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| import numpy as np, parselmouth, torch, pdb, sys, os | |
| from time import time as ttime | |
| import torch.nn.functional as F | |
| import torchcrepe # Fork feature. Use the crepe f0 algorithm. New dependency (pip install torchcrepe) | |
| from torch import Tensor | |
| import scipy.signal as signal | |
| import pyworld, os, traceback, faiss, librosa, torchcrepe | |
| from scipy import signal | |
| from functools import lru_cache | |
| now_dir = os.getcwd() | |
| sys.path.append(now_dir) | |
| bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000) | |
| input_audio_path2wav = {} | |
| def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period): | |
| audio = input_audio_path2wav[input_audio_path] | |
| f0, t = pyworld.harvest( | |
| audio, | |
| fs=fs, | |
| f0_ceil=f0max, | |
| f0_floor=f0min, | |
| frame_period=frame_period, | |
| ) | |
| f0 = pyworld.stonemask(audio, f0, t, fs) | |
| return f0 | |
| def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频，2是输出音频,rate是2的占比 | |
| # print(data1.max(),data2.max()) | |
| rms1 = librosa.feature.rms( | |
| y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2 | |
| ) # 每半秒一个点 | |
| rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2) | |
| rms1 = torch.from_numpy(rms1) | |
| rms1 = F.interpolate( | |
| rms1.unsqueeze(0), size=data2.shape[0], mode="linear" | |
| ).squeeze() | |
| rms2 = torch.from_numpy(rms2) | |
| rms2 = F.interpolate( | |
| rms2.unsqueeze(0), size=data2.shape[0], mode="linear" | |
| ).squeeze() | |
| rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6) | |
| data2 *= ( | |
| torch.pow(rms1, torch.tensor(1 - rate)) | |
| * torch.pow(rms2, torch.tensor(rate - 1)) | |
| ).numpy() | |
| return data2 | |
| class VC(object): | |
| def __init__(self, tgt_sr, config): | |
| self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = ( | |
| config.x_pad, | |
| config.x_query, | |
| config.x_center, | |
| config.x_max, | |
| config.is_half, | |
| ) | |
| self.sr = 16000 # hubert输入采样率 | |
| self.window = 160 # 每帧点数 | |
| self.t_pad = self.sr * self.x_pad # 每条前后pad时间 | |
| self.t_pad_tgt = tgt_sr * self.x_pad | |
| self.t_pad2 = self.t_pad * 2 | |
| self.t_query = self.sr * self.x_query # 查询切点前后查询时间 | |
| self.t_center = self.sr * self.x_center # 查询切点位置 | |
| self.t_max = self.sr * self.x_max # 免查询时长阈值 | |
| self.device = config.device | |
| # Fork Feature: Get the best torch device to use for f0 algorithms that require a torch device. Will return the type (torch.device) | |
| def get_optimal_torch_device(self, index: int = 0) -> torch.device: | |
| # Get cuda device | |
| if torch.cuda.is_available(): | |
| return torch.device( | |
| f"cuda:{index % torch.cuda.device_count()}" | |
| ) # Very fast | |
| elif torch.backends.mps.is_available(): | |
| return torch.device("mps") | |
| # Insert an else here to grab "xla" devices if available. TO DO later. Requires the torch_xla.core.xla_model library | |
| # Else wise return the "cpu" as a torch device, | |
| return torch.device("cpu") | |
| # Fork Feature: Compute f0 with the crepe method | |
| def get_f0_crepe_computation( | |
| self, | |
| x, | |
| f0_min, | |
| f0_max, | |
| p_len, | |
| hop_length=160, # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time. | |
| model="full", # Either use crepe-tiny "tiny" or crepe "full". Default is full | |
| ): | |
| x = x.astype( | |
| np.float32 | |
| ) # fixes the F.conv2D exception. We needed to convert double to float. | |
| x /= np.quantile(np.abs(x), 0.999) | |
| torch_device = self.get_optimal_torch_device() | |
| audio = torch.from_numpy(x).to(torch_device, copy=True) | |
| audio = torch.unsqueeze(audio, dim=0) | |
| if audio.ndim == 2 and audio.shape[0] > 1: | |
| audio = torch.mean(audio, dim=0, keepdim=True).detach() | |
| audio = audio.detach() | |
| print("Initiating prediction with a crepe_hop_length of: " + str(hop_length)) | |
| pitch: Tensor = torchcrepe.predict( | |
| audio, | |
| self.sr, | |
| hop_length, | |
| f0_min, | |
| f0_max, | |
| model, | |
| batch_size=hop_length * 2, | |
| device=torch_device, | |
| pad=True, | |
| ) | |
| p_len = p_len or x.shape[0] // hop_length | |
