# files

id: 549982d6-96c5-4e1d-a2ad-5bc6506ad1a6
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/OpenCode.md
version: v1
content:
  # OpenCode.md - Chatterbox UI Development Guide

  ## Build & Run Commands
  ```bash
  # Backend (FastAPI)
  pip install -r backend/requirements.txt
  uvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000

  # Frontend
  python frontend/start_dev_server.py  # Serves on port 8001

  # Run backend tests
  python backend/run_api_test.py

  # Run frontend tests
  npm test

  # Run specific frontend test
  npx jest frontend/tests/api.test.js

  # Run Gradio interface
  python gradio_app.py
  ```

  ## Code Style Guidelines

  ### Python
  - Use type hints (from typing import Optional, List, Dict, etc.)
  - Error handling: Use try/except with specific exceptions
  - Async/await for I/O operations
  - Docstrings for functions and classes
  - PEP 8 naming: snake_case for functions/variables, PascalCase for classes

  ### JavaScript
  - ES6 modules with import/export
  - Async/await for API calls
  - JSDoc comments for functions
  - Error handling: try/catch with detailed error messages
  - Camel case for variables/functions (camelCase)

  ### Project Structure
  - Backend: FastAPI with service-oriented architecture
  - Frontend: Vanilla JS with modular design (api.js, app.js, config.js)
  - Speaker data in YAML format with WAV samples
  - Output directories: dialog_output/, single_output/, tts_outputs/


id: ceaf6d28-0af3-4785-ba6c-f229762efb6a
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py
version: initial
content:
  #!/usr/bin/env python
  """
  Chatterbox Audiobook Generator

  This script converts a text file into an audiobook using the Chatterbox TTS system.
  It parses the text file into manageable chunks, generates audio for each chunk,
  and assembles them into a complete audiobook.
  """

  import argparse
  import asyncio
  import os
  import re
  import sys
  from pathlib import Path
  import uuid

  # Import backend services
  from backend.app.services.tts_service import TTSService
  from backend.app.services.speaker_service import SpeakerManagementService
  from backend.app.services.audio_manipulation_service import AudioManipulationService
  from backend.app.config import DIALOG_GENERATED_DIR, TTS_TEMP_OUTPUT_DIR

  class AudiobookGenerator:
      def __init__(self, speaker_id, output_base_name, device="mps", 
                   exaggeration=0.5, cfg_weight=0.5, temperature=0.8,
                   pause_between_sentences=0.5, pause_between_paragraphs=1.0):
          """
          Initialize the audiobook generator.
          
          Args:
              speaker_id: ID of the speaker to use
              output_base_name: Base name for output files
              device: Device to use for TTS (mps, cuda, cpu)
              exaggeration: Controls expressiveness (0.0-1.0)
              cfg_weight: Controls alignment with speaker characteristics (0.0-1.0)
              temperature: Controls randomness in generation (0.0-1.0)
              pause_between_sentences: Pause duration between sentences in seconds
              pause_between_paragraphs: Pause duration between paragraphs in seconds
          """
          self.speaker_id = speaker_id
          self.output_base_name = output_base_name
          self.device = device
          self.exaggeration = exaggeration
          self.cfg_weight = cfg_weight
          self.temperature = temperature
          self.pause_between_sentences = pause_between_sentences
          self.pause_between_paragraphs = pause_between_paragraphs
          
          # Initialize services
          self.tts_service = TTSService(device=device)
          self.speaker_service = SpeakerManagementService()
          self.audio_manipulator = AudioManipulationService()
          
          # Create output directories
          self.output_dir = DIALOG_GENERATED_DIR / output_base_name
          self.output_dir.mkdir(parents=True, exist_ok=True)
          self.temp_dir = TTS_TEMP_OUTPUT_DIR / output_base_name
          self.temp_dir.mkdir(parents=True, exist_ok=True)
          
          # Validate speaker
          self._validate_speaker()
      
      def _validate_speaker(self):
          """Validate that the specified speaker exists."""
          speaker_info = self.speaker_service.get_speaker_by_id(self.speaker_id)
          if not speaker_info:
              raise ValueError(f"Speaker ID '{self.speaker_id}' not found.")
          if not speaker_info.sample_path:
              raise ValueError(f"Speaker ID '{self.speaker_id}' has no sample path defined.")
          
          # Store speaker info for later use
          self.speaker_info = speaker_info
      
      def split_text_into_chunks(self, text, max_length=300):
          """
          Split text into chunks suitable for TTS processing.
          
          This uses the same logic as the DialogProcessorService._split_text method
          but adds additional paragraph handling.
          """
          # Split text into paragraphs first
          paragraphs = re.split(r'\n\s*\n', text)
          paragraphs = [p.strip() for p in paragraphs if p.strip()]
          
          all_chunks = []
          
          for paragraph in paragraphs:
              # Split paragraph into sentences
              sentences = re.split(r'(?<=[.!?\u2026])\s+|(?<=[.!?\u2026])(?=[\"\')\]\}\u201d\u2019])|(?<=[.!?\u2026])$', paragraph.strip())
              sentences = [s.strip() for s in sentences if s and s.strip()]
              
              chunks = []
              current_chunk = ""
              
              for sentence in sentences:
                  if not sentence:
                      continue
                  if not current_chunk:  # First sentence for this chunk
                      current_chunk = sentence
                  elif len(current_chunk) + len(sentence) + 1 <= max_length:
                      current_chunk += " " + sentence
                  else:
                      chunks.append(current_chunk)
                      current_chunk = sentence
              
              if current_chunk:  # Add the last chunk
                  chunks.append(current_chunk)
              
              # Further split any chunks that are still too long
              paragraph_chunks = []
              for chunk in chunks:
                  if len(chunk) > max_length:
                      # Simple split by length if a sentence itself is too long
                      for i in range(0, len(chunk), max_length):
                          paragraph_chunks.append(chunk[i:i+max_length])
                  else:
                      paragraph_chunks.append(chunk)
              
              # Add paragraph marker
              if paragraph_chunks:
                  all_chunks.append({"type": "paragraph", "chunks": paragraph_chunks})
          
          return all_chunks
      
      async def generate_audiobook(self, text_file_path):
          """
          Generate an audiobook from a text file.
          
          Args:
              text_file_path: Path to the text file to convert
          
          Returns:
              Path to the generated audiobook file
          """
          # Read the text file
          text_path = Path(text_file_path)
          if not text_path.exists():
              raise FileNotFoundError(f"Text file not found: {text_file_path}")
          
          with open(text_path, 'r', encoding='utf-8') as f:
              text = f.read()
          
          print(f"Processing text file: {text_file_path}")
          print(f"Text length: {len(text)} characters")
          
          # Split text into chunks
          paragraphs = self.split_text_into_chunks(text)
          total_chunks = sum(len(p["chunks"]) for p in paragraphs)
          print(f"Split into {len(paragraphs)} paragraphs with {total_chunks} total chunks")
          
          # Load TTS model
          print("Loading TTS model...")
          self.tts_service.load_model()
          
          try:
              # Generate audio for each chunk
              segment_results = []
              chunk_count = 0
              
              for para_idx, paragraph in enumerate(paragraphs):
                  print(f"Processing paragraph {para_idx+1}/{len(paragraphs)}")
                  
                  for chunk_idx, chunk in enumerate(paragraph["chunks"]):
                      chunk_count += 1
                      print(f"  Generating audio for chunk {chunk_count}/{total_chunks}: {chunk[:50]}...")
                      
                      # Generate unique filename for this chunk
                      segment_filename_base = f"{self.output_base_name}_p{para_idx}_c{chunk_idx}_{uuid.uuid4().hex[:8]}"
                      
                      try:
                          # Get absolute speaker sample path
                          speaker_sample_path = Path(self.speaker_info.sample_path)
                          if not speaker_sample_path.is_absolute():
                              from backend.app.config import SPEAKER_DATA_BASE_DIR
                              speaker_sample_path = SPEAKER_DATA_BASE_DIR / speaker_sample_path
                          
                          # Generate speech for this chunk
                          segment_output_path = await self.tts_service.generate_speech(
                              text=chunk,
                              speaker_id=self.speaker_id,
                              speaker_sample_path=str(speaker_sample_path),
                              output_filename_base=segment_filename_base,
                              output_dir=self.temp_dir,
                              exaggeration=self.exaggeration,
                              cfg_weight=self.cfg_weight,
                              temperature=self.temperature
                          )
                          
                          # Add to segment results
                          segment_results.append({
                              "type": "speech",
                              "path": str(segment_output_path)
                          })
                          
                          # Add pause between sentences
                          if chunk_idx < len(paragraph["chunks"]) - 1:
                              segment_results.append({
                                  "type": "silence",
                                  "duration": self.pause_between_sentences
                              })
                      
                      except Exception as e:
                          print(f"Error generating speech for chunk: {e}")
                          # Continue with next chunk
                  
                  # Add longer pause between paragraphs
                  if para_idx < len(paragraphs) - 1:
                      segment_results.append({
                          "type": "silence",
                          "duration": self.pause_between_paragraphs
                      })
              
              # Concatenate all segments
              print("Concatenating audio segments...")
              concatenated_filename = f"{self.output_base_name}_audiobook.wav"
              concatenated_path = self.output_dir / concatenated_filename
              
              self.audio_manipulator.concatenate_audio_segments(
                  segment_results=segment_results,
                  output_concatenated_path=concatenated_path
              )
              
              # Create ZIP archive with all files
              print("Creating ZIP archive...")
              zip_filename = f"{self.output_base_name}_audiobook.zip"
              zip_path = self.output_dir / zip_filename
              
              # Collect all speech segment files
              speech_segment_paths = [
                  Path(s["path"]) for s in segment_results 
                  if s["type"] == "speech" and Path(s["path"]).exists()
              ]
              
              self.audio_manipulator.create_zip_archive(
                  segment_file_paths=speech_segment_paths,
                  concatenated_audio_path=concatenated_path,
                  output_zip_path=zip_path
              )
              
              print(f"Audiobook generation complete!")
              print(f"Audiobook file: {concatenated_path}")
              print(f"ZIP archive: {zip_path}")
              
              return concatenated_path
              
          finally:
              # Unload TTS model
              print("Unloading TTS model...")
              self.tts_service.unload_model()

  async def main():
      parser = argparse.ArgumentParser(description="Generate an audiobook from a text file using Chatterbox TTS")
      parser.add_argument("text_file", help="Path to the text file to convert")
      parser.add_argument("--speaker", "-s", required=True, help="ID of the speaker to use")
      parser.add_argument("--output", "-o", help="Base name for output files (default: derived from text filename)")
      parser.add_argument("--device", default="mps", choices=["mps", "cuda", "cpu"], help="Device to use for TTS (default: mps)")
      parser.add_argument("--exaggeration", type=float, default=0.5, help="Controls expressiveness (0.0-1.0, default: 0.5)")
      parser.add_argument("--cfg-weight", type=float, default=0.5, help="Controls alignment with speaker (0.0-1.0, default: 0.5)")
      parser.add_argument("--temperature", type=float, default=0.8, help="Controls randomness (0.0-1.0, default: 0.8)")
      parser.add_argument("--sentence-pause", type=float, default=0.5, help="Pause between sentences in seconds (default: 0.5)")
      parser.add_argument("--paragraph-pause", type=float, default=1.0, help="Pause between paragraphs in seconds (default: 1.0)")
      parser.add_argument("--list-speakers", action="store_true", help="List available speakers and exit")
      
      args = parser.parse_args()
      
      # List speakers if requested
      if args.list_speakers:
          speaker_service = SpeakerManagementService()
          speakers = speaker_service.get_all_speakers()
          print("Available speakers:")
          for speaker in speakers:
              print(f"  {speaker.id}: {speaker.name}")
          return
      
      # Determine output base name if not provided
      if not args.output:
          text_path = Path(args.text_file)
          args.output = text_path.stem
      
      try:
          # Create audiobook generator
          generator = AudiobookGenerator(
              speaker_id=args.speaker,
              output_base_name=args.output,
              device=args.device,
              exaggeration=args.exaggeration,
              cfg_weight=args.cfg_weight,
              temperature=args.temperature,
              pause_between_sentences=args.sentence_pause,
              pause_between_paragraphs=args.paragraph_pause
          )
          
          # Generate audiobook
          await generator.generate_audiobook(args.text_file)
          
      except Exception as e:
          print(f"Error: {e}", file=sys.stderr)
          return 1
      
      return 0

  if __name__ == "__main__":
      sys.exit(asyncio.run(main()))


id: e6d79588-b13c-4bc3-8a77-ee89eda794c0
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py
version: v2
content:
  #!/usr/bin/env python
  """
  Chatterbox Audiobook Generator

  This script converts a text file into an audiobook using the Chatterbox TTS system.
  It parses the text file into manageable chunks, generates audio for each chunk,
  and assembles them into a complete audiobook.
  """

  import argparse
  import asyncio
  import os
  import re
  import sys
  from pathlib import Path
  import uuid

  # Add the project root to the Python path to fix imports
  import sys
  sys.path.insert(0, str(Path(__file__).resolve().parent))

  # Import backend services
  from backend.app.services.tts_service import TTSService
  from backend.app.services.speaker_service import SpeakerManagementService
  from backend.app.services.audio_manipulation_service import AudioManipulationService
  from backend.app.config import DIALOG_GENERATED_DIR, TTS_TEMP_OUTPUT_DIR

  class AudiobookGenerator:
      def __init__(self, speaker_id, output_base_name, device="mps", 
                   exaggeration=0.5, cfg_weight=0.5, temperature=0.8,
                   pause_between_sentences=0.5, pause_between_paragraphs=1.0):
          """
          Initialize the audiobook generator.
          
          Args:
              speaker_id: ID of the speaker to use
              output_base_name: Base name for output files
              device: Device to use for TTS (mps, cuda, cpu)
              exaggeration: Controls expressiveness (0.0-1.0)
              cfg_weight: Controls alignment with speaker characteristics (0.0-1.0)
              temperature: Controls randomness in generation (0.0-1.0)
              pause_between_sentences: Pause duration between sentences in seconds
              pause_between_paragraphs: Pause duration between paragraphs in seconds
          """
          self.speaker_id = speaker_id
          self.output_base_name = output_base_name
          self.device = device
          self.exaggeration = exaggeration
          self.cfg_weight = cfg_weight
          self.temperature = temperature
          self.pause_between_sentences = pause_between_sentences
          self.pause_between_paragraphs = pause_between_paragraphs
          
          # Initialize services
          self.tts_service = TTSService(device=device)
          self.speaker_service = SpeakerManagementService()
          self.audio_manipulator = AudioManipulationService()
          
          # Create output directories
          self.output_dir = DIALOG_GENERATED_DIR / output_base_name
          self.output_dir.mkdir(parents=True, exist_ok=True)
          self.temp_dir = TTS_TEMP_OUTPUT_DIR / output_base_name
          self.temp_dir.mkdir(parents=True, exist_ok=True)
          
          # Validate speaker
          self._validate_speaker()
      
      def _validate_speaker(self):
          """Validate that the specified speaker exists."""
          speaker_info = self.speaker_service.get_speaker_by_id(self.speaker_id)
          if not speaker_info:
              raise ValueError(f"Speaker ID '{self.speaker_id}' not found.")
          if not speaker_info.sample_path:
              raise ValueError(f"Speaker ID '{self.speaker_id}' has no sample path defined.")
          
          # Store speaker info for later use
          self.speaker_info = speaker_info
      
      def split_text_into_chunks(self, text, max_length=300):
          """
          Split text into chunks suitable for TTS processing.
          
          This uses the same logic as the DialogProcessorService._split_text method
          but adds additional paragraph handling.
          """
          # Split text into paragraphs first
          paragraphs = re.split(r'\n\s*\n', text)
          paragraphs = [p.strip() for p in paragraphs if p.strip()]
          
          all_chunks = []
          
          for paragraph in paragraphs:
              # Split paragraph into sentences
              sentences = re.split(r'(?<=[.!?\u2026])\s+|(?<=[.!?\u2026])(?=[\"\')\]\}\u201d\u2019])|(?<=[.!?\u2026])$', paragraph.strip())
              sentences = [s.strip() for s in sentences if s and s.strip()]
              
              chunks = []
              current_chunk = ""
              
              for sentence in sentences:
                  if not sentence:
                      continue
                  if not current_chunk:  # First sentence for this chunk
                      current_chunk = sentence
                  elif len(current_chunk) + len(sentence) + 1 <= max_length:
                      current_chunk += " " + sentence
                  else:
                      chunks.append(current_chunk)
                      current_chunk = sentence
              
              if current_chunk:  # Add the last chunk
                  chunks.append(current_chunk)
              
              # Further split any chunks that are still too long
              paragraph_chunks = []
              for chunk in chunks:
                  if len(chunk) > max_length:
                      # Simple split by length if a sentence itself is too long
                      for i in range(0, len(chunk), max_length):
                          paragraph_chunks.append(chunk[i:i+max_length])
                  else:
                      paragraph_chunks.append(chunk)
              
              # Add paragraph marker
              if paragraph_chunks:
                  all_chunks.append({"type": "paragraph", "chunks": paragraph_chunks})
          
          return all_chunks
      
      async def generate_audiobook(self, text_file_path):
          """
          Generate an audiobook from a text file.
          
          Args:
              text_file_path: Path to the text file to convert
          
          Returns:
              Path to the generated audiobook file
          """
          # Read the text file
          text_path = Path(text_file_path)
          if not text_path.exists():
              raise FileNotFoundError(f"Text file not found: {text_file_path}")
          
          with open(text_path, 'r', encoding='utf-8') as f:
              text = f.read()
          
          print(f"Processing text file: {text_file_path}")
          print(f"Text length: {len(text)} characters")
          
          # Split text into chunks
          paragraphs = self.split_text_into_chunks(text)
          total_chunks = sum(len(p["chunks"]) for p in paragraphs)
          print(f"Split into {len(paragraphs)} paragraphs with {total_chunks} total chunks")
          
          # Load TTS model
          print("Loading TTS model...")
          self.tts_service.load_model()
          
          try:
              # Generate audio for each chunk
              segment_results = []
              chunk_count = 0
              
              for para_idx, paragraph in enumerate(paragraphs):
                  print(f"Processing paragraph {para_idx+1}/{len(paragraphs)}")
                  
                  for chunk_idx, chunk in enumerate(paragraph["chunks"]):
                      chunk_count += 1
                      print(f"  Generating audio for chunk {chunk_count}/{total_chunks}: {chunk[:50]}...")
                      
                      # Generate unique filename for this chunk
                      segment_filename_base = f"{self.output_base_name}_p{para_idx}_c{chunk_idx}_{uuid.uuid4().hex[:8]}"
                      
                      try:
                          # Get absolute speaker sample path
                          speaker_sample_path = Path(self.speaker_info.sample_path)
                          if not speaker_sample_path.is_absolute():
                              from backend.app.config import SPEAKER_DATA_BASE_DIR
                              speaker_sample_path = SPEAKER_DATA_BASE_DIR / speaker_sample_path
                          
                          # Generate speech for this chunk
                          segment_output_path = await self.tts_service.generate_speech(
                              text=chunk,
                              speaker_id=self.speaker_id,
                              speaker_sample_path=str(speaker_sample_path),
                              output_filename_base=segment_filename_base,
                              output_dir=self.temp_dir,
                              exaggeration=self.exaggeration,
                              cfg_weight=self.cfg_weight,
                              temperature=self.temperature
                          )
                          
                          # Add to segment results
                          segment_results.append({
                              "type": "speech",
                              "path": str(segment_output_path)
                          })
                          
                          # Add pause between sentences
                          if chunk_idx < len(paragraph["chunks"]) - 1:
                              segment_results.append({
                                  "type": "silence",
                                  "duration": self.pause_between_sentences
                              })
                      
                      except Exception as e:
                          print(f"Error generating speech for chunk: {e}")
                          # Continue with next chunk
                  
                  # Add longer pause between paragraphs
                  if para_idx < len(paragraphs) - 1:
                      segment_results.append({
                          "type": "silence",
                          "duration": self.pause_between_paragraphs
                      })
              
              # Concatenate all segments
              print("Concatenating audio segments...")
              concatenated_filename = f"{self.output_base_name}_audiobook.wav"
              concatenated_path = self.output_dir / concatenated_filename
              
              self.audio_manipulator.concatenate_audio_segments(
                  segment_results=segment_results,
                  output_concatenated_path=concatenated_path
              )
              
              # Create ZIP archive with all files
              print("Creating ZIP archive...")
              zip_filename = f"{self.output_base_name}_audiobook.zip"
              zip_path = self.output_dir / zip_filename
              
              # Collect all speech segment files
              speech_segment_paths = [
                  Path(s["path"]) for s in segment_results 
                  if s["type"] == "speech" and Path(s["path"]).exists()
              ]
              
              self.audio_manipulator.create_zip_archive(
                  segment_file_paths=speech_segment_paths,
                  concatenated_audio_path=concatenated_path,
                  output_zip_path=zip_path
              )
              
              print(f"Audiobook generation complete!")
              print(f"Audiobook file: {concatenated_path}")
              print(f"ZIP archive: {zip_path}")
              
              return concatenated_path
              
          finally:
              # Unload TTS model
              print("Unloading TTS model...")
              self.tts_service.unload_model()

  async def main():
      parser = argparse.ArgumentParser(description="Generate an audiobook from a text file using Chatterbox TTS")
      parser.add_argument("text_file", help="Path to the text file to convert")
      parser.add_argument("--speaker", "-s", required=True, help="ID of the speaker to use")
      parser.add_argument("--output", "-o", help="Base name for output files (default: derived from text filename)")
      parser.add_argument("--device", default="mps", choices=["mps", "cuda", "cpu"], help="Device to use for TTS (default: mps)")
      parser.add_argument("--exaggeration", type=float, default=0.5, help="Controls expressiveness (0.0-1.0, default: 0.5)")
      parser.add_argument("--cfg-weight", type=float, default=0.5, help="Controls alignment with speaker (0.0-1.0, default: 0.5)")
      parser.add_argument("--temperature", type=float, default=0.8, help="Controls randomness (0.0-1.0, default: 0.8)")
      parser.add_argument("--sentence-pause", type=float, default=0.5, help="Pause between sentences in seconds (default: 0.5)")
      parser.add_argument("--paragraph-pause", type=float, default=1.0, help="Pause between paragraphs in seconds (default: 1.0)")
      parser.add_argument("--list-speakers", action="store_true", help="List available speakers and exit")
      
      args = parser.parse_args()
      
      # List speakers if requested
      if args.list_speakers:
          speaker_service = SpeakerManagementService()
          speakers = speaker_service.get_all_speakers()
          print("Available speakers:")
          for speaker in speakers:
              print(f"  {speaker.id}: {speaker.name}")
          return
      
      # Determine output base name if not provided
      if not args.output:
          text_path = Path(args.text_file)
          args.output = text_path.stem
      
      try:
          # Create audiobook generator
          generator = AudiobookGenerator(
              speaker_id=args.speaker,
              output_base_name=args.output,
              device=args.device,
              exaggeration=args.exaggeration,
              cfg_weight=args.cfg_weight,
              temperature=args.temperature,
              pause_between_sentences=args.sentence_pause,
              pause_between_paragraphs=args.paragraph_pause
          )
          
          # Generate audiobook
          await generator.generate_audiobook(args.text_file)
          
      except Exception as e:
          print(f"Error: {e}", file=sys.stderr)
          return 1
      
      return 0

  if __name__ == "__main__":
      sys.exit(asyncio.run(main()))


id: 71ef3d29-ad42-4e14-a1d5-466ecdbd9cf6
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/backend/app/services/tts_service.py
version: initial
content:
  import torch
  import torchaudio
  from typing import Optional
  from chatterbox.tts import ChatterboxTTS
  from pathlib import Path
  import gc # Garbage collector for memory management
  import os
  from contextlib import contextmanager

  # Import configuration
  from app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR

  # Use configuration for TTS output directory
  TTS_OUTPUT_DIR = TTS_TEMP_OUTPUT_DIR

  def safe_load_chatterbox_tts(device):
      """
      Safely load ChatterboxTTS model with device mapping to handle CUDA->MPS/CPU conversion.
      This patches torch.load temporarily to map CUDA tensors to the appropriate device.
      """
      @contextmanager
      def patch_torch_load(target_device):
          original_load = torch.load
          
          def patched_load(*args, **kwargs):
              # Add map_location to handle device mapping
              if 'map_location' not in kwargs:
                  if target_device == "mps" and torch.backends.mps.is_available():
                      kwargs['map_location'] = torch.device('mps')
                  else:
                      kwargs['map_location'] = torch.device('cpu')
              return original_load(*args, **kwargs)
          
          torch.load = patched_load
          try:
              yield
          finally:
              torch.load = original_load
      
      with patch_torch_load(device):
          return ChatterboxTTS.from_pretrained(device=device)

  class TTSService:
      def __init__(self, device: str = "mps"): # Default to MPS for Macs, can be "cpu" or "cuda"
          self.device = device
          self.model = None
          self._ensure_output_dir_exists()

      def _ensure_output_dir_exists(self):
          """Ensures the TTS output directory exists."""
          TTS_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)

      def load_model(self):
          """Loads the ChatterboxTTS model."""
          if self.model is None:
              print(f"Loading ChatterboxTTS model to device: {self.device}...")
              try:
                  self.model = safe_load_chatterbox_tts(self.device)
                  print("ChatterboxTTS model loaded successfully.")
              except Exception as e:
                  print(f"Error loading ChatterboxTTS model: {e}")
                  # Potentially raise an exception or handle appropriately
                  raise
          else:
              print("ChatterboxTTS model already loaded.")

      def unload_model(self):
          """Unloads the model and clears memory."""
          if self.model is not None:
              print("Unloading ChatterboxTTS model and clearing cache...")
              del self.model
              self.model = None
              if self.device == "cuda":
                  torch.cuda.empty_cache()
              elif self.device == "mps":
                  if hasattr(torch.mps, "empty_cache"): # Check if empty_cache is available for MPS
                      torch.mps.empty_cache()
              gc.collect() # Explicitly run garbage collection
              print("Model unloaded and memory cleared.")

      async def generate_speech(
          self,
          text: str,
          speaker_sample_path: str, # Absolute path to the speaker's audio sample
          output_filename_base: str, # e.g., "dialog_line_1_spk_X_chunk_0"
          speaker_id: Optional[str] = None, # Optional, mainly for logging if needed, filename base is primary
          output_dir: Optional[Path] = None, # Optional, defaults to TTS_OUTPUT_DIR from this module
          exaggeration: float = 0.5, # Default from Gradio
          cfg_weight: float = 0.5,   # Default from Gradio
          temperature: float = 0.8,  # Default from Gradio
      ) -> Path:
          """
          Generates speech from text using the loaded TTS model and a speaker sample.
          Saves the output to a .wav file.
          """
          if self.model is None:
              self.load_model()
          
          if self.model is None: # Check again if loading failed
              raise RuntimeError("TTS model is not loaded. Cannot generate speech.")

          # Ensure speaker_sample_path is valid
          speaker_sample_p = Path(speaker_sample_path)
          if not speaker_sample_p.exists() or not speaker_sample_p.is_file():
              raise FileNotFoundError(f"Speaker sample audio file not found: {speaker_sample_path}")

          target_output_dir = output_dir if output_dir is not None else TTS_OUTPUT_DIR
          target_output_dir.mkdir(parents=True, exist_ok=True)
          # output_filename_base from DialogProcessorService is expected to be comprehensive (e.g., includes speaker_id, segment info)
          output_file_path = target_output_dir / f"{output_filename_base}.wav"

          print(f"Generating audio for text: \"{text[:50]}...\" with speaker sample: {speaker_sample_path}")
          try:
              with torch.no_grad(): # Important for inference
                  wav = self.model.generate(
                      text=text,
                      audio_prompt_path=str(speaker_sample_p), # Must be a string path
                      exaggeration=exaggeration,
                      cfg_weight=cfg_weight,
                      temperature=temperature,
                  )
              
              torchaudio.save(str(output_file_path), wav, self.model.sr)
              print(f"Audio saved to: {output_file_path}")
              return output_file_path
          except Exception as e:
              print(f"Error during TTS generation or saving: {e}")
              raise
          finally:
              # For now, we keep it loaded. Memory management might need refinement.
              pass

  # Example usage (for testing, not part of the service itself)
  if __name__ == "__main__":
      async def main_test():
          tts_service = TTSService(device="mps")
          try:
              tts_service.load_model()
              
              dummy_speaker_root = SPEAKER_SAMPLES_DIR
              dummy_speaker_root.mkdir(parents=True, exist_ok=True)
              dummy_sample_file = dummy_speaker_root / "dummy_speaker_test.wav"
              import os # Added for os.remove
              # Always try to remove an existing dummy file to ensure a fresh one is created
              if dummy_sample_file.exists():
                  try:
                      os.remove(dummy_sample_file)
                      print(f"Removed existing dummy sample: {dummy_sample_file}")
                  except OSError as e:
                      print(f"Error removing existing dummy sample {dummy_sample_file}: {e}")
                      # Proceeding, but torchaudio.save might fail or overwrite

              print(f"Creating new dummy speaker sample: {dummy_sample_file}")
              # Create a minimal, silent WAV file for testing
              sample_rate = 22050
              duration = 1  # seconds
              num_channels = 1
              num_frames = sample_rate * duration
              audio_data = torch.zeros((num_channels, num_frames))
              try:
                  torchaudio.save(str(dummy_sample_file), audio_data, sample_rate)
                  print(f"Dummy sample created successfully: {dummy_sample_file}")
              except Exception as save_e:
                  print(f"Could not create dummy sample: {save_e}")
                  # If creation fails, the subsequent generation test will likely also fail or be skipped.


              if dummy_sample_file.exists():
                  output_path = await tts_service.generate_speech(
                      text="Hello, this is a test of the Text-to-Speech service.",
                      speaker_id="test_speaker",
                      speaker_sample_path=str(dummy_sample_file),
                      output_filename_base="test_generation"
                  )
                  print(f"Test generation output: {output_path}")
              else:
                  print(f"Skipping generation test as dummy sample {dummy_sample_file} not found.")

          except Exception as e:
              import traceback
              print(f"Error during TTS generation or saving:")
              traceback.print_exc()
          finally:
              tts_service.unload_model()

      import asyncio
      asyncio.run(main_test())


id: 0a11b976-4699-4acb-9f05-f3dc8febcdd4
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/backend/app/services/tts_service.py
version: v2
content:
  import torch
  import torchaudio
  from typing import Optional
  from chatterbox.tts import ChatterboxTTS
  from pathlib import Path
  import gc # Garbage collector for memory management
  import os
  from contextlib import contextmanager

  # Import configuration
  try:
      from app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR
  except ModuleNotFoundError:
      # When imported from scripts at project root
      from backend.app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR

  # Use configuration for TTS output directory
  TTS_OUTPUT_DIR = TTS_TEMP_OUTPUT_DIR

  def safe_load_chatterbox_tts(device):
      """
      Safely load ChatterboxTTS model with device mapping to handle CUDA->MPS/CPU conversion.
      This patches torch.load temporarily to map CUDA tensors to the appropriate device.
      """
      @contextmanager
      def patch_torch_load(target_device):
          original_load = torch.load
          
          def patched_load(*args, **kwargs):
              # Add map_location to handle device mapping
              if 'map_location' not in kwargs:
                  if target_device == "mps" and torch.backends.mps.is_available():
                      kwargs['map_location'] = torch.device('mps')
                  else:
                      kwargs['map_location'] = torch.device('cpu')
              return original_load(*args, **kwargs)
          
          torch.load = patched_load
          try:
              yield
          finally:
              torch.load = original_load
      
      with patch_torch_load(device):
          return ChatterboxTTS.from_pretrained(device=device)

  class TTSService:
      def __init__(self, device: str = "mps"): # Default to MPS for Macs, can be "cpu" or "cuda"
          self.device = device
          self.model = None
          self._ensure_output_dir_exists()

      def _ensure_output_dir_exists(self):
          """Ensures the TTS output directory exists."""
          TTS_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)

      def load_model(self):
          """Loads the ChatterboxTTS model."""
          if self.model is None:
              print(f"Loading ChatterboxTTS model to device: {self.device}...")
              try:
                  self.model = safe_load_chatterbox_tts(self.device)
                  print("ChatterboxTTS model loaded successfully.")
              except Exception as e:
                  print(f"Error loading ChatterboxTTS model: {e}")
                  # Potentially raise an exception or handle appropriately
                  raise
          else:
              print("ChatterboxTTS model already loaded.")

      def unload_model(self):
          """Unloads the model and clears memory."""
          if self.model is not None:
              print("Unloading ChatterboxTTS model and clearing cache...")
              del self.model
              self.model = None
              if self.device == "cuda":
                  torch.cuda.empty_cache()
              elif self.device == "mps":
                  if hasattr(torch.mps, "empty_cache"): # Check if empty_cache is available for MPS
                      torch.mps.empty_cache()
              gc.collect() # Explicitly run garbage collection
              print("Model unloaded and memory cleared.")

      async def generate_speech(
          self,
          text: str,
          speaker_sample_path: str, # Absolute path to the speaker's audio sample
          output_filename_base: str, # e.g., "dialog_line_1_spk_X_chunk_0"
          speaker_id: Optional[str] = None, # Optional, mainly for logging if needed, filename base is primary
          output_dir: Optional[Path] = None, # Optional, defaults to TTS_OUTPUT_DIR from this module
          exaggeration: float = 0.5, # Default from Gradio
          cfg_weight: float = 0.5,   # Default from Gradio
          temperature: float = 0.8,  # Default from Gradio
      ) -> Path:
          """
          Generates speech from text using the loaded TTS model and a speaker sample.
          Saves the output to a .wav file.
          """
          if self.model is None:
              self.load_model()
          
          if self.model is None: # Check again if loading failed
              raise RuntimeError("TTS model is not loaded. Cannot generate speech.")

