ayoubelfallah1
/

whisper-darija-v3

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
+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
+---