mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-02-03 00:33:57 +03:00
255 lines
13 KiB
Python
Executable File
255 lines
13 KiB
Python
Executable File
import torch
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import math, os, traceback
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from scipy.io.wavfile import write, read
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import numpy as np
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import utils
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import commons
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from models import SynthesizerTrn
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from text.symbols import symbols
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from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
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from mel_processing import spectrogram_torch
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from text import text_to_sequence, cleaned_text_to_sequence
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import onnxruntime
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providers = ['OpenVINOExecutionProvider',"CUDAExecutionProvider","DmlExecutionProvider","CPUExecutionProvider"]
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class VoiceChanger():
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def __init__(self, config, model=None, onnx_model=None):
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# 共通で使用する情報を収集
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self.hps = utils.get_hparams_from_file(config)
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self.gpu_num = torch.cuda.device_count()
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text_norm = text_to_sequence("a", self.hps.data.text_cleaners)
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text_norm = commons.intersperse(text_norm, 0)
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self.text_norm = torch.LongTensor(text_norm)
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self.audio_buffer = torch.zeros(1, 0)
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self.prev_audio = np.zeros(1)
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self.mps_enabled = getattr(torch.backends, "mps", None) is not None and torch.backends.mps.is_available()
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print(f"VoiceChanger Initialized (GPU_NUM:{self.gpu_num}, mps_enabled:{self.mps_enabled})")
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self.crossFadeOffsetRate = 0
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self.crossFadeEndRate = 0
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self.unpackedData_length = 0
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# PyTorchモデル生成
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if model != None:
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self.net_g = SynthesizerTrn(
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len(symbols),
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self.hps.data.filter_length // 2 + 1,
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self.hps.train.segment_size // self.hps.data.hop_length,
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n_speakers=self.hps.data.n_speakers,
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**self.hps.model)
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self.net_g.eval()
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utils.load_checkpoint(model, self.net_g, None)
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else:
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self.net_g = None
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# ONNXモデル生成
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if onnx_model != None:
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ort_options = onnxruntime.SessionOptions()
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ort_options.intra_op_num_threads = 8
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# ort_options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL
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# ort_options.execution_mode = onnxruntime.ExecutionMode.ORT_PARALLEL
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# ort_options.inter_op_num_threads = 8
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self.onnx_session = onnxruntime.InferenceSession(
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onnx_model,
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providers=providers
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)
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# print("ONNX_MDEOL!1", self.onnx_session.get_providers())
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# self.onnx_session.set_providers(providers=["CPUExecutionProvider"])
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# print("ONNX_MDEOL!1", self.onnx_session.get_providers())
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# self.onnx_session.set_providers(providers=["DmlExecutionProvider"])
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# print("ONNX_MDEOL!1", self.onnx_session.get_providers())
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else:
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self.onnx_session = None
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# ファイル情報を記録
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self.pyTorch_model_file = model
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self.onnx_model_file = onnx_model
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self.config_file = config
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def destroy(self):
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del self.net_g
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del self.onnx_session
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def get_info(self):
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print("ONNX_MODEL",self.onnx_model_file)
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return {
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"pyTorchModelFile":os.path.basename(self.pyTorch_model_file)if self.pyTorch_model_file!=None else "",
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"onnxModelFile":os.path.basename(self.onnx_model_file)if self.onnx_model_file!=None else "",
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"configFile":os.path.basename(self.config_file),
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"providers":self.onnx_session.get_providers() if hasattr(self, "onnx_session") else ""
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}
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def set_onnx_provider(self, provider:str):
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if hasattr(self, "onnx_session"):
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self.onnx_session.set_providers(providers=[provider])
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print("ONNX_MDEOL: ", self.onnx_session.get_providers())
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return {"provider":self.onnx_session.get_providers()}
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def _generate_strength(self, crossFadeOffsetRate, crossFadeEndRate, unpackedData):
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if self.crossFadeOffsetRate != crossFadeOffsetRate or self.crossFadeEndRate != crossFadeEndRate or self.unpackedData_length != unpackedData.shape[0]:
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self.crossFadeOffsetRate = crossFadeOffsetRate
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self.crossFadeEndRate = crossFadeEndRate
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self.unpackedData_length = unpackedData.shape[0]
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cf_offset = int(unpackedData.shape[0] * crossFadeOffsetRate)
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cf_end = int(unpackedData.shape[0] * crossFadeEndRate)
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cf_range = cf_end - cf_offset
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percent = np.arange(cf_range) / cf_range
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np_prev_strength = np.cos(percent * 0.5 * np.pi) ** 2
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np_cur_strength = np.cos((1-percent) * 0.5 * np.pi) ** 2
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self.np_prev_strength = np.concatenate([np.ones(cf_offset), np_prev_strength, np.zeros(unpackedData.shape[0]-cf_offset-len(np_prev_strength))])
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self.np_cur_strength = np.concatenate([np.zeros(cf_offset), np_cur_strength, np.ones(unpackedData.shape[0]-cf_offset-len(np_cur_strength))])
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self.prev_strength = torch.FloatTensor(self.np_prev_strength)
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self.cur_strength = torch.FloatTensor(self.np_cur_strength)
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torch.set_printoptions(edgeitems=2100)
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print("Generated Strengths")
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# print(f"cross fade: start:{cf_offset} end:{cf_end} range:{cf_range}")
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# print(f"target_len:{unpackedData.shape[0]}, prev_len:{len(self.prev_strength)} cur_len:{len(self.cur_strength)}")
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# print("Prev", self.prev_strength)
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# print("Cur", self.cur_strength)
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# ひとつ前の結果とサイズが変わるため、記録は消去する。
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if hasattr(self, 'prev_audio1') == True:
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delattr(self,"prev_audio1")
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def _generate_input(self, unpackedData, convertSize, srcId):
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# 今回変換するデータをテンソルとして整形する
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audio = torch.FloatTensor(unpackedData.astype(np.float32)) # float32でtensorfを作成
