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Sadou
/
mms-1b-wolof-finetuned

Automatic Speech Recognition
Safetensors
Wolof
wav2vec2
audio
wolof
mms
senegal
africa
low-resource
Eval Results (legacy)
Model card Files
xet
 Community
mms-1b-wolof-finetuned
File size: 4,359 Bytes
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---
language: wo
license: apache-2.0
tags:
- automatic-speech-recognition
- audio
- wolof
- mms
- senegal
- africa
- low-resource
datasets:
- vonewman/wolof-audio-data
metrics:
- wer
base_model: facebook/mms-1b-all
model-index:
- name: mms-1b-wolof-finetuned
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: vonewman/wolof-audio-data
      type: vonewman/wolof-audio-data
    metrics:
    - type: wer
      value: 29.56
      name: Word Error Rate
---

# MMS-1B Wolof Fine-Tuned 🇸🇳

Fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) for **Wolof Automatic Speech Recognition (ASR)**.

This model transcribes Wolof speech audio into Wolof text. It's the result of full fine-tuning (all 964M parameters) on 20,000 quality-filtered audio samples.

## 📊 Performance

| Metric | Value |
|--------|-------|
| **WER (Word Error Rate)** | **29.56%** |
| Test set | 500 examples |
| Training samples | 20,000 |
| Total audio | 25.6 hours |
| Mode | Full fine-tuning (964M params) |

## 🎯 Use Cases

- 🎙️ Transcription of Wolof audio (radio, podcasts, conversations)
- 🌍 First step in a Wolof → French translation pipeline
- ♿ Accessibility tools for Wolof-speaking communities
- 🔬 Research on low-resource African languages

## 🚀 Quick Start

```python
from transformers import Wav2Vec2ForCTC, AutoProcessor
import torch
import torchaudio

# Load model and processor
model_id = "Sadou/mms-1b-wolof-finetuned"
processor = AutoProcessor.from_pretrained(model_id)
model = Wav2Vec2ForCTC.from_pretrained(model_id)

# Load audio (must be 16kHz, mono)
waveform, sr = torchaudio.load("your_audio.wav")
if sr != 16000:
    waveform = torchaudio.transforms.Resample(sr, 16000)(waveform)
if waveform.shape[0] > 1:
    waveform = waveform.mean(dim=0, keepdim=True)

# Transcribe
inputs = processor(waveform[0].numpy(), sampling_rate=16000, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
print(f"Transcription: {transcription}")
```

## 📈 Training Details

### Dataset
- **Source**: [vonewman/wolof-audio-data](https://huggingface.co/datasets/vonewman/wolof-audio-data)
- **Combines**: ALFFA + FLEURS + Urban Bus + Kallama
- **Quality filters applied**:
  - Audio duration: 2-15s
  - Text: 3-30 words
  - Speech rate: 1.0-4.5 words/sec
  - Retention rate: 75.5%

### Training Configuration
- **Base model**: `facebook/mms-1b-all`
- **Mode**: Full fine-tuning (all 964M parameters)
- **Precision**: bf16 (Brain Float 16)
- **Effective batch size**: 32 (16 × 2 gradient accumulation)
- **Gradient checkpointing**: Enabled
- **Learning rate**: 3e-5 (initial), then 5e-6 (final epochs)
- **Epochs**: 6 (4 + 2 with LR decay)
- **Hardware**: NVIDIA RTX PRO 6000 Blackwell

### Training Progression

| Step | WER | Phase |
|------|-----|-------|
| 300 | 47.67% | Initial training |
| 900 | 38.86% | |
| 1500 | 33.35% | |
| 2100 | 30.77% | |
| 2400 | 30.11% | Plateau detected |
| 2700 | 30.56% | LR decay started |
| 3300 | 29.56% | |
| 3600 | **29.56%** | Final ✅ |

## ⚠️ Limitations

- **Code-switching**: Difficulty with French words mixed in Wolof speech
- **Word segmentation**: Sometimes merges or splits words incorrectly
- **Background noise**: Performance degrades on noisy audio
- **Long audios**: For audios > 30s, use chunking with stride

## 🔄 Long Audio Inference

For audios longer than 30 seconds, use HuggingFace pipeline with chunking:

```python
from transformers import pipeline

pipe = pipeline(
    'automatic-speech-recognition',
    model="Sadou/mms-1b-wolof-finetuned",
    chunk_length_s=30,
    stride_length_s=(4, 2),
    device=0,
)

result = pipe("long_audio.wav")
print(result['text'])
```

## 🙏 Acknowledgments

- Meta AI for [MMS](https://huggingface.co/facebook/mms-1b-all)
- [vonewman](https://huggingface.co/vonewman) for the Wolof audio dataset
- [GalsenAI](https://huggingface.co/galsenai) and the Senegalese AI community

## 📚 Citation

```bibtex
@misc{wolof-mms-2026,
  author = {Sadou Barry},
  title = {MMS-1B Wolof Fine-Tuned},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Sadou/mms-1b-wolof-finetuned}
}
```