Instructions to use KitsuMate/chatterbox-turbo-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chatterbox
How to use KitsuMate/chatterbox-turbo-onnx 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
Chatterbox Turbo ONNX (Q4F16)
KitsuMate mirror of the official
ResembleAI/chatterbox-turbo-ONNX
Q4F16 profile for reproducible Unity and ONNX Runtime downloads.
Contents
The four graphs under onnx/ form one complete Q4F16 pipeline:
| Stage | File |
|---|---|
| Reference speech encoder | speech_encoder_q4f16.onnx |
| Text and speech-token embeddings | embed_tokens_q4f16.onnx |
| Autoregressive language model | language_model_q4f16.onnx |
| Conditional acoustic decoder | conditional_decoder_q4f16.onnx |
Each graph must remain beside its matching .onnx_data file. The model produces
24 kHz English speech and supports the paralinguistic tags provided by Chatterbox
Turbo.
Reproducibility
- Official ONNX source revision:
d21799bd0354adb85e348b8a0442a8405110a2cf - Original implementation:
resemble-ai/chatterbox - Original model:
ResembleAI/chatterbox-turbo - File hashes and byte sizes:
SHA256SUMS
default_voice.wav is redistributed unchanged from
onnx-community/chatterbox-ONNX
revision 3cab09af388d3f02bba43443fce88c1f4525ac43, under the same MIT license. Its
SHA-256 is 3ebc531cdaba358a327099c1c4f0448026719957bcf4d8e9868767f227e02f4e.
License and attribution
Chatterbox and its model weights are Copyright (c) 2025 Resemble AI and are licensed under the MIT License. This repository preserves the original license and provides no additional model-weight license.
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