Update main.py

main.py

@@ -1,4 +1,7 @@
 import os
+os.environ["NUMBA_CACHE_DIR"] = "/tmp"       # Ensure a writable cache directory
+os.environ["NUMBA_DISABLE_CACHE"] = "1"        # Disable Numba caching to avoid errors
+
 import torch
 import librosa
 import numpy as np
@@ -8,24 +11,16 @@ from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
 from librosa.sequence import dtw
 from contextlib import asynccontextmanager
 
-os.environ["NUMBA_CACHE_DIR"] = "/tmp"  # Ensure Numba caching works in container environments
-
-
 # --- Core Class Definition ---
 class QuranRecitationComparer:
     def __init__(self, model_name="jonatasgrosman/wav2vec2-large-xlsr-53-arabic", auth_token=None):
-        """
-        Initialize the Quran recitation comparer with a specific Wav2Vec2 model.
-        """
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        
         if auth_token:
             self.processor = Wav2Vec2Processor.from_pretrained(model_name, token=auth_token)
             self.model = Wav2Vec2ForCTC.from_pretrained(model_name, token=auth_token)
         else:
             self.processor = Wav2Vec2Processor.from_pretrained(model_name)
             self.model = Wav2Vec2ForCTC.from_pretrained(model_name)
-        
         self.model = self.model.to(self.device)
         self.model.eval()
         self.embedding_cache = {}
@@ -46,10 +41,8 @@ class QuranRecitationComparer:
             sampling_rate=sr,
             return_tensors="pt"
         ).input_values.to(self.device)
-
         with torch.no_grad():
             outputs = self.model(input_values, output_hidden_states=True)
-
         hidden_states = outputs.hidden_states[-1]
         embedding_seq = hidden_states.squeeze(0).cpu().numpy()
         return embedding_seq
@@ -105,7 +98,6 @@ class QuranRecitationComparer:
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     global comparer
-    # Use environment variables or a secure configuration in production
     auth_token = os.environ.get("HF_TOKEN")
     comparer = QuranRecitationComparer(
         model_name="jonatasgrosman/wav2vec2-large-xlsr-53-arabic",