WIP: switch base from trainer to client

.gitignore

@@ -4,6 +4,7 @@ __pycache__
 
 server/upload_dir/
 server/MMVC_Trainer/
+server/MMVC_Client/
 server/key
 server/info
 

server/MMVCServerSIO.py

@@ -8,8 +8,9 @@ from distutils.util import strtobool
 
 from scipy.io.wavfile import write, read
 
-sys.path.append("MMVC_Trainer")
-sys.path.append("MMVC_Trainer/text")
+# sys.path.append("MMVC_Trainer")
+# sys.path.append("MMVC_Trainer/text")
+sys.path.append("MMVC_Client/python")
 
 from fastapi.routing import APIRoute
 from fastapi import HTTPException,  FastAPI, UploadFile, File, Form

server/misc/log_control.py

@@ -2,13 +2,15 @@ import logging
 
 # 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
 

server/voice_changer/TrainerFunctions.py

@@ -0,0 +1,151 @@
+import torch
+import os, sys, json
+import logging
+logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+logger = logging
+
+hann_window = {}
+def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
+    if torch.min(y) < -1.:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.:
+        print('max value is ', torch.max(y))
+
+    global hann_window
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    if wnsize_dtype_device not in hann_window:
+        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
+
+    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
+    y = y.squeeze(1)
+
+    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
+    spec = torch.view_as_real(spec)
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    return spec
+
+class TextAudioSpeakerCollate():
+    """ Zero-pads model inputs and targets
+    """
+    def __init__(self, return_ids=False, no_text = False):
+        self.return_ids = return_ids
+        self.no_text = no_text
+
+    def __call__(self, batch):
+        """Collate's training batch from normalized text, audio and speaker identities
+        PARAMS
+        ------
+        batch: [text_normalized, spec_normalized, wav_normalized, sid]
+        """
+        # Right zero-pad all one-hot text sequences to max input length
+        _, ids_sorted_decreasing = torch.sort(
+            torch.LongTensor([x[1].size(1) for x in batch]),
+            dim=0, descending=True)
+
+        max_text_len = max([len(x[0]) for x in batch])
+        max_spec_len = max([x[1].size(1) for x in batch])
+        max_wav_len = max([x[2].size(1) for x in batch])
+
+        text_lengths = torch.LongTensor(len(batch))
+        spec_lengths = torch.LongTensor(len(batch))
+        wav_lengths = torch.LongTensor(len(batch))
+        sid = torch.LongTensor(len(batch))
+
+        text_padded = torch.LongTensor(len(batch), max_text_len)
+        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
+        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
+        text_padded.zero_()
+        spec_padded.zero_()
+        wav_padded.zero_()
+        for i in range(len(ids_sorted_decreasing)):
+            row = batch[ids_sorted_decreasing[i]]
+
+            text = row[0]
+            text_padded[i, :text.size(0)] = text
+            text_lengths[i] = text.size(0)
+
+            spec = row[1]
+            spec_padded[i, :, :spec.size(1)] = spec
+            spec_lengths[i] = spec.size(1)
+
+            wav = row[2]
+            wav_padded[i, :, :wav.size(1)] = wav
+            wav_lengths[i] = wav.size(1)
+
+            sid[i] = row[3]
+
+        if self.return_ids:
+            return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
+        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
+
+
+def load_checkpoint(checkpoint_path, model, optimizer=None):
+  assert os.path.isfile(checkpoint_path), f"No such file or directory: {checkpoint_path}"
+  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
+  iteration = checkpoint_dict['iteration']
+  learning_rate = checkpoint_dict['learning_rate']
+  if optimizer is not None:
+    optimizer.load_state_dict(checkpoint_dict['optimizer'])
+  saved_state_dict = checkpoint_dict['model']
+  if hasattr(model, 'module'):
+    state_dict = model.module.state_dict()
+  else:
+    state_dict = model.state_dict()
+  new_state_dict= {}
+  for k, v in state_dict.items():
+    try:
+      new_state_dict[k] = saved_state_dict[k]
+    except:
+      logger.info("%s is not in the checkpoint" % k)
+      new_state_dict[k] = v
+  if hasattr(model, 'module'):
+    model.module.load_state_dict(new_state_dict)
+  else:
+    model.load_state_dict(new_state_dict)
+  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
+    checkpoint_path, iteration))
+  return model, optimizer, learning_rate, iteration
+
+
+def get_hparams_from_file(config_path):
+  with open(config_path, "r") as f:
+    data = f.read()
+  config = json.loads(data)
+
+  hparams =HParams(**config)
+  return hparams
+
+class HParams():
+  def __init__(self, **kwargs):
+    for k, v in kwargs.items():
+      if type(v) == dict:
+        v = HParams(**v)
+      self[k] = v
+    
+  def keys(self):
+    return self.__dict__.keys()
+
+  def items(self):
+    return self.__dict__.items()
+
+  def values(self):
+    return self.__dict__.values()
+
+  def __len__(self):
+    return len(self.__dict__)
+
+  def __getitem__(self, key):
+    return getattr(self, key)
+
+  def __setitem__(self, key, value):
+    return setattr(self, key, value)
+
+  def __contains__(self, key):
+    return key in self.__dict__
+
+  def __repr__(self):
+    return self.__dict__.__repr__()
+

