from const import ERROR_NO_ONNX_SESSION, TMP_DIR import torch import os import traceback import numpy as np from dataclasses import dataclass, asdict import onnxruntime from symbols import symbols from models import SynthesizerTrn import pyworld as pw # from voice_changer.TrainerFunctions import TextAudioSpeakerCollate, spectrogram_torch, load_checkpoint, get_hparams_from_file from voice_changer.client_modules import convert_continuos_f0, spectrogram_torch, TextAudioSpeakerCollate, get_hparams_from_file, load_checkpoint providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"] import wave import matplotlib matplotlib.use('Agg') import pylab import librosa import librosa.display SAMPLING_RATE = 24000 class MockStream: """ オーディオストリーミング入出力をファイル入出力にそのまま置き換えるためのモック """ def __init__(self, sampling_rate): self.sampling_rate = sampling_rate self.start_count = 2 self.end_count = 2 self.fr = None self.fw = None def open_inputfile(self, input_filename): self.fr = wave.open(input_filename, 'rb') def open_outputfile(self, output_filename): self.fw = wave.open(output_filename, 'wb') self.fw.setnchannels(1) self.fw.setsampwidth(2) self.fw.setframerate(self.sampling_rate) def read(self, length, exception_on_overflow=False): if self.start_count > 0: wav = bytes(length * 2) self.start_count -= 1 # 最初の2回はダミーの空データ送る else: wav = self.fr.readframes(length) if len(wav) <= 0: # データなくなってから最後の2回はダミーの空データを送る wav = bytes(length * 2) self.end_count -= 1 if self.end_count < 0: Hyperparameters.VC_END_FLAG = True return wav def write(self, wav): self.fw.writeframes(wav) def stop_stream(self): pass def close(self): if self.fr != None: self.fr.close() self.fr = None if self.fw != None: self.fw.close() self.fw = None @dataclass class VocieChangerSettings(): gpu: int = 0 srcId: int = 107 dstId: int = 100 crossFadeOffsetRate: float = 0.1 crossFadeEndRate: float = 0.9 crossFadeOverlapRate: float = 0.9 convertChunkNum: int = 32 minConvertSize: int = 0 framework: str = "PyTorch" # PyTorch or ONNX f0Factor: float = 1.0 f0Detector: str = "dio" # dio or harvest recordIO: int = 1 # 0:off, 1:on pyTorchModelFile: str = "" onnxModelFile: str = "" configFile: str = "" # ↓mutableな物だけ列挙 intData = ["gpu", "srcId", "dstId", "convertChunkNum", "minConvertSize", "recordIO"] floatData = ["crossFadeOffsetRate", "crossFadeEndRate", "crossFadeOverlapRate", "f0Factor"] strData = ["framework", "f0Detector"] class VoiceChanger(): def __init__(self): # 初期化 self.settings = VocieChangerSettings() self.unpackedData_length = 0 self.net_g = None self.onnx_session = None self.currentCrossFadeOffsetRate = 0 self.currentCrossFadeEndRate = 0 self.currentCrossFadeOverlapRate = 0 self.gpu_num = torch.cuda.device_count() 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() self._setupRecordIO() print(f"VoiceChanger Initialized (GPU_NUM:{self.gpu_num}, mps_enabled:{self.mps_enabled})") def _setupRecordIO(self): # IO Recorder Setup mock_stream_out = MockStream(24000) stream_output_file = os.path.join(TMP_DIR, "out.wav") if os.path.exists(stream_output_file): os.remove(stream_output_file) mock_stream_out.open_outputfile(stream_output_file) self.stream_out = mock_stream_out mock_stream_in = MockStream(24000) stream_input_file = os.path.join(TMP_DIR, "in.wav") if os.path.exists(stream_input_file): os.remove(stream_input_file) mock_stream_in.open_outputfile(stream_input_file) self.stream_in = mock_stream_in def loadModel(self, config: str, pyTorch_model_file: str = None, onnx_model_file: str = None): self.settings.configFile = config self.hps = get_hparams_from_file(config) if