          # Ensure speaker_sample_path is valid
          speaker_sample_p = Path(speaker_sample_path)
          if not speaker_sample_p.exists() or not speaker_sample_p.is_file():
              raise FileNotFoundError(f"Speaker sample audio file not found: {speaker_sample_path}")

          target_output_dir = output_dir if output_dir is not None else TTS_OUTPUT_DIR
          target_output_dir.mkdir(parents=True, exist_ok=True)
          # output_filename_base from DialogProcessorService is expected to be comprehensive (e.g., includes speaker_id, segment info)
          output_file_path = target_output_dir / f"{output_filename_base}.wav"

          print(f"Generating audio for text: \"{text[:50]}...\" with speaker sample: {speaker_sample_path}")
          try:
              with torch.no_grad(): # Important for inference
                  wav = self.model.generate(
                      text=text,
                      audio_prompt_path=str(speaker_sample_p), # Must be a string path
                      exaggeration=exaggeration,
                      cfg_weight=cfg_weight,
                      temperature=temperature,
                  )
              
              torchaudio.save(str(output_file_path), wav, self.model.sr)
              print(f"Audio saved to: {output_file_path}")
              return output_file_path
          except Exception as e:
              print(f"Error during TTS generation or saving: {e}")
              raise
          finally:
              # For now, we keep it loaded. Memory management might need refinement.
              pass

  # Example usage (for testing, not part of the service itself)
  if __name__ == "__main__":
      async def main_test():
          tts_service = TTSService(device="mps")
          try:
              tts_service.load_model()
              
              dummy_speaker_root = SPEAKER_SAMPLES_DIR
              dummy_speaker_root.mkdir(parents=True, exist_ok=True)
              dummy_sample_file = dummy_speaker_root / "dummy_speaker_test.wav"
              import os # Added for os.remove
              # Always try to remove an existing dummy file to ensure a fresh one is created
              if dummy_sample_file.exists():
                  try:
                      os.remove(dummy_sample_file)
                      print(f"Removed existing dummy sample: {dummy_sample_file}")
                  except OSError as e:
                      print(f"Error removing existing dummy sample {dummy_sample_file}: {e}")
                      # Proceeding, but torchaudio.save might fail or overwrite

              print(f"Creating new dummy speaker sample: {dummy_sample_file}")
              # Create a minimal, silent WAV file for testing
              sample_rate = 22050
              duration = 1  # seconds
              num_channels = 1
              num_frames = sample_rate * duration
              audio_data = torch.zeros((num_channels, num_frames))
              try:
                  torchaudio.save(str(dummy_sample_file), audio_data, sample_rate)
                  print(f"Dummy sample created successfully: {dummy_sample_file}")
              except Exception as save_e:
                  print(f"Could not create dummy sample: {save_e}")
                  # If creation fails, the subsequent generation test will likely also fail or be skipped.


              if dummy_sample_file.exists():
                  output_path = await tts_service.generate_speech(
                      text="Hello, this is a test of the Text-to-Speech service.",
                      speaker_id="test_speaker",
                      speaker_sample_path=str(dummy_sample_file),
                      output_filename_base="test_generation"
                  )
                  print(f"Test generation output: {output_path}")
              else:
                  print(f"Skipping generation test as dummy sample {dummy_sample_file} not found.")

          except Exception as e:
              import traceback
              print(f"Error during TTS generation or saving:")
              traceback.print_exc()
          finally:
              tts_service.unload_model()

      import asyncio
      asyncio.run(main_test())


id: 095cacbe-e5ad-4404-86c3-3625ef09352b
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/OpenCode.md
version: v3
content:
  # OpenCode.md - Chatterbox UI Development Guide

  ## Build & Run Commands
  ```bash
  # Backend (FastAPI)
  pip install -r backend/requirements.txt
  uvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000

  # Frontend
  python frontend/start_dev_server.py  # Serves on port 8001

  # Run backend tests
  python backend/run_api_test.py

  # Run frontend tests
  npm test

  # Run specific frontend test
  npx jest frontend/tests/api.test.js

  # Run Gradio interface
  python gradio_app.py

  # Run utility scripts
  python cbx-audiobook.py --list-speakers  # List available speakers
  python cbx-audiobook.py sample-audiobook.txt --speaker <speaker_id>
  python cbx-dialog-generate.py sample-dialog.md
  ```

  ## Code Style Guidelines

  ### Python
  - Use type hints (from typing import Optional, List, Dict, etc.)
  - Error handling: Use try/except with specific exceptions
  - Async/await for I/O operations
  - Docstrings for functions and classes
  - PEP 8 naming: snake_case for functions/variables, PascalCase for classes

  ### JavaScript
  - ES6 modules with import/export
  - Async/await for API calls
  - JSDoc comments for functions
  - Error handling: try/catch with detailed error messages
  - Camel case for variables/functions (camelCase)

  ### Project Structure
  - Backend: FastAPI with service-oriented architecture
  - Frontend: Vanilla JS with modular design (api.js, app.js, config.js)
  - Speaker data in YAML format with WAV samples
  - Output directories: dialog_output/, single_output/, tts_outputs/


id: f5396eab-d210-4dd2-82d1-6f67f8b26a0c
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/backend/app/services/speaker_service.py
version: initial
content:
  import yaml
  import uuid
  import os
  import io # Added for BytesIO
  import torchaudio # Added for audio processing
  from pathlib import Path
  from typing import List, Dict, Optional, Any

  from fastapi import UploadFile, HTTPException
  from app.models.speaker_models import Speaker, SpeakerCreate
  from app import config

  class SpeakerManagementService:
      def __init__(self):
          self._ensure_data_files_exist()
          self.speakers_data = self._load_speakers_data()

      def _ensure_data_files_exist(self):
          """Ensures the speaker data directory and YAML file exist."""
          config.SPEAKER_DATA_BASE_DIR.mkdir(parents=True, exist_ok=True)
          config.SPEAKER_SAMPLES_DIR.mkdir(parents=True, exist_ok=True)
          if not config.SPEAKERS_YAML_FILE.exists():
              with open(config.SPEAKERS_YAML_FILE, 'w') as f:
                  yaml.dump({}, f) # Initialize with an empty dict, as per previous fixes

      def _load_speakers_data(self) -> Dict[str, Any]: # Changed return type to Dict
          """Loads speaker data from the YAML file."""
          try:
              with open(config.SPEAKERS_YAML_FILE, 'r') as f:
                  data = yaml.safe_load(f)
                  return data if isinstance(data, dict) else {} # Ensure it's a dict
          except FileNotFoundError:
              return {}
          except yaml.YAMLError:
              # Handle corrupted YAML file, e.g., log error and return empty list
              print(f"Error: Corrupted speakers YAML file at {config.SPEAKERS_YAML_FILE}")
              return {}


      def _save_speakers_data(self):
          """Saves the current speaker data to the YAML file."""
          with open(config.SPEAKERS_YAML_FILE, 'w') as f:
              yaml.dump(self.speakers_data, f, sort_keys=False)

      def get_speakers(self) -> List[Speaker]:
          """Returns a list of all speakers."""
          # self.speakers_data is now a dict: {speaker_id: {name: ..., sample_path: ...}}
          return [Speaker(id=spk_id, **spk_attrs) for spk_id, spk_attrs in self.speakers_data.items()]

      def get_speaker_by_id(self, speaker_id: str) -> Optional[Speaker]:
          """Retrieves a speaker by their ID."""
          if speaker_id in self.speakers_data:
              speaker_attributes = self.speakers_data[speaker_id]
              return Speaker(id=speaker_id, **speaker_attributes)
          return None

      async def add_speaker(self, name: str, audio_file: UploadFile) -> Speaker:
          """Adds a new speaker, converts sample to WAV, saves it, and updates YAML."""
          speaker_id = str(uuid.uuid4())
          
          # Define standardized sample filename and path (always WAV)
          sample_filename = f"{speaker_id}.wav"
          sample_path = config.SPEAKER_SAMPLES_DIR / sample_filename
          
          try:
              content = await audio_file.read()
              # Use BytesIO to handle the in-memory audio data for torchaudio
              audio_buffer = io.BytesIO(content)
              
              # Load audio data using torchaudio, this handles various formats (MP3, WAV, etc.)
              # waveform is a tensor, sample_rate is an int
              waveform, sample_rate = torchaudio.load(audio_buffer)
              
              # Save the audio data as WAV
              # Ensure the SPEAKER_SAMPLES_DIR exists (though _ensure_data_files_exist should handle it)
              config.SPEAKER_SAMPLES_DIR.mkdir(parents=True, exist_ok=True)
              torchaudio.save(str(sample_path), waveform, sample_rate, format="wav")
              
          except torchaudio.TorchaudioException as e:
              # More specific error for torchaudio issues (e.g. unsupported format, corrupted file)
              raise HTTPException(status_code=400, detail=f"Error processing audio file: {e}. Ensure it's a valid audio format (e.g., WAV, MP3).")
          except Exception as e:
              # General error handling for other issues (e.g., file system errors)
              raise HTTPException(status_code=500, detail=f"Could not save audio file: {e}")
          finally:
              await audio_file.close()

          new_speaker_data = {
              "id": speaker_id,
              "name": name,
              "sample_path": str(sample_path.relative_to(config.SPEAKER_DATA_BASE_DIR)) # Store path relative to speaker_data dir
          }
          
          # self.speakers_data is now a dict
          self.speakers_data[speaker_id] = {
              "name": name,
              "sample_path": str(sample_path.relative_to(config.SPEAKER_DATA_BASE_DIR))
          }
          self._save_speakers_data()
          # Construct Speaker model for return, including the ID
          return Speaker(id=speaker_id, name=name, sample_path=str(sample_path.relative_to(config.SPEAKER_DATA_BASE_DIR)))

      def delete_speaker(self, speaker_id: str) -> bool:
          """Deletes a speaker and their audio sample."""
          # Speaker data is now a dictionary, keyed by speaker_id
          speaker_to_delete = self.speakers_data.pop(speaker_id, None)
          
          if speaker_to_delete:
              self._save_speakers_data()
              sample_path_str = speaker_to_delete.get("sample_path")
              if sample_path_str:
                  # sample_path_str is relative to SPEAKER_DATA_BASE_DIR
                  full_sample_path = config.SPEAKER_DATA_BASE_DIR / sample_path_str
                  try:
                      if full_sample_path.is_file(): # Check if it's a file before removing
                          os.remove(full_sample_path)
                  except OSError as e:
                      # Log error if file deletion fails but proceed
                      print(f"Error deleting sample file {full_sample_path}: {e}")
              return True
          return False

  # Example usage (for testing, not part of the service itself)
  if __name__ == "__main__":
      service = SpeakerManagementService()
      print("Initial speakers:", service.get_speakers())
      
      # This part would require a mock UploadFile to run directly
      # print("\nAdding a new speaker (manual test setup needed for UploadFile)")
      # class MockUploadFile:
      #     def __init__(self, filename, content):
      #         self.filename = filename
      #         self._content = content
      #     async def read(self): return self._content
      #     async def close(self): pass
      # import asyncio
      # async def test_add():
      #     mock_file = MockUploadFile("test.wav", b"dummy audio content")
      #     new_speaker = await service.add_speaker(name="Test Speaker", audio_file=mock_file)
      #     print("\nAdded speaker:", new_speaker)
      #     print("Speakers after add:", service.get_speakers())
      #     return new_speaker.id
      # speaker_id_to_delete = asyncio.run(test_add())
      # if speaker_id_to_delete:
      #     print(f"\nDeleting speaker {speaker_id_to_delete}")
      #     service.delete_speaker(speaker_id_to_delete)
      #     print("Speakers after delete:", service.get_speakers())


id: 25e77304-9a6e-4624-95f1-f73561589433
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/backend/app/services/speaker_service.py
version: v2
content:
  import yaml
  import uuid
  import os
  import io # Added for BytesIO
  import torchaudio # Added for audio processing
  from pathlib import Path
  from typing import List, Dict, Optional, Any

  from fastapi import UploadFile, HTTPException
  try:
      from app.models.speaker_models import Speaker, SpeakerCreate
      from app import config
  except ModuleNotFoundError:
      # When imported from scripts at project root
      from backend.app.models.speaker_models import Speaker, SpeakerCreate
      from backend.app import config

  class SpeakerManagementService:
      def __init__(self):
          self._ensure_data_files_exist()
          self.speakers_data = self._load_speakers_data()

      def _ensure_data_files_exist(self):
          """Ensures the speaker data directory and YAML file exist."""
          config.SPEAKER_DATA_BASE_DIR.mkdir(parents=True, exist_ok=True)
          config.SPEAKER_SAMPLES_DIR.mkdir(parents=True, exist_ok=True)
          if not config.SPEAKERS_YAML_FILE.exists():
              with open(config.SPEAKERS_YAML_FILE, 'w') as f:
                  yaml.dump({}, f) # Initialize with an empty dict, as per previous fixes

      def _load_speakers_data(self) -> Dict[str, Any]: # Changed return type to Dict
          """Loads speaker data from the YAML file."""
          try:
              with open(config.SPEAKERS_YAML_FILE, 'r') as f:
                  data = yaml.safe_load(f)
                  return data if isinstance(data, dict) else {} # Ensure it's a dict
          except FileNotFoundError:
              return {}
          except yaml.YAMLError:
              # Handle corrupted YAML file, e.g., log error and return empty list
              print(f"Error: Corrupted speakers YAML file at {config.SPEAKERS_YAML_FILE}")
              return {}


      def _save_speakers_data(self):
          """Saves the current speaker data to the YAML file."""
          with open(config.SPEAKERS_YAML_FILE, 'w') as f:
              yaml.dump(self.speakers_data, f, sort_keys=False)

      def get_speakers(self) -> List[Speaker]:
          """Returns a list of all speakers."""
          # self.speakers_data is now a dict: {speaker_id: {name: ..., sample_path: ...}}
          return [Speaker(id=spk_id, **spk_attrs) for spk_id, spk_attrs in self.speakers_data.items()]

      def get_speaker_by_id(self, speaker_id: str) -> Optional[Speaker]:
          """Retrieves a speaker by their ID."""
          if speaker_id in self.speakers_data:
              speaker_attributes = self.speakers_data[speaker_id]
              return Speaker(id=speaker_id, **speaker_attributes)
          return None

      async def add_speaker(self, name: str, audio_file: UploadFile) -> Speaker:
          """Adds a new speaker, converts sample to WAV, saves it, and updates YAML."""
          speaker_id = str(uuid.uuid4())
          
          # Define standardized sample filename and path (always WAV)
          sample_filename = f"{speaker_id}.wav"
          sample_path = config.SPEAKER_SAMPLES_DIR / sample_filename
          
          try:
              content = await audio_file.read()
              # Use BytesIO to handle the in-memory audio data for torchaudio
              audio_buffer = io.BytesIO(content)
              
              # Load audio data using torchaudio, this handles various formats (MP3, WAV, etc.)
              # waveform is a tensor, sample_rate is an int
              waveform, sample_rate = torchaudio.load(audio_buffer)
              
              # Save the audio data as WAV
              # Ensure the SPEAKER_SAMPLES_DIR exists (though _ensure_data_files_exist should handle it)
              config.SPEAKER_SAMPLES_DIR.mkdir(parents=True, exist_ok=True)
              torchaudio.save(str(sample_path), waveform, sample_rate, format="wav")
              
          except torchaudio.TorchaudioException as e:
              # More specific error for torchaudio issues (e.g. unsupported format, corrupted file)
              raise HTTPException(status_code=400, detail=f"Error processing audio file: {e}. Ensure it's a valid audio format (e.g., WAV, MP3).")
          except Exception as e:
              # General error handling for other issues (e.g., file system errors)
              raise HTTPException(status_code=500, detail=f"Could not save audio file: {e}")
          finally:
              await audio_file.close()

          new_speaker_data = {
              "id": speaker_id,
              "name": name,
              "sample_path": str(sample_path.relative_to(config.SPEAKER_DATA_BASE_DIR)) # Store path relative to speaker_data dir
          }
          
          # self.speakers_data is now a dict
          self.speakers_data[speaker_id] = {
              "name": name,
              "sample_path": str(sample_path.relative_to(config.SPEAKER_DATA_BASE_DIR))
          }
          self._save_speakers_data()
          # Construct Speaker model for return, including the ID
          return Speaker(id=speaker_id, name=name, sample_path=str(sample_path.relative_to(config.SPEAKER_DATA_BASE_DIR)))

      def delete_speaker(self, speaker_id: str) -> bool:
          """Deletes a speaker and their audio sample."""
          # Speaker data is now a dictionary, keyed by speaker_id
          speaker_to_delete = self.speakers_data.pop(speaker_id, None)
          
          if speaker_to_delete:
              self._save_speakers_data()
              sample_path_str = speaker_to_delete.get("sample_path")
              if sample_path_str:
                  # sample_path_str is relative to SPEAKER_DATA_BASE_DIR
                  full_sample_path = config.SPEAKER_DATA_BASE_DIR / sample_path_str
                  try:
                      if full_sample_path.is_file(): # Check if it's a file before removing
                          os.remove(full_sample_path)
                  except OSError as e:
                      # Log error if file deletion fails but proceed
                      print(f"Error deleting sample file {full_sample_path}: {e}")
              return True
          return False

  # Example usage (for testing, not part of the service itself)
  if __name__ == "__main__":
      service = SpeakerManagementService()
      print("Initial speakers:", service.get_speakers())
      
      # This part would require a mock UploadFile to run directly
      # print("\nAdding a new speaker (manual test setup needed for UploadFile)")
      # class MockUploadFile:
      #     def __init__(self, filename, content):
      #         self.filename = filename
      #         self._content = content
      #     async def read(self): return self._content
      #     async def close(self): pass
      # import asyncio
      # async def test_add():
      #     mock_file = MockUploadFile("test.wav", b"dummy audio content")
      #     new_speaker = await service.add_speaker(name="Test Speaker", audio_file=mock_file)
      #     print("\nAdded speaker:", new_speaker)
      #     print("Speakers after add:", service.get_speakers())
      #     return new_speaker.id
      # speaker_id_to_delete = asyncio.run(test_add())
      # if speaker_id_to_delete:
      #     print(f"\nDeleting speaker {speaker_id_to_delete}")
      #     service.delete_speaker(speaker_id_to_delete)
      #     print("Speakers after delete:", service.get_speakers())


id: 56b1cde3-21cd-42f2-894f-9ce3e0e88d75
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/backend/app/services/dialog_processor_service.py
version: initial
content:
  from pathlib import Path
  from typing import List, Dict, Any, Union
  import re

  from .tts_service import TTSService
  from .speaker_service import SpeakerManagementService
  from app import config
  # Potentially models for dialog structure if we define them
  # from ..models.dialog_models import DialogItem # Example

  class DialogProcessorService:
      def __init__(self, tts_service: TTSService, speaker_service: SpeakerManagementService):
          self.tts_service = tts_service
          self.speaker_service = speaker_service
          # Base directory for storing individual audio segments during processing
          self.temp_audio_dir = config.TTS_TEMP_OUTPUT_DIR
          self.temp_audio_dir.mkdir(parents=True, exist_ok=True)

      def _split_text(self, text: str, max_length: int = 300) -> List[str]:
          """
          Splits text into chunks suitable for TTS processing, attempting to respect sentence boundaries.
          Similar to split_text_at_sentence_boundaries from the original Gradio app.
          Max_length is approximate, as it tries to finish sentences.
          """
          # Basic sentence splitting using common delimiters. More sophisticated NLP could be used.
          # This regex tries to split by '.', '!', '?', '...', followed by space or end of string.
          # It also handles cases where these delimiters might be followed by quotes or parentheses.
          sentences = re.split(r'(?<=[.!?\u2026])\s+|(?<=[.!?\u2026])(?=["\')\]\}\u201d\u2019])|(?<=[.!?\u2026])$', text.strip())
          sentences = [s.strip() for s in sentences if s and s.strip()]

          chunks = []
          current_chunk = ""
          for sentence in sentences:
              if not sentence:
                  continue
              if not current_chunk: # First sentence for this chunk
                  current_chunk = sentence
              elif len(current_chunk) + len(sentence) + 1 <= max_length:
                  current_chunk += " " + sentence
              else:
                  chunks.append(current_chunk)
                  current_chunk = sentence
          
          if current_chunk: # Add the last chunk
              chunks.append(current_chunk)
          
          # Further split any chunks that are still too long (e.g., a single very long sentence)
          final_chunks = []
          for chunk in chunks:
              if len(chunk) > max_length:
                  # Simple split by length if a sentence itself is too long
                  for i in range(0, len(chunk), max_length):
                      final_chunks.append(chunk[i:i+max_length])
              else:
                  final_chunks.append(chunk)
          return final_chunks

      async def process_dialog(self, dialog_items: List[Dict[str, Any]], output_base_name: str) -> Dict[str, Any]:
          """
          Processes a list of dialog items (speech or silence) to generate audio segments.
          
          Args:
              dialog_items: A list of dictionaries, where each item has:
                            - 'type': 'speech' or 'silence'
                            - For 'speech': 'speaker_id': str, 'text': str
                            - For 'silence': 'duration': float (in seconds)
              output_base_name: The base name for the output files.

          Returns:
              A dictionary containing paths to generated segments and other processing info.
              Example: {
                  "log": "Processing complete...",
                  "segment_files": [
                      {"type": "speech", "path": "/path/to/segment1.wav", "speaker_id": "X", "text_chunk": "..."},
                      {"type": "silence", "duration": 0.5},
                      {"type": "speech", "path": "/path/to/segment2.wav", "speaker_id": "Y", "text_chunk": "..."}
                  ],
                  "temp_dir": str(self.temp_audio_dir / output_base_name)
              }
          """
          segment_results = []
          processing_log = []
          
          # Create a unique subdirectory for this dialog's temporary files
          dialog_temp_dir = self.temp_audio_dir / output_base_name
          dialog_temp_dir.mkdir(parents=True, exist_ok=True)
          processing_log.append(f"Created temporary directory for segments: {dialog_temp_dir}")

          import shutil
          segment_idx = 0
          for i, item in enumerate(dialog_items):
              item_type = item.get("type")
              processing_log.append(f"Processing item {i+1}: type='{item_type}'")

              # --- Universal: Handle reuse of existing audio for both speech and silence ---
              use_existing_audio = item.get("use_existing_audio", False)
              audio_url = item.get("audio_url")
              if use_existing_audio and audio_url:
                  # Determine source path (handle both absolute and relative)
                  # Map web URL to actual file location in tts_generated_dialogs
                  if audio_url.startswith("/generated_audio/"):
                      src_audio_path = config.DIALOG_OUTPUT_DIR / audio_url[len("/generated_audio/"):]
                  else:
                      src_audio_path = Path(audio_url)
                      if not src_audio_path.is_absolute():
                          # Assume relative to the generated audio root dir
                          src_audio_path = config.DIALOG_OUTPUT_DIR / audio_url.lstrip("/\\")
                  # Now src_audio_path should point to the real file in tts_generated_dialogs
                  if src_audio_path.is_file():
                      segment_filename = f"{output_base_name}_seg{segment_idx}_reused.wav"
                      dest_path = (self.temp_audio_dir / output_base_name / segment_filename)
                      try:
                          if not src_audio_path.exists():
                              processing_log.append(f"[REUSE] Source audio file does not exist: {src_audio_path}")
                          else:
                              processing_log.append(f"[REUSE] Source audio file exists: {src_audio_path}, size={src_audio_path.stat().st_size} bytes")
                          shutil.copyfile(src_audio_path, dest_path)
                          if not dest_path.exists():
                              processing_log.append(f"[REUSE] Destination audio file was not created: {dest_path}")
                          else:
                              processing_log.append(f"[REUSE] Destination audio file created: {dest_path}, size={dest_path.stat().st_size} bytes")
                          # Only include 'type' and 'path' so the concatenator always includes this segment
                          segment_results.append({
                              "type": item_type,
                              "path": str(dest_path)
                          })
                          processing_log.append(f"Reused existing audio for item {i+1}: copied from {src_audio_path} to {dest_path}")
                      except Exception as e:
                          error_message = f"Failed to copy reused audio for item {i+1}: {e}"
                          processing_log.append(error_message)
                          segment_results.append({"type": "error", "message": error_message})
                      segment_idx += 1
                      continue
                  else:
                      error_message = f"Audio file for reuse not found at {src_audio_path} for item {i+1}."
                      processing_log.append(error_message)
                      segment_results.append({"type": "error", "message": error_message})
                      segment_idx += 1
                      continue

              if item_type == "speech":
                  speaker_id = item.get("speaker_id")
                  text = item.get("text")
                  if not speaker_id or not text:
                      processing_log.append(f"Skipping speech item {i+1} due to missing speaker_id or text.")
                      segment_results.append({"type": "error", "message": "Missing speaker_id or text"})
                      continue

                  # Validate speaker_id and get speaker_sample_path
                  speaker_info = self.speaker_service.get_speaker_by_id(speaker_id)
                  if not speaker_info:
                      processing_log.append(f"Speaker ID '{speaker_id}' not found. Skipping item {i+1}.")
                      segment_results.append({"type": "error", "message": f"Speaker ID '{speaker_id}' not found"})
                      continue
                  if not speaker_info.sample_path:
                      processing_log.append(f"Speaker ID '{speaker_id}' has no sample path defined. Skipping item {i+1}.")
                      segment_results.append({"type": "error", "message": f"Speaker ID '{speaker_id}' has no sample path defined"})
                      continue

                  # speaker_info.sample_path is relative to config.SPEAKER_DATA_BASE_DIR
                  abs_speaker_sample_path = config.SPEAKER_DATA_BASE_DIR / speaker_info.sample_path
                  if not abs_speaker_sample_path.is_file():
                      processing_log.append(f"Speaker sample file not found or is not a file at '{abs_speaker_sample_path}' for speaker ID '{speaker_id}'. Skipping item {i+1}.")
                      segment_results.append({"type": "error", "message": f"Speaker sample not a file or not found: {abs_speaker_sample_path}"})
                      continue

                  text_chunks = self._split_text(text)
                  processing_log.append(f"Split text for speaker '{speaker_id}' into {len(text_chunks)} chunk(s).")

                  for chunk_idx, text_chunk in enumerate(text_chunks):
                      segment_filename_base = f"{output_base_name}_seg{segment_idx}_spk{speaker_id}_chunk{chunk_idx}"
                      processing_log.append(f"Generating speech for chunk: '{text_chunk[:50]}...' using speaker '{speaker_id}'")
                      
                      try:
                          segment_output_path = await self.tts_service.generate_speech(
                              text=text_chunk,
                              speaker_id=speaker_id, # For metadata, actual sample path is used by TTS
                              speaker_sample_path=str(abs_speaker_sample_path),
                              output_filename_base=segment_filename_base,
                              output_dir=dialog_temp_dir, # Save to the dialog's temp dir
                              exaggeration=item.get('exaggeration', 0.5), # Default from Gradio, Pydantic model should provide this
                              cfg_weight=item.get('cfg_weight', 0.5),     # Default from Gradio, Pydantic model should provide this
                              temperature=item.get('temperature', 0.8)  # Default from Gradio, Pydantic model should provide this
                          )
                          segment_results.append({
                              "type": "speech", 
                              "path": str(segment_output_path), 
                              "speaker_id": speaker_id, 
                              "text_chunk": text_chunk
                          })
                          processing_log.append(f"Successfully generated segment: {segment_output_path}")
                      except Exception as e:
                          error_message = f"Error generating speech for chunk '{text_chunk[:50]}...': {repr(e)}"
                          processing_log.append(error_message)
                          segment_results.append({"type": "error", "message": error_message, "text_chunk": text_chunk})
                      segment_idx += 1
              
              elif item_type == "silence":
                  duration = item.get("duration")
                  if duration is None or duration < 0:
                      processing_log.append(f"Skipping silence item {i+1} due to invalid duration.")
                      segment_results.append({"type": "error", "message": "Invalid duration for silence"})
                      continue
                  segment_results.append({"type": "silence", "duration": float(duration)})
                  processing_log.append(f"Added silence of {duration}s.")
              
              else:
                  processing_log.append(f"Unknown item type '{item_type}' at item {i+1}. Skipping.")
                  segment_results.append({"type": "error", "message": f"Unknown item type: {item_type}"})

          # Log the full segment_results list for debugging
          processing_log.append("[DEBUG] Final segment_results list:")
          for idx, seg in enumerate(segment_results):
              processing_log.append(f"  [{idx}] {seg}")

          return {
              "log": "\n".join(processing_log),
              "segment_files": segment_results,
              "temp_dir": str(dialog_temp_dir) # For cleanup or zipping later
          }

  if __name__ == "__main__":
      import asyncio
      import pprint

      async def main_test():
          # Initialize services
          tts_service = TTSService(device="mps") # or your preferred device
          speaker_service = SpeakerManagementService()
          dialog_processor = DialogProcessorService(tts_service, speaker_service)

          # Ensure dummy speaker sample exists (TTSService test block usually creates this)
          # For robustness, we can call the TTSService test logic or ensure it's run prior.
          # Here, we assume dummy_speaker_test.wav is available as per previous steps.
          # If not, the 'test_speaker_for_dialog_proc' will fail file validation.

          # First, ensure the dummy speaker file is created by TTSService's own test logic
          # This is a bit of a hack for testing; ideally, test assets are managed independently.
          try:
              print("Ensuring dummy speaker sample is created by running TTSService's main_test logic...")
              from .tts_service import main_test as tts_main_test
              await tts_main_test() # This will create the dummy_speaker_test.wav
              print("TTSService main_test completed, dummy sample should exist.")
          except ImportError:
              print("Could not import tts_service.main_test directly. Ensure dummy_speaker_test.wav exists.")
          except Exception as e:
              print(f"Error running tts_service.main_test for dummy sample creation: {e}")
              print("Proceeding, but 'test_speaker_for_dialog_proc' might fail if sample is missing.")

          sample_dialog_items = [
              {
                  "type": "speech",
                  "speaker_id": "test_speaker_for_dialog_proc", # Defined in speakers.yaml
                  "text": "Hello world! This is the first speech segment."
              },
              {
                  "type": "silence",
                  "duration": 0.75
              },
              {
                  "type": "speech",
                  "speaker_id": "test_speaker_for_dialog_proc",
                  "text": "This is a much longer piece of text that should definitely be split into multiple, smaller chunks by the dialog processor. It contains several sentences. Let's see how it handles this. The maximum length is set to 300 characters, but it tries to respect sentence boundaries. This sentence itself is quite long and might even be split mid-sentence if it exceeds the hard limit after sentence splitting. We will observe the output carefully to ensure it works as expected, creating multiple audio files for this single text block if necessary."
              },
              {
                  "type": "speech",
                  "speaker_id": "non_existent_speaker_id",
                  "text": "This should fail because the speaker does not exist."
              },
              {
                  "type": "invalid_type", 
                  "text": "This item has an invalid type."
              },
              {
                  "type": "speech",
                  "speaker_id": "test_speaker_for_dialog_proc",
                  "text": None # Test missing text
              },
              {
                  "type": "speech",
                  "speaker_id": None, # Test missing speaker_id
                  "text": "This is a test with a missing speaker ID."
              },
              {
                  "type": "silence",
                  "duration": -0.5 # Invalid duration
              }
          ]

          output_base_name = "dialog_processor_test_run"

          try:
              print(f"\nLoading TTS model for DialogProcessorService test...")
              # TTSService's generate_speech will load the model if not already loaded.
              # However, explicit load/unload is good practice for a test block.
              tts_service.load_model()
              
              print(f"\nProcessing dialog items with base name: {output_base_name}...")
              results = await dialog_processor.process_dialog(sample_dialog_items, output_base_name)
              
              print("\n--- Processing Log ---")
              print(results.get("log"))
              print("\n--- Segment Files / Results ---")
              pprint.pprint(results.get("segment_files"))
              print(f"\nTemporary directory used: {results.get('temp_dir')}")
              print("\nPlease check the temporary directory for generated audio segments.")

          except Exception as e:
              import traceback
              print(f"\nAn error occurred during the DialogProcessorService test:")
              traceback.print_exc()
          finally:
              print("\nUnloading TTS model...")
              tts_service.unload_model()
              print("DialogProcessorService test finished.")

      asyncio.run(main_test())


id: c907efc1-ae79-4a78-bb7d-d4a65c8ab8c4
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/backend/app/services/dialog_processor_service.py
version: v2
content:
  from pathlib import Path
  from typing import List, Dict, Any, Union
  import re

  from .tts_service import TTSService
  from .speaker_service import SpeakerManagementService
  try:
      from app import config
  except ModuleNotFoundError:
      # When imported from scripts at project root
      from backend.app import config
  # Potentially models for dialog structure if we define them
  # from ..models.dialog_models import DialogItem # Example

  class DialogProcessorService:
      def __init__(self, tts_service: TTSService, speaker_service: SpeakerManagementService):
          self.tts_service = tts_service
          self.speaker_service = speaker_service
          # Base directory for storing individual audio segments during processing
          self.temp_audio_dir = config.TTS_TEMP_OUTPUT_DIR
          self.temp_audio_dir.mkdir(parents=True, exist_ok=True)

      def _split_text(self, text: str, max_length: int = 300) -> List[str]:
          """
          Splits text into chunks suitable for TTS processing, attempting to respect sentence boundaries.
          Similar to split_text_at_sentence_boundaries from the original Gradio app.
          Max_length is approximate, as it tries to finish sentences.
          """
          # Basic sentence splitting using common delimiters. More sophisticated NLP could be used.
          # This regex tries to split by '.', '!', '?', '...', followed by space or end of string.
          # It also handles cases where these delimiters might be followed by quotes or parentheses.
          sentences = re.split(r'(?<=[.!?\u2026])\s+|(?<=[.!?\u2026])(?=["\')\]\}\u201d\u2019])|(?<=[.!?\u2026])$', text.strip())
          sentences = [s.strip() for s in sentences if s and s.strip()]

          chunks = []
          current_chunk = ""
          for sentence in sentences:
              if not sentence:
                  continue
              if not current_chunk: # First sentence for this chunk
                  current_chunk = sentence
              elif len(current_chunk) + len(sentence) + 1 <= max_length:
                  current_chunk += " " + sentence
              else:
                  chunks.append(current_chunk)
                  current_chunk = sentence
          
          if current_chunk: # Add the last chunk
              chunks.append(current_chunk)
          
          # Further split any chunks that are still too long (e.g., a single very long sentence)
          final_chunks = []
          for chunk in chunks:
              if len(chunk) > max_length:
                  # Simple split by length if a sentence itself is too long
                  for i in range(0, len(chunk), max_length):
                      final_chunks.append(chunk[i:i+max_length])
              else:
                  final_chunks.append(chunk)
          return final_chunks

      async def process_dialog(self, dialog_items: List[Dict[str, Any]], output_base_name: str) -> Dict[str, Any]:
          """
          Processes a list of dialog items (speech or silence) to generate audio segments.
          
          Args:
              dialog_items: A list of dictionaries, where each item has:
                            - 'type': 'speech' or 'silence'
                            - For 'speech': 'speaker_id': str, 'text': str
                            - For 'silence': 'duration': float (in seconds)
              output_base_name: The base name for the output files.

          Returns:
              A dictionary containing paths to generated segments and other processing info.
              Example: {
                  "log": "Processing complete...",
                  "segment_files": [
                      {"type": "speech", "path": "/path/to/segment1.wav", "speaker_id": "X", "text_chunk": "..."},
                      {"type": "silence", "duration": 0.5},
                      {"type": "speech", "path": "/path/to/segment2.wav", "speaker_id": "Y", "text_chunk": "..."}
                  ],
                  "temp_dir": str(self.temp_audio_dir / output_base_name)
              }
          """
          segment_results = []
          processing_log = []
          
          # Create a unique subdirectory for this dialog's temporary files
          dialog_temp_dir = self.temp_audio_dir / output_base_name
          dialog_temp_dir.mkdir(parents=True, exist_ok=True)
          processing_log.append(f"Created temporary directory for segments: {dialog_temp_dir}")

          import shutil
          segment_idx = 0
          for i, item in enumerate(dialog_items):
              item_type = item.get("type")
              processing_log.append(f"Processing item {i+1}: type='{item_type}'")

              # --- Universal: Handle reuse of existing audio for both speech and silence ---
              use_existing_audio = item.get("use_existing_audio", False)
              audio_url = item.get("audio_url")
              if use_existing_audio and audio_url:
                  # Determine source path (handle both absolute and relative)
                  # Map web URL to actual file location in tts_generated_dialogs
                  if audio_url.startswith("/generated_audio/"):
                      src_audio_path = config.DIALOG_OUTPUT_DIR / audio_url[len("/generated_audio/"):]
                  else:
                      src_audio_path = Path(audio_url)
                      if not src_audio_path.is_absolute():
                          # Assume relative to the generated audio root dir
                          src_audio_path = config.DIALOG_OUTPUT_DIR / audio_url.lstrip("/\\")
                  # Now src_audio_path should point to the real file in tts_generated_dialogs
                  if src_audio_path.is_file():
                      segment_filename = f"{output_base_name}_seg{segment_idx}_reused.wav"
                      dest_path = (self.temp_audio_dir / output_base_name / segment_filename)
                      try:
                          if not src_audio_path.exists():
                              processing_log.append(f"[REUSE] Source audio file does not exist: {src_audio_path}")
                          else:
                              processing_log.append(f"[REUSE] Source audio file exists: {src_audio_path}, size={src_audio_path.stat().st_size} bytes")
                          shutil.copyfile(src_audio_path, dest_path)
                          if not dest_path.exists():
                              processing_log.append(f"[REUSE] Destination audio file was not created: {dest_path}")
                          else:
                              processing_log.append(f"[REUSE] Destination audio file created: {dest_path}, size={dest_path.stat().st_size} bytes")
                          # Only include 'type' and 'path' so the concatenator always includes this segment
                          segment_results.append({
                              "type": item_type,
                              "path": str(dest_path)
                          })
                          processing_log.append(f"Reused existing audio for item {i+1}: copied from {src_audio_path} to {dest_path}")
                      except Exception as e:
                          error_message = f"Failed to copy reused audio for item {i+1}: {e}"
                          processing_log.append(error_message)
                          segment_results.append({"type": "error", "message": error_message})
                      segment_idx += 1
                      continue
                  else:
                      error_message = f"Audio file for reuse not found at {src_audio_path} for item {i+1}."
                      processing_log.append(error_message)
                      segment_results.append({"type": "error", "message": error_message})
                      segment_idx += 1
                      continue

              if item_type == "speech":
                  speaker_id = item.get("speaker_id")
                  text = item.get("text")
                  if not speaker_id or not text:
                      processing_log.append(f"Skipping speech item {i+1} due to missing speaker_id or text.")
                      segment_results.append({"type": "error", "message": "Missing speaker_id or text"})
                      continue

                  # Validate speaker_id and get speaker_sample_path
                  speaker_info = self.speaker_service.get_speaker_by_id(speaker_id)
                  if not speaker_info:
                      processing_log.append(f"Speaker ID '{speaker_id}' not found. Skipping item {i+1}.")
                      segment_results.append({"type": "error", "message": f"Speaker ID '{speaker_id}' not found"})
                      continue
                  if not speaker_info.sample_path:
                      processing_log.append(f"Speaker ID '{speaker_id}' has no sample path defined. Skipping item {i+1}.")
                      segment_results.append({"type": "error", "message": f"Speaker ID '{speaker_id}' has no sample path defined"})
                      continue

                  # speaker_info.sample_path is relative to config.SPEAKER_DATA_BASE_DIR
                  abs_speaker_sample_path = config.SPEAKER_DATA_BASE_DIR / speaker_info.sample_path
                  if not abs_speaker_sample_path.is_file():
                      processing_log.append(f"Speaker sample file not found or is not a file at '{abs_speaker_sample_path}' for speaker ID '{speaker_id}'. Skipping item {i+1}.")
                      segment_results.append({"type": "error", "message": f"Speaker sample not a file or not found: {abs_speaker_sample_path}"})
                      continue

                  text_chunks = self._split_text(text)
                  processing_log.append(f"Split text for speaker '{speaker_id}' into {len(text_chunks)} chunk(s).")

                  for chunk_idx, text_chunk in enumerate(text_chunks):
                      segment_filename_base = f"{output_base_name}_seg{segment_idx}_spk{speaker_id}_chunk{chunk_idx}"
                      processing_log.append(f"Generating speech for chunk: '{text_chunk[:50]}...' using speaker '{speaker_id}'")
                      
                      try:
                          segment_output_path = await self.tts_service.generate_speech(
                              text=text_chunk,
                              speaker_id=speaker_id, # For metadata, actual sample path is used by TTS
                              speaker_sample_path=str(abs_speaker_sample_path),
                              output_filename_base=segment_filename_base,
                              output_dir=dialog_temp_dir, # Save to the dialog's temp dir
                              exaggeration=item.get('exaggeration', 0.5), # Default from Gradio, Pydantic model should provide this
                              cfg_weight=item.get('cfg_weight', 0.5),     # Default from Gradio, Pydantic model should provide this
                              temperature=item.get('temperature', 0.8)  # Default from Gradio, Pydantic model should provide this
                          )
                          segment_results.append({
                              "type": "speech", 
                              "path": str(segment_output_path), 
                              "speaker_id": speaker_id, 
                              "text_chunk": text_chunk
                          })
                          processing_log.append(f"Successfully generated segment: {segment_output_path}")
                      except Exception as e:
                          error_message = f"Error generating speech for chunk '{text_chunk[:50]}...': {repr(e)}"
                          processing_log.append(error_message)
                          segment_results.append({"type": "error", "message": error_message, "text_chunk": text_chunk})
                      segment_idx += 1
              
              elif item_type == "silence":
                  duration = item.get("duration")
                  if duration is None or duration < 0:
                      processing_log.append(f"Skipping silence item {i+1} due to invalid duration.")
                      segment_results.append({"type": "error", "message": "Invalid duration for silence"})
                      continue
                  segment_results.append({"type": "silence", "duration": float(duration)})
                  processing_log.append(f"Added silence of {duration}s.")
              
              else:
                  processing_log.append(f"Unknown item type '{item_type}' at item {i+1}. Skipping.")
                  segment_results.append({"type": "error", "message": f"Unknown item type: {item_type}"})

          # Log the full segment_results list for debugging
          processing_log.append("[DEBUG] Final segment_results list:")
          for idx, seg in enumerate(segment_results):
              processing_log.append(f"  [{idx}] {seg}")

          return {
              "log": "\n".join(processing_log),
              "segment_files": segment_results,
              "temp_dir": str(dialog_temp_dir) # For cleanup or zipping later
          }

  if __name__ == "__main__":
      import asyncio
      import pprint

      async def main_test():
          # Initialize services
          tts_service = TTSService(device="mps") # or your preferred device
          speaker_service = SpeakerManagementService()
          dialog_processor = DialogProcessorService(tts_service, speaker_service)

          # Ensure dummy speaker sample exists (TTSService test block usually creates this)
          # For robustness, we can call the TTSService test logic or ensure it's run prior.
          # Here, we assume dummy_speaker_test.wav is available as per previous steps.
          # If not, the 'test_speaker_for_dialog_proc' will fail file validation.

