mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-24 22:15:02 +03:00
259 lines
9.9 KiB
Python
259 lines
9.9 KiB
Python
import numpy as np
|
||
from typing import Any
|
||
import math
|
||
import torch
|
||
import torch.nn.functional as F
|
||
from torch.cuda.amp import autocast
|
||
from Exceptions import (
|
||
DeviceCannotSupportHalfPrecisionException,
|
||
DeviceChangingException,
|
||
HalfPrecisionChangingException,
|
||
NotEnoughDataExtimateF0,
|
||
)
|
||
|
||
from voice_changer.RVC.embedder.Embedder import Embedder
|
||
from voice_changer.RVC.inferencer.Inferencer import Inferencer
|
||
from voice_changer.RVC.inferencer.OnnxRVCInferencer import OnnxRVCInferencer
|
||
from voice_changer.RVC.inferencer.OnnxRVCInferencerNono import OnnxRVCInferencerNono
|
||
|
||
from voice_changer.RVC.pitchExtractor.PitchExtractor import PitchExtractor
|
||
|
||
|
||
class Pipeline(object):
|
||
embedder: Embedder
|
||
inferencer: Inferencer
|
||
pitchExtractor: PitchExtractor
|
||
|
||
index: Any | None
|
||
big_npy: Any | None
|
||
# feature: Any | None
|
||
|
||
targetSR: int
|
||
device: torch.device
|
||
isHalf: bool
|
||
|
||
def __init__(
|
||
self,
|
||
embedder: Embedder,
|
||
inferencer: Inferencer,
|
||
pitchExtractor: PitchExtractor,
|
||
index: Any | None,
|
||
# feature: Any | None,
|
||
targetSR,
|
||
device,
|
||
isHalf,
|
||
):
|
||
self.embedder = embedder
|
||
self.inferencer = inferencer
|
||
self.pitchExtractor = pitchExtractor
|
||
print("GENERATE INFERENCER", self.inferencer)
|
||
print("GENERATE EMBEDDER", self.embedder)
|
||
print("GENERATE PITCH EXTRACTOR", self.pitchExtractor)
|
||
|
||
self.index = index
|
||
self.big_npy = index.reconstruct_n(0, index.ntotal) if index is not None else None
|
||
# self.feature = feature
|
||
|
||
self.targetSR = targetSR
|
||
self.device = device
|
||
self.isHalf = isHalf
|
||
|
||
self.sr = 16000
|
||
self.window = 160
|
||
|
||
def getPipelineInfo(self):
|
||
inferencerInfo = self.inferencer.getInferencerInfo() if self.inferencer else {}
|
||
embedderInfo = self.embedder.getEmbedderInfo()
|
||
pitchExtractorInfo = self.pitchExtractor.getPitchExtractorInfo()
|
||
return {"inferencer": inferencerInfo, "embedder": embedderInfo, "pitchExtractor": pitchExtractorInfo, "isHalf": self.isHalf}
|
||
|
||
def setPitchExtractor(self, pitchExtractor: PitchExtractor):
|
||
self.pitchExtractor = pitchExtractor
|
||
|
||
def exec(
|
||
self,
|
||
sid,
|
||
audio, # torch.tensor [n]
|
||
pitchf, # np.array [m]
|
||
feature, # np.array [m, feat]
|
||
f0_up_key,
|
||
index_rate,
|
||
if_f0,
|
||
silence_front,
|
||
embOutputLayer,
|
||
useFinalProj,
|
||
repeat,
|
||
protect=0.5,
|
||
out_size=None,
|
||
):
|
||
# 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。
|
||
|
||
search_index = self.index is not None and self.big_npy is not None and index_rate != 0
|
||
# self.t_pad = self.sr * repeat # 1秒
|
||
# 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)
|
||
audio = audio.unsqueeze(0)
|
||
|
||
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。
|
||
|
||
self.t_pad = round(self.sr * quality_padding_sec) # 前後に音声を追加
|
||
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)
|
||
audio_pad = F.pad(audio, (self.t_pad, self.t_pad), mode="reflect").squeeze(0)
|
||
p_len = audio_pad.shape[0] // self.window
|
||
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
|
||
|
||
# RVC QualityがOnのときにはsilence_frontをオフに。
|
||
silence_front = silence_front if repeat == 0 else 0
|
||
pitchf = pitchf if repeat == 0 else np.zeros(p_len)
|
||
out_size = out_size if repeat == 0 else None
|
||
|
||
# ピッチ検出
|
||
try:
|
||
if if_f0 == 1:
|
||
pitch, pitchf = self.pitchExtractor.extract(
|
||
audio_pad,
|
||
pitchf,
|
||
f0_up_key,
|
||
self.sr,
|
||
self.window,
|
||
silence_front=silence_front,
|
||
)
|
||
# pitch = pitch[:p_len]
|
||
# pitchf = pitchf[:p_len]
|
||
pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
|
||
pitchf = torch.tensor(pitchf, device=self.device, dtype=torch.float).unsqueeze(0)
|
||
else:
|
||
pitch = None
|
||
pitchf = None
|
||
except IndexError:
|
||
# print(e)
|
||
raise NotEnoughDataExtimateF0()
|
||
|
||
# tensor型調整
|
||
feats = audio_pad
|
||
if feats.dim() == 2: # double channels
|
||
feats = feats.mean(-1)
|
||
