asr.py

import librosa
import torch
import numpy as np
import langid
from transformers import Wav2Vec2ForCTC, AutoProcessor

ASR_SAMPLING_RATE = 16_000
MODEL_ID = "facebook/mms-1b-all"  # Or your model ID

# 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):
    lang, _ = langid.classify(text)
    return lang if lang in ["en", "sw"] else "en"

def transcribe_auto(audio_data=None):
    if not audio_data:
        return "<<ERROR: Empty Audio Input>>"

    # ... (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}>>"