Create app.py

app.py

@@ -0,0 +1,33 @@
+from fastapi import FastAPI, HTTPException
+from transformers import pipeline
+from pydantic import BaseModel
+import os
+
+# Load the Whisper model for transcription
+transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-small")
+
+app = FastAPI()
+
+class TranscriptionResponse(BaseModel):
+    text: str
+
+@app.post("/transcribe/", response_model=TranscriptionResponse)
+async def transcribe(file_name: str):
+    file_path = os.path.join("/app/audio_files", file_name)  # Assuming audio files are stored in /app/audio_files directory
+
+    # Check if file exists
+    if not os.path.exists(file_path):
+        raise HTTPException(status_code=404, detail="File not found")
+    
+    # Read the file content
+    with open(file_path, "rb") as audio_file:
+        audio_content = audio_file.read()
+    
+    # Perform the transcription
+    transcription = transcriber(audio_content)["text"]
+    
+    return TranscriptionResponse(text=transcription)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)