          # First, ensure the dummy speaker file is created by TTSService's own test logic
          # This is a bit of a hack for testing; ideally, test assets are managed independently.
          try:
              print("Ensuring dummy speaker sample is created by running TTSService's main_test logic...")
              from .tts_service import main_test as tts_main_test
              await tts_main_test() # This will create the dummy_speaker_test.wav
              print("TTSService main_test completed, dummy sample should exist.")
          except ImportError:
              print("Could not import tts_service.main_test directly. Ensure dummy_speaker_test.wav exists.")
          except Exception as e:
              print(f"Error running tts_service.main_test for dummy sample creation: {e}")
              print("Proceeding, but 'test_speaker_for_dialog_proc' might fail if sample is missing.")

          sample_dialog_items = [
              {
                  "type": "speech",
                  "speaker_id": "test_speaker_for_dialog_proc", # Defined in speakers.yaml
                  "text": "Hello world! This is the first speech segment."
              },
              {
                  "type": "silence",
                  "duration": 0.75
              },
              {
                  "type": "speech",
                  "speaker_id": "test_speaker_for_dialog_proc",
                  "text": "This is a much longer piece of text that should definitely be split into multiple, smaller chunks by the dialog processor. It contains several sentences. Let's see how it handles this. The maximum length is set to 300 characters, but it tries to respect sentence boundaries. This sentence itself is quite long and might even be split mid-sentence if it exceeds the hard limit after sentence splitting. We will observe the output carefully to ensure it works as expected, creating multiple audio files for this single text block if necessary."
              },
              {
                  "type": "speech",
                  "speaker_id": "non_existent_speaker_id",
                  "text": "This should fail because the speaker does not exist."
              },
              {
                  "type": "invalid_type", 
                  "text": "This item has an invalid type."
              },
              {
                  "type": "speech",
                  "speaker_id": "test_speaker_for_dialog_proc",
                  "text": None # Test missing text
              },
              {
                  "type": "speech",
                  "speaker_id": None, # Test missing speaker_id
                  "text": "This is a test with a missing speaker ID."
              },
              {
                  "type": "silence",
                  "duration": -0.5 # Invalid duration
              }
          ]

          output_base_name = "dialog_processor_test_run"

          try:
              print(f"\nLoading TTS model for DialogProcessorService test...")
              # TTSService's generate_speech will load the model if not already loaded.
              # However, explicit load/unload is good practice for a test block.
              tts_service.load_model()
              
              print(f"\nProcessing dialog items with base name: {output_base_name}...")
              results = await dialog_processor.process_dialog(sample_dialog_items, output_base_name)
              
              print("\n--- Processing Log ---")
              print(results.get("log"))
              print("\n--- Segment Files / Results ---")
              pprint.pprint(results.get("segment_files"))
              print(f"\nTemporary directory used: {results.get('temp_dir')}")
              print("\nPlease check the temporary directory for generated audio segments.")

          except Exception as e:
              import traceback
              print(f"\nAn error occurred during the DialogProcessorService test:")
              traceback.print_exc()
          finally:
              print("\nUnloading TTS model...")
              tts_service.unload_model()
              print("DialogProcessorService test finished.")

      asyncio.run(main_test())


id: ea1f8a8f-bab7-4c38-a43f-7d4b1d5ce9f1
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/import_helper.py
version: v1
content:
  """
  Import helper module for Chatterbox UI.

  This module provides a function to add the project root to the Python path,
  which helps resolve import issues when running scripts from different locations.
  """

  import sys
  import os
  from pathlib import Path

  def setup_python_path():
      """
      Add the project root to the Python path.
      This allows imports to work correctly regardless of where the script is run from.
      """
      # Get the project root (parent of the directory containing this file)
      project_root = Path(__file__).resolve().parent
      
      # Add the project root to the Python path if it's not already there
      if str(project_root) not in sys.path:
          sys.path.insert(0, str(project_root))
          print(f"Added {project_root} to Python path")
      
      # Set environment variable for other modules to use
      os.environ["PROJECT_ROOT"] = str(project_root)
      
      return project_root

  # Run setup when this module is imported
  project_root = setup_python_path()


id: b7454f15-26f2-468c-8f95-80cf40def865
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py
version: v4
content:
  #!/usr/bin/env python
  """
  Chatterbox Audiobook Generator

  This script converts a text file into an audiobook using the Chatterbox TTS system.
  It parses the text file into manageable chunks, generates audio for each chunk,
  and assembles them into a complete audiobook.
  """

  import argparse
  import asyncio
  import os
  import re
  import sys
  from pathlib import Path
  import uuid

  # Import helper to fix Python path
  import import_helper

  # Import backend services
  from backend.app.services.tts_service import TTSService
  from backend.app.services.speaker_service import SpeakerManagementService
  from backend.app.services.audio_manipulation_service import AudioManipulationService
  from backend.app.config import DIALOG_GENERATED_DIR, TTS_TEMP_OUTPUT_DIR

  class AudiobookGenerator:
      def __init__(self, speaker_id, output_base_name, device="mps", 
                   exaggeration=0.5, cfg_weight=0.5, temperature=0.8,
                   pause_between_sentences=0.5, pause_between_paragraphs=1.0):
          """
          Initialize the audiobook generator.
          
          Args:
              speaker_id: ID of the speaker to use
              output_base_name: Base name for output files
              device: Device to use for TTS (mps, cuda, cpu)
              exaggeration: Controls expressiveness (0.0-1.0)
              cfg_weight: Controls alignment with speaker characteristics (0.0-1.0)
              temperature: Controls randomness in generation (0.0-1.0)
              pause_between_sentences: Pause duration between sentences in seconds
              pause_between_paragraphs: Pause duration between paragraphs in seconds
          """
          self.speaker_id = speaker_id
          self.output_base_name = output_base_name
          self.device = device
          self.exaggeration = exaggeration
          self.cfg_weight = cfg_weight
          self.temperature = temperature
          self.pause_between_sentences = pause_between_sentences
          self.pause_between_paragraphs = pause_between_paragraphs
          
          # Initialize services
          self.tts_service = TTSService(device=device)
          self.speaker_service = SpeakerManagementService()
          self.audio_manipulator = AudioManipulationService()
          
          # Create output directories
          self.output_dir = DIALOG_GENERATED_DIR / output_base_name
          self.output_dir.mkdir(parents=True, exist_ok=True)
          self.temp_dir = TTS_TEMP_OUTPUT_DIR / output_base_name
          self.temp_dir.mkdir(parents=True, exist_ok=True)
          
          # Validate speaker
          self._validate_speaker()
      
      def _validate_speaker(self):
          """Validate that the specified speaker exists."""
          speaker_info = self.speaker_service.get_speaker_by_id(self.speaker_id)
          if not speaker_info:
              raise ValueError(f"Speaker ID '{self.speaker_id}' not found.")
          if not speaker_info.sample_path:
              raise ValueError(f"Speaker ID '{self.speaker_id}' has no sample path defined.")
          
          # Store speaker info for later use
          self.speaker_info = speaker_info
      
      def split_text_into_chunks(self, text, max_length=300):
          """
          Split text into chunks suitable for TTS processing.
          
          This uses the same logic as the DialogProcessorService._split_text method
          but adds additional paragraph handling.
          """
          # Split text into paragraphs first
          paragraphs = re.split(r'\n\s*\n', text)
          paragraphs = [p.strip() for p in paragraphs if p.strip()]
          
          all_chunks = []
          
          for paragraph in paragraphs:
              # Split paragraph into sentences
              sentences = re.split(r'(?<=[.!?\u2026])\s+|(?<=[.!?\u2026])(?=[\"\')\]\}\u201d\u2019])|(?<=[.!?\u2026])$', paragraph.strip())
              sentences = [s.strip() for s in sentences if s and s.strip()]
              
              chunks = []
              current_chunk = ""
              
              for sentence in sentences:
                  if not sentence:
                      continue
                  if not current_chunk:  # First sentence for this chunk
                      current_chunk = sentence
                  elif len(current_chunk) + len(sentence) + 1 <= max_length:
                      current_chunk += " " + sentence
                  else:
                      chunks.append(current_chunk)
                      current_chunk = sentence
              
              if current_chunk:  # Add the last chunk
                  chunks.append(current_chunk)
              
              # Further split any chunks that are still too long
              paragraph_chunks = []
              for chunk in chunks:
                  if len(chunk) > max_length:
                      # Simple split by length if a sentence itself is too long
                      for i in range(0, len(chunk), max_length):
                          paragraph_chunks.append(chunk[i:i+max_length])
                  else:
                      paragraph_chunks.append(chunk)
              
              # Add paragraph marker
              if paragraph_chunks:
                  all_chunks.append({"type": "paragraph", "chunks": paragraph_chunks})
          
          return all_chunks
      
      async def generate_audiobook(self, text_file_path):
          """
          Generate an audiobook from a text file.
          
          Args:
              text_file_path: Path to the text file to convert
          
          Returns:
              Path to the generated audiobook file
          """
          # Read the text file
          text_path = Path(text_file_path)
          if not text_path.exists():
              raise FileNotFoundError(f"Text file not found: {text_file_path}")
          
          with open(text_path, 'r', encoding='utf-8') as f:
              text = f.read()
          
          print(f"Processing text file: {text_file_path}")
          print(f"Text length: {len(text)} characters")
          
          # Split text into chunks
          paragraphs = self.split_text_into_chunks(text)
          total_chunks = sum(len(p["chunks"]) for p in paragraphs)
          print(f"Split into {len(paragraphs)} paragraphs with {total_chunks} total chunks")
          
          # Load TTS model
          print("Loading TTS model...")
          self.tts_service.load_model()
          
          try:
              # Generate audio for each chunk
              segment_results = []
              chunk_count = 0
              
              for para_idx, paragraph in enumerate(paragraphs):
                  print(f"Processing paragraph {para_idx+1}/{len(paragraphs)}")
                  
                  for chunk_idx, chunk in enumerate(paragraph["chunks"]):
                      chunk_count += 1
                      print(f"  Generating audio for chunk {chunk_count}/{total_chunks}: {chunk[:50]}...")
                      
                      # Generate unique filename for this chunk
                      segment_filename_base = f"{self.output_base_name}_p{para_idx}_c{chunk_idx}_{uuid.uuid4().hex[:8]}"
                      
                      try:
                          # Get absolute speaker sample path
                          speaker_sample_path = Path(self.speaker_info.sample_path)
                          if not speaker_sample_path.is_absolute():
                              from backend.app.config import SPEAKER_DATA_BASE_DIR
                              speaker_sample_path = SPEAKER_DATA_BASE_DIR / speaker_sample_path
                          
                          # Generate speech for this chunk
                          segment_output_path = await self.tts_service.generate_speech(
                              text=chunk,
                              speaker_id=self.speaker_id,
                              speaker_sample_path=str(speaker_sample_path),
                              output_filename_base=segment_filename_base,
                              output_dir=self.temp_dir,
                              exaggeration=self.exaggeration,
                              cfg_weight=self.cfg_weight,
                              temperature=self.temperature
                          )
                          
                          # Add to segment results
                          segment_results.append({
                              "type": "speech",
                              "path": str(segment_output_path)
                          })
                          
                          # Add pause between sentences
                          if chunk_idx < len(paragraph["chunks"]) - 1:
                              segment_results.append({
                                  "type": "silence",
                                  "duration": self.pause_between_sentences
                              })
                      
                      except Exception as e:
                          print(f"Error generating speech for chunk: {e}")
                          # Continue with next chunk
                  
                  # Add longer pause between paragraphs
                  if para_idx < len(paragraphs) - 1:
                      segment_results.append({
                          "type": "silence",
                          "duration": self.pause_between_paragraphs
                      })
              
              # Concatenate all segments
              print("Concatenating audio segments...")
              concatenated_filename = f"{self.output_base_name}_audiobook.wav"
              concatenated_path = self.output_dir / concatenated_filename
              
              self.audio_manipulator.concatenate_audio_segments(
                  segment_results=segment_results,
                  output_concatenated_path=concatenated_path
              )
              
              # Create ZIP archive with all files
              print("Creating ZIP archive...")
              zip_filename = f"{self.output_base_name}_audiobook.zip"
              zip_path = self.output_dir / zip_filename
              
              # Collect all speech segment files
              speech_segment_paths = [
                  Path(s["path"]) for s in segment_results 
                  if s["type"] == "speech" and Path(s["path"]).exists()
              ]
              
              self.audio_manipulator.create_zip_archive(
                  segment_file_paths=speech_segment_paths,
                  concatenated_audio_path=concatenated_path,
                  output_zip_path=zip_path
              )
              
              print(f"Audiobook generation complete!")
              print(f"Audiobook file: {concatenated_path}")
              print(f"ZIP archive: {zip_path}")
              
              return concatenated_path
              
          finally:
              # Unload TTS model
              print("Unloading TTS model...")
              self.tts_service.unload_model()

  async def main():
      parser = argparse.ArgumentParser(description="Generate an audiobook from a text file using Chatterbox TTS")
      parser.add_argument("text_file", help="Path to the text file to convert")
      parser.add_argument("--speaker", "-s", required=True, help="ID of the speaker to use")
      parser.add_argument("--output", "-o", help="Base name for output files (default: derived from text filename)")
      parser.add_argument("--device", default="mps", choices=["mps", "cuda", "cpu"], help="Device to use for TTS (default: mps)")
      parser.add_argument("--exaggeration", type=float, default=0.5, help="Controls expressiveness (0.0-1.0, default: 0.5)")
      parser.add_argument("--cfg-weight", type=float, default=0.5, help="Controls alignment with speaker (0.0-1.0, default: 0.5)")
      parser.add_argument("--temperature", type=float, default=0.8, help="Controls randomness (0.0-1.0, default: 0.8)")
      parser.add_argument("--sentence-pause", type=float, default=0.5, help="Pause between sentences in seconds (default: 0.5)")
      parser.add_argument("--paragraph-pause", type=float, default=1.0, help="Pause between paragraphs in seconds (default: 1.0)")
      parser.add_argument("--list-speakers", action="store_true", help="List available speakers and exit")
      
      args = parser.parse_args()
      
      # List speakers if requested
      if args.list_speakers:
          speaker_service = SpeakerManagementService()
          speakers = speaker_service.get_all_speakers()
          print("Available speakers:")
          for speaker in speakers:
              print(f"  {speaker.id}: {speaker.name}")
          return
      
      # Determine output base name if not provided
      if not args.output:
          text_path = Path(args.text_file)
          args.output = text_path.stem
      
      try:
          # Create audiobook generator
          generator = AudiobookGenerator(
              speaker_id=args.speaker,
              output_base_name=args.output,
              device=args.device,
              exaggeration=args.exaggeration,
              cfg_weight=args.cfg_weight,
              temperature=args.temperature,
              pause_between_sentences=args.sentence_pause,
              pause_between_paragraphs=args.paragraph_pause
          )
          
          # Generate audiobook
          await generator.generate_audiobook(args.text_file)
          
      except Exception as e:
          print(f"Error: {e}", file=sys.stderr)
          return 1
      
      return 0

  if __name__ == "__main__":
      sys.exit(asyncio.run(main()))


id: 73404983-cc7c-4d53-9f1e-4b38c6e1af3f
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/OpenCode.md
version: v5
content:
  # OpenCode.md - Chatterbox UI Development Guide

  ## Build & Run Commands
  ```bash
  # Backend (FastAPI)
  pip install -r backend/requirements.txt
  uvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000

  # Frontend
  python frontend/start_dev_server.py  # Serves on port 8001

  # Run backend tests
  python backend/run_api_test.py

  # Run frontend tests
  npm test

  # Run specific frontend test
  npx jest frontend/tests/api.test.js

  # Run Gradio interface
  python gradio_app.py

  # Run utility scripts
  python cbx-audiobook.py --list-speakers  # List available speakers
  python cbx-audiobook.py sample-audiobook.txt --speaker <speaker_id>
  python cbx-dialog-generate.py sample-dialog.md
  ```

  ## Code Style Guidelines

  ### Python
  - Use type hints (from typing import Optional, List, Dict, etc.)
  - Error handling: Use try/except with specific exceptions
  - Async/await for I/O operations
  - Docstrings for functions and classes
  - PEP 8 naming: snake_case for functions/variables, PascalCase for classes

  ### JavaScript
  - ES6 modules with import/export
  - Async/await for API calls
  - JSDoc comments for functions
  - Error handling: try/catch with detailed error messages
  - Camel case for variables/functions (camelCase)

  ## Import Structure
  - When importing from scripts, use `import import_helper` first to fix Python path
  - Backend modules use relative imports within the app package
  - Services are in `backend.app.services`
  - Models are in `backend.app.models`
  - Configuration is in `backend.app.config`


id: e11056bc-d8a8-44b7-87a8-9dd1758b8733
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/cbx-dialog-generate.py
version: initial
content:
  #!/usr/bin/env python3
  import argparse
  import re
  import os
  import yaml
  import torchaudio as ta
  from chatterbox.tts import ChatterboxTTS

  def split_text_at_sentence_boundaries(text, max_length=300):
      """
      Split text at sentence boundaries, ensuring each chunk is <= max_length.
      Returns a list of text chunks.
      """
      # Simple regex for sentence boundaries (period, question mark, exclamation mark followed by space or end)
      sentence_pattern = r'[.!?](?:\s|$)'
      sentences = re.split(f'({sentence_pattern})', text)
      
      # Recombine the split parts (the regex split keeps the delimiters as separate items)
      actual_sentences = []
      current = ""
      for i in range(0, len(sentences), 2):
          if i+1 < len(sentences):
              current = sentences[i] + sentences[i+1]
          else:
              current = sentences[i]
          if current:
              actual_sentences.append(current)
      
      # Group sentences into chunks <= max_length
      chunks = []
      current_chunk = ""
      
      for sentence in actual_sentences:
          # If adding this sentence would exceed max_length and we already have content,
          # finish the current chunk and start a new one
          if len(current_chunk) + len(sentence) > max_length and current_chunk:
              chunks.append(current_chunk.strip())
              current_chunk = sentence
          else:
              current_chunk += sentence
      
      # Add the last chunk if it has content
      if current_chunk:
          chunks.append(current_chunk.strip())
      
      return chunks

  def parse_dialog_line(line):
      """
      Parse a dialog line in the format: Name: "Text"
      Returns a tuple of (speaker, text) or None if the line doesn't match the pattern.
      """
      pattern = r'^([^:]+):\s*"([^"]+)"$'
      match = re.match(pattern, line.strip())
      if match:
          speaker = match.group(1).strip()
          text = match.group(2).strip()
          return (speaker, text)
      return None

  def main():
      parser = argparse.ArgumentParser(description="Generate dialog audio from markdown file using Chatterbox TTS")
      parser.add_argument('--config', required=True, type=str, help='YAML config file mapping speaker names to audio samples')
      parser.add_argument('--dialog', required=True, type=str, help='Markdown dialog file')
      parser.add_argument('--output-base', required=True, type=str, help='Base name for output files (e.g., "output" for "001-output.wav")')
      parser.add_argument('--reinit-each-line', action='store_true', help='Re-initialize the model after each line to reduce memory usage')
      args = parser.parse_args()
      
      # Load the YAML config
      with open(args.config, 'r') as f:
          speaker_samples = yaml.safe_load(f)
      
      # Load the dialog file
      with open(args.dialog, 'r') as f:
          dialog_lines = f.readlines()
      
      # Initialize model only once if not reinitializing per line
      model = None
      if not args.reinit_each_line:
          print("Loading ChatterboxTTS model once for all lines...")
          model = ChatterboxTTS.from_pretrained(device="mps")
      
      # Process each dialog line
      file_counter = 1
      summary = []
      
      for line_num, line in enumerate(dialog_lines, 1):
          parsed = parse_dialog_line(line)
          if not parsed:
              print(f"Skipping line {line_num}: Not in the expected format")
              continue
          
          speaker, text = parsed
          
          # Check if the speaker is in the config
          if speaker not in speaker_samples:
              print(f"Warning: Speaker '{speaker}' not found in config, skipping line {line_num}")
              continue
          
          sample_path = speaker_samples[speaker]
          
          # Reinitialize model if needed
          if args.reinit_each_line or model is None:
              if args.reinit_each_line:
                  print(f"Reinitializing model for line {line_num}...")
              model = ChatterboxTTS.from_pretrained(device="mps")
          
          # Check if the text needs to be split (> 300 chars)
          if len(text) > 300:
              chunks = split_text_at_sentence_boundaries(text)
              chunk_files = []
              
              for chunk in chunks:
                  output_file = f"{file_counter:03d}-{args.output_base}.wav"
                  
                  # Generate audio for this chunk
                  wav = model.generate(chunk, audio_prompt_path=sample_path)
                  ta.save(output_file, wav, model.sr)
                  
                  chunk_files.append(output_file)
                  summary.append(f"File {output_file}: {speaker} (chunk) - {chunk[:50]}...")
                  file_counter += 1
              
              print(f"Generated {len(chunks)} files for line {line_num} (speaker: {speaker})")
          else:
              # Generate a single file for this line
              output_file = f"{file_counter:03d}-{args.output_base}.wav"
              
              # Generate audio
              wav = model.generate(text, audio_prompt_path=sample_path)
              ta.save(output_file, wav, model.sr)
              
              summary.append(f"File {output_file}: {speaker} - {text[:50]}...")
              file_counter += 1
              print(f"Generated file for line {line_num} (speaker: {speaker})")
      
      # Print summary
      print("\nSummary of generated files:")
      for entry in summary:
          print(entry)

  if __name__ == '__main__':
      main()


id: b4a5ebf7-2ba1-41f5-8ebe-bbaa02c364b7
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/cbx-dialog-generate.py
version: v2
content:
  #!/usr/bin/env python3
  import argparse
  import re
  import os
  import yaml
  import torchaudio as ta
  from chatterbox.tts import ChatterboxTTS

  # Import helper to fix Python path
  import import_helper

  def split_text_at_sentence_boundaries(text, max_length=300):
      """
      Split text at sentence boundaries, ensuring each chunk is <= max_length.
      Returns a list of text chunks.
      """
      # Simple regex for sentence boundaries (period, question mark, exclamation mark followed by space or end)
      sentence_pattern = r'[.!?](?:\s|$)'
      sentences = re.split(f'({sentence_pattern})', text)
      
      # Recombine the split parts (the regex split keeps the delimiters as separate items)
      actual_sentences = []
      current = ""
      for i in range(0, len(sentences), 2):
          if i+1 < len(sentences):
              current = sentences[i] + sentences[i+1]
          else:
              current = sentences[i]
          if current:
              actual_sentences.append(current)
      
      # Group sentences into chunks <= max_length
      chunks = []
      current_chunk = ""
      
      for sentence in actual_sentences:
          # If adding this sentence would exceed max_length and we already have content,
          # finish the current chunk and start a new one
          if len(current_chunk) + len(sentence) > max_length and current_chunk:
              chunks.append(current_chunk.strip())
              current_chunk = sentence
          else:
              current_chunk += sentence
      
      # Add the last chunk if it has content
      if current_chunk:
          chunks.append(current_chunk.strip())
      
      return chunks

  def parse_dialog_line(line):
      """
      Parse a dialog line in the format: Name: "Text"
      Returns a tuple of (speaker, text) or None if the line doesn't match the pattern.
      """
      pattern = r'^([^:]+):\s*"([^"]+)"$'
      match = re.match(pattern, line.strip())
      if match:
          speaker = match.group(1).strip()
          text = match.group(2).strip()
          return (speaker, text)
      return None

  def main():
      parser = argparse.ArgumentParser(description="Generate dialog audio from markdown file using Chatterbox TTS")
      parser.add_argument('--config', required=True, type=str, help='YAML config file mapping speaker names to audio samples')
      parser.add_argument('--dialog', required=True, type=str, help='Markdown dialog file')
      parser.add_argument('--output-base', required=True, type=str, help='Base name for output files (e.g., "output" for "001-output.wav")')
      parser.add_argument('--reinit-each-line', action='store_true', help='Re-initialize the model after each line to reduce memory usage')
      args = parser.parse_args()
      
      # Load the YAML config
      with open(args.config, 'r') as f:
          speaker_samples = yaml.safe_load(f)
      
      # Load the dialog file
      with open(args.dialog, 'r') as f:
          dialog_lines = f.readlines()
      
      # Initialize model only once if not reinitializing per line
      model = None
      if not args.reinit_each_line:
          print("Loading ChatterboxTTS model once for all lines...")
          model = ChatterboxTTS.from_pretrained(device="mps")
      
      # Process each dialog line
      file_counter = 1
      summary = []
      
      for line_num, line in enumerate(dialog_lines, 1):
          parsed = parse_dialog_line(line)
          if not parsed:
              print(f"Skipping line {line_num}: Not in the expected format")
              continue
          
          speaker, text = parsed
          
          # Check if the speaker is in the config
          if speaker not in speaker_samples:
              print(f"Warning: Speaker '{speaker}' not found in config, skipping line {line_num}")
              continue
          
          sample_path = speaker_samples[speaker]
          
          # Reinitialize model if needed
          if args.reinit_each_line or model is None:
              if args.reinit_each_line:
                  print(f"Reinitializing model for line {line_num}...")
              model = ChatterboxTTS.from_pretrained(device="mps")
          
          # Check if the text needs to be split (> 300 chars)
          if len(text) > 300:
              chunks = split_text_at_sentence_boundaries(text)
              chunk_files = []
              
              for chunk in chunks:
                  output_file = f"{file_counter:03d}-{args.output_base}.wav"
                  
                  # Generate audio for this chunk
                  wav = model.generate(chunk, audio_prompt_path=sample_path)
                  ta.save(output_file, wav, model.sr)
                  
                  chunk_files.append(output_file)
                  summary.append(f"File {output_file}: {speaker} (chunk) - {chunk[:50]}...")
                  file_counter += 1
              
              print(f"Generated {len(chunks)} files for line {line_num} (speaker: {speaker})")
          else:
              # Generate a single file for this line
              output_file = f"{file_counter:03d}-{args.output_base}.wav"
              
              # Generate audio
              wav = model.generate(text, audio_prompt_path=sample_path)
              ta.save(output_file, wav, model.sr)
              
              summary.append(f"File {output_file}: {speaker} - {text[:50]}...")
              file_counter += 1
              print(f"Generated file for line {line_num} (speaker: {speaker})")
      
      # Print summary
      print("\nSummary of generated files:")
      for entry in summary:
          print(entry)

  if __name__ == '__main__':
      main()


id: 60c2883e-fa75-4dcd-9987-868d8ddda5df
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/cbx-generate.py
version: initial
content:
  import argparse
  import torchaudio as ta
  from chatterbox.tts import ChatterboxTTS

  def main():
      parser = argparse.ArgumentParser(description="Chatterbox TTS audio generation")
      parser.add_argument('--sample', required=True, type=str, help='Prompt/reference audio file (e.g. .wav, .mp3) for the voice')
      parser.add_argument('--output', required=True, type=str, help='Output audio file path (should end with .wav)')
      parser.add_argument('--text', required=True, type=str, help='Text to synthesize')
      args = parser.parse_args()

      # Load model on MPS (for Apple Silicon)
      model = ChatterboxTTS.from_pretrained(device="mps")

      # Generate the audio
      wav = model.generate(args.text, audio_prompt_path=args.sample)
      # Save to output .wav
      ta.save(args.output, wav, model.sr)
      print(f"Generated audio saved to {args.output}")

  if __name__ == '__main__':
      main()


id: c10a3fe0-9607-4aff-b435-7a4502b2b0ba
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/cbx-generate.py
version: v2
content:
  import argparse
  import torchaudio as ta
  from chatterbox.tts import ChatterboxTTS

  # Import helper to fix Python path
  import import_helper

  def main():
      parser = argparse.ArgumentParser(description="Chatterbox TTS audio generation")
      parser.add_argument('--sample', required=True, type=str, help='Prompt/reference audio file (e.g. .wav, .mp3) for the voice')
      parser.add_argument('--output', required=True, type=str, help='Output audio file path (should end with .wav)')
      parser.add_argument('--text', required=True, type=str, help='Text to synthesize')
      args = parser.parse_args()

      # Load model on MPS (for Apple Silicon)
      model = ChatterboxTTS.from_pretrained(device="mps")

      # Generate the audio
      wav = model.generate(args.text, audio_prompt_path=args.sample)
      # Save to output .wav
      ta.save(args.output, wav, model.sr)
      print(f"Generated audio saved to {args.output}")

  if __name__ == '__main__':
      main()


id: cfbd67a3-5552-4ca7-bb3a-005ec20ebdad
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/OpenCode.md
version: v7
content:
  # OpenCode.md - Chatterbox UI Development Guide

  ## Build & Run Commands
  ```bash
  # Backend (FastAPI)
  pip install -r backend/requirements.txt
  uvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000

  # Frontend
  python frontend/start_dev_server.py  # Serves on port 8001

  # Run backend tests
  python backend/run_api_test.py

  # Run frontend tests
  npm test

  # Run specific frontend test
  npx jest frontend/tests/api.test.js

  # Run Gradio interface
  python gradio_app.py

  # Run utility scripts
  python cbx-audiobook.py --list-speakers  # List available speakers
  python cbx-audiobook.py sample-audiobook.txt --speaker <speaker_id>
  python cbx-dialog-generate.py sample-dialog.md
  ```

  ## Code Style Guidelines

  ### Python
  - Use type hints (from typing import Optional, List, Dict, etc.)
  - Error handling: Use try/except with specific exceptions
  - Async/await for I/O operations
  - Docstrings for functions and classes
  - PEP 8 naming: snake_case for functions/variables, PascalCase for classes

  ### JavaScript
  - ES6 modules with import/export
  - Async/await for API calls
  - JSDoc comments for functions
  - Error handling: try/catch with detailed error messages
  - Camel case for variables/functions (camelCase)

  ## Import Structure
  - When importing from scripts, use `import import_helper` first to fix Python path
  - Backend modules use relative imports within the app package
  - Services are in `backend.app.services`
  - Models are in `backend.app.models`
  - Configuration is in `backend.app.config`

  ## Project Structure
  - Backend: FastAPI with service-oriented architecture
  - Frontend: Vanilla JS with modular design (api.js, app.js, config.js)
  - Speaker data in YAML format with WAV samples
  - Output directories: dialog_output/, single_output/, tts_outputs/

  ## Common Issues
  - Import errors: Make sure to use `import import_helper` in scripts
  - Speaker samples must be WAV format in `speaker_data/speaker_samples/`
  - TTS model requires GPU (CUDA) or Apple Silicon (MPS)


id: f2ef5f21-f328-430b-b71d-93800136cfa4
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
path: /Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py
version: v6
content:
  #!/usr/bin/env python
  """
  Chatterbox Audiobook Generator

  This script converts a text file into an audiobook using the Chatterbox TTS system.
  It parses the text file into manageable chunks, generates audio for each chunk,
  and assembles them into a complete audiobook.
  """

  import argparse
  import asyncio
  import os
  import re
  import sys
  from pathlib import Path
  import uuid

  # Import helper to fix Python path
  import import_helper

  # Import backend services
  from backend.app.services.tts_service import TTSService
  from backend.app.services.speaker_service import SpeakerManagementService
  from backend.app.services.audio_manipulation_service import AudioManipulationService
  from backend.app.config import DIALOG_GENERATED_DIR, TTS_TEMP_OUTPUT_DIR

  class AudiobookGenerator:
      def __init__(self, speaker_id, output_base_name, device="mps", 
                   exaggeration=0.5, cfg_weight=0.5, temperature=0.8,
                   pause_between_sentences=0.5, pause_between_paragraphs=1.0):
          """
          Initialize the audiobook generator.
          
          Args:
              speaker_id: ID of the speaker to use
              output_base_name: Base name for output files
              device: Device to use for TTS (mps, cuda, cpu)
              exaggeration: Controls expressiveness (0.0-1.0)
              cfg_weight: Controls alignment with speaker characteristics (0.0-1.0)
              temperature: Controls randomness in generation (0.0-1.0)
              pause_between_sentences: Pause duration between sentences in seconds
              pause_between_paragraphs: Pause duration between paragraphs in seconds
          """
          self.speaker_id = speaker_id
          self.output_base_name = output_base_name
          self.device = device
          self.exaggeration = exaggeration
          self.cfg_weight = cfg_weight
          self.temperature = temperature
          self.pause_between_sentences = pause_between_sentences
          self.pause_between_paragraphs = pause_between_paragraphs
          
          # Initialize services
          self.tts_service = TTSService(device=device)
          self.speaker_service = SpeakerManagementService()
          self.audio_manipulator = AudioManipulationService()
          
          # Create output directories
          self.output_dir = DIALOG_GENERATED_DIR / output_base_name
          self.output_dir.mkdir(parents=True, exist_ok=True)
          self.temp_dir = TTS_TEMP_OUTPUT_DIR / output_base_name
          self.temp_dir.mkdir(parents=True, exist_ok=True)
          
          # Validate speaker
          self._validate_speaker()
      
      def _validate_speaker(self):
          """Validate that the specified speaker exists."""
          speaker_info = self.speaker_service.get_speaker_by_id(self.speaker_id)
          if not speaker_info:
              raise ValueError(f"Speaker ID '{self.speaker_id}' not found.")
          if not speaker_info.sample_path:
              raise ValueError(f"Speaker ID '{self.speaker_id}' has no sample path defined.")
          
          # Store speaker info for later use
          self.speaker_info = speaker_info
      
      def split_text_into_chunks(self, text, max_length=300):
          """
          Split text into chunks suitable for TTS processing.
          
          This uses the same logic as the DialogProcessorService._split_text method
          but adds additional paragraph handling.
          """
          # Split text into paragraphs first
          paragraphs = re.split(r'\n\s*\n', text)
          paragraphs = [p.strip() for p in paragraphs if p.strip()]
          
          all_chunks = []
          
          for paragraph in paragraphs:
              # Split paragraph into sentences
              sentences = re.split(r'(?<=[.!?\u2026])\s+|(?<=[.!?\u2026])(?=[\"\')\]\}\u201d\u2019])|(?<=[.!?\u2026])$', paragraph.strip())
              sentences = [s.strip() for s in sentences if s and s.strip()]
              
              chunks = []
              current_chunk = ""
              
              for sentence in sentences:
                  if not sentence:
                      continue
                  if not current_chunk:  # First sentence for this chunk
                      current_chunk = sentence
                  elif len(current_chunk) + len(sentence) + 1 <= max_length:
                      current_chunk += " " + sentence
                  else:
                      chunks.append(current_chunk)
                      current_chunk = sentence
              
              if current_chunk:  # Add the last chunk
                  chunks.append(current_chunk)
              
              # Further split any chunks that are still too long
              paragraph_chunks = []
              for chunk in chunks:
                  if len(chunk) > max_length:
                      # Simple split by length if a sentence itself is too long
                      for i in range(0, len(chunk), max_length):
                          paragraph_chunks.append(chunk[i:i+max_length])
                  else:
                      paragraph_chunks.append(chunk)
              
              # Add paragraph marker
              if paragraph_chunks:
                  all_chunks.append({"type": "paragraph", "chunks": paragraph_chunks})
          
          return all_chunks
      
      async def generate_audiobook(self, text_file_path):
          """
          Generate an audiobook from a text file.
          
          Args:
              text_file_path: Path to the text file to convert
          
          Returns:
              Path to the generated audiobook file
          """
          # Read the text file
          text_path = Path(text_file_path)
          if not text_path.exists():
              raise FileNotFoundError(f"Text file not found: {text_file_path}")
          
          with open(text_path, 'r', encoding='utf-8') as f:
              text = f.read()
          
          print(f"Processing text file: {text_file_path}")
          print(f"Text length: {len(text)} characters")
          
          # Split text into chunks
          paragraphs = self.split_text_into_chunks(text)
          total_chunks = sum(len(p["chunks"]) for p in paragraphs)
          print(f"Split into {len(paragraphs)} paragraphs with {total_chunks} total chunks")
          
          # Load TTS model
          print("Loading TTS model...")
          self.tts_service.load_model()
          
          try:
              # Generate audio for each chunk
              segment_results = []
              chunk_count = 0
              
              for para_idx, paragraph in enumerate(paragraphs):
                  print(f"Processing paragraph {para_idx+1}/{len(paragraphs)}")
                  
                  for chunk_idx, chunk in enumerate(paragraph["chunks"]):
                      chunk_count += 1
                      print(f"  Generating audio for chunk {chunk_count}/{total_chunks}: {chunk[:50]}...")
                      
