mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-23 21:45:00 +03:00
284 lines
11 KiB
Python
284 lines
11 KiB
Python
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 torch.cuda.amp import autocast
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from Exceptions import (
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DeviceCannotSupportHalfPrecisionException,
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DeviceChangingException,
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HalfPrecisionChangingException,
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NotEnoughDataExtimateF0,
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)
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from mods.log_control import VoiceChangaerLogger
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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.inferencer.OnnxRVCInferencer import OnnxRVCInferencer
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from voice_changer.RVC.inferencer.OnnxRVCInferencerNono import OnnxRVCInferencerNono
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from voice_changer.RVC.pitchExtractor.PitchExtractor import PitchExtractor
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from voice_changer.utils.Timer import Timer2
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logger = VoiceChangaerLogger.get_instance().getLogger()
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class Pipeline(object):
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embedder: Embedder
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inferencer: Inferencer
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pitchExtractor: PitchExtractor
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index: Any | None
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big_npy: Any | None
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# feature: Any | None
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targetSR: int
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device: torch.device
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isHalf: bool
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def __init__(
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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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index: Any | None,
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# feature: Any | None,
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targetSR,
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device,
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isHalf,
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):
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self.embedder = embedder
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self.inferencer = inferencer
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self.pitchExtractor = pitchExtractor
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logger.info("GENERATE INFERENCER" + str(self.inferencer))
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logger.info("GENERATE EMBEDDER" + str(self.embedder))
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logger.info("GENERATE PITCH EXTRACTOR" + str(self.pitchExtractor))
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self.index = index
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self.big_npy = index.reconstruct_n(0, index.ntotal) if index is not None else None
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# self.feature = feature
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self.targetSR = targetSR
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self.device = device
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self.isHalf = isHalf
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self.sr = 16000
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self.window = 160
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def getPipelineInfo(self):
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inferencerInfo = self.inferencer.getInferencerInfo() if self.inferencer else {}
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embedderInfo = self.embedder.getEmbedderInfo()
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pitchExtractorInfo = self.pitchExtractor.getPitchExtractorInfo()
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return {"inferencer": inferencerInfo, "embedder": embedderInfo, "pitchExtractor": pitchExtractorInfo, "isHalf": self.isHalf}
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def setPitchExtractor(self, pitchExtractor: PitchExtractor):
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self.pitchExtractor = pitchExtractor
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def extractPitch(self, audio_pad, if_f0, pitchf, f0_up_key, silence_front):
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try:
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if if_f0 == 1:
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pitch, pitchf = self.pitchExtractor.extract(
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audio_pad,
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pitchf,
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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(pitchf, device=self.device, dtype=torch.float).unsqueeze(0)
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else:
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pitch = None
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pitchf = None
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except IndexError as e: # NOQA
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print(e)
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import traceback
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traceback.print_exc()
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raise NotEnoughDataExtimateF0()
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return pitch, pitchf
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def extractFeatures(self, feats, embOutputLayer, useFinalProj):
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with autocast(enabled=self.isHalf):
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try:
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feats = self.embedder.extractFeatures(feats, embOutputLayer, useFinalProj)
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if torch.isnan(feats).all():
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raise DeviceCannotSupportHalfPrecisionException()
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return feats
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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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elif "same device" in e.__str__():
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raise DeviceChangingException()
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else:
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raise e
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def infer(self, feats, p_len, pitch, pitchf, sid, out_size):
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try:
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with torch.no_grad():
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with autocast(enabled=self.isHalf):
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audio1 = self.inferencer.infer(feats, p_len, pitch, pitchf, sid, out_size)
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audio1 = (audio1 * 32767.5).data.to(dtype=torch.int16)
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return audio1
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except RuntimeError as e:
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if "HALF" in e.__str__().upper():
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print("HalfPresicion Error:", e)
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raise HalfPrecisionChangingException()
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else:
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raise e
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def exec(
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self,
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sid,
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audio, # torch.tensor [n]
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pitchf, # np.array [m]
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feature, # np.array [m, feat]
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f0_up_key,
