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
metadata
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