diff --git a/.claude/settings.json b/.claude/settings.json new file mode 100644 index 00000000..2d53febb --- /dev/null +++ b/.claude/settings.json @@ -0,0 +1,7 @@ +{ + "permissions": { + "allow": [ + "Bash(uv venv:*)" + ] + } +} diff --git a/.gitignore b/.gitignore index 1f6b14cc..e4ec7e1f 100644 --- a/.gitignore +++ b/.gitignore @@ -21,4 +21,13 @@ wheels/ .idea/ .vscode/ venv/ -env/ \ No newline at end of file +env/ +.venv/ +.DS_Store +.claude/ + +# Models directory (large files from Hugging Face) +models/ + +# Example output directory +example_output/ \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 00000000..9f421b89 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,567 @@ +# CLAUDE.md - AI Assistant Guide for Qwen3-TTS + +This document provides AI assistants with essential information about the Qwen3-TTS codebase structure, development workflows, conventions, and best practices. + +## Project Overview + +**Qwen3-TTS** is a comprehensive speech synthesis system developed by Alibaba Qwen Team. It provides: +- Voice cloning capabilities (3-second rapid cloning) +- Voice design from natural language descriptions +- Custom voice generation with premium timbres +- Ultra-high-quality human-like speech generation +- Natural language-based voice control +- Multilingual support (10+ languages) + +### Key Features +- **12Hz/25Hz Tokenizers**: High-fidelity acoustic compression +- **Streaming Support**: Low-latency streaming generation (97ms end-to-end) +- **Instruction-Based Control**: Control tone, emotion, prosody via natural language +- **Model Sizes**: 0.6B and 1.7B parameter variants +- **Languages**: Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian + +## Repository Structure + +``` +Qwen3-TTS/ +├── qwen_tts/ # Main Python package +│ ├── core/ # Core implementations +│ │ ├── models/ # TTS model architecture (configuration, modeling, processing) +│ │ ├── tokenizer_12hz/ # 12Hz tokenizer implementation +│ │ └── tokenizer_25hz/ # 25Hz tokenizer implementation (VQ-based) +│ ├── inference/ # High-level inference APIs +│ │ ├── qwen3_tts_model.py # Main TTS model interface +│ │ └── qwen3_tts_tokenizer.py # Tokenizer interface +│ ├── cli/ # CLI tools +│ │ └── demo.py # Gradio web demo +│ └── __init__.py # Package exports +├── examples/ # Usage examples +│ ├── test_model_12hz_base.py # Voice clone examples +│ ├── test_model_12hz_custom_voice.py # Custom voice examples +│ ├── test_model_12hz_voice_design.py # Voice design examples +│ └── test_tokenizer_12hz.py # Tokenizer usage +├── finetuning/ # Fine-tuning workflows +│ ├── prepare_data.py # Data preparation script +│ ├── sft_12hz.py # Fine-tuning script +│ ├── dataset.py # Dataset utilities +│ └── README.md # Fine-tuning guide +├── assets/ # Documentation assets +├── .github/ # GitHub configuration +│ ├── ISSUE_TEMPLATE/ # Issue templates +│ └── workflows/ # CI/CD workflows +├── README.md # Main documentation +├── LICENSE # Apache-2.0 license +├── pyproject.toml # Python package configuration +└── MANIFEST.in # Package manifest + +``` + +## Available Models + +### Tokenizers +- **Qwen3-TTS-Tokenizer-12Hz**: Encodes/decodes speech into/from codes (16 codebooks, 2048 vocab, 12.5 FPS) +- **Qwen3-TTS-Tokenizer-25Hz**: Alternative tokenizer (25 FPS) + +### TTS Models (12Hz Series) + +| Model | Size | Features | Streaming | Instruction Control | +|-------|------|----------|-----------|---------------------| +| Qwen3-TTS-12Hz-1.7B-VoiceDesign | 1.7B | Voice design from descriptions | ✅ | ✅ | +| Qwen3-TTS-12Hz-1.7B-CustomVoice | 1.7B | 9 premium timbres + instruction control | ✅ | ✅ | +| Qwen3-TTS-12Hz-1.7B-Base | 1.7B | 3-sec voice clone, fine-tuning | ✅ | ❌ | +| Qwen3-TTS-12Hz-0.6B-CustomVoice | 0.6B | 9 premium timbres | ✅ | ❌ | +| Qwen3-TTS-12Hz-0.6B-Base | 0.6B | 3-sec voice clone, fine-tuning | ✅ | ❌ | + +### Premium Speakers (CustomVoice Models) +- **Vivian**: Bright young female (Chinese) +- **Serena**: Warm young female (Chinese) +- **Uncle_Fu**: Seasoned male with mellow timbre (Chinese) +- **Dylan**: Beijing male with clear timbre (Chinese - Beijing Dialect) +- **Eric**: Chengdu male with husky brightness (Chinese - Sichuan Dialect) +- **Ryan**: Dynamic male with rhythmic drive (English) +- **Aiden**: Sunny American male (English) +- **Ono_Anna**: Playful Japanese female (Japanese) +- **Sohee**: Warm Korean female (Korean) + +## Code Organization + +### Package Structure +The `qwen_tts` package follows a modular architecture: + +1. **Core Layer** (`qwen_tts/core/`) + - Model architecture implementations + - Tokenizer implementations (12Hz and 25Hz variants) + - Configuration classes + - Processing utilities + +2. **Inference Layer** (`qwen_tts/inference/`) + - High-level user-facing APIs + - `Qwen3TTSModel`: Main TTS interface + - `Qwen3TTSTokenizer`: Audio encoding/decoding interface + +3. **CLI Layer** (`qwen_tts/cli/`) + - Command-line interfaces + - Gradio web demo + +### Key Classes + +#### Qwen3TTSModel +Primary interface for TTS generation: +- `from_pretrained()`: Load models from HuggingFace/ModelScope or local paths +- `generate_custom_voice()`: Generate with premium speakers +- `generate_voice_design()`: Generate from natural language descriptions +- `generate_voice_clone()`: Clone voices from reference audio +- `create_voice_clone_prompt()`: Create reusable voice prompts +- `get_supported_speakers()`: List available speakers +- `get_supported_languages()`: List supported languages + +#### Qwen3TTSTokenizer +Audio tokenization interface: +- `from_pretrained()`: Load tokenizer +- `encode()`: Convert audio to codes +- `decode()`: Convert codes to audio + +## Development Workflows + +### Environment Setup + +**Recommended Python Version**: 3.12 (supports 3.9-3.13) + +```bash +# Create clean environment +conda create -n qwen3-tts python=3.12 -y +conda activate qwen3-tts + +# Install from PyPI +pip install -U qwen-tts + +# Install from source (for development) +git clone https://github.com/QwenLM/Qwen3-TTS.git +cd Qwen3-TTS +pip install -e . + +# Optional: FlashAttention 2 (for GPU memory optimization) +pip install -U flash-attn --no-build-isolation +# OR with limited cores: +MAX_JOBS=4 pip install -U flash-attn --no-build-isolation +``` + +### Dependencies +Core dependencies (from pyproject.toml): +- `transformers==4.57.3` (HuggingFace Transformers) +- `accelerate==1.12.0` (Multi-GPU support) +- `gradio` (Web UI) +- `librosa`, `torchaudio`, `soundfile`, `sox` (Audio processing) +- `onnxruntime` (ONNX support) +- `einops` (Tensor operations) + +### Model Loading Pattern + +```python +import torch +from qwen_tts import Qwen3TTSModel + +model = Qwen3TTSModel.from_pretrained( + "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", # HF model ID or local path + device_map="cuda:0", # Device placement + dtype=torch.bfloat16, # Precision (bfloat16 recommended) + attn_implementation="flash_attention_2", # Use FlashAttention if available +) +``` + +### Inference Patterns + +#### Custom Voice Generation +```python +wavs, sr = model.generate_custom_voice( + text="Your text here", + language="Chinese", # or "Auto" for auto-detection + speaker="Vivian", # Use model.get_supported_speakers() + instruct="语气描述", # Optional instruction +) +``` + +#### Voice Design +```python +wavs, sr = model.generate_voice_design( + text="Your text here", + language="Chinese", + instruct="描述目标音色特征", # Natural language voice description +) +``` + +#### Voice Clone +```python +wavs, sr = model.generate_voice_clone( + text="Your text here", + language="English", + ref_audio="path/to/reference.wav", # URL, path, base64, or (array, sr) + ref_text="Reference transcript", +) + +# Reusable prompt for efficiency +prompt = model.create_voice_clone_prompt( + ref_audio="path/to/reference.wav", + ref_text="Reference transcript", +) +wavs, sr = model.generate_voice_clone( + text=["Text 1", "Text 2"], + language=["English", "English"], + voice_clone_prompt=prompt, # Reuse across generations +) +``` + +### Fine-tuning Workflow + +Located in `finetuning/` directory: + +1. **Prepare JSONL Data** + ```jsonl + {"audio":"./data/utt0001.wav","text":"Transcript","ref_audio":"./data/ref.wav"} + ``` + +2. **Extract Audio Codes** + ```bash + python prepare_data.py \ + --device cuda:0 \ + --tokenizer_model_path Qwen/Qwen3-TTS-Tokenizer-12Hz \ + --input_jsonl train_raw.jsonl \ + --output_jsonl train_with_codes.jsonl + ``` + +3. **Run Fine-tuning** + ```bash + python sft_12hz.py \ + --init_model_path Qwen/Qwen3-TTS-12Hz-1.7B-Base \ + --output_model_path output \ + --train_jsonl train_with_codes.jsonl \ + --batch_size 2 \ + --lr 2e-5 \ + --num_epochs 3 \ + --speaker_name custom_speaker + ``` + +4. **Test Fine-tuned Model** + ```python + model = Qwen3TTSModel.from_pretrained("output/checkpoint-epoch-2", ...) + wavs, sr = model.generate_custom_voice(text="...", speaker="custom_speaker") + ``` + +### Web Demo + +```bash +# Launch Gradio demo +qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --ip 0.0.0.0 --port 8000 + +# For Base model with microphone (requires HTTPS) +openssl req -x509 -newkey rsa:2048 -keyout key.pem -out cert.pem -days 365 -nodes -subj "/CN=localhost" +qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-Base \ + --ssl-certfile cert.pem --ssl-keyfile key.pem --no-ssl-verify +``` + +## Coding Conventions & Best Practices + +### Code Style +- **License Headers**: All Python files include Apache-2.0 license headers +- **Copyright**: "Copyright 2026 The Alibaba Qwen team." +- **Encoding**: UTF-8 (`# coding=utf-8`) +- **SPDX**: `# SPDX-License-Identifier: Apache-2.0` + +### Import Organization +Standard structure: +```python +# coding=utf-8 +# Copyright 2026 The Alibaba Qwen team. +# SPDX-License-Identifier: Apache-2.0 + +# Standard library imports +import time +import torch + +# Third-party imports +import soundfile as sf + +# Local imports +from qwen_tts import Qwen3TTSModel +``` + +### Model Loading Best Practices +1. **Always use `torch.bfloat16`** for optimal quality/performance +2. **Smart model path detection**: Use `get_model_path()` to automatically check for local models before downloading +3. **Device detection**: Use `get_optimal_device()` for automatic device selection (MPS > CUDA > CPU) +4. **FlashAttention**: Use `get_attention_implementation()` to get device-appropriate attention (auto-skipped on non-CUDA) +5. **Explicit device placement**: Can override with `device_map="cuda:0"`, `device_map="mps"`, or `device_map="cpu"` if needed +6. **Batch inference**: Pass lists to generate functions for efficiency +7. **Device-agnostic timing**: Use `device_synchronize()` instead of `torch.cuda.synchronize()` + +**Complete Example:** +```python +from qwen_tts import Qwen3TTSModel +from qwen_tts.core.device_utils import ( + get_model_path, + get_optimal_device, + get_attention_implementation, +) +import torch + +# Smart model path: checks ./models/ first, then downloads if needed +model_path = get_model_path("Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice") + +# Auto-detects optimal device (MPS > CUDA > CPU) +device = get_optimal_device() + +# Gets appropriate attention implementation for device +attn_impl = get_attention_implementation(device) + +# Load model +model = Qwen3TTSModel.from_pretrained( + model_path, + device_map=device, + dtype=torch.bfloat16, + attn_implementation=attn_impl, +) +``` + +**Model Organization:** +For smart path detection to work, organize downloaded models in `./models/` directory: +``` +./models/ +├── Qwen3-TTS-Tokenizer-12Hz/ +├── Qwen3-TTS-12Hz-1.7B-CustomVoice/ +├── Qwen3-TTS-12Hz-1.7B-VoiceDesign/ +└── ... +``` + +### Audio Handling +- **Output format**: `(wavs, sample_rate)` tuple +- **Sample rate**: Typically 16000 Hz +- **Batch output**: `wavs` is a list of numpy arrays +- **Supported inputs**: Local paths, URLs, base64 strings, (array, sr) tuples + +### Language Specification +- Use `"Auto"` for automatic language detection +- Explicitly set language when known for better quality +- Supported: Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian + +### Error Handling & Edge Cases +1. **Reference audio**: 3+ seconds recommended for voice clone +2. **Same ref_audio**: Use identical reference across fine-tuning dataset +3. **Streaming**: All models support streaming mode +4. **x_vector_only_mode**: Falls back to speaker embedding only (lower quality) + +## Testing & Validation + +### Example Scripts +Run examples to verify installation: +```bash +python examples/test_model_12hz_custom_voice.py +python examples/test_model_12hz_voice_design.py +python examples/test_model_12hz_base.py +python examples/test_tokenizer_12hz.py +``` + +### Performance Benchmarks +- **Latency**: 97ms end-to-end synthesis (streaming) +- **WER**: 0.77 (Chinese), 1.24 (English) on Seed-TTS test +- **Speaker Similarity**: 0.8+ cosine similarity +- **Quality**: 4.16 UTMOS score + +## Common Development Tasks + +### macOS / Apple Silicon Development + +When developing and testing on macOS with Apple Silicon (M1/M2/M3/M4): + +1. **Device Utilities**: Always use device detection functions from `qwen_tts.core.device_utils`: + - `get_optimal_device()` - Auto-detects MPS > CUDA > CPU + - `get_attention_implementation()` - Returns appropriate attention backend for device + - `device_synchronize()` - Device-agnostic synchronization for timing + - `get_device_info()` - Human-readable device description + +2. **Example Development Pattern**: + ```python + from qwen_tts import Qwen3TTSModel + from qwen_tts.core.device_utils import ( + get_optimal_device, + get_attention_implementation, + device_synchronize, + get_device_info, + ) + import torch + import time + + # Auto-detect device + device = get_optimal_device() + print(f"Using device: {get_device_info(device)}") + + # Get appropriate attention for this device + attn_impl = get_attention_implementation(device) + + # Load model + model = Qwen3TTSModel.from_pretrained( + "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", + device_map=device, + dtype=torch.bfloat16, + attn_implementation=attn_impl, + ) + + # Device-agnostic timing + device_synchronize(device) + t0 = time.time() + wavs, sr = model.generate_custom_voice(...) + device_synchronize(device) + print(f"Time: {time.time() - t0:.3f}s") + ``` + +3. **FlashAttention on macOS**: FlashAttention 2 is CUDA-only and not available on macOS. The device utilities automatically handle this: + - On macOS: `get_attention_implementation()` returns `None`, uses default attention + - On CUDA: Returns `"flash_attention_2"` if `flash-attn` is installed + - Explicit requests for FlashAttention on non-CUDA devices are warned and skipped + +4. **Performance Expectations**: + - MPS is 2-3x faster than CPU + - Typically slower than modern NVIDIA GPUs + - Good for development and testing + +5. **Memory Management**: + - M-series uses unified memory (CPU + GPU shared) + - If MPS runs out of memory: Use `device_map="cpu"` as fallback + - Monitor with Activity Monitor's GPU Memory + +### Adding New Speakers (Fine-tuning) +1. Collect 10-50 utterances from target speaker +2. Prepare JSONL with consistent `ref_audio` +3. Run `prepare_data.py` to extract codes +4. Fine-tune with `sft_12hz.py` +5. Test with custom speaker name + +### Integrating into Applications +```python +from qwen_tts import Qwen3TTSModel +import torch + +# Initialize once +tts = Qwen3TTSModel.from_pretrained( + "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", + device_map="cuda:0", + dtype=torch.bfloat16, + attn_implementation="flash_attention_2", +) + +# Generate on demand +def synthesize(text, speaker="Vivian", language="Auto"): + wavs, sr = tts.generate_custom_voice( + text=text, speaker=speaker, language=language + ) + return wavs[0], sr +``` + +### Batch Processing +```python +texts = ["Text 1", "Text 2", "Text 3"] +languages = ["Chinese", "English", "Japanese"] +speakers = ["Vivian", "Ryan", "Ono_Anna"] + +wavs, sr = model.generate_custom_voice( + text=texts, + language=languages, + speaker=speakers, +) + +# Save all outputs +for i, wav in enumerate(wavs): + sf.write(f"output_{i}.wav", wav, sr) +``` + +## Model Download + +Models auto-download from HuggingFace or ModelScope. Manual download: + +```bash +# Via ModelScope (recommended for China) +pip install -U modelscope +modelscope download --model Qwen/Qwen3-TTS-Tokenizer-12Hz --local_dir ./models/tokenizer +modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --local_dir ./models/custom + +# Via HuggingFace +pip install -U "huggingface_hub[cli]" +huggingface-cli download Qwen/Qwen3-TTS-Tokenizer-12Hz --local-dir ./models/tokenizer +huggingface-cli download Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --local-dir ./models/custom +``` + +Then use local paths: +```python +model = Qwen3TTSModel.from_pretrained("./models/custom", ...) +``` + +## vLLM Integration + +For production deployment, use vLLM-Omni: +```bash +git clone https://github.com/vllm-project/vllm-omni.git +cd vllm-omni/examples/offline_inference/qwen3_tts + +# Run inference +python end2end.py --query-type CustomVoice +python end2end.py --query-type VoiceDesign +python end2end.py --query-type Base --mode-tag icl +``` + +## Documentation & Resources + +- **Main README**: `/README.md` - Comprehensive usage guide +- **Fine-tuning Guide**: `/finetuning/README.md` - SFT workflow +- **Examples**: `/examples/` - Code samples for each feature +- **Paper**: https://arxiv.org/abs/2601.15621 +- **Blog**: https://qwen.ai/blog?id=qwen3tts-0115 +- **HuggingFace**: https://huggingface.co/collections/Qwen/qwen3-tts +- **ModelScope**: https://modelscope.cn/collections/Qwen/Qwen3-TTS + +## AI Assistant Guidelines + +When working with this codebase: + +1. **Read before modifying**: Always read relevant files before suggesting changes +2. **Use examples**: Reference `/examples/` for usage patterns +3. **Check model compatibility**: Verify model variant supports requested features +4. **Respect license**: Maintain Apache-2.0 headers in all files +5. **Test changes**: Suggest running example scripts after modifications +6. **Device awareness**: Consider GPU availability and memory constraints +7. **Batch optimization**: Recommend batch processing for multiple inputs +8. **Language handling**: Use explicit language settings when language is known +9. **Error context**: Check model size, device memory, and input formats +10. **Documentation**: Update README.md if adding features or changing APIs + +### Common Pitfalls to Avoid +- Don't use Base models for instruction-based control (not supported) +- Don't omit `ref_text` in voice clone unless using `x_vector_only_mode=True` +- Don't use FP32 dtype (use bfloat16 for quality and memory efficiency) +- Don't assume FlashAttention is available (check hardware compatibility) +- Don't use relative imports outside package structure +- Don't modify core model architectures without understanding tokenizer coupling + +### When Suggesting Changes +1. Verify the change aligns with existing code patterns +2. Consider backward compatibility +3. Test with multiple model variants if applicable +4. Update docstrings and type hints +5. Consider performance implications (batching, device placement) +6. Maintain consistency with HuggingFace Transformers conventions + +## Version Information + +- **Package Version**: 0.0.4 (as of pyproject.toml) +- **Python Support**: 3.9 - 3.13 +- **Transformers Version**: 4.57.3 +- **Accelerate Version**: 1.12.0 +- **Release Date**: January 2026 (Qwen3-TTS-12Hz series) + +--- + +**Last Updated**: 2026-01-23 + +This guide should be updated when: +- New models are released +- API changes are introduced +- New features are added +- Best practices evolve +- Dependencies are updated \ No newline at end of file diff --git a/README.md b/README.md index 8287bac4..b49b907f 100644 --- a/README.md +++ b/README.md @@ -27,6 +27,7 @@ We release **Qwen3-TTS**, a series of powerful speech generation capabilities de - [Released Models Description and Download](#released-models-description-and-download) - [Quickstart](#quickstart) - [Environment Setup](#environment-setup) + - [macOS / Apple Silicon Support](#macos--apple-silicon-m1m2m3m4-support) - [Python Package Usage](#python-package-usage) - [Custom Voice Generation](#custom-voice-generate) - [Voice Design](#voice-design) @@ -78,26 +79,77 @@ Below is an introduction and download information for the Qwen3-TTS models that | Qwen3-TTS-12Hz-0.6B-CustomVoice | Supports 9 premium timbres covering various combinations of gender, age, language, and dialect. | Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian | ✅ | | | Qwen3-TTS-12Hz-0.6B-Base | Base model capable of 3-second rapid voice clone from user audio input; can be used for fine-tuning (FT) other models. | Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian | ✅ | | -During model loading in the qwen-tts package or vLLM, model weights will be automatically downloaded based on the model name. However, if your runtime environment is not conducive to downloading weights during execution, you can refer to the following commands to manually download the model weights to a local directory: +### Model Loading and Caching + +During model loading in the qwen-tts package or vLLM, model weights will be automatically downloaded based on the model name. The library provides **smart model path detection** that automatically checks for locally downloaded models in the `./models/` directory before downloading from the internet. + +**Smart Model Path Detection:** + +All examples and scripts automatically check for locally downloaded models in the `./models/` directory. If a model is found locally, it will be used. Otherwise, the model will be automatically downloaded from HuggingFace or ModelScope. + +```bash +# Recommended directory structure for local models +./models/ +├── Qwen3-TTS-Tokenizer-12Hz/ +├── Qwen3-TTS-12Hz-1.7B-CustomVoice/ +├── Qwen3-TTS-12Hz-1.7B-VoiceDesign/ +├── Qwen3-TTS-12Hz-1.7B-Base/ +├── Qwen3-TTS-12Hz-0.6B-CustomVoice/ +└── Qwen3-TTS-12Hz-0.6B-Base/ +``` + +**Manual Model Download (Optional):** + +If your runtime environment is not conducive to downloading weights during execution, you can manually download the model weights to the `./models/` directory using the following commands to download the model weights to a local directory: ```bash # Download through ModelScope (recommended for users in Mainland China) pip install -U modelscope -modelscope download --model Qwen/Qwen3-TTS-Tokenizer-12Hz --local_dir ./Qwen3-TTS-Tokenizer-12Hz -modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --local_dir ./Qwen3-TTS-12Hz-1.7B-CustomVoice -modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign --local_dir ./Qwen3-TTS-12Hz-1.7B-VoiceDesign -modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-Base --local_dir ./Qwen3-TTS-12Hz-1.7B-Base -modelscope download --model Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice --local_dir ./Qwen3-TTS-12Hz-0.6B-CustomVoice -modelscope download --model Qwen/Qwen3-TTS-12Hz-0.6B-Base --local_dir ./Qwen3-TTS-12Hz-0.6B-Base +modelscope download --model Qwen/Qwen3-TTS-Tokenizer-12Hz --local_dir ./models/Qwen3-TTS-Tokenizer-12Hz +modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --local_dir ./models/Qwen3-TTS-12Hz-1.7B-CustomVoice +modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign --local_dir ./models/Qwen3-TTS-12Hz-1.7B-VoiceDesign +modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-Base --local_dir ./models/Qwen3-TTS-12Hz-1.7B-Base +modelscope download --model Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice --local_dir ./models/Qwen3-TTS-12Hz-0.6B-CustomVoice +modelscope download --model Qwen/Qwen3-TTS-12Hz-0.6B-Base --local_dir ./models/Qwen3-TTS-12Hz-0.6B-Base # Download through Hugging Face pip install -U "huggingface_hub[cli]" -huggingface-cli download Qwen/Qwen3-TTS-Tokenizer-12Hz --local-dir ./Qwen3-TTS-Tokenizer-12Hz -huggingface-cli download Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --local-dir ./Qwen3-TTS-12Hz-1.7B-CustomVoice -huggingface-cli download Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign --local-dir ./Qwen3-TTS-12Hz-1.7B-VoiceDesign -huggingface-cli download Qwen/Qwen3-TTS-12Hz-1.7B-Base --local-dir ./Qwen3-TTS-12Hz-1.7B-Base -huggingface-cli download Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice --local-dir ./Qwen3-TTS-12Hz-0.6B-CustomVoice -huggingface-cli download Qwen/Qwen3-TTS-12Hz-0.6B-Base --local-dir ./Qwen3-TTS-12Hz-0.6B-Base +huggingface-cli download Qwen/Qwen3-TTS-Tokenizer-12Hz --local-dir ./models/Qwen3-TTS-Tokenizer-12Hz +huggingface-cli download Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --local-dir ./models/Qwen3-TTS-12Hz-1.7B-CustomVoice +huggingface-cli download Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign --local-dir ./models/Qwen3-TTS-12Hz-1.7B-VoiceDesign +huggingface-cli download Qwen/Qwen3-TTS-12Hz-1.7B-Base --local-dir ./models/Qwen3-TTS-12Hz-1.7B-Base +huggingface-cli download Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice --local-dir ./models/Qwen3-TTS-12Hz-0.6B-CustomVoice +huggingface-cli download Qwen/Qwen3-TTS-12Hz-0.6B-Base --local-dir ./models/Qwen3-TTS-12Hz-0.6B-Base +``` + +Then use the models with smart path detection: + +```python +from qwen_tts import Qwen3TTSModel + +# Library automatically checks ./models/ directory +# If model found locally → uses it +# If not found → downloads from HuggingFace +model = Qwen3TTSModel.from_pretrained( + "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", + dtype=torch.bfloat16, +) + +# Or use explicit local path +model = Qwen3TTSModel.from_pretrained( + "./models/Qwen3-TTS-12Hz-1.7B-CustomVoice", + dtype=torch.bfloat16, +) +``` + +**Console output examples:** + +```bash +# If local model found +Found local model: ./models/Qwen3-TTS-12Hz-1.7B-CustomVoice + +# If downloading from HuggingFace +Local model not found at ./models/Qwen3-TTS-12Hz-1.7B-CustomVoice, will download from HuggingFace... ``` @@ -140,6 +192,61 @@ MAX_JOBS=4 pip install -U flash-attn --no-build-isolation Also, you should have hardware that is compatible with FlashAttention 2. Read more about it in the official documentation of the [FlashAttention repository](https://github.com/Dao-AILab/flash-attention). FlashAttention 2 can only be used when a model is loaded in `torch.float16` or `torch.bfloat16`. +### macOS / Apple Silicon (M1/M2/M3/M4) Support + +Qwen3-TTS fully supports Apple Silicon Macs via Metal Performance Shaders (MPS). Device selection is automatic - the library will detect and use MPS when available. + +**Installation on macOS:** + +```bash +conda create -n qwen3-tts python=3.12 -y +conda activate qwen3-tts +pip install -U qwen-tts + +# Note: FlashAttention is NOT available on macOS (CUDA-only) +# The library automatically uses default attention +``` + +**Device Auto-Detection:** + +Qwen3-TTS automatically detects the optimal device (MPS > CUDA > CPU). No explicit device selection is usually needed: + +```python +from qwen_tts import Qwen3TTSModel +import torch + +# Auto-detection (recommended) +model = Qwen3TTSModel.from_pretrained( + "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", + dtype=torch.bfloat16, # Works great on MPS +) + +# Explicit device +model = Qwen3TTSModel.from_pretrained( + "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", + device_map="mps", # or "cpu" for CPU-only + dtype=torch.bfloat16, +) +``` + +**macOS Limitations:** + +- **FlashAttention 2**: Not available (CUDA-only). Default attention is automatically used instead. +- **Performance**: MPS is typically 2-3x faster than CPU but slower than modern NVIDIA GPUs. +- **Memory**: M-series Unified Memory is shared between CPU and GPU. + +**Troubleshooting:** + +```python +# If MPS runs out of memory, use CPU fallback +model = Qwen3TTSModel.from_pretrained(..., device_map="cpu") +``` + +```bash +# Verify MPS availability +python -c "import torch; print(f'MPS available: {torch.backends.mps.is_available()}')" +``` + ### Python Package Usage diff --git a/examples/test_model_12hz_base.py b/examples/test_model_12hz_base.py index ec7ea4b5..b949ca58 100644 --- a/examples/test_model_12hz_base.py +++ b/examples/test_model_12hz_base.py @@ -19,19 +19,26 @@ import soundfile as sf from qwen_tts import Qwen3TTSModel +from qwen_tts.core.device_utils import ( + get_optimal_device, + get_attention_implementation, + device_synchronize, + get_device_info, + get_model_path, +) def ensure_dir(d: str): os.makedirs(d, exist_ok=True) -def run_case(tts: Qwen3TTSModel, out_dir: str, case_name: str, call_fn): - torch.cuda.synchronize() +def run_case(tts: Qwen3TTSModel, out_dir: str, case_name: str, call_fn, device: str): + device_synchronize(device) t0 = time.time() wavs, sr = call_fn() - torch.cuda.synchronize() + device_synchronize(device) t1 = time.time() print(f"[{case_name}] time: {t1 - t0:.3f}s, n_wavs={len(wavs)}, sr={sr}") @@ -40,16 +47,21 @@ def run_case(tts: Qwen3TTSModel, out_dir: str, case_name: str, call_fn): def main(): - device = "cuda:0" - MODEL_PATH = "Qwen/Qwen3-TTS-12Hz-1.7B-Base/" + device = get_optimal_device() + print(f"Using device: {get_device_info(device)}\n") + + # Use local model if available, otherwise download from HuggingFace + model_path = get_model_path("Qwen/Qwen3-TTS-12Hz-1.7B-Base") OUT_DIR = "qwen3_tts_test_voice_clone_output_wav" ensure_dir(OUT_DIR) + attn_impl = get_attention_implementation(device) + tts = Qwen3TTSModel.from_pretrained( - MODEL_PATH, + model_path, device_map=device, dtype=torch.bfloat16, - attn_implementation="flash_attention_2", + attn_implementation=attn_impl, ) # Reference audio(s) @@ -102,6 +114,7 @@ def main(): x_vector_only_mode=xvec_only, **common_gen_kwargs, ), + device, ) # Case 1b: prompt single + synth single, via create_voice_clone_prompt @@ -121,6 +134,7 @@ def _case1b(): run_case( tts, OUT_DIR, f"case1_promptSingle_synSingle_promptThenGen_{mode_tag}", _case1b, + device, ) # Case 2: prompt single + synth batch, direct @@ -134,6 +148,7 @@ def _case1b(): x_vector_only_mode=xvec_only, **common_gen_kwargs, ), + device, ) # Case 2b: prompt single + synth batch, via create_voice_clone_prompt @@ -153,6 +168,7 @@ def _case2b(): run_case( tts, OUT_DIR, f"case2_promptSingle_synBatch_promptThenGen_{mode_tag}", _case2b, + device, ) # Case 3: prompt batch + synth batch, direct @@ -166,6 +182,7 @@ def _case2b(): x_vector_only_mode=[xvec_only, xvec_only], **common_gen_kwargs, ), + device, ) # Case 3b: prompt batch + synth batch, via create_voice_clone_prompt @@ -185,6 +202,7 @@ def _case3b(): run_case( tts, OUT_DIR, f"case3_promptBatch_synBatch_promptThenGen_{mode_tag}", _case3b, + device, ) diff --git a/examples/test_model_12hz_custom_voice.py b/examples/test_model_12hz_custom_voice.py index 560ecd13..7779a9f8 100644 --- a/examples/test_model_12hz_custom_voice.py +++ b/examples/test_model_12hz_custom_voice.py @@ -13,26 +13,43 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import os import time import torch import soundfile as sf from qwen_tts import Qwen3TTSModel +from qwen_tts.core.device_utils import ( + get_optimal_device, + get_attention_implementation, + device_synchronize, + get_device_info, + get_model_path, +) def main(): - device = "cuda:0" - MODEL_PATH = "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice/" + # Setup output directory + output_dir = "example_output" + os.makedirs(output_dir, exist_ok=True) + + device = get_optimal_device() + print(f"Using device: {get_device_info(device)}\n") + + # Use local model if available, otherwise download from HuggingFace + model_path = get_model_path("Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice") + + attn_impl = get_attention_implementation(device) tts = Qwen3TTSModel.from_pretrained( - MODEL_PATH, + model_path, device_map=device, dtype=torch.bfloat16, - attn_implementation="flash_attention_2", + attn_implementation=attn_impl, ) # -------- Single (with instruct) -------- - torch.cuda.synchronize() + device_synchronize(device) t0 = time.time() wavs, sr = tts.generate_custom_voice( @@ -42,11 +59,11 @@ def main(): instruct="用特别愤怒的语气说", ) - torch.cuda.synchronize() + device_synchronize(device) t1 = time.time() print(f"[CustomVoice Single] time: {t1 - t0:.3f}s") - sf.write("qwen3_tts_test_custom_single.wav", wavs[0], sr) + sf.write(os.path.join(output_dir, "qwen3_tts_test_custom_single.wav"), wavs[0], sr) # -------- Batch (some empty instruct) -------- texts = ["其实我真的有发现，我是一个特别善于观察别人情绪的人。", "She said she would be here by noon."] @@ -54,7 +71,7 @@ def main(): speakers = ["Vivian", "Ryan"] instructs = ["", "Very happy."] - torch.cuda.synchronize() + device_synchronize(device) t0 = time.time() wavs, sr = tts.generate_custom_voice( @@ -65,12 +82,12 @@ def main(): max_new_tokens=2048, ) - torch.cuda.synchronize() + device_synchronize(device) t1 = time.time() print(f"[CustomVoice Batch] time: {t1 - t0:.3f}s") for i, w in