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| title: tiny-voca | |
| emoji: 🗣️ | |
| colorFrom: pink | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 6.32.0 | |
| app_file: app.py | |
| python_version: "3.12" | |
| short_description: 4.5M-param English TTS, two voices, 24 kHz, runs on CPU | |
| # tiny-voca | |
| A demo of [n4ze3m/tiny-voca](https://huggingface.co/n4ze3m/tiny-voca): a 4.5M-parameter | |
| English text-to-speech model — two voices (F2 female, M2 male), 24 kHz mono audio, | |
| ~70× real-time on CPU. Weights and phonemizer data are Apache-2.0. | |
| Text is turned into phonemes by the author's own | |
| [dizione](https://github.com/n4ze3m/dizione) phonemizer (vendored in this Space): | |
| a CMUdict-derived lexicon, a small neural G2P model for unknown words, and a | |
| homograph classifier — so *"I read the book you read yesterday"* comes out right. | |
| The ONNX export of the model (21 MB) is downloaded from the model repo at | |
| startup and run on CPU via onnxruntime. Long text is split into sentences, | |
| long sentences into utterances that fit the model's 512-token limit, and the | |
| resulting audio chunks are concatenated — the same pipeline as the author's | |
| browser demo. | |