Model Description
This model is based on OpenAI Whisper and has been further fine-tuned specifically for Moroccan Darija speech in the travel domain.
The model recognizes spoken requests such as:
- Book a flight
- Search for flights
- Departure city
- Destination city
- Travel date
- Number of passengers
- Flight-related conversational requests
This model is intended to be integrated into intelligent flight booking assistants.
Base Model
- Base Model: ilyaslbern7347/whisper-darija-stage1
Intended Uses
This model is suitable for:
- Automatic Speech Recognition (ASR)
- Moroccan Darija transcription
- Voice assistants
- Flight booking assistants
- Conversational AI
- Travel applications
Limitations
This model was fine-tuned only on Moroccan Darija speech related to flight booking.
Performance may decrease for:
- General conversations
- Medical vocabulary
- Legal vocabulary
- Noisy audio
- Strong regional accents not represented in the training data
Dataset
The model was trained on a custom Moroccan Darija speech dataset containing flight-related queries.
The dataset includes recordings covering:
- Departure cities
- Arrival cities
- Dates
- Passenger counts
- Flight reservations
- Flight search requests
Training Procedure
Hyperparameters
Table with columns: Parameter, Value| Parameter | Value |
|---|
| Learning Rate | 5e-6 |
| Train Batch Size | 8 |
| Eval Batch Size | 8 |
| Gradient Accumulation | 2 |
| Total Batch Size | 16 |
| Warmup Steps | 40 |
| Max Training Steps | 200 |
| Weight Decay | 0.01 |
| FP16 | True |
Training Results
Table with columns: Step, Training Loss, Validation Loss, WER| Step | Training Loss | Validation Loss | WER |
|---|
| 50 | 1.5132 | 0.4115 | 30.17 |
| 100 | 0.1160 | 0.2923 | 25.51 |
| 150 | 0.0102 | 0.2779 | 23.28 |
| 200 | 0.0048 | 0.2775 | 23.46 |
Best checkpoint:
- Validation Loss: 0.2779
- WER: 23.28
Evaluation
The model achieved:
- Word Error Rate (WER): 23.28%
This represents a significant improvement over the base model for Moroccan Darija flight-related speech recognition.
Example Use Cases
The model can transcribe requests such as:
"بغيت نحجز رحلة من كازا لباريس."
"شنو أرخص رحلة لغدا؟"
"بغيت نمشي لطنجة نهار الجمعة."
Framework Versions
- Transformers
- PyTorch
- Datasets
- Tokenizers