Delete whisper_server.py

whisper_server.py

@@ -1,63 +0,0 @@
-
-import os
-import tempfile
-from flask import request, jsonify
-from transformers import pipeline
-import torch
-import traceback
-
-# Define a writable directory for the model cache.
-# This now respects the HF_HOME environment variable set in the Dockerfile.
-cache_dir = os.environ.get("HF_HOME", "/tmp/.cache")
-os.makedirs(cache_dir, exist_ok=True)
-
-
-print("Loading openai/whisper-tiny model via transformers pipeline...")
-
-# Determine device
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-# Initialize the ASR pipeline with a more lightweight model
-# Using openai/whisper-tiny is much less memory intensive than the Hindi-specific one
-model = pipeline(
-    "automatic-speech-recognition",
-    model="openai/whisper-tiny",
-    device=device,
-    model_kwargs={"cache_dir": cache_dir}
-)
-
-print("Whisper model loaded.")
-
-def handle_transcribe():
-    try:
-        # Step 1: Validate request - looking for 'audio' key from frontend
-        if 'audio' not in request.files:
-            print("Error: 'audio' key not in request.files")
-            return jsonify({'error': 'No audio file part in the request'}), 400
-        
-        file = request.files['audio']
-        
-        if file.filename == '':
-            print("Error: No selected file")
-            return jsonify({'error': 'No selected file'}), 400
-
-        # Step 2: Use a temporary file to save the upload
-        with tempfile.NamedTemporaryFile(delete=True, suffix=".webm") as temp_audio:
-            file.save(temp_audio.name)
-            
-            print(f"Transcribing file: {temp_audio.name} with openai/whisper-tiny pipeline")
-            
-            # Step 3: Transcribe using the pipeline
-            # The pipeline is robust and can handle various formats directly, leveraging ffmpeg.
-            result = model(temp_audio.name)
-            
-            transcribed_text = result.get('text', '')
-
-            print("Transcription successful.")
-            return jsonify({'text': transcribed_text})
-        
-    except Exception as e:
-        # Step 4: Robust error logging
-        print("❌ Error in handle_transcribe():")
-        traceback.print_exc()
-        return jsonify({'error': f"An unexpected error occurred during transcription: {str(e)}"}), 500