pyTorch_model_file != None: self.settings.pyTorchModelFile = pyTorch_model_file if onnx_model_file: self.settings.onnxModelFile = onnx_model_file # PyTorchモデル生成 if pyTorch_model_file != None: self.net_g = SynthesizerTrn( spec_channels=self.hps.data.filter_length // 2 + 1, segment_size=self.hps.train.segment_size // self.hps.data.hop_length, inter_channels=self.hps.model.inter_channels, hidden_channels=self.hps.model.hidden_channels, upsample_rates=self.hps.model.upsample_rates, upsample_initial_channel=self.hps.model.upsample_initial_channel, upsample_kernel_sizes=self.hps.model.upsample_kernel_sizes, n_flow=self.hps.model.n_flow, dec_out_channels=1, dec_kernel_size=7, n_speakers=self.hps.data.n_speakers, gin_channels=self.hps.model.gin_channels, requires_grad_pe=self.hps.requires_grad.pe, requires_grad_flow=self.hps.requires_grad.flow, requires_grad_text_enc=self.hps.requires_grad.text_enc, requires_grad_dec=self.hps.requires_grad.dec ) self.net_g.eval() 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: ort_options = onnxruntime.SessionOptions() ort_options.intra_op_num_threads = 8 self.onnx_session = onnxruntime.InferenceSession( onnx_model_file, providers=providers ) return self.get_info() def destroy(self): del self.net_g del self.onnx_session def get_info(self): data = asdict(self.settings) data["onnxExecutionProvider"] = self.onnx_session.get_providers() if self.onnx_session != None else [] files = ["configFile", "pyTorchModelFile", "onnxModelFile"] for f in files: if data[f] != None and os.path.exists(data[f]): data[f] = os.path.basename(data[f]) else: data[f] = "" return data def _get_f0_dio(self, y, sr=SAMPLING_RATE): _f0, time = pw.dio(y, sr, frame_period=5) f0 = pw.stonemask(y, _f0, time, sr) time = np.linspace(0, y.shape[0] / sr, len(time)) return f0, time def _get_f0_harvest(self, y, sr=SAMPLING_RATE): _f0, time = pw.harvest(y, sr, frame_period=5) f0 = pw.stonemask(y, _f0, time, sr) time = np.linspace(0, y.shape[0] / sr, len(time)) return f0, time def update_setteings(self, key: str, val: any): if key == "onnxExecutionProvider" and self.onnx_session != None: if val == "CUDAExecutionProvider": if self.settings.gpu < 0 or self.settings.gpu >= self.gpu_num: self.settings.gpu = 0 provider_options = [{'device_id': self.settings.gpu}] self.onnx_session.set_providers(providers=[val], provider_options=provider_options) else: self.onnx_session.set_providers(providers=[val]) elif key in self.settings.intData: setattr(self.settings, key, int(val)) if key == "gpu" and val >= 0 and val < self.gpu_num and self.onnx_session != None: providers = self.onnx_session.get_providers() print("Providers:", providers) if "CUDAExecutionProvider" in providers: provider_options = [{'device_id': self.settings.gpu}] self.onnx_session.set_providers(providers=["CUDAExecutionProvider"], provider_options=provider_options) if key == "crossFadeOffsetRate" or key == "crossFadeEndRate": self.unpackedData_length = 0 if key == "recordIO" and val == 1: self._setupRecordIO() if key == "recordIO" and val == 0: pass if key == "recordIO" and val == 2: try: stream_input_file = os.path.join(TMP_DIR, "in.wav") analyze_file_dio = os.path.join(TMP_DIR, "analyze-dio.png") analyze_file_harvest = os.path.join(TMP_DIR, "analyze-harvest.png") y, sr = librosa.load(stream_input_file, SAMPLING_RATE) y = y.astype(np.float64) spec = librosa.amplitude_to_db(np.abs(librosa.stft(y, n_fft=2048, win_length=2048, hop_length=128)), ref=np.max) f0_dio, times = self._get_f0_dio(y) f0_harvest, times = self._get_f0_harvest(y) pylab.close() HOP_LENGTH = 128 img = librosa.display.specshow(spec, sr=SAMPLING_RATE, hop_length=HOP_LENGTH, x_axis='time', y_axis='log', ) pylab.plot(times, f0_dio, label='f0', color=(0, 1, 1, 0.6), linewidth=3) pylab.savefig(analyze_file_dio) pylab.close() HOP_LENGTH = 128 img = librosa.display.specshow(spec, sr=SAMPLING_RATE, hop_length=HOP_LENGTH, x_axis='time', y_axis='log', ) pylab.plot(times, f0_harvest, label='f0', color=(0, 1, 1, 0.6), linewidth=3) pylab.savefig(analyze_file_harvest) except Exception as e: print("recordIO exception", e) elif key in self.settings.floatData: setattr(self.settings, key, float(val)) elif key in self.settings.strData: setattr(self.settings, key, str(val)) else: print(f"{key} is not mutalbe variable!") return self.get_info() def _generate_strength(self, unpackedData): if self.unpackedData_length != unpackedData.shape[0] or self.currentCrossFadeOffsetRate != self.settings.crossFadeOffsetRate or self.currentCrossFadeEndRate != self.settings.crossFadeEndRate or self.currentCrossFadeOverlapRate != self.settings.crossFadeOverlapRate: self.unpackedData_length = unpackedData.shape[0] self.currentCrossFadeOffsetRate = self.settings.crossFadeOffsetRate self.currentCrossFadeEndRate = self.settings.crossFadeEndRate self.currentCrossFadeOverlapRate = self.settings.crossFadeOverlapRate overlapSize = int(unpackedData.shape[0] * self.settings.crossFadeOverlapRate) cf_offset = int(overlapSize * self.settings.crossFadeOffsetRate) cf_end = int(overlapSize * self.settings.crossFadeEndRate) cf_range = cf_end - cf_offset percent = np.arange(cf_range) / cf_range np_prev_strength = np.cos(percent * 0.5 * np.pi) ** 2 np_cur_strength = np.cos((1 - percent) * 0.5 * np.pi) ** 2 self.np_prev_strength = np.concatenate([np.ones(cf_offset), np_prev_strength, np.zeros(overlapSize - cf_offset - len(np_prev_strength))]) self.np_cur_strength = np.concatenate([np.zeros(cf_offset), np_cur_strength, np.ones(overlapSize - cf_offset - len(np_cur_strength))]) self.prev_strength = torch.FloatTensor(self.np_prev_strength) self.cur_strength = torch.FloatTensor(self.np_cur_strength) # torch.set_printoptions(edgeitems=2100) print("Generated Strengths") # print(f"cross fade: start:{cf_offset} end:{cf_end} range:{cf_range}") # print(f"target_len:{unpackedData.shape[0]}, prev_len:{len(self.prev_strength)} cur_len:{len(self.cur_strength)}") # print("Prev", self.prev_strength) # print("Cur", self.cur_strength) # ひとつ前の結果とサイズが変わるため、記録は消去する。 if hasattr(self, 'prev_audio1') == True: delattr(self, "prev_audio1") def _generate_input(self, unpackedData: any, convertSize: int): # 今回変換するデータをテンソルとして整形する audio = torch.FloatTensor(unpackedData.astype(np.float32)) # float32でtensorfを作成 audio_norm = audio / self.hps.data.max_wav_value # normalize audio_norm = audio_norm.unsqueeze(0) # unsqueeze self.audio_buffer = torch.cat([self.audio_buffer, audio_norm], axis=1) # 過去のデータに連結 # audio_norm = self.audio_buffer[:, -(convertSize + 1280 * 2):] # 変換対象の部分だけ抽出 audio_norm = self.audio_buffer[:, -(convertSize):] # 変換対象の部分だけ抽出 self.audio_buffer = audio_norm # TBD: numpy <--> pytorch変換が行ったり来たりしているが、まずは動かすことを最優先。 