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Tilas/qwen2audio-darija-multitask

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Qwen2-Audio Fine-tuned for Moroccan Darija (Multi-task)

This is a LoRA adapter for Qwen/Qwen2-Audio-7B-Instruct, fine-tuned on Moroccan Darija (Arabic dialect) for three tasks:

Tasks

1.ASR: Audio → Darija transcription (Arabic script)
2.Translation: Audio → English translation
3.Transliteration: Audio → Darija in Latin script (Arabizi)

Training Details

—Base model: Qwen2-Audio-7B-Instruct (8.2B params)
—Method: LoRA (r=32, alpha=32, ~109M trainable params / 1.28%)
—Training data: ~19,100 samples
—Casablanca Morocco (UBC-NLP): ~1,045 ASR samples
—DODa-audio (AtlasIA): ~12,700 samples (ASR + Translation + Transliteration)
—Epochs: 2
—Best eval loss: 0.7265

Results on Casablanca Morocco Test Set (held out)

Model	WER	CER
Baseline (zero-shot)	121.48%	94.17%
Phase 1 (Casablanca only)	82.69%	39.03%
Multi-task (this model)	67.94%	29.26%

Usage

python

from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor
from peft import PeftModel
import librosa

# Load base model + adapter
base_model = Qwen2AudioForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2-Audio-7B-Instruct", torch_dtype=torch.bfloat16
).to("cuda")
model = PeftModel.from_pretrained(base_model, "Tilas/qwen2audio-darija-multitask")
processor = AutoProcessor.from_pretrained("Tilas/qwen2audio-darija-multitask")

# Transcribe
audio, sr = librosa.load("audio.wav", sr=16000)
conversation = [{"role": "user", "content": [
    {"type": "audio", "audio_url": "placeholder"},
    {"type": "text", "text": "Transcribe this speech exactly as spoken, using Arabic script."},
]}]
text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
inputs = processor(text=text, audios=[audio], sampling_rate=16000, return_tensors="pt", padding=True).to("cuda")
output = model.generate(**inputs, max_new_tokens=256)
print(processor.batch_decode(output[:, inputs.input_ids.size(1):], skip_special_tokens=True)[0])

Citation

If you use this model, please cite the underlying datasets:

bibtex

@article{talafha2024casablanca,
  title={Casablanca: Data and Models for Multidialectal Arabic Speech Recognition},
  author={Talafha, Bashar and others},
  journal={arXiv preprint arXiv:2410.04527},
  year={2024}
}