|
--- |
|
license: apache-2.0 |
|
datasets: |
|
- Ayoub-Laachir/Darija_Dataset |
|
language: |
|
- dj |
|
metrics: |
|
- wer |
|
- cer |
|
base_model: |
|
- openai/whisper-large-v3 |
|
pipeline_tag: automatic-speech-recognition |
|
--- |
|
# Model Card for Fine-tuned Whisper Large V3 (Moroccan Darija) |
|
|
|
## Model Overview |
|
**Model Name:** Whisper Large V3 (Fine-tuned for Moroccan Darija) |
|
**Author:** Ayoub Laachir |
|
**License:** apache-2.0 |
|
**Repository:** [Ayoub-Laachir/MaghrebVoice](https://huggingface.co/Ayoub-Laachir/MaghrebVoice) |
|
**Dataset:** [Ayoub-Laachir/Darija_Dataset](https://huggingface.co/datasets/Ayoub-Laachir/Darija_Dataset) |
|
|
|
## Description |
|
This model is a fine-tuned version of OpenAI’s Whisper Large V3, specifically adapted for recognizing and transcribing Moroccan Darija, a dialect influenced by Arabic, Berber, French, and Spanish. The project aims to improve technological accessibility for millions of Moroccans and serve as a blueprint for similar advancements in underrepresented languages. |
|
|
|
## Technologies Used |
|
- **Whisper Large V3:** OpenAI’s state-of-the-art speech recognition model |
|
- **PEFT (Parameter-Efficient Fine-Tuning) with LoRA (Low-Rank Adaptation):** An efficient fine-tuning technique |
|
- **Google Colab:** Cloud environment for training the model |
|
- **Hugging Face:** Hosting the dataset and final model |
|
|
|
## Dataset Preparation |
|
The dataset preparation involved several steps: |
|
1. **Cleaning:** Correcting bad transcriptions and standardizing word spellings. |
|
2. **Audio Processing:** Converting all samples to a 16 kHz sample rate. |
|
3. **Dataset Split:** Creating a training set of 3,312 samples and a test set of 150 samples. |
|
4. **Format Conversion:** Transforming the dataset into the parquet file format. |
|
5. **Uploading:** The prepared dataset was uploaded to the Hugging Face Hub. |
|
|
|
## Training Process |
|
The model was fine-tuned using the following parameters: |
|
- **Per device train batch size:** 8 |
|
- **Gradient accumulation steps:** 1 |
|
- **Learning rate:** 1e-4 (0.0001) |
|
- **Warmup steps:** 200 |
|
- **Number of train epochs:** 2 |
|
- **Logging and evaluation:** every 50 steps |
|
- **Weight decay:** 0.01 |
|
|
|
Training progress showed a steady decrease in both training and validation loss over 8000 steps. |
|
|
|
## Testing and Evaluation |
|
The model was evaluated using: |
|
- **Word Error Rate (WER):** 3.1467% |
|
- **Character Error Rate (CER):** 2.3893% |
|
|
|
These metrics demonstrate the model's ability to accurately transcribe Moroccan Darija speech. |
|
|
|
|
|
The fine-tuned model shows improved handling of Darija-specific words, sentence structure, and overall accuracy. |
|
|
|
## Challenges and Future Improvements |
|
### Challenges Encountered |
|
- Diverse spellings of words in Moroccan Darija |
|
- Cleaning and standardizing the dataset |
|
|
|
### Future Improvements |
|
- Expand the dataset to include more Darija accents and expressions |
|
- Further fine-tune the model for specific Moroccan regional dialects |
|
- Explore integration into practical applications like voice assistants and transcription services |
|
|
|
## Conclusion |
|
This project marks a significant step towards making AI more accessible for Moroccan Arabic speakers. The success of this fine-tuned model highlights the potential for adapting advanced AI technologies to underrepresented languages, serving as a model for similar initiatives in North Africa. |
|
|
