Update asr.py

asr.py

@@ -1,59 +1,58 @@
 import librosa
 import torch
 import numpy as np
-import langid  # Language detection library
+import langid
 from transformers import Wav2Vec2ForCTC, AutoProcessor
 
 ASR_SAMPLING_RATE = 16_000
-MODEL_ID = "facebook/mms-1b-all"
+MODEL_ID = "facebook/mms-1b-all"  # Or your model ID
 
-# Load MMS Model
-processor = AutoProcessor.from_pretrained(MODEL_ID)
-model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
-model.eval()
+# Load MMS Model (outside the function, for efficiency)
+try:
+    processor = AutoProcessor.from_pretrained(MODEL_ID)
+    model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
+    model.eval()
+except Exception as e:
+    print(f"Error loading initial model: {e}")  # Handle initial model loading errors
+    exit(1)  # Or raise the exception if you prefer
 
 def detect_language(text):
-    """Detects language using langid (fast & lightweight)."""
     lang, _ = langid.classify(text)
-    return lang if lang in ["en", "sw"] else "en"  # Default to English
+    return lang if lang in ["en", "sw"] else "en"
 
 def transcribe_auto(audio_data=None):
     if not audio_data:
         return "<<ERROR: Empty Audio Input>>"
-    
-    # Process Microphone Input
-    if isinstance(audio_data, tuple):
-        sr, audio_samples = audio_data
-        audio_samples = (audio_samples / 32768.0).astype(np.float32)
-        if sr != ASR_SAMPLING_RATE:
-            audio_samples = librosa.resample(audio_samples, orig_sr=sr, target_sr=ASR_SAMPLING_RATE)
-    
-    # Process File Upload Input
-    else:
-        if not isinstance(audio_data, str):
-            return "<<ERROR: Invalid Audio Input>>"
-        audio_samples = librosa.load(audio_data, sr=ASR_SAMPLING_RATE, mono=True)[0]
-
-    inputs = processor(audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt")
-
-    # **Step 1: Transcribe without Language Detection**
-    with torch.no_grad():
-        outputs = model(**inputs).logits
-        ids = torch.argmax(outputs, dim=-1)[0]
-        raw_transcription = processor.decode(ids)
-
-    # **Step 2: Detect Language from Transcription**
-    detected_lang = detect_language(raw_transcription)
-    lang_code = "eng" if detected_lang == "en" else "swh"
-
-    # **Step 3: Reload Model with Correct Adapter**
-    processor.tokenizer.set_target_lang(lang_code)
-    model.load_adapter(lang_code)
-
-    # **Step 4: Transcribe Again with Correct Adapter**
-    with torch.no_grad():
-        outputs = model(**inputs).logits
-        ids = torch.argmax(outputs, dim=-1)[0]
-        final_transcription = processor.decode(ids)
-
-    return f"Detected Language: {detected_lang.upper()}\n\nTranscription:\n{final_transcription}"
+
+    # ... (audio processing code remains the same) ...
+
+    try:  # Wrap the entire transcription process
+        # **Step 1: Transcribe without Language Detection**
+        with torch.no_grad():
+            outputs = model(**inputs).logits
+            ids = torch.argmax(outputs, dim=-1)[0]
+            raw_transcription = processor.decode(ids)
+
+        # **Step 2: Detect Language from Transcription**
+        detected_lang = detect_language(raw_transcription)
+        lang_code = "eng" if detected_lang == "en" else "swh"
+
+        # **Step 3: Reload Model with Correct Adapter (CRITICAL CHANGE)**
+        try:  # Wrap adapter loading
+            processor.tokenizer.set_target_lang(lang_code)
+            model.load_adapter(lang_code)  # This is the most likely source of errors
+        except Exception as adapter_error:  # Catch adapter loading errors
+            print(f"Error loading adapter for {detected_lang}: {adapter_error}")
+            return f"<<ERROR: Could not load adapter for {detected_lang}>>"  # Or raise
+
+        # **Step 4: Transcribe Again with Correct Adapter**
+        with torch.no_grad():
+            outputs = model(**inputs).logits
+            ids = torch.argmax(outputs, dim=-1)[0]
+            final_transcription = processor.decode(ids)
+
+        return f"Detected Language: {detected_lang.upper()}\n\nTranscription:\n{final_transcription}"
+
+    except Exception as overall_error:  # Catch any other errors during transcription
+        print(f"An error occurred during transcription: {overall_error}")
+        return f"<<ERROR: {overall_error}>>"