Or4kool/wolof_asr

Wolof ASR (wav2vec2 CTC)

Fine-tuned wav2vec2 CTC model for transcribing Wolof speech to text.

Model details

• Architecture: wav2vec2 CTC (AutoModelForCTC)
• Task: Automatic speech recognition (ASR)
• Language: Wolof (wo)
• Sample rate: 16 kHz mono
• Output: Plain text transcription

Expected input format

Field	Requirement
Audio format	WAV, FLAC, OGG, MP3, M4A (decoded server-side)
Channels	Mono preferred (stereo is averaged to mono)
Sample rate	Any (resampled to 16 kHz automatically)
Duration	Up to 60 seconds per request
Encoding	Raw bytes, base64, multipart upload, or HTTPS URL

Quick start

from transformers import AutoModelForCTC, AutoProcessor
import librosa

model_id = "Or4kool/wolof_asr"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForCTC.from_pretrained(model_id)
model.eval()

audio, sr = librosa.load("sample.wav", sr=16000, mono=True)
inputs = processor(audio, sampling_rate=16000, return_tensors="pt")

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

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

Pipeline usage

from transformers import pipeline

asr = pipeline(
    "automatic-speech-recognition",
    model="Or4kool/wolof_asr",
)
print(asr("sample.wav")["text"])

API service (this repository)

This model powers the myagro-audio service. Example requests:

JSON (base64)

curl -X POST https://your-endpoint/transcribe \
  -H "Content-Type: application/json" \
  -d '{
    "audio_base64": "<base64-audio>",
    "language": "wol",
    "return_timestamps": false
  }'

Multipart file upload

curl -X POST https://your-endpoint/transcribe/upload \
  -F "file=@sample.wav" \
  -F "language=wol" \
  -F "return_timestamps=true"

Batch (RunPod-friendly)

{
  "input": {
    "items": [
      {"id": "clip-1", "audio_base64": "..."},
      {"id": "clip-2", "audio_base64": "..."}
    ],
    "return_timestamps": false
  }
}

Response format

{
  "text": "transcribed wolof text",
  "language": "wol",
  "duration_seconds": 2.5,
  "request_id": "uuid",
  "inference_ms": 120.4,
  "words": [
    {"word": "example", "start": 0.12, "end": 0.48}
  ]
}

words is optional and only included when return_timestamps=true.

Limitations

• Best performance on clear, close-mic speech at 16 kHz
• Word timestamps are approximate CTC alignments, not forced alignments
• Not intended for real-time streaming transcription
• Long-form audio should be split into chunks under 60 seconds

Training data

Citation

@misc{or4kool-wolof-asr,
  author = {Samuel Oriaku},
  title = {Wolof ASR wav2vec2 CTC},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Or4kool/wolof_asr}}
}

Uploading this card to Hugging Face

huggingface-cli login
huggingface-cli upload Or4kool/wolof_asr huggingface_model_card/README.md README.md