├── requirements.txt ├── README.md ├── LICENSE └── chattered.py /requirements.txt: -------------------------------------------------------------------------------- 1 | gradio>=4.0.0 2 | torch>=2.0.0 3 | torchaudio>=2.0.0 4 | numpy>=1.21.0 5 | pathlib 6 | requests>=2.25.0 7 | huggingface-hub>=0.16.0 8 | tqdm>=4.62.0 9 | chatterbox-tts -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Chattered 2 | 3 | A Gradio-based web interface for the Chatterbox Text-to-Speech model, providing voice cloning capabilities and speech synthesis through an intuitive browser interface. 4 | 5 | ## Screenshots 6 | ![image](https://github.com/user-attachments/assets/bff94192-86de-458b-a6f9-93348d83c837) ![image](https://github.com/user-attachments/assets/90b589f1-c345-4c27-90b1-3863b6d4b6b3) ![image](https://github.com/user-attachments/assets/24857b44-3eff-4dbd-990b-e0f2c7c951e8) 7 | 8 | 9 | 10 | 11 | 12 | ## Overview 13 | 14 | Chattered enables users to generate high-quality synthetic speech using the Chatterbox TTS model. The application supports voice cloning from uploaded audio samples, automatic text processing for long content, and real-time speech generation with configurable parameters. 15 | 16 | ## Features 17 | 18 | - Voice cloning with uploaded audio samples 19 | - Voice sample management and storage system 20 | - Automatic text chunking for long content processing 21 | - CUDA GPU acceleration with CPU fallback 22 | - Adjustable emotion and variation parameters 23 | - Web-based interface accessible via browser 24 | - Support for multiple audio formats (WAV, MP3, FLAC) 25 | 26 | ## System Requirements 27 | 28 | - Python 3.8 or higher 29 | - 4GB RAM minimum (8GB recommended) 30 | - 2GB storage space for model files 31 | - Optional: CUDA-compatible GPU for accelerated processing 32 | - Internet connection required for initial model download 33 | 34 | ## Installation 35 | 36 | 1. Clone the repository: 37 | ```bash 38 | git clone https://github.com/RemmyLee/chattered.git 39 | cd chattered 40 | ``` 41 | 42 | 2. Create and activate virtual environment: 43 | ```bash 44 | python -m venv venv 45 | source venv/bin/activate 46 | ``` 47 | 48 | 3. Install dependencies: 49 | ```bash 50 | pip install -r requirements.txt 51 | ``` 52 | 53 | 4. Run the application: 54 | ```bash 55 | python chattered.py 56 | ``` 57 | 58 | 5. Access the interface at `http://localhost:7860` 59 | 60 | ## Usage 61 | 62 | ### Text-to-Speech Generation 63 | 64 | 1. Enter text in the synthesis field 65 | 2. Configure emotion and variation parameters 66 | 3. Click "Generate Speech" to produce audio 67 | 68 | ### Voice Cloning 69 | 70 | 1. Upload a clear audio sample (5-30 seconds recommended) 71 | 2. Optionally save the voice sample with a custom label 72 | 3. Select saved voices from the dropdown menu 73 | 4. Generate speech using the cloned voice characteristics 74 | 75 | ### Voice Sample Management 76 | 77 | - Save uploaded audio samples for future use 78 | - Preview saved voice samples before selection 79 | - Delete unwanted voice samples from storage 80 | - Organize samples with custom labels and descriptions 81 | 82 | ## Configuration 83 | 84 | ### Parameters 85 | 86 | - **Emotion (0.0-1.0)**: Controls emotional expression intensity 87 | - **Variation (0.1-1.0)**: Controls speech randomness and naturalness 88 | 89 | ### Performance Options 90 | 91 | - Automatic GPU detection and utilization 92 | - CPU fallback for systems without CUDA support 93 | - Memory management with automatic cache clearing 94 | - Text chunking for processing long content 95 | 96 | ## Technical Details 97 | 98 | The application uses the Chatterbox TTS model by Resemble AI, automatically downloading required model files on first run. Voice samples are stored locally in the `voice_samples` directory with accompanying metadata. 99 | 100 | ### File Structure 101 | 102 | ``` 103 | chattered/ 104 | ├── chattered.py # Main application 105 | ├── requirements.txt # Python dependencies 106 | ├── voice_samples/ # Stored voice samples 107 | └── README.md # Documentation 108 | ``` 109 | 110 | ## Troubleshooting 111 | 112 | ### Model Download Issues 113 | 114 | Clear the model cache and restart: 115 | ```bash 116 | rm -rf ~/.cache/chatterbox/ 117 | python chattered.py 118 | ``` 119 | 120 | ### Memory Issues 121 | 122 | - Reduce input text length 123 | - Close other GPU applications 124 | - Application automatically falls back to CPU processing 125 | 126 | ### Audio Quality 127 | 128 | - Use high-quality voice samples with clear speech 129 | - Minimize background noise in samples 130 | - Ensure adequate sample length (5-30 seconds) 131 | 132 | ## License 133 | 134 | This project is licensed under the GPL-V3 License. See the LICENSE file for details. 