diff --git a/server/voice_changer/RVC/RVC.py b/server/voice_changer/RVC/RVC.py index c627a964..eb0e2006 100644 --- a/server/voice_changer/RVC/RVC.py +++ b/server/voice_changer/RVC/RVC.py @@ -15,6 +15,8 @@ import numpy as np import torch from fairseq import checkpoint_utils +import traceback +import faiss from const import TMP_DIR # type:ignore @@ -169,12 +171,15 @@ class RVC: self.settings.modelSlots[slot].deprecated = tmp_onnx_session.getDeprecated() def prepareModel(self, slot: int): + if slot < 0: + return self.get_info() print("[Voice Changer] Prepare Model of slot:", slot) onnxModelFile = self.settings.modelSlots[slot].onnxModelFile isONNX = ( True if self.settings.modelSlots[slot].onnxModelFile is not None else False ) + # モデルのロード if isONNX: print("[Voice Changer] Loading ONNX Model...") self.next_onnx_session = ModelWrapper(onnxModelFile) @@ -214,8 +219,36 @@ class RVC: self.next_net_g = net_g self.next_onnx_session = None + # Indexのロード + print("[Voice Changer] Loading index...") self.next_feature_file = self.settings.modelSlots[slot].featureFile self.next_index_file = self.settings.modelSlots[slot].indexFile + + if ( + self.settings.modelSlots[slot].featureFile is not None + and self.settings.modelSlots[slot].indexFile is not None + ): + if ( + os.path.exists(self.settings.modelSlots[slot].featureFile) is True + and os.path.exists(self.settings.modelSlots[slot].indexFile) is True + ): + try: + self.next_index = faiss.read_index( + self.settings.modelSlots[slot].indexFile + ) + self.next_feature = np.load( + self.settings.modelSlots[slot].featureFile + ) + except: + print("[Voice Changer] load index failed. Use no index.") + traceback.print_exc() + self.next_index = self.next_feature = None + else: + print("[Voice Changer] Index file is not found. Use no index.") + self.next_index = self.next_feature = None + else: + self.next_index = self.next_feature = None + self.next_trans = self.settings.modelSlots[slot].defaultTrans self.next_samplingRate = self.settings.modelSlots[slot].samplingRate self.next_framework = ( @@ -232,6 +265,8 @@ class RVC: self.onnx_session = self.next_onnx_session self.feature_file = self.next_feature_file self.index_file = self.next_index_file + self.feature = self.next_feature + self.index = self.next_index self.settings.tran = self.next_trans self.settings.framework = self.next_framework self.settings.modelSamplingRate = self.next_samplingRate @@ -436,14 +471,10 @@ class RVC: repeat *= self.settings.rvcQuality # 0 or 3 vc = VC(self.settings.modelSamplingRate, dev, self.is_half, repeat) sid = 0 - times = [0, 0, 0] f0_up_key = self.settings.tran f0_method = self.settings.f0Detector - file_index = self.index_file if self.index_file is not None else "" - file_big_npy = self.feature_file if self.feature_file is not None else "" index_rate = self.settings.indexRatio if_f0 = 1 if self.settings.modelSlots[self.currentSlot].f0 else 0 - f0_file = None embChannels = self.settings.modelSlots[self.currentSlot].embChannels audio_out = vc.pipeline( @@ -451,14 +482,12 @@ class RVC: self.net_g, sid, audio, - times, f0_up_key, f0_method, - file_index, - file_big_npy, + self.index, + self.feature, index_rate, if_f0, - f0_file=f0_file, silence_front=self.settings.extraConvertSize / self.settings.modelSamplingRate, embChannels=embChannels, diff --git a/server/voice_changer/RVC/custom_vc_infer_pipeline.py b/server/voice_changer/RVC/custom_vc_infer_pipeline.py index ba53c73a..1aafdc63 100644 --- a/server/voice_changer/RVC/custom_vc_infer_pipeline.py +++ b/server/voice_changer/RVC/custom_vc_infer_pipeline.py @@ -1,16 +1,10 @@ import numpy as np import parselmouth import torch -import pdb -from time import time as ttime import torch.nn.functional as F from config import x_pad, x_query, x_center, x_max import scipy.signal as signal import pyworld -import os -import traceback -import faiss -# from .const import RVC_MODEL_TYPE_NORMAL, RVC_MODEL_TYPE_PITCHLESS, RVC_MODEL_TYPE_WEBUI_256_NORMAL, RVC_MODEL_TYPE_WEBUI_768_NORMAL, RVC_MODEL_TYPE_WEBUI_256_PITCHLESS, RVC_MODEL_TYPE_WEBUI_768_PITCHLESS from .const import RVC_MODEL_TYPE_RVC, RVC_MODEL_TYPE_WEBUI @@ -20,7 +14,6 @@ class VC(object): self.window = 160 # 每帧点数 self.t_pad = self.sr * x_pad # 每条前后pad时间 self.t_pad_tgt = tgt_sr * x_pad - self.t_pad2 = self.t_pad * 2 self.t_query = self.sr * x_query # 查询切点前后查询时间 self.t_center = self.sr * x_center # 查询切点位置 self.t_max = self.sr * x_max # 免查询时长阈值 @@ -28,26 +21,34 @@ class VC(object): self.is_half = is_half def get_f0(self, audio, p_len, f0_up_key, f0_method, inp_f0=None, silence_front=0): - n_frames = int(len(audio) // self.window) + 1 start_frame = int(silence_front * self.sr / self.window) real_silence_front = start_frame * self.window / self.sr - audio = audio[int(np.round(real_silence_front * self.sr)):] + audio = audio[int(np.round(real_silence_front * self.sr)) :] time_step = self.window / self.sr * 1000 f0_min = 50 f0_max = 1100 f0_mel_min = 1127 * np.log(1 + f0_min / 700) f0_mel_max = 1127 * np.log(1 + f0_max / 700) - if (f0_method == "pm"): - f0 = parselmouth.Sound(audio, self.sr).to_pitch_ac( - time_step=time_step / 1000, voicing_threshold=0.6, - pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency'] + if f0_method == "pm": + f0 = ( + parselmouth.Sound(audio, self.sr) + .to_pitch_ac( + time_step=time_step / 1000, + voicing_threshold=0.6, + pitch_floor=f0_min, + pitch_ceiling=f0_max, + ) + .selected_array["frequency"] + ) pad_size = (p_len - len(f0) + 1) // 2 - if (pad_size > 0 or p_len - len(f0) - pad_size > 0): - f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode='constant') - elif (f0_method == "harvest"): + if pad_size > 0 or p_len - len(f0) - pad_size > 0: + f0 = np.pad( + f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant" + ) + elif f0_method == "harvest": f0, t = pyworld.harvest( audio.astype(np.double), fs=self.sr, @@ -57,36 +58,65 @@ class VC(object): f0 = pyworld.stonemask(audio.astype(np.double), f0, t, self.sr) f0 = signal.medfilt(f0, 3) - f0 = np.pad(f0.astype('float'), (start_frame, n_frames - len(f0) - start_frame)) + f0 = np.pad( + f0.astype("float"), (start_frame, n_frames - len(f0) - start_frame) + ) else: print("[Voice Changer] invalid f0 detector, use pm.", f0_method) - f0 = parselmouth.Sound(audio, self.sr).to_pitch_ac( - time_step=time_step / 1000, voicing_threshold=0.6, - pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency'] + f0 = ( + parselmouth.Sound(audio, self.sr) + .to_pitch_ac( + time_step=time_step / 1000, + voicing_threshold=0.6, + pitch_floor=f0_min, + pitch_ceiling=f0_max, + ) + .selected_array["frequency"] + ) pad_size = (p_len - len(f0) + 1) // 2 - if (pad_size > 0 or p_len - len(f0) - pad_size > 0): - f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode='constant') + if pad_size > 0 or p_len - len(f0) - pad_size > 0: + f0 = np.pad( + f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant" + ) f0 *= pow(2, f0_up_key / 12) # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) tf0 = self.sr // self.window # 每秒f0点数 - if (inp_f0 is not None): - delta_t = np.round((inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1).astype("int16") - replace_f0 = np.interp(list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]) - shape = f0[x_pad * tf0:x_pad * tf0 + len(replace_f0)].shape[0] - f0[x_pad * tf0:x_pad * tf0 + len(replace_f0)] = replace_f0[:shape] + if inp_f0 is not None: + delta_t = np.round( + (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1 + ).astype("int16") + replace_f0 = np.interp( + list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1] + ) + shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0] + f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape] # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) f0bak = f0.copy() f0_mel = 1127 * np.log(1 + f0 / 700) - f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1 + f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / ( + f0_mel_max - f0_mel_min + ) + 1 f0_mel[f0_mel <= 1] = 1 f0_mel[f0_mel > 255] = 255 f0_coarse = np.rint(f0_mel).astype(np.int) return f0_coarse, f0bak # 1-0 - def vc(self, model, net_g, sid, audio0, pitch, pitchf, times, index, big_npy, index_rate, embChannels=256): # ,file_index,file_big_npy + def vc( + self, + model, + net_g, + sid, + audio0, + pitch, + pitchf, + index, + big_npy, + index_rate, + embChannels=256, + ): # ,file_index,file_big_npy feats = torch.from_numpy(audio0) - if (self.is_half == True): + if