Instructions to use Dvirile/chatterbox-turbo-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chatterbox
How to use Dvirile/chatterbox-turbo-lora with Chatterbox:
# pip install chatterbox-tts import torchaudio as ta from chatterbox.tts import ChatterboxTTS model = ChatterboxTTS.from_pretrained(device="cuda") text = "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill." wav = model.generate(text) ta.save("test-1.wav", wav, model.sr) # If you want to synthesize with a different voice, specify the audio prompt AUDIO_PROMPT_PATH="YOUR_FILE.wav" wav = model.generate(text, audio_prompt_path=AUDIO_PROMPT_PATH) ta.save("test-2.wav", wav, model.sr) - Notebooks
- Google Colab
- Kaggle
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license: mit
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---
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license: mit
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language:
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- en
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tags:
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- text-to-speech
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- chatterbox
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- lora
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- onnx
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base_model: ResembleAI/chatterbox
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# Chatterbox-Turbo with LoRA input ports
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Re-exported Chatterbox-Turbo ONNX with 192 optional LoRA adapter input
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ports added to the language_model graph. Enables per-speaker fine-tuning
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via ORT's `Ort::LoraAdapter` API.
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## Files
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- `language_model_q4f16.onnx` — graph file (~277 KB)
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- `language_model_q4f16.onnx_data` — external weights file (~700 MB,
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fp32 — quantization to q4f16 is a TODO)
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## Modifications vs upstream
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- 96 additive LoRA branches (`MatMul → MatMul → Add`) spliced into each
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GPT2 block's c_attn / c_proj / c_fc / mlp.c_proj projections
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- 192 named optional graph inputs: `lora.layers.{N}.{parent}_{child}.{A|B}`
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- Zero-sized default initializers so the un-adapted graph runs identically
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to the original
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## Original work
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Based on [ResembleAI/chatterbox](https://huggingface.co/ResembleAI/chatterbox)
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by Resemble AI, licensed under MIT. This derivative is also MIT-licensed.
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