Update app.py

app.py

@@ -5,7 +5,6 @@ import uuid
 from pathlib import Path
 from threading import Lock
 from typing import Dict, Optional
-import time
 
 import requests
 import torch
@@ -29,16 +28,11 @@ MODEL_DIR = os.getenv("MODEL_DIR", "/data/indextts2")
 MAX_TEXT_LENGTH = 1000
 DEFAULT_LANGUAGE = "en"
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-USE_GPU = DEVICE == "cuda"
 
 # Job management
 JOBS: Dict[str, Dict[str, str]] = {}
 JOB_LOCK = Lock()
 
-# Connection pooling for faster URL downloads
-HTTP_SESSION = requests.Session()
-HTTP_SESSION.headers.update({"User-Agent": "IndexTTS2-API/1.0"})
-
 # Set token in environment before importing
 if HF_TOKEN:
     os.environ["HUGGING_FACE_HUB_TOKEN"] = HF_TOKEN
@@ -51,6 +45,7 @@ if HF_TOKEN:
 
 # Download model checkpoints from Hugging Face
 os.makedirs(MODEL_DIR, exist_ok=True)
+
 try:
     from huggingface_hub import snapshot_download
     
@@ -67,7 +62,7 @@ except Exception as exc:
     print(f"Warning: Could not download model: {exc}")
     # Continue anyway - model might already be present
 
-# Initialize IndexTTS2 with GPU optimizations if available
+# Initialize IndexTTS2
 try:
     from indextts.infer_v2 import IndexTTS2
     
@@ -77,20 +72,14 @@ try:
             f"Config file not found at {cfg_path}. Model may not be downloaded."
         )
     
-    print(f"Loading IndexTTS2 model on {DEVICE}...")
-    load_start = time.time()
-    
     tts_model = IndexTTS2(
         cfg_path=cfg_path,
         model_dir=MODEL_DIR,
-        use_fp16=False,  # Keep disabled - can cause issues
-        use_cuda_kernel=False,  # Keep disabled for stability
-        use_deepspeed=False,  # Keep disabled for stability
+        use_fp16=False,  # CPU doesn't support FP16
+        use_cuda_kernel=False,  # CPU mode
+        use_deepspeed=False,  # CPU mode
     )
-    
-    load_time = time.time() - load_start
-    print(f"IndexTTS2 model loaded successfully in {load_time:.2f}s on {DEVICE}")
-
+    print("IndexTTS2 model loaded successfully.")
 except Exception as exc:
     raise RuntimeError(f"Failed to load IndexTTS2 model: {exc}") from exc
 
@@ -113,8 +102,8 @@ def _require_api_key(x_api_key: Optional[str]):
 
 
 def _write_temp_audio_from_url(url: HttpUrl) -> str:
-    """Download audio from URL to temporary file using connection pooling."""
-    response = HTTP_SESSION.get(str(url), stream=True, timeout=30)
+    """Download audio from URL to temporary file."""
+    response = requests.get(url, stream=True, timeout=30)
     if response.status_code >= 400:
         raise HTTPException(
             status_code=400,
@@ -161,8 +150,6 @@ def _preprocess_audio_wav(
     - convert to mono
     - resample to target_sr
     - peak-normalize to target_peak (avoid clipping)
-    
-    Optimized to minimize disk I/O.
     """
     wav, sr = torchaudio.load(path)
     
@@ -223,30 +210,22 @@ def _run_generate_job(job_id: str, payload: Dict[str, str]):
     _set_job(job_id, status="processing")
     
     try:
-        start_time = time.time()
-        
-        # Download/decode speaker audio
         speaker_file = _temp_speaker_file(payload["speaker_wav"])
         speaker_file = _preprocess_audio_wav(speaker_file)
-        prep_time = time.time() - start_time
         
         output_file = os.path.join(
             tempfile.gettempdir(), 
             f"indextts2-{uuid.uuid4()}.wav"
         )
         
-        # Run inference (no wrapper - let the model handle its own optimizations)
-        infer_start = time.time()
         tts_model.infer(
             spk_audio_prompt=speaker_file,
             text=payload["text"],
             output_path=output_file,
             use_random=False,
-            verbose=True,  # Keep verbose for timing info
+            verbose=False,
         )
-        infer_time = time.time() - infer_start
         
-        # Post-process output
         output_file = _preprocess_audio_wav(output_file)
         
         if not Path(output_file).exists():
@@ -254,13 +233,9 @@ def _run_generate_job(job_id: str, payload: Dict[str, str]):
                 f"TTS generation failed: output file was not created at {output_file}"
             )
         
-        total_time = time.time() - start_time
-        print(f">> Job {job_id[:8]} completed: prep={prep_time:.2f}s, infer={infer_time:.2f}s, total={total_time:.2f}s")
-        
         _cleanup_files(speaker_file)
         _set_job(job_id, status="completed", output_file=output_file)
     except Exception as exc:
-        print(f">> Job {job_id[:8]} failed: {exc}")
         _cleanup_files(speaker_file, output_file)
         _set_job(job_id, status="error", error=str(exc))
 
@@ -269,13 +244,7 @@ def _run_generate_job(job_id: str, payload: Dict[str, str]):
 def health(x_api_key: Optional[str] = Header(default=None)):
     """Health check endpoint."""
     _require_api_key(x_api_key)
-    return {
-        "status": "ok", 
-        "model": "indextts2", 
-        "device": DEVICE,
-        "gpu_enabled": USE_GPU,
-        "fp16_enabled": USE_GPU,
-    }
+    return {"status": "ok", "model": "indextts2", "device": DEVICE}
 
 
 @app.post("/generate")
@@ -368,8 +337,6 @@ def root():
     """API root with available endpoints."""
     return {
         "name": "indextts2-api",
-        "device": DEVICE,
-        "gpu_enabled": USE_GPU,
         "endpoints": [
             "/health", 
             "/generate",