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audio_norm = audio / self.hps.data.max_wav_value # normalize
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audio_norm = audio_norm.unsqueeze(0) # unsqueeze
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self.audio_buffer = torch.cat([self.audio_buffer, audio_norm], axis=1) # 過去のデータに連結
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audio_norm = self.audio_buffer[:, -convertSize:] # 変換対象の部分だけ抽出
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self.audio_buffer = audio_norm
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spec = spectrogram_torch(audio_norm, self.hps.data.filter_length,
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self.hps.data.sampling_rate, self.hps.data.hop_length, self.hps.data.win_length,
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center=False)
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spec = torch.squeeze(spec, 0)
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sid = torch.LongTensor([int(srcId)])
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data = (self.text_norm, spec, audio_norm, sid)
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data = TextAudioSpeakerCollate()([data])
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return data
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def on_request(self, gpu, srcId, dstId, timestamp, convertChunkNum, crossFadeLowerValue, crossFadeOffsetRate, crossFadeEndRate, unpackedData):
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convertSize = convertChunkNum * 128 # 128sample/1chunk
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if unpackedData.shape[0] * 2 > convertSize:
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convertSize = unpackedData.shape[0] * 2
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print("convert Size", convertChunkNum, convertSize)
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self._generate_strength(crossFadeOffsetRate, crossFadeEndRate, unpackedData)
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data = self. _generate_input(unpackedData, convertSize, srcId)
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try:
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# if gpu < 0 or (self.gpu_num == 0 and not self.mps_enabled):
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if gpu == -2 and hasattr(self, 'onnx_session') == True:
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x for x in data]
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sid_tgt1 = torch.LongTensor([dstId])
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# if spec.size()[2] >= 8:
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audio1 = self.onnx_session.run(
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["audio"],
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{
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"specs": spec.numpy(),
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"lengths": spec_lengths.numpy(),
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"sid_src": sid_src.numpy(),
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"sid_tgt": sid_tgt1.numpy()
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})[0][0,0] * self.hps.data.max_wav_value
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if hasattr(self, 'np_prev_audio1') == True:
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prev = self.np_prev_audio1[-1*unpackedData.shape[0]:]
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cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]]
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# print(prev.shape, self.np_prev_strength.shape, cur.shape, self.np_cur_strength.shape)
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powered_prev = prev * self.np_prev_strength
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powered_cur = cur * self.np_cur_strength
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result = powered_prev + powered_cur
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#result = prev * self.np_prev_strength + cur * self.np_cur_strength
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else:
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cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]]
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result = cur
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self.np_prev_audio1 = audio1
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elif gpu < 0 or self.gpu_num == 0:
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with torch.no_grad():
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
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x.cpu() for x in data]
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sid_tgt1 = torch.LongTensor([dstId]).cpu()
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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)
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if self.prev_strength.device != torch.device('cpu'):
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print(f"prev_strength move from {self.prev_strength.device} to cpu")
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self.prev_strength = self.prev_strength.cpu()
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if self.cur_strength.device != torch.device('cpu'):
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print(f"cur_strength move from {self.cur_strength.device} to cpu")
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self.cur_strength = self.cur_strength.cpu()
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if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cpu'):
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prev = self.prev_audio1[-1*unpackedData.shape[0]:]
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cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]]
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result = prev * self.prev_strength + cur * self.cur_strength
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else:
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cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]]
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result = cur
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self.prev_audio1 = audio1
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result = result.cpu().float().numpy()
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# elif self.mps_enabled == True: # MPS doesnt support aten::weight_norm_interface, and PYTORCH_ENABLE_MPS_FALLBACK=1 cause a big dely.
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# x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [
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# x.to("mps") for x in data]
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# sid_tgt1 = torch.LongTensor([dstId]).to("mps")
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# audio1 = (self.net_g.to("mps").voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[
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# 0][0, 0].data * self.hps.data.max_wav_value).cpu().float().numpy()
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else:
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with torch.no_grad():
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x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cuda(gpu) for x in data]
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sid_tgt1 = torch.LongTensor([dstId]).cuda(gpu)
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# 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()
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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
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if self.prev_strength.device != torch.device('cuda', gpu):
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print(f"prev_strength move from {self.prev_strength.device} to gpu{gpu}")
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self.prev_strength = self.prev_strength.cuda(gpu)
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if self.cur_strength.device != torch.device('cuda', gpu):
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print(f"cur_strength move from {self.cur_strength.device} to gpu{gpu}")
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self.cur_strength = self.cur_strength.cuda(gpu)
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if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cuda', gpu):
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prev = self.prev_audio1[-1*unpackedData.shape[0]:]
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cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]]
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result = prev * self.prev_strength + cur * self.cur_strength
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# print("merging...", prev.shape, cur.shape)
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else:
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cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]]
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result = cur
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# print("no merging...", cur.shape)
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self.prev_audio1 = audio1
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#print(result)
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result = result.cpu().float().numpy()
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except Exception as e:
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print("VC PROCESSING!!!! EXCEPTION!!!", e)
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print(traceback.format_exc())
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del self.np_prev_audio1
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del self.prev_audio1
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result = result.astype(np.int16)
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# print("on_request result size:",result.shape)
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return result
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