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FarmerlineML
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Model Details
Model Provider
FarmerlineML
Model Tree
Qwen/Qwen2-Audio-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
import librosa, torchfrom transformers import AutoProcessor, Qwen2AudioForConditionalGenerationfrom peft import PeftModel BASE = "Qwen/Qwen2-Audio-7B-Instruct"REPO = "FarmerlineML/dagbani-asr-qwen2audio-lora"SYSTEM = ( "You are a Dagbani speech recognition system. " "Transcribe the audio exactly as spoken in Dagbani. " "Return only the Dagbani transcript, nothing else.") processor = AutoProcessor.from_pretrained(REPO)model = Qwen2AudioForConditionalGeneration.from_pretrained( BASE, torch_dtype=torch.bfloat16, device_map="cuda", attn_implementation="sdpa")model = PeftModel.from_pretrained(model, REPO)model = model.merge_and_unload()model.eval() def transcribe(audio_path: str) -> str: audio, _ = librosa.load(audio_path, sr=processor.feature_extractor.sampling_rate) conversation = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": [ {"type": "audio", "audio_url": audio_path}, {"type": "text", "text": "Transcribe this Dagbani audio exactly."}, ]}, ] text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False) inputs = processor( text=text, audio=audio, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ).to("cuda") with torch.no_grad(): out = model.generate(**inputs, max_new_tokens=256, do_sample=False) n = inputs["input_ids"].shape[1] return processor.batch_decode(out[:, n:], skip_special_tokens=True)[0].strip() print(transcribe("dagbani_audio.wav"))import librosa, torchfrom transformers import AutoProcessor, Qwen2AudioForConditionalGenerationfrom peft import PeftModel BASE = "Qwen/Qwen2-Audio-7B-Instruct"REPO = "FarmerlineML/dagbani-asr-qwen2audio-lora"SYSTEM = ( "You are a Dagbani speech recognition system. " "Transcribe the audio exactly as spoken in Dagbani. " "Return only the Dagbani transcript, nothing else.") processor = AutoProcessor.from_pretrained(REPO)model = Qwen2AudioForConditionalGeneration.from_pretrained( BASE, torch_dtype=torch.bfloat16, device_map="cuda", attn_implementation="sdpa")model = PeftModel.from_pretrained(model, REPO)model = model.merge_and_unload()model.eval() def transcribe(audio_path: str) -> str: audio, _ = librosa.load(audio_path, sr=processor.feature_extractor.sampling_rate) conversation = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": [ {"type": "audio", "audio_url": audio_path}, {"type": "text", "text": "Transcribe this Dagbani audio exactly."}, ]}, ] text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False) inputs = processor( text=text, audio=audio, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ).to("cuda") with torch.no_grad(): out = model.generate(**inputs, max_new_tokens=256, do_sample=False) n = inputs["input_ids"].shape[1] return processor.batch_decode(out[:, n:], skip_special_tokens=True)[0].strip() print(transcribe("dagbani_audio.wav"))