import os import sys import traceback import logging logger = logging.getLogger(__name__) from functools import lru_cache from time import time as ttime from torch import Tensor import faiss import librosa import numpy as np import parselmouth import pyworld import torch.nn.functional as F from scipy import signal from tqdm import tqdm import random now_dir = os.getcwd() sys.path.append(now_dir) import re from functools import partial bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000) input_audio_path2wav = {} import torchcrepe # Fork Feature. Crepe algo for training and preprocess import torch from lib.infer.infer_libs.rmvpe import RMVPE @lru_cache 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 Pipeline(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 self.model_rmvpe = RMVPE("%s/rmvpe.pt" % os.environ["rmvpe_root"], is_half=self.is_half, device=self.device) self.note_dict = [ 65.41, 69.30, 73.42, 77.78, 82.41, 87.31, 92.50, 98.00, 103.83, 110.00, 116.54, 123.47, 130.81, 138.59, 146.83, 155.56, 164.81, 174.61, 185.00, 196.00, 207.65, 220.00, 233.08, 246.94, 261.63, 277.18, 293.66, 311.13, 329.63, 349.23, 369.99, 392.00, 415.30, 440.00, 466.16, 493.88, 523.25, 554.37, 587.33, 622.25, 659.25, 698.46, 739.99, 783.99, 830.61, 880.00, 932.33, 987.77, 1046.50, 1108.73, 1174.66, 1244.51, 1318.51, 1396.91, 1479.98, 1567.98, 1661.22, 1760.00, 1864.66, 1975.53, 2093.00, 2217.46, 2349.32, 2489.02, 2637.02, 2793.83, 2959.96, 3135.96, 3322.44, 3520.00, 3729.31, 3951.07 ] # 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: 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") 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, *args, # 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. **kwargs, # 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() hop_length = kwargs.get('crepe_hop_length', 160) model = kwargs.get('model', 'full') 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, *args, **kwargs ): # 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() model = kwargs.get('model', 'full') 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 def get_pm(self, x, p_len, *args, **kwargs): f0 = parselmouth.Sound(x, self.sr).to_pitch_ac( time_step=160 / 16000, voicing_threshold=0.6, pitch_floor=kwargs.get('f0_min'), pitch_ceiling=kwargs.get('f0_max'), ).selected_array["frequency"] return np.pad( f0, [[max(0, (p_len - len(f0) + 1) // 2), max(0, p_len - len(f0) - (p_len - len(f0) + 1) // 2)]], mode="constant" ) def get_harvest(self, x, *args, **kwargs): f0_spectral = pyworld.harvest( x.astype(np.double), fs=self.sr, f0_ceil=kwargs.get('f0_max'), f0_floor=kwargs.get('f0_min'), frame_period=1000 * kwargs.get('hop_length', 160) / self.sr, ) return pyworld.stonemask(x.astype(np.double), *f0_spectral, self.sr) def get_dio(self, x, *args, **kwargs): f0_spectral = pyworld.dio( x.astype(np.double), fs=self.sr, f0_ceil=kwargs.get('f0_max'), f0_floor=kwargs.get('f0_min'), frame_period=1000 * kwargs.get('hop_length', 160) / self.sr, ) return pyworld.stonemask(x.astype(np.double), *f0_spectral, self.sr) def get_rmvpe(self, x, *args, **kwargs): if not hasattr(self, "model_rmvpe"): from lib.infer.infer_libs.rmvpe import RMVPE logger.info( "Loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"] ) self.model_rmvpe = RMVPE( "%s/rmvpe.pt" % os.environ["rmvpe_root"], is_half=self.is_half, device=self.device, ) f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03) if "privateuseone" in str(self.device): # clean ortruntime memory del self.model_rmvpe.model del self.model_rmvpe logger.info("Cleaning ortruntime memory") return f0 def get_pitch_dependant_rmvpe(self, x, f0_min=1, f0_max=40000, *args, **kwargs): if not hasattr(self, "model_rmvpe"): from lib.infer.infer_libs.rmvpe import RMVPE logger.info( "Loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"] ) self.model_rmvpe = RMVPE( "%s/rmvpe.pt" % os.environ["rmvpe_root"], is_half=self.is_half, device=self.device, ) f0 = self.model_rmvpe.infer_from_audio_with_pitch(x, thred=0.03, f0_min=f0_min, f0_max=f0_max) if "privateuseone" in str(self.device): # clean ortruntime memory del self.model_rmvpe.model del self.model_rmvpe logger.info("Cleaning ortruntime memory") return f0 def autotune_f0(self, f0): autotuned_f0 = [] for freq in f0: closest_notes = [x for x in self.note_dict if abs(x - freq) == min(abs(n - freq) for n in self.note_dict)] autotuned_f0.append(random.choice(closest_notes)) return np.array(autotuned_f0, np.float64) # 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 methods_str = re.search('hybrid\[(.+)\]', methods_str) if methods_str: # Ensure a match was found methods = [method.strip() for method in methods_str.group(1).