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https://github.com/w-okada/voice-changer.git
synced 2025-01-23 21:45:00 +03:00
optimize convert
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wataru
parent
bfeef443f5
commit
2e96f8072a
@ -280,8 +280,11 @@ class RVC:
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crossfadeSize: int,
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solaSearchFrame: int = 0,
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):
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newData = newData.astype(np.float32) / 32768.0
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newData = (
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newData.astype(np.float32) / 32768.0
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) # RVCのモデルのサンプリングレートで入ってきている。（extraDataLength, Crossfade等も同じSRで処理）(★１)
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print("newData", newData.shape, crossfadeSize, solaSearchFrame)
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if self.audio_buffer is not None:
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# 過去のデータに連結
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self.audio_buffer = np.concatenate([self.audio_buffer, newData], 0)
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@ -292,8 +295,10 @@ class RVC:
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inputSize + crossfadeSize + solaSearchFrame + self.settings.extraConvertSize
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)
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print("convertSize1", convertSize)
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if convertSize % 128 != 0: # モデルの出力のホップサイズで切り捨てが発生するので補う。
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convertSize = convertSize + (128 - (convertSize % 128))
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print("convertSize2", convertSize)
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convertOffset = -1 * convertSize
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self.audio_buffer = self.audio_buffer[convertOffset:] # 変換対象の部分だけ抽出
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@ -314,6 +319,7 @@ class RVC:
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vol = torch.sqrt(torch.square(crop).mean()).detach().cpu().numpy()
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vol = max(vol, self.prevVol * 0.0)
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self.prevVol = vol
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print("inf0 : ", audio_buffer.shape, convertSize)
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return (audio_buffer, convertSize, vol)
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@ -341,6 +347,7 @@ class RVC:
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if vol < self.settings.silentThreshold:
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return np.zeros(convertSize).astype(np.int16)
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print("inf1 : ", audio.shape)
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audio = torchaudio.functional.resample(
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audio, self.settings.modelSamplingRate, 16000, rolloff=0.99
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)
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@ -360,7 +367,8 @@ class RVC:
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f0_up_key,
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index_rate,
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if_f0,
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self.settings.extraConvertSize / self.settings.modelSamplingRate,
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self.settings.extraConvertSize
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/ self.settings.modelSamplingRate, # extaraDataSizeの秒数。RVCのモデルのサンプリングレートで処理(★１)。
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embOutputLayer,
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useFinalProj,
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repeat,
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@ -1,6 +1,6 @@
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import numpy as np
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from typing import Any
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import math
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import torch
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import torch.nn.functional as F
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from Exceptions import (
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@ -90,7 +90,7 @@ class Pipeline(object):
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)
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self.t_pad = self.sr * repeat
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self.t_pad_tgt = self.targetSR * repeat
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print("Audio Feature1", audio.shape) # 16000のサンプリングレートで入ってきている。以降この世界は16000で処理。
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audio_pad = F.pad(
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audio.unsqueeze(0), (self.t_pad, self.t_pad), mode="reflect"
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).squeeze(0)
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@ -130,9 +130,21 @@ class Pipeline(object):
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feats = feats.view(1, -1)
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# embedding
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print("audio feature", feats.shape)
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padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
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try:
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# testFeat = feats.clone()
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# while True:
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# print("embedding audio;", testFeat.shape)
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# testFeatOut = self.embedder.extractFeatures(
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# testFeat, embOutputLayer, useFinalProj
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# )
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# testFeat = testFeat[:, 1:]
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# print("embedding vector;", testFeatOut.shape)
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print("embedding audio;", feats.shape)
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feats = self.embedder.extractFeatures(feats, embOutputLayer, useFinalProj)
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print("embedding vector;", feats.shape)
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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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@ -147,6 +159,20 @@ class Pipeline(object):
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# if self.index is not None and self.feature is not None and index_rate != 0:
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if search_index:
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npy = feats[0].cpu().numpy()
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print("npy shape", npy.shape, npy.shape[0] * 16000)
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npyOffset = math.floor(silence_front * 16000) // 360
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print(
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"npyOffset",
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silence_front,
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self.targetSR,
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(silence_front * self.targetSR),
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npyOffset,
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)
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npy = npy[npyOffset:]
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print(
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"npy trimmed shape",
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npy.shape,
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)
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if self.isHalf is True:
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npy = npy.astype("float32")
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# D, I = self.index.search(npy, 1)
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@ -156,6 +182,7 @@ class Pipeline(object):
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k = 1
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if k == 1:
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_, ix = self.index.search(npy, 1)
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print("ix shape", ix.shape)
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npy = self.big_npy[ix.squeeze()]
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else:
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score, ix = self.index.search(npy, k=8)
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@ -166,6 +193,11 @@ class Pipeline(object):
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if self.isHalf is True:
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npy = npy.astype("float16")
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npy = np.concatenate([np.zeros([npyOffset, npy.shape[1]]), npy])
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print(
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"npy last shape",
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npy.shape,
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)
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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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@ -195,6 +227,22 @@ class Pipeline(object):
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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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npyOffset = math.floor(silence_front * 16000) // 360
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print(
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"npy last shape2",
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feats.shape,
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)
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feats = feats[:, npyOffset * 2 :, :]
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feats_len = feats.shape[1]
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pitch = pitch[:, -feats_len:]
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pitchf = pitchf[:, -feats_len:]
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p_len = torch.tensor([feats_len], device=self.device).long()
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print(
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"npy last shape3",
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feats.shape,
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feats_len,
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)
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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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@ -435,7 +435,7 @@ class VoiceChanger:
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raise RuntimeError("Voice Changer is not selected.")
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processing_sampling_rate = self.voiceChanger.get_processing_sampling_rate()
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print("original frame", receivedData.shape[0])
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# 前処理
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with Timer("pre-process") as t:
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if self.settings.inputSampleRate != processing_sampling_rate:
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@ -453,6 +453,7 @@ class VoiceChanger:
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sola_search_frame = int(0.012 * processing_sampling_rate)
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# sola_search_frame = 0
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block_frame = newData.shape[0]
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print("block frame", newData.shape[0])
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crossfade_frame = min(self.settings.crossFadeOverlapSize, block_frame)
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self._generate_strength(crossfade_frame)
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@ -472,8 +473,7 @@ class VoiceChanger:
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sola_search_frame + crossfade_frame + block_frame
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)
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audio = audio[audio_offset:]
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a = 0
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audio = audio[a:]
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# SOLA algorithm from https://github.com/yxlllc/DDSP-SVC, https://github.com/liujing04/Retrieval-based-Voice-Conversion-WebUI
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cor_nom = np.convolve(
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audio[: crossfade_frame + sola_search_frame],
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