audio_norm_np = audio_norm.squeeze().numpy().astype(np.float64) if self.settings.f0Detector == "dio": _f0, _time = pw.dio(audio_norm_np, self.hps.data.sampling_rate, frame_period=5.5) f0 = pw.stonemask(audio_norm_np, _f0, _time, self.hps.data.sampling_rate) else: f0, t = pw.harvest(audio_norm_np, self.hps.data.sampling_rate, frame_period=5.5, f0_floor=71.0, f0_ceil=1000.0) f0 = convert_continuos_f0(f0, int(audio_norm_np.shape[0] / self.hps.data.hop_length)) f0 = torch.from_numpy(f0.astype(np.float32)) 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) # dispose_stft_specs = 2 # spec = spec[:, dispose_stft_specs:-dispose_stft_specs] # f0 = f0[dispose_stft_specs:-dispose_stft_specs] spec = torch.squeeze(spec, 0) sid = torch.LongTensor([int(self.settings.srcId)]) # data = (self.text_norm, spec, audio_norm, sid) # data = TextAudioSpeakerCollate()([data]) data = TextAudioSpeakerCollate( sample_rate=self.hps.data.sampling_rate, hop_size=self.hps.data.hop_length, f0_factor=self.settings.f0Factor )([(spec, sid, f0)]) return data, f0.numpy() def _onnx_inference(self, data, inputSize): if hasattr(self, "onnx_session") == False or self.onnx_session == None: print("[Voice Changer] No ONNX session.") return np.zeros(1).astype(np.int16) x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x for x in data] sid_tgt1 = torch.LongTensor([self.settings.dstId]) # if spec.size()[2] >= 8: audio1 = self.onnx_session.run( ["audio"], { "specs": spec.numpy(), "lengths": spec_lengths.numpy(), "sid_src": sid_src.numpy(), "sid_tgt": sid_tgt1.numpy() })[0][0, 0] * self.hps.data.max_wav_value if hasattr(self, 'np_prev_audio1') == True: overlapSize = int(inputSize * self.settings.crossFadeOverlapRate) prev_overlap = self.np_prev_audio1[-1 * overlapSize:] cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize] # print(prev_overlap.shape, self.np_prev_strength.shape, cur_overlap.shape, self.np_cur_strength.shape) # print(">>>>>>>>>>>", -1*(inputSize + overlapSize) , -1*inputSize) powered_prev = prev_overlap * self.np_prev_strength powered_cur = cur_overlap * self.np_cur_strength powered_result = powered_prev + powered_cur cur = audio1[-1 * inputSize:-1 * overlapSize] result = np.concatenate([powered_result, cur], axis=0) else: result = np.zeros(1).astype(np.int16) self.np_prev_audio1 = audio1 return result def _pyTorch_inference(self, data, inputSize): if hasattr(self, "net_g") == False or self.net_g == None: print("[Voice Changer] No pyTorch session.") return np.zeros(1).astype(np.int16) if self.settings.gpu < 0 or self.gpu_num == 0: with torch.no_grad(): spec, spec_lengths, sid_src, sin, d = data spec = spec.cpu() spec_lengths = spec_lengths.cpu() sid_src = sid_src.cpu() sin = sin.cpu() d = tuple([d[:1].cpu() for d in d]) sid_target = torch.LongTensor([self.settings.dstId]).cpu() audio1 = self.net_g.cpu().voice_conversion(spec, spec_lengths, sin, d, sid_src, sid_target)[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") self.prev_strength = self.prev_strength.cpu() if self.cur_strength.device != torch.device('cpu'): print(f"cur_strength move from {self.cur_strength.device} to cpu") self.cur_strength = self.cur_strength.cpu() if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cpu'): # prev_audio1が所望のデバイスに無い場合は一回休み。 overlapSize = int(inputSize * self.settings.crossFadeOverlapRate) prev_overlap = self.prev_audio1[-1 * overlapSize:] cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize] powered_prev = prev_overlap * self.prev_strength powered_cur = cur_overlap * self.cur_strength powered_result = powered_prev + powered_cur cur = audio1[-1 * inputSize:-1 * overlapSize] # 今回のインプットの生部分。(インプット - 次回のCrossfade部分)。 