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 == "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 == "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(f"Calculating hybrid median f0 from the stack of: {str(methods)}") 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, f0_autotune, inp_f0=None, f0_min=50, f0_max=1100, ): 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": 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() 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 not hasattr(self, "model_rmvpe"): from lib.infer.infer_libs.rmvpe import RMVPE logger.info( "Loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"] ) self.model_rmvpe = RMVPE( "%s/rmvpe.pt" % os.environ["rmvpe_root"], is_half=self.is_half, device=self.device, ) f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03) if "privateuseone" in str(self.device): # clean ortruntime memory del self.model_rmvpe.model del self.model_rmvpe logger.info("Cleaning ortruntime memory") elif f0_method == "rmvpe+": params = {'x': x, 'p_len': p_len, 'f0_up_key': f0_up_key, 'f0_min': f0_min, 'f0_max': f0_max, 'time_step': time_step, 'filter_radius': filter_radius, 'crepe_hop_length': crepe_hop_length, 'model': "full" } f0 = self.get_pitch_dependant_rmvpe(**params) 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, ) print("Autotune:", f0_autotune) if f0_autotune: f0 = self.autotune_f0(f0) 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.int32) 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 is not None and pitchf is not None: feats0 = feats.clone() if ( not isinstance(index, type(None)) and not isinstance(big_npy, type(None)) 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 is not None and pitchf is not 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 is not None and pitchf is not None: pitch = pitch[:, :p_len] pitchf = pitchf[:, :p_len] if protect < 0.5 and pitch is not None and pitchf is not 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(): hasp = pitch is not None and pitchf is not None arg = (feats, p_len, pitch, pitchf, sid) if hasp else (feats, p_len, sid) audio1 = (net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy() del hasp, arg 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 process_t(self, t, s, window, audio_pad, pitch, pitchf, times, index, big_npy, index_rate, version, protect, t_pad_tgt, if_f0, sid, model, net_g): t = t // window * window if if_f0 == 1: return self.vc( model, net_g, sid, audio_pad[s : t + t_pad_tgt + window], pitch[:, s // window : (t + t_pad_tgt) // window], pitchf[:, s // window : (t + t_pad_tgt) // window], times, index, big_npy, index_rate, version, protect, )[t_pad_tgt : -t_pad_tgt] else: return self.vc( model, net_g, sid, audio_pad[s : t + t_pad_tgt + window], None, None, times, index, big_npy, index_rate, version, protect, )[t_pad_tgt : -t_pad_tgt] def pipeline( self, model, net_g, sid, audio, input_audio_path, times, f0_up_key, f0_method, file_index, index_rate, if_f0, filter_radius, tgt_sr, resample_sr, rms_mix_rate, version, protect, crepe_hop_length, f0_autotune, f0_file=None, f0_min=50, f0_max=1100 ): if ( file_index != "" # and file_big_npy != "" # and os.path.exists(file_big_npy) == True and os.path.exists(file_index) 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"): 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: pitch, pitchf = self.get_f0( input_audio_path, audio_pad, p_len, f0_up_key, f0_method, filter_radius, crepe_hop_length, f0_autotune, inp_f0, f0_min, f0_max ) pitch = pitch[:p_len] pitchf = pitchf[:p_len] if "mps" not in str(self.device) or "xpu" not in str(self.device): 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 with tqdm(total=len(opt_ts), desc="Processing", unit="window") as pbar: for i, t in enumerate(opt_ts): t = t // self.window * self.window start = s end = t + self.t_pad2 + self.window audio_slice = audio_pad[start:end] pitch_slice = pitch[:, start // self.window:end // self.window] if if_f0 else None pitchf_slice = pitchf[:, start // self.window:end // self.window] if if_f0 else None audio_opt.append(self.vc(model, net_g, sid, audio_slice, pitch_slice, pitchf_slice, times, index, big_npy, index_rate, version, protect)[self.t_pad_tgt : -self.t_pad_tgt]) s = t pbar.update(1) pbar.refresh() audio_slice = audio_pad[t:] pitch_slice = pitch[:, t // self.window:] if if_f0 and t is not None else pitch pitchf_slice = pitchf[:, t // self.window:] if if_f0 and t is not None else pitchf audio_opt.append(self.vc(model, net_g, sid, audio_slice, pitch_slice, pitchf_slice, 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 tgt_sr != resample_sr >= 16000: 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() print("Returning completed audio...") return audio_opt