import torch import math 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})") self.crossFadeOffsetRate = 0 self.crossFadeEndRate = 0 self.unpackedData_length = 0 def destroy(self): del self.net_g def on_request(self, gpu, srcId, dstId, timestamp, convertChunkNum, crossFadeLowerValue, crossFadeOffsetRate, crossFadeEndRate, unpackedData): # convertSize = unpackedData.shape[0] + (convertChunkNum * 128) # 128sample/1chunk convertSize = convertChunkNum * 128 # 128sample/1chunk # print("on_request", unpackedData.shape[0], convertChunkNum* 128 ) if unpackedData.shape[0] * 2 > convertSize: # print(f"Convert sample_num = {128 * convertChunkNum} (128 * {convertChunkNum}) is less than input sample_num x2 ({unpackedData.shape[0]}) x2. Chage to {unpackedData.shape[0] * 2} samples") convertSize = unpackedData.shape[0] * 2 if self.crossFadeOffsetRate != crossFadeOffsetRate or self.crossFadeEndRate != crossFadeEndRate or self.unpackedData_length != unpackedData.shape[0]: self.crossFadeOffsetRate = crossFadeOffsetRate self.crossFadeEndRate = crossFadeEndRate self.unpackedData_length = unpackedData.shape[0] cf_offset = int(unpackedData.shape[0] * crossFadeOffsetRate) cf_end = int(unpackedData.shape[0] * 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 np_prev_strength = np.concatenate([np.ones(cf_offset), np_prev_strength, np.zeros(unpackedData.shape[0]-cf_offset-len(np_prev_strength))]) np_cur_strength = np.concatenate([np.zeros(cf_offset), np_cur_strength, np.ones(unpackedData.shape[0]-cf_offset-len(np_cur_strength))]) self.prev_strength = torch.FloatTensor(np_prev_strength) self.cur_strength = torch.FloatTensor(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。 delattr(self,"prev_audio1") try: # 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:] # 変換対象の部分だけ抽出 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() 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 if self.prev_strength.device != torch.device('cuda', gpu): print(f"prev_strength move from {self.prev_strength.device} to gpu{gpu}") self.prev_strength = self.prev_strength.cuda(gpu) if self.cur_strength.device != torch.device('cuda', gpu): print(f"cur_strength move from {self.cur_strength.device} to gpu{gpu}") self.cur_strength = self.cur_strength.cuda(gpu) if hasattr(self, 'prev_audio1') == True: prev = self.prev_audio1[-1*unpackedData.shape[0]:] cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]] result = prev * self.prev_strength + cur * self.cur_strength # print("merging...", prev.shape, cur.shape) else: cur = audio1[-2*unpackedData.shape[0]:-1*unpackedData.shape[0]] result = cur # print("no merging...", cur.shape) self.prev_audio1 = audio1 #print(result) result = result.cpu().float().numpy() except Exception as e: print("VC PROCESSING!!!! EXCEPTION!!!", e) print(traceback.format_exc()) result = result.astype(np.int16) print("on_request result size:",result.shape) return result def on_request_old(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