Update asr.py

asr.py

@@ -1,33 +1,46 @@
 import librosa
 from transformers import Wav2Vec2ForCTC, AutoProcessor
 import torch
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.DEBUG)
 
 ASR_SAMPLING_RATE = 16_000
 
 MODEL_ID = "facebook/wav2vec2-large-960h-lv60-self"
 
-processor = AutoProcessor.from_pretrained(MODEL_ID)
-model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
+try:
+    processor = AutoProcessor.from_pretrained(MODEL_ID)
+    model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
+    logging.info("ASR model and processor loaded successfully.")
+except Exception as e:
+    logging.error(f"Error loading ASR model or processor: {e}")
 
 def transcribe(audio):
-    if audio is None:
-        return "ERROR: You have to either use the microphone or upload an audio file"
-    
-    audio_samples = librosa.load(audio, sr=ASR_SAMPLING_RATE, mono=True)[0]
-    inputs = processor(audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt")
-    
-    # Set language ID for Faroese
-    language_id = 'fao'  # ISO 639-3 code for Faroese
-    processor.tokenizer.set_lang(language_id)
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model.to(device)
-    inputs = inputs.to(device)
-
-    with torch.no_grad():
-        outputs = model(**inputs).logits
-
-    ids = torch.argmax(outputs, dim=-1)[0]
-    transcription = processor.decode(ids)
-    
-    return transcription
+    try:
+        if audio is None:
+            return "ERROR: You have to either use the microphone or upload an audio file"
+        
+        audio_samples = librosa.load(audio, sr=ASR_SAMPLING_RATE, mono=True)[0]
+        inputs = processor(audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt")
+        
+        # Set language ID for Faroese
+        language_id = 'fao'  # ISO 639-3 code for Faroese
+        processor.tokenizer.set_lang(language_id)
+
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        model.to(device)
+        inputs = inputs.to(device)
+
+        with torch.no_grad():
+            outputs = model(**inputs).logits
+
+        ids = torch.argmax(outputs, dim=-1)[0]
+        transcription = processor.decode(ids)
+        
+        logging.info("Transcription completed successfully.")
+        return transcription
+    except Exception as e:
+        logging.error(f"Error during transcription: {e}")
+        return "ERROR"