Update app.py

app.py

@@ -1,6 +1,6 @@
 from fastapi import FastAPI, File, UploadFile, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
-from transformers import pipeline, AutoProcessor, AutoModelForAudioClassification
+from transformers import pipeline
 import tempfile
 import os
 import uvicorn
@@ -11,9 +11,11 @@ from datetime import datetime
 import torch
 from contextlib import asynccontextmanager
 
-# Configurar cache de Hugging Face
+# Configurar cache y deshabilitar numba cache
 os.environ['TRANSFORMERS_CACHE'] = '/tmp/transformers_cache'
 os.environ['HF_HOME'] = '/tmp/huggingface'
+os.environ['NUMBA_CACHE_DIR'] = '/tmp/numba_cache'
+os.environ['NUMBA_DISABLE_JIT'] = '1'  # Deshabilitar JIT de numba
 
 # Configurar logging
 logging.basicConfig(
@@ -34,9 +36,10 @@ async def load_model():
         # Crear directorios de cache
         os.makedirs('/tmp/transformers_cache', exist_ok=True)
         os.makedirs('/tmp/huggingface', exist_ok=True)
+        os.makedirs('/tmp/numba_cache', exist_ok=True)
         
-        # Usar modelo más simple y confiable
-        model_name = "superb/hubert-base-superb-ic"
+        # Usar modelo más simple para audio
+        model_name = "facebook/wav2vec2-base-960h"
         
         logger.info(f"Cargando modelo: {model_name}")
         
@@ -51,20 +54,7 @@ async def load_model():
         
     except Exception as e:
         logger.error(f"Error cargando modelo: {e}")
-        
-        # Fallback a modelo básico
-        try:
-            logger.info("Intentando modelo alternativo...")
-            classifier = pipeline(
-                "audio-classification",
-                model="facebook/wav2vec2-base",
-                device=-1,
-                return_all_scores=True
-            )
-            logger.info("Modelo alternativo cargado")
-        except Exception as e2:
-            logger.error(f"Error con modelo alternativo: {e2}")
-            classifier = None
+        classifier = None
 
 async def cleanup_model():
     """Limpiar recursos"""
@@ -121,6 +111,24 @@ async def health_check():
         "cache_dir": "/tmp/transformers_cache"
     }
 
+def load_audio_simple(file_path):
+    """Cargar audio sin usar funciones complejas de librosa"""
+    try:
+        # Cargar audio de forma simple
+        y, sr = librosa.load(file_path, sr=16000, mono=True)
+        return y, sr
+    except Exception as e:
+        logger.error(f"Error con librosa.load: {e}")
+        # Fallback usando soundfile
+        import soundfile as sf
+        y, sr = sf.read(file_path)
+        if sr != 16000:
+            # Resample simple
+            from scipy import signal
+            y = signal.resample(y, int(len(y) * 16000 / sr))
+            sr = 16000
+        return y, sr
+
 @app.post("/detect")
 async def detect_instrument(audio: UploadFile = File(...)):
     """Detectar instrumentos musicales en archivo de audio"""
@@ -161,12 +169,8 @@ async def detect_instrument(audio: UploadFile = File(...)):
         try:
             logger.info("Cargando audio...")
             
-            # Cargar audio
-            audio_data, sample_rate = librosa.load(
-                temp_path, 
-                sr=16000,
-                mono=True
-            )
+            # Cargar audio con función simple
+            audio_data, sample_rate = load_audio_simple(temp_path)
             
             duration = len(audio_data) / sample_rate
             logger.info(f"Audio: {duration:.2f}s, {sample_rate}Hz")