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Sub-second streaming TTS on Chatterbox-Turbo via adaptive buffer-ratchet chunking. First audio in ~0.5s (vs ~5s one-shot) with no quality compromise — chunk joins land on natural sentence pauses and the stream converges to one large near-full-context chunk within 2-3 joins. Works because the engine runs faster than realtime; the no-starvation guarantee is proven in a GPU-free simulation (tests/test_scheduler.py). - chatterbox_fast/scheduler.py: the adaptive-chunk scheduler (pure logic, no GPU) - chatterbox_fast/app.py: FastAPI server (POST /tts streaming, /voices, /health) - bench.py: streaming client (ground-truth TTFB + starvation check) - Self-contained Dockerfile (slim base + chatterbox-tts from PyPI) - Three public-domain LibriVox starter voices baked in (see voices/ATTRIBUTION.md) MIT licensed.
20 lines
923 B
Markdown
20 lines
923 B
Markdown
# voices
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Each `*.wav` in this directory is a predefined voice. The file **stem** becomes
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the voice name returned by `GET /voices` and selectable via the `voice` request
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field. The server clones the reference on the fly — no training, no enrollment.
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**A good reference clip is:** 5–30 seconds, a single speaker, clean (minimal
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noise/music), 16 kHz or higher, mono. Match the clip's language to your text.
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Add a voice by dropping a wav in here (or mounting your own directory at
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`CBF_VOICES_DIR`); `/voices` re-scans on every call, so no restart is needed.
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## Licensing note
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The voices shipped in this open-source repository are **redistributable** (public
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domain / explicitly licensed for redistribution). If you add your own voices,
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make sure you have the right to use — and, if you redistribute the image, to
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share — those clips. Don't ship voices of real people or copyrighted characters
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without permission.
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