Instructions to use gabar-tech/chatterbox-amharic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use gabar-tech/chatterbox-amharic with PEFT:
Task type is invalid.
- Chatterbox
How to use gabar-tech/chatterbox-amharic 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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demo/README.md
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# Demo audio
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Same sentences, same reference voice, two models:
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- `NN_stock.wav` — stock Chatterbox Multilingual v3, given the identical
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- `NN_finetuned.wav` — v3 + this adapter
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`temperature=0.6`, `cfg_weight=0.5`.
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Texts are in `sentences.txt` (tab-separated: index, text).
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# Demo audio
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Same sentences, same reference voice, same call, two models:
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- `NN_stock.wav` — stock Chatterbox Multilingual v3, given the identical
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normalized text, no language tag (the base has no Amharic mode and no
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Fidel tokenizer coverage; this is what "no support" sounds like).
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- `NN_finetuned.wav` — v3 + this adapter through the released loader, no
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language tag, `temperature=0.6`, `cfg_weight=0.5`.
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Texts are in `sentences.txt` (tab-separated: index, text). `reference.wav`
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is the voice both models cloned (one of us). All generated clips carry the
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PerTh watermark; `before_after.mp4` is all eight pairs in one video for
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GitHub.
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