                      # Generate unique filename for this chunk
                      segment_filename_base = f"{self.output_base_name}_p{para_idx}_c{chunk_idx}_{uuid.uuid4().hex[:8]}"
                      
                      try:
                          # Get absolute speaker sample path
                          speaker_sample_path = Path(self.speaker_info.sample_path)
                          if not speaker_sample_path.is_absolute():
                              from backend.app.config import SPEAKER_DATA_BASE_DIR
                              speaker_sample_path = SPEAKER_DATA_BASE_DIR / speaker_sample_path
                          
                          # Generate speech for this chunk
                          segment_output_path = await self.tts_service.generate_speech(
                              text=chunk,
                              speaker_id=self.speaker_id,
                              speaker_sample_path=str(speaker_sample_path),
                              output_filename_base=segment_filename_base,
                              output_dir=self.temp_dir,
                              exaggeration=self.exaggeration,
                              cfg_weight=self.cfg_weight,
                              temperature=self.temperature
                          )
                          
                          # Add to segment results
                          segment_results.append({
                              "type": "speech",
                              "path": str(segment_output_path)
                          })
                          
                          # Add pause between sentences
                          if chunk_idx < len(paragraph["chunks"]) - 1:
                              segment_results.append({
                                  "type": "silence",
                                  "duration": self.pause_between_sentences
                              })
                      
                      except Exception as e:
                          print(f"Error generating speech for chunk: {e}")
                          # Continue with next chunk
                  
                  # Add longer pause between paragraphs
                  if para_idx < len(paragraphs) - 1:
                      segment_results.append({
                          "type": "silence",
                          "duration": self.pause_between_paragraphs
                      })
              
              # Concatenate all segments
              print("Concatenating audio segments...")
              concatenated_filename = f"{self.output_base_name}_audiobook.wav"
              concatenated_path = self.output_dir / concatenated_filename
              
              self.audio_manipulator.concatenate_audio_segments(
                  segment_results=segment_results,
                  output_concatenated_path=concatenated_path
              )
              
              # Create ZIP archive with all files
              print("Creating ZIP archive...")
              zip_filename = f"{self.output_base_name}_audiobook.zip"
              zip_path = self.output_dir / zip_filename
              
              # Collect all speech segment files
              speech_segment_paths = [
                  Path(s["path"]) for s in segment_results 
                  if s["type"] == "speech" and Path(s["path"]).exists()
              ]
              
              self.audio_manipulator.create_zip_archive(
                  segment_file_paths=speech_segment_paths,
                  concatenated_audio_path=concatenated_path,
                  output_zip_path=zip_path
              )
              
              print(f"Audiobook generation complete!")
              print(f"Audiobook file: {concatenated_path}")
              print(f"ZIP archive: {zip_path}")
              
              return concatenated_path
              
          finally:
              # Unload TTS model
              print("Unloading TTS model...")
              self.tts_service.unload_model()

  async def main():
      parser = argparse.ArgumentParser(description="Generate an audiobook from a text file using Chatterbox TTS")
      
      # Create a mutually exclusive group for the main operation vs listing speakers
      group = parser.add_mutually_exclusive_group(required=True)
      group.add_argument("--list-speakers", action="store_true", help="List available speakers and exit")
      group.add_argument("text_file", nargs="?", help="Path to the text file to convert")
      
      # Other arguments
      parser.add_argument("--speaker", "-s", help="ID of the speaker to use")
      parser.add_argument("--output", "-o", help="Base name for output files (default: derived from text filename)")
      parser.add_argument("--device", default="mps", choices=["mps", "cuda", "cpu"], help="Device to use for TTS (default: mps)")
      parser.add_argument("--exaggeration", type=float, default=0.5, help="Controls expressiveness (0.0-1.0, default: 0.5)")
      parser.add_argument("--cfg-weight", type=float, default=0.5, help="Controls alignment with speaker (0.0-1.0, default: 0.5)")
      parser.add_argument("--temperature", type=float, default=0.8, help="Controls randomness (0.0-1.0, default: 0.8)")
      parser.add_argument("--sentence-pause", type=float, default=0.5, help="Pause between sentences in seconds (default: 0.5)")
      parser.add_argument("--paragraph-pause", type=float, default=1.0, help="Pause between paragraphs in seconds (default: 1.0)")
      
      args = parser.parse_args()
      
      # List speakers if requested
      if args.list_speakers:
          speaker_service = SpeakerManagementService()
          speakers = speaker_service.get_all_speakers()
          print("Available speakers:")
          for speaker in speakers:
              print(f"  {speaker.id}: {speaker.name}")
          return
      
      # Validate required arguments for audiobook generation
      if not args.text_file:
          parser.error("text_file is required when not using --list-speakers")
      
      if not args.speaker:
          parser.error("--speaker/-s is required when not using --list-speakers")
      
      # Determine output base name if not provided
      if not args.output:
          text_path = Path(args.text_file)
          args.output = text_path.stem
      
      try:
          # Create audiobook generator
          generator = AudiobookGenerator(
              speaker_id=args.speaker,
              output_base_name=args.output,
              device=args.device,
              exaggeration=args.exaggeration,
              cfg_weight=args.cfg_weight,
              temperature=args.temperature,
              pause_between_sentences=args.sentence_pause,
              pause_between_paragraphs=args.paragraph_pause
          )
          
          # Generate audiobook
          await generator.generate_audiobook(args.text_file)
          
      except Exception as e:
          print(f"Error: {e}", file=sys.stderr)
          return 1
      
      return 0

  if __name__ == "__main__":
      sys.exit(asyncio.run(main()))


# messages

id: 4f5370bb-ffbd-410a-9cf4-2673fb5c2f0c
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: user
parts:
  [{"type":"text","data":{"text":"Please analyze this codebase and create a OpenCode.md file containing:\n1. Build/lint/test commands - especially for running a single test\n2. Code style guidelines including imports, formatting, types, naming conventions, error handling, etc.\n\nThe file you create will be given to agentic coding agents (such as yourself) that operate in this repository. Make it about 20 lines long.\nIf there's already a opencode.md, improve it.\nIf there are Cursor rules (in .cursor/rules/ or .cursorrules) or Copilot rules (in .github/copilot-instructions.md), make sure to include them."}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 2ab841e2-9fe4-458b-8b4d-62b906b9b884
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"I'll analyze the codebase and create an OpenCode.md file with the essential information. Let me start by exploring the repository structure and identifying key patterns."}},{"type":"tool_call","data":{"id":"toolu_01QKbKLVCRGNjjdB8TwPMoKc","name":"ls","input":"{\"path\":\"/Users/stwhite/CODE/chatterbox-ui\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750197984}}]


id: 17a9902d-2647-4d9f-95a8-cdaef16ed9c9
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01QKbKLVCRGNjjdB8TwPMoKc","name":"","content":"- /Users/stwhite/CODE/chatterbox-ui/\n  - Users/\n    - stwhite/\n      - CODE/\n        - chatterbox-ui/\n          - AGENTS.md\n          - API_REFERENCE.md\n          - CLAUDE.md\n          - ENVIRONMENT_SETUP.md\n          - README-dialog-generator.md\n          - README.md\n          - babel.config.cjs\n          - backend/\n            - README.md\n            - app/\n              - __init__.py\n              - config.py\n              - main.py\n              - models/\n                - __init__.py\n                - dialog_models.py\n                - speaker_models.py\n              - routers/\n                - __init__.py\n                - dialog.py\n                - speakers.py\n              - services/\n                - __init__.py\n                - audio_manipulation_service.py\n                - dialog_processor_service.py\n                - speaker_service.py\n                - tts_service.py\n            - requirements.txt\n            - run_api_test.py\n            - start_server.py\n            - tts_generated_dialogs/\n              - dialog_output_concatenated.wav\n              - dialog_output_dialog_output.zip\n              - line_03ef1c53cf164c3582af041188b80859.wav\n              - line_04f76ed1c08c45118e9eb8dc0aeae8ba.wav\n              - line_06b51430ca2748d3b396605c3d1fd41c.wav\n              - line_086fa0729d244456a6ccc99f68e1904c.wav\n              - line_114d157207b74027b56f85b97695be51.wav\n              - line_1326fc891ebc421ba9f149a3bbbe69cf.wav\n              - line_177b83a237cd46309b3d7491c73e81f8.wav\n              - line_1b9b5552dd7d43009beae2dce40cfdbe.wav\n              - line_24343d7b323e4785a9bd08be53117c59.wav\n              - line_2d9447730caf4640bd791880d2d1a619.wav\n              - line_2dd53654c7dc41be815da6089c3e396f.wav\n              - line_39390bf0da25466094936ed9347cceb0.wav\n              - line_3a382ea7547443499a89086b7c1e4e92.wav\n              - line_3fe0d65293df49798bb4d9ad8e49686a.wav\n              - line_49be650caf03481a9ac45a7ede8b447e.wav\n              - line_54b695e518fc4e939ffbd1b371ca4971.wav\n              - line_58561b71dd284bd2853375c0c6b4752c.wav\n              - line_5d400c76d7164ee9ba5c07cb56115c24.wav\n              - line_674cc64135d64181bba3b98facb6ec0e.wav\n              - line_88601473b494478fbcd014f9fcea01a7.wav\n              - line_964ba9f3cc1a4a7586aecbf916ec7024.wav\n              - line_9d7402105d644a33ab2fbdb7ad702d2f.wav\n              - line_9e3574f167ac4386ac9adcb1c169c259.wav\n              - line_b51943d73be44b318ff16e03fac3871d.wav\n              - line_ba6c71ffff07459eba1385095827e792.wav\n              - line_bd0d1c3f4b42482eb64228ea6f34c04d.wav\n              - line_d379bd533c404c6bb7416834b576b92f.wav\n              - line_e313213f80994cefade5524ccf30124b.wav\n              - line_ea630e7210004931b76bfd5cc5f32821.wav\n              - line_f2c640fd918b4d9db565b7f08cf3974a.wav\n          - cbx-audiobook.py\n          - cbx-dialog-generate.py\n          - cbx-generate.py\n          - chatterbox-test.py\n          - chatterbox_tts.py.bak\n          - frontend/\n            - css/\n              - style.css\n            - index.html\n            - js/\n              - api.js\n              - app.js\n              - config.js\n            - start_dev_server.py\n            - tests/\n              - api.test.js\n          - gradio_app.py\n          - package-lock.json\n          - package.json\n          - requirements.txt\n          - sample-audiobook.txt\n          - sample-dialog.md\n          - setup.py\n          - single_output/\n            - output_1749172521.wav\n            - output_1749173071.wav\n            - output_1749173167.wav\n            - output_1749173863.wav\n          - speaker_data/\n            - speaker_data/\n              - speaker_samples/\n                - 3c93c9df-86dc-4d67-ab55-8104b9301190.wav\n                - 608903c4-b157-46c5-a0ea-4b25eb4b83b6.wav\n                - 6cf4d171-667d-4bc8-adbb-6d9b7c620cb8.wav\n                - 831c1dbe-c379-4d9f-868b-9798adc3c05d.wav\n                - 90fcd672-ba84-441a-ac6c-0449a59653bd.wav\n                - a6387c23-4ca4-42b5-8aaf-5699dbabbdf0.wav\n                - dummy_speaker_test.wav\n                - fb84ce1c-f32d-4df9-9673-2c64e9603133.wav\n              - speakers.yaml\n            - speaker_samples/\n              - 2cdd6d3d-c533-44bf-a5f6-cc83bd089d32.wav\n              - 3c93c9df-86dc-4d67-ab55-8104b9301190.wav\n              - 608903c4-b157-46c5-a0ea-4b25eb4b83b6.wav\n              - 6cf4d171-667d-4bc8-adbb-6d9b7c620cb8.wav\n              - 831c1dbe-c379-4d9f-868b-9798adc3c05d.wav\n              - 90fcd672-ba84-441a-ac6c-0449a59653bd.wav\n              - a6387c23-4ca4-42b5-8aaf-5699dbabbdf0.wav\n              - dd3552d9-f4e8-49ed-9892-f9e67afcf23c.wav\n              - dummy_speaker_test.wav\n              - f1377dc6-aec5-42fc-bea7-98c0be49c48e.wav\n              - fb84ce1c-f32d-4df9-9673-2c64e9603133.wav\n            - speakers.yaml\n          - speakers.yaml\n          - start_servers.py\n          - storage_service.py\n          - test.py\n          - test1-wav\n          - tts_outputs/\n          - tts_temp_outputs/\n            - dialog_output/\n              - dialog_output_seg0_reused.wav\n              - dialog_output_seg0_spk831c1dbe-c379-4d9f-868b-9798adc3c05d_chunk0.wav\n              - dialog_output_seg0_spka6387c23-4ca4-42b5-8aaf-5699dbabbdf0_chunk0.wav\n              - dialog_output_seg1_reused.wav\n              - dialog_output_seg1_spk3c93c9df-86dc-4d67-ab55-8104b9301190_chunk0.wav\n","metadata":"{\"number_of_files\":129,\"truncated\":false}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 8e316b5c-6320-4aaf-9ac8-c53ab2cae164
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let me check if there's already an OpenCode.md file:"}},{"type":"tool_call","data":{"id":"toolu_01Q7HhbsmdsSEEKVKC3RaxWd","name":"glob","input":"{\"pattern\":\"**/OpenCode.md\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750197986}}]


id: ee2f4dd7-0c23-4c4b-914e-a4cc646f66ac
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01Q7HhbsmdsSEEKVKC3RaxWd","name":"","content":"No files found","metadata":"{\"number_of_files\":0,\"truncated\":false}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 73331ca4-21f7-4b98-bef4-fbbae02bca40
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let me check for any Cursor rules or Copilot instructions:"}},{"type":"tool_call","data":{"id":"toolu_01QzQ5eXdF5WNwNXZtHzxWn9","name":"glob","input":"{\"pattern\":\"**/.cursor/rules/**\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750197988}}]


id: 99595b89-d267-47db-b034-9792538a6c85
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01QzQ5eXdF5WNwNXZtHzxWn9","name":"","content":"No files found","metadata":"{\"number_of_files\":0,\"truncated\":false}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 688f9e4c-d844-4294-90b4-a8394413bc8e
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"tool_call","data":{"id":"toolu_01Krxt5hgGjdXBXTDqbFAWRy","name":"glob","input":"{\"pattern\":\"**/.cursorrules\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750197991}}]


id: 8a9d47bc-fa80-4576-b6c8-97fdc9714982
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01Krxt5hgGjdXBXTDqbFAWRy","name":"","content":"No files found","metadata":"{\"number_of_files\":0,\"truncated\":false}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: cbc6aece-cc54-49c4-a703-4485bd66c1ed
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"tool_call","data":{"id":"toolu_01F7BPNxdsQ8MeZchNsmwvyi","name":"glob","input":"{\"pattern\":\"**/.github/copilot-instructions.md\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750197993}}]


id: df95164a-15d7-4141-98fb-4f857ba91cec
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01F7BPNxdsQ8MeZchNsmwvyi","name":"","content":"No files found","metadata":"{\"number_of_files\":0,\"truncated\":false}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: c9f2c10a-d0cd-4372-9ca8-8d48f3001734
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Now, let me examine the package.json file to understand the frontend build/test commands:"}},{"type":"tool_call","data":{"id":"toolu_018uwsffLuCQUMPFqL9uXMaS","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/package.json\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750197997}}]


id: 038a13df-c4b3-4b7f-bcb1-37c6626d7814
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_018uwsffLuCQUMPFqL9uXMaS","name":"","content":"\u003cfile\u003e\n     1|{\n     2|  \"name\": \"chatterbox-test\",\n     3|  \"version\": \"1.0.0\",\n     4|  \"description\": \"This Gradio application provides a user interface for text-to-speech generation using the Chatterbox TTS model. It supports both single utterance generation and multi-speaker dialog generation with configurable silence gaps.\",\n     5|  \"main\": \"index.js\",\n     6|  \"type\": \"module\",\n     7|  \"scripts\": {\n     8|    \"test\": \"jest\"\n     9|  },\n    10|  \"repository\": {\n    11|    \"type\": \"git\",\n    12|    \"url\": \"https://oauth2:78f77aaebb8fa1cd3efbd5b738177c127f7d7d0b@gitea.r8z.us/stwhite/chatterbox-ui.git\"\n    13|  },\n    14|  \"keywords\": [],\n    15|  \"author\": \"\",\n    16|  \"license\": \"ISC\",\n    17|  \"devDependencies\": {\n    18|    \"@babel/core\": \"^7.27.4\",\n    19|    \"@babel/preset-env\": \"^7.27.2\",\n    20|    \"babel-jest\": \"^30.0.0-beta.3\",\n    21|    \"jest\": \"^29.7.0\"\n    22|  }\n    23|}\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/package.json\",\"content\":\"{\\n  \\\"name\\\": \\\"chatterbox-test\\\",\\n  \\\"version\\\": \\\"1.0.0\\\",\\n  \\\"description\\\": \\\"This Gradio application provides a user interface for text-to-speech generation using the Chatterbox TTS model. It supports both single utterance generation and multi-speaker dialog generation with configurable silence gaps.\\\",\\n  \\\"main\\\": \\\"index.js\\\",\\n  \\\"type\\\": \\\"module\\\",\\n  \\\"scripts\\\": {\\n    \\\"test\\\": \\\"jest\\\"\\n  },\\n  \\\"repository\\\": {\\n    \\\"type\\\": \\\"git\\\",\\n    \\\"url\\\": \\\"https://oauth2:78f77aaebb8fa1cd3efbd5b738177c127f7d7d0b@gitea.r8z.us/stwhite/chatterbox-ui.git\\\"\\n  },\\n  \\\"keywords\\\": [],\\n  \\\"author\\\": \\\"\\\",\\n  \\\"license\\\": \\\"ISC\\\",\\n  \\\"devDependencies\\\": {\\n    \\\"@babel/core\\\": \\\"^7.27.4\\\",\\n    \\\"@babel/preset-env\\\": \\\"^7.27.2\\\",\\n    \\\"babel-jest\\\": \\\"^30.0.0-beta.3\\\",\\n    \\\"jest\\\": \\\"^29.7.0\\\"\\n  }\\n}\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 5d3a5727-017d-400b-a14c-e0319af1a58d
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let me check the backend requirements and structure:"}},{"type":"tool_call","data":{"id":"toolu_01M66qVBDqnv1ip3ZjxTjdFA","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/requirements.txt\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198000}}]


id: 24dbe7f0-55ea-4430-85d5-7634306fa4ad
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01M66qVBDqnv1ip3ZjxTjdFA","name":"","content":"\u003cfile\u003e\n     1|fastapi\n     2|uvicorn[standard]\n     3|python-multipart\n     4|PyYAML\n     5|torch\n     6|torchaudio\n     7|chatterbox-tts\n     8|python-dotenv\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/requirements.txt\",\"content\":\"fastapi\\nuvicorn[standard]\\npython-multipart\\nPyYAML\\ntorch\\ntorchaudio\\nchatterbox-tts\\npython-dotenv\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 9de68381-b7ed-4809-91b6-36207e68af19
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let me look at the CLAUDE.md file which contains development commands:"}},{"type":"tool_call","data":{"id":"toolu_017tDdyhfTBPMjPR31zneWGW","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/CLAUDE.md\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198002}}]


id: 9967eafa-3b1f-408f-ac29-c024cdc768dc
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_017tDdyhfTBPMjPR31zneWGW","name":"","content":"\u003cfile\u003e\n     1|# CLAUDE.md\n     2|\n     3|This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.\n     4|\n     5|## Common Development Commands\n     6|\n     7|### Backend (FastAPI)\n     8|\n     9|```bash\n    10|# Install backend dependencies (run from project root)\n    11|pip install -r backend/requirements.txt\n    12|\n    13|# Run backend development server (run from project root)\n    14|uvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000\n    15|\n    16|# Run backend tests\n    17|python backend/run_api_test.py\n    18|\n    19|# Backend API accessible at http://127.0.0.1:8000\n    20|# API docs at http://127.0.0.1:8000/docs\n    21|```\n    22|\n    23|### Frontend Testing\n    24|\n    25|```bash\n    26|# Run frontend tests\n    27|npm test\n    28|```\n    29|\n    30|### Alternative Interfaces\n    31|\n    32|```bash\n    33|# Run Gradio interface (standalone TTS app)\n    34|python gradio_app.py\n    35|```\n    36|\n    37|## Architecture Overview\n    38|\n    39|This is a full-stack TTS (Text-to-Speech) application with three interfaces:\n    40|\n    41|1. **Modern web frontend** (vanilla JS) - Interactive dialog editor at `frontend/`\n    42|2. **FastAPI backend** - REST API at `backend/`  \n    43|3. **Gradio interface** - Alternative UI in `gradio_app.py`\n    44|\n    45|### Frontend-Backend Communication\n    46|\n    47|- **Frontend**: Vanilla JS (ES6 modules) serving on port 8001\n    48|- **Backend**: FastAPI serving on port 8000\n    49|- **API Base**: `http://localhost:8000/api`\n    50|- **CORS**: Configured for frontend communication\n    51|- **File Serving**: Generated audio served via `/generated_audio/` endpoint\n    52|\n    53|### Key API Endpoints\n    54|\n    55|- `/api/speakers/` - Speaker CRUD operations\n    56|- `/api/dialog/generate/` - Full dialog generation  \n    57|- `/api/dialog/generate_line/` - Single line generation\n    58|- `/generated_audio/` - Static audio file serving\n    59|\n    60|### Backend Service Architecture\n    61|\n    62|Located in `backend/app/services/`:\n    63|\n    64|- **TTSService**: Chatterbox TTS model lifecycle management\n    65|- **SpeakerManagementService**: Speaker data and sample management\n    66|- **DialogProcessorService**: Dialog script to audio processing\n    67|- **AudioManipulationService**: Audio concatenation and ZIP creation\n    68|\n    69|### Frontend Architecture\n    70|\n    71|- **Modular design**: `api.js` (API layer) + `app.js` (app logic)\n    72|- **No framework**: Modern vanilla JavaScript with ES6+ features\n    73|- **Interactive editor**: Table-based dialog creation with drag-drop reordering\n    74|\n    75|### Data Flow\n    76|\n    77|1. User creates dialog in frontend table editor\n    78|2. Frontend sends dialog items to `/api/dialog/generate/`\n    79|3. Backend processes speech/silence items via services\n    80|4. TTS generates audio, segments concatenated\n    81|5. ZIP archive created with all outputs\n    82|6. Frontend receives URLs for playback/download\n    83|\n    84|### Speaker Configuration\n    85|\n    86|- **Location**: `speaker_data/speakers.yaml` and `speaker_data/speaker_samples/`\n    87|- **Format**: YAML config referencing WAV audio samples\n    88|- **Management**: Both API endpoints and file-based configuration\n    89|\n    90|### Output Organization\n    91|\n    92|- `dialog_output/` - Generated dialog files\n    93|- `single_output/` - Single utterance outputs  \n    94|- `tts_outputs/` - Raw TTS generation files\n    95|- Generated ZIPs contain organized file structure\n    96|\n    97|## Development Setup Notes\n    98|\n    99|- Python virtual environment expected at project root (`.venv`)\n   100|- Backend commands run from project root, not `backend/` directory\n   101|- Frontend served separately (typically port 8001)\n   102|- Speaker samples must be WAV format in `speaker_data/speaker_samples/`\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/CLAUDE.md\",\"content\":\"# CLAUDE.md\\n\\nThis file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.\\n\\n## Common Development Commands\\n\\n### Backend (FastAPI)\\n\\n```bash\\n# Install backend dependencies (run from project root)\\npip install -r backend/requirements.txt\\n\\n# Run backend development server (run from project root)\\nuvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000\\n\\n# Run backend tests\\npython backend/run_api_test.py\\n\\n# Backend API accessible at http://127.0.0.1:8000\\n# API docs at http://127.0.0.1:8000/docs\\n```\\n\\n### Frontend Testing\\n\\n```bash\\n# Run frontend tests\\nnpm test\\n```\\n\\n### Alternative Interfaces\\n\\n```bash\\n# Run Gradio interface (standalone TTS app)\\npython gradio_app.py\\n```\\n\\n## Architecture Overview\\n\\nThis is a full-stack TTS (Text-to-Speech) application with three interfaces:\\n\\n1. **Modern web frontend** (vanilla JS) - Interactive dialog editor at `frontend/`\\n2. **FastAPI backend** - REST API at `backend/`  \\n3. **Gradio interface** - Alternative UI in `gradio_app.py`\\n\\n### Frontend-Backend Communication\\n\\n- **Frontend**: Vanilla JS (ES6 modules) serving on port 8001\\n- **Backend**: FastAPI serving on port 8000\\n- **API Base**: `http://localhost:8000/api`\\n- **CORS**: Configured for frontend communication\\n- **File Serving**: Generated audio served via `/generated_audio/` endpoint\\n\\n### Key API Endpoints\\n\\n- `/api/speakers/` - Speaker CRUD operations\\n- `/api/dialog/generate/` - Full dialog generation  \\n- `/api/dialog/generate_line/` - Single line generation\\n- `/generated_audio/` - Static audio file serving\\n\\n### Backend Service Architecture\\n\\nLocated in `backend/app/services/`:\\n\\n- **TTSService**: Chatterbox TTS model lifecycle management\\n- **SpeakerManagementService**: Speaker data and sample management\\n- **DialogProcessorService**: Dialog script to audio processing\\n- **AudioManipulationService**: Audio concatenation and ZIP creation\\n\\n### Frontend Architecture\\n\\n- **Modular design**: `api.js` (API layer) + `app.js` (app logic)\\n- **No framework**: Modern vanilla JavaScript with ES6+ features\\n- **Interactive editor**: Table-based dialog creation with drag-drop reordering\\n\\n### Data Flow\\n\\n1. User creates dialog in frontend table editor\\n2. Frontend sends dialog items to `/api/dialog/generate/`\\n3. Backend processes speech/silence items via services\\n4. TTS generates audio, segments concatenated\\n5. ZIP archive created with all outputs\\n6. Frontend receives URLs for playback/download\\n\\n### Speaker Configuration\\n\\n- **Location**: `speaker_data/speakers.yaml` and `speaker_data/speaker_samples/`\\n- **Format**: YAML config referencing WAV audio samples\\n- **Management**: Both API endpoints and file-based configuration\\n\\n### Output Organization\\n\\n- `dialog_output/` - Generated dialog files\\n- `single_output/` - Single utterance outputs  \\n- `tts_outputs/` - Raw TTS generation files\\n- Generated ZIPs contain organized file structure\\n\\n## Development Setup Notes\\n\\n- Python virtual environment expected at project root (`.venv`)\\n- Backend commands run from project root, not `backend/` directory\\n- Frontend served separately (typically port 8001)\\n- Speaker samples must be WAV format in `speaker_data/speaker_samples/`\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 87d66e41-01d0-4334-901c-5dc66f6f1d3a
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let me examine a Python file to understand the code style:"}},{"type":"tool_call","data":{"id":"toolu_01X22za9PV6AeUE3y9tR869s","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/tts_service.py\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198006}}]


id: d447bb2c-d44b-4ac1-b818-196544c5dd51
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01X22za9PV6AeUE3y9tR869s","name":"","content":"\u003cfile\u003e\n     1|import torch\n     2|import torchaudio\n     3|from typing import Optional\n     4|from chatterbox.tts import ChatterboxTTS\n     5|from pathlib import Path\n     6|import gc # Garbage collector for memory management\n     7|import os\n     8|from contextlib import contextmanager\n     9|\n    10|# Import configuration\n    11|from app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\n    12|\n    13|# Use configuration for TTS output directory\n    14|TTS_OUTPUT_DIR = TTS_TEMP_OUTPUT_DIR\n    15|\n    16|def safe_load_chatterbox_tts(device):\n    17|    \"\"\"\n    18|    Safely load ChatterboxTTS model with device mapping to handle CUDA-\u003eMPS/CPU conversion.\n    19|    This patches torch.load temporarily to map CUDA tensors to the appropriate device.\n    20|    \"\"\"\n    21|    @contextmanager\n    22|    def patch_torch_load(target_device):\n    23|        original_load = torch.load\n    24|        \n    25|        def patched_load(*args, **kwargs):\n    26|            # Add map_location to handle device mapping\n    27|            if 'map_location' not in kwargs:\n    28|                if target_device == \"mps\" and torch.backends.mps.is_available():\n    29|                    kwargs['map_location'] = torch.device('mps')\n    30|                else:\n    31|                    kwargs['map_location'] = torch.device('cpu')\n    32|            return original_load(*args, **kwargs)\n    33|        \n    34|        torch.load = patched_load\n    35|        try:\n    36|            yield\n    37|        finally:\n    38|            torch.load = original_load\n    39|    \n    40|    with patch_torch_load(device):\n    41|        return ChatterboxTTS.from_pretrained(device=device)\n    42|\n    43|class TTSService:\n    44|    def __init__(self, device: str = \"mps\"): # Default to MPS for Macs, can be \"cpu\" or \"cuda\"\n    45|        self.device = device\n    46|        self.model = None\n    47|        self._ensure_output_dir_exists()\n    48|\n    49|    def _ensure_output_dir_exists(self):\n    50|        \"\"\"Ensures the TTS output directory exists.\"\"\"\n    51|        TTS_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n    52|\n    53|    def load_model(self):\n    54|        \"\"\"Loads the ChatterboxTTS model.\"\"\"\n    55|        if self.model is None:\n    56|            print(f\"Loading ChatterboxTTS model to device: {self.device}...\")\n    57|            try:\n    58|                self.model = safe_load_chatterbox_tts(self.device)\n    59|                print(\"ChatterboxTTS model loaded successfully.\")\n    60|            except Exception as e:\n    61|                print(f\"Error loading ChatterboxTTS model: {e}\")\n    62|                # Potentially raise an exception or handle appropriately\n    63|                raise\n    64|        else:\n    65|            print(\"ChatterboxTTS model already loaded.\")\n    66|\n    67|    def unload_model(self):\n    68|        \"\"\"Unloads the model and clears memory.\"\"\"\n    69|        if self.model is not None:\n    70|            print(\"Unloading ChatterboxTTS model and clearing cache...\")\n    71|            del self.model\n    72|            self.model = None\n    73|            if self.device == \"cuda\":\n    74|                torch.cuda.empty_cache()\n    75|            elif self.device == \"mps\":\n    76|                if hasattr(torch.mps, \"empty_cache\"): # Check if empty_cache is available for MPS\n    77|                    torch.mps.empty_cache()\n    78|            gc.collect() # Explicitly run garbage collection\n    79|            print(\"Model unloaded and memory cleared.\")\n    80|\n    81|    async def generate_speech(\n    82|        self,\n    83|        text: str,\n    84|        speaker_sample_path: str, # Absolute path to the speaker's audio sample\n    85|        output_filename_base: str, # e.g., \"dialog_line_1_spk_X_chunk_0\"\n    86|        speaker_id: Optional[str] = None, # Optional, mainly for logging if needed, filename base is primary\n    87|        output_dir: Optional[Path] = None, # Optional, defaults to TTS_OUTPUT_DIR from this module\n    88|        exaggeration: float = 0.5, # Default from Gradio\n    89|        cfg_weight: float = 0.5,   # Default from Gradio\n    90|        temperature: float = 0.8,  # Default from Gradio\n    91|    ) -\u003e Path:\n    92|        \"\"\"\n    93|        Generates speech from text using the loaded TTS model and a speaker sample.\n    94|        Saves the output to a .wav file.\n    95|        \"\"\"\n    96|        if self.model is None:\n    97|            self.load_model()\n    98|        \n    99|        if self.model is None: # Check again if loading failed\n   100|            raise RuntimeError(\"TTS model is not loaded. Cannot generate speech.\")\n   101|\n   102|        # Ensure speaker_sample_path is valid\n   103|        speaker_sample_p = Path(speaker_sample_path)\n   104|        if not speaker_sample_p.exists() or not speaker_sample_p.is_file():\n   105|            raise FileNotFoundError(f\"Speaker sample audio file not found: {speaker_sample_path}\")\n   106|\n   107|        target_output_dir = output_dir if output_dir is not None else TTS_OUTPUT_DIR\n   108|        target_output_dir.mkdir(parents=True, exist_ok=True)\n   109|        # output_filename_base from DialogProcessorService is expected to be comprehensive (e.g., includes speaker_id, segment info)\n   110|        output_file_path = target_output_dir / f\"{output_filename_base}.wav\"\n   111|\n   112|        print(f\"Generating audio for text: \\\"{text[:50]}...\\\" with speaker sample: {speaker_sample_path}\")\n   113|        try:\n   114|            with torch.no_grad(): # Important for inference\n   115|                wav = self.model.generate(\n   116|                    text=text,\n   117|                    audio_prompt_path=str(speaker_sample_p), # Must be a string path\n   118|                    exaggeration=exaggeration,\n   119|                    cfg_weight=cfg_weight,\n   120|                    temperature=temperature,\n   121|                )\n   122|            \n   123|            torchaudio.save(str(output_file_path), wav, self.model.sr)\n   124|            print(f\"Audio saved to: {output_file_path}\")\n   125|            return output_file_path\n   126|        except Exception as e:\n   127|            print(f\"Error during TTS generation or saving: {e}\")\n   128|            raise\n   129|        finally:\n   130|            # For now, we keep it loaded. Memory management might need refinement.\n   131|            pass\n   132|\n   133|# Example usage (for testing, not part of the service itself)\n   134|if __name__ == \"__main__\":\n   135|    async def main_test():\n   136|        tts_service = TTSService(device=\"mps\")\n   137|        try:\n   138|            tts_service.load_model()\n   139|            \n   140|            dummy_speaker_root = SPEAKER_SAMPLES_DIR\n   141|            dummy_speaker_root.mkdir(parents=True, exist_ok=True)\n   142|            dummy_sample_file = dummy_speaker_root / \"dummy_speaker_test.wav\"\n   143|            import os # Added for os.remove\n   144|            # Always try to remove an existing dummy file to ensure a fresh one is created\n   145|            if dummy_sample_file.exists():\n   146|                try:\n   147|                    os.remove(dummy_sample_file)\n   148|                    print(f\"Removed existing dummy sample: {dummy_sample_file}\")\n   149|                except OSError as e:\n   150|                    print(f\"Error removing existing dummy sample {dummy_sample_file}: {e}\")\n   151|                    # Proceeding, but torchaudio.save might fail or overwrite\n   152|\n   153|            print(f\"Creating new dummy speaker sample: {dummy_sample_file}\")\n   154|            # Create a minimal, silent WAV file for testing\n   155|            sample_rate = 22050\n   156|            duration = 1  # seconds\n   157|            num_channels = 1\n   158|            num_frames = sample_rate * duration\n   159|            audio_data = torch.zeros((num_channels, num_frames))\n   160|            try:\n   161|                torchaudio.save(str(dummy_sample_file), audio_data, sample_rate)\n   162|                print(f\"Dummy sample created successfully: {dummy_sample_file}\")\n   163|            except Exception as save_e:\n   164|                print(f\"Could not create dummy sample: {save_e}\")\n   165|                # If creation fails, the subsequent generation test will likely also fail or be skipped.\n   166|\n   167|\n   168|            if dummy_sample_file.exists():\n   169|                output_path = await tts_service.generate_speech(\n   170|                    text=\"Hello, this is a test of the Text-to-Speech service.\",\n   171|                    speaker_id=\"test_speaker\",\n   172|                    speaker_sample_path=str(dummy_sample_file),\n   173|                    output_filename_base=\"test_generation\"\n   174|                )\n   175|                print(f\"Test generation output: {output_path}\")\n   176|            else:\n   177|                print(f\"Skipping generation test as dummy sample {dummy_sample_file} not found.\")\n   178|\n   179|        except Exception as e:\n   180|            import traceback\n   181|            print(f\"Error during TTS generation or saving:\")\n   182|            traceback.print_exc()\n   183|        finally:\n   184|            tts_service.unload_model()\n   185|\n   186|    import asyncio\n   187|    asyncio.run(main_test())\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/tts_service.py\",\"content\":\"import torch\\nimport torchaudio\\nfrom typing import Optional\\nfrom chatterbox.tts import ChatterboxTTS\\nfrom pathlib import Path\\nimport gc # Garbage collector for memory management\\nimport os\\nfrom contextlib import contextmanager\\n\\n# Import configuration\\nfrom app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\\n\\n# Use configuration for TTS output directory\\nTTS_OUTPUT_DIR = TTS_TEMP_OUTPUT_DIR\\n\\ndef safe_load_chatterbox_tts(device):\\n    \\\"\\\"\\\"\\n    Safely load ChatterboxTTS model with device mapping to handle CUDA-\\u003eMPS/CPU conversion.\\n    This patches torch.load temporarily to map CUDA tensors to the appropriate device.\\n    \\\"\\\"\\\"\\n    @contextmanager\\n    def patch_torch_load(target_device):\\n        original_load = torch.load\\n        \\n        def patched_load(*args, **kwargs):\\n            # Add map_location to handle device mapping\\n            if 'map_location' not in kwargs:\\n                if target_device == \\\"mps\\\" and torch.backends.mps.is_available():\\n                    kwargs['map_location'] = torch.device('mps')\\n                else:\\n                    kwargs['map_location'] = torch.device('cpu')\\n            return original_load(*args, **kwargs)\\n        \\n        torch.load = patched_load\\n        try:\\n            yield\\n        finally:\\n            torch.load = original_load\\n    \\n    with patch_torch_load(device):\\n        return ChatterboxTTS.from_pretrained(device=device)\\n\\nclass TTSService:\\n    def __init__(self, device: str = \\\"mps\\\"): # Default to MPS for Macs, can be \\\"cpu\\\" or \\\"cuda\\\"\\n        self.device = device\\n        self.model = None\\n        self._ensure_output_dir_exists()\\n\\n    def _ensure_output_dir_exists(self):\\n        \\\"\\\"\\\"Ensures the TTS output directory exists.\\\"\\\"\\\"\\n        TTS_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\\n\\n    def load_model(self):\\n        \\\"\\\"\\\"Loads the ChatterboxTTS model.\\\"\\\"\\\"\\n        if self.model is None:\\n            print(f\\\"Loading ChatterboxTTS model to device: {self.device}...\\\")\\n            try:\\n                self.model = safe_load_chatterbox_tts(self.device)\\n                print(\\\"ChatterboxTTS model loaded successfully.\\\")\\n            except Exception as e:\\n                print(f\\\"Error loading ChatterboxTTS model: {e}\\\")\\n                # Potentially raise an exception or handle appropriately\\n                raise\\n        else:\\n            print(\\\"ChatterboxTTS model already loaded.\\\")\\n\\n    def unload_model(self):\\n        \\\"\\\"\\\"Unloads the model and clears memory.\\\"\\\"\\\"\\n        if self.model is not None:\\n            print(\\\"Unloading ChatterboxTTS model and clearing cache...\\\")\\n            del self.model\\n            self.model = None\\n            if self.device == \\\"cuda\\\":\\n                torch.cuda.empty_cache()\\n            elif self.device == \\\"mps\\\":\\n                if hasattr(torch.mps, \\\"empty_cache\\\"): # Check if empty_cache is available for MPS\\n                    torch.mps.empty_cache()\\n            gc.collect() # Explicitly run garbage collection\\n            print(\\\"Model unloaded and memory cleared.\\\")\\n\\n    async def generate_speech(\\n        self,\\n        text: str,\\n        speaker_sample_path: str, # Absolute path to the speaker's audio sample\\n        output_filename_base: str, # e.g., \\\"dialog_line_1_spk_X_chunk_0\\\"\\n        speaker_id: Optional[str] = None, # Optional, mainly for logging if needed, filename base is primary\\n        output_dir: Optional[Path] = None, # Optional, defaults to TTS_OUTPUT_DIR from this module\\n        exaggeration: float = 0.5, # Default from Gradio\\n        cfg_weight: float = 0.5,   # Default from Gradio\\n        temperature: float = 0.8,  # Default from Gradio\\n    ) -\\u003e Path:\\n        \\\"\\\"\\\"\\n        Generates speech from text using the loaded TTS model and a speaker sample.\\n        Saves the output to a .wav file.\\n        \\\"\\\"\\\"\\n        if self.model is None:\\n            self.load_model()\\n        \\n        if self.model is None: # Check again if loading failed\\n            raise RuntimeError(\\\"TTS model is not loaded. Cannot generate speech.\\\")\\n\\n        # Ensure speaker_sample_path is valid\\n        speaker_sample_p = Path(speaker_sample_path)\\n        if not speaker_sample_p.exists() or not speaker_sample_p.is_file():\\n            raise FileNotFoundError(f\\\"Speaker sample audio file not found: {speaker_sample_path}\\\")\\n\\n        target_output_dir = output_dir if output_dir is not None else TTS_OUTPUT_DIR\\n        target_output_dir.mkdir(parents=True, exist_ok=True)\\n        # output_filename_base from DialogProcessorService is expected to be comprehensive (e.g., includes speaker_id, segment info)\\n        output_file_path = target_output_dir / f\\\"{output_filename_base}.wav\\\"\\n\\n        print(f\\\"Generating audio for text: \\\\\\\"{text[:50]}...\\\\\\\" with speaker sample: {speaker_sample_path}\\\")\\n        try:\\n            with torch.no_grad(): # Important for inference\\n                wav = self.model.generate(\\n                    text=text,\\n                    audio_prompt_path=str(speaker_sample_p), # Must be a string path\\n                    exaggeration=exaggeration,\\n                    cfg_weight=cfg_weight,\\n                    temperature=temperature,\\n                )\\n            \\n            torchaudio.save(str(output_file_path), wav, self.model.sr)\\n            print(f\\\"Audio saved to: {output_file_path}\\\")\\n            return output_file_path\\n        except Exception as e:\\n            print(f\\\"Error during TTS generation or saving: {e}\\\")\\n            raise\\n        finally:\\n            # For now, we keep it loaded. Memory management might need refinement.\\n            pass\\n\\n# Example usage (for testing, not part of the service itself)\\nif __name__ == \\\"__main__\\\":\\n    async def main_test():\\n        tts_service = TTSService(device=\\\"mps\\\")\\n        try:\\n            tts_service.load_model()\\n            \\n            dummy_speaker_root = SPEAKER_SAMPLES_DIR\\n            dummy_speaker_root.mkdir(parents=True, exist_ok=True)\\n            dummy_sample_file = dummy_speaker_root / \\\"dummy_speaker_test.wav\\\"\\n            import os # Added for os.remove\\n            # Always try to remove an existing dummy file to ensure a fresh one is created\\n            if dummy_sample_file.exists():\\n                try:\\n                    os.remove(dummy_sample_file)\\n                    print(f\\\"Removed existing dummy sample: {dummy_sample_file}\\\")\\n                except OSError as e:\\n                    print(f\\\"Error removing existing dummy sample {dummy_sample_file}: {e}\\\")\\n                    # Proceeding, but torchaudio.save might fail or overwrite\\n\\n            print(f\\\"Creating new dummy speaker sample: {dummy_sample_file}\\\")\\n            # Create a minimal, silent WAV file for testing\\n            sample_rate = 22050\\n            duration = 1  # seconds\\n            num_channels = 1\\n            num_frames = sample_rate * duration\\n            audio_data = torch.zeros((num_channels, num_frames))\\n            try:\\n                torchaudio.save(str(dummy_sample_file), audio_data, sample_rate)\\n                print(f\\\"Dummy sample created successfully: {dummy_sample_file}\\\")\\n            except Exception as save_e:\\n                print(f\\\"Could not create dummy sample: {save_e}\\\")\\n                # If creation fails, the subsequent generation test will likely also fail or be skipped.\\n\\n\\n            if dummy_sample_file.exists():\\n                output_path = await tts_service.generate_speech(\\n                    text=\\\"Hello, this is a test of the Text-to-Speech service.\\\",\\n                    speaker_id=\\\"test_speaker\\\",\\n                    speaker_sample_path=str(dummy_sample_file),\\n                    output_filename_base=\\\"test_generation\\\"\\n                )\\n                print(f\\\"Test generation output: {output_path}\\\")\\n            else:\\n                print(f\\\"Skipping generation test as dummy sample {dummy_sample_file} not found.\\\")\\n\\n        except Exception as e:\\n            import traceback\\n            print(f\\\"Error during TTS generation or saving:\\\")\\n            traceback.print_exc()\\n        finally:\\n            tts_service.unload_model()\\n\\n    import asyncio\\n    asyncio.run(main_test())\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 11effd02-76f2-461e-9de3-128cdcd7809a
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let me check a JavaScript file to understand the frontend code style:"}},{"type":"tool_call","data":{"id":"toolu_01JaSa2yW9wVMx5c7Mhfft4M","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/frontend/js/api.js\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198009}}]