self.is_half == True: feats = feats.half() else: feats = feats.float() @@ -107,7 +137,6 @@ class VC(object): "padding_mask": padding_mask, } - t0 = ttime() with torch.no_grad(): logits = model.extract_features(**inputs) if embChannels == 256: @@ -115,82 +144,121 @@ class VC(object): else: feats = logits[0] - if (isinstance(index, type(None)) == False and isinstance(big_npy, type(None)) == False and index_rate != 0): + if ( + isinstance(index, type(None)) is False + and isinstance(big_npy, type(None)) is False + and index_rate != 0 + ): npy = feats[0].cpu().numpy() - if (self.is_half == True): + if self.is_half is True: npy = npy.astype("float32") D, I = index.search(npy, 1) npy = big_npy[I.squeeze()] - if (self.is_half == True): + if self.is_half is True: npy = npy.astype("float16") - feats = torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate + (1 - index_rate) * feats + + feats = ( + torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate + + (1 - index_rate) * feats + ) feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) - t1 = ttime() p_len = audio0.shape[0] // self.window - if (feats.shape[1] < p_len): + if feats.shape[1] < p_len: p_len = feats.shape[1] - if (pitch != None and pitchf != None): + if pitch is not None and pitchf is not None: pitch = pitch[:, :p_len] pitchf = pitchf[:, :p_len] p_len = torch.tensor([p_len], device=self.device).long() with torch.no_grad(): - if pitch != None: - audio1 = (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16) + if pitch is not None: + audio1 = ( + (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768) + .data.cpu() + .float() + .numpy() + .astype(np.int16) + ) else: if hasattr(net_g, "infer_pitchless"): - audio1 = (net_g.infer_pitchless(feats, p_len, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16) + audio1 = ( + (net_g.infer_pitchless(feats, p_len, sid)[0][0, 0] * 32768) + .data.cpu() + .float() + .numpy() + .astype(np.int16) + ) else: - audio1 = (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16) - - # audio1 = (net_g.infer(feats, p_len, None, pitchf, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16) + audio1 = ( + (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768) + .data.cpu() + .float() + .numpy() + .astype(np.int16) + ) del feats, p_len, padding_mask torch.cuda.empty_cache() - t2 = ttime() - times[0] += (t1 - t0) - times[2] += (t2 - t1) + return audio1 - def pipeline(self, model, net_g, sid, audio, times, f0_up_key, f0_method, file_index, file_big_npy, index_rate, if_f0, f0_file=None, silence_front=0, embChannels=256): - if (file_big_npy != "" and file_index != "" and os.path.exists(file_big_npy) == True and os.path.exists(file_index) == True and index_rate != 0): - try: - index = faiss.read_index(file_index) - big_npy = np.load(file_big_npy) - except: - traceback.print_exc() - index = big_npy = None - else: - index = big_npy = None - - audio_opt = [] - t = None - t1 = ttime() - audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode='reflect') + def pipeline( + self, + embedder, + model, + sid, + audio, + f0_up_key, + f0_method, + index, + big_npy, + index_rate, + if_f0, + silence_front=0, + embChannels=256, + ): + audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect") p_len = audio_pad.shape[0] // self.window inp_f0 = None - sid = torch.tensor(sid, device=self.device).unsqueeze(0).long() + + # ピッチ検出 pitch, pitchf = None, None - if (if_f0 == 1): - pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0, silence_front=silence_front) + if if_f0 == 1: + pitch, pitchf = self.get_f0( + audio_pad, + p_len, + f0_up_key, + f0_method, + inp_f0, + silence_front=silence_front, + ) pitch = pitch[:p_len] pitchf = pitchf[:p_len] pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long() - pitchf = torch.tensor(pitchf, device=self.device, dtype=torch.float).unsqueeze(0) + pitchf = torch.tensor( + pitchf, device=self.device, dtype=torch.float + ).unsqueeze(0) - t2 = ttime() - times[1] += (t2 - t1) - if self.t_pad_tgt == 0: - audio_opt.append(self.vc(model, net_g, sid, audio_pad[t:], pitch[:, t // self.window:]if t is not None else pitch, - pitchf[:, t // self.window:]if t is not None else pitchf, times, index, big_npy, index_rate, embChannels)) - else: - audio_opt.append(self.vc(model, net_g, sid, audio_pad[t:], pitch[:, t // self.window:]if t is not None else pitch, - pitchf[:, t // self.window:]if t is not None else pitchf, times, index, big_npy, index_rate, embChannels)[self.t_pad_tgt:-self.t_pad_tgt]) + output = self.vc( + embedder, + model, + sid, + audio_pad, + pitch, + pitchf, + index, + big_npy, + index_rate, + embChannels, + ) + if self.t_pad_tgt != 0: + offset = self.t_pad_tgt + end = -1 * self.t_pad_tgt + output = output[offset:end] - audio_opt = np.concatenate(audio_opt) del pitch, pitchf, sid torch.cuda.empty_cache() - return audio_opt + return output diff --git a/server/voice_changer/RVC/custom_vc_infer_pipeline_backup.py b/server/voice_changer/RVC/custom_vc_infer_pipeline_backup.py new file mode 100644 index 00000000..bd9f4070 --- /dev/null +++ b/server/voice_changer/RVC/custom_vc_infer_pipeline_backup.py @@ -0,0 +1,315 @@ +import numpy as np +import parselmouth +import torch +import pdb +from time import time as ttime +import torch.nn.functional as F +from config import x_pad, x_query, x_center, x_max +import scipy.signal as signal +import pyworld +import os +import traceback +import faiss +from .const import RVC_MODEL_TYPE_RVC, RVC_MODEL_TYPE_WEBUI + + +class VC(object): + def __init__(self, tgt_sr, device, is_half, x_pad): + self.sr = 16000 # hubert输入采样率 + self.window = 160 # 每帧点数 + self.t_pad = self.sr * x_pad # 每条前后pad时间 + self.t_pad_tgt = tgt_sr * x_pad + self.t_pad2 = self.t_pad * 2 + self.t_query = self.sr * x_query # 查询切点前后查询时间 + self.t_center = self.sr * x_center # 查询切点位置 + self.t_max = self.sr * x_max # 免查询时长阈值 + self.device = device + self.is_half = is_half + + def get_f0(self, audio, p_len, f0_up_key, f0_method, inp_f0=None, silence_front=0): + n_frames = int(len(audio) // self.window) + 1 + start_frame = int(silence_front * self.sr / self.window) + real_silence_front = start_frame * self.window / self.sr + + audio = audio[int(np.round(real_silence_front * self.sr)) :] + + time_step = self.window / self.sr * 1000 + f0_min = 50 + f0_max = 1100 + f0_mel_min = 1127 * np.log(1 + f0_min / 700) + f0_mel_max = 1127 * np.log(1 + f0_max / 700) + if f0_method == "pm": + f0 = ( + parselmouth.Sound(audio, self.sr) + .to_pitch_ac( + time_step=time_step / 1000, + voicing_threshold=0.6, + pitch_floor=f0_min, + pitch_ceiling=f0_max, + ) + .selected_array["frequency"] + ) + pad_size = (p_len - len(f0) + 1) // 2 + if pad_size > 0 or p_len - len(f0) - pad_size > 0: + f0 = np.pad( + f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant" + ) + elif f0_method == "harvest": + f0, t = pyworld.harvest( + audio.astype(np.double), + fs=self.sr, + f0_ceil=f0_max, + frame_period=10, + ) + f0 = pyworld.stonemask(audio.astype(np.double), f0, t, self.sr) + f0 = signal.medfilt(f0, 3) + + f0 = np.pad( + f0.astype("float"), (start_frame, n_frames - len(f0) - start_frame) + ) + else: + print("[Voice Changer] invalid f0 detector, use pm.", f0_method) + f0 = ( + parselmouth.Sound(audio, self.sr) + .to_pitch_ac( + time_step=time_step / 1000, + voicing_threshold=0.6, + pitch_floor=f0_min, + pitch_ceiling=f0_max, + ) + .selected_array["frequency"] + ) + pad_size = (p_len - len(f0) + 1) // 2 + if pad_size > 0 or p_len - len(f0) - pad_size > 0: + f0 = np.pad( + f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant" + ) + + f0 *= pow(2, f0_up_key / 12) + # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) + tf0 = self.sr // self.window # 每秒f0点数 + if inp_f0 is not None: + delta_t = np.round( + (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1 + ).astype("int16") + replace_f0 = np.interp( + list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1] + ) + shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0] + f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape] + # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) + f0bak = f0.copy() + f0_mel = 1127 * np.log(1 + f0 / 700) + f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / ( + f0_mel_max - f0_mel_min + ) + 1 + f0_mel[f0_mel <= 1] = 1 + f0_mel[f0_mel > 255] = 255 + f0_coarse = np.rint(f0_mel).astype(np.int) + return f0_coarse, f0bak # 1-0 + + def