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Axiveri/
Renpiper-mms-onnx-V1

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Renpiper MMS ONNX V1

This is an ONNX conversion of Meta AI's facebook/mms-tts checkpoints, converted using the approach documented by the sherpa-onnx project (https://k2-fsa.github.io/sherpa/onnx/tts/mms.html), for on-device mobile inference via the sherpa_onnx runtime. The weights are unmodified from Meta AI's originals -- no fine-tuning has been applied, only a format conversion (PyTorch checkpoint -> ONNX) and vocabulary re-export (vocab.txt -> tokens.txt).

Language status

This table reflects exactly what this repo's own build notebook found and produced -- not an assumption about MMS's general language coverage.

Language	Status	ONNX size	Sample rate
Yoruba (`yor`)	Converted	108.8 MB	16000 Hz
Hausa (`hau`)	Converted	108.8 MB	16000 Hz
Igbo (`ibo`)	No raw checkpoint found (tried `models/ibo`, `models/ig`, `models/igbo`, `models/ib`, `full_models/ibo`, `full_models/ig`). A separate transformers-wrapped `facebook/mms-tts-ibo` repo does not exist, for reference -- not usable for this notebook's ONNX pipeline either way.	--	--
Nigerian Pidgin (`pcm`)	Converted	108.8 MB	16000 Hz

Each converted language lives in its own subfolder (yor/, hau/, ibo/, pcm/ -- whichever succeeded) containing model.onnx and tokens.txt.

What this is NOT

Not an original or independently trained model.
Not fine-tuned or adapted beyond the format conversion described above.
Not available for commercial use or commercial relicensing under any name -- see License below.
Not a claim that every language listed in Meta's general "1107 languages" MMS coverage is available here -- only what this notebook actually verified and converted.

License

CC-BY-NC 4.0, inherited unchanged from the source checkpoints. Non-commercial use only. This means this repo cannot be used as the basis for a paid product, client deliverable, or commercial deployment (including under a different product name) without a separate commercial license from Meta AI.

Citation

@article{pratap2023mms,
  title={Scaling Speech Technology to 1,000+ Languages},
  author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello
    and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and
    Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi
    and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli},
  journal={arXiv},
  year={2023}
}

Model developed by Vineel Pratap et al., Meta AI. All credit for the underlying model belongs to Meta AI / the MMS project, not to this repo's maintainer.

Inference (sherpa-onnx)

python

# pip install sherpa-onnx
import sherpa_onnx

tts = sherpa_onnx.OfflineTts(
    sherpa_onnx.OfflineTtsConfig(
        model=sherpa_onnx.OfflineTtsModelConfig(
            vits=sherpa_onnx.OfflineTtsVitsModelConfig(
                model="yor/model.onnx",
                tokens="yor/tokens.txt",
            ),
        ),
    )
)
audio = tts.generate("Your Yoruba text here")

Axiveri/Renpiper-mms-onnx-V1