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
File size: 1,046 Bytes
025c8df 17cd630 025c8df 17cd630 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ---
license: mit
language:
- en
tags:
- text-to-speech
- chatterbox
- lora
- onnx
base_model: ResembleAI/chatterbox
---
# Chatterbox-Turbo with LoRA input ports
Re-exported Chatterbox-Turbo ONNX with 192 optional LoRA adapter input
ports added to the language_model graph. Enables per-speaker fine-tuning
via ORT's `Ort::LoraAdapter` API.
## Files
- `language_model_q4f16.onnx` — graph file (~277 KB)
- `language_model_q4f16.onnx_data` — external weights file (~700 MB,
fp32 — quantization to q4f16 is a TODO)
## Modifications vs upstream
- 96 additive LoRA branches (`MatMul → MatMul → Add`) spliced into each
GPT2 block's c_attn / c_proj / c_fc / mlp.c_proj projections
- 192 named optional graph inputs: `lora.layers.{N}.{parent}_{child}.{A|B}`
- Zero-sized default initializers so the un-adapted graph runs identically
to the original
## Original work
Based on [ResembleAI/chatterbox](https://huggingface.co/ResembleAI/chatterbox)
by Resemble AI, licensed under MIT. This derivative is also MIT-licensed.
--- |