| --- |
| language: |
| - ar |
| - ary |
| library_name: transformers |
| license: apache-2.0 |
| base_model: ilyaslbern7347/whisper-darija-stage1 |
| pipeline_tag: automatic-speech-recognition |
|
|
| tags: |
| - whisper |
| - automatic-speech-recognition |
| - speech-recognition |
| - darija |
| - moroccan-arabic |
| - whisper-finetuned |
| - generated_from_trainer |
|
|
| metrics: |
| - wer |
|
|
| model-index: |
| - name: whisper-darija-vols-stage2 |
| results: |
| - task: |
| type: automatic-speech-recognition |
| name: Automatic Speech Recognition |
| dataset: |
| name: Moroccan Darija Flight Queries |
| type: custom |
| metrics: |
| - type: wer |
| value: 23.28 |
| name: Word Error Rate |
| --- |
| |
| # Whisper Darija Flights Stage 2 |
|
|
| This model is a fine-tuned version of **ilyaslbern7347/whisper-darija-stage1** using a custom dataset of Moroccan Darija flight-related speech. |
|
|
| The objective of this model is to improve speech recognition accuracy for Moroccan Darija users searching for flights using natural language. |
|
|
| ## 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 |
|
|
| | 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 | |
| | Gradient Checkpointing | True | |
| | Optimizer | AdamW | |
| | LR Scheduler | Linear | |
|
|
| --- |
|
|
| # Training Results |
|
|
| | 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 |
|
|
| --- |
