457 lines
16 KiB
Python
457 lines
16 KiB
Python
import os
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import sys
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import traceback
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from functools import lru_cache
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from time import time as ttime
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import faiss
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import librosa
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import numpy as np
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import parselmouth
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import pyworld
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import torch
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import torch.nn.functional as F
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import torchcrepe
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from scipy import signal
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
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input_audio_path2wav = {}
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@lru_cache
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def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
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audio = input_audio_path2wav[input_audio_path]
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f0, t = pyworld.harvest(
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audio,
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fs=fs,
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f0_ceil=f0max,
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f0_floor=f0min,
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frame_period=frame_period,
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)
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f0 = pyworld.stonemask(audio, f0, t, fs)
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return f0
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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
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# print(data1.max(),data2.max())
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rms1 = librosa.feature.rms(
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y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
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) # 每半秒一个点
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rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
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rms1 = torch.from_numpy(rms1)
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rms1 = F.interpolate(
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rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
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).squeeze()
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rms2 = torch.from_numpy(rms2)
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rms2 = F.interpolate(
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rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
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).squeeze()
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rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
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data2 *= (
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torch.pow(rms1, torch.tensor(1 - rate))
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* torch.pow(rms2, torch.tensor(rate - 1))
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).numpy()
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return data2
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class Pipeline(object):
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def __init__(self, tgt_sr, config):
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self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
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config.x_pad,
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config.x_query,
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config.x_center,
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config.x_max,
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config.is_half,
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)
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self.sr = 16000 # hubert输入采样率
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self.window = 160 # 每帧点数
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self.t_pad = self.sr * self.x_pad # 每条前后pad时间
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self.t_pad_tgt = tgt_sr * self.x_pad
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self.t_pad2 = self.t_pad * 2
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self.t_query = self.sr * self.x_query # 查询切点前后查询时间
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self.t_center = self.sr * self.x_center # 查询切点位置
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self.t_max = self.sr * self.x_max # 免查询时长阈值
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self.device = config.device
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def get_f0(
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self,
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input_audio_path,
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x,
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p_len,
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f0_up_key,
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f0_method,
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filter_radius,
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inp_f0=None,
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):
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global input_audio_path2wav
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time_step = self.window / self.sr * 1000
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f0_min = 50
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f0_max = 1100
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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if f0_method == "pm":
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f0 = (
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parselmouth.Sound(x, self.sr)
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.to_pitch_ac(
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time_step=time_step / 1000,
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voicing_threshold=0.6,
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pitch_floor=f0_min,
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pitch_ceiling=f0_max,
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)
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.selected_array["frequency"]
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)
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pad_size = (p_len - len(f0) + 1) // 2
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if pad_size > 0 or p_len - len(f0) - pad_size > 0:
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f0 = np.pad(
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f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
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)
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elif f0_method == "harvest":
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input_audio_path2wav[input_audio_path] = x.astype(np.double)
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f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
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if filter_radius > 2:
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f0 = signal.medfilt(f0, 3)
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elif f0_method == "crepe":
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model = "full"
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# Pick a batch size that doesn't cause memory errors on your gpu
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batch_size = 512
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# Compute pitch using first gpu
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audio = torch.tensor(np.copy(x))[None].float()
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f0, pd = torchcrepe.predict(
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audio,
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self.sr,
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self.window,
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f0_min,
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f0_max,
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model,
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batch_size=batch_size,
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device=self.device,
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return_periodicity=True,
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)
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pd = torchcrepe.filter.median(pd, 3)
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f0 = torchcrepe.filter.mean(f0, 3)
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f0[pd < 0.1] = 0
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f0 = f0[0].cpu().numpy()
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elif f0_method == "rmvpe":
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if not hasattr(self, "model_rmvpe"):
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from infer.lib.rmvpe import RMVPE
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print("loading rmvpe model,%s"%"%s/rmvpe.pt"%os.environ["rmvpe_root"])
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self.model_rmvpe = RMVPE(
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"%s/rmvpe.pt"%os.environ["rmvpe_root"], is_half=self.is_half, device=self.device
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)
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f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
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if "privateuseone" in str(self.device): # clean ortruntime memory
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del self.model_rmvpe.model
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del self.model_rmvpe
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print("cleaning ortruntime memory")
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f0 *= pow(2, f0_up_key / 12)
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# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
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tf0 = self.sr // self.window # 每秒f0点数