135 | 136 | ## Dependencies 137 | 138 | - Gradio: Web interface framework 139 | - PyTorch: Deep learning framework 140 | - Torchaudio: Audio processing 141 | - Chatterbox-TTS: Text-to-speech model 142 | - NumPy: Numerical computing 143 | - Hugging Face Hub: Model repository access 144 | 145 | ## Contributing 146 | 147 | 1. Fork the repository 148 | 2. Create a feature branch 149 | 3. Implement changes with appropriate testing 150 | 4. Submit a pull request with detailed description 151 | 152 | ## Support 153 | 154 | For issues and bug reports, please use the GitHub Issues tracker. 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Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /chattered.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import gradio as gr 4 | import torch 5 | import torchaudio as ta 6 | import tempfile 7 | import os 8 | import numpy as np 9 | from pathlib import Path 10 | import warnings 11 | import sys 12 | import re 13 | import gc 14 | import json 15 | import shutil 16 | from datetime import datetime 17 | from tqdm import tqdm 18 | import requests 19 | from huggingface_hub import hf_hub_download, snapshot_download 20 | from huggingface_hub.utils import HfHubHTTPError, RepositoryNotFoundError 21 | 22 | warnings.filterwarnings("ignore") 23 | 24 | # Enable CUDA debugging 25 | os.environ["CUDA_LAUNCH_BLOCKING"] = "1" 26 | 27 | try: 28 | from chatterbox.tts import ChatterboxTTS 29 | except ImportError: 30 | print("Error: chatterbox-tts not installed. Please run: pip install chatterbox-tts") 31 | exit(1) 32 | 33 | 34 | class VoiceSampleManager: 35 | def __init__(self, voice_samples_dir): 36 | self.voice_samples_dir = Path(voice_samples_dir) 37 | self.voice_samples_dir.mkdir(parents=True, exist_ok=True) 38 | self.metadata_file = self.voice_samples_dir / "voice_samples.json" 39 | self.metadata = self.load_metadata() 40 | 41 | def load_metadata(self): 42 | """Load voice sample metadata from JSON file""" 43 | if self.metadata_file.exists(): 44 | try: 45 | with open(self.metadata_file, "r") as f: 46 | return json.load(f) 47 | except Exception as e: 48 | print(f"Error loading voice metadata: {e}") 49 | return {} 50 | return {} 51 | 52 | def save_metadata(self): 53 | """Save voice sample metadata to JSON file""" 54 | try: 55 | with open(self.metadata_file, "w") as f: 56 | json.dump(self.metadata, f, indent=2) 57 | except Exception as e: 58 | print(f"Error saving voice metadata: {e}") 59 | 60 | def save_voice_sample(self, audio_data, sample_rate, label, description=""): 61 | """Save voice sample with metadata""" 62 | if not label or not label.strip(): 63 | return False, "Label cannot be empty" 64 | 65 | # Sanitize label for filename 66 | safe_label = re.sub(r"[^\w\-_\.]", "_", label.strip()) 67 | if safe_label != label.strip(): 68 | print(f"Label sanitized from '{label.strip()}' to '{safe_label}'") 69 | 70 | # Check if label already exists 71 | if safe_label in self.metadata: 72 | return False, f"Voice sample '{safe_label}' already exists" 73 | 74 | try: 75 | # Generate filename 76 | timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") 77 | filename = f"{safe_label}_{timestamp}.wav" 78 | filepath = self.voice_samples_dir / filename 79 | 80 | # Convert audio data to tensor if needed 81 | if isinstance(audio_data, tuple): 82 | sample_rate, audio_array = audio_data 83 | audio_tensor = torch.from_numpy(audio_array.astype(np.float32)) 84 | else: 85 | audio_tensor = torch.from_numpy(audio_data.astype(np.float32)) 86 | 87 | # Ensure proper tensor shape 88 | if audio_tensor.dim() == 1: 89 | audio_tensor = audio_tensor.unsqueeze(0) 90 | elif ( 91 | audio_tensor.dim() == 2 92 | and audio_tensor.shape[0] > audio_tensor.shape[1] 93 | ): 94 | audio_tensor = audio_tensor.T 95 | 96 | # Save audio file 97 | ta.save(str(filepath), audio_tensor, sample_rate) 98 | print(f"DEBUG: Voice sample saved to: {filepath}") 99 | 100 | # Save metadata 101 | self.metadata[safe_label] = { 102 | "filename": filename, 103 | "filepath": str(filepath), 104 | "label": label.strip(), 105 | "description": description, 106 | "sample_rate": sample_rate, 107 | "created_at": datetime.now().isoformat(), 108 | "duration_seconds": audio_tensor.shape[1] / sample_rate, 109 | } 110 | 111 | self.save_metadata() 112 | print(f"DEBUG: Metadata updated for voice sample: {safe_label}") 113 | return True, f"Voice sample '{safe_label}' saved successfully" 114 | 115 | except Exception as e: 116 | return False, f"Error saving voice sample: {str(e)}" 117 | 118 | def get_voice_sample_path(self, label): 119 | """Get the file path for a voice sample""" 120 | if label in self.metadata: 121 | return self.metadata[label]["filepath"] 122 | return None 123 | 124 | def get_voice_sample_list(self): 125 | """Get list of available voice samples for dropdown""" 126 | if not self.metadata: 127 | return ["None"] 128 | return ["None"] + list(self.metadata.keys()) 129 | 130 | def get_voice_sample_info(self, label): 131 | """Get detailed information about a voice sample""" 132 | if label in self.metadata: 133 | info = self.metadata[label] 134 | return f"Description: {info.get('description', 'N/A')}\nDuration: {info.get('duration_seconds', 0):.1f}s\nCreated: {info.get('created_at', 'Unknown')}" 135 | return "No information available" 136 | 137 | def delete_voice_sample(self, label): 138 | """Delete a voice sample and its metadata""" 139 | if label not in self.metadata: 140 | return False, f"Voice sample '{label}' not found" 141 | 142 | try: 143 | # Delete file 144 | filepath = Path(self.metadata[label]["filepath"]) 145 | if filepath.exists(): 146 | filepath.unlink() 147 | 148 | # Remove from metadata 149 | del self.metadata[label] 150 | self.save_metadata() 151 | 152 | return True, f"Voice sample '{label}' deleted successfully" 153 | 154 | except Exception as e: 155 | return False, f"Error deleting voice sample: {str(e)}" 156 | 157 | 158 | class ChatterboxGradioApp: 159 | def __init__(self): 160 | self.model = None 161 | self.device = self.get_optimal_device() 162 | self.model_id = "ResembleAI/chatterbox" 163 | self.cache_dir = Path.home() / ".cache" / "chatterbox" 164 | 165 | # Store voice samples relative to script location, not cache 166 | script_dir = Path(__file__).parent if __file__ else Path.cwd() 167 | self.voice_samples_dir = script_dir / "voice_samples" 168 | 169 | self.model_loaded = False 170 | self.chunk_size = 200 # Much smaller chunks - about 10-15 seconds of speech 171 | self.max_chunk_words = 30 # Hard limit on words per chunk 172 | 173 | print(f"Device: {self.device}") 174 | print(f"Cache directory: {self.cache_dir}") 175 | print(f"Voice samples directory: {self.voice_samples_dir}") 176 | 177 | # Create directories if they don't exist 178 | self.cache_dir.mkdir(parents=True, exist_ok=True) 179 | self.voice_samples_dir.mkdir(parents=True, exist_ok=True) 180 | 181 | # Initialize voice sample manager 182 | self.voice_manager = VoiceSampleManager(self.voice_samples_dir) 183 | 184 | # Initialize model loading 185 | self.initialize_model() 186 | 187 | def get_optimal_device(self): 188 | """Determine the best device to use with proper CUDA checks""" 189 | if not torch.cuda.is_available(): 190 | return "cpu" 191 | 192 | try: 193 | # Test CUDA functionality 194 | test_tensor = torch.tensor([1.0]).cuda() 195 | test_result = test_tensor * 2 196 | del test_tensor, test_result 197 | torch.cuda.empty_cache() 198 | return "cuda" 199 | except Exception as e: 200 | print(f"CUDA test failed, falling back to CPU: {e}") 201 | return "cpu" 202 | 203 | def sanitize_text(self, text): 204 | """Sanitize text to prevent tokenization issues""" 205 | try: 206 | # First, normalize unicode encoding 207 | import unicodedata 208 | 209 | text = unicodedata.normalize("NFKC", text) 210 | 211 | # Remove non-printable characters except common ones 212 | import string 213 | 214 | printable = set(string.printable) 215 | sanitized = "".join(filter(lambda x: x in printable, text)) 216 | 217 | # Replace multiple spaces with single space 218 | sanitized = re.sub(r"\s+", " ", sanitized) 219 | 220 | # Remove leading/trailing whitespace 221 | sanitized = sanitized.strip() 222 | 223 | # Ensure text ends with proper punctuation 224 | if sanitized and sanitized[-1] not in ".!?": 225 | sanitized += "." 226 | 227 | # Replace problematic character sequences that might cause tokenization issues 228 | replacements = { 229 | '"': '"', # Smart quotes to regular quotes 230 | '"': '"', 231 | """: "'", 232 | """: "'", 233 | "…": "...", 234 | "–": "-", 235 | "—": "-", 236 | "\u200b": "", # Zero-width space 237 | "\u200c": "", # Zero-width non-joiner 238 | "\u200d": "", # Zero-width joiner 239 | "\ufeff": "", # Byte order mark 240 | "\xa0": " ", # Non-breaking space 241 | "\t": " ", # Tab to space 242 | "\n": " ", # Newline to space 243 | "\r": " ", # Carriage return to space 244 | } 245 | 246 | for old, new in replacements.items(): 247 | sanitized = sanitized.replace(old, new) 248 | 249 | # Remove any remaining control characters 250 | sanitized = "".join( 251 | char for char in sanitized if ord(char) >= 32 or char in "\t\n\r" 252 | ) 253 | 254 | # Validate that remaining characters are reasonable 255 | # Keep only ASCII and common extended ASCII 256 | sanitized = "".join(char for char in sanitized if ord(char) < 256) 257 | 258 | # Limit length to prevent extremely long inputs 259 | max_length = 5000 260 | if len(sanitized) > max_length: 261 | # Try to cut at sentence boundary 262 | sentences = re.split(r"(?