| # Resize the pitch for final f0 | |
| source = np.array(pitch.squeeze(0).cpu().float().numpy()) | |
| source[source < 0.001] = np.nan | |
| target = np.interp( | |
| np.arange(0, len(source) * p_len, len(source)) / p_len, | |
| np.arange(0, len(source)), | |
| source, | |
| ) | |
| f0 = np.nan_to_num(target) | |
| return f0 # Resized f0 | |
| def get_f0_official_crepe_computation( | |
| self, | |
| x, | |
| f0_min, | |
| f0_max, | |
| model="full", | |
| ): | |
| # Pick a batch size that doesn't cause memory errors on your gpu | |
| batch_size = 512 | |
| # Compute pitch using first gpu | |
| audio = torch.tensor(np.copy(x))[None].float() | |
| f0, pd = torchcrepe.predict( | |
| audio, | |
| self.sr, | |
| self.window, | |
| f0_min, | |
| f0_max, | |
| model, | |
| batch_size=batch_size, | |
| device=self.device, | |
| return_periodicity=True, | |
| ) | |
| pd = torchcrepe.filter.median(pd, 3) | |
| f0 = torchcrepe.filter.mean(f0, 3) | |
| f0[pd < 0.1] = 0 | |
| f0 = f0[0].cpu().numpy() | |
| return f0 | |
| # Fork Feature: Compute pYIN f0 method | |
| def get_f0_pyin_computation(self, x, f0_min, f0_max): | |
| y, sr = librosa.load("saudio/Sidney.wav", self.sr, mono=True) | |
| f0, _, _ = librosa.pyin(y, sr=self.sr, fmin=f0_min, fmax=f0_max) | |
| f0 = f0[1:] # Get rid of extra first frame | |
| return f0 | |
| # Fork Feature: Acquire median hybrid f0 estimation calculation | |
| def get_f0_hybrid_computation( | |
| self, | |
| methods_str, | |
| input_audio_path, | |
| x, | |
| f0_min, | |
| f0_max, | |
| p_len, | |
| filter_radius, | |
| crepe_hop_length, | |
| time_step, | |
| ): | |
| # Get various f0 methods from input to use in the computation stack | |
| s = methods_str | |
| s = s.split("hybrid")[1] | |
| s = s.replace("[", "").replace("]", "") | |
| methods = s.split("+") | |
| f0_computation_stack = [] | |
| print("Calculating f0 pitch estimations for methods: %s" % str(methods)) | |
| x = x.astype(np.float32) | |
| x /= np.quantile(np.abs(x), 0.999) | |
| # Get f0 calculations for all methods specified | |
| for method in methods: | |
| f0 = None | |
| if method == "pm": | |
| f0 = ( | |
| parselmouth.Sound(x, 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 method == "crepe": | |
| f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max) | |
| f0 = f0[1:] # Get rid of extra first frame | |
| elif method == "crepe-tiny": | |
| f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny") | |
| f0 = f0[1:] # Get rid of extra first frame | |
| elif method == "mangio-crepe": | |
| f0 = self.get_f0_crepe_computation( | |
| x, f0_min, f0_max, p_len, crepe_hop_length | |
| ) | |
| elif method == "mangio-crepe-tiny": | |
| f0 = self.get_f0_crepe_computation( | |
| x, f0_min, f0_max, p_len, crepe_hop_length, "tiny" | |
| ) | |
| elif method == "harvest": | |
| f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10) | |
| if filter_radius > 2: | |
| f0 = signal.medfilt(f0, 3) | |
| f0 = f0[1:] # Get rid of first frame. | |
| elif method == "rmvpe": | |
| if hasattr(self, "model_rmvpe") == False: | |
| from rmvpe import RMVPE | |
| print("loading rmvpe model") | |
| self.model_rmvpe = RMVPE( | |
| "rmvpe.pt", is_half=self.is_half, device=self.device | |
| ) | |
| f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03) | |
| f0 = f0[1:] # Get rid of first frame. | |
| elif method == "dio": # Potentially buggy? | |
| f0, t = pyworld.dio( | |
| x.astype(np.double), | |
| fs=self.sr, | |
| f0_ceil=f0_max, | |
| f0_floor=f0_min, | |
| frame_period=10, | |
| ) | |
| f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr) | |
| f0 = signal.medfilt(f0, 3) | |
| f0 = f0[1:] | |
| # elif method == "pyin": Not Working just yet | |
| # f0 = self.get_f0_pyin_computation(x, f0_min, f0_max) | |
| # Push method to the stack | |
| f0_computation_stack.append(f0) | |
| for fc in f0_computation_stack: | |
| print(len(fc)) | |
| print("Calculating hybrid median f0 from the stack of: %s" % str(methods)) | |
| f0_median_hybrid = None | |
| if len(f0_computation_stack) == 1: | |
| f0_median_hybrid = f0_computation_stack[0] | |
| else: | |
| f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0) | |
