WAXAL MMS-TTS — Wolof (wol)

Fine-tuning-ready checkpoint for Wolof (wol).

⚠️ Proxy checkpoint

facebook/mms-tts-wol does not exist in MMS-TTS coverage (1107 languages). This repository fine-tunes from the closest available linguistic donor:

Proximity rationale: Wolof (wol) has no MMS checkpoint. Proxy: ful (Fula; same Atlantic-Congo/Senegambian branch — Wolof and Fula are the two major Senegambian languages). Secondary: bam (Bambara, Mande; different branch but geographically adjacent in West Africa / Senegal region).

Each recipient language is fine-tuned independently from the same donor base. Donor weights provide acoustic/prosodic warm-start; WAXAL fine-tuning adapts them to the target language.

What this repository adds

facebook/mms-tts-* Hub checkpoints are inference-only releases that crash run_vits_finetuning.py. This repository applies three patches:

Model weights are not stored here. _name_or_path in config.json points to facebook/mms-tts-ful, so run_vits_finetuning.py loads weights from that checkpoint at training time.

preprocessor_config.json

Downloaded verbatim from ylacombe/mms-tts-eng-train. Values are VITS architecture constants shared by all MMS-TTS languages.

Usage in finetune-hf-vits

{
 "model_name_or_path": "rnjema-unima/mms-tts-wol-baseline",
 "feature_extractor_name": "rnjema-unima/mms-tts-wol-baseline",
 "dataset_name": "google/WaxalNLP",
 "dataset_config_name": "wol_tts",
 "audio_column_name": "audio",
 "text_column_name": "text",
 "train_split_name": "train",
 "eval_split_name": "validation"
}

Inference (after fine-tuning)

from transformers import VitsModel, VitsTokenizer
import torch, scipy

model = VitsModel.from_pretrained("your-org/your-finetuned-model")
tokenizer = VitsTokenizer.from_pretrained("your-org/your-finetuned-model")

inputs = tokenizer("Your text in Wolof.", return_tensors="pt")
with torch.no_grad():
 out = model(**inputs)

scipy.io.wavfile.write("output.wav", model.config.sampling_rate,
 out.waveform.squeeze().numpy())

Technical details

References

BibTex Citation:

This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:

@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} }

Notable Acknowledgements:

• WAXAL paper — arXiv:2602.02734
• WAXAL dataset
• finetune-hf-vits
• facebook/mms-tts-ful
• preprocessor_config.json source — ylacombe/mms-tts-eng-train

Base model

facebook/mms-tts-ful

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