ayoubelfallah1/whisper-darija-v3

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.

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Base Model

• Base Model: ilyaslbern7347/whisper-darija-stage1

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Intended Uses

This model is suitable for:

• Automatic Speech Recognition (ASR)
• Moroccan Darija transcription
• Voice assistants
• Flight booking assistants
• Conversational AI
• Travel applications

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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

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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

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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

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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

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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.

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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."

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Framework Versions

• Transformers
• PyTorch
• Datasets
• Tokenizers

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