separate log control

server/MMVCServerSIO.py

@@ -1,16 +1,17 @@
 import sys, os, struct, argparse, logging, shutil, base64, traceback
-logging.getLogger('numba').setLevel(logging.WARNING)
+# logging.getLogger('numba').setLevel(logging.WARNING)
 
-class UvicornSuppressFilter(logging.Filter):
-    def filter(self, record):
-        return False
+# class UvicornSuppressFilter(logging.Filter):
+#     def filter(self, record):
+#         return False
 
-logger = logging.getLogger("uvicorn.error")
-logger.addFilter(UvicornSuppressFilter())
+# logger = logging.getLogger("uvicorn.error")
+# logger.addFilter(UvicornSuppressFilter())
+# # logger.propagate = False
+# logger = logging.getLogger("multipart.multipart")
 # logger.propagate = False
-logger = logging.getLogger("multipart.multipart")
-logger.propagate = False
 
+import misc.log_control
 
 from dataclasses import dataclass
 from datetime import datetime

server/misc/log_control.py

@@ -0,0 +1,13 @@
+import logging
+
+logging.getLogger('numba').setLevel(logging.WARNING)
+
+class UvicornSuppressFilter(logging.Filter):
+    def filter(self, record):
+        return False
+
+logger = logging.getLogger("uvicorn.error")
+logger.addFilter(UvicornSuppressFilter())
+# logger.propagate = False
+logger = logging.getLogger("multipart.multipart")
+logger.propagate = False

server/voice_changer/VoiceChanger.py

@@ -0,0 +1,106 @@
+import torch
+
+from scipy.io.wavfile import write, read
+import numpy as np
+import traceback
+
+import utils
+import commons
+from models import SynthesizerTrn
+
+from text.symbols import symbols
+from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
+
+from mel_processing import spectrogram_torch
+from text import text_to_sequence, cleaned_text_to_sequence
+
+
+
+class VoiceChanger():
+    def __init__(self, config, model):
+        self.hps = utils.get_hparams_from_file(config)
+        self.net_g = SynthesizerTrn(
+            len(symbols),
+            self.hps.data.filter_length // 2 + 1,
+            self.hps.train.segment_size // self.hps.data.hop_length,
+            n_speakers=self.hps.data.n_speakers,
+            **self.hps.model)
+        self.net_g.eval()
+        self.gpu_num = torch.cuda.device_count()
+        utils.load_checkpoint(model, self.net_g, None)
+
+        text_norm = text_to_sequence("a", self.hps.data.text_cleaners)
+        text_norm = commons.intersperse(text_norm, 0)
+        self.text_norm = torch.LongTensor(text_norm)
+        self.audio_buffer = torch.zeros(1, 0)
+        self.prev_audio = np.zeros(1)
+        self.mps_enabled = getattr(
+            torch.backends, "mps", None) is not None and torch.backends.mps.is_available()
+
+        print(
+            f"VoiceChanger Initialized (GPU_NUM:{self.gpu_num}, mps_enabled:{self.mps_enabled})")
+
+    def destroy(self):
+        del self.net_g
+
+    def on_request(self, gpu, srcId, dstId, timestamp, prefixChunkSize, wav):
+        unpackedData = wav
+        convertSize = unpackedData.shape[0] + (prefixChunkSize * 512)
+
+        try:
+
+            audio = torch.FloatTensor(unpackedData.astype(np.float32))
+            audio_norm = audio / self.hps.data.max_wav_value
+            audio_norm = audio_norm.unsqueeze(0)
+            self.audio_buffer = torch.cat(
+                [self.audio_buffer, audio_norm], axis=1)
+            audio_norm = self.audio_buffer[:, -convertSize:]
+            self.audio_buffer = audio_norm
+
+            spec = spectrogram_torch(audio_norm, self.hps.data.filter_length,
+                                     self.hps.data.sampling_rate, self.hps.data.hop_length, self.hps.data.win_length,
+                                     center=False)
+            spec = torch.squeeze(spec, 0)
+            sid = torch.LongTensor([int(srcId)])
+
+            data = (self.text_norm, spec, audio_norm, sid)
+            data = TextAudioSpeakerCollate()([data])
+
+            # if gpu < 0 or (self.gpu_num == 0 and not self.mps_enabled):
+            if gpu < 0 or self.gpu_num == 0:
+                with torch.no_grad():
+                    x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
+                        x.cpu() for x in data]
+                    sid_tgt1 = torch.LongTensor([dstId]).cpu()
+                    audio1 = (self.net_g.cpu().voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[
+                              0][0, 0].data * self.hps.data.max_wav_value).cpu().float().numpy()
+            # elif self.mps_enabled == True: # MPS doesnt support aten::weight_norm_interface, and PYTORCH_ENABLE_MPS_FALLBACK=1 cause a big dely.
+            #         x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
+            #             x.to("mps") for x in data]
+            #         sid_tgt1 = torch.LongTensor([dstId]).to("mps")
+            #         audio1 = (self.net_g.to("mps").voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[
+            #                   0][0, 0].data * self.hps.data.max_wav_value).cpu().float().numpy()
+
+            else:
+                with torch.no_grad():
+                    x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
+                        x.cuda(gpu) for x in data]
+                    sid_tgt1 = torch.LongTensor([dstId]).cuda(gpu)
+                    audio1 = (self.net_g.cuda(gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[
+                              0][0, 0].data * self.hps.data.max_wav_value).cpu().float().numpy()
+
+            # if len(self.prev_audio) > unpackedData.shape[0]:
+            #     prevLastFragment = self.prev_audio[-unpackedData.shape[0]:]
+            #     curSecondLastFragment = audio1[-unpackedData.shape[0]*2:-unpackedData.shape[0]]
+            #     print("prev, cur", prevLastFragment.shape, curSecondLastFragment.shape)
+            # self.prev_audio = audio1
+            # print("self.prev_audio", self.prev_audio.shape)
+
+            audio1 = audio1[-unpackedData.shape[0]*2:]
+
+        except Exception as e:
+            print("VC PROCESSING!!!! EXCEPTION!!!", e)
+            print(traceback.format_exc())
+
+        audio1 = audio1.astype(np.int16)
+        return audio1

server/voice_changer/VoiceChangerManager.py

@@ -1,5 +1,5 @@
 import numpy as np
-from mods.VoiceChanger import VoiceChanger
+from voice_changer.VoiceChanger import VoiceChanger
 
 class VoiceChangerManager():
     @classmethod