Whisper Small Darija - Lyte (Yassine Ennour)
This is a fine-tuned version of OpenAI's whisper-small
model on the DarijaTTS-clean dataset. The goal of this project is to improve automatic speech recognition (ASR) for Moroccan Darija (ary).
Model Details
- Model Name:
Lyte/Whisper-Small-Darija
- Base Model:
openai/whisper-small
- Fine-Tuned On:
Lyte/DarijaTTS-clean
- Language: Moroccan Darija (ary)
- Task: Automatic Speech Recognition (ASR)
- Dataset Size: 19.9k training samples, 1k test samples
Training Progress
Training was started but interrupted. The training will be resumed from step 600. Below is the progress so far:
Step | Training Loss | Validation Loss | WER |
---|---|---|---|
200 | 1.0142 | 1.0804 | 129.35 |
400 | 0.8288 | 0.9905 | 72.44 |
600 | 0.7618 | 0.9656 | 70.41 |
Usage
You can use this model with Hugging Face's transformers
library:
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torch
model_id = "Lyte/whisper-small-darija"
processor = WhisperProcessor.from_pretrained(model_id)
model = WhisperForConditionalGeneration.from_pretrained(model_id)
# Load an audio file and preprocess
input_features = processor("path_to_audio.wav", return_tensors="pt").input_features
generated_ids = model.generate(input_features)
predicted_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(predicted_text)
Next Steps
- Resume training from step 600 to further improve WER.
- Optimize hyperparameters to reduce validation loss.
- Expand dataset for better generalization.
Acknowledgments
Special thanks to OpenAI for Whisper and Hugging Face for their amazing platform. This model is built as part of my ongoing research in ASR for Darija.
For updates and more details, stay tuned to this repository!
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Base model
openai/whisper-small