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if inp_f0 is not None:
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delta_t = np.round(
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(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
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).astype("int16")
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replace_f0 = np.interp(
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list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
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)
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shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
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f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
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:shape
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]
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# with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
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f0bak = f0.copy()
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f0_mel = 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
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f0_mel_max - f0_mel_min
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) + 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > 255] = 255
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f0_coarse = np.rint(f0_mel).astype(np.int32)
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return f0_coarse, f0bak # 1-0
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def vc(
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self,
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model,
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net_g,
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sid,
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audio0,
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pitch,
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pitchf,
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times,
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index,
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big_npy,
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index_rate,
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version,
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protect,
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): # ,file_index,file_big_npy
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feats = torch.from_numpy(audio0)
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if self.is_half:
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feats = feats.half()
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else:
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feats = feats.float()
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if feats.dim() == 2: # double channels
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feats = feats.mean(-1)
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assert feats.dim() == 1, feats.dim()
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feats = feats.view(1, -1)
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padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
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inputs = {
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"source": feats.to(self.device),
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"padding_mask": padding_mask,
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"output_layer": 9 if version == "v1" else 12,
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}
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t0 = ttime()
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with torch.no_grad():
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logits = model.extract_features(**inputs)
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feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
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if protect < 0.5 and pitch is not None and pitchf is not None:
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feats0 = feats.clone()
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if (
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not isinstance(index, type(None))
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and not isinstance(big_npy, type(None))
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and index_rate != 0
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):
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npy = feats[0].cpu().numpy()
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if self.is_half:
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npy = npy.astype("float32")
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# _, I = index.search(npy, 1)
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# npy = big_npy[I.squeeze()]
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score, ix = index.search(npy, k=8)
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weight = np.square(1 / score)
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weight /= weight.sum(axis=1, keepdims=True)
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npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
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if self.is_half:
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npy = npy.astype("float16")
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feats = (
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torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
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+ (1 - index_rate) * feats
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)
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feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
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if protect < 0.5 and pitch is not None and pitchf is not None:
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feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
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0, 2, 1
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)
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t1 = ttime()
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p_len = audio0.shape[0] // self.window
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if feats.shape[1] < p_len:
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p_len = feats.shape[1]
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if pitch is not None and pitchf is not None:
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pitch = pitch[:, :p_len]
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pitchf = pitchf[:, :p_len]
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if protect < 0.5 and pitch is not None and pitchf is not None:
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pitchff = pitchf.clone()
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pitchff[pitchf > 0] = 1
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pitchff[pitchf < 1] = protect
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pitchff = pitchff.unsqueeze(-1)
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feats = feats * pitchff + feats0 * (1 - pitchff)
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feats = feats.to(feats0.dtype)
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p_len = torch.tensor([p_len], device=self.device).long()
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with torch.no_grad():
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if pitch is not None and pitchf is not None:
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audio1 = (
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(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
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.data.cpu()
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.float()
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.numpy()
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)
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else:
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audio1 = (
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(net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
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)
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del feats, p_len, padding_mask
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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t2 = ttime()
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times[0] += t1 - t0
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times[2] += t2 - t1
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return audio1
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def pipeline(
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self,
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model,
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net_g,
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sid,
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audio,
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input_audio_path,
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times,
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f0_up_key,
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f0_method,
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file_index,
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index_rate,
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if_f0,
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filter_radius,
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tgt_sr,
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resample_sr,
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rms_mix_rate,
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version,
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protect,
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f0_file=None,
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):
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if (
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file_index != ""
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# and file_big_npy != ""
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# and os.path.exists(file_big_npy) == True
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and os.path.exists(file_index)
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and index_rate != 0
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):
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try:
