Create app.py

app.py

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+import gradio as gr
+from peft import PeftModel, PeftConfig
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+import torch
+import torchaudio
+import os
+
+# Check if CUDA is available and set the device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+def load_model():
+    peft_model_id = "TArtx/MinD_CH_PEFT_ID"
+    peft_config = PeftConfig.from_pretrained(peft_model_id)
+    
+    model = WhisperForConditionalGeneration.from_pretrained(
+        "BELLE-2/Belle-whisper-large-v3-zh",
+        device_map=None
+    ).to(device)
+    
+    model = PeftModel.from_pretrained(model, peft_model_id)
+    return model
+
+def transcribe(audio_path):
+    if audio_path is None:
+        return "Please upload an audio file."
+    
+    try:
+        # Load and resample audio
+        waveform, sample_rate = torchaudio.load(audio_path)
+        waveform = waveform.to(device)
+        
+        # Convert to mono if stereo
+        if waveform.shape[0] > 1:
+            waveform = torch.mean(waveform, dim=0, keepdim=True)
+        
+        # Resample to 16kHz if needed
+        if sample_rate != 16000:
+            resampler = torchaudio.transforms.Resample(sample_rate, 16000)
+            waveform = resampler(waveform)
+        
+        # Convert to numpy array
+        audio_array = waveform.squeeze().cpu().numpy()
+        
+        # Process audio input
+        inputs = processor(
+            audio_array,
+            sampling_rate=16000,
+            return_tensors="pt"
+        ).to(device)
+        
+        # Generate transcription
+        predicted_ids = model.generate(**inputs)
+        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+        return transcription
+        
+    except Exception as e:
+        return f"Error during transcription: {str(e)}"
+
+# Initialize model and processor
+print("Loading model...")
+model = load_model()
+processor = WhisperProcessor.from_pretrained(
+    "BELLE-2/Belle-whisper-large-v3-zh",
+    language="Chinese",
+    task="transcribe"
+)
+print("Model loaded!")
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(type="filepath"),
+    outputs="text",
+    title="Chinese-Mindong Speech Recognition",
+    description="Upload an audio file for transcription. Model optimized for Eastern Min dialect."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()