vc( + self, + model, + net_g, + sid, + audio0, + pitch, + pitchf, + times, + index, + big_npy, + index_rate, + embChannels=256, + ): # ,file_index,file_big_npy + feats = torch.from_numpy(audio0) + if self.is_half == True: + feats = feats.half() + else: + feats = feats.float() + if feats.dim() == 2: # double channels + feats = feats.mean(-1) + assert feats.dim() == 1, feats.dim() + feats = feats.view(1, -1) + padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False) + if embChannels == 256: + inputs = { + "source": feats.to(self.device), + "padding_mask": padding_mask, + "output_layer": 9, # layer 9 + } + else: + inputs = { + "source": feats.to(self.device), + "padding_mask": padding_mask, + } + + t0 = ttime() + with torch.no_grad(): + logits = model.extract_features(**inputs) + if embChannels == 256: + feats = model.final_proj(logits[0]) + else: + feats = logits[0] + + if ( + isinstance(index, type(None)) == False + and isinstance(big_npy, type(None)) == False + and index_rate != 0 + ): + npy = feats[0].cpu().numpy() + if self.is_half == True: + npy = npy.astype("float32") + D, I = index.search(npy, 1) + npy = big_npy[I.squeeze()] + if self.is_half == True: + npy = npy.astype("float16") + feats = ( + torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate + + (1 - index_rate) * feats + ) + + feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) + + t1 = ttime() + p_len = audio0.shape[0] // self.window + if feats.shape[1] < p_len: + p_len = feats.shape[1] + if pitch != None and pitchf != None: + pitch = pitch[:, :p_len] + pitchf = pitchf[:, :p_len] + p_len = torch.tensor([p_len], device=self.device).long() + + with torch.no_grad(): + if pitch != None: + audio1 = ( + (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768) + .data.cpu() + .float() + .numpy() + .astype(np.int16) + ) + else: + if hasattr(net_g, "infer_pitchless"): + audio1 = ( + (net_g.infer_pitchless(feats, p_len, sid)[0][0, 0] * 32768) + .data.cpu() + .float() + .numpy() + .astype(np.int16) + ) + else: + audio1 = ( + (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768) + .data.cpu() + .float() + .numpy() + .astype(np.int16) + ) + + # audio1 = (net_g.infer(feats, p_len, None, pitchf, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16) + + del feats, p_len, padding_mask + torch.cuda.empty_cache() + t2 = ttime() + times[0] += t1 - t0 + times[2] += t2 - t1 + return audio1 + + def pipeline( + self, + model, + net_g, + sid, + audio, + times, + f0_up_key, + f0_method, + file_index, + file_big_npy, + index_rate, + if_f0, + f0_file=None, + silence_front=0, + embChannels=256, + ): + if ( + file_big_npy != "" + and file_index != "" + and os.path.exists(file_big_npy) == True + and os.path.exists(file_index) == True + and index_rate != 0 + ): + try: + index = faiss.read_index(file_index) + big_npy = np.load(file_big_npy) + except: + traceback.print_exc() + index = big_npy = None + else: + index = big_npy = None + + audio_opt = [] + t = None + t1 = ttime() + audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect") + p_len = audio_pad.shape[0] // self.window + inp_f0 = None + + sid = torch.tensor(sid, device=self.device).unsqueeze(0).long() + pitch, pitchf = None, None + if if_f0 == 1: + pitch, pitchf = self.get_f0( + audio_pad, + p_len, + f0_up_key, + f0_method, + inp_f0, + silence_front=silence_front, + ) + pitch = pitch[:p_len] + pitchf = pitchf[:p_len] + pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long() + pitchf = torch.tensor( + pitchf, device=self.device, dtype=torch.float + ).unsqueeze(0) + + t2 = ttime() + times[1] += t2 - t1 + if self.t_pad_tgt == 0: + audio_opt.append( + self.vc( + model, + net_g, + sid, + audio_pad[t:], + pitch[:, t // self.window :] if t is not None else pitch, + pitchf[:, t // self.window :] if t is not None else pitchf, + times, + index, + big_npy, + index_rate, + embChannels, + ) + ) + else: + audio_opt.append( + self.vc( + model, + net_g, + sid, + audio_pad[t:], + pitch[:, t // self.window :] if t is not None else pitch, + pitchf[:, t // self.window :] if t is not None else pitchf, + times, + index, + big_npy, + index_rate, + embChannels, + )[self.t_pad_tgt : -self.t_pad_tgt] + ) + + audio_opt = np.concatenate(audio_opt) + del pitch, pitchf, sid + torch.cuda.empty_cache() + return audio_opt