| return f0_median_hybrid | |
| def get_f0( | |
| self, | |
| input_audio_path, | |
| x, | |
| p_len, | |
| f0_up_key, | |
| f0_method, | |
| filter_radius, | |
| crepe_hop_length, | |
| inp_f0=None, | |
| ): | |
| global input_audio_path2wav | |
| 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(x, 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": | |
| input_audio_path2wav[input_audio_path] = x.astype(np.double) | |
| f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10) | |
| if filter_radius > 2: | |
| f0 = signal.medfilt(f0, 3) | |
| elif f0_method == "dio": # Potentially Buggy? | |
| f0, t = pyworld.dio( | |
| x.astype(np.double), | |
| fs=self.sr, | |
| f0_ceil=f0_max, | |
| f0_floor=f0_min, | |
| frame_period=10, | |
| ) | |
| f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr) | |
| f0 = signal.medfilt(f0, 3) | |
| elif f0_method == "crepe": | |
| f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max) | |
| elif f0_method == "crepe-tiny": | |
| f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny") | |
| elif f0_method == "mangio-crepe": | |
| f0 = self.get_f0_crepe_computation( | |
| x, f0_min, f0_max, p_len, crepe_hop_length | |
| ) | |
| elif f0_method == "mangio-crepe-tiny": | |
| f0 = self.get_f0_crepe_computation( | |
| x, f0_min, f0_max, p_len, crepe_hop_length, "tiny" | |
| ) | |
| elif f0_method == "rmvpe": | |
| if hasattr(self, "model_rmvpe") == False: | |
| from rmvpe import RMVPE | |
| print("loading rmvpe model") | |
| self.model_rmvpe = RMVPE( | |
| "rmvpe.pt", is_half=self.is_half, device=self.device | |
| ) | |
| f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03) | |
| elif "hybrid" in f0_method: | |
| # Perform hybrid median pitch estimation | |
| input_audio_path2wav[input_audio_path] = x.astype(np.double) | |
| f0 = self.get_f0_hybrid_computation( | |
| f0_method, | |
| input_audio_path, | |
| x, | |
| f0_min, | |
| f0_max, | |
| p_len, | |
| filter_radius, | |
| crepe_hop_length, | |
| time_step, | |
| ) | |
| 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[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0] | |
| f0[self.x_pad * tf0 : self.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, | |
| version, | |
| protect, | |
| ): # ,file_index,file_big_npy | |
| feats = torch.from_numpy(audio0) | |
| if self.is_half: | |
| 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) | |
| inputs = { | |
| "source": feats.to(self.device), | |
| "padding_mask": padding_mask, | |
| "output_layer": 9 if version == "v1" else 12, | |
| } | |
| t0 = ttime() | |
| with torch.no_grad(): | |
| logits = model.extract_features(**inputs) | |
| feats = model.final_proj(logits[0]) if version == "v1" else logits[0] | |
| if protect < 0.5 and pitch != None and pitchf != None: | |
| feats0 = feats.clone() | |
| 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: | |
| npy = npy.astype("float32") | |
| # _, I = index.search(npy, 1) | |
| # npy = big_npy[I.squeeze()] | |
| score, ix = index.search(npy, k=8) | |
| weight = np.square(1 / score) | |
| weight /= weight.sum(axis=1, keepdims=True) | |
| npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1) | |
| if self.is_half: | |
| 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) | |
| if protect < 0.5 and pitch != None and pitchf != None: | |
| feats0 = F.interpolate(feats0.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] | |
| if protect < 0.5 and pitch != None and pitchf != None: | |
| pitchff = pitchf.clone() | |
| pitchff[pitchf > 0] = 1 | |
| pitchff[pitchf < 1] = protect | |
| pitchff = pitchff.unsqueeze(-1) | |
| feats = feats * pitchff + feats0 * (1 - pitchff) | |
| feats = feats.to(feats0.dtype) | |
| p_len = torch.tensor([p_len], device=self.device).long() | |
| with torch.no_grad(): | |
| if pitch != None and pitchf != None: | |
| audio1 = ( | |
| (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]) | |
| .data.cpu() | |
| .float() | |
| .numpy() | |
| ) | |
| else: | |
| audio1 = ( | |
| (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy() | |
| ) | |
| del feats, p_len, padding_mask | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| t2 = ttime() | |