<=[.!?])\s+", sanitized[:max_length]) 263 | if len(sentences) > 1: 264 | sanitized = " ".join(sentences[:-1]) 265 | else: 266 | sanitized = sanitized[:max_length] 267 | 268 | # Final cleanup - ensure no empty result 269 | sanitized = sanitized.strip() 270 | if not sanitized: 271 | sanitized = "Hello." 272 | 273 | return sanitized 274 | 275 | except Exception as e: 276 | print(f"Error sanitizing text: {e}") 277 | # Return basic cleaned version as fallback 278 | cleaned = re.sub(r"[^\w\s.,!?-]", "", text)[:1000].strip() 279 | return cleaned if cleaned else "Hello." 280 | 281 | def test_model_with_text(self, text): 282 | """Test model with specific text to catch issues early""" 283 | try: 284 | # Test with a simple, known-good text first 285 | test_wav = self.model.generate("Hello.", temperature=0.7, exaggeration=0.5) 286 | if test_wav is None: 287 | return False, "Model failed basic test" 288 | del test_wav 289 | self.clear_cuda_cache() 290 | 291 | # Test with the actual text (first 100 chars) 292 | test_text = text[:100] + "." if len(text) > 100 else text 293 | test_wav = self.model.generate(test_text, temperature=0.7, exaggeration=0.5) 294 | if test_wav is None: 295 | return False, "Model failed with provided text" 296 | del test_wav 297 | self.clear_cuda_cache() 298 | 299 | return True, "Model test successful" 300 | 301 | except Exception as e: 302 | return False, f"Model test failed: {str(e)}" 303 | 304 | def generate_with_fallbacks(self, text, **kwargs): 305 | """Generate audio with multiple fallback strategies""" 306 | 307 | # Strategy 1: Try with CUDA (if available) 308 | if self.device == "cuda": 309 | try: 310 | return self.model.generate(text, **kwargs) 311 | except Exception as cuda_error: 312 | print(f"CUDA generation failed: {cuda_error}") 313 | 314 | # Strategy 2: Try moving model to CPU temporarily 315 | try: 316 | print("Attempting CPU generation...") 317 | original_device = ( 318 | self.model.device if hasattr(self.model, "device") else None 319 | ) 320 | 321 | # Check if model has a method to move to CPU 322 | if hasattr(self.model, "cpu"): 323 | self.model.cpu() 324 | elif hasattr(self.model, "to"): 325 | self.model.to("cpu") 326 | 327 | result = self.model.generate(text, **kwargs) 328 | 329 | # Try to move back to CUDA if it was there before 330 | if original_device and "cuda" in str(original_device): 331 | try: 332 | if hasattr(self.model, "cuda"): 333 | self.model.cuda() 334 | elif hasattr(self.model, "to"): 335 | self.model.to("cuda") 336 | except: 337 | print("Could not move model back to CUDA, staying on CPU") 338 | self.device = "cpu" 339 | 340 | return result 341 | 342 | except Exception as cpu_error: 343 | print(f"CPU generation also failed: {cpu_error}") 344 | 345 | def clear_cuda_cache(self): 346 | """Clear CUDA cache and run garbage collection""" 347 | if self.device == "cuda": 348 | torch.cuda.empty_cache() 349 | gc.collect() 350 | 351 | def check_model_exists(self): 352 | """Check if the model files exist locally""" 353 | try: 354 | # Check if essential model files exist 355 | model_files = ["s3gen.pt", "conds.pt"] 356 | for filename in model_files: 357 | file_path = self.cache_dir / filename 358 | if not file_path.exists(): 359 | return False 360 | return True 361 | except Exception as e: 362 | print(f"Error checking model files: {e}") 363 | return False 364 | 365 | def download_model_with_progress(self): 366 | """Download model with progress bar""" 367 | print("Downloading Chatterbox model files...") 368 | 369 | try: 370 | # Download the entire repository 371 | print("Downloading model repository...") 372 | snapshot_download( 373 | repo_id=self.model_id, 374 | cache_dir=str(self.cache_dir.parent), 375 | local_dir=str(self.cache_dir), 376 | local_dir_use_symlinks=False, 377 | ) 378 | print("Model download completed successfully!") 379 | return True 380 | 381 | except (HfHubHTTPError, RepositoryNotFoundError) as e: 382 | print(f"Error downloading from Hugging Face: {e}") 383 | return False 384 | except Exception as e: 385 | print(f"Unexpected error during download: {e}") 386 | return False 387 | 388 | def download_individual_files(self): 389 | """Fallback method to download individual files""" 390 | print("Attempting to download individual model files...") 391 | 392 | files_to_download = ["s3gen.pt", "conds.pt", "README.md"] 393 | 394 | try: 395 | for filename in files_to_download: 396 | print(f"Downloading {filename}...") 