Fix: token issue

.gitignore

@@ -0,0 +1,4 @@
+.env
+hello.wav
+venv/
+__pycache__/

diagnosis/ai_engine/detect_stuttering.py

@@ -1,4 +1,5 @@
 # diagnosis/ai_engine/detect_stuttering.py
+import os
 import librosa
 import torch
 import torchaudio
@@ -25,6 +26,7 @@ logger = logging.getLogger(__name__)
 MODEL_ID = "ai4bharat/indicwav2vec-hindi"
 LID_MODEL_ID = "facebook/mms-lid-126"
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+HF_TOKEN = os.getenv("HF_TOKEN")  # Hugging Face token for authenticated model access
 
 INDIAN_LANGUAGES = {
     'hindi': 'hin', 'english': 'eng', 'tamil': 'tam', 'telugu': 'tel',
@@ -137,11 +139,19 @@ class AdvancedStutterDetector:
 
     def __init__(self):
         logger.info(f"🚀 Initializing Advanced AI Engine on {DEVICE}...")
+        if HF_TOKEN:
+            logger.info("✅ HF_TOKEN found - using authenticated model access")
+        else:
+            logger.warning("⚠️ HF_TOKEN not found - model access may fail if authentication is required")
         try:
             # Wav2Vec2 Model Loading - IndicWav2Vec Hindi Model
-            self.processor = AutoProcessor.from_pretrained(MODEL_ID)
+            self.processor = AutoProcessor.from_pretrained(
+                MODEL_ID,
+                token=HF_TOKEN
+            )
             self.model = Wav2Vec2ForCTC.from_pretrained(
                 MODEL_ID,
+                token=HF_TOKEN,
                 torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32
             ).to(DEVICE)
             self.model.eval()
@@ -167,8 +177,14 @@ class AdvancedStutterDetector:
         """Detect language using MMS LID model"""
         try:
             from transformers import Wav2Vec2ForSequenceClassification
-            lid_model = Wav2Vec2ForSequenceClassification.from_pretrained(LID_MODEL_ID).to(DEVICE)
-            lid_processor = AutoFeatureExtractor.from_pretrained(LID_MODEL_ID)
+            lid_model = Wav2Vec2ForSequenceClassification.from_pretrained(
+                LID_MODEL_ID,
+                token=HF_TOKEN
+            ).to(DEVICE)
+            lid_processor = AutoFeatureExtractor.from_pretrained(
+                LID_MODEL_ID,
+                token=HF_TOKEN
+            )
             
             audio, sr = librosa.load(audio_path, sr=16000)
             inputs = lid_processor(audio, sampling_rate=16000, return_tensors="pt").to(DEVICE)