WAXAL MMS-TTS — Igbo (ibo)

Fine-tuning-ready checkpoint for Igbo (ibo).

⚠️ Proxy checkpoint

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

Proximity rationale: Igbo (ibo) has no MMS checkpoint. Proxy: yor (Yoruba; same Niger-Congo/Volta-Congo branch, West African tonal language, geographically co-located in southern Nigeria). Secondary: pcm (Nigerian Pidgin, same recording environment). Tertiary: hau (Hausa, same region, but Afroasiatic — more distant).

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-yor, 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-ibo-baseline",
  "feature_extractor_name": "rnjema-unima/mms-tts-ibo-baseline",
  "dataset_name":           "google/WaxalNLP",
  "dataset_config_name":    "ibo_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 Igbo.", 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-yor
• preprocessor_config.json source — ylacombe/mms-tts-eng-train