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index_rate,
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if_f0,
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silence_front,
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embOutputLayer,
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useFinalProj,
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repeat,
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protect=0.5,
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out_size=None,
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):
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# print(f"pipeline exec input, audio:{audio.shape}, pitchf:{pitchf.shape}, feature:{feature.shape}")
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# print(f"pipeline exec input, silence_front:{silence_front}, out_size:{out_size}")
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with Timer2("Pipeline-Exec", False) as t: # NOQA
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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 = self.index is not None and self.big_npy is not None and index_rate != 0
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# self.t_pad = self.sr * repeat # 1秒
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# self.t_pad_tgt = self.targetSR * repeat # 1秒 出力時のトリミング(モデルのサンプリングで出力される)
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audio = audio.unsqueeze(0)
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quality_padding_sec = (repeat * (audio.shape[1] - 1)) / self.sr # padding(reflect)のサイズは元のサイズより小さい必要がある。
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self.t_pad = round(self.sr * quality_padding_sec) # 前後に音声を追加
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self.t_pad_tgt = round(self.targetSR * quality_padding_sec) # 前後に音声を追加 出力時のトリミング(モデルのサンプリングで出力される)
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audio_pad = F.pad(audio, (self.t_pad, self.t_pad), mode="reflect").squeeze(0)
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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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# RVC QualityがOnのときにはsilence_frontをオフに。
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silence_front = silence_front if repeat == 0 else 0
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pitchf = pitchf if repeat == 0 else np.zeros(p_len)
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out_size = out_size if repeat == 0 else None
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# tensor型調整
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feats = audio_pad
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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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t.record("pre-process")
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# ピッチ検出
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pitch, pitchf = self.extractPitch(audio_pad, if_f0, pitchf, f0_up_key, silence_front)
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t.record("extract-pitch")
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# embedding
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feats = self.extractFeatures(feats, embOutputLayer, useFinalProj)
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t.record("extract-feats")
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# Index - feature抽出
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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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# apply silent front for indexsearch
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npyOffset = math.floor(silence_front * 16000) // 360
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npy = npy[npyOffset:]
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if self.isHalf is True:
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npy = npy.astype("float32")
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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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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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weight = np.square(1 / score)
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weight /= weight.sum(axis=1, keepdims=True)
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npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
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# recover silient font
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npy = np.concatenate([np.zeros([npyOffset, npy.shape[1]], dtype=np.float32), feature[:npyOffset:2].astype("float32"), npy])[-feats.shape[1]:]
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feats = torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate + (1 - index_rate) * feats
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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 search_index:
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feats0 = feats.clone()
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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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feats_len = feats.shape[1]
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if pitch is not None and pitchf is not None:
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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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# pitchの推定が上手くいかない(pitchf=0)場合、検索前の特徴を混ぜる
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# pitchffの作り方の疑問はあるが、本家通りなので、このまま使うことにする。
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# https://github.com/w-okada/voice-changer/pull/276#issuecomment-1571336929
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if protect < 0.5 and search_index:
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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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# apply silent front for inference
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if type(self.inferencer) in [OnnxRVCInferencer, OnnxRVCInferencerNono]:
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npyOffset = math.floor(silence_front * 16000) // 360
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feats = feats[:, npyOffset * 2 :, :] # NOQA
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feats_len = feats.shape[1]
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if pitch is not None and pitchf is not None:
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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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t.record("mid-precess")
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# 推論実行
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audio1 = self.infer(feats, p_len, pitch, pitchf, sid, out_size)
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t.record("infer")
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feats_buffer = feats.squeeze(0).detach().cpu()
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if pitchf is not None:
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pitchf_buffer = pitchf.squeeze(0).detach().cpu()
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else:
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pitchf_buffer = None
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del p_len, pitch, pitchf, feats
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# torch.cuda.empty_cache()
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# inferで出力されるサンプリングレートはモデルのサンプリングレートになる。
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# pipelineに（入力されるときはhubertように16k）
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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 sid
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t.record("post-process")
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# torch.cuda.empty_cache()
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# print("EXEC AVERAGE:", t.avrSecs)
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return audio1, pitchf_buffer, feats_buffer
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def __del__(self):
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del self.embedder
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del self.inferencer
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del self.pitchExtractor
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print('Pipeline has been deleted')
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