mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-03-15 04:13:57 +03:00
138 lines
4.3 KiB
Python
138 lines
4.3 KiB
Python
import numpy as np
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# import parselmouth
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import torch
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import torch.nn.functional as F
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from Exceptions import HalfPrecisionChangingException
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from voice_changer.RVC.embedder.Embedder import Embedder
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from voice_changer.RVC.inferencer.Inferencer import Inferencer
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from voice_changer.RVC.pitchExtractor.PitchExtractor import PitchExtractor
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class VC(object):
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def __init__(self, tgt_sr, device, is_half, x_pad):
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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 * x_pad # 每条前后pad时间
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self.t_pad_tgt = tgt_sr * x_pad
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self.device = device
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self.is_half = is_half
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def pipeline(
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self,
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embedder: Embedder,
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inferencer: Inferencer,
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pitchExtractor: PitchExtractor,
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sid,
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audio,
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f0_up_key,
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index,
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big_npy,
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index_rate,
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if_f0,
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silence_front=0,
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embChannels=256,
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):
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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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sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
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# ピッチ検出
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pitch, pitchf = None, None
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if if_f0 == 1:
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pitch, pitchf = pitchExtractor.extract(
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audio_pad,
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f0_up_key,
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self.sr,
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self.window,
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silence_front=silence_front,
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)
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pitch = pitch[:p_len]
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pitchf = pitchf[:p_len]
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pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
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pitchf = torch.tensor(
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pitchf, device=self.device, dtype=torch.float
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).unsqueeze(0)
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# tensor型調整
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feats = torch.from_numpy(audio_pad)
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if self.is_half is True:
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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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# embedding
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padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
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try:
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feats = embedder.extractFeatures(feats, embChannels)
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except RuntimeError as e:
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if "HALF" in e.__str__().upper():
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raise HalfPrecisionChangingException()
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else:
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raise e
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# Index - feature抽出
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if (
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isinstance(index, type(None)) is False
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and isinstance(big_npy, type(None)) is False
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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 is True:
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npy = npy.astype("float32")
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D, I = index.search(npy, 1)
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npy = big_npy[I.squeeze()]
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if self.is_half is True:
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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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# ピッチサイズ調整
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p_len = audio_pad.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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p_len = torch.tensor([p_len], device=self.device).long()
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# 推論実行
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try:
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with torch.no_grad():
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audio1 = (
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(
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inferencer.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
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* 32768
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)
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.data.cpu()
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.float()
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.numpy()
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.astype(np.int16)
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)
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except RuntimeError as e:
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if "HALF" in e.__str__().upper():
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raise HalfPrecisionChangingException()
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else:
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raise e
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del feats, p_len, padding_mask
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torch.cuda.empty_cache()
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if self.t_pad_tgt != 0:
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offset = self.t_pad_tgt
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end = -1 * self.t_pad_tgt
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audio1 = audio1[offset:end]
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del pitch, pitchf, sid
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torch.cuda.empty_cache()
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return audio1
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