Automatic Speech Recognition
Transformers
Safetensors
Arabic
wav2vec2
hassaniya
mauritania
mms
speech-recognition
asr
Instructions to use Hassen80/hassaniya-mms-asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hassen80/hassaniya-mms-asr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Hassen80/hassaniya-mms-asr")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Hassen80/hassaniya-mms-asr") model = AutoModelForCTC.from_pretrained("Hassen80/hassaniya-mms-asr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Hassaniya MMS ASR
MMS-1B fine-tuned for Hassaniya speech recognition (Mauritanian Arabic).
Usage
from transformers import Wav2Vec2ForCTC, AutoProcessor
import torch
processor = AutoProcessor.from_pretrained("Hassen80/hassaniya-mms-asr")
model = Wav2Vec2ForCTC.from_pretrained("Hassen80/hassaniya-mms-asr")
# Process audio (16kHz, mono)
inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
predicted_ids = torch.argmax(logits, dim=-1)
text = processor.batch_decode(predicted_ids)[0]
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