MoulSot v0.3 — Automatic Speech Recognition for Moroccan Darija
Model Description
MoulSot v0.3 is an Automatic Speech Recognition (ASR) model developed by Atlasia for Moroccan Darija, designed to handle real-world speech with strong robustness to code-switching (Darija ↔ French ↔ Arabic).
The model is trained on a large, curated dataset and aims to provide high-quality transcription for conversational, media, and user-generated audio in Moroccan dialect.
- Model type: End-to-end ASR
- Language(s): Moroccan Darija (primary), with code-switching (French, English, Arabic)
- Version: v0.3
- Repository: https://huggingface.co/atlasia/moulsot.v0.3
- Data pipeline: https://github.com/atlasia-ma/MoulSot
Key Features
- 🔊 1,500 hours of curated Darija audio
- 🏆 80 hours of gold-standard transcriptions
- ⚡ Native support for code-switching
- 🎙️ Designed for real-world noisy and conversational speech
Training Data
Overview
The MoulSot dataset is built from a large-scale data pipeline focused on collecting, filtering, and curating Moroccan Darija audio.
Total audio: ~1,500 hours
High-quality labeled subset: 80 hours
Speech characteristics:
- Spontaneous and conversational
- Multi-domain (media, informal speech, etc.)
- Code-switching between Darija, English, French, and Modern Standard Arabic
Data Pipeline
The full data processing pipeline is open-sourced: 👉 https://github.com/atlasia-ma/MoulSot
It includes:
- Audio collection and aggregation
- Cleaning and normalization
- Segmentation
- Annotation workflows
- Quality filtering
Training Procedure
While full technical details are released progressively, the model training includes:
- Supervised training on high-quality labeled data (80h)
- Optimization for multilingual/code-switched contexts
- Iterative refinement across versions
More details will be available in the technical blog.
Intended Use
Primary Use Cases
- Transcription of Moroccan Darija audio
- Voice interfaces and assistants
- Media and content indexing
- Call center and conversational AI
- Speech analytics in Moroccan context
Out-of-Scope Use
- Critical decision-making systems without human validation
- Languages outside Darija/English/French/Arabic code-switching context
- Highly specialized domains without adaptation
Performance
MoulSot v0.3 is positioned as a state-of-the-art Darija ASR system, with strong qualitative performance in:
- Conversational fluency
- Handling mixed-language speech
- Robustness to accents and informal usage
Benchmark Comparison
Results (↓ lower is better):
- MoulSot v0.1 — WER: 66.78 / CER: 21.30
- MoulSot v0.3 — WER: 38.99 / CER: 12.58
- ISMA — WER: 39.20 / CER: 13.47
MoulSot v0.3 significantly improves over v0.1 and achieves state-of-the-art performance, slightly outperforming ISMA on both WER and CER.
Leaderboard Ranking
MoulSot v0.3 ranks #1 on the Moroccan Darija ASR leaderboard:
👉 https://huggingface.co/spaces/abdeljalilELmajjodi/moroccan_darija_asr_leaderboard
Key takeaways:
- 🥇 Ranked 1st place among evaluated Darija ASR models
- 📊 Confirms strong real-world performance beyond internal benchmarks
- 🔍 Validates improvements over previous versions and competing systems
Quantitative benchmarks and detailed evaluations will be expanded in the technical report.
Limitations
Performance may degrade on:
- Extremely noisy audio
- Rare dialectal variations
- Domain-specific jargon
Code-switching handling is strong but not perfect
Limited publicly available evaluation benchmarks for Darija
Ethical Considerations
The dataset is curated with attention to quality and representativeness, but may still contain biases.
Users should:
- Validate outputs in critical applications
- Be mindful of potential transcription inaccuracies
- Avoid misuse in surveillance or harmful contexts
Usage
Install (Python)
uv pip install qwen_asr
Inference (PyTorch / CUDA)
from qwen_asr import Qwen3ASRModel
model = Qwen3ASRModel.from_pretrained(
"atlasia/moulsot.v0.3",
dtype="bfloat16",
device_map="cuda",
)
result = model.transcribe(audio="your_audio.wav", language="Arabic")
print(result.text)
MLX (Apple Silicon)
Install:
uv pip install mlx-audio
Run:
from mlx_audio.stt.utils import load
model = load("atlasia/moulsot.v0.3")
audio_path = "your_audio.wav"
transcription = model.generate(audio_path).text
print(transcription)
Citation
@misc{moulsot2026,
title={MoulSot v0.3: The New Champ of Darija ASR},
author={Atlasia},
year={2026},
url={https://huggingface.co/atlasia/moulsot.v0.3}
}
Resources & Roadmap
Coming soon:
- Full technical blog
- Dataset release
- Training details
- Future model versions
Stay tuned via: 👉 https://atlasia.ma
Acknowledgements
Developed by Atlasia as part of ongoing efforts to advance AI for Moroccan languages and dialects.
License
Apache 2.0 — same as the base model.
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