Add Darija (Moroccan Arabic) finetuning example

[Mohcinimohamed]

• Add darija_toy_dataset/ with 2 Darija speech samples (.wav + .json)
• Update README.md with Darija training and inference commands
• Uses customized_context with Darija-specific hotwords
• Audio recorded by the contributor via a community data collection app

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[pengzhiliang]

Thanks so much for this contribution @Mohcinimohamed! It's really exciting to see community members working on low-resource languages like Darija — this is exactly the kind of work we'd love to highlight.

Before we merge, would you be able to share:

1. Evaluation results — any WER/CER metrics or qualitative observations on how the fine-tuned model performs on Darija compared to the base model?
2. LoRA weights — if you could upload the trained LoRA weights to your Hugging Face account, we'd love to link to them as a community example. This would make it much easier for others interested in Darija ASR to get started.

This would really help us showcase this as a reference example for fine-tuning on new languages. Thanks again for the great work!

[Mohcinimohamed]

Hi @pengzhiliang ! Thanks so much for the kind words. We're thrilled to contribute to VibeVoice and help expand its capabilities to low-resource languages like Moroccan Darija.

Here are the details you requested:

1. LoRA Weights
I have uploaded the trained LoRA adapter to Hugging Face. You can find it here:
👉 Mohcinimohamed/vibevoice-asr-darija-lora
The model card includes all our training configurations (QLoRA 4-bit, rank 16) and a Python code snippet demonstrating how to load the adapter with the base model for inference.

2. Evaluation Results & Observations

• Qualitative: The base microsoft/VibeVoice-ASR model naturally struggles with Darija—it tends to "correct" spoken Darija into Modern Standard Arabic (MSA) and completely breaks down during French/Arabic code-switching, which is extremely common in Moroccan speech. With this LoRA adapter, the model now accurately transcribes strictly dialectal vocabulary (e.g., labas, bezzaf, kifash) and smoothly handles Franco-Arab code-switching without hallucinating.
• Quantitative (WER/CER): The currently uploaded adapter is an early checkpoint (checkpoint-500). We are still finishing the full staged training curriculum on our dataset of ~18k samples. I am currently preparing the formal WER/CER benchmark on our 997-sample validation set and will share the exact metrics here as soon as the final training epoch is done. However, the qualitative improvements are already massive.

Let me know if you need anything else to get this merged. Thanks again!

[pengzhiliang]

This is awesome, thanks so much for the detailed update! The qualitative improvements you described — especially around code-switching — sound really promising.

No rush at all on the WER/CER numbers. Let's wait until your full training is done and the final metrics are ready before merging. Feel free to update this PR whenever you're ready!