assert feats.dim() == 1, feats.dim()
|
||
feats = feats.view(1, -1)
|
||
|
||
# embedding
|
||
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
|
||
with autocast(enabled=self.isHalf):
|
||
try:
|
||
feats = self.embedder.extractFeatures(feats, embOutputLayer, useFinalProj)
|
||
if torch.isnan(feats).all():
|
||
raise DeviceCannotSupportHalfPrecisionException()
|
||
except RuntimeError as e:
|
||
if "HALF" in e.__str__().upper():
|
||
raise HalfPrecisionChangingException()
|
||
elif "same device" in e.__str__():
|
||
raise DeviceChangingException()
|
||
else:
|
||
raise e
|
||
if protect < 0.5 and search_index:
|
||
feats0 = feats.clone()
|
||
|
||
# Index - feature抽出
|
||
# if self.index is not None and self.feature is not None and index_rate != 0:
|
||
if search_index:
|
||
npy = feats[0].cpu().numpy()
|
||
# apply silent front for indexsearch
|
||
npyOffset = math.floor(silence_front * 16000) // 360
|
||
npy = npy[npyOffset:]
|
||
|
||
if self.isHalf is True:
|
||
npy = npy.astype("float32")
|
||
|
||
# TODO: kは調整できるようにする
|
||
k = 1
|
||
if k == 1:
|
||
_, ix = self.index.search(npy, 1)
|
||
npy = self.big_npy[ix.squeeze()]
|
||
else:
|
||
score, ix = self.index.search(npy, k=8)
|
||
weight = np.square(1 / score)
|
||
weight /= weight.sum(axis=1, keepdims=True)
|
||
npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
|
||
|
||
# recover silient font
|
||
npy = np.concatenate([np.zeros([npyOffset, npy.shape[1]], dtype=np.float32), feature[:npyOffset:2].astype("float32"), npy])[-feats.shape[1]:]
|
||
feats = torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate + (1 - index_rate) * feats
|
||
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
||
if protect < 0.5 and search_index:
|
||
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
||
|
||
# ピッチサイズ調整
|
||
p_len = audio_pad.shape[0] // self.window
|
||
if feats.shape[1] < p_len:
|
||
p_len = feats.shape[1]
|
||
if pitch is not None and pitchf is not None:
|
||
pitch = pitch[:, :p_len]
|
||
pitchf = pitchf[:, :p_len]
|
||
|
||
feats_len = feats.shape[1]
|
||
if pitch is not None and pitchf is not None:
|
||
pitch = pitch[:, -feats_len:]
|
||
pitchf = pitchf[:, -feats_len:]
|
||
p_len = torch.tensor([feats_len], device=self.device).long()
|
||
|
||
# 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
|
||
# 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。
|
||
# https://github.com/w-okada/voice-changer/pull/276#issuecomment-1571336929
|
||
if protect < 0.5 and search_index:
|
||
pitchff = pitchf.clone()
|
||
pitchff[pitchf > 0] = 1
|
||
pitchff[pitchf < 1] = protect
|
||
pitchff = pitchff.unsqueeze(-1)
|
||
feats = feats * pitchff + feats0 * (1 - pitchff)
|
||
feats = feats.to(feats0.dtype)
|
||
p_len = torch.tensor([p_len], device=self.device).long()
|
||
|
||
# apply silent front for inference
|
||
if type(self.inferencer) in [OnnxRVCInferencer, OnnxRVCInferencerNono]:
|
||
npyOffset = math.floor(silence_front * 16000) // 360
|
||
feats = feats[:, npyOffset * 2 :, :] # NOQA
|
||
|
||
feats_len = feats.shape[1]
|
||
if pitch is not None and pitchf is not None:
|
||
pitch = pitch[:, -feats_len:]
|
||
pitchf = pitchf[:, -feats_len:]
|
||
p_len = torch.tensor([feats_len], device=self.device).long()
|
||
|
||
# 推論実行
|
||
try:
|
||
with torch.no_grad():
|
||
with autocast(enabled=self.isHalf):
|
||
audio1 = (
|
||
torch.clip(
|
||
self.inferencer.infer(feats, p_len, pitch, pitchf, sid, out_size)[0][0, 0].to(dtype=torch.float32),
|
||
-1.0,
|
||
1.0,
|
||
)
|
||
* 32767.5
|
||
).data.to(dtype=torch.int16)
|
||
except RuntimeError as e:
|
||
if "HALF" in e.__str__().upper():
|
||
print("11", e)
|
||
raise HalfPrecisionChangingException()
|
||
else:
|
||
raise e
|
||
|
||
feats_buffer = feats.squeeze(0).detach().cpu()
|
||
if pitchf is not None:
|
||
pitchf_buffer = pitchf.squeeze(0).detach().cpu()
|
||
else:
|
||
pitchf_buffer = None
|
||
|
||
del p_len, padding_mask, pitch, pitchf, feats
|
||
torch.cuda.empty_cache()
|
||
|
||
# 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。
|
||
# pipelineに（入力されるときはhubertように16k）
|
||
if self.t_pad_tgt != 0:
|
||
offset = self.t_pad_tgt
|
||
end = -1 * self.t_pad_tgt
|
||
audio1 = audio1[offset:end]
|
||
|
||
del sid
|
||
torch.cuda.empty_cache()
|
||
return audio1, pitchf_buffer, feats_buffer
|