server/voice_changer/VoiceChanger.py

@@ -4,17 +4,34 @@ import math, os, traceback
 from scipy.io.wavfile import write, read
 import numpy as np
 from dataclasses import dataclass, asdict
-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
 import onnxruntime
 
+
+# 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
+
+
+################
+from symbols import symbols
+# from mmvc_client import get_hparams_from_file, load_checkpoint
+from models import SynthesizerTrn
+################
+
+# from voice_changer.utils import get_hparams_from_file, load_checkpoint
+# from voice_changer.models import SynthesizerTrn
+# from voice_changer.symbols import symbols
+
+from voice_changer.TrainerFunctions import TextAudioSpeakerCollate, spectrogram_torch, load_checkpoint, get_hparams_from_file
+
 providers = ['OpenVINOExecutionProvider',"CUDAExecutionProvider","DmlExecutionProvider","CPUExecutionProvider"]
 
 @dataclass
@@ -49,12 +66,17 @@ class VoiceChanger():
         self.currentCrossFadeOverlapRate=0
         
         # 共通で使用する情報を収集
-        self.hps = utils.get_hparams_from_file(config)
+        # self.hps = utils.get_hparams_from_file(config)
+        self.hps = get_hparams_from_file(config)
         self.gpu_num = torch.cuda.device_count()
 
-        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)
+        # text_norm = text_to_sequence("a", self.hps.data.text_cleaners)
+        # print("text_norm1: ",text_norm)
+        # text_norm = commons.intersperse(text_norm, 0)
+        # print("text_norm2: ",text_norm)
+        # self.text_norm = torch.LongTensor(text_norm)
+
+        self.text_norm = torch.LongTensor([0, 6, 0])        
         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()
@@ -77,7 +99,8 @@ class VoiceChanger():
                 n_speakers=self.hps.data.n_speakers,
                 **self.hps.model)
             self.net_g.eval()
-            utils.load_checkpoint(pyTorch_model_file, self.net_g, None)
+            load_checkpoint(pyTorch_model_file, self.net_g, None)
+            # utils.load_checkpoint(pyTorch_model_file, self.net_g, None)
 
         # ONNXモデル生成
         if onnx_model_file != None:
@@ -232,7 +255,7 @@ class VoiceChanger():
             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([self.settings.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)
+                audio1 = (self.net_g.cpu().voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0, 0].data * self.hps.data.max_wav_value)
 
                 if self.prev_strength.device != torch.device('cpu'):
                     print(f"prev_strength move from {self.prev_strength.device} to cpu")
@@ -263,7 +286,7 @@ class VoiceChanger():
             with torch.no_grad():
                 x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cuda(self.settings.gpu) for x in data]
                 sid_tgt1 = torch.LongTensor([self.settings.dstId]).cuda(self.settings.gpu)
-                audio1 = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0][0, 0].data * self.hps.data.max_wav_value
+                audio1 = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0, 0].data * self.hps.data.max_wav_value
 
                 if self.prev_strength.device != torch.device('cuda', self.settings.gpu):
                     print(f"prev_strength move from {self.prev_strength.device} to gpu{self.settings.gpu}")