397 | try: 398 | downloaded_file = hf_hub_download( 399 | repo_id=self.model_id, 400 | filename=filename, 401 | cache_dir=str(self.cache_dir.parent), 402 | local_dir=str(self.cache_dir), 403 | local_dir_use_symlinks=False, 404 | ) 405 | print(f"Successfully downloaded {filename}") 406 | except Exception as e: 407 | print(f"Failed to download {filename}: {e}") 408 | if filename in ["s3gen.pt", "conds.pt"]: # Critical files 409 | return False 410 | 411 | return True 412 | 413 | except Exception as e: 414 | print(f"Error in individual file download: {e}") 415 | return False 416 | 417 | def initialize_model(self): 418 | """Initialize model with automatic download if needed""" 419 | try: 420 | if not self.check_model_exists(): 421 | print("Model not found locally. Starting download...") 422 | 423 | # Try snapshot download first 424 | if not self.download_model_with_progress(): 425 | print( 426 | "Snapshot download failed. Trying individual file download..." 427 | ) 428 | if not self.download_individual_files(): 429 | raise Exception("Failed to download model files") 430 | 431 | print("Model download completed. Verifying files...") 432 | if not self.check_model_exists(): 433 | raise Exception("Model files verification failed after download") 434 | 435 | # Load the model 436 | self.load_model() 437 | 438 | except Exception as e: 439 | print(f"Error initializing model: {e}") 440 | raise e 441 | 442 | def load_model(self): 443 | """Load the Chatterbox TTS model with robust error handling""" 444 | print(f"Loading Chatterbox TTS model on {self.device}...") 445 | try: 446 | # Clear any existing CUDA memory 447 | self.clear_cuda_cache() 448 | 449 | # Set environment variable for model cache if needed 450 | if str(self.cache_dir) not in os.environ.get("HF_HOME", ""): 451 | os.environ["TRANSFORMERS_CACHE"] = str(self.cache_dir.parent) 452 | os.environ["HF_HOME"] = str(self.cache_dir.parent) 453 | 454 | # Try loading on specified device 455 | try: 456 | self.model = ChatterboxTTS.from_pretrained(device=self.device) 457 | self.model_loaded = True 458 | print("Model loaded successfully!") 459 | 460 | # Test model with a safe sample 461 | test_success, test_message = self.test_model_with_text("Hello world.") 462 | if not test_success: 463 | raise Exception(f"Model validation failed: {test_message}") 464 | 465 | print("Model validation successful!") 466 | 467 | except Exception as cuda_error: 468 | print(f"Failed to load on {self.device}: {cuda_error}") 469 | if self.device == "cuda": 470 | print("Attempting to load on CPU instead...") 471 | self.device = "cpu" 472 | self.clear_cuda_cache() 473 | self.model = ChatterboxTTS.from_pretrained(device=self.device) 474 | self.model_loaded = True 475 | print("Model loaded successfully on CPU!") 476 | 477 | # Test CPU model 478 | test_success, test_message = self.test_model_with_text( 479 | "Hello world." 480 | ) 481 | if not test_success: 482 | raise Exception(f"CPU model validation failed: {test_message}") 483 | print("CPU model validation successful!") 484 | else: 485 | raise cuda_error 486 | 487 | except Exception as e: 488 | print(f"Error loading model: {e}") 489 | self.model_loaded = False 490 | raise e 491 | 492 | def split_text_into_chunks(self, text): 493 | """Split text into manageable chunks at sentence boundaries""" 494 | # Split by sentences first 495 | sentences = re.split(r"(?<=[.!?])\s+", text.strip()) 496 | 497 | chunks = [] 498 | current_chunk = "" 499 | 500 | for sentence in sentences: 501 | # If adding this sentence would exceed chunk size, start new chunk 502 | if ( 503 | len(current_chunk) + len(sentence) + 1 > self.chunk_size 504 | and current_chunk 505 | ): 506 | chunks.append(current_chunk.strip()) 507 | current_chunk = sentence 508 | else: 509 | if current_chunk: 510 | current_chunk += " " + sentence 511 | else: 512 | current_chunk = sentence 513 | 514 | # Add the last chunk if it exists 515 | if current_chunk.strip(): 516 | chunks.append(current_chunk.strip()) 517 | 518 | # If no sentence boundaries found and text is too long, split by words 519 | if not chunks and len(text) > self.chunk_size: 520 | words = text.split() 521 | current_chunk = "" 522 | for word in words: 523 | if ( 524 | len(current_chunk) + len(word) + 1 > self.chunk_size 525 | and current_chunk 526 | ): 527 | chunks.append(current_chunk.strip()) 528 | current_chunk = word 529 | else: 530 | if current_chunk: 531 | current_chunk += " " + word 532 | else: 533 | current_chunk = word 534 | if current_chunk.strip(): 535 | chunks.append(current_chunk.strip()) 536 | 537 | return chunks if