result = torch.cat([powered_result, cur], axis=0) # Crossfadeと今回のインプットの生部分を結合 else: cur = audio1[-2 * inputSize:-1 * inputSize] result = cur self.prev_audio1 = audio1 result = result.cpu().float().numpy() else: with torch.no_grad(): spec, spec_lengths, sid_src, sin, d = data spec = spec.cuda(self.settings.gpu) spec_lengths = spec_lengths.cuda(self.settings.gpu) sid_src = sid_src.cuda(self.settings.gpu) sin = sin.cuda(self.settings.gpu) d = tuple([d[:1].cuda(self.settings.gpu) for d in d]) sid_target = 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].data * self.hps.data.max_wav_value audio1 = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sin, d, sid_src, sid_target)[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}") self.prev_strength = self.prev_strength.cuda(self.settings.gpu) if self.cur_strength.device != torch.device('cuda', self.settings.gpu): print(f"cur_strength move from {self.cur_strength.device} to gpu{self.settings.gpu}") self.cur_strength = self.cur_strength.cuda(self.settings.gpu) if hasattr(self, 'prev_audio1') == True and self.prev_audio1.device == torch.device('cuda', self.settings.gpu): overlapSize = int(inputSize * self.settings.crossFadeOverlapRate) prev_overlap = self.prev_audio1[-1 * overlapSize:] cur_overlap = audio1[-1 * (inputSize + overlapSize):-1 * inputSize] powered_prev = prev_overlap * self.prev_strength powered_cur = cur_overlap * self.cur_strength powered_result = powered_prev + powered_cur cur = audio1[-1 * inputSize:-1 * overlapSize] # 今回のインプットの生部分。(インプット - 次回のCrossfade部分)。 result = torch.cat([powered_result, cur], axis=0) # Crossfadeと今回のインプットの生部分を結合 else: cur = audio1[-2 * inputSize:-1 * inputSize] result = cur self.prev_audio1 = audio1 result = result.cpu().float().numpy() return result def on_request(self, unpackedData: any): convertSize = self.settings.convertChunkNum * 128 # 128sample/1chunk # print("convsize:", unpackedData.shape[0] * (1 + self.settings.crossFadeOverlapRate)) if unpackedData.shape[0] * (1 + self.settings.crossFadeOverlapRate) + 1024 > convertSize: convertSize = int(unpackedData.shape[0] * (1 + self.settings.crossFadeOverlapRate)) + 1024 if convertSize < self.settings.minConvertSize: convertSize = self.settings.minConvertSize # print("convert Size", unpackedData.shape[0], unpackedData.shape[0]*(1 + self.settings.crossFadeOverlapRate), convertSize, self.settings.minConvertSize) # convertSize = 8192 self._generate_strength(unpackedData) # f0はデバッグ用 data, f0 = self._generate_input(unpackedData, convertSize) try: if self.settings.framework == "ONNX": result = self._onnx_inference(data, unpackedData.shape[0]) else: result = self._pyTorch_inference(data, unpackedData.shape[0]) except Exception as e: print("VC PROCESSING!!!! EXCEPTION!!!", e) print(traceback.format_exc()) if hasattr(self, "np_prev_audio1"): del self.np_prev_audio1 if hasattr(self, "prev_audio1"): del self.prev_audio1 return np.zeros(1).astype(np.int16) result = result.astype(np.int16) # print("on_request result size:",result.shape) if self.settings.recordIO == 1: self.stream_in.write(unpackedData.astype(np.int16).tobytes()) self.stream_out.write(result.tobytes()) return result ######################################################################################### def overlap_merge(self, now_wav, prev_wav, overlap_length): """ 生成したwavデータを前回生成したwavデータとoverlap_lengthだけ重ねてグラデーション的にマージします 終端のoverlap_lengthぶんは次回マージしてから再生するので削除します Parameters ---------- now_wav: 今回生成した音声wavデータ prev_wav: 前回生成した音声wavデータ overlap_length: 重ねる長さ """ if overlap_length == 0: return now_wav gradation = np.arange(overlap_length) / overlap_length now = np.frombuffer(now_wav, dtype='int16') prev = np.frombuffer(prev_wav, dtype='int16') now_head = now[:overlap_length] prev_tail = prev[-overlap_length:] print("merge params:", gradation.shape, now.shape, prev.shape, now_head.shape, prev_tail.shape) merged = prev_tail * (np.cos(gradation * np.pi * 0.5) ** 2) + now_head * (np.cos((1 - gradation) * np.pi * 0.5) ** 2) # merged = prev_tail * (1 - gradation) + now_head * gradation overlapped = np.append(merged, now[overlap_length:-overlap_length]) signal = np.round(overlapped, decimals=0) signal = signal.astype(np.int16) # signal = signal.astype(np.int16).tobytes() return signal def on_request_(self, unpackedData: any): self._generate_strength(unpackedData) convertSize = 8192 unpackedData = unpackedData.astype(np.int16) if hasattr(self, 'stored_raw_input') == False: self.stored_raw_input = unpackedData else: self.stored_raw_input = np.concatenate([self.stored_raw_input, unpackedData]) self.stored_raw_input = self.stored_raw_input[-1 * (convertSize):] processing_input = self.stored_raw_input print("signal_shape1", unpackedData.shape, processing_input.shape, processing_input.dtype) processing_input = processing_input / self.hps.data.max_wav_value print("type:", processing_input.dtype) _f0, _time = pw.dio(processing_input, self.hps.data.sampling_rate, frame_period=5.5) f0 = pw.stonemask(processing_input, _f0, _time, self.hps.data.sampling_rate) f0 = convert_continuos_f0(f0, int(processing_input.shape[0] / self.hps.data.hop_length)) f0 = torch.from_numpy(f0.astype(np.float32)) print("signal_shape2", f0.shape) processing_input = torch.from_numpy(processing_input.astype(np.float32)).clone() with torch.no_grad(): trans_length = processing_input.size()[0] # spec, sid = get_audio_text_speaker_pair(signal.view(1, trans_length), Hyperparameters.SOURCE_ID) processing_input_v = processing_input.view(1, trans_length) # unsqueezeと同じ print("processing_input_v shape:", processing_input_v.shape) spec = spectrogram_torch(processing_input_v, 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(self.settings.srcId)]) dispose_stft_specs = 2 spec = spec[:, dispose_stft_specs:-dispose_stft_specs] f0 = f0[dispose_stft_specs:-dispose_stft_specs] print("spec shape:", spec.shape) data = TextAudioSpeakerCollate( sample_rate=self.hps.data.sampling_rate, hop_size=self.hps.data.hop_length, f0_factor=self.settings.f0Factor )([(spec, sid, f0)]) if self.settings.gpu >= 0 or self.gpu_num > 0: # spec, spec_lengths, sid_src, sin, d = [x.cuda(Hyperparameters.GPU_ID) for x in data] spec, spec_lengths, sid_src, sin, d = data spec = spec.cuda(self.settings.gpu) spec_lengths = spec_lengths.cuda(self.settings.gpu) sid_src = sid_src.cuda(self.settings.gpu) sin = sin.cuda(self.settings.gpu) d = tuple([d[:1].cuda(self.settings.gpu) for d in d]) sid_target = torch.LongTensor([self.settings.dstId]).cuda(self.settings.gpu) audio = self.net_g.cuda(self.settings.gpu).voice_conversion(spec, spec_lengths, sin, d, sid_src, sid_target)[0, 0].data.cpu().float().numpy() else: spec, spec_lengths, sid_src, sin, d = data sid_target = torch.LongTensor([self.settings.dstId]) audio = self.net_g.voice_conversion(spec, spec_lengths, sin, d, sid_src, sid_target)[0, 0].data.cpu().float().numpy() dispose_conv1d_length = 1280 audio = audio[dispose_conv1d_length:-dispose_conv1d_length] audio = audio * self.hps.data.max_wav_value audio = audio.astype(np.int16) print("fin audio shape:", audio.shape) audio = audio.tobytes() if hasattr(self, "prev_audio"): try: audio1 = self.overlap_merge(audio, self.prev_audio, 1024) except: audio1 = np.zeros(1).astype(np.int16) pass # return np.zeros(1).astype(np.int16) else: audio1 = np.zeros(1).astype(np.int16) self.prev_audio = audio self.out.write(audio) self.stream_in.write(unpackedData.tobytes()) # print(audio1) return audio1