Create app.py

app.py

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+import torch
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import soundfile as sf
+
+# Load the processor and model
+processor = Wav2Vec2Processor.from_pretrained("openbmb/MiniCPM-o-2_6")
+model = Wav2Vec2ForCTC.from_pretrained("openbmb/MiniCPM-o-2_6")
+
+def transcribe_audio(file_path):
+    # Load audio file
+    audio_input, sample_rate = sf.read(file_path)
+    
+    # Preprocess the audio
+    input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
+    
+    # Perform inference
+    with torch.no_grad():
+        logits = model(input_values).logits
+    
+    # Decode the logits to text
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)
+    
+    return transcription[0]
+
+if __name__ == "__main__":
+    audio_file_path = "CAR0005.mp3"
+    transcription = transcribe_audio(audio_file_path)
+    print("Transcription:", transcription)