chunks else [text] 538 | 539 | def concatenate_audio_files(self, audio_files, sample_rate): 540 | """Concatenate multiple audio tensors into a single tensor""" 541 | if not audio_files: 542 | return None 543 | 544 | if len(audio_files) == 1: 545 | return audio_files[0] 546 | 547 | # Load all audio files and concatenate 548 | concatenated = None 549 | for audio_file in audio_files: 550 | audio, sr = ta.load(audio_file) 551 | 552 | # Resample if necessary 553 | if sr != sample_rate: 554 | resampler = ta.transforms.Resample(sr, sample_rate) 555 | audio = resampler(audio) 556 | 557 | if concatenated is None: 558 | concatenated = audio 559 | else: 560 | concatenated = torch.cat([concatenated, audio], dim=1) 561 | 562 | return concatenated 563 | 564 | def save_voice_sample(self, audio_data, label, description): 565 | """Save uploaded voice sample for future use""" 566 | if not audio_data: 567 | return "No audio data provided", gr.update() 568 | 569 | if not label or not label.strip(): 570 | return "Label cannot be empty", gr.update() 571 | 572 | success, message = self.voice_manager.save_voice_sample( 573 | audio_data, 22050, label, description 574 | ) 575 | 576 | # Update dropdown choices 577 | new_choices = self.voice_manager.get_voice_sample_list() 578 | 579 | if success: 580 | # Use the sanitized label (which is the key in metadata) for dropdown value 581 | sanitized_label = re.sub(r"[^\w\-_\.]", "_", label.strip()) 582 | return message, gr.update(choices=new_choices, value=sanitized_label) 583 | else: 584 | return message, gr.update(choices=new_choices) 585 | 586 | def save_voice_sample_with_clear(self, audio_data, label, description): 587 | """Save voice sample and clear inputs""" 588 | if not audio_data: 589 | return "No audio data provided", gr.update(), label, description 590 | 591 | if not label or not label.strip(): 592 | return "Label cannot be empty", gr.update(), label, description 593 | 594 | success, message = self.voice_manager.save_voice_sample( 595 | audio_data, 22050, label, description 596 | ) 597 | 598 | new_choices = self.voice_manager.get_voice_sample_list() 599 | 600 | if success: 601 | sanitized_label = re.sub(r"[^\w\-_\.]", "_", label.strip()) 602 | return ( 603 | message, 604 | gr.update(choices=new_choices, value=sanitized_label), 605 | "", 606 | "", 607 | ) 608 | else: 609 | return message, gr.update(choices=new_choices), label, description 610 | 611 | def delete_voice_sample(self, selected_voice): 612 | """Delete selected voice sample""" 613 | if not selected_voice or selected_voice == "None": 614 | return "No voice sample selected", gr.update() 615 | 616 | success, message = self.voice_manager.delete_voice_sample(selected_voice) 617 | new_choices = self.voice_manager.get_voice_sample_list() 618 | 619 | return message, gr.update(choices=new_choices, value="None") 620 | """Delete selected voice sample""" 621 | if not selected_voice or selected_voice == "None": 622 | return "No voice sample selected", gr.update() 623 | 624 | success, message = self.voice_manager.delete_voice_sample(selected_voice) 625 | new_choices = self.voice_manager.get_voice_sample_list() 626 | 627 | return message, gr.update(choices=new_choices, value="None") 628 | 629 | def verify_voice_sample(self, voice_path): 630 | """Verify that a voice sample is readable and in correct format""" 631 | try: 632 | if not Path(voice_path).exists(): 633 | return False, f"Voice sample file does not exist: {voice_path}" 634 | 635 | # Try to load the audio file 636 | audio, sr = ta.load(voice_path) 637 | duration = audio.shape[1] / sr 638 | 639 | print(f"DEBUG: Voice sample verification - Path: {voice_path}") 640 | print( 641 | f"DEBUG: Voice sample verification - Duration: {duration:.2f}s, Sample rate: {sr}, Shape: {audio.shape}" 642 | ) 643 | 644 | # Only check minimum duration - remove maximum limitation 645 | if duration < 1.0: 646 | return ( 647 | False, 648 | f"Voice sample too short: {duration:.2f}s (minimum 1s required)", 649 | ) 650 | 651 | return True, f"Voice sample verified: {duration:.2f}s at {sr}Hz" 652 | 653 | except Exception as e: 654 | return False, f"Error verifying voice sample: {str(e)}" 655 | 656 | def get_voice_preview(self, selected_voice): 657 | """Get preview audio for selected voice sample""" 658 | if not selected_voice or selected_voice == "None": 659 | return None 660 | 661 | audio_path = self.voice_manager.get_voice_sample_path(selected_voice) 662 | return audio_path 663 | 664 | def preprocess_audio_file(self, audio_path): 665 | """Preprocess audio file to match expected format for voice cloning""" 666 | try: 667 | # Load the audio file 668 | audio, sr = ta.load(audio_path) 669 | 670 | print(f"DEBUG: Original audio shape: {audio.shape}, sample rate: {sr}") 671 | print( 672 | f"DEBUG: Original audio range: {audio.min().item():.4f} to {audio.max().item():.4f}" 673 | ) 674 | 675 | # Convert stereo to mono if needed 676 | if audio.shape[0] > 1: 677 | audio = torch.mean(audio, dim=0, keepdim=True) 678 | print(f"DEBUG: Converted to mono: {audio.shape}") 679 | 680 | # Normalize audio to [-1, 1] range 681 | max_val = torch.max(torch.abs(audio)) 682 | if max_val > 1.0: 683 | audio = audio / max_val 684 | print( 685 | f"DEBUG: Normalized audio, max abs value was: {max_val.item():.4f}" 686 | ) 687 | 688 | print( 689 | f"DEBUG: Final audio range: {audio.min().item():.4f} to {audio.max().item():.4f}" 690 | ) 691 | 692 | # Create temporary file with properly processed audio 693 | with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_file: 694 | ta.save(tmp_file.name, audio, sr) 695 | return tmp_file.name 696 | 697 | except Exception as e: 698 | print(f"Error preprocessing audio: {e}") 699 | return None 700 | 701 | def generate_tts( 702 | self, 703 | text, 704 | audio_upload, 705 | selected_voice, 706 | exaggeration, 707 | temperature, 708 | ): 709 | """Generate TTS audio with simplified voice handling""" 710 | if not self.model_loaded: 711 | return None, "Model not loaded" 712 | 713 | if not text.strip(): 714 | return None, "Please enter text to synthesize" 715 | 716 | try: 717 | # Determine audio source for voice cloning 718 | audio_prompt_path = None 719 | temp_processed_file = None 720 | 721 | if selected_voice and selected_voice != "None": 722 | # Use saved voice sample 723 | saved_audio_path = self.voice_manager.get_voice_sample_path( 724 | selected_voice 725 | ) 726 | if saved_audio_path and Path(saved_audio_path).exists(): 727 | is_valid, _ = self.verify_voice_sample(saved_audio_path) 728 | if is_valid: 729 | temp_processed_file = self.preprocess_audio_file( 730 | saved_audio_path 731 | ) 732 | audio_prompt_path = temp_processed_file 733 | 734 | elif audio_upload is not None: 735 | # Use uploaded audio for one-time cloning 736 | with tempfile.NamedTemporaryFile( 737 | suffix=".wav", delete=False 738 | ) as tmp_file: 739 | if isinstance(audio_upload, tuple): 740 | sample_rate, audio_data = audio_upload 741 | if audio_data.dtype != np.float32: 742 | if audio_data.dtype == np.int16: 743 | audio_data = audio_data.astype(np.float32) / 32768.0 744 | elif audio_data.dtype == np.int32: 745 | audio_data = ( 746 | audio_data.astype(np.float32) / 2147483648.0 747 | ) 748 | 749 | audio_tensor = torch.from_numpy(audio_data) 750 | if audio_tensor.dim() == 1: 751 | audio_tensor = audio_tensor.unsqueeze(0) 752 | elif ( 753 | audio_tensor.dim() == 2 754 | and audio_tensor.shape[0] > audio_tensor.shape[1] 755 | ): 756 | audio_tensor = audio_tensor.T 757 | 758 | ta.save(tmp_file.name, audio_tensor, sample_rate) 759 | audio_prompt_path = tmp_file.name 760 | 761 | # Split text into chunks 762 | text_chunks = self.split_text_into_chunks(text.strip()) 763 | 764 | # Generate audio for each chunk 765 | audio_files = [] 766 | temp_files = [] 767 | 768 | kwargs = { 769 | "exaggeration": float(exaggeration), 770 | "temperature": float(temperature), 771 | } 772 | 773 | for i, chunk in enumerate(text_chunks): 774 | if audio_prompt_path: 775 | wav = self.model.generate( 776 | chunk, audio_prompt_path=audio_prompt_path, **kwargs 777 | ) 778 | else: 779 | wav = self.model.generate(chunk, **kwargs) 780 | 781 | with tempfile.NamedTemporaryFile( 782 | suffix=f"_chunk_{i}.wav", delete=False 783 | ) as tmp_file: 784 | ta.save(tmp_file.name, wav, self.model.sr) 785 | audio_files.append(tmp_file.name) 786 | temp_files.append(tmp_file.name) 787 | 788 | # Concatenate audio chunks 789 | if len(audio_files) > 1: 790 | concatenated_audio = self.concatenate_audio_files( 791 | audio_files, self.model.sr 792 | ) 793 | else: 794 | concatenated_audio, _ = ta.load(audio_files[0]) 795 | 796 | # Clean up temporary files 797 | for temp_file in temp_files: 798 | try: 799 | os.unlink(temp_file) 800 | except: 801 | pass 802 | 803 | if temp_processed_file: 804 | try: 805 | os.unlink(temp_processed_file) 806 | except: 807 | pass 808 | 809 | # Save final output 810 | with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as final_file: 811 | ta.save(final_file.name, concatenated_audio, self.model.sr) 812 | 813 | voice_info = ( 814 | f" with {selected_voice} voice" 815 | if selected_voice and selected_voice != "None" 816 | else "" 817 | ) 818 | voice_info = voice_info or ( 819 | " with uploaded voice" if audio_upload else "" 820 | ) 821 | 822 | status_msg = f"Generated {len(text_chunks)} chunk(s){voice_info}" 823 | return final_file.name, status_msg 824 | 825 | except Exception as e: 826 | return None, f"Generation failed: {str(e)}" 827 | 828 | def create_interface(self): 829 | """Create the streamlined Gradio interface""" 830 | with gr.Blocks(title="Chatterbox TTS", theme=gr.themes.Soft()) as interface: 831 | 832 | gr.Markdown("# Chatterbox TTS") 833 | 834 | with gr.Row(): 835 | with gr.Column(scale=3): 836 | text_input = gr.Textbox( 837 | label="Text to Synthesize", 838 | placeholder="Enter the text you want to convert to speech...", 839 | lines=4, 840 | max_lines=8, 841 | ) 842 | 843 | with gr.Row(): 844 | exaggeration = gr.Slider( 845 | minimum=0.0, 846 | maximum=1.0, 847 | value=0.5, 848 | step=0.1, 849 | label="Emotion (0.0 = subtle, 1.0 = dramatic)", 850 | ) 851 | 852 | temperature = gr.Slider( 853 | minimum=0.1, 854 | maximum=1.0, 855 | value=0.7, 856 | step=0.1, 857 | label="Variation (Controls randomness)", 858 | ) 859 | 860 | generate_btn = gr.Button( 861 | "🎵 Generate Speech", 862 | variant="primary", 863 | size="lg", 864 | interactive=self.model_loaded, 865 | ) 866 | 867 | with gr.Column(scale=2): 868 | with gr.Group(): 869 | gr.Markdown("### Voice Options") 870 | 871 | saved_voices_dropdown = gr.Dropdown( 872 | label="Saved Voices (Use a previously saved voice)", 873 | choices=self.voice_manager.get_voice_sample_list(), 874 | value="None", 875 | ) 876 | 877 | voice_preview = gr.Audio( 878 | label="Voice Preview", interactive=False, visible=False 879 | ) 880 | 881 | audio_upload = gr.Audio( 882 | label="Upload Voice Sample (5-30 seconds of clear speech)", 883 | type="numpy", 884 | ) 885 | 886 | with gr.Row(): 887 | delete_btn = gr.Button( 888 | "🗑️ Delete", variant="secondary", size="sm" 889 | ) 890 | 891 | with gr.Accordion( 892 | "💾 Save Voice Sample", open=False 893 | ) as save_accordion: 894 | label = gr.Textbox( 895 | label="Voice Name", 896 | placeholder="e.g., 'John_Speaker'", 897 | max_lines=1, 898 | ) 899 | 900 | description = gr.Textbox( 901 | label="Description", 902 | placeholder="Optional description", 903 | lines=2, 904 | ) 905 | 906 | save_btn = gr.Button( 907 | "💾 Save Voice", variant="primary", size="sm" 908 | ) 909 | 910 | with gr.Row(): 911 | with gr.Column(): 912 | output_audio = gr.Audio( 913 | label="Generated Speech", type="filepath", interactive=False 914 | ) 915 | 916 | status_msg = gr.Textbox(label="Status", interactive=False, lines=1) 917 | 918 | # Event handlers 919 | def update_voice_preview(selected_voice): 920 | if selected_voice and selected_voice != "None": 921 | preview_path = self.get_voice_preview(selected_voice) 922 | return gr.Audio(visible=True, value=preview_path) 923 | return gr.Audio(visible=False) 924 | 925 | saved_voices_dropdown.change( 926 | fn=update_voice_preview, 927 | inputs=[saved_voices_dropdown], 928 | outputs=[voice_preview], 929 | ) 930 | 931 | delete_btn.click( 932 | fn=self.delete_voice_sample, 933 | inputs=[saved_voices_dropdown], 934 | outputs=[status_msg, saved_voices_dropdown], 935 | ) 936 | 937 | save_btn.click( 938 | fn=self.save_voice_sample_with_clear, 939 | inputs=[audio_upload, label, description], 940 | outputs=[status_msg, saved_voices_dropdown, label, description], 941 | ) 942 | 943 | generate_btn.click( 944 | fn=self.generate_tts, 945 | inputs=[ 946 | text_input, 947 | audio_upload, 948 | saved_voices_dropdown, 949 | exaggeration, 950 | temperature, 951 | ], 952 | outputs=[output_audio, status_msg], 953 | ) 954 | 955 | return interface 956 | 957 | def cleanup(self): 958 | """Clean up resources and clear CUDA cache""" 959 | try: 960 | if self.model: 961 | del self.model 962 | self.clear_cuda_cache() 963 | print("Cleanup completed successfully.") 964 | except Exception as e: 965 | print(f"Error during cleanup: {e}") 966 | 967 | 968 | def main(): 969 | print("Initializing Chatterbox TTS...") 970 | print("=" * 50) 971 | 972 | app = None 973 | try: 974 | app = ChatterboxGradioApp() 975 | interface = app.create_interface() 976 | 977 | print("=" * 50) 978 | print("Starting interface...") 979 | print("Local URL: http://localhost:7860") 980 | print("=" * 50) 981 | 982 | interface.launch( 983 | server_name="0.0.0.0", 984 | server_port=7860, 985 | share=False, 986 | show_error=True, 987 | allowed_paths=[str(app.voice_samples_dir)], 988 | ) 989 | 990 | except KeyboardInterrupt: 991 | print("\nShutting down...") 992 | except Exception as e: 993 | print(f"Error: {e}") 994 | sys.exit(1) 995 | finally: 996 | if app: 997 | app.cleanup() 998 | 999 | 1000 | if __name__ == "__main__": 1001 | main() 1002 | --------------------------------------------------------------------------------