| times[0] += t1 - t0 | |
| times[2] += t2 - t1 | |
| return audio1 | |
| def pipeline( | |
| self, | |
| model, | |
| net_g, | |
| sid, | |
| audio, | |
| input_audio_path, | |
| times, | |
| f0_up_key, | |
| f0_method, | |
| file_index, | |
| # file_big_npy, | |
| index_rate, | |
| if_f0, | |
| filter_radius, | |
| tgt_sr, | |
| resample_sr, | |
| rms_mix_rate, | |
| version, | |
| protect, | |
| crepe_hop_length, | |
| f0_file=None, | |
| ): | |
| if ( | |
| file_index != "" | |
| # and file_big_npy != "" | |
| # 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) | |
| big_npy = index.reconstruct_n(0, index.ntotal) | |
| except: | |
| traceback.print_exc() | |
| index = big_npy = None | |
| else: | |
| index = big_npy = None | |
| audio = signal.filtfilt(bh, ah, audio) | |
| audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect") | |
| opt_ts = [] | |
| if audio_pad.shape[0] > self.t_max: | |
| audio_sum = np.zeros_like(audio) | |
| for i in range(self.window): | |
| audio_sum += audio_pad[i : i - self.window] | |
| for t in range(self.t_center, audio.shape[0], self.t_center): | |
| opt_ts.append( | |
| t | |
| - self.t_query | |
| + np.where( | |
| np.abs(audio_sum[t - self.t_query : t + self.t_query]) | |
| == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min() | |
| )[0][0] | |
| ) | |
| s = 0 | |
| 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 | |
| if hasattr(f0_file, "name") == True: | |
| try: | |
| with open(f0_file.name, "r") as f: | |
| lines = f.read().strip("\n").split("\n") | |
| inp_f0 = [] | |
| for line in lines: | |
| inp_f0.append([float(i) for i in line.split(",")]) | |
| inp_f0 = np.array(inp_f0, dtype="float32") | |
| except: | |
| traceback.print_exc() | |
| sid = torch.tensor(sid, device=self.device).unsqueeze(0).long() | |
| pitch, pitchf = None, None | |
| if if_f0 == 1: | |
| pitch, pitchf = self.get_f0( | |
| input_audio_path, | |
| audio_pad, | |
| p_len, | |
| f0_up_key, | |
| f0_method, | |
| filter_radius, | |
| crepe_hop_length, | |
| inp_f0, | |
| ) | |
| pitch = pitch[:p_len] | |
| pitchf = pitchf[:p_len] | |
| if self.device == "mps": | |
| pitchf = pitchf.astype(np.float32) | |
| pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long() | |
| pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float() | |
| t2 = ttime() | |
| times[1] += t2 - t1 | |
| for t in opt_ts: | |
| t = t // self.window * self.window | |
| if if_f0 == 1: | |
| audio_opt.append( | |
| self.vc( | |
| model, | |
| net_g, | |
| sid, | |
| audio_pad[s : t + self.t_pad2 + self.window], | |
| pitch[:, s // self.window : (t + self.t_pad2) // self.window], | |
| pitchf[:, s // self.window : (t + self.t_pad2) // self.window], | |
| times, | |
| index, | |
| big_npy, | |
| index_rate, | |
| version, | |
| protect, | |
| )[self.t_pad_tgt : -self.t_pad_tgt] | |
| ) | |
| else: | |
| audio_opt.append( | |
| self.vc( | |
| model, | |
| net_g, | |
| sid, | |
| audio_pad[s : t + self.t_pad2 + self.window], | |
| None, | |
| None, | |
| times, | |
| index, | |
| big_npy, | |
| index_rate, | |
| version, | |
| protect, | |
| )[self.t_pad_tgt : -self.t_pad_tgt] | |
| ) | |
| s = t | |
| if if_f0 == 1: | |
| 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, | |
| version, | |
| protect, | |
| )[self.t_pad_tgt : -self.t_pad_tgt] | |
| ) | |
| else: | |
| audio_opt.append( | |
| self.vc( | |
| model, | |
| net_g, | |
| sid, | |
| audio_pad[t:], | |
| None, | |
| None, | |
| times, | |
| index, | |
| big_npy, | |
| index_rate, | |
| version, | |
| protect, | |
| )[self.t_pad_tgt : -self.t_pad_tgt] | |
| ) | |
| audio_opt = np.concatenate(audio_opt) | |
| if rms_mix_rate != 1: | |
| audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate) | |
| if resample_sr >= 16000 and tgt_sr != resample_sr: | |
| audio_opt = librosa.resample( | |
| audio_opt, orig_sr=tgt_sr, target_sr=resample_sr | |
| ) | |
| audio_max = np.abs(audio_opt).max() / 0.99 | |
| max_int16 = 32768 | |
| if audio_max > 1: | |
| max_int16 /= audio_max | |
| audio_opt = (audio_opt * max_int16).astype(np.int16) | |
| del pitch, pitchf, sid | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| return audio_opt | |