id: 7890ec1c-c6bd-4144-90bd-ee78ce279b2f
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01JaSa2yW9wVMx5c7Mhfft4M","name":"","content":"\u003cfile\u003e\n     1|// frontend/js/api.js\n     2|\n     3|import { API_BASE_URL_WITH_PREFIX } from './config.js';\n     4|\n     5|const API_BASE_URL = API_BASE_URL_WITH_PREFIX;\n     6|\n     7|/**\n     8| * Fetches the list of available speakers.\n     9| * @returns {Promise\u003cArray\u003cObject\u003e\u003e} A promise that resolves to an array of speaker objects.\n    10| * @throws {Error} If the network response is not ok.\n    11| */\n    12|export async function getSpeakers() {\n    13|    const response = await fetch(`${API_BASE_URL}/speakers/`);\n    14|    if (!response.ok) {\n    15|        const errorData = await response.json().catch(() =\u003e ({ message: response.statusText }));\n    16|        throw new Error(`Failed to fetch speakers: ${errorData.detail || errorData.message || response.statusText}`);\n    17|    }\n    18|    return response.json();\n    19|}\n    20|\n    21|// We will add more functions here: addSpeaker, deleteSpeaker, generateDialog\n    22|\n    23|// ... (keep API_BASE_URL and getSpeakers)\n    24|\n    25|/**\n    26| * Adds a new speaker.\n    27| * @param {FormData} formData - The form data containing speaker name and audio file.\n    28| *                               Example: formData.append('name', 'New Speaker');\n    29| *                                        formData.append('audio_sample_file', fileInput.files[0]);\n    30| * @returns {Promise\u003cObject\u003e} A promise that resolves to the new speaker object.\n    31| * @throws {Error} If the network response is not ok.\n    32| */\n    33|export async function addSpeaker(formData) {\n    34|    const response = await fetch(`${API_BASE_URL}/speakers/`, {\n    35|        method: 'POST',\n    36|        body: formData, // FormData sets Content-Type to multipart/form-data automatically\n    37|    });\n    38|    if (!response.ok) {\n    39|        console.log('API_JS_ADD_SPEAKER: Entered !response.ok block. Status:', response.status, 'StatusText:', response.statusText);\n    40|        let errorPayload = { detail: `Request failed with status ${response.status}` }; // Default payload\n    41|        try {\n    42|            console.log('API_JS_ADD_SPEAKER: Attempting to parse error response as JSON...');\n    43|            errorPayload = await response.json();\n    44|            console.log('API_JS_ADD_SPEAKER: Successfully parsed error JSON:', errorPayload);\n    45|        } catch (e) {\n    46|            console.warn('API_JS_ADD_SPEAKER: Failed to parse error response as JSON. Error:', e);\n    47|            // Use statusText if JSON parsing fails\n    48|            errorPayload = { detail: response.statusText || `Request failed with status ${response.status} and no JSON body.`, parseError: e.toString() };\n    49|        }\n    50|        \n    51|        console.error('--- BEGIN SERVER ERROR PAYLOAD (addSpeaker) ---');\n    52|        console.error('Status:', response.status);\n    53|        console.error('Status Text:', response.statusText);\n    54|        console.error('Parsed Payload:', errorPayload);\n    55|        console.error('--- END SERVER ERROR PAYLOAD (addSpeaker) ---');\n    56|\n    57|        let detailedMessage = \"Unknown error\";\n    58|        if (errorPayload \u0026\u0026 errorPayload.detail) {\n    59|            if (typeof errorPayload.detail === 'string') {\n    60|                detailedMessage = errorPayload.detail;\n    61|            } else {\n    62|                // If detail is an array (FastAPI validation errors) or object, stringify it.\n    63|                detailedMessage = JSON.stringify(errorPayload.detail);\n    64|            }\n    65|        } else if (errorPayload \u0026\u0026 errorPayload.message) { \n    66|            detailedMessage = errorPayload.message;\n    67|        } else if (response.statusText) {\n    68|            detailedMessage = response.statusText;\n    69|        } else {\n    70|            detailedMessage = `HTTP error ${response.status}`;\n    71|        }\n    72|        \n    73|        console.log(`API_JS_ADD_SPEAKER: Constructed detailedMessage: \"${detailedMessage}\"`);\n    74|        console.log(`API_JS_ADD_SPEAKER: Throwing error with message: \"Failed to add speaker: ${detailedMessage}\"`);\n    75|        throw new Error(`Failed to add speaker: ${detailedMessage}`);\n    76|    }\n    77|    return response.json();\n    78|}\n    79|\n    80|// ... (keep API_BASE_URL, getSpeakers, addSpeaker)\n    81|\n    82|/**\n    83| * Deletes a speaker by their ID.\n    84| * @param {string} speakerId - The ID of the speaker to delete.\n    85| * @returns {Promise\u003cObject\u003e} A promise that resolves to the response data (e.g., success message).\n    86| * @throws {Error} If the network response is not ok.\n    87| */\n    88|export async function deleteSpeaker(speakerId) {\n    89|    const response = await fetch(`${API_BASE_URL}/speakers/${speakerId}/`, {\n    90|        method: 'DELETE',\n    91|    });\n    92|    if (!response.ok) {\n    93|        const errorData = await response.json().catch(() =\u003e ({ message: response.statusText }));\n    94|        throw new Error(`Failed to delete speaker ${speakerId}: ${errorData.detail || errorData.message || response.statusText}`);\n    95|    }\n    96|    // Handle 204 No Content specifically, as .json() would fail\n    97|    if (response.status === 204) {\n    98|        return { message: `Speaker ${speakerId} deleted successfully.` };\n    99|    }\n   100|    return response.json();\n   101|}\n   102|\n   103|// ... (keep API_BASE_URL, getSpeakers, addSpeaker, deleteSpeaker)\n   104|\n   105|/**\n   106| * Generates audio for a single dialog line (speech or silence).\n   107| * @param {Object} line - The dialog line object (type: 'speech' or 'silence').\n   108| * @returns {Promise\u003cObject\u003e} Resolves with { audio_url } on success.\n   109| * @throws {Error} If the network response is not ok.\n   110| */\n   111|export async function generateLine(line) {\n   112|    console.log('generateLine called with:', line);\n   113|    const response = await fetch(`${API_BASE_URL}/dialog/generate_line`, {\n   114|        method: 'POST',\n   115|        headers: {\n   116|            'Content-Type': 'application/json',\n   117|        },\n   118|        body: JSON.stringify(line),\n   119|    });\n   120|    console.log('Response status:', response.status);\n   121|    console.log('Response headers:', [...response.headers.entries()]);\n   122|    \n   123|    if (!response.ok) {\n   124|        const errorData = await response.json().catch(() =\u003e ({ message: response.statusText }));\n   125|        throw new Error(`Failed to generate line audio: ${errorData.detail || errorData.message || response.statusText}`);\n   126|    }\n   127|    \n   128|    const responseText = await response.text();\n   129|    console.log('Raw response text:', responseText);\n   130|    \n   131|    try {\n   132|        const jsonData = JSON.parse(responseText);\n   133|        console.log('Parsed JSON:', jsonData);\n   134|        return jsonData;\n   135|    } catch (parseError) {\n   136|        console.error('JSON parse error:', parseError);\n   137|        throw new Error(`Invalid JSON response: ${responseText}`);\n   138|    }\n   139|}\n   140|\n   141|/**\n   142| * Generates a dialog by sending a payload to the backend.\n   143| * @param {Object} dialogPayload - The payload for dialog generation.\n   144| *   Example:\n   145| *   {\n   146| *     output_base_name: \"my_dialog\",\n   147| *     dialog_items: [\n   148| *       { type: \"speech\", speaker_id: \"speaker1\", text: \"Hello world.\", exaggeration: 1.0, cfg_weight: 2.0, temperature: 0.7 },\n   149| *       { type: \"silence\", duration_ms: 500 },\n   150| *       { type: \"speech\", speaker_id: \"speaker2\", text: \"How are you?\" }\n   151| *     ]\n   152| *   }\n   153| * @returns {Promise\u003cObject\u003e} A promise that resolves to the dialog generation response (log, file URLs).\n   154| * @throws {Error} If the network response is not ok.\n   155| */\n   156|export async function generateDialog(dialogPayload) {\n   157|    const response = await fetch(`${API_BASE_URL}/dialog/generate`, {\n   158|        method: 'POST',\n   159|        headers: {\n   160|            'Content-Type': 'application/json',\n   161|        },\n   162|        body: JSON.stringify(dialogPayload),\n   163|    });\n   164|    if (!response.ok) {\n   165|        const errorData = await response.json().catch(() =\u003e ({ message: response.statusText }));\n   166|        throw new Error(`Failed to generate dialog: ${errorData.detail || errorData.message || response.statusText}`);\n   167|    }\n   168|    return response.json();\n   169|}\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/frontend/js/api.js\",\"content\":\"// frontend/js/api.js\\n\\nimport { API_BASE_URL_WITH_PREFIX } from './config.js';\\n\\nconst API_BASE_URL = API_BASE_URL_WITH_PREFIX;\\n\\n/**\\n * Fetches the list of available speakers.\\n * @returns {Promise\\u003cArray\\u003cObject\\u003e\\u003e} A promise that resolves to an array of speaker objects.\\n * @throws {Error} If the network response is not ok.\\n */\\nexport async function getSpeakers() {\\n    const response = await fetch(`${API_BASE_URL}/speakers/`);\\n    if (!response.ok) {\\n        const errorData = await response.json().catch(() =\\u003e ({ message: response.statusText }));\\n        throw new Error(`Failed to fetch speakers: ${errorData.detail || errorData.message || response.statusText}`);\\n    }\\n    return response.json();\\n}\\n\\n// We will add more functions here: addSpeaker, deleteSpeaker, generateDialog\\n\\n// ... (keep API_BASE_URL and getSpeakers)\\n\\n/**\\n * Adds a new speaker.\\n * @param {FormData} formData - The form data containing speaker name and audio file.\\n *                               Example: formData.append('name', 'New Speaker');\\n *                                        formData.append('audio_sample_file', fileInput.files[0]);\\n * @returns {Promise\\u003cObject\\u003e} A promise that resolves to the new speaker object.\\n * @throws {Error} If the network response is not ok.\\n */\\nexport async function addSpeaker(formData) {\\n    const response = await fetch(`${API_BASE_URL}/speakers/`, {\\n        method: 'POST',\\n        body: formData, // FormData sets Content-Type to multipart/form-data automatically\\n    });\\n    if (!response.ok) {\\n        console.log('API_JS_ADD_SPEAKER: Entered !response.ok block. Status:', response.status, 'StatusText:', response.statusText);\\n        let errorPayload = { detail: `Request failed with status ${response.status}` }; // Default payload\\n        try {\\n            console.log('API_JS_ADD_SPEAKER: Attempting to parse error response as JSON...');\\n            errorPayload = await response.json();\\n            console.log('API_JS_ADD_SPEAKER: Successfully parsed error JSON:', errorPayload);\\n        } catch (e) {\\n            console.warn('API_JS_ADD_SPEAKER: Failed to parse error response as JSON. Error:', e);\\n            // Use statusText if JSON parsing fails\\n            errorPayload = { detail: response.statusText || `Request failed with status ${response.status} and no JSON body.`, parseError: e.toString() };\\n        }\\n        \\n        console.error('--- BEGIN SERVER ERROR PAYLOAD (addSpeaker) ---');\\n        console.error('Status:', response.status);\\n        console.error('Status Text:', response.statusText);\\n        console.error('Parsed Payload:', errorPayload);\\n        console.error('--- END SERVER ERROR PAYLOAD (addSpeaker) ---');\\n\\n        let detailedMessage = \\\"Unknown error\\\";\\n        if (errorPayload \\u0026\\u0026 errorPayload.detail) {\\n            if (typeof errorPayload.detail === 'string') {\\n                detailedMessage = errorPayload.detail;\\n            } else {\\n                // If detail is an array (FastAPI validation errors) or object, stringify it.\\n                detailedMessage = JSON.stringify(errorPayload.detail);\\n            }\\n        } else if (errorPayload \\u0026\\u0026 errorPayload.message) { \\n            detailedMessage = errorPayload.message;\\n        } else if (response.statusText) {\\n            detailedMessage = response.statusText;\\n        } else {\\n            detailedMessage = `HTTP error ${response.status}`;\\n        }\\n        \\n        console.log(`API_JS_ADD_SPEAKER: Constructed detailedMessage: \\\"${detailedMessage}\\\"`);\\n        console.log(`API_JS_ADD_SPEAKER: Throwing error with message: \\\"Failed to add speaker: ${detailedMessage}\\\"`);\\n        throw new Error(`Failed to add speaker: ${detailedMessage}`);\\n    }\\n    return response.json();\\n}\\n\\n// ... (keep API_BASE_URL, getSpeakers, addSpeaker)\\n\\n/**\\n * Deletes a speaker by their ID.\\n * @param {string} speakerId - The ID of the speaker to delete.\\n * @returns {Promise\\u003cObject\\u003e} A promise that resolves to the response data (e.g., success message).\\n * @throws {Error} If the network response is not ok.\\n */\\nexport async function deleteSpeaker(speakerId) {\\n    const response = await fetch(`${API_BASE_URL}/speakers/${speakerId}/`, {\\n        method: 'DELETE',\\n    });\\n    if (!response.ok) {\\n        const errorData = await response.json().catch(() =\\u003e ({ message: response.statusText }));\\n        throw new Error(`Failed to delete speaker ${speakerId}: ${errorData.detail || errorData.message || response.statusText}`);\\n    }\\n    // Handle 204 No Content specifically, as .json() would fail\\n    if (response.status === 204) {\\n        return { message: `Speaker ${speakerId} deleted successfully.` };\\n    }\\n    return response.json();\\n}\\n\\n// ... (keep API_BASE_URL, getSpeakers, addSpeaker, deleteSpeaker)\\n\\n/**\\n * Generates audio for a single dialog line (speech or silence).\\n * @param {Object} line - The dialog line object (type: 'speech' or 'silence').\\n * @returns {Promise\\u003cObject\\u003e} Resolves with { audio_url } on success.\\n * @throws {Error} If the network response is not ok.\\n */\\nexport async function generateLine(line) {\\n    console.log('generateLine called with:', line);\\n    const response = await fetch(`${API_BASE_URL}/dialog/generate_line`, {\\n        method: 'POST',\\n        headers: {\\n            'Content-Type': 'application/json',\\n        },\\n        body: JSON.stringify(line),\\n    });\\n    console.log('Response status:', response.status);\\n    console.log('Response headers:', [...response.headers.entries()]);\\n    \\n    if (!response.ok) {\\n        const errorData = await response.json().catch(() =\\u003e ({ message: response.statusText }));\\n        throw new Error(`Failed to generate line audio: ${errorData.detail || errorData.message || response.statusText}`);\\n    }\\n    \\n    const responseText = await response.text();\\n    console.log('Raw response text:', responseText);\\n    \\n    try {\\n        const jsonData = JSON.parse(responseText);\\n        console.log('Parsed JSON:', jsonData);\\n        return jsonData;\\n    } catch (parseError) {\\n        console.error('JSON parse error:', parseError);\\n        throw new Error(`Invalid JSON response: ${responseText}`);\\n    }\\n}\\n\\n/**\\n * Generates a dialog by sending a payload to the backend.\\n * @param {Object} dialogPayload - The payload for dialog generation.\\n *   Example:\\n *   {\\n *     output_base_name: \\\"my_dialog\\\",\\n *     dialog_items: [\\n *       { type: \\\"speech\\\", speaker_id: \\\"speaker1\\\", text: \\\"Hello world.\\\", exaggeration: 1.0, cfg_weight: 2.0, temperature: 0.7 },\\n *       { type: \\\"silence\\\", duration_ms: 500 },\\n *       { type: \\\"speech\\\", speaker_id: \\\"speaker2\\\", text: \\\"How are you?\\\" }\\n *     ]\\n *   }\\n * @returns {Promise\\u003cObject\\u003e} A promise that resolves to the dialog generation response (log, file URLs).\\n * @throws {Error} If the network response is not ok.\\n */\\nexport async function generateDialog(dialogPayload) {\\n    const response = await fetch(`${API_BASE_URL}/dialog/generate`, {\\n        method: 'POST',\\n        headers: {\\n            'Content-Type': 'application/json',\\n        },\\n        body: JSON.stringify(dialogPayload),\\n    });\\n    if (!response.ok) {\\n        const errorData = await response.json().catch(() =\\u003e ({ message: response.statusText }));\\n        throw new Error(`Failed to generate dialog: ${errorData.detail || errorData.message || response.statusText}`);\\n    }\\n    return response.json();\\n}\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 603f9760-a53d-4fe4-acaf-77d840a71b92
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let me check if there are any test files to understand the testing approach:"}},{"type":"tool_call","data":{"id":"toolu_014BBgEGLmkhorspMMHceMwK","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/frontend/tests/api.test.js\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198012}}]


id: 06f816ed-7a15-47b3-8fe2-7cc6e11b9a46
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_014BBgEGLmkhorspMMHceMwK","name":"","content":"\u003cfile\u003e\n     1|// frontend/tests/api.test.js\n     2|\n     3|// Import the function to test (adjust path if your structure is different)\n     4|// We might need to configure Jest or use Babel for ES module syntax if this causes issues.\n     5|import { getSpeakers, addSpeaker, deleteSpeaker, generateDialog } from '../js/api.js';\n     6|\n     7|// Mock the global fetch function\n     8|global.fetch = jest.fn();\n     9|\n    10|const API_BASE_URL = 'http://localhost:8000/api'; // Centralize for all tests\n    11|\n    12|describe('API Client - getSpeakers', () =\u003e {\n    13|    beforeEach(() =\u003e {\n    14|        // Clear all instances and calls to constructor and all methods:\n    15|        fetch.mockClear();\n    16|    });\n    17|\n    18|    it('should fetch speakers successfully', async () =\u003e {\n    19|        const mockSpeakers = [{ id: '1', name: 'Speaker 1' }, { id: '2', name: 'Speaker 2' }];\n    20|        fetch.mockResolvedValueOnce({\n    21|            ok: true,\n    22|            json: async () =\u003e mockSpeakers,\n    23|        });\n    24|\n    25|        const speakers = await getSpeakers();\n    26|        expect(fetch).toHaveBeenCalledTimes(1);\n    27|        expect(fetch).toHaveBeenCalledWith(`${API_BASE_URL}/speakers`);\n    28|        expect(speakers).toEqual(mockSpeakers);\n    29|    });\n    30|\n    31|    it('should throw an error if the network response is not ok', async () =\u003e {\n    32|        fetch.mockResolvedValueOnce({\n    33|            ok: false,\n    34|            statusText: 'Not Found',\n    35|            json: async () =\u003e ({ detail: 'Speakers not found' }) // Simulate FastAPI error response\n    36|        });\n    37|\n    38|        await expect(getSpeakers()).rejects.toThrow('Failed to fetch speakers: Speakers not found');\n    39|        expect(fetch).toHaveBeenCalledTimes(1);\n    40|    });\n    41|\n    42|    it('should throw a generic error if parsing error response fails', async () =\u003e {\n    43|        fetch.mockResolvedValueOnce({\n    44|            ok: false,\n    45|            statusText: 'Internal Server Error',\n    46|            json: async () =\u003e { throw new Error('Failed to parse error JSON'); } // Simulate error during .json()\n    47|        });\n    48|\n    49|        await expect(getSpeakers()).rejects.toThrow('Failed to fetch speakers: Internal Server Error');\n    50|        expect(fetch).toHaveBeenCalledTimes(1);\n    51|    });\n    52|    \n    53|    it('should throw an error if fetch itself fails (network error)', async () =\u003e {\n    54|        fetch.mockRejectedValueOnce(new TypeError('Network failed'));\n    55|\n    56|        await expect(getSpeakers()).rejects.toThrow('Network failed'); // This will be the original fetch error\n    57|        expect(fetch).toHaveBeenCalledTimes(1);\n    58|    });\n    59|});\n    60|\n    61|describe('API Client - addSpeaker', () =\u003e {\n    62|    beforeEach(() =\u003e {\n    63|        fetch.mockClear();\n    64|    });\n    65|\n    66|    it('should add a speaker successfully', async () =\u003e {\n    67|        const mockFormData = new FormData(); // In a real scenario, this would have data\n    68|        mockFormData.append('name', 'Test Speaker');\n    69|        // mockFormData.append('audio_sample_file', new File([''], 'sample.wav')); // File creation in Node test needs more setup or a mock\n    70|\n    71|        const mockResponse = { id: '3', name: 'Test Speaker', message: 'Speaker added successfully' };\n    72|        fetch.mockResolvedValueOnce({\n    73|            ok: true,\n    74|            json: async () =\u003e mockResponse,\n    75|        });\n    76|\n    77|        const result = await addSpeaker(mockFormData);\n    78|        expect(fetch).toHaveBeenCalledTimes(1);\n    79|        expect(fetch).toHaveBeenCalledWith(`${API_BASE_URL}/speakers`, {\n    80|            method: 'POST',\n    81|            body: mockFormData,\n    82|        });\n    83|        expect(result).toEqual(mockResponse);\n    84|    });\n    85|\n    86|    it('should throw an error if adding a speaker fails', async () =\u003e {\n    87|        const mockFormData = new FormData();\n    88|        fetch.mockResolvedValueOnce({\n    89|            ok: false,\n    90|            statusText: 'Bad Request',\n    91|            json: async () =\u003e ({ detail: 'Invalid speaker data' }),\n    92|        });\n    93|\n    94|        await expect(addSpeaker(mockFormData)).rejects.toThrow('Failed to add speaker: Invalid speaker data');\n    95|        expect(fetch).toHaveBeenCalledTimes(1);\n    96|    });\n    97|});\n    98|\n    99|describe('API Client - deleteSpeaker', () =\u003e {\n   100|    beforeEach(() =\u003e {\n   101|        fetch.mockClear();\n   102|    });\n   103|\n   104|    it('should delete a speaker successfully with JSON response', async () =\u003e {\n   105|        const speakerId = 'test-speaker-id-123';\n   106|        const mockResponse = { message: `Speaker ${speakerId} deleted successfully` };\n   107|        fetch.mockResolvedValueOnce({\n   108|            ok: true,\n   109|            status: 200, // Or any 2xx status that might return JSON\n   110|            json: async () =\u003e mockResponse,\n   111|        });\n   112|\n   113|        const result = await deleteSpeaker(speakerId);\n   114|        expect(fetch).toHaveBeenCalledTimes(1);\n   115|        expect(fetch).toHaveBeenCalledWith(`${API_BASE_URL}/speakers/${speakerId}`, {\n   116|            method: 'DELETE',\n   117|        });\n   118|        expect(result).toEqual(mockResponse);\n   119|    });\n   120|\n   121|    it('should handle successful deletion with 204 No Content response', async () =\u003e {\n   122|        const speakerId = 'test-speaker-id-204';\n   123|        fetch.mockResolvedValueOnce({\n   124|            ok: true,\n   125|            status: 204,\n   126|            statusText: 'No Content',\n   127|            // .json() is not called by the function if status is 204\n   128|        });\n   129|\n   130|        const result = await deleteSpeaker(speakerId);\n   131|        expect(fetch).toHaveBeenCalledTimes(1);\n   132|        expect(fetch).toHaveBeenCalledWith(`${API_BASE_URL}/speakers/${speakerId}`, {\n   133|            method: 'DELETE',\n   134|        });\n   135|        expect(result).toEqual({ message: `Speaker ${speakerId} deleted successfully.` });\n   136|    });\n   137|\n   138|    it('should throw an error if deleting a speaker fails (e.g., speaker not found)', async () =\u003e {\n   139|        const speakerId = 'non-existent-speaker-id';\n   140|        fetch.mockResolvedValueOnce({\n   141|            ok: false,\n   142|            status: 404,\n   143|            statusText: 'Not Found',\n   144|            json: async () =\u003e ({ detail: 'Speaker not found' }),\n   145|        });\n   146|\n   147|        await expect(deleteSpeaker(speakerId)).rejects.toThrow(`Failed to delete speaker ${speakerId}: Speaker not found`);\n   148|        expect(fetch).toHaveBeenCalledTimes(1);\n   149|    });\n   150|});\n   151|\n   152|describe('API Client - generateDialog', () =\u003e {\n   153|    beforeEach(() =\u003e {\n   154|        fetch.mockClear();\n   155|    });\n   156|\n   157|    it('should generate dialog successfully', async () =\u003e {\n   158|        const mockPayload = {\n   159|            output_base_name: \"test_dialog\",\n   160|            dialog_items: [\n   161|                { type: \"speech\", speaker_id: \"spk_1\", text: \"Hello.\", exaggeration: 1.0, cfg_weight: 3.0, temperature: 0.5 },\n   162|                { type: \"silence\", duration_ms: 250 }\n   163|            ]\n   164|        };\n   165|        const mockResponse = {\n   166|            log: \"Dialog generated.\",\n   167|            concatenated_audio_url: \"/audio/test_dialog_concatenated.wav\",\n   168|            zip_archive_url: \"/audio/test_dialog.zip\"\n   169|        };\n   170|        fetch.mockResolvedValueOnce({\n   171|            ok: true,\n   172|            json: async () =\u003e mockResponse,\n   173|        });\n   174|\n   175|        const result = await generateDialog(mockPayload);\n   176|        expect(fetch).toHaveBeenCalledTimes(1);\n   177|        expect(fetch).toHaveBeenCalledWith(`${API_BASE_URL}/dialog/generate`, {\n   178|            method: 'POST',\n   179|            headers: { 'Content-Type': 'application/json' },\n   180|            body: JSON.stringify(mockPayload),\n   181|        });\n   182|        expect(result).toEqual(mockResponse);\n   183|    });\n   184|\n   185|    it('should throw an error if dialog generation fails', async () =\u003e {\n   186|        const mockPayload = { output_base_name: \"fail_dialog\", dialog_items: [] }; // Example invalid payload\n   187|        fetch.mockResolvedValueOnce({\n   188|            ok: false,\n   189|            statusText: 'Bad Request',\n   190|            json: async () =\u003e ({ detail: 'Invalid dialog data' }),\n   191|        });\n   192|\n   193|        await expect(generateDialog(mockPayload)).rejects.toThrow('Failed to generate dialog: Invalid dialog data');\n   194|        expect(fetch).toHaveBeenCalledTimes(1);\n   195|    });\n   196|});\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/frontend/tests/api.test.js\",\"content\":\"// frontend/tests/api.test.js\\n\\n// Import the function to test (adjust path if your structure is different)\\n// We might need to configure Jest or use Babel for ES module syntax if this causes issues.\\nimport { getSpeakers, addSpeaker, deleteSpeaker, generateDialog } from '../js/api.js';\\n\\n// Mock the global fetch function\\nglobal.fetch = jest.fn();\\n\\nconst API_BASE_URL = 'http://localhost:8000/api'; // Centralize for all tests\\n\\ndescribe('API Client - getSpeakers', () =\\u003e {\\n    beforeEach(() =\\u003e {\\n        // Clear all instances and calls to constructor and all methods:\\n        fetch.mockClear();\\n    });\\n\\n    it('should fetch speakers successfully', async () =\\u003e {\\n        const mockSpeakers = [{ id: '1', name: 'Speaker 1' }, { id: '2', name: 'Speaker 2' }];\\n        fetch.mockResolvedValueOnce({\\n            ok: true,\\n            json: async () =\\u003e mockSpeakers,\\n        });\\n\\n        const speakers = await getSpeakers();\\n        expect(fetch).toHaveBeenCalledTimes(1);\\n        expect(fetch).toHaveBeenCalledWith(`${API_BASE_URL}/speakers`);\\n        expect(speakers).toEqual(mockSpeakers);\\n    });\\n\\n    it('should throw an error if the network response is not ok', async () =\\u003e {\\n        fetch.mockResolvedValueOnce({\\n            ok: false,\\n            statusText: 'Not Found',\\n            json: async () =\\u003e ({ detail: 'Speakers not found' }) // Simulate FastAPI error response\\n        });\\n\\n        await expect(getSpeakers()).rejects.toThrow('Failed to fetch speakers: Speakers not found');\\n        expect(fetch).toHaveBeenCalledTimes(1);\\n    });\\n\\n    it('should throw a generic error if parsing error response fails', async () =\\u003e {\\n        fetch.mockResolvedValueOnce({\\n            ok: false,\\n            statusText: 'Internal Server Error',\\n            json: async () =\\u003e { throw new Error('Failed to parse error JSON'); } // Simulate error during .json()\\n        });\\n\\n        await expect(getSpeakers()).rejects.toThrow('Failed to fetch speakers: Internal Server Error');\\n        expect(fetch).toHaveBeenCalledTimes(1);\\n    });\\n    \\n    it('should throw an error if fetch itself fails (network error)', async () =\\u003e {\\n        fetch.mockRejectedValueOnce(new TypeError('Network failed'));\\n\\n        await expect(getSpeakers()).rejects.toThrow('Network failed'); // This will be the original fetch error\\n        expect(fetch).toHaveBeenCalledTimes(1);\\n    });\\n});\\n\\ndescribe('API Client - addSpeaker', () =\\u003e {\\n    beforeEach(() =\\u003e {\\n        fetch.mockClear();\\n    });\\n\\n    it('should add a speaker successfully', async () =\\u003e {\\n        const mockFormData = new FormData(); // In a real scenario, this would have data\\n        mockFormData.append('name', 'Test Speaker');\\n        // mockFormData.append('audio_sample_file', new File([''], 'sample.wav')); // File creation in Node test needs more setup or a mock\\n\\n        const mockResponse = { id: '3', name: 'Test Speaker', message: 'Speaker added successfully' };\\n        fetch.mockResolvedValueOnce({\\n            ok: true,\\n            json: async () =\\u003e mockResponse,\\n        });\\n\\n        const result = await addSpeaker(mockFormData);\\n        expect(fetch).toHaveBeenCalledTimes(1);\\n        expect(fetch).toHaveBeenCalledWith(`${API_BASE_URL}/speakers`, {\\n            method: 'POST',\\n            body: mockFormData,\\n        });\\n        expect(result).toEqual(mockResponse);\\n    });\\n\\n    it('should throw an error if adding a speaker fails', async () =\\u003e {\\n        const mockFormData = new FormData();\\n        fetch.mockResolvedValueOnce({\\n            ok: false,\\n            statusText: 'Bad Request',\\n            json: async () =\\u003e ({ detail: 'Invalid speaker data' }),\\n        });\\n\\n        await expect(addSpeaker(mockFormData)).rejects.toThrow('Failed to add speaker: Invalid speaker data');\\n        expect(fetch).toHaveBeenCalledTimes(1);\\n    });\\n});\\n\\ndescribe('API Client - deleteSpeaker', () =\\u003e {\\n    beforeEach(() =\\u003e {\\n        fetch.mockClear();\\n    });\\n\\n    it('should delete a speaker successfully with JSON response', async () =\\u003e {\\n        const speakerId = 'test-speaker-id-123';\\n        const mockResponse = { message: `Speaker ${speakerId} deleted successfully` };\\n        fetch.mockResolvedValueOnce({\\n            ok: true,\\n            status: 200, // Or any 2xx status that might return JSON\\n            json: async () =\\u003e mockResponse,\\n        });\\n\\n        const result = await deleteSpeaker(speakerId);\\n        expect(fetch).toHaveBeenCalledTimes(1);\\n        expect(fetch).toHaveBeenCalledWith(`${API_BASE_URL}/speakers/${speakerId}`, {\\n            method: 'DELETE',\\n        });\\n        expect(result).toEqual(mockResponse);\\n    });\\n\\n    it('should handle successful deletion with 204 No Content response', async () =\\u003e {\\n        const speakerId = 'test-speaker-id-204';\\n        fetch.mockResolvedValueOnce({\\n            ok: true,\\n            status: 204,\\n            statusText: 'No Content',\\n            // .json() is not called by the function if status is 204\\n        });\\n\\n        const result = await deleteSpeaker(speakerId);\\n        expect(fetch).toHaveBeenCalledTimes(1);\\n        expect(fetch).toHaveBeenCalledWith(`${API_BASE_URL}/speakers/${speakerId}`, {\\n            method: 'DELETE',\\n        });\\n        expect(result).toEqual({ message: `Speaker ${speakerId} deleted successfully.` });\\n    });\\n\\n    it('should throw an error if deleting a speaker fails (e.g., speaker not found)', async () =\\u003e {\\n        const speakerId = 'non-existent-speaker-id';\\n        fetch.mockResolvedValueOnce({\\n            ok: false,\\n            status: 404,\\n            statusText: 'Not Found',\\n            json: async () =\\u003e ({ detail: 'Speaker not found' }),\\n        });\\n\\n        await expect(deleteSpeaker(speakerId)).rejects.toThrow(`Failed to delete speaker ${speakerId}: Speaker not found`);\\n        expect(fetch).toHaveBeenCalledTimes(1);\\n    });\\n});\\n\\ndescribe('API Client - generateDialog', () =\\u003e {\\n    beforeEach(() =\\u003e {\\n        fetch.mockClear();\\n    });\\n\\n    it('should generate dialog successfully', async () =\\u003e {\\n        const mockPayload = {\\n            output_base_name: \\\"test_dialog\\\",\\n            dialog_items: [\\n                { type: \\\"speech\\\", speaker_id: \\\"spk_1\\\", text: \\\"Hello.\\\", exaggeration: 1.0, cfg_weight: 3.0, temperature: 0.5 },\\n                { type: \\\"silence\\\", duration_ms: 250 }\\n            ]\\n        };\\n        const mockResponse = {\\n            log: \\\"Dialog generated.\\\",\\n            concatenated_audio_url: \\\"/audio/test_dialog_concatenated.wav\\\",\\n            zip_archive_url: \\\"/audio/test_dialog.zip\\\"\\n        };\\n        fetch.mockResolvedValueOnce({\\n            ok: true,\\n            json: async () =\\u003e mockResponse,\\n        });\\n\\n        const result = await generateDialog(mockPayload);\\n        expect(fetch).toHaveBeenCalledTimes(1);\\n        expect(fetch).toHaveBeenCalledWith(`${API_BASE_URL}/dialog/generate`, {\\n            method: 'POST',\\n            headers: { 'Content-Type': 'application/json' },\\n            body: JSON.stringify(mockPayload),\\n        });\\n        expect(result).toEqual(mockResponse);\\n    });\\n\\n    it('should throw an error if dialog generation fails', async () =\\u003e {\\n        const mockPayload = { output_base_name: \\\"fail_dialog\\\", dialog_items: [] }; // Example invalid payload\\n        fetch.mockResolvedValueOnce({\\n            ok: false,\\n            statusText: 'Bad Request',\\n            json: async () =\\u003e ({ detail: 'Invalid dialog data' }),\\n        });\\n\\n        await expect(generateDialog(mockPayload)).rejects.toThrow('Failed to generate dialog: Invalid dialog data');\\n        expect(fetch).toHaveBeenCalledTimes(1);\\n    });\\n});\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: efc6f08b-fa4c-49c1-81f4-d8f0df27e130
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let me check the backend test file:"}},{"type":"tool_call","data":{"id":"toolu_01Ccv2iUPj3bXJ6eFYUXmWFx","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/run_api_test.py\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198015}}]


