sokho2/mms-300m-wolof

MMS-300m Fine-tuned for Wolof ASR

Fine-tuned facebook/mms-300m for Wolof (Wolof: wol) automatic speech recognition using CTC.

Evaluation Results

Metric	Value
WER	13.64%
CER	4.51%
RTF	263x

on ALFFA Wolof + Google FLEURS

Training Details

Base Model

• Architecture: Wav2Vec2 (300M parameters)
• Base checkpoint: facebook/mms-300m

Datasets

Dataset	Samples	Text Column
Or4kool/wolof_speech_dataset	~9,260 (filtered)	text
Alwaly/wolof_asr_bg	~81,361	transcription
(data augmentation)	~72,863	transcription
Total	~163K

• Evaluation on 10% held-out

Data Augmentation (on-the-fly)

• Speed perturbation
• Volume perturbation
• Time masking:

Hyperparameters

• Hardware: 8× NVIDIA V100 32GB
• Epochs: 20
• Batch size: 8 per GPU × 8 GPUs = 64 effective
• Learning rate: 1e-4 (cosine schedule)
• Warmup steps: 1,000
• Precision: fp16 mixed precision
• Gradient clipping: max_grad_norm=1.0
• Gradient checkpointing: enabled
• Feature encoder: frozen (freeze_feature_encoder=True)
• CTC blank token: <pad> (index 0)

Training Stability

• CTCTrainer: Custom Trainer
• CTC validity filter
• DDP: find_unused_parameters=True for frozen feature encoder compatibility

Usage

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
import soundfile as sf

processor = Wav2Vec2Processor.from_pretrained("sokho2/mms-300m-wolof-v001")
model = Wav2Vec2ForCTC.from_pretrained("sokho2/mms-300m-wolof-v001")

# Load audio (16kHz mono)
audio, sr = sf.read("audio.wav")

inputs = processor(audio, sampling_rate=16000, return_tensors="pt", padding=True)
with torch.no_grad():
    logits = model(**inputs).logits

predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
print(transcription[0])

Comparison with Other Models

Model	WER	CER	RTF	Params
sokho2/mms-300m-wolof-v001 (this model)	13.64%	4.51%	263x	300M
Omnilingual ASR CTC 1B	28.01%	7.45%	163x	1B
bilalfaye/wav2vec2-large-mms-1b-wolof	29.00%	8.60%	138x	1B
vonewman/wav2vec2-large-mms-1b-wolof	32.15%	8.94%	140x	1B
M9and2M/marone_wolof_wav2vec2-xls-r-300m	39.11%	21.77%	264x	300M
BenDaouda/wav2vec2-large-xls-wolof-asr	41.31%	14.70%	267x	300M
BenDaouda/wav2vec2-large-xls-r-1b-wolof-VoiceToText	48.08%	15.83%	267x	1B
Or4kool/mms_300m_wolof_stt	72.19%	35.61%	267x	300M
kingabzpro/wav2vec2-large-xlsr-53-wolof	96.93%	53.47%	271x	300M

Evaluated on 2,337 samples from ALFFA Wolof (dev + test) + Google FLEURS Wolof (wo_sn) test set.

Limitations

• Wolof has significant dialectal variation and French code-switching (~36% of training data contains some French); the model may struggle with heavily French-mixed utterances
• Short utterances (< 2 seconds) tend to have inflated WER due to single-word errors having outsized impact
• No punctuation
• Spelling variants in Wolof (e.g., "xam" vs "ham") are penalized as errors even when phonetically equivalent

License

CC-BY-NC-4.0 (following the base MMS model license)

@misc{sokho2-mms-300m-wolof,
  author = {Sokho2},
  title = {MMS-300M Wolof ASR},
  year = {2026},
  url = {https://huggingface.co/sokho2/mms-300m-wolof}
}