enumerate(wavs): - sf.write(f"qwen3_tts_test_custom_batch_{i}.wav", w, sr) + sf.write(os.path.join(output_dir, f"qwen3_tts_test_custom_batch_{i}.wav"), w, sr) if __name__ == "__main__": diff --git a/examples/test_model_12hz_voice_design.py b/examples/test_model_12hz_voice_design.py index 7e755391..86474ad8 100644 --- a/examples/test_model_12hz_voice_design.py +++ b/examples/test_model_12hz_voice_design.py @@ -13,26 +13,43 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import os import time import torch import soundfile as sf from qwen_tts import Qwen3TTSModel +from qwen_tts.core.device_utils import ( + get_optimal_device, + get_attention_implementation, + device_synchronize, + get_device_info, + get_model_path, +) def main(): - device = "cuda:0" - MODEL_PATH = "Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign/" + # Setup output directory + output_dir = "example_output" + os.makedirs(output_dir, exist_ok=True) + + device = get_optimal_device() + print(f"Using device: {get_device_info(device)}\n") + + # Use local model if available, otherwise download from HuggingFace + model_path = get_model_path("Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign") + + attn_impl = get_attention_implementation(device) tts = Qwen3TTSModel.from_pretrained( - MODEL_PATH, + model_path, device_map=device, dtype=torch.bfloat16, - attn_implementation="flash_attention_2", + attn_implementation=attn_impl, ) # -------- Single -------- - torch.cuda.synchronize() + device_synchronize(device) t0 = time.time() wavs, sr = tts.generate_voice_design( @@ -41,11 +58,11 @@ def main(): instruct="体现撒娇稚嫩的萝莉女声，音调偏高且起伏明显，营造出黏人、做作又刻意卖萌的听觉效果。", ) - torch.cuda.synchronize() + device_synchronize(device) t1 = time.time() print(f"[VoiceDesign Single] time: {t1 - t0:.3f}s") - sf.write("qwen3_tts_test_voice_design_single.wav", wavs[0], sr) + sf.write(os.path.join(output_dir, "qwen3_tts_test_voice_design_single.wav"), wavs[0], sr) # -------- Batch -------- texts = [ @@ -58,7 +75,7 @@ def main(): "Speak in an incredulous tone, but with a hint of panic beginning to creep into your voice." ] - torch.cuda.synchronize() + device_synchronize(device) t0 = time.time() wavs, sr = tts.generate_voice_design( @@ -68,12 +85,12 @@ def main(): max_new_tokens=2048, ) - torch.cuda.synchronize() + device_synchronize(device) t1 = time.time() print(f"[VoiceDesign Batch] time: {t1 - t0:.3f}s") for i, w in enumerate(wavs): - sf.write(f"qwen3_tts_test_voice_design_batch_{i}.wav", w, sr) + sf.write(os.path.join(output_dir, f"qwen3_tts_test_voice_design_batch_{i}.wav"), w, sr) if __name__ == "__main__": diff --git a/examples/test_tokenizer_12hz.py b/examples/test_tokenizer_12hz.py index 595809e3..6321ca05 100644 --- a/examples/test_tokenizer_12hz.py +++ b/examples/test_tokenizer_12hz.py @@ -14,52 +14,65 @@ # See the License for the specific language governing permissions and # limitations under the License. import io +import os import requests import soundfile as sf from qwen_tts import Qwen3TTSTokenizer +from qwen_tts.core.device_utils import get_optimal_device, get_device_info, get_model_path audio_1 = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-TTS-Repo/tokenizer_demo_1.wav" audio_2 = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-TTS-Repo/tokenizer_demo_2.wav" +# Setup output directory +output_dir = "example_output" +os.makedirs(output_dir, exist_ok=True) + +# Auto-detect device +device = get_optimal_device() +print(f"Using device: {get_device_info(device)}\n") + +# Use local model if available, otherwise download from HuggingFace +tokenizer_path = get_model_path("Qwen/Qwen3-TTS-Tokenizer-12Hz") + # -------- Single input: wav path -------- tokenizer_12hz = Qwen3TTSTokenizer.from_pretrained( - "Qwen/Qwen3-TTS-Tokenizer-12Hz", - device_map="cuda:0", + tokenizer_path, + device_map=device, ) enc1 = tokenizer_12hz.encode(audio_1) wavs1, out_sr1 = tokenizer_12hz.decode(enc1) -sf.write("decoded_single_12hz.wav", wavs1[0], out_sr1) +sf.write(os.path.join(output_dir, "decoded_single_12hz.wav"), wavs1[0], out_sr1) # -------- Batch input: wav path list -------- enc2 = tokenizer_12hz.encode([audio_1, audio_2]) wavs2, out_sr2 = tokenizer_12hz.decode(enc2) for i, w in enumerate(wavs2): - sf.write(f"decoded_batch_12hz_{i}.wav", w, out_sr2) + sf.write(os.path.join(output_dir, f"decoded_batch_12hz_{i}.wav"), w, out_sr2) # -------- Decode input as dict (12hz) -------- # Take the first sample codes and pass as a dict. dict_input_12hz = {"audio_codes": enc2.audio_codes[0]} # torch.Tensor wavs_d1, out_sr_d1 = tokenizer_12hz.decode(dict_input_12hz) -sf.write("decoded_dict_12hz.wav", wavs_d1[0], out_sr_d1) +sf.write(os.path.join(output_dir, "decoded_dict_12hz.wav"), wavs_d1[0], out_sr_d1) # -------- Decode input as list[dict] (12hz) -------- list_dict_input_12hz = [{"audio_codes": c} for c in enc2.audio_codes] # list of torch.Tensor wavs_d2, out_sr_d2 = tokenizer_12hz.decode(list_dict_input_12hz) for i, w in enumerate(wavs_d2): - sf.write(f"decoded_listdict_12hz_{i}.wav", w, out_sr_d2) + sf.write(os.path.join(output_dir, f"decoded_listdict_12hz_{i}.wav"), w, out_sr_d2) # -------- Decode input as list[dict] with numpy (12hz) -------- # Convert codes to numpy to simulate "serialized" payload. list_dict_numpy_12hz = [{"audio_codes": c.cpu().numpy()} for c in enc2.audio_codes] wavs_d3, out_sr_d3 = tokenizer_12hz.decode(list_dict_numpy_12hz) for i, w in enumerate(wavs_d3): - sf.write(f"decoded_listdict_numpy_12hz_{i}.wav", w, out_sr_d3) + sf.write(os.path.join(output_dir, f"decoded_listdict_numpy_12hz_{i}.wav"), w, out_sr_d3) # -------- Numpy input (must pass sr) -------- data = requests.get(audio_2, timeout=30).content y, sr = sf.read(io.BytesIO(data)) enc3 = tokenizer_12hz.encode(y, sr=sr) wavs3, out_sr3 = tokenizer_12hz.decode(enc3) -sf.write("decoded_numpy_12hz.wav", wavs3[0], out_sr3) \ No newline at end of file +sf.write(os.path.join(output_dir, "decoded_numpy_12hz.wav"), wavs3[0], out_sr3) \ No newline at end of file diff --git a/finetuning/prepare_data.py b/finetuning/prepare_data.py index 7e646108..814f3d2a 100644 --- a/finetuning/prepare_data.py +++ b/finetuning/prepare_data.py @@ -18,20 +18,28 @@ import json from qwen_tts import Qwen3TTSTokenizer +from qwen_tts.core.device_utils import get_optimal_device, get_device_info, get_model_path BATCH_INFER_NUM = 32 def main(): parser = argparse.ArgumentParser() - parser.add_argument("--device", type=str, default="cuda:0") + parser.add_argument("--device", type=str, default=None, help="Device for device_map (e.g., cpu, cuda, mps). If not specified, auto-detects optimal