Update voice_processing.py

voice_processing.py

@@ -1,18 +1,11 @@
-import asyncio
-import datetime
-import logging
 import os
 import time
 import traceback
-import tempfile
-from concurrent.futures import ThreadPoolExecutor
-
-import edge_tts
-import librosa
 import torch
+import numpy as np
+import librosa
 from fairseq import checkpoint_utils
-import uuid
-
+from rmvpe import RMVPE
 from config import Config
 from lib.infer_pack.models import (
     SynthesizerTrnMs256NSFsid,
@@ -20,39 +13,63 @@ from lib.infer_pack.models import (
     SynthesizerTrnMs768NSFsid,
     SynthesizerTrnMs768NSFsid_nono,
 )
-from rmvpe import RMVPE
 from vc_infer_pipeline import VC
-
-# Set logging levels
-logging.getLogger("fairseq").setLevel(logging.WARNING)
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-limitation = os.getenv("SYSTEM") == "spaces"
+import uuid
 
 config = Config()
 
-# Edge TTS
-tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-tts_voices = ["mn-MN-BataaNeural", "mn-MN-YesuiNeural"]  # Specific voices
+# Global models loaded once
+hubert_model = None
+rmvpe_model = None
+model_cache = {}  # Cache for RVC models
 
-# RVC models
-model_root = "weights"
-models = [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")]
-models.sort()
+def load_hubert():
+    global hubert_model
+    if hubert_model is None:
+        print("Loading Hubert model...")
+        models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
+            ["hubert_base.pt"],
+            suffix="",
+        )
+        hubert_model = models[0]
+        hubert_model = hubert_model.to(config.device)
+        if config.is_half:
+            hubert_model = hubert_model.half()
+        else:
+            hubert_model = hubert_model.float()
+        hubert_model.eval()
+        print("Hubert model loaded.")
+    return hubert_model
+
+def load_rmvpe():
+    global rmvpe_model
+    if rmvpe_model is None:
+        print("Loading RMVPE model...")
+        rmvpe_model = RMVPE("rmvpe.pt", config.is_half, config.device)
+        print("RMVPE model loaded.")
+    return rmvpe_model
 
 def get_unique_filename(extension):
     return f"{uuid.uuid4()}.{extension}"
 
+def get_model_names():
+    model_root = "weights"  # Assuming this is where your models are stored
+    return [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")]
+
 def model_data(model_name):
-    pth_path = [
-        f"{model_root}/{model_name}/{f}"
-        for f in os.listdir(f"{model_root}/{model_name}")
-        if f.endswith(".pth")
-    ][0]
-    print(f"Loading {pth_path}")
+    global model_cache
+    if model_name in model_cache:
+        # Return cached model data
+        return model_cache[model_name]
+
+    model_root = "weights"
+    pth_files = [
+        f for f in os.listdir(f"{model_root}/{model_name}") if f.endswith(".pth")
+    ]
+    if not pth_files:
+        raise FileNotFoundError(f"No .pth file found for model '{model_name}'")
+    pth_path = f"{model_root}/{model_name}/{pth_files[0]}"
+    print(f"Loading model from {pth_path}")
     cpt = torch.load(pth_path, map_location="cpu")
     tgt_sr = cpt["config"][-1]
     cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
@@ -70,62 +87,33 @@ def model_data(model_name):
             net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
     else:
         raise ValueError("Unknown version")
+
     del net_g.enc_q
     net_g.load_state_dict(cpt["weight"], strict=False)
-    print("Model loaded")
     net_g.eval().to(config.device)
     if config.is_half:
         net_g = net_g.half()
     else:
         net_g = net_g.float()
+    print(f"Model '{model_name}' loaded.")
+
     vc = VC(tgt_sr, config)
 
     index_files = [
-        f"{model_root}/{model_name}/{f}"
-        for f in os.listdir(f"{model_root}/{model_name}")
-        if f.endswith(".index")
+        f for f in os.listdir(f"{model_root}/{model_name}") if f.endswith(".index")
     ]
-    if len(index_files) == 0:
-        print("No index file found")
-        index_file = ""
-    else:
-        index_file = index_files[0]
+    if index_files:
+        index_file = f"{model_root}/{model_name}/{index_files[0]}"
         print(f"Index file found: {index_file}")
-
-    return tgt_sr, net_g, vc, version, index_file, if_f0
-
-def load_hubert():
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(config.device)
-    if config.is_half:
-        hubert_model = hubert_model.half()
     else:
-        hubert_model = hubert_model.float()
-    return hubert_model.eval()
+        index_file = ""
+        print("No index file found.")
 