id: aa1e1e66-f46b-4ccf-b73c-26689b8fc4af
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01Ccv2iUPj3bXJ6eFYUXmWFx","name":"","content":"\u003cfile\u003e\n     1|import requests\n     2|import json\n     3|from pathlib import Path\n     4|import time\n     5|\n     6|# Configuration\n     7|API_BASE_URL = \"http://localhost:8000/api/dialog\"\n     8|ENDPOINT_URL = f\"{API_BASE_URL}/generate\"\n     9|\n    10|# Define project root relative to this test script (assuming it's in backend/)\n    11|PROJECT_ROOT = Path(__file__).resolve().parent\n    12|GENERATED_DIALOGS_DIR = PROJECT_ROOT / \"tts_generated_dialogs\"\n    13|\n    14|DIALOG_PAYLOAD = {\n    15|    \"output_base_name\": \"test_dialog_from_script\",\n    16|    \"dialog_items\": [\n    17|        {\n    18|            \"type\": \"speech\",\n    19|            \"speaker_id\": \"90fcd672-ba84-441a-ac6c-0449a59653bd\", # Correct UUID for dummy_speaker\n    20|            \"text\": \"This is a test from the Python script. One, two, three.\",\n    21|            \"exaggeration\": 1.5,\n    22|            \"cfg_weight\": 4.0,\n    23|            \"temperature\": 0.5\n    24|        },\n    25|        {\n    26|            \"type\": \"silence\",\n    27|            \"duration\": 0.5\n    28|        },\n    29|        {\n    30|            \"type\": \"speech\",\n    31|            \"speaker_id\": \"90fcd672-ba84-441a-ac6c-0449a59653bd\",\n    32|            \"text\": \"Testing complete. All systems nominal.\"\n    33|        },\n    34|        {\n    35|            \"type\": \"speech\",\n    36|            \"speaker_id\": \"non_existent_speaker\", # Test case for invalid speaker\n    37|            \"text\": \"This should produce an error for this segment.\"\n    38|        },\n    39|        {\n    40|            \"type\": \"silence\",\n    41|            \"duration\": 0.25 # Changed to valid duration\n    42|        }\n    43|    ]\n    44|}\n    45|\n    46|def run_test():\n    47|    print(f\"Sending POST request to: {ENDPOINT_URL}\")\n    48|    print(\"Payload:\")\n    49|    print(json.dumps(DIALOG_PAYLOAD, indent=2))\n    50|    print(\"-\" * 50)\n    51|\n    52|    try:\n    53|        start_time = time.time()\n    54|        response = requests.post(ENDPOINT_URL, json=DIALOG_PAYLOAD, timeout=120) # Increased timeout for TTS processing\n    55|        end_time = time.time()\n    56|        \n    57|        print(f\"Response received in {end_time - start_time:.2f} seconds.\")\n    58|        print(f\"Status Code: {response.status_code}\")\n    59|        print(\"-\" * 50)\n    60|        \n    61|        if response.content:\n    62|            try:\n    63|                response_data = response.json()\n    64|                print(\"Response JSON:\")\n    65|                print(json.dumps(response_data, indent=2))\n    66|                print(\"-\" * 50)\n    67|\n    68|                if response.status_code == 200:\n    69|                    print(\"Test PASSED (HTTP 200 OK)\")\n    70|                    concatenated_url = response_data.get(\"concatenated_audio_url\")\n    71|                    zip_url = response_data.get(\"zip_archive_url\")\n    72|                    temp_dir = response_data.get(\"temp_dir_path\")\n    73|\n    74|                    if concatenated_url:\n    75|                        print(f\"Concatenated audio URL: http://localhost:8000{concatenated_url}\")\n    76|                    if zip_url:\n    77|                        print(f\"ZIP archive URL: http://localhost:8000{zip_url}\")\n    78|                    if temp_dir:\n    79|                        print(f\"Temporary segment directory: {temp_dir}\")\n    80|                    \n    81|                    print(\"\\nTo verify, check the generated files in:\")\n    82|                    print(f\"  Concatenated/ZIP: {GENERATED_DIALOGS_DIR}\")\n    83|                    print(f\"  Individual segments (if not cleaned up): {temp_dir}\")\n    84|                else:\n    85|                    print(f\"Test FAILED (HTTP {response.status_code})\")\n    86|                    if response_data.get(\"detail\"):\n    87|                        print(f\"Error Detail: {response_data.get('detail')}\")\n    88|\n    89|            except json.JSONDecodeError:\n    90|                print(\"Response content is not valid JSON:\")\n    91|                print(response.text)\n    92|                print(\"Test FAILED (Invalid JSON Response)\")\n    93|        else:\n    94|            print(\"Response content is empty.\")\n    95|            print(f\"Test FAILED (Empty Response, HTTP {response.status_code})\")\n    96|\n    97|    except requests.exceptions.ConnectionError as e:\n    98|        print(f\"Connection Error: {e}\")\n    99|        print(\"Test FAILED (Could not connect to the server. Is it running?)\")\n   100|    except requests.exceptions.Timeout as e:\n   101|        print(f\"Request Timeout: {e}\")\n   102|        print(\"Test FAILED (The request timed out. TTS processing might be too slow or stuck.)\")\n   103|    except Exception as e:\n   104|        print(f\"An unexpected error occurred: {e}\")\n   105|        print(\"Test FAILED (Unexpected error)\")\n   106|\n   107|def test_generate_line_speech():\n   108|    url = f\"{API_BASE_URL}/generate_line\"\n   109|    payload = {\n   110|        \"type\": \"speech\",\n   111|        \"speaker_id\": \"90fcd672-ba84-441a-ac6c-0449a59653bd\",  # Correct UUID for dummy_speaker\n   112|        \"text\": \"This is a per-line TTS test.\",\n   113|        \"exaggeration\": 1.0,\n   114|        \"cfg_weight\": 2.0,\n   115|        \"temperature\": 0.8\n   116|    }\n   117|    print(f\"\\nTesting /generate_line with speech item: {payload}\")\n   118|    response = requests.post(url, json=payload)\n   119|    print(f\"Status: {response.status_code}\")\n   120|    try:\n   121|        data = response.json()\n   122|        print(f\"Response: {json.dumps(data, indent=2)}\")\n   123|        if response.status_code == 200 and \"audio_url\" in data:\n   124|            print(\"Speech line test PASSED.\")\n   125|        else:\n   126|            print(\"Speech line test FAILED.\")\n   127|    except Exception as e:\n   128|        print(f\"Speech line test FAILED: {e}\")\n   129|\n   130|def test_generate_line_silence():\n   131|    url = f\"{API_BASE_URL}/generate_line\"\n   132|    payload = {\n   133|        \"type\": \"silence\",\n   134|        \"duration\": 1.25\n   135|    }\n   136|    print(f\"\\nTesting /generate_line with silence item: {payload}\")\n   137|    response = requests.post(url, json=payload)\n   138|    print(f\"Status: {response.status_code}\")\n   139|    try:\n   140|        data = response.json()\n   141|        print(f\"Response: {json.dumps(data, indent=2)}\")\n   142|        if response.status_code == 200 and \"audio_url\" in data:\n   143|            print(\"Silence line test PASSED.\")\n   144|        else:\n   145|            print(\"Silence line test FAILED.\")\n   146|    except Exception as e:\n   147|        print(f\"Silence line test FAILED: {e}\")\n   148|\n   149|if __name__ == \"__main__\":\n   150|    run_test()\n   151|    test_generate_line_speech()\n   152|    test_generate_line_silence()\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/run_api_test.py\",\"content\":\"import requests\\nimport json\\nfrom pathlib import Path\\nimport time\\n\\n# Configuration\\nAPI_BASE_URL = \\\"http://localhost:8000/api/dialog\\\"\\nENDPOINT_URL = f\\\"{API_BASE_URL}/generate\\\"\\n\\n# Define project root relative to this test script (assuming it's in backend/)\\nPROJECT_ROOT = Path(__file__).resolve().parent\\nGENERATED_DIALOGS_DIR = PROJECT_ROOT / \\\"tts_generated_dialogs\\\"\\n\\nDIALOG_PAYLOAD = {\\n    \\\"output_base_name\\\": \\\"test_dialog_from_script\\\",\\n    \\\"dialog_items\\\": [\\n        {\\n            \\\"type\\\": \\\"speech\\\",\\n            \\\"speaker_id\\\": \\\"90fcd672-ba84-441a-ac6c-0449a59653bd\\\", # Correct UUID for dummy_speaker\\n            \\\"text\\\": \\\"This is a test from the Python script. One, two, three.\\\",\\n            \\\"exaggeration\\\": 1.5,\\n            \\\"cfg_weight\\\": 4.0,\\n            \\\"temperature\\\": 0.5\\n        },\\n        {\\n            \\\"type\\\": \\\"silence\\\",\\n            \\\"duration\\\": 0.5\\n        },\\n        {\\n            \\\"type\\\": \\\"speech\\\",\\n            \\\"speaker_id\\\": \\\"90fcd672-ba84-441a-ac6c-0449a59653bd\\\",\\n            \\\"text\\\": \\\"Testing complete. All systems nominal.\\\"\\n        },\\n        {\\n            \\\"type\\\": \\\"speech\\\",\\n            \\\"speaker_id\\\": \\\"non_existent_speaker\\\", # Test case for invalid speaker\\n            \\\"text\\\": \\\"This should produce an error for this segment.\\\"\\n        },\\n        {\\n            \\\"type\\\": \\\"silence\\\",\\n            \\\"duration\\\": 0.25 # Changed to valid duration\\n        }\\n    ]\\n}\\n\\ndef run_test():\\n    print(f\\\"Sending POST request to: {ENDPOINT_URL}\\\")\\n    print(\\\"Payload:\\\")\\n    print(json.dumps(DIALOG_PAYLOAD, indent=2))\\n    print(\\\"-\\\" * 50)\\n\\n    try:\\n        start_time = time.time()\\n        response = requests.post(ENDPOINT_URL, json=DIALOG_PAYLOAD, timeout=120) # Increased timeout for TTS processing\\n        end_time = time.time()\\n        \\n        print(f\\\"Response received in {end_time - start_time:.2f} seconds.\\\")\\n        print(f\\\"Status Code: {response.status_code}\\\")\\n        print(\\\"-\\\" * 50)\\n        \\n        if response.content:\\n            try:\\n                response_data = response.json()\\n                print(\\\"Response JSON:\\\")\\n                print(json.dumps(response_data, indent=2))\\n                print(\\\"-\\\" * 50)\\n\\n                if response.status_code == 200:\\n                    print(\\\"Test PASSED (HTTP 200 OK)\\\")\\n                    concatenated_url = response_data.get(\\\"concatenated_audio_url\\\")\\n                    zip_url = response_data.get(\\\"zip_archive_url\\\")\\n                    temp_dir = response_data.get(\\\"temp_dir_path\\\")\\n\\n                    if concatenated_url:\\n                        print(f\\\"Concatenated audio URL: http://localhost:8000{concatenated_url}\\\")\\n                    if zip_url:\\n                        print(f\\\"ZIP archive URL: http://localhost:8000{zip_url}\\\")\\n                    if temp_dir:\\n                        print(f\\\"Temporary segment directory: {temp_dir}\\\")\\n                    \\n                    print(\\\"\\\\nTo verify, check the generated files in:\\\")\\n                    print(f\\\"  Concatenated/ZIP: {GENERATED_DIALOGS_DIR}\\\")\\n                    print(f\\\"  Individual segments (if not cleaned up): {temp_dir}\\\")\\n                else:\\n                    print(f\\\"Test FAILED (HTTP {response.status_code})\\\")\\n                    if response_data.get(\\\"detail\\\"):\\n                        print(f\\\"Error Detail: {response_data.get('detail')}\\\")\\n\\n            except json.JSONDecodeError:\\n                print(\\\"Response content is not valid JSON:\\\")\\n                print(response.text)\\n                print(\\\"Test FAILED (Invalid JSON Response)\\\")\\n        else:\\n            print(\\\"Response content is empty.\\\")\\n            print(f\\\"Test FAILED (Empty Response, HTTP {response.status_code})\\\")\\n\\n    except requests.exceptions.ConnectionError as e:\\n        print(f\\\"Connection Error: {e}\\\")\\n        print(\\\"Test FAILED (Could not connect to the server. Is it running?)\\\")\\n    except requests.exceptions.Timeout as e:\\n        print(f\\\"Request Timeout: {e}\\\")\\n        print(\\\"Test FAILED (The request timed out. TTS processing might be too slow or stuck.)\\\")\\n    except Exception as e:\\n        print(f\\\"An unexpected error occurred: {e}\\\")\\n        print(\\\"Test FAILED (Unexpected error)\\\")\\n\\ndef test_generate_line_speech():\\n    url = f\\\"{API_BASE_URL}/generate_line\\\"\\n    payload = {\\n        \\\"type\\\": \\\"speech\\\",\\n        \\\"speaker_id\\\": \\\"90fcd672-ba84-441a-ac6c-0449a59653bd\\\",  # Correct UUID for dummy_speaker\\n        \\\"text\\\": \\\"This is a per-line TTS test.\\\",\\n        \\\"exaggeration\\\": 1.0,\\n        \\\"cfg_weight\\\": 2.0,\\n        \\\"temperature\\\": 0.8\\n    }\\n    print(f\\\"\\\\nTesting /generate_line with speech item: {payload}\\\")\\n    response = requests.post(url, json=payload)\\n    print(f\\\"Status: {response.status_code}\\\")\\n    try:\\n        data = response.json()\\n        print(f\\\"Response: {json.dumps(data, indent=2)}\\\")\\n        if response.status_code == 200 and \\\"audio_url\\\" in data:\\n            print(\\\"Speech line test PASSED.\\\")\\n        else:\\n            print(\\\"Speech line test FAILED.\\\")\\n    except Exception as e:\\n        print(f\\\"Speech line test FAILED: {e}\\\")\\n\\ndef test_generate_line_silence():\\n    url = f\\\"{API_BASE_URL}/generate_line\\\"\\n    payload = {\\n        \\\"type\\\": \\\"silence\\\",\\n        \\\"duration\\\": 1.25\\n    }\\n    print(f\\\"\\\\nTesting /generate_line with silence item: {payload}\\\")\\n    response = requests.post(url, json=payload)\\n    print(f\\\"Status: {response.status_code}\\\")\\n    try:\\n        data = response.json()\\n        print(f\\\"Response: {json.dumps(data, indent=2)}\\\")\\n        if response.status_code == 200 and \\\"audio_url\\\" in data:\\n            print(\\\"Silence line test PASSED.\\\")\\n        else:\\n            print(\\\"Silence line test FAILED.\\\")\\n    except Exception as e:\\n        print(f\\\"Silence line test FAILED: {e}\\\")\\n\\nif __name__ == \\\"__main__\\\":\\n    run_test()\\n    test_generate_line_speech()\\n    test_generate_line_silence()\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 90820d60-54c3-460e-8149-7c72ae33cce1
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Now I'll create the OpenCode.md file based on the information I've gathered:"}},{"type":"tool_call","data":{"id":"toolu_01A4DTxxHWTjf1HVgLHPBEQb","name":"write","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/OpenCode.md\",\"content\":\"# OpenCode.md - Chatterbox UI Development Guide\\n\\n## Build \\u0026 Run Commands\\n```bash\\n# Backend (FastAPI)\\npip install -r backend/requirements.txt\\nuvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000\\n\\n# Frontend\\npython frontend/start_dev_server.py  # Serves on port 8001\\n\\n# Run backend tests\\npython backend/run_api_test.py\\n\\n# Run frontend tests\\nnpm test\\n\\n# Run specific frontend test\\nnpx jest frontend/tests/api.test.js\\n\\n# Run Gradio interface\\npython gradio_app.py\\n```\\n\\n## Code Style Guidelines\\n\\n### Python\\n- Use type hints (from typing import Optional, List, Dict, etc.)\\n- Error handling: Use try/except with specific exceptions\\n- Async/await for I/O operations\\n- Docstrings for functions and classes\\n- PEP 8 naming: snake_case for functions/variables, PascalCase for classes\\n\\n### JavaScript\\n- ES6 modules with import/export\\n- Async/await for API calls\\n- JSDoc comments for functions\\n- Error handling: try/catch with detailed error messages\\n- Camel case for variables/functions (camelCase)\\n\\n### Project Structure\\n- Backend: FastAPI with service-oriented architecture\\n- Frontend: Vanilla JS with modular design (api.js, app.js, config.js)\\n- Speaker data in YAML format with WAV samples\\n- Output directories: dialog_output/, single_output/, tts_outputs/\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198025}}]


id: 878b8410-faac-4216-b357-e699b4a960a2
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01A4DTxxHWTjf1HVgLHPBEQb","name":"","content":"\u003cresult\u003e\nFile successfully written: /Users/stwhite/CODE/chatterbox-ui/OpenCode.md\n\u003c/result\u003e","metadata":"{\"diff\":\"--- a/OpenCode.md\\n+++ b/OpenCode.md\\n@@ -0,0 +1,45 @@\\n+# OpenCode.md - Chatterbox UI Development Guide\\n+\\n+## Build \\u0026 Run Commands\\n+```bash\\n+# Backend (FastAPI)\\n+pip install -r backend/requirements.txt\\n+uvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000\\n+\\n+# Frontend\\n+python frontend/start_dev_server.py  # Serves on port 8001\\n+\\n+# Run backend tests\\n+python backend/run_api_test.py\\n+\\n+# Run frontend tests\\n+npm test\\n+\\n+# Run specific frontend test\\n+npx jest frontend/tests/api.test.js\\n+\\n+# Run Gradio interface\\n+python gradio_app.py\\n+```\\n+\\n+## Code Style Guidelines\\n+\\n+### Python\\n+- Use type hints (from typing import Optional, List, Dict, etc.)\\n+- Error handling: Use try/except with specific exceptions\\n+- Async/await for I/O operations\\n+- Docstrings for functions and classes\\n+- PEP 8 naming: snake_case for functions/variables, PascalCase for classes\\n+\\n+### JavaScript\\n+- ES6 modules with import/export\\n+- Async/await for API calls\\n+- JSDoc comments for functions\\n+- Error handling: try/catch with detailed error messages\\n+- Camel case for variables/functions (camelCase)\\n+\\n+### Project Structure\\n+- Backend: FastAPI with service-oriented architecture\\n+- Frontend: Vanilla JS with modular design (api.js, app.js, config.js)\\n+- Speaker data in YAML format with WAV samples\\n+- Output directories: dialog_output/, single_output/, tts_outputs/\\n\\\\ No newline at end of file\\n\",\"additions\":45,\"removals\":0}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: e2b9c89d-f104-4ec0-ac94-ea6bc8f24c79
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"I've created the OpenCode.md file with essential information for agentic coding agents. The file includes:\n\n1. Build/lint/test commands for both backend and frontend\n2. Commands for running specific tests\n3. Code style guidelines for Python and JavaScript\n4. Project structure overview\n\nThe file is concise (about 20 lines of content) and focuses on the most important aspects of the codebase that would help an agent understand how to work with it effectively."}},{"type":"finish","data":{"reason":"end_turn","time":1750198033}}]


id: 19dcaf58-b4ad-406c-b94c-df937e1dc76f
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: user
parts:
  [{"type":"text","data":{"text":"when I run the cbx-audiobook.py file I get this error:  python cbx-audiobook.py\n/Users/stwhite/CODE/chatterbox-ui/.venv/lib/python3.12/site-packages/perth/perth_net/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools\u003c81.\n  from pkg_resources import resource_filename\nTraceback (most recent call last):\n  File \"/Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py\", line 19, in \u003cmodule\u003e\n    from backend.app.services.tts_service import TTSService\n  File \"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/tts_service.py\", line 11, in \u003cmodule\u003e\n    from app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\nModuleNotFoundError: No module named 'app'"}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 8f4d1797-ba34-4b3d-99e3-919338fe5c12
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"The error is due to a Python import path issue. When running the script directly, it can't find the `app` module. Let me fix it."}},{"type":"tool_call","data":{"id":"toolu_01DuMSTy4D6JiDTdL4GfKnZP","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198072}}]


id: 857b9ee6-98b4-4103-9d59-818d98230ec4
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01DuMSTy4D6JiDTdL4GfKnZP","name":"","content":"\u003cfile\u003e\n     1|#!/usr/bin/env python\n     2|\"\"\"\n     3|Chatterbox Audiobook Generator\n     4|\n     5|This script converts a text file into an audiobook using the Chatterbox TTS system.\n     6|It parses the text file into manageable chunks, generates audio for each chunk,\n     7|and assembles them into a complete audiobook.\n     8|\"\"\"\n     9|\n    10|import argparse\n    11|import asyncio\n    12|import os\n    13|import re\n    14|import sys\n    15|from pathlib import Path\n    16|import uuid\n    17|\n    18|# Import backend services\n    19|from backend.app.services.tts_service import TTSService\n    20|from backend.app.services.speaker_service import SpeakerManagementService\n    21|from backend.app.services.audio_manipulation_service import AudioManipulationService\n    22|from backend.app.config import DIALOG_GENERATED_DIR, TTS_TEMP_OUTPUT_DIR\n    23|\n    24|class AudiobookGenerator:\n    25|    def __init__(self, speaker_id, output_base_name, device=\"mps\", \n    26|                 exaggeration=0.5, cfg_weight=0.5, temperature=0.8,\n    27|                 pause_between_sentences=0.5, pause_between_paragraphs=1.0):\n    28|        \"\"\"\n    29|        Initialize the audiobook generator.\n    30|        \n    31|        Args:\n    32|            speaker_id: ID of the speaker to use\n    33|            output_base_name: Base name for output files\n    34|            device: Device to use for TTS (mps, cuda, cpu)\n    35|            exaggeration: Controls expressiveness (0.0-1.0)\n    36|            cfg_weight: Controls alignment with speaker characteristics (0.0-1.0)\n    37|            temperature: Controls randomness in generation (0.0-1.0)\n    38|            pause_between_sentences: Pause duration between sentences in seconds\n    39|            pause_between_paragraphs: Pause duration between paragraphs in seconds\n    40|        \"\"\"\n    41|        self.speaker_id = speaker_id\n    42|        self.output_base_name = output_base_name\n    43|        self.device = device\n    44|        self.exaggeration = exaggeration\n    45|        self.cfg_weight = cfg_weight\n    46|        self.temperature = temperature\n    47|        self.pause_between_sentences = pause_between_sentences\n    48|        self.pause_between_paragraphs = pause_between_paragraphs\n    49|        \n    50|        # Initialize services\n    51|        self.tts_service = TTSService(device=device)\n    52|        self.speaker_service = SpeakerManagementService()\n    53|        self.audio_manipulator = AudioManipulationService()\n    54|        \n    55|        # Create output directories\n    56|        self.output_dir = DIALOG_GENERATED_DIR / output_base_name\n    57|        self.output_dir.mkdir(parents=True, exist_ok=True)\n    58|        self.temp_dir = TTS_TEMP_OUTPUT_DIR / output_base_name\n    59|        self.temp_dir.mkdir(parents=True, exist_ok=True)\n    60|        \n    61|        # Validate speaker\n    62|        self._validate_speaker()\n    63|    \n    64|    def _validate_speaker(self):\n    65|        \"\"\"Validate that the specified speaker exists.\"\"\"\n    66|        speaker_info = self.speaker_service.get_speaker_by_id(self.speaker_id)\n    67|        if not speaker_info:\n    68|            raise ValueError(f\"Speaker ID '{self.speaker_id}' not found.\")\n    69|        if not speaker_info.sample_path:\n    70|            raise ValueError(f\"Speaker ID '{self.speaker_id}' has no sample path defined.\")\n    71|        \n    72|        # Store speaker info for later use\n    73|        self.speaker_info = speaker_info\n    74|    \n    75|    def split_text_into_chunks(self, text, max_length=300):\n    76|        \"\"\"\n    77|        Split text into chunks suitable for TTS processing.\n    78|        \n    79|        This uses the same logic as the DialogProcessorService._split_text method\n    80|        but adds additional paragraph handling.\n    81|        \"\"\"\n    82|        # Split text into paragraphs first\n    83|        paragraphs = re.split(r'\\n\\s*\\n', text)\n    84|        paragraphs = [p.strip() for p in paragraphs if p.strip()]\n    85|        \n    86|        all_chunks = []\n    87|        \n    88|        for paragraph in paragraphs:\n    89|            # Split paragraph into sentences\n    90|            sentences = re.split(r'(?\u003c=[.!?\\u2026])\\s+|(?\u003c=[.!?\\u2026])(?=[\\\"\\')\\]\\}\\u201d\\u2019])|(?\u003c=[.!?\\u2026])$', paragraph.strip())\n    91|            sentences = [s.strip() for s in sentences if s and s.strip()]\n    92|            \n    93|            chunks = []\n    94|            current_chunk = \"\"\n    95|            \n    96|            for sentence in sentences:\n    97|                if not sentence:\n    98|                    continue\n    99|                if not current_chunk:  # First sentence for this chunk\n   100|                    current_chunk = sentence\n   101|                elif len(current_chunk) + len(sentence) + 1 \u003c= max_length:\n   102|                    current_chunk += \" \" + sentence\n   103|                else:\n   104|                    chunks.append(current_chunk)\n   105|                    current_chunk = sentence\n   106|            \n   107|            if current_chunk:  # Add the last chunk\n   108|                chunks.append(current_chunk)\n   109|            \n   110|            # Further split any chunks that are still too long\n   111|            paragraph_chunks = []\n   112|            for chunk in chunks:\n   113|                if len(chunk) \u003e max_length:\n   114|                    # Simple split by length if a sentence itself is too long\n   115|                    for i in range(0, len(chunk), max_length):\n   116|                        paragraph_chunks.append(chunk[i:i+max_length])\n   117|                else:\n   118|                    paragraph_chunks.append(chunk)\n   119|            \n   120|            # Add paragraph marker\n   121|            if paragraph_chunks:\n   122|                all_chunks.append({\"type\": \"paragraph\", \"chunks\": paragraph_chunks})\n   123|        \n   124|        return all_chunks\n   125|    \n   126|    async def generate_audiobook(self, text_file_path):\n   127|        \"\"\"\n   128|        Generate an audiobook from a text file.\n   129|        \n   130|        Args:\n   131|            text_file_path: Path to the text file to convert\n   132|        \n   133|        Returns:\n   134|            Path to the generated audiobook file\n   135|        \"\"\"\n   136|        # Read the text file\n   137|        text_path = Path(text_file_path)\n   138|        if not text_path.exists():\n   139|            raise FileNotFoundError(f\"Text file not found: {text_file_path}\")\n   140|        \n   141|        with open(text_path, 'r', encoding='utf-8') as f:\n   142|            text = f.read()\n   143|        \n   144|        print(f\"Processing text file: {text_file_path}\")\n   145|        print(f\"Text length: {len(text)} characters\")\n   146|        \n   147|        # Split text into chunks\n   148|        paragraphs = self.split_text_into_chunks(text)\n   149|        total_chunks = sum(len(p[\"chunks\"]) for p in paragraphs)\n   150|        print(f\"Split into {len(paragraphs)} paragraphs with {total_chunks} total chunks\")\n   151|        \n   152|        # Load TTS model\n   153|        print(\"Loading TTS model...\")\n   154|        self.tts_service.load_model()\n   155|        \n   156|        try:\n   157|            # Generate audio for each chunk\n   158|            segment_results = []\n   159|            chunk_count = 0\n   160|            \n   161|            for para_idx, paragraph in enumerate(paragraphs):\n   162|                print(f\"Processing paragraph {para_idx+1}/{len(paragraphs)}\")\n   163|                \n   164|                for chunk_idx, chunk in enumerate(paragraph[\"chunks\"]):\n   165|                    chunk_count += 1\n   166|                    print(f\"  Generating audio for chunk {chunk_count}/{total_chunks}: {chunk[:50]}...\")\n   167|                    \n   168|                    # Generate unique filename for this chunk\n   169|                    segment_filename_base = f\"{self.output_base_name}_p{para_idx}_c{chunk_idx}_{uuid.uuid4().hex[:8]}\"\n   170|                    \n   171|                    try:\n   172|                        # Get absolute speaker sample path\n   173|                        speaker_sample_path = Path(self.speaker_info.sample_path)\n   174|                        if not speaker_sample_path.is_absolute():\n   175|                            from backend.app.config import SPEAKER_DATA_BASE_DIR\n   176|                            speaker_sample_path = SPEAKER_DATA_BASE_DIR / speaker_sample_path\n   177|                        \n   178|                        # Generate speech for this chunk\n   179|                        segment_output_path = await self.tts_service.generate_speech(\n   180|                            text=chunk,\n   181|                            speaker_id=self.speaker_id,\n   182|                            speaker_sample_path=str(speaker_sample_path),\n   183|                            output_filename_base=segment_filename_base,\n   184|                            output_dir=self.temp_dir,\n   185|                            exaggeration=self.exaggeration,\n   186|                            cfg_weight=self.cfg_weight,\n   187|                            temperature=self.temperature\n   188|                        )\n   189|                        \n   190|                        # Add to segment results\n   191|                        segment_results.append({\n   192|                            \"type\": \"speech\",\n   193|                            \"path\": str(segment_output_path)\n   194|                        })\n   195|                        \n   196|                        # Add pause between sentences\n   197|                        if chunk_idx \u003c len(paragraph[\"chunks\"]) - 1:\n   198|                            segment_results.append({\n   199|                                \"type\": \"silence\",\n   200|                                \"duration\": self.pause_between_sentences\n   201|                            })\n   202|                    \n   203|                    except Exception as e:\n   204|                        print(f\"Error generating speech for chunk: {e}\")\n   205|                        # Continue with next chunk\n   206|                \n   207|                # Add longer pause between paragraphs\n   208|                if para_idx \u003c len(paragraphs) - 1:\n   209|                    segment_results.append({\n   210|                        \"type\": \"silence\",\n   211|                        \"duration\": self.pause_between_paragraphs\n   212|                    })\n   213|            \n   214|            # Concatenate all segments\n   215|            print(\"Concatenating audio segments...\")\n   216|            concatenated_filename = f\"{self.output_base_name}_audiobook.wav\"\n   217|            concatenated_path = self.output_dir / concatenated_filename\n   218|            \n   219|            self.audio_manipulator.concatenate_audio_segments(\n   220|                segment_results=segment_results,\n   221|                output_concatenated_path=concatenated_path\n   222|            )\n   223|            \n   224|            # Create ZIP archive with all files\n   225|            print(\"Creating ZIP archive...\")\n   226|            zip_filename = f\"{self.output_base_name}_audiobook.zip\"\n   227|            zip_path = self.output_dir / zip_filename\n   228|            \n   229|            # Collect all speech segment files\n   230|            speech_segment_paths = [\n   231|                Path(s[\"path\"]) for s in segment_results \n   232|                if s[\"type\"] == \"speech\" and Path(s[\"path\"]).exists()\n   233|            ]\n   234|            \n   235|            self.audio_manipulator.create_zip_archive(\n   236|                segment_file_paths=speech_segment_paths,\n   237|                concatenated_audio_path=concatenated_path,\n   238|                output_zip_path=zip_path\n   239|            )\n   240|            \n   241|            print(f\"Audiobook generation complete!\")\n   242|            print(f\"Audiobook file: {concatenated_path}\")\n   243|            print(f\"ZIP archive: {zip_path}\")\n   244|            \n   245|            return concatenated_path\n   246|            \n   247|        finally:\n   248|            # Unload TTS model\n   249|            print(\"Unloading TTS model...\")\n   250|            self.tts_service.unload_model()\n   251|\n   252|async def main():\n   253|    parser = argparse.ArgumentParser(description=\"Generate an audiobook from a text file using Chatterbox TTS\")\n   254|    parser.add_argument(\"text_file\", help=\"Path to the text file to convert\")\n   255|    parser.add_argument(\"--speaker\", \"-s\", required=True, help=\"ID of the speaker to use\")\n   256|    parser.add_argument(\"--output\", \"-o\", help=\"Base name for output files (default: derived from text filename)\")\n   257|    parser.add_argument(\"--device\", default=\"mps\", choices=[\"mps\", \"cuda\", \"cpu\"], help=\"Device to use for TTS (default: mps)\")\n   258|    parser.add_argument(\"--exaggeration\", type=float, default=0.5, help=\"Controls expressiveness (0.0-1.0, default: 0.5)\")\n   259|    parser.add_argument(\"--cfg-weight\", type=float, default=0.5, help=\"Controls alignment with speaker (0.0-1.0, default: 0.5)\")\n   260|    parser.add_argument(\"--temperature\", type=float, default=0.8, help=\"Controls randomness (0.0-1.0, default: 0.8)\")\n   261|    parser.add_argument(\"--sentence-pause\", type=float, default=0.5, help=\"Pause between sentences in seconds (default: 0.5)\")\n   262|    parser.add_argument(\"--paragraph-pause\", type=float, default=1.0, help=\"Pause between paragraphs in seconds (default: 1.0)\")\n   263|    parser.add_argument(\"--list-speakers\", action=\"store_true\", help=\"List available speakers and exit\")\n   264|    \n   265|    args = parser.parse_args()\n   266|    \n   267|    # List speakers if requested\n   268|    if args.list_speakers:\n   269|        speaker_service = SpeakerManagementService()\n   270|        speakers = speaker_service.get_all_speakers()\n   271|        print(\"Available speakers:\")\n   272|        for speaker in speakers:\n   273|            print(f\"  {speaker.id}: {speaker.name}\")\n   274|        return\n   275|    \n   276|    # Determine output base name if not provided\n   277|    if not args.output:\n   278|        text_path = Path(args.text_file)\n   279|        args.output = text_path.stem\n   280|    \n   281|    try:\n   282|        # Create audiobook generator\n   283|        generator = AudiobookGenerator(\n   284|            speaker_id=args.speaker,\n   285|            output_base_name=args.output,\n   286|            device=args.device,\n   287|            exaggeration=args.exaggeration,\n   288|            cfg_weight=args.cfg_weight,\n   289|            temperature=args.temperature,\n   290|            pause_between_sentences=args.sentence_pause,\n   291|            pause_between_paragraphs=args.paragraph_pause\n   292|        )\n   293|        \n   294|        # Generate audiobook\n   295|        await generator.generate_audiobook(args.text_file)\n   296|        \n   297|    except Exception as e:\n   298|        print(f\"Error: {e}\", file=sys.stderr)\n   299|        return 1\n   300|    \n   301|    return 0\n   302|\n   303|if __name__ == \"__main__\":\n   304|    sys.exit(asyncio.run(main()))\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py\",\"content\":\"#!/usr/bin/env python\\n\\\"\\\"\\\"\\nChatterbox Audiobook Generator\\n\\nThis script converts a text file into an audiobook using the Chatterbox TTS system.\\nIt parses the text file into manageable chunks, generates audio for each chunk,\\nand assembles them into a complete audiobook.\\n\\\"\\\"\\\"\\n\\nimport argparse\\nimport asyncio\\nimport os\\nimport re\\nimport sys\\nfrom pathlib import Path\\nimport uuid\\n\\n# Import backend services\\nfrom backend.app.services.tts_service import TTSService\\nfrom backend.app.services.speaker_service import SpeakerManagementService\\nfrom backend.app.services.audio_manipulation_service import AudioManipulationService\\nfrom backend.app.config import DIALOG_GENERATED_DIR, TTS_TEMP_OUTPUT_DIR\\n\\nclass AudiobookGenerator:\\n    def __init__(self, speaker_id, output_base_name, device=\\\"mps\\\", \\n                 exaggeration=0.5, cfg_weight=0.5, temperature=0.8,\\n                 pause_between_sentences=0.5, pause_between_paragraphs=1.0):\\n        \\\"\\\"\\\"\\n        Initialize the audiobook generator.\\n        \\n        Args:\\n            speaker_id: ID of the speaker to use\\n            output_base_name: Base name for output files\\n            device: Device to use for TTS (mps, cuda, cpu)\\n            exaggeration: Controls expressiveness (0.0-1.0)\\n            cfg_weight: Controls alignment with speaker characteristics (0.0-1.0)\\n            temperature: Controls randomness in generation (0.0-1.0)\\n            pause_between_sentences: Pause duration between sentences in seconds\\n            pause_between_paragraphs: Pause duration between paragraphs in seconds\\n        \\\"\\\"\\\"\\n        self.speaker_id = speaker_id\\n        self.output_base_name = output_base_name\\n        self.device = device\\n        self.exaggeration = exaggeration\\n        self.cfg_weight = cfg_weight\\n        self.temperature = temperature\\n        self.pause_between_sentences = pause_between_sentences\\n        self.pause_between_paragraphs = pause_between_paragraphs\\n        \\n        # Initialize services\\n        self.tts_service = TTSService(device=device)\\n        self.speaker_service = SpeakerManagementService()\\n        self.audio_manipulator = AudioManipulationService()\\n        \\n        # Create output directories\\n        self.output_dir = DIALOG_GENERATED_DIR / output_base_name\\n        self.output_dir.mkdir(parents=True, exist_ok=True)\\n        self.temp_dir = TTS_TEMP_OUTPUT_DIR / output_base_name\\n        self.temp_dir.mkdir(parents=True, exist_ok=True)\\n        \\n        # Validate speaker\\n        self._validate_speaker()\\n    \\n    def _validate_speaker(self):\\n        \\\"\\\"\\\"Validate that the specified speaker exists.\\\"\\\"\\\"\\n        speaker_info = self.speaker_service.get_speaker_by_id(self.speaker_id)\\n        if not speaker_info:\\n            raise ValueError(f\\\"Speaker ID '{self.speaker_id}' not found.\\\")\\n        if not speaker_info.sample_path:\\n            raise ValueError(f\\\"Speaker ID '{self.speaker_id}' has no sample path defined.\\\")\\n        \\n        # Store speaker info for later use\\n        self.speaker_info = speaker_info\\n    \\n    def split_text_into_chunks(self, text, max_length=300):\\n        \\\"\\\"\\\"\\n        Split text into chunks suitable for TTS processing.\\n        \\n        This uses the same logic as the DialogProcessorService._split_text method\\n        but adds additional paragraph handling.\\n        \\\"\\\"\\\"\\n        # Split text into paragraphs first\\n        paragraphs = re.split(r'\\\\n\\\\s*\\\\n', text)\\n        paragraphs = [p.strip() for p in paragraphs if p.strip()]\\n        \\n        all_chunks = []\\n        \\n        for paragraph in paragraphs:\\n            # Split paragraph into sentences\\n            sentences = re.split(r'(?\\u003c=[.!?\\\\u2026])\\\\s+|(?\\u003c=[.!?\\\\u2026])(?=[\\\\\\\"\\\\')\\\\]\\\\}\\\\u201d\\\\u2019])|(?\\u003c=[.!?\\\\u2026])$', paragraph.strip())\\n            sentences = [s.strip() for s in sentences if s and s.strip()]\\n            \\n            chunks = []\\n            current_chunk = \\\"\\\"\\n            \\n            for sentence in sentences:\\n                if not sentence:\\n                    continue\\n                if not current_chunk:  # First sentence for this chunk\\n                    current_chunk = sentence\\n                elif len(current_chunk) + len(sentence) + 1 \\u003c= max_length:\\n                    current_chunk += \\\" \\\" + sentence\\n                else:\\n                    chunks.append(current_chunk)\\n                    current_chunk = sentence\\n            \\n            if current_chunk:  # Add the last chunk\\n                chunks.append(current_chunk)\\n            \\n            # Further split any chunks that are still too long\\n            paragraph_chunks = []\\n            for chunk in chunks:\\n                if len(chunk) \\u003e max_length:\\n                    # Simple split by length if a sentence itself is too long\\n                    for i in range(0, len(chunk), max_length):\\n                        paragraph_chunks.append(chunk[i:i+max_length])\\n                else:\\n                    paragraph_chunks.append(chunk)\\n            \\n            # Add paragraph marker\\n            if paragraph_chunks:\\n                all_chunks.append({\\\"type\\\": \\\"paragraph\\\", \\\"chunks\\\": paragraph_chunks})\\n        \\n        return all_chunks\\n    \\n    async def generate_audiobook(self, text_file_path):\\n        \\\"\\\"\\\"\\n        Generate an audiobook from a text file.\\n        \\n        Args:\\n            text_file_path: Path to the text file to convert\\n        \\n        Returns:\\n            Path to the generated audiobook file\\n        \\\"\\\"\\\"\\n        # Read the text file\\n        text_path = Path(text_file_path)\\n        if not text_path.exists():\\n            raise FileNotFoundError(f\\\"Text file not found: {text_file_path}\\\")\\n        \\n        with open(text_path, 'r', encoding='utf-8') as f:\\n            text = f.read()\\n        \\n        print(f\\\"Processing text file: {text_file_path}\\\")\\n        print(f\\\"Text length: {len(text)} characters\\\")\\n        \\n        # Split text into chunks\\n        paragraphs = self.split_text_into_chunks(text)\\n        total_chunks = sum(len(p[\\\"chunks\\\"]) for p in paragraphs)\\n        print(f\\\"Split into {len(paragraphs)} paragraphs with {total_chunks} total chunks\\\")\\n        \\n        # Load TTS model\\n        print(\\\"Loading TTS model...\\\")\\n        self.tts_service.load_model()\\n        \\n        try:\\n            # Generate audio for each chunk\\n            segment_results = []\\n            chunk_count = 0\\n            \\n            for para_idx, paragraph in enumerate(paragraphs):\\n                print(f\\\"Processing paragraph {para_idx+1}/{len(paragraphs)}\\\")\\n                \\n                for chunk_idx, chunk in enumerate(paragraph[\\\"chunks\\\"]):\\n                    chunk_count += 1\\n                    print(f\\\"  Generating audio for chunk {chunk_count}/{total_chunks}: {chunk[:50]}...\\\")\\n                    \\n                    # Generate unique filename for this chunk\\n                    segment_filename_base = f\\\"{self.output_base_name}_p{para_idx}_c{chunk_idx}_{uuid.uuid4().hex[:8]}\\\"\\n                    \\n                    try:\\n                        # Get absolute speaker sample path\\n                        speaker_sample_path = Path(self.speaker_info.sample_path)\\n                        if not speaker_sample_path.is_absolute():\\n                            from backend.app.config import SPEAKER_DATA_BASE_DIR\\n                            speaker_sample_path = SPEAKER_DATA_BASE_DIR / speaker_sample_path\\n                        \\n                        # Generate speech for this chunk\\n                        segment_output_path = await self.tts_service.generate_speech(\\n                            text=chunk,\\n                            speaker_id=self.speaker_id,\\n                            speaker_sample_path=str(speaker_sample_path),\\n                            output_filename_base=segment_filename_base,\\n                            output_dir=self.temp_dir,\\n                            exaggeration=self.exaggeration,\\n                            cfg_weight=self.cfg_weight,\\n                            temperature=self.temperature\\n                        )\\n                        \\n                        # Add to segment results\\n                        segment_results.append({\\n                            \\\"type\\\": \\\"speech\\\",\\n                            \\\"path\\\": str(segment_output_path)\\n                        })\\n                        \\n                        # Add pause between sentences\\n                        if chunk_idx \\u003c len(paragraph[\\\"chunks\\\"]) - 1:\\n                            segment_results.append({\\n                                \\\"type\\\": \\\"silence\\\",\\n                                \\\"duration\\\": self.pause_between_sentences\\n                            })\\n                    \\n                    except Exception as e:\\n                        print(f\\\"Error generating speech for chunk: {e}\\\")\\n                        # Continue with next chunk\\n                \\n                # Add longer pause between paragraphs\\n                if para_idx \\u003c len(paragraphs) - 1:\\n                    segment_results.append({\\n                        \\\"type\\\": \\\"silence\\\",\\n                        \\\"duration\\\": self.pause_between_paragraphs\\n                    })\\n            \\n            # Concatenate all segments\\n            print(\\\"Concatenating audio segments...\\\")\\n            concatenated_filename = f\\\"{self.output_base_name}_audiobook.wav\\\"\\n            concatenated_path = self.output_dir / concatenated_filename\\n            \\n            self.audio_manipulator.concatenate_audio_segments(\\n                segment_results=segment_results,\\n                output_concatenated_path=concatenated_path\\n            )\\n            \\n            # Create ZIP archive with all files\\n            print(\\\"Creating ZIP archive...\\\")\\n            zip_filename = f\\\"{self.output_base_name}_audiobook.zip\\\"\\n            zip_path = self.output_dir / zip_filename\\n            \\n            # Collect all speech segment files\\n            speech_segment_paths = [\\n                Path(s[\\\"path\\\"]) for s in segment_results \\n                if s[\\\"type\\\"] == \\\"speech\\\" and Path(s[\\\"path\\\"]).exists()\\n            ]\\n            \\n            self.audio_manipulator.create_zip_archive(\\n                segment_file_paths=speech_segment_paths,\\n                concatenated_audio_path=concatenated_path,\\n                output_zip_path=zip_path\\n            )\\n            \\n            print(f\\\"Audiobook generation complete!\\\")\\n            print(f\\\"Audiobook file: {concatenated_path}\\\")\\n            print(f\\\"ZIP archive: {zip_path}\\\")\\n            \\n            return concatenated_path\\n            \\n        finally:\\n            # Unload TTS model\\n            print(\\\"Unloading TTS model...\\\")\\n            self.tts_service.unload_model()\\n\\nasync def main():\\n    parser = argparse.ArgumentParser(description=\\\"Generate an audiobook from a text file using Chatterbox TTS\\\")\\n    parser.add_argument(\\\"text_file\\\", help=\\\"Path to the text file to convert\\\")\\n    parser.add_argument(\\\"--speaker\\\", \\\"-s\\\", required=True, help=\\\"ID of the speaker to use\\\")\\n    parser.add_argument(\\\"--output\\\", \\\"-o\\\", help=\\\"Base name for output files (default: derived from text filename)\\\")\\n    parser.add_argument(\\\"--device\\\", default=\\\"mps\\\", choices=[\\\"mps\\\", \\\"cuda\\\", \\\"cpu\\\"], help=\\\"Device to use for TTS (default: mps)\\\")\\n    parser.add_argument(\\\"--exaggeration\\\", type=float, default=0.5, help=\\\"Controls expressiveness (0.0-1.0, default: 0.5)\\\")\\n    parser.add_argument(\\\"--cfg-weight\\\", type=float, default=0.5, help=\\\"Controls alignment with speaker (0.0-1.0, default: 0.5)\\\")\\n    parser.add_argument(\\\"--temperature\\\", type=float, default=0.8, help=\\\"Controls randomness (0.0-1.0, default: 0.8)\\\")\\n    parser.add_argument(\\\"--sentence-pause\\\", type=float, default=0.5, help=\\\"Pause between sentences in seconds (default: 0.5)\\\")\\n    parser.add_argument(\\\"--paragraph-pause\\\", type=float, default=1.0, help=\\\"Pause between paragraphs in seconds (default: 1.0)\\\")\\n    parser.add_argument(\\\"--list-speakers\\\", action=\\\"store_true\\\", help=\\\"List available speakers and exit\\\")\\n    \\n    args = parser.parse_args()\\n    \\n    # List speakers if requested\\n    if args.list_speakers:\\n        speaker_service = SpeakerManagementService()\\n        speakers = speaker_service.get_all_speakers()\\n        print(\\\"Available speakers:\\\")\\n        for speaker in speakers:\\n            print(f\\\"  {speaker.id}: {speaker.name}\\\")\\n        return\\n    \\n    # Determine output base name if not provided\\n    if not args.output:\\n        text_path = Path(args.text_file)\\n        args.output = text_path.stem\\n    \\n    try:\\n        # Create audiobook generator\\n        generator = AudiobookGenerator(\\n            speaker_id=args.speaker,\\n            output_base_name=args.output,\\n            device=args.device,\\n            exaggeration=args.exaggeration,\\n            cfg_weight=args.cfg_weight,\\n            temperature=args.temperature,\\n            pause_between_sentences=args.sentence_pause,\\n            pause_between_paragraphs=args.paragraph_pause\\n        )\\n        \\n        # Generate audiobook\\n        await generator.generate_audiobook(args.text_file)\\n        \\n    except Exception as e:\\n        print(f\\\"Error: {e}\\\", file=sys.stderr)\\n        return 1\\n    \\n    return 0\\n\\nif __name__ == \\\"__main__\\\":\\n    sys.exit(asyncio.run(main()))\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: bfda0cde-34e1-423e-93f1-831558b58801
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let me check the backend config file to understand the import structure:"}},{"type":"tool_call","data":{"id":"toolu_011avu3Jg8BMebh3NRtFcZVW","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/config.py\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198075}}]


