mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-23 13:35:12 +03:00
WIP: refactoring
This commit is contained in:
wataru
parent
4fc57153e7
commit
83083a68ac
@ -15,6 +15,8 @@ import numpy as np
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import torch
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from fairseq import checkpoint_utils
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import traceback
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import faiss
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from const import TMP_DIR # type:ignore
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@ -169,12 +171,15 @@ class RVC:
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self.settings.modelSlots[slot].deprecated = tmp_onnx_session.getDeprecated()
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def prepareModel(self, slot: int):
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if slot < 0:
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return self.get_info()
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print("[Voice Changer] Prepare Model of slot:", slot)
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onnxModelFile = self.settings.modelSlots[slot