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index = faiss.read_index(file_index)
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# big_npy = np.load(file_big_npy)
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big_npy = index.reconstruct_n(0, index.ntotal)
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except:
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traceback.print_exc()
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index = big_npy = None
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else:
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index = big_npy = None
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audio = signal.filtfilt(bh, ah, audio)
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audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
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opt_ts = []
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if audio_pad.shape[0] > self.t_max:
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audio_sum = np.zeros_like(audio)
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for i in range(self.window):
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audio_sum += audio_pad[i : i - self.window]
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for t in range(self.t_center, audio.shape[0], self.t_center):
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opt_ts.append(
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t
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- self.t_query
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+ np.where(
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np.abs(audio_sum[t - self.t_query : t + self.t_query])
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== np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
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)[0][0]
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)
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s = 0
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audio_opt = []
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t = None
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t1 = ttime()
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audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
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p_len = audio_pad.shape[0] // self.window
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inp_f0 = None
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if hasattr(f0_file, "name"):
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try:
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with open(f0_file.name, "r") as f:
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lines = f.read().strip("\n").split("\n")
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inp_f0 = []
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for line in lines:
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inp_f0.append([float(i) for i in line.split(",")])
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inp_f0 = np.array(inp_f0, dtype="float32")
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except:
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traceback.print_exc()
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sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
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pitch, pitchf = None, None
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if if_f0 == 1:
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pitch, pitchf = self.get_f0(
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input_audio_path,
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audio_pad,
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p_len,
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f0_up_key,
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f0_method,
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filter_radius,
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inp_f0,
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)
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pitch = pitch[:p_len]
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pitchf = pitchf[:p_len]
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if self.device == "mps":
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pitchf = pitchf.astype(np.float32)
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pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
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pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
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t2 = ttime()
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times[1] += t2 - t1
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for t in opt_ts:
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t = t // self.window * self.window
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if if_f0 == 1:
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audio_opt.append(
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self.vc(
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model,
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net_g,
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sid,
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audio_pad[s : t + self.t_pad2 + self.window],
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pitch[:, s // self.window : (t + self.t_pad2) // self.window],
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pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
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times,
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index,
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big_npy,
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index_rate,
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version,
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protect,
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)[self.t_pad_tgt : -self.t_pad_tgt]
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)
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else:
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audio_opt.append(
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self.vc(
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model,
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net_g,
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sid,
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audio_pad[s : t + self.t_pad2 + self.window],
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None,
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None,
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times,
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index,
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big_npy,
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index_rate,
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version,
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protect,
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)[self.t_pad_tgt : -self.t_pad_tgt]
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)
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s = t
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if if_f0 == 1:
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audio_opt.append(
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self.vc(
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model,
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net_g,
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sid,
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audio_pad[t:],
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pitch[:, t // self.window :] if t is not None else pitch,
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pitchf[:, t // self.window :] if t is not None else pitchf,
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times,
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index,
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big_npy,
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index_rate,
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version,
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protect,
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)[self.t_pad_tgt : -self.t_pad_tgt]
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)
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else:
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audio_opt.append(
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self.vc(
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model,
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net_g,
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sid,
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audio_pad[t:],
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None,
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None,
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times,
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index,
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big_npy,
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index_rate,
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version,
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protect,
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)[self.t_pad_tgt : -self.t_pad_tgt]
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)
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audio_opt = np.concatenate(audio_opt)
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if rms_mix_rate != 1:
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audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
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if tgt_sr != resample_sr >= 16000:
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audio_opt = librosa.resample(
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audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
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)
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audio_max = np.abs(audio_opt).max() / 0.99
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max_int16 = 32768
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if audio_max > 1:
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max_int16 /= audio_max
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audio_opt = (audio_opt * max_int16).astype(np.int16)
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del pitch, pitchf, sid
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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return audio_opt
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