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https://github.com/w-okada/voice-changer.git
synced 2025-01-23 21:45:00 +03:00
optimize starting RVC
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wataru
parent
2e96f8072a
commit
3a556ebc5f
@ -284,7 +284,6 @@ class RVC:
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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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@ -295,10 +294,14 @@ 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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# バッファがたまっていない場合はzeroで補う
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if self.audio_buffer.shape[0] < convertSize:
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self.audio_buffer = np.concatenate(
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[np.zeros([convertSize]), self.audio_buffer]
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)
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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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@ -319,7 +322,6 @@ 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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@ -347,7 +349,6 @@ 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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@ -14,10 +14,6 @@ from voice_changer.RVC.inferencer.Inferencer import Inferencer
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from voice_changer.RVC.pitchExtractor.PitchExtractor import PitchExtractor
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# isHalfが変わる場合はPipeline作り直し
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# device(GPU, isHalf変更が伴わない場合), pitchExtractorの変更は、入れ替えで対応
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class Pipeline(object):
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embedder: Embedder
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inferencer: Inferencer
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@ -85,12 +81,13 @@ class Pipeline(object):
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repeat,
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protect=0.5,
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):
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# 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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search_index = (
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self.index is not None and self.big_npy is not None and index_rate != 0
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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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@ -130,21 +127,9 @@ 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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@ -159,30 +144,17 @@ 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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# apply silent front for indexsearch
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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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# npy = self.feature[I.squeeze()]
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# TODO: kは調整できるようにする
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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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@ -193,11 +165,9 @@ class Pipeline(object):
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if self.isHalf is True:
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npy = npy.astype("float16")
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# recover silient font
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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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@ -207,6 +177,7 @@ class Pipeline(object):
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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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# 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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@ -227,22 +198,14 @@ 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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# apply silent front for inference
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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 +435,6 @@ 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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@ -453,7 +452,6 @@ 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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