device.") parser.add_argument("--tokenizer_model_path", type=str, default="Qwen/Qwen3-TTS-Tokenizer-12Hz") parser.add_argument("--input_jsonl", type=str, required=True) parser.add_argument("--output_jsonl", type=str, required=True) args = parser.parse_args() + # Auto-detect device if not specified + device = get_optimal_device(args.device) + print(f"Using device: {get_device_info(device)}\n") + + # Use local model if available, otherwise download from HuggingFace + tokenizer_path = get_model_path(args.tokenizer_model_path) + tokenizer_12hz = Qwen3TTSTokenizer.from_pretrained( - args.tokenizer_model_path, - device_map=args.device, + tokenizer_path, + device_map=device, ) total_lines = open(args.input_jsonl).readlines() diff --git a/finetuning/sft_12hz.py b/finetuning/sft_12hz.py index 9ed8c480..528b30c6 100644 --- a/finetuning/sft_12hz.py +++ b/finetuning/sft_12hz.py @@ -86,7 +86,7 @@ def train(): input_text_ids = input_ids[:, :, 0] input_codec_ids = input_ids[:, :, 1] - input_text_embedding = model.talker.model.text_embedding(input_text_ids) * text_embedding_mask + input_text_embedding = model.talker.text_projection(model.talker.get_text_embeddings()(input_text_ids)) * text_embedding_mask input_codec_embedding = model.talker.model.codec_embedding(input_codec_ids) * codec_embedding_mask input_codec_embedding[:, 6, :] = speaker_embedding diff --git a/pyproject.toml b/pyproject.toml index b2b180ad..0434c7db 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,10 +7,9 @@ name = "qwen-tts" version = "0.0.4" description = "Qwen-TTS python package" readme = "README.md" -requires-python = ">=3.9" +requires-python = ">=3.10" classifiers = [ "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", diff --git a/qwen_tts/cli/demo.py b/qwen_tts/cli/demo.py index e267861d..2c563b7c 100644 --- a/qwen_tts/cli/demo.py +++ b/qwen_tts/cli/demo.py @@ -28,6 +28,12 @@ import torch from .. import Qwen3TTSModel, VoiceClonePromptItem +from ..core.device_utils import ( + get_optimal_device, + get_attention_implementation, + get_device_info, + get_model_path, +) def _title_case_display(s: str) -> str: @@ -91,8 +97,8 @@ def build_parser() -> argparse.ArgumentParser: # Model loading / from_pretrained args parser.add_argument( "--device", - default="cuda:0", - help="Device for device_map, e.g. cpu, cuda, cuda:0 (default: cuda:0).", + default=None, + help="Device for device_map, e.g. cpu, cuda, cuda:0, mps. If not specified, auto-detects optimal device (MPS > CUDA > CPU).", ) parser.add_argument( "--dtype", @@ -602,12 +608,20 @@ def main(argv=None) -> int: ckpt = _resolve_checkpoint(args) + # Use local model if available, otherwise use specified checkpoint + model_path = get_model_path(ckpt) + + # Auto-detect device if not specified + device = get_optimal_device(args.device) + print(f"Using device: {get_device_info(device)}") + dtype = _dtype_from_str(args.dtype) - attn_impl = "flash_attention_2" if args.flash_attn else None + # Get appropriate attention implementation for the device + attn_impl = get_attention_implementation(device, "flash_attention_2" if args.flash_attn else None) tts = Qwen3TTSModel.from_pretrained( - ckpt, - device_map=args.device, + model_path, + device_map=device, dtype=dtype, attn_implementation=attn_impl, ) diff --git a/qwen_tts/core/device_utils.py b/qwen_tts/core/device_utils.py new file mode 100644 index 00000000..2268a4ed --- /dev/null +++ b/qwen_tts/core/device_utils.py @@ -0,0 +1,257 @@ +# coding=utf-8 +# Copyright 2026 The Alibaba Qwen team. +# SPDX-License-Identifier: Apache-2.0 + +"""Device detection and management utilities for cross-platform support. + +Provides intelligent device selection and device-agnostic operations for PyTorch +models. Supports CUDA, Apple Metal Performance Shaders (MPS), and CPU with +automatic fallback (MPS > CUDA > CPU). +""" + +import os +import warnings +from typing import Optional, Union + +import torch + + +def get_optimal_device(preferred_device: Optional[str] = None) -> str: + """ + Detect optimal PyTorch device with intelligent fallback. + + Priority order: + 1. User-specified device (if valid) + 2. MPS (Apple Silicon) if available + 3. CUDA if available + 4. CPU (fallback) + + Args: + preferred_device: User-specified device string (e.g., "cuda:0", "mps", "cpu"). + If not specified, auto-detects optimal device. + + Returns: + Device string suitable for device_map parameter in model loading. + + Example: + >>> device = get_optimal_device() # Auto-detect + >>> device = get_optimal_device("cuda:1") # Prefer specific GPU + """ + # If user explicitly specifies device, validate and return + if preferred_device is not None: + preferred = str(preferred_device).lower().strip() + + # Validate user preference + if preferred.startswith("cuda"): + if not torch.cuda.is_available(): + warnings.warn( + f"CUDA device '{preferred}' requested but CUDA not available. " + "Falling back to auto-detection.", + RuntimeWarning, + ) + else: + return preferred_device + elif preferred == "mps": + if not torch.backends.mps.is_available(): + warnings.warn( + "MPS device requested but not available. Falling back to auto-detection.", + RuntimeWarning, + ) + else: + return "mps" + elif preferred == "cpu": + return "cpu" + else: + # Let it pass through for other device types + return preferred_device + + # Auto-detection with priority order + if torch.backends.mps.is_available(): + return "mps" + elif torch.cuda.is_available(): + return "cuda:0" + else: + warnings.warn( + "No GPU detected. Using CPU. Performance will be significantly slower.", + RuntimeWarning, + ) + return "cpu" + + +def get_optimal_dtype( + device: Union[str, torch.device], preferred_dtype: Optional[torch.dtype] = None +) -> torch.dtype: + """ + Select optimal dtype for given device. + + Args: + device: Device string or torch.device object. + preferred_dtype: User-specified dtype preference. + + Returns: + Recommended torch.dtype. + + Note: + bfloat16 is recommended for all modern hardware (best quality/performance). + float16 is also supported as a fallback. + MPS has good bfloat16 support in PyTorch 2.0+. + """ + if preferred_dtype is not None: + return preferred_dtype + + # bfloat16 is optimal for all modern hardware + return torch.bfloat16 + + +def get_attention_implementation( + device: Union[str, torch.device], preferred: Optional[str] = None +) -> Optional[str]: + """ + Select appropriate attention implementation for device. + + Args: + device: Device string or torch.device object. + preferred: User preference (e.g., "flash_attention_2", "eager", "sdpa"). + + Returns: + Attention implementation string or None