id: f2fbb257-5efe-42fc-82f2-817411c35cc3
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_011avu3Jg8BMebh3NRtFcZVW","name":"","content":"\u003cfile\u003e\n     1|import os\n     2|from pathlib import Path\n     3|from dotenv import load_dotenv\n     4|\n     5|# Load environment variables from .env file\n     6|load_dotenv()\n     7|\n     8|# Project root - can be overridden by environment variable\n     9|PROJECT_ROOT = Path(os.getenv(\"PROJECT_ROOT\", Path(__file__).parent.parent.parent)).resolve()\n    10|\n    11|# Directory paths\n    12|SPEAKER_DATA_BASE_DIR = Path(os.getenv(\"SPEAKER_DATA_BASE_DIR\", str(PROJECT_ROOT / \"speaker_data\")))\n    13|SPEAKER_SAMPLES_DIR = Path(os.getenv(\"SPEAKER_SAMPLES_DIR\", str(SPEAKER_DATA_BASE_DIR / \"speaker_samples\")))\n    14|SPEAKERS_YAML_FILE = Path(os.getenv(\"SPEAKERS_YAML_FILE\", str(SPEAKER_DATA_BASE_DIR / \"speakers.yaml\")))\n    15|\n    16|# TTS temporary output path (used by DialogProcessorService)\n    17|TTS_TEMP_OUTPUT_DIR = Path(os.getenv(\"TTS_TEMP_OUTPUT_DIR\", str(PROJECT_ROOT / \"tts_temp_outputs\")))\n    18|\n    19|# Final dialog output path (used by Dialog router and served by main app)\n    20|# These are stored within the 'backend' directory to be easily servable.\n    21|DIALOG_OUTPUT_PARENT_DIR = PROJECT_ROOT / \"backend\"\n    22|DIALOG_GENERATED_DIR = Path(os.getenv(\"DIALOG_GENERATED_DIR\", str(DIALOG_OUTPUT_PARENT_DIR / \"tts_generated_dialogs\")))\n    23|\n    24|# Alias for clarity and backward compatibility\n    25|DIALOG_OUTPUT_DIR = DIALOG_GENERATED_DIR\n    26|\n    27|# Server configuration\n    28|HOST = os.getenv(\"HOST\", \"0.0.0.0\")\n    29|PORT = int(os.getenv(\"PORT\", \"8000\"))\n    30|RELOAD = os.getenv(\"RELOAD\", \"true\").lower() == \"true\"\n    31|\n    32|# CORS configuration\n    33|CORS_ORIGINS = [origin.strip() for origin in os.getenv(\"CORS_ORIGINS\", \"http://localhost:8001,http://127.0.0.1:8001\").split(\",\")]\n    34|\n    35|# Device configuration\n    36|DEVICE = os.getenv(\"DEVICE\", \"auto\")\n    37|\n    38|# Ensure directories exist\n    39|SPEAKER_SAMPLES_DIR.mkdir(parents=True, exist_ok=True)\n    40|TTS_TEMP_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n    41|DIALOG_GENERATED_DIR.mkdir(parents=True, exist_ok=True)\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/config.py\",\"content\":\"import os\\nfrom pathlib import Path\\nfrom dotenv import load_dotenv\\n\\n# Load environment variables from .env file\\nload_dotenv()\\n\\n# Project root - can be overridden by environment variable\\nPROJECT_ROOT = Path(os.getenv(\\\"PROJECT_ROOT\\\", Path(__file__).parent.parent.parent)).resolve()\\n\\n# Directory paths\\nSPEAKER_DATA_BASE_DIR = Path(os.getenv(\\\"SPEAKER_DATA_BASE_DIR\\\", str(PROJECT_ROOT / \\\"speaker_data\\\")))\\nSPEAKER_SAMPLES_DIR = Path(os.getenv(\\\"SPEAKER_SAMPLES_DIR\\\", str(SPEAKER_DATA_BASE_DIR / \\\"speaker_samples\\\")))\\nSPEAKERS_YAML_FILE = Path(os.getenv(\\\"SPEAKERS_YAML_FILE\\\", str(SPEAKER_DATA_BASE_DIR / \\\"speakers.yaml\\\")))\\n\\n# TTS temporary output path (used by DialogProcessorService)\\nTTS_TEMP_OUTPUT_DIR = Path(os.getenv(\\\"TTS_TEMP_OUTPUT_DIR\\\", str(PROJECT_ROOT / \\\"tts_temp_outputs\\\")))\\n\\n# Final dialog output path (used by Dialog router and served by main app)\\n# These are stored within the 'backend' directory to be easily servable.\\nDIALOG_OUTPUT_PARENT_DIR = PROJECT_ROOT / \\\"backend\\\"\\nDIALOG_GENERATED_DIR = Path(os.getenv(\\\"DIALOG_GENERATED_DIR\\\", str(DIALOG_OUTPUT_PARENT_DIR / \\\"tts_generated_dialogs\\\")))\\n\\n# Alias for clarity and backward compatibility\\nDIALOG_OUTPUT_DIR = DIALOG_GENERATED_DIR\\n\\n# Server configuration\\nHOST = os.getenv(\\\"HOST\\\", \\\"0.0.0.0\\\")\\nPORT = int(os.getenv(\\\"PORT\\\", \\\"8000\\\"))\\nRELOAD = os.getenv(\\\"RELOAD\\\", \\\"true\\\").lower() == \\\"true\\\"\\n\\n# CORS configuration\\nCORS_ORIGINS = [origin.strip() for origin in os.getenv(\\\"CORS_ORIGINS\\\", \\\"http://localhost:8001,http://127.0.0.1:8001\\\").split(\\\",\\\")]\\n\\n# Device configuration\\nDEVICE = os.getenv(\\\"DEVICE\\\", \\\"auto\\\")\\n\\n# Ensure directories exist\\nSPEAKER_SAMPLES_DIR.mkdir(parents=True, exist_ok=True)\\nTTS_TEMP_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\\nDIALOG_GENERATED_DIR.mkdir(parents=True, exist_ok=True)\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 15e73dcf-7864-43cc-a2a6-52e2198390cf
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Now I'll check the tts_service.py file to understand the import issue:"}},{"type":"tool_call","data":{"id":"toolu_01YXQXz3K5aU32KXcRQaddrM","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/tts_service.py\",\"limit\":20}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198078}}]


id: d8510a92-554a-420b-9e4a-58cfbc3b7372
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01YXQXz3K5aU32KXcRQaddrM","name":"","content":"\u003cfile\u003e\n     1|import torch\n     2|import torchaudio\n     3|from typing import Optional\n     4|from chatterbox.tts import ChatterboxTTS\n     5|from pathlib import Path\n     6|import gc # Garbage collector for memory management\n     7|import os\n     8|from contextlib import contextmanager\n     9|\n    10|# Import configuration\n    11|from app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\n    12|\n    13|# Use configuration for TTS output directory\n    14|TTS_OUTPUT_DIR = TTS_TEMP_OUTPUT_DIR\n    15|\n    16|def safe_load_chatterbox_tts(device):\n    17|    \"\"\"\n    18|    Safely load ChatterboxTTS model with device mapping to handle CUDA-\u003eMPS/CPU conversion.\n    19|    This patches torch.load temporarily to map CUDA tensors to the appropriate device.\n    20|    \"\"\"\n\n(File has more lines. Use 'offset' parameter to read beyond line 20)\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/tts_service.py\",\"content\":\"import torch\\nimport torchaudio\\nfrom typing import Optional\\nfrom chatterbox.tts import ChatterboxTTS\\nfrom pathlib import Path\\nimport gc # Garbage collector for memory management\\nimport os\\nfrom contextlib import contextmanager\\n\\n# Import configuration\\nfrom app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\\n\\n# Use configuration for TTS output directory\\nTTS_OUTPUT_DIR = TTS_TEMP_OUTPUT_DIR\\n\\ndef safe_load_chatterbox_tts(device):\\n    \\\"\\\"\\\"\\n    Safely load ChatterboxTTS model with device mapping to handle CUDA-\\u003eMPS/CPU conversion.\\n    This patches torch.load temporarily to map CUDA tensors to the appropriate device.\\n    \\\"\\\"\\\"\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: c1d07842-e8db-4a81-b405-a00aebd833f6
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"I see the issue. Let me fix the cbx-audiobook.py file by modifying the import paths:"}},{"type":"tool_call","data":{"id":"toolu_018581g9yrAL8tfTHPrYreRu","name":"edit","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py\",\"old_string\":\"# Import backend services\\nfrom backend.app.services.tts_service import TTSService\\nfrom backend.app.services.speaker_service import SpeakerManagementService\\nfrom backend.app.services.audio_manipulation_service import AudioManipulationService\\nfrom backend.app.config import DIALOG_GENERATED_DIR, TTS_TEMP_OUTPUT_DIR\",\"new_string\":\"# Add the project root to the Python path to fix imports\\nimport sys\\nsys.path.insert(0, str(Path(__file__).resolve().parent))\\n\\n# Import backend services\\nfrom backend.app.services.tts_service import TTSService\\nfrom backend.app.services.speaker_service import SpeakerManagementService\\nfrom backend.app.services.audio_manipulation_service import AudioManipulationService\\nfrom backend.app.config import DIALOG_GENERATED_DIR, TTS_TEMP_OUTPUT_DIR\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198085}}]


id: e2fbc0c9-7612-4c8e-9d7d-c90841c41735
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_018581g9yrAL8tfTHPrYreRu","name":"","content":"\u003cresult\u003e\nContent replaced in file: /Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py\n\u003c/result\u003e\n","metadata":"{\"diff\":\"--- a/cbx-audiobook.py\\n+++ b/cbx-audiobook.py\\n@@ -13,7 +13,11 @@\\n import re\\n import sys\\n from pathlib import Path\\n-import uuid\\n+import uuid\\n+\\n+# Add the project root to the Python path to fix imports\\n+import sys\\n+sys.path.insert(0, str(Path(__file__).resolve().parent))\\n \\n # Import backend services\\n from backend.app.services.tts_service import TTSService\\n\",\"additions\":5,\"removals\":1}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: d4fec189-d7ea-4ef3-ac5b-a95b884c8e51
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Now I need to fix the import in the tts_service.py file:"}},{"type":"tool_call","data":{"id":"toolu_01Mmiii6kU3a2KAEeisn6Lx5","name":"edit","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/tts_service.py\",\"old_string\":\"# Import configuration\\nfrom app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\",\"new_string\":\"# Import configuration\\ntry:\\n    from app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\\nexcept ModuleNotFoundError:\\n    # When imported from scripts at project root\\n    from backend.app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198110}}]


id: b68c112a-4ea1-4474-8c37-e16d10be1af7
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01Mmiii6kU3a2KAEeisn6Lx5","name":"","content":"\u003cresult\u003e\nContent replaced in file: /Users/stwhite/CODE/chatterbox-ui/backend/app/services/tts_service.py\n\u003c/result\u003e\n","metadata":"{\"diff\":\"--- a/backend/app/services/tts_service.py\\n+++ b/backend/app/services/tts_service.py\\n@@ -8,7 +8,11 @@\\n from contextlib import contextmanager\\n \\n # Import configuration\\n-from app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\\n+try:\\n+    from app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\\n+except ModuleNotFoundError:\\n+    # When imported from scripts at project root\\n+    from backend.app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\\n \\n # Use configuration for TTS output directory\\n TTS_OUTPUT_DIR = TTS_TEMP_OUTPUT_DIR\\n\",\"additions\":5,\"removals\":1}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 3d71ea19-5687-4e2b-9cbf-638e414de7ff
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let's also update the OpenCode.md file to include information about running the scripts:"}},{"type":"tool_call","data":{"id":"toolu_01TU8aKskueEf4FYRnetSfnA","name":"edit","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/OpenCode.md\",\"new_string\":\"# OpenCode.md - Chatterbox UI Development Guide\\n\\n## Build \\u0026 Run Commands\\n```bash\\n# Backend (FastAPI)\\npip install -r backend/requirements.txt\\nuvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000\\n\\n# Frontend\\npython frontend/start_dev_server.py  # Serves on port 8001\\n\\n# Run backend tests\\npython backend/run_api_test.py\\n\\n# Run frontend tests\\nnpm test\\n\\n# Run specific frontend test\\nnpx jest frontend/tests/api.test.js\\n\\n# Run Gradio interface\\npython gradio_app.py\\n\\n# Run utility scripts\\npython cbx-audiobook.py --list-speakers  # List available speakers\\npython cbx-audiobook.py sample-audiobook.txt --speaker \\u003cspeaker_id\\u003e\\npython cbx-dialog-generate.py sample-dialog.md\\n```\",\"old_string\":\"# OpenCode.md - Chatterbox UI Development Guide\\n\\n## Build \\u0026 Run Commands\\n```bash\\n# Backend (FastAPI)\\npip install -r backend/requirements.txt\\nuvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000\\n\\n# Frontend\\npython frontend/start_dev_server.py  # Serves on port 8001\\n\\n# Run backend tests\\npython backend/run_api_test.py\\n\\n# Run frontend tests\\nnpm test\\n\\n# Run specific frontend test\\nnpx jest frontend/tests/api.test.js\\n\\n# Run Gradio interface\\npython gradio_app.py\\n```\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198127}}]


id: dc2fccc9-41a1-47a7-81ff-52b37dc6f656
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01TU8aKskueEf4FYRnetSfnA","name":"","content":"\u003cresult\u003e\nContent replaced in file: /Users/stwhite/CODE/chatterbox-ui/OpenCode.md\n\u003c/result\u003e\n","metadata":"{\"diff\":\"--- a/OpenCode.md\\n+++ b/OpenCode.md\\n@@ -19,7 +19,12 @@\\n npx jest frontend/tests/api.test.js\\n \\n # Run Gradio interface\\n-python gradio_app.py\\n+python gradio_app.py\\n+\\n+# Run utility scripts\\n+python cbx-audiobook.py --list-speakers  # List available speakers\\n+python cbx-audiobook.py sample-audiobook.txt --speaker \\u003cspeaker_id\\u003e\\n+python cbx-dialog-generate.py sample-dialog.md\\n ```\\n \\n ## Code Style Guidelines\\n\",\"additions\":6,\"removals\":1}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: b72a3475-6ad5-4878-b2c2-e54eed3d06ba
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"I've fixed the import issue in two ways:\n\n1. Added `sys.path.insert(0, str(Path(__file__).resolve().parent))` to cbx-audiobook.py to ensure the project root is in the Python path.\n\n2. Modified the import in tts_service.py to handle both cases:\n   ```python\n   try:\n       from app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\n   except ModuleNotFoundError:\n       # When imported from scripts at project root\n       from backend.app.config import TTS_TEMP_OUTPUT_DIR, SPEAKER_SAMPLES_DIR\n   ```\n\nThis should fix the ModuleNotFoundError when running cbx-audiobook.py directly. I also updated the OpenCode.md file to include information about running the utility scripts.\n\nTry running the script again with:\n```\npython cbx-audiobook.py --list-speakers\n```"}},{"type":"finish","data":{"reason":"end_turn","time":1750198134}}]


id: a93245f8-6417-4a2e-b08c-a2dc9b202728
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: user
parts:
  [{"type":"text","data":{"text":"I get this error \npython cbx-audiobook.py --help\n/Users/stwhite/CODE/chatterbox-ui/.venv/lib/python3.12/site-packages/perth/perth_net/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools\u003c81.\n  from pkg_resources import resource_filename\nTraceback (most recent call last):\n  File \"/Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py\", line 24, in \u003cmodule\u003e\n    from backend.app.services.speaker_service import SpeakerManagementService\n  File \"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/speaker_service.py\", line 10, in \u003cmodule\u003e\n    from app.models.speaker_models import Speaker, SpeakerCreate\nModuleNotFoundError: No module named 'app'\n(base)"}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: dec4bcec-6988-4140-83d8-6382e77e80be
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let me fix the speaker_service.py file as well to handle the import path issue:"}},{"type":"tool_call","data":{"id":"toolu_01B1Ry4bHAhFEhroS79JmxDG","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/speaker_service.py\",\"limit\":20}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198180}}]


id: ef061b1f-b1ff-4eab-915a-6771fb617c72
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01B1Ry4bHAhFEhroS79JmxDG","name":"","content":"\u003cfile\u003e\n     1|import yaml\n     2|import uuid\n     3|import os\n     4|import io # Added for BytesIO\n     5|import torchaudio # Added for audio processing\n     6|from pathlib import Path\n     7|from typing import List, Dict, Optional, Any\n     8|\n     9|from fastapi import UploadFile, HTTPException\n    10|from app.models.speaker_models import Speaker, SpeakerCreate\n    11|from app import config\n    12|\n    13|class SpeakerManagementService:\n    14|    def __init__(self):\n    15|        self._ensure_data_files_exist()\n    16|        self.speakers_data = self._load_speakers_data()\n    17|\n    18|    def _ensure_data_files_exist(self):\n    19|        \"\"\"Ensures the speaker data directory and YAML file exist.\"\"\"\n    20|        config.SPEAKER_DATA_BASE_DIR.mkdir(parents=True, exist_ok=True)\n\n(File has more lines. Use 'offset' parameter to read beyond line 20)\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/speaker_service.py\",\"content\":\"import yaml\\nimport uuid\\nimport os\\nimport io # Added for BytesIO\\nimport torchaudio # Added for audio processing\\nfrom pathlib import Path\\nfrom typing import List, Dict, Optional, Any\\n\\nfrom fastapi import UploadFile, HTTPException\\nfrom app.models.speaker_models import Speaker, SpeakerCreate\\nfrom app import config\\n\\nclass SpeakerManagementService:\\n    def __init__(self):\\n        self._ensure_data_files_exist()\\n        self.speakers_data = self._load_speakers_data()\\n\\n    def _ensure_data_files_exist(self):\\n        \\\"\\\"\\\"Ensures the speaker data directory and YAML file exist.\\\"\\\"\\\"\\n        config.SPEAKER_DATA_BASE_DIR.mkdir(parents=True, exist_ok=True)\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 3d3f1eef-9b36-4a21-9085-90fc32cd8710
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let me fix the import paths in speaker_service.py:"}},{"type":"tool_call","data":{"id":"toolu_017GL4oE3HY96SyJCRnFBr5n","name":"edit","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/speaker_service.py\",\"new_string\":\"import yaml\\nimport uuid\\nimport os\\nimport io # Added for BytesIO\\nimport torchaudio # Added for audio processing\\nfrom pathlib import Path\\nfrom typing import List, Dict, Optional, Any\\n\\nfrom fastapi import UploadFile, HTTPException\\ntry:\\n    from app.models.speaker_models import Speaker, SpeakerCreate\\n    from app import config\\nexcept ModuleNotFoundError:\\n    # When imported from scripts at project root\\n    from backend.app.models.speaker_models import Speaker, SpeakerCreate\\n    from backend.app import config\",\"old_string\":\"import yaml\\nimport uuid\\nimport os\\nimport io # Added for BytesIO\\nimport torchaudio # Added for audio processing\\nfrom pathlib import Path\\nfrom typing import List, Dict, Optional, Any\\n\\nfrom fastapi import UploadFile, HTTPException\\nfrom app.models.speaker_models import Speaker, SpeakerCreate\\nfrom app import config\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198185}}]


id: 7c4f9008-aa47-4f15-8a66-d29b5f606c97
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_017GL4oE3HY96SyJCRnFBr5n","name":"","content":"\u003cresult\u003e\nContent replaced in file: /Users/stwhite/CODE/chatterbox-ui/backend/app/services/speaker_service.py\n\u003c/result\u003e\n","metadata":"{\"diff\":\"--- a/backend/app/services/speaker_service.py\\n+++ b/backend/app/services/speaker_service.py\\n@@ -7,8 +7,13 @@\\n from typing import List, Dict, Optional, Any\\n \\n from fastapi import UploadFile, HTTPException\\n-from app.models.speaker_models import Speaker, SpeakerCreate\\n+try:\\n+    from app.models.speaker_models import Speaker, SpeakerCreate\\n-from app import config\\n+    from app import config\\n+except ModuleNotFoundError:\\n+    # When imported from scripts at project root\\n+    from backend.app.models.speaker_models import Speaker, SpeakerCreate\\n+    from backend.app import config\\n \\n class SpeakerManagementService:\\n     def __init__(self):\\n\",\"additions\":7,\"removals\":2}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: f6604915-a9fc-44c2-8813-3ad1701d3d62
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Now let's check the audio_manipulation_service.py file as well:"}},{"type":"tool_call","data":{"id":"toolu_01PdaeUx2EGVVRm7VXMHmbk6","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/audio_manipulation_service.py\",\"limit\":20}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198191}}]


id: c2f5715d-f0a3-430e-8844-fe9ffa547cef
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01PdaeUx2EGVVRm7VXMHmbk6","name":"","content":"\u003cfile\u003e\n     1|import torch\n     2|import torchaudio\n     3|from pathlib import Path\n     4|from typing import List, Dict, Union, Tuple\n     5|import zipfile\n     6|\n     7|# Define a common sample rate, e.g., from the TTS model. This should ideally be configurable or dynamically obtained.\n     8|# For now, let's assume the TTS model (ChatterboxTTS) outputs at a known sample rate.\n     9|# The ChatterboxTTS model.sr is 24000.\n    10|DEFAULT_SAMPLE_RATE = 24000 \n    11|\n    12|class AudioManipulationService:\n    13|    def __init__(self, default_sample_rate: int = DEFAULT_SAMPLE_RATE):\n    14|        self.sample_rate = default_sample_rate\n    15|\n    16|    def _load_audio(self, file_path: Union[str, Path]) -\u003e Tuple[torch.Tensor, int]:\n    17|        \"\"\"Loads an audio file and returns the waveform and sample rate.\"\"\"\n    18|        try:\n    19|            waveform, sr = torchaudio.load(file_path)\n    20|            return waveform, sr\n\n(File has more lines. Use 'offset' parameter to read beyond line 20)\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/audio_manipulation_service.py\",\"content\":\"import torch\\nimport torchaudio\\nfrom pathlib import Path\\nfrom typing import List, Dict, Union, Tuple\\nimport zipfile\\n\\n# Define a common sample rate, e.g., from the TTS model. This should ideally be configurable or dynamically obtained.\\n# For now, let's assume the TTS model (ChatterboxTTS) outputs at a known sample rate.\\n# The ChatterboxTTS model.sr is 24000.\\nDEFAULT_SAMPLE_RATE = 24000 \\n\\nclass AudioManipulationService:\\n    def __init__(self, default_sample_rate: int = DEFAULT_SAMPLE_RATE):\\n        self.sample_rate = default_sample_rate\\n\\n    def _load_audio(self, file_path: Union[str, Path]) -\\u003e Tuple[torch.Tensor, int]:\\n        \\\"\\\"\\\"Loads an audio file and returns the waveform and sample rate.\\\"\\\"\\\"\\n        try:\\n            waveform, sr = torchaudio.load(file_path)\\n            return waveform, sr\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 5b7d623d-8366-42b1-b8a2-1f764dbe55d3
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let's check if there are any other imports in the dialog_processor_service.py file:"}},{"type":"tool_call","data":{"id":"toolu_01NaeWHMiyRBq64CTb9huWGi","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/dialog_processor_service.py\",\"limit\":20}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198194}}]


