Upload whisper_server (1).py

whisper_server (1).py

@@ -0,0 +1,66 @@
+
+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", "./.cache")
+os.makedirs(cache_dir, exist_ok=True)
+
+
+print("Loading collabora/whisper-tiny-hindi model via transformers pipeline...")
+
+# Determine device
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+# Initialize the ASR pipeline with the specified model
+# Using the transformers pipeline is the correct way to load custom models from the Hub.
+model = pipeline(
+    "automatic-speech-recognition",
+    model="collabora/whisper-tiny-hindi",
+    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 collabora/whisper-tiny-hindi 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})
+
+        return jsonify({'error': 'File processing failed'}), 500
+        
+    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
+