for default implementation. + + Note: + FlashAttention 2 is CUDA-only and will be skipped on MPS/CPU. + If user requests FlashAttention on non-CUDA device, a warning is issued + and default attention is used instead. + """ + device_str = str(device).lower() + + # If user explicitly requests FlashAttention on non-CUDA, warn them + if preferred == "flash_attention_2": + if "cuda" not in device_str: + warnings.warn( + f"FlashAttention 2 is CUDA-only and not available on device '{device}'. " + "Using default attention implementation.", + RuntimeWarning, + ) + return None # Let transformers choose default + return "flash_attention_2" + + # Return user preference if specified + if preferred is not None: + return preferred + + # Auto-select: FlashAttention on CUDA if available, otherwise default + if "cuda" in device_str: + # Check if flash-attn is actually installed + try: + import flash_attn # noqa: F401 + return "flash_attention_2" + except ImportError: + return None + + return None # Use default for MPS/CPU + + +def device_synchronize(device: Union[str, torch.device, None] = None) -> None: + """ + Device-agnostic synchronization for accurate timing measurements. + + Ensures all previous GPU operations are complete before returning. + Required for accurate timing measurements across different device types. + + Args: + device: Device to synchronize (inferred from default if None). + + Example: + >>> device_synchronize(device) + >>> t0 = time.time() + >>> # ... model inference ... + >>> device_synchronize(device) + >>> t1 = time.time() + >>> print(f"Inference time: {t1 - t0:.3f}s") + """ + if device is None: + # Try to infer from default device + if torch.cuda.is_available(): + torch.cuda.synchronize() + elif torch.backends.mps.is_available(): + # MPS synchronization (available in PyTorch 2.0+) + if hasattr(torch.mps, "synchronize"): + torch.mps.synchronize() + return + + device_str = str(device).lower() + + if "cuda" in device_str: + torch.cuda.synchronize() + elif "mps" in device_str: + # MPS synchronization (available in PyTorch 2.0+) + if hasattr(torch, "mps") and hasattr(torch.mps, "synchronize"): + torch.mps.synchronize() + # CPU doesn't need explicit synchronization (operations are synchronous) + + +def get_device_info(device: Union[str, torch.device]) -> str: + """ + Get human-readable device information. + + Args: + device: Device to describe. + + Returns: + Descriptive string about the device. + + Example: + >>> device = "mps" + >>> print(get_device_info(device)) + "Apple Metal Performance Shaders (MPS) - Apple Silicon GPU" + """ + device_str = str(device).lower() + + if "cuda" in device_str: + if torch.cuda.is_available(): + try: + gpu_name = torch.cuda.get_device_name(device) + return f"CUDA GPU: {gpu_name}" + except Exception: + return f"CUDA GPU: {device}" + return "CUDA (not available)" + elif "mps" in device_str: + if torch.backends.mps.is_available(): + return "Apple Metal Performance Shaders (MPS) - Apple Silicon GPU" + return "MPS (not available)" + elif "cpu" in device_str: + return "CPU" + else: + return f"Device: {device}" + + +def get_model_path( + model_id: str, local_models_dir: str = "./models" +) -> str: + """ + Get model path, preferring local directory if it exists. + + Checks if a model is available locally, and uses it if found. + Otherwise, returns the HuggingFace model ID for auto-download. + + Args: + model_id: HuggingFace model ID (e.g., "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice") + local_models_dir: Directory containing locally downloaded models (default: "./models") + + Returns: + Local path if model exists locally, otherwise HuggingFace model ID. + + Example: + >>> path = get_model_path("Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice") + # If ./models/Qwen3-TTS-12Hz-1.7B-CustomVoice exists, returns that path + # Otherwise returns "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice" + """ + # Extract model name from HuggingFace ID + model_name = model_id.split("/")[-1] + local_path = os.path.join(local_models_dir, model_name) + + if os.path.isdir(local_path): + print(f"Found local model: {local_path}") + return local_path + + print(f"Local model not found at {local_path}, will download from HuggingFace...") + return model_id diff --git a/scripts/download_models.py b/scripts/download_models.py new file mode 100755 index 00000000..914ceb94 --- /dev/null +++ b/scripts/download_models.py @@ -0,0 +1,96 @@ +#!/usr/bin/env python3 +""" +Script to download Qwen3-TTS models from Hugging Face. +Usage: python download_models.py [--models MODEL1 MODEL2 ...] [--output-dir ./models] +""" + +import argparse +import os +import sys +from pathlib import Path +from huggingface_hub import snapshot_download + + +# Available models +DEFAULT_MODELS = [ + "Qwen/Qwen3-TTS-Tokenizer-12Hz", + "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", + "Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", + "Qwen/Qwen3-TTS-12Hz-1.7B-Base", + "Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", + "Qwen/Qwen3-TTS-12Hz-0.6B-Base", +] + + +def download_models(models, output_dir="./models"): + """Download models from Hugging Face.""" + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + print(f"Downloading models to {output_dir.resolve()}") + print("-" * 60) + + failed = [] + for model_id in models: + model_name = model_id.split("/")[-1] + local_dir = output_dir / model_name + + print(f"\nDownloading {model_name}...") + try: + snapshot_download( + repo_id=model_id, + local_dir=str(local_dir), + repo_type="model", + ) + print(f"✓ Successfully downloaded {model_name}") + except Exception as e: + print(f"✗ Failed to download {model_name}: {e}") + failed.append(model_name) + + print("\n" + "=" * 60) + print(f"Download completed!") + print(f"Models location: {output_dir.resolve()}") + + if failed: + print(f"\nFailed downloads ({len(failed)}):") + for model in failed: + print(f" - {model}") + return False + return True + + +def main(): + parser = argparse.ArgumentParser( + description="Download Qwen3-TTS models from Hugging Face" + ) + parser.add_argument( + "--models", + nargs="+", + default=DEFAULT_MODELS, + help="Models to download (default: all available models)", + ) + parser.add_argument( + "--output-dir", + default="./models", + help="Output directory for models (default: ./models)", + ) + parser.add_argument( + "--list", + action="store_true", + help="List available models and exit", + ) + + args = parser.parse_args() + + if args.list: + print("Available models:") + for 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