id: 0e8082c5-3a8c-4a44-90fc-2cff7a0ba89a
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01NaeWHMiyRBq64CTb9huWGi","name":"","content":"\u003cfile\u003e\n     1|from pathlib import Path\n     2|from typing import List, Dict, Any, Union\n     3|import re\n     4|\n     5|from .tts_service import TTSService\n     6|from .speaker_service import SpeakerManagementService\n     7|from app import config\n     8|# Potentially models for dialog structure if we define them\n     9|# from ..models.dialog_models import DialogItem # Example\n    10|\n    11|class DialogProcessorService:\n    12|    def __init__(self, tts_service: TTSService, speaker_service: SpeakerManagementService):\n    13|        self.tts_service = tts_service\n    14|        self.speaker_service = speaker_service\n    15|        # Base directory for storing individual audio segments during processing\n    16|        self.temp_audio_dir = config.TTS_TEMP_OUTPUT_DIR\n    17|        self.temp_audio_dir.mkdir(parents=True, exist_ok=True)\n    18|\n    19|    def _split_text(self, text: str, max_length: int = 300) -\u003e List[str]:\n    20|        \"\"\"\n\n(File has more lines. Use 'offset' parameter to read beyond line 20)\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/dialog_processor_service.py\",\"content\":\"from pathlib import Path\\nfrom typing import List, Dict, Any, Union\\nimport re\\n\\nfrom .tts_service import TTSService\\nfrom .speaker_service import SpeakerManagementService\\nfrom app import config\\n# Potentially models for dialog structure if we define them\\n# from ..models.dialog_models import DialogItem # Example\\n\\nclass DialogProcessorService:\\n    def __init__(self, tts_service: TTSService, speaker_service: SpeakerManagementService):\\n        self.tts_service = tts_service\\n        self.speaker_service = speaker_service\\n        # Base directory for storing individual audio segments during processing\\n        self.temp_audio_dir = config.TTS_TEMP_OUTPUT_DIR\\n        self.temp_audio_dir.mkdir(parents=True, exist_ok=True)\\n\\n    def _split_text(self, text: str, max_length: int = 300) -\\u003e List[str]:\\n        \\\"\\\"\\\"\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 4d585a29-e6e1-426b-b5d9-32cc3296e71a
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let's fix the dialog_processor_service.py file as well:"}},{"type":"tool_call","data":{"id":"toolu_017GtDGqMXzxpzgLDUAXxfiF","name":"edit","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/backend/app/services/dialog_processor_service.py\",\"new_string\":\"from pathlib import Path\\nfrom typing import List, Dict, Any, Union\\nimport re\\n\\nfrom .tts_service import TTSService\\nfrom .speaker_service import SpeakerManagementService\\ntry:\\n    from app import config\\nexcept ModuleNotFoundError:\\n    # When imported from scripts at project root\\n    from backend.app import config\\n# Potentially models for dialog structure if we define them\\n# from ..models.dialog_models import DialogItem # Example\",\"old_string\":\"from pathlib import Path\\nfrom typing import List, Dict, Any, Union\\nimport re\\n\\nfrom .tts_service import TTSService\\nfrom .speaker_service import SpeakerManagementService\\nfrom app import config\\n# Potentially models for dialog structure if we define them\\n# from ..models.dialog_models import DialogItem # Example\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198199}}]


id: 1dc38a75-de36-41ab-a885-601630643bd4
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_017GtDGqMXzxpzgLDUAXxfiF","name":"","content":"\u003cresult\u003e\nContent replaced in file: /Users/stwhite/CODE/chatterbox-ui/backend/app/services/dialog_processor_service.py\n\u003c/result\u003e\n","metadata":"{\"diff\":\"--- a/backend/app/services/dialog_processor_service.py\\n+++ b/backend/app/services/dialog_processor_service.py\\n@@ -4,7 +4,11 @@\\n \\n from .tts_service import TTSService\\n from .speaker_service import SpeakerManagementService\\n-from app import config\\n+try:\\n+    from app import config\\n+except ModuleNotFoundError:\\n+    # When imported from scripts at project root\\n+    from backend.app import config\\n # Potentially models for dialog structure if we define them\\n # from ..models.dialog_models import DialogItem # Example\\n \\n\",\"additions\":5,\"removals\":1}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: a50a54b6-1f83-4246-9425-b5261157ce97
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let's create a more comprehensive fix by adding a helper module to handle imports properly. This will be a cleaner solution than adding try/except blocks in every file:"}},{"type":"tool_call","data":{"id":"toolu_01M6afJyAcicDmcpYv96Ckhh","name":"write","input":"{\"content\":\"\\\"\\\"\\\"\\nImport helper module for Chatterbox UI.\\n\\nThis module provides a function to add the project root to the Python path,\\nwhich helps resolve import issues when running scripts from different locations.\\n\\\"\\\"\\\"\\n\\nimport sys\\nimport os\\nfrom pathlib import Path\\n\\ndef setup_python_path():\\n    \\\"\\\"\\\"\\n    Add the project root to the Python path.\\n    This allows imports to work correctly regardless of where the script is run from.\\n    \\\"\\\"\\\"\\n    # Get the project root (parent of the directory containing this file)\\n    project_root = Path(__file__).resolve().parent\\n    \\n    # Add the project root to the Python path if it's not already there\\n    if str(project_root) not in sys.path:\\n        sys.path.insert(0, str(project_root))\\n        print(f\\\"Added {project_root} to Python path\\\")\\n    \\n    # Set environment variable for other modules to use\\n    os.environ[\\\"PROJECT_ROOT\\\"] = str(project_root)\\n    \\n    return project_root\\n\\n# Run setup when this module is imported\\nproject_root = setup_python_path()\",\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/import_helper.py\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198213}}]


id: bfe6da3b-a07a-4c98-baff-ec7423daa684
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01M6afJyAcicDmcpYv96Ckhh","name":"","content":"\u003cresult\u003e\nFile successfully written: /Users/stwhite/CODE/chatterbox-ui/import_helper.py\n\u003c/result\u003e","metadata":"{\"diff\":\"--- a/import_helper.py\\n+++ b/import_helper.py\\n@@ -0,0 +1,31 @@\\n+\\\"\\\"\\\"\\n+Import helper module for Chatterbox UI.\\n+\\n+This module provides a function to add the project root to the Python path,\\n+which helps resolve import issues when running scripts from different locations.\\n+\\\"\\\"\\\"\\n+\\n+import sys\\n+import os\\n+from pathlib import Path\\n+\\n+def setup_python_path():\\n+    \\\"\\\"\\\"\\n+    Add the project root to the Python path.\\n+    This allows imports to work correctly regardless of where the script is run from.\\n+    \\\"\\\"\\\"\\n+    # Get the project root (parent of the directory containing this file)\\n+    project_root = Path(__file__).resolve().parent\\n+    \\n+    # Add the project root to the Python path if it's not already there\\n+    if str(project_root) not in sys.path:\\n+        sys.path.insert(0, str(project_root))\\n+        print(f\\\"Added {project_root} to Python path\\\")\\n+    \\n+    # Set environment variable for other modules to use\\n+    os.environ[\\\"PROJECT_ROOT\\\"] = str(project_root)\\n+    \\n+    return project_root\\n+\\n+# Run setup when this module is imported\\n+project_root = setup_python_path()\\n\\\\ No newline at end of file\\n\",\"additions\":31,\"removals\":0}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 35e4f296-2884-4695-982b-7541ffe97815
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Now let's update the cbx-audiobook.py file to use this helper:"}},{"type":"tool_call","data":{"id":"toolu_01PPrMxvN9c3KiBc5MJQb6Jv","name":"edit","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py\",\"new_string\":\"#!/usr/bin/env python\\n\\\"\\\"\\\"\\nChatterbox Audiobook Generator\\n\\nThis script converts a text file into an audiobook using the Chatterbox TTS system.\\nIt parses the text file into manageable chunks, generates audio for each chunk,\\nand assembles them into a complete audiobook.\\n\\\"\\\"\\\"\\n\\nimport argparse\\nimport asyncio\\nimport os\\nimport re\\nimport sys\\nfrom pathlib import Path\\nimport uuid\\n\\n# Import helper to fix Python path\\nimport import_helper\\n\\n# Import backend services\\nfrom backend.app.services.tts_service import TTSService\\nfrom backend.app.services.speaker_service import SpeakerManagementService\\nfrom backend.app.services.audio_manipulation_service import AudioManipulationService\\nfrom backend.app.config import DIALOG_GENERATED_DIR, TTS_TEMP_OUTPUT_DIR\",\"old_string\":\"#!/usr/bin/env python\\n\\\"\\\"\\\"\\nChatterbox Audiobook Generator\\n\\nThis script converts a text file into an audiobook using the Chatterbox TTS system.\\nIt parses the text file into manageable chunks, generates audio for each chunk,\\nand assembles them into a complete audiobook.\\n\\\"\\\"\\\"\\n\\nimport argparse\\nimport asyncio\\nimport os\\nimport re\\nimport sys\\nfrom pathlib import Path\\nimport uuid\\n\\n# Add the project root to the Python path to fix imports\\nimport sys\\nsys.path.insert(0, str(Path(__file__).resolve().parent))\\n\\n# Import backend services\\nfrom backend.app.services.tts_service import TTSService\\nfrom backend.app.services.speaker_service import SpeakerManagementService\\nfrom backend.app.services.audio_manipulation_service import AudioManipulationService\\nfrom backend.app.config import DIALOG_GENERATED_DIR, TTS_TEMP_OUTPUT_DIR\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198224}}]


id: 6f5f86a4-c727-4005-bd90-85afb7a68b0f
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01PPrMxvN9c3KiBc5MJQb6Jv","name":"","content":"\u003cresult\u003e\nContent replaced in file: /Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py\n\u003c/result\u003e\n","metadata":"{\"diff\":\"--- a/cbx-audiobook.py\\n+++ b/cbx-audiobook.py\\n@@ -15,9 +15,8 @@\\n from pathlib import Path\\n import uuid\\n \\n-# Add the project root to the Python path to fix imports\\n-import sys\\n-sys.path.insert(0, str(Path(__file__).resolve().parent))\\n+# Import helper to fix Python path\\n+import import_helper\\n \\n # Import backend services\\n from backend.app.services.tts_service import TTSService\\n\",\"additions\":2,\"removals\":3}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 3a887394-80b3-491a-8682-7d1ba27e6e9b
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let's update the OpenCode.md file to include information about the import helper:"}},{"type":"tool_call","data":{"id":"toolu_01FP8xeUu7joRbFNpdVJwmEc","name":"edit","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/OpenCode.md\",\"new_string\":\"## Import Structure\\n- When importing from scripts, use `import import_helper` first to fix Python path\\n- Backend modules use relative imports within the app package\\n- Services are in `backend.app.services`\\n- Models are in `backend.app.models`\\n- Configuration is in `backend.app.config`\",\"old_string\":\"### Project Structure\\n- Backend: FastAPI with service-oriented architecture\\n- Frontend: Vanilla JS with modular design (api.js, app.js, config.js)\\n- Speaker data in YAML format with WAV samples\\n- Output directories: dialog_output/, single_output/, tts_outputs/\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198233}}]


id: 9ea954b4-b70e-45d3-a4d3-a128a9ba41c7
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01FP8xeUu7joRbFNpdVJwmEc","name":"","content":"\u003cresult\u003e\nContent replaced in file: /Users/stwhite/CODE/chatterbox-ui/OpenCode.md\n\u003c/result\u003e\n","metadata":"{\"diff\":\"--- a/OpenCode.md\\n+++ b/OpenCode.md\\n@@ -43,8 +43,9 @@\\n - Error handling: try/catch with detailed error messages\\n - Camel case for variables/functions (camelCase)\\n \\n-### Project Structure\\n+## Import Structure\\n-- Backend: FastAPI with service-oriented architecture\\n-- Frontend: Vanilla JS with modular design (api.js, app.js, config.js)\\n-- Speaker data in YAML format with WAV samples\\n-- Output directories: dialog_output/, single_output/, tts_outputs/\\n\\\\ No newline at end of file\\n+- When importing from scripts, use `import import_helper` first to fix Python path\\n+- Backend modules use relative imports within the app package\\n+- Services are in `backend.app.services`\\n+- Models are in `backend.app.models`\\n+- Configuration is in `backend.app.config`\\n\\\\ No newline at end of file\\n\",\"additions\":6,\"removals\":5}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 6754c7ac-2fab-4f38-8325-9c89a18f11b2
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let's also update the other script files to use the import helper:"}},{"type":"tool_call","data":{"id":"toolu_01YSqowBEX4RNcMmgkpViEkh","name":"glob","input":"{\"pattern\":\"cbx-*.py\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198239}}]


id: ad869fa1-d8f2-45b6-a29f-dea8791d5ca9
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01YSqowBEX4RNcMmgkpViEkh","name":"","content":"/Users/stwhite/CODE/chatterbox-ui/cbx-generate.py\n/Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py\n/Users/stwhite/CODE/chatterbox-ui/cbx-dialog-generate.py","metadata":"{\"number_of_files\":3,\"truncated\":false}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 54813d14-28be-4551-acdb-ff87ef0c973c
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let's check and update the other script files:"}},{"type":"tool_call","data":{"id":"toolu_01Vpe7NWELBexfs559oD8LVU","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-dialog-generate.py\",\"limit\":20}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198241}}]


id: bbc7d432-c882-4751-a898-a43ba25e6426
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01Vpe7NWELBexfs559oD8LVU","name":"","content":"\u003cfile\u003e\n     1|#!/usr/bin/env python3\n     2|import argparse\n     3|import re\n     4|import os\n     5|import yaml\n     6|import torchaudio as ta\n     7|from chatterbox.tts import ChatterboxTTS\n     8|\n     9|def split_text_at_sentence_boundaries(text, max_length=300):\n    10|    \"\"\"\n    11|    Split text at sentence boundaries, ensuring each chunk is \u003c= max_length.\n    12|    Returns a list of text chunks.\n    13|    \"\"\"\n    14|    # Simple regex for sentence boundaries (period, question mark, exclamation mark followed by space or end)\n    15|    sentence_pattern = r'[.!?](?:\\s|$)'\n    16|    sentences = re.split(f'({sentence_pattern})', text)\n    17|    \n    18|    # Recombine the split parts (the regex split keeps the delimiters as separate items)\n    19|    actual_sentences = []\n    20|    current = \"\"\n\n(File has more lines. Use 'offset' parameter to read beyond line 20)\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-dialog-generate.py\",\"content\":\"#!/usr/bin/env python3\\nimport argparse\\nimport re\\nimport os\\nimport yaml\\nimport torchaudio as ta\\nfrom chatterbox.tts import ChatterboxTTS\\n\\ndef split_text_at_sentence_boundaries(text, max_length=300):\\n    \\\"\\\"\\\"\\n    Split text at sentence boundaries, ensuring each chunk is \\u003c= max_length.\\n    Returns a list of text chunks.\\n    \\\"\\\"\\\"\\n    # Simple regex for sentence boundaries (period, question mark, exclamation mark followed by space or end)\\n    sentence_pattern = r'[.!?](?:\\\\s|$)'\\n    sentences = re.split(f'({sentence_pattern})', text)\\n    \\n    # Recombine the split parts (the regex split keeps the delimiters as separate items)\\n    actual_sentences = []\\n    current = \\\"\\\"\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 0ecd6b3c-9a65-46d4-ab1b-5ec93c4553ea
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"tool_call","data":{"id":"toolu_01XNVqULkXdxrAnBTPXygBUG","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-generate.py\",\"limit\":20}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198243}}]


id: 3f1c2bec-2cd1-451f-b16e-4524a0bcecc0
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01XNVqULkXdxrAnBTPXygBUG","name":"","content":"\u003cfile\u003e\n     1|import argparse\n     2|import torchaudio as ta\n     3|from chatterbox.tts import ChatterboxTTS\n     4|\n     5|def main():\n     6|    parser = argparse.ArgumentParser(description=\"Chatterbox TTS audio generation\")\n     7|    parser.add_argument('--sample', required=True, type=str, help='Prompt/reference audio file (e.g. .wav, .mp3) for the voice')\n     8|    parser.add_argument('--output', required=True, type=str, help='Output audio file path (should end with .wav)')\n     9|    parser.add_argument('--text', required=True, type=str, help='Text to synthesize')\n    10|    args = parser.parse_args()\n    11|\n    12|    # Load model on MPS (for Apple Silicon)\n    13|    model = ChatterboxTTS.from_pretrained(device=\"mps\")\n    14|\n    15|    # Generate the audio\n    16|    wav = model.generate(args.text, audio_prompt_path=args.sample)\n    17|    # Save to output .wav\n    18|    ta.save(args.output, wav, model.sr)\n    19|    print(f\"Generated audio saved to {args.output}\")\n    20|\n\n(File has more lines. Use 'offset' parameter to read beyond line 20)\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-generate.py\",\"content\":\"import argparse\\nimport torchaudio as ta\\nfrom chatterbox.tts import ChatterboxTTS\\n\\ndef main():\\n    parser = argparse.ArgumentParser(description=\\\"Chatterbox TTS audio generation\\\")\\n    parser.add_argument('--sample', required=True, type=str, help='Prompt/reference audio file (e.g. .wav, .mp3) for the voice')\\n    parser.add_argument('--output', required=True, type=str, help='Output audio file path (should end with .wav)')\\n    parser.add_argument('--text', required=True, type=str, help='Text to synthesize')\\n    args = parser.parse_args()\\n\\n    # Load model on MPS (for Apple Silicon)\\n    model = ChatterboxTTS.from_pretrained(device=\\\"mps\\\")\\n\\n    # Generate the audio\\n    wav = model.generate(args.text, audio_prompt_path=args.sample)\\n    # Save to output .wav\\n    ta.save(args.output, wav, model.sr)\\n    print(f\\\"Generated audio saved to {args.output}\\\")\\n\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: de209549-d24c-48ae-9b2a-12912464e3de
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let's update the cbx-dialog-generate.py and cbx-generate.py files to use the import helper:"}},{"type":"tool_call","data":{"id":"toolu_01XtPfX9moFbWfGWzcSArUXr","name":"edit","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-dialog-generate.py\",\"new_string\":\"#!/usr/bin/env python3\\nimport argparse\\nimport re\\nimport os\\nimport yaml\\nimport torchaudio as ta\\nfrom chatterbox.tts import ChatterboxTTS\\n\\n# Import helper to fix Python path\\nimport import_helper\",\"old_string\":\"#!/usr/bin/env python3\\nimport argparse\\nimport re\\nimport os\\nimport yaml\\nimport torchaudio as ta\\nfrom chatterbox.tts import ChatterboxTTS\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198247}}]


id: ee927fec-4ac9-4978-9339-87475bf174dc
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01XtPfX9moFbWfGWzcSArUXr","name":"","content":"\u003cresult\u003e\nContent replaced in file: /Users/stwhite/CODE/chatterbox-ui/cbx-dialog-generate.py\n\u003c/result\u003e\n","metadata":"{\"diff\":\"--- a/cbx-dialog-generate.py\\n+++ b/cbx-dialog-generate.py\\n@@ -4,7 +4,10 @@\\n import os\\n import yaml\\n import torchaudio as ta\\n-from chatterbox.tts import ChatterboxTTS\\n+from chatterbox.tts import ChatterboxTTS\\n+\\n+# Import helper to fix Python path\\n+import import_helper\\n \\n def split_text_at_sentence_boundaries(text, max_length=300):\\n     \\\"\\\"\\\"\\n\",\"additions\":4,\"removals\":1}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: ae036d5e-a463-4e34-b4d8-bf6b081c2598
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"tool_call","data":{"id":"toolu_0169h71zUc38f1yT4TudQcXk","name":"edit","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-generate.py\",\"new_string\":\"import argparse\\nimport torchaudio as ta\\nfrom chatterbox.tts import ChatterboxTTS\\n\\n# Import helper to fix Python path\\nimport import_helper\",\"old_string\":\"import argparse\\nimport torchaudio as ta\\nfrom chatterbox.tts import ChatterboxTTS\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198254}}]


id: 4e53b81f-a3e9-4ef8-812c-e16082271717
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_0169h71zUc38f1yT4TudQcXk","name":"","content":"\u003cresult\u003e\nContent replaced in file: /Users/stwhite/CODE/chatterbox-ui/cbx-generate.py\n\u003c/result\u003e\n","metadata":"{\"diff\":\"--- a/cbx-generate.py\\n+++ b/cbx-generate.py\\n@@ -1,6 +1,9 @@\\n import argparse\\n import torchaudio as ta\\n-from chatterbox.tts import ChatterboxTTS\\n+from chatterbox.tts import ChatterboxTTS\\n+\\n+# Import helper to fix Python path\\n+import import_helper\\n \\n def main():\\n     parser = argparse.ArgumentParser(description=\\\"Chatterbox TTS audio generation\\\")\\n\",\"additions\":4,\"removals\":1}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 5de9cd32-4130-4b33-9559-7e98ba4fe467
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let's update the OpenCode.md file with a more comprehensive guide:"}},{"type":"tool_call","data":{"id":"toolu_019JiYN57NK6v6cpGNkD4smU","name":"write","input":"{\"content\":\"# OpenCode.md - Chatterbox UI Development Guide\\n\\n## Build \\u0026 Run Commands\\n```bash\\n# Backend (FastAPI)\\npip install -r backend/requirements.txt\\nuvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000\\n\\n# Frontend\\npython frontend/start_dev_server.py  # Serves on port 8001\\n\\n# Run backend tests\\npython backend/run_api_test.py\\n\\n# Run frontend tests\\nnpm test\\n\\n# Run specific frontend test\\nnpx jest frontend/tests/api.test.js\\n\\n# Run Gradio interface\\npython gradio_app.py\\n\\n# Run utility scripts\\npython cbx-audiobook.py --list-speakers  # List available speakers\\npython cbx-audiobook.py sample-audiobook.txt --speaker \\u003cspeaker_id\\u003e\\npython cbx-dialog-generate.py sample-dialog.md\\n```\\n\\n## Code Style Guidelines\\n\\n### Python\\n- Use type hints (from typing import Optional, List, Dict, etc.)\\n- Error handling: Use try/except with specific exceptions\\n- Async/await for I/O operations\\n- Docstrings for functions and classes\\n- PEP 8 naming: snake_case for functions/variables, PascalCase for classes\\n\\n### JavaScript\\n- ES6 modules with import/export\\n- Async/await for API calls\\n- JSDoc comments for functions\\n- Error handling: try/catch with detailed error messages\\n- Camel case for variables/functions (camelCase)\\n\\n## Import Structure\\n- When importing from scripts, use `import import_helper` first to fix Python path\\n- Backend modules use relative imports within the app package\\n- Services are in `backend.app.services`\\n- Models are in `backend.app.models`\\n- Configuration is in `backend.app.config`\\n\\n## Project Structure\\n- Backend: FastAPI with service-oriented architecture\\n- Frontend: Vanilla JS with modular design (api.js, app.js, config.js)\\n- Speaker data in YAML format with WAV samples\\n- Output directories: dialog_output/, single_output/, tts_outputs/\\n\\n## Common Issues\\n- Import errors: Make sure to use `import import_helper` in scripts\\n- Speaker samples must be WAV format in `speaker_data/speaker_samples/`\\n- TTS model requires GPU (CUDA) or Apple Silicon (MPS)\",\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/OpenCode.md\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198272}}]


id: b2931100-12f4-4e4b-8d2b-519c29793f0e
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_019JiYN57NK6v6cpGNkD4smU","name":"","content":"\u003cresult\u003e\nFile successfully written: /Users/stwhite/CODE/chatterbox-ui/OpenCode.md\n\u003c/result\u003e","metadata":"{\"diff\":\"--- a/OpenCode.md\\n+++ b/OpenCode.md\\n@@ -48,4 +48,15 @@\\n - Backend modules use relative imports within the app package\\n - Services are in `backend.app.services`\\n - Models are in `backend.app.models`\\n-- Configuration is in `backend.app.config`\\n\\\\ No newline at end of file\\n+- Configuration is in `backend.app.config`\\n+\\n+## Project Structure\\n+- Backend: FastAPI with service-oriented architecture\\n+- Frontend: Vanilla JS with modular design (api.js, app.js, config.js)\\n+- Speaker data in YAML format with WAV samples\\n+- Output directories: dialog_output/, single_output/, tts_outputs/\\n+\\n+## Common Issues\\n+- Import errors: Make sure to use `import import_helper` in scripts\\n+- Speaker samples must be WAV format in `speaker_data/speaker_samples/`\\n+- TTS model requires GPU (CUDA) or Apple Silicon (MPS)\\n\\\\ No newline at end of file\\n\",\"additions\":12,\"removals\":1}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 5c6a716a-11d2-4af5-90b9-95b274463a7c
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"I've made several changes to fix the import issues:\n\n1. Created an `import_helper.py` module that adds the project root to the Python path when imported\n2. Updated all the script files (cbx-*.py) to use this helper\n3. Fixed the import paths in service modules to handle both direct imports and imports from scripts\n4. Created a comprehensive OpenCode.md file with:\n   - Build/lint/test commands\n   - Code style guidelines\n   - Import structure information\n   - Project structure overview\n   - Common issues and solutions\n\nTry running the script again with:\n```\npython cbx-audiobook.py --list-speakers\n```\n\nThis should now work correctly. The import_helper.py module provides a clean solution that will work for all scripts in the project."}},{"type":"finish","data":{"reason":"end_turn","time":1750198280}}]


id: 5009494c-ed41-4f51-8ffc-41a15173eadc
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: user
parts:
  [{"type":"text","data":{"text":"python cbx-audiobook.py --list-speakers\n/Users/stwhite/CODE/chatterbox-ui/.venv/lib/python3.12/site-packages/perth/perth_net/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools\u003c81.\n  from pkg_resources import resource_filename\nusage: cbx-audiobook.py [-h] --speaker SPEAKER [--output OUTPUT] [--device {mps,cuda,cpu}] [--exaggeration EXAGGERATION]\n                        [--cfg-weight CFG_WEIGHT] [--temperature TEMPERATURE] [--sentence-pause SENTENCE_PAUSE]\n                        [--paragraph-pause PARAGRAPH_PAUSE] [--list-speakers]\n                        text_file\ncbx-audiobook.py: error: the following arguments are required: text_file, --speaker/-s"}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: c66b930e-7571-4303-a8fa-f1c8ac7b65ab
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"The error message shows that the script is now finding the modules correctly, but there's an issue with the command-line arguments. When using the `--list-speakers` flag, you shouldn't need to provide the other required arguments.\n\nLet's fix the argument parsing in the cbx-audiobook.py file:"}},{"type":"tool_call","data":{"id":"toolu_01TTpfqb1xYLocwzeMwiwQaW","name":"view","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py\",\"offset\":250,\"limit\":50}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198392}}]


id: 873fc6ea-a242-4375-825e-f3e8d13e0719
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: tool
parts:
  [{"type":"tool_result","data":{"tool_call_id":"toolu_01TTpfqb1xYLocwzeMwiwQaW","name":"","content":"\u003cfile\u003e\n   251|            # Unload TTS model\n   252|            print(\"Unloading TTS model...\")\n   253|            self.tts_service.unload_model()\n   254|\n   255|async def main():\n   256|    parser = argparse.ArgumentParser(description=\"Generate an audiobook from a text file using Chatterbox TTS\")\n   257|    parser.add_argument(\"text_file\", help=\"Path to the text file to convert\")\n   258|    parser.add_argument(\"--speaker\", \"-s\", required=True, help=\"ID of the speaker to use\")\n   259|    parser.add_argument(\"--output\", \"-o\", help=\"Base name for output files (default: derived from text filename)\")\n   260|    parser.add_argument(\"--device\", default=\"mps\", choices=[\"mps\", \"cuda\", \"cpu\"], help=\"Device to use for TTS (default: mps)\")\n   261|    parser.add_argument(\"--exaggeration\", type=float, default=0.5, help=\"Controls expressiveness (0.0-1.0, default: 0.5)\")\n   262|    parser.add_argument(\"--cfg-weight\", type=float, default=0.5, help=\"Controls alignment with speaker (0.0-1.0, default: 0.5)\")\n   263|    parser.add_argument(\"--temperature\", type=float, default=0.8, help=\"Controls randomness (0.0-1.0, default: 0.8)\")\n   264|    parser.add_argument(\"--sentence-pause\", type=float, default=0.5, help=\"Pause between sentences in seconds (default: 0.5)\")\n   265|    parser.add_argument(\"--paragraph-pause\", type=float, default=1.0, help=\"Pause between paragraphs in seconds (default: 1.0)\")\n   266|    parser.add_argument(\"--list-speakers\", action=\"store_true\", help=\"List available speakers and exit\")\n   267|    \n   268|    args = parser.parse_args()\n   269|    \n   270|    # List speakers if requested\n   271|    if args.list_speakers:\n   272|        speaker_service = SpeakerManagementService()\n   273|        speakers = speaker_service.get_all_speakers()\n   274|        print(\"Available speakers:\")\n   275|        for speaker in speakers:\n   276|            print(f\"  {speaker.id}: {speaker.name}\")\n   277|        return\n   278|    \n   279|    # Determine output base name if not provided\n   280|    if not args.output:\n   281|        text_path = Path(args.text_file)\n   282|        args.output = text_path.stem\n   283|    \n   284|    try:\n   285|        # Create audiobook generator\n   286|        generator = AudiobookGenerator(\n   287|            speaker_id=args.speaker,\n   288|            output_base_name=args.output,\n   289|            device=args.device,\n   290|            exaggeration=args.exaggeration,\n   291|            cfg_weight=args.cfg_weight,\n   292|            temperature=args.temperature,\n   293|            pause_between_sentences=args.sentence_pause,\n   294|            pause_between_paragraphs=args.paragraph_pause\n   295|        )\n   296|        \n   297|        # Generate audiobook\n   298|        await generator.generate_audiobook(args.text_file)\n   299|        \n   300|    except Exception as e:\n\n(File has more lines. Use 'offset' parameter to read beyond line 300)\n\u003c/file\u003e\n","metadata":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py\",\"content\":\"            # Unload TTS model\\n            print(\\\"Unloading TTS model...\\\")\\n            self.tts_service.unload_model()\\n\\nasync def main():\\n    parser = argparse.ArgumentParser(description=\\\"Generate an audiobook from a text file using Chatterbox TTS\\\")\\n    parser.add_argument(\\\"text_file\\\", help=\\\"Path to the text file to convert\\\")\\n    parser.add_argument(\\\"--speaker\\\", \\\"-s\\\", required=True, help=\\\"ID of the speaker to use\\\")\\n    parser.add_argument(\\\"--output\\\", \\\"-o\\\", help=\\\"Base name for output files (default: derived from text filename)\\\")\\n    parser.add_argument(\\\"--device\\\", default=\\\"mps\\\", choices=[\\\"mps\\\", \\\"cuda\\\", \\\"cpu\\\"], help=\\\"Device to use for TTS (default: mps)\\\")\\n    parser.add_argument(\\\"--exaggeration\\\", type=float, default=0.5, help=\\\"Controls expressiveness (0.0-1.0, default: 0.5)\\\")\\n    parser.add_argument(\\\"--cfg-weight\\\", type=float, default=0.5, help=\\\"Controls alignment with speaker (0.0-1.0, default: 0.5)\\\")\\n    parser.add_argument(\\\"--temperature\\\", type=float, default=0.8, help=\\\"Controls randomness (0.0-1.0, default: 0.8)\\\")\\n    parser.add_argument(\\\"--sentence-pause\\\", type=float, default=0.5, help=\\\"Pause between sentences in seconds (default: 0.5)\\\")\\n    parser.add_argument(\\\"--paragraph-pause\\\", type=float, default=1.0, help=\\\"Pause between paragraphs in seconds (default: 1.0)\\\")\\n    parser.add_argument(\\\"--list-speakers\\\", action=\\\"store_true\\\", help=\\\"List available speakers and exit\\\")\\n    \\n    args = parser.parse_args()\\n    \\n    # List speakers if requested\\n    if args.list_speakers:\\n        speaker_service = SpeakerManagementService()\\n        speakers = speaker_service.get_all_speakers()\\n        print(\\\"Available speakers:\\\")\\n        for speaker in speakers:\\n            print(f\\\"  {speaker.id}: {speaker.name}\\\")\\n        return\\n    \\n    # Determine output base name if not provided\\n    if not args.output:\\n        text_path = Path(args.text_file)\\n        args.output = text_path.stem\\n    \\n    try:\\n        # Create audiobook generator\\n        generator = AudiobookGenerator(\\n            speaker_id=args.speaker,\\n            output_base_name=args.output,\\n            device=args.device,\\n            exaggeration=args.exaggeration,\\n            cfg_weight=args.cfg_weight,\\n            temperature=args.temperature,\\n            pause_between_sentences=args.sentence_pause,\\n            pause_between_paragraphs=args.paragraph_pause\\n        )\\n        \\n        # Generate audiobook\\n        await generator.generate_audiobook(args.text_file)\\n        \\n    except Exception as e:\"}","is_error":false}},{"type":"finish","data":{"reason":"stop","time":0}}]


id: 6d0fc448-3075-4e65-92d9-c284d4cbe47f
session_id: e17e0be8-e6be-42f9-a7b5-8b1d50020cef
role: assistant
model: claude-3.7-sonnet
parts:
  [{"type":"text","data":{"text":"Let's fix the argument parsing in the cbx-audiobook.py file:"}},{"type":"tool_call","data":{"id":"toolu_01LxYkL5qJMdVySXnAARUgmL","name":"edit","input":"{\"file_path\":\"/Users/stwhite/CODE/chatterbox-ui/cbx-audiobook.py\",\"new_string\":\"async def main():\\n    parser = argparse.ArgumentParser(description=\\\"Generate an audiobook from a text file using Chatterbox TTS\\\")\\n    \\n    # Create a mutually exclusive group for the main operation vs listing speakers\\n    group = parser.add_mutually_exclusive_group(required=True)\\n    group.add_argument(\\\"--list-speakers\\\", action=\\\"store_true\\\", help=\\\"List available speakers and exit\\\")\\n    group.add_argument(\\\"text_file\\\", nargs=\\\"?\\\", help=\\\"Path to the text file to convert\\\")\\n    \\n    # Other arguments\\n    parser.add_argument(\\\"--speaker\\\", \\\"-s\\\", help=\\\"ID of the speaker to use\\\")\\n    parser.add_argument(\\\"--output\\\", \\\"-o\\\", help=\\\"Base name for output files (default: derived from text filename)\\\")\\n    parser.add_argument(\\\"--device\\\", default=\\\"mps\\\", choices=[\\\"mps\\\", \\\"cuda\\\", \\\"cpu\\\"], help=\\\"Device to use for TTS (default: mps)\\\")\\n    parser.add_argument(\\\"--exaggeration\\\", type=float, default=0.5, help=\\\"Controls expressiveness (0.0-1.0, default: 0.5)\\\")\\n    parser.add_argument(\\\"--cfg-weight\\\", type=float, default=0.5, help=\\\"Controls alignment with speaker (0.0-1.0, default: 0.5)\\\")\\n    parser.add_argument(\\\"--temperature\\\", type=float, default=0.8, help=\\\"Controls randomness (0.0-1.0, default: 0.8)\\\")\\n    parser.add_argument(\\\"--sentence-pause\\\", type=float, default=0.5, help=\\\"Pause between sentences in seconds (default: 0.5)\\\")\\n    parser.add_argument(\\\"--paragraph-pause\\\", type=float, default=1.0, help=\\\"Pause between paragraphs in seconds (default: 1.0)\\\")\\n    \\n    args = parser.parse_args()\\n    \\n    # List speakers if requested\\n    if args.list_speakers:\\n        speaker_service = SpeakerManagementService()\\n        speakers = speaker_service.get_all_speakers()\\n        print(\\\"Available speakers:\\\")\\n        for speaker in speakers:\\n            print(f\\\"  {speaker.id}: {speaker.name}\\\")\\n        return\\n    \\n    # Validate required arguments for audiobook generation\\n    if not args.text_file:\\n        parser.error(\\\"text_file is required when not using --list-speakers\\\")\\n    \\n    if not args.speaker:\\n        parser.error(\\\"--speaker/-s is required when not using --list-speakers\\\")\\n    \\n    # Determine output base name if not provided\\n    if not args.output:\\n        text_path = Path(args.text_file)\\n        args.output = text_path.stem\",\"old_string\":\"async def main():\\n    parser = argparse.ArgumentParser(description=\\\"Generate an audiobook from a text file using Chatterbox TTS\\\")\\n    parser.add_argument(\\\"text_file\\\", help=\\\"Path to the text file to convert\\\")\\n    parser.add_argument(\\\"--speaker\\\", \\\"-s\\\", required=True, help=\\\"ID of the speaker to use\\\")\\n    parser.add_argument(\\\"--output\\\", \\\"-o\\\", help=\\\"Base name for output files (default: derived from text filename)\\\")\\n    parser.add_argument(\\\"--device\\\", default=\\\"mps\\\", choices=[\\\"mps\\\", \\\"cuda\\\", \\\"cpu\\\"], help=\\\"Device to use for TTS (default: mps)\\\")\\n    parser.add_argument(\\\"--exaggeration\\\", type=float, default=0.5, help=\\\"Controls expressiveness (0.0-1.0, default: 0.5)\\\")\\n    parser.add_argument(\\\"--cfg-weight\\\", type=float, default=0.5, help=\\\"Controls alignment with speaker (0.0-1.0, default: 0.5)\\\")\\n    parser.add_argument(\\\"--temperature\\\", type=float, default=0.8, help=\\\"Controls randomness (0.0-1.0, default: 0.8)\\\")\\n    parser.add_argument(\\\"--sentence-pause\\\", type=float, default=0.5, help=\\\"Pause between sentences in seconds (default: 0.5)\\\")\\n    parser.add_argument(\\\"--paragraph-pause\\\", type=float, default=1.0, help=\\\"Pause between paragraphs in seconds (default: 1.0)\\\")\\n    parser.add_argument(\\\"--list-speakers\\\", action=\\\"store_true\\\", help=\\\"List available speakers and exit\\\")\\n    \\n    args = parser.parse_args()\\n    \\n    # List speakers if requested\\n    if args.list_speakers:\\n        speaker_service = SpeakerManagementService()\\n        speakers = speaker_service.get_all_speakers()\\n        print(\\\"Available speakers:\\\")\\n        for speaker in speakers:\\n            print(f\\\"  {speaker.id}: {speaker.name}\\\")\\n        return\\n    \\n    # Determine output base name if not provided\\n    if not args.output:\\n        text_path = Path(args.text_file)\\n        args.output = text_path.stem\"}","type":"tool_use","finished":true}},{"type":"finish","data":{"reason":"tool_use","time":1750198407}}]


