TaloCreations/whisper-darija-finetuned

Model Card for Whisper Darija (Fine-Tuned)

This is a fine-tuned OpenAI Whisper small model on Moroccan Darija speech transcription. It is trained to transcribe Moroccan dialectal Arabic from audio.

Model Details

Model Description

This model is a fine-tuned version of giannitto/whisper-morocco-model using a dataset of Moroccan Darija audio and transcriptions. The fine-tuning process aimed to improve the model's Word Error Rate (WER) for spoken Darija, which is underrepresented in many multilingual speech models.

• —Developed by: Bentaleb Ali
• —Model type: Automatic Speech Recognition (ASR)
• —Language(s): Moroccan Darija (Arabic dialect)
• —License: Apache 2.0
• —Finetuned from model: giannitto/whisper-morocco-model

Model Sources

• —Repository: https://huggingface.co/TaloCreations/whisper-darija-finetuned

Uses

Direct Use

This model is intended for transcription of Moroccan Darija audio into text. It can be used in:

• —Voice assistants
• —Media subtitling
• —Dialectal speech processing
• —Linguistic research

Out-of-Scope Use

• —Translation tasks (this model is for transcription, not translation)
• —Other Arabic dialects outside Moroccan Darija

Bias, Risks, and Limitations

• —The model may perform poorly on noisy or low-quality recordings.
• —The model may not generalize well to other dialects of Arabic.
• —Biases in the training data (e.g., gender, age, region) may affect transcription accuracy.

Recommendations

Carefully evaluate outputs when using the model in sensitive applications. Avoid using it in high-risk domains without human verification.

How to Get Started with the Model

from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
import torch, torchaudio

# Load model and processor
processor = AutoProcessor.from_pretrained("TaloCreations/whisper-darija-finetuned")
model = AutoModelForSpeechSeq2Seq.from_pretrained("TaloCreations/whisper-darija-finetuned")
model.eval()

speech, sr = torchaudio.load("path_to_record.wav")

if sr != 16000:
    resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=16000)
    speech = resampler(speech)

# Preprocess and generate
inputs = processor(speech[0], sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
    generated_ids = model.generate(**inputs)
    transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print("📢 Transcription:", transcription)

Training Details

Training Data

The model was trained on:

• —atlasia/DODa-audio-dataset Viewer
• —adiren7/darija_speech_to_text

These datasets contain manually transcribed audio samples of Moroccan Darija.

Training Procedure

Preprocessing

• —All audio was resampled to 16kHz
• —Mel spectrograms were padded to 3000 frames (30s max)
• —Transcripts were tokenized and clipped to <=448 tokens
• —Decoder prompts were injected to ensure language/task alignment

Training Hyperparameters

• —Batch size: 8 (gradient accumulation = 2)
• —Epochs: 10
• —Learning rate: 2e-6
• —Mixed precision: fp16
• —Weight decay: 0.01
• —Warmup steps: 500

Evaluation

Testing Data, Factors & Metrics

Testing Data

A held-out subset (10%) of the training datasets.

Metrics

• —Word Error Rate (WER)

Results

📊 Training Progress

Summary

After 10 epochs, the model achieved a WER of ~50%, a significant improvement over baseline multilingual Whisper models on Moroccan Darija.

Environmental Impact

Estimated based on training on a single A100 GPU for ~6.5 hours.

• —Hardware Type: A100
• —Hours used: ~6.5
• —Cloud Provider: Google Cloud (Colab)
• —Compute Region: Morocco

Technical Specifications

Model Architecture and Objective

• —Whisper (small) encoder-decoder architecture
• —Objective: sequence-to-sequence transcription

Compute Infrastructure

• —Google Colab Pro
• —1x A100 GPU
• —PyTorch + Transformers 4.39

Citation

  title={Whisper Darija: Fine-tuned Whisper Model for Moroccan Arabic Speech},
  author={Bentaleb, Ali},
  year={2025},
}

Model Card Authors

• —Ali Bentaleb @TaloCreations

Model Card Contact

• —📧 alitennis131800@gmail.com