-def get_model_names():
-    model_root = "weights"  # Assuming this is where your models are stored
-    return [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")]
+    # Cache the loaded model data
+    model_cache[model_name] = (tgt_sr, net_g, vc, version, index_file, if_f0)
+    return tgt_sr, net_g, vc, version, index_file, if_f0
 
-# Add this helper function to ensure a new event loop is created if none exists
-def run_async_in_thread(fn, *args):
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    result = loop.run_until_complete(fn(*args))
-    loop.close()
-    return result
-
-def parallel_tts(tasks):
-    with ThreadPoolExecutor() as executor:
-        futures = [executor.submit(run_async_in_thread, tts, *task) for task in tasks]
-        results = [future.result() for future in futures]
-    return results
-
-async def tts(
+def tts(
     model_name,
     tts_text,
     tts_voice,
@@ -133,8 +121,11 @@ async def tts(
     use_uploaded_voice,
     uploaded_voice,
 ):
+    # Load models if not already loaded
+    load_hubert()
+    load_rmvpe()
+
     # Default values for parameters used in EdgeTTS
-    speed = 0  # Default speech speed
     f0_up_key = 0  # Default pitch adjustment
     f0_method = "rmvpe"  # Default pitch extraction method
     protect = 0.33  # Default protect value
@@ -144,52 +135,42 @@ async def tts(
     edge_time = 0  # Initialize edge_time
 
     edge_output_filename = get_unique_filename("mp3")
+    audio = None
+    sr = 16000  # Default sample rate
 
     try:
         if use_uploaded_voice:
             if uploaded_voice is None:
                 return "No voice file uploaded.", None, None
-            
+
             # Process the uploaded voice file
             with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
                 tmp_file.write(uploaded_voice)
                 uploaded_file_path = tmp_file.name
 
             audio, sr = librosa.load(uploaded_file_path, sr=16000, mono=True)
+            input_audio_path = uploaded_file_path
         else:
             # EdgeTTS processing
-            if limitation and len(tts_text) > 12000:
-                return (
-                    f"Text characters should be at most 12000 in this huggingface space, but got {len(tts_text)} characters.",
-                    None,
-                    None,
-                )
-            
-            # Invoke Edge TTS
+            # Note: EdgeTTS code may need to be adjusted based on your implementation
+            import edge_tts
             t0 = time.time()
+            speed = 0  # Default speech speed
             speed_str = f"+{speed}%" if speed >= 0 else f"{speed}%"
-            await edge_tts.Communicate(
+            communicate = edge_tts.Communicate(
                 tts_text, tts_voice, rate=speed_str
-            ).save(edge_output_filename)
+            )
+            asyncio.run(communicate.save(edge_output_filename))
             t1 = time.time()
             edge_time = t1 - t0
 
             audio, sr = librosa.load(edge_output_filename, sr=16000, mono=True)
+            input_audio_path = edge_output_filename
 
-        # Common processing after loading the audio
-        duration = len(audio) / sr
-        print(f"Audio duration: {duration}s")
-        if limitation and duration >= 20000:
-            return (
-                f"Audio should be less than 20 seconds in this huggingface space, but got {duration}s.",
-                None,
-                None,
-            )
-
-        f0_up_key = int(f0_up_key)
+        # Load the specified RVC model
         tgt_sr, net_g, vc, version, index_file, if_f0 = model_data(model_name)
 
-        # Setup for RMVPE or other pitch extraction methods
+        # Set RMVPE model for pitch extraction
         if f0_method == "rmvpe":
             vc.model_rmvpe = rmvpe_model
 
@@ -198,9 +179,9 @@ async def tts(
         audio_opt = vc.pipeline(
             hubert_model,
             net_g,
-            0,
+            0,  # Speaker ID
             audio,
-            edge_output_filename if not use_uploaded_voice else uploaded_file_path,
+            input_audio_path,
             times,
             f0_up_key,
             f0_method,
@@ -218,31 +199,28 @@ async def tts(
 
         if tgt_sr != resample_sr and resample_sr >= 16000:
             tgt_sr = resample_sr
-        
-        info = f"Success. Time: tts: {edge_time}s, npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s"
+
+        info = f"Success. Time: tts: {edge_time:.2f}s, npy: {times[0]:.2f}s, f0: {times[1]:.2f}s, infer: {times[2]:.2f}s"
         print(info)
         return (
-            info,
-            edge_output_filename if not use_uploaded_voice else None,   
+            {"info": info},
+            None,  # Return None for edge_output_filename as it's not needed
             (tgt_sr, audio_opt),
         )
 
     except EOFError:
         info = (
-            "output not valid. This may occur when input text and speaker do not match."
+            "Output not valid. This may occur when input text and speaker do not match."
         )
         print(info)
-        return info, None, None
+        return {"error": info}, None, None
     except Exception as e:
         traceback_info = traceback.format_exc()
         print(traceback_info)
-        return str(e), None, None
+        return {"error": str(e)}, None, None
 
+# Voice mapping dictionary
 voice_mapping = {
     "Mongolian Male": "mn-MN-BataaNeural",
     "Mongolian Female": "mn-MN-YesuiNeural"
 }
-
-hubert_model = load_hubert()
-
-rmvpe_model = RMVPE("rmvpe.pt", config.is_half, config.device)