| |
| """Transcribe one Urdu call-audio file with PakUrdu-Conversational-ASR-r12. |
| |
| Usage: |
| python inference_example.py call.wav [more.wav ...] |
| |
| Loads the public base model from the HF hub, applies this LoRA adapter, |
| merges, and decodes with the certified settings (language=ur, greedy). |
| """ |
| import sys |
|
|
| import soundfile as sf |
| import torch |
| from peft import PeftModel |
| from transformers import WhisperForConditionalGeneration, WhisperProcessor |
|
|
| BASE = "openai/whisper-large-v3-turbo" |
| ADAPTER = "KhiredNetworks/PakUrdu-Conversational-ASR" |
|
|
| if len(sys.argv) < 2: |
| sys.exit(__doc__) |
|
|
| processor = WhisperProcessor.from_pretrained(BASE) |
| model = WhisperForConditionalGeneration.from_pretrained(BASE) |
| model = PeftModel.from_pretrained(model, ADAPTER).merge_and_unload() |
|
|
| for path in sys.argv[1:]: |
| speech, sr = sf.read(path, dtype="float32") |
| if speech.ndim > 1: |
| speech = speech.mean(axis=1) |
| inputs = processor(speech, sampling_rate=sr, return_tensors="pt") |
| with torch.no_grad(): |
| ids = model.generate(inputs.input_features, language="ur", |
| task="transcribe", do_sample=False) |
| text = processor.batch_decode(ids, skip_special_tokens=True)[0].strip() |
| print(f"{path}: {text}") |
|
|
