Create asr.py

asr.py

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+import torchaudio
+import torch
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
+
+# Load processor and model
+processor = AutoProcessor.from_pretrained("ixxan/whisper-small-ug-cv-15")
+model = AutoModelForSpeechSeq2Seq.from_pretrained("ixxan/whisper-small-ug-cv-15")
+
+def transcribe(audio_path: str) -> str:
+    """
+    Transcribes audio to text using the Whisper model for Uyghur.
+
+    Args:
+    - audio_path (str): Path to the audio file to transcribe.
+
+    Returns:
+    - str: The transcription of the audio.
+    """
+
+    # Load audio file
+    audio_input, sampling_rate = torchaudio.load(audio_path)
+
+    # Resample if needed
+    if sampling_rate != processor.feature_extractor.sampling_rate:
+        resampler = torchaudio.transforms.Resample(sampling_rate, processor.feature_extractor.sampling_rate)
+        audio_input = resampler(audio_input)
+
+    # Preprocess the audio input
+    inputs = processor(audio_input.squeeze(), sampling_rate=16000, return_tensors="pt")
+
+    # Move model to GPU if available
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model.to(device)
+    inputs = {key: val.to(device) for key, val in inputs.items()}
+
+    # Generate transcription
+    with torch.no_grad():
+        generated_ids = model.generate(inputs["input_features"], max_length=225)
+
+    # Decode the output to get the transcription text
+    transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    
+    return transcription