Delete asr.py(auto/audio)

asr.py(auto/audio)

@@ -1,59 +0,0 @@
-import librosa
-import torch
-import numpy as np
-import langid  # Language detection library
-from transformers import Wav2Vec2ForCTC, AutoProcessor
-
-ASR_SAMPLING_RATE = 16_000
-MODEL_ID = "facebook/mms-1b-all"
-
-# Load MMS Model
-processor = AutoProcessor.from_pretrained(MODEL_ID)
-model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
-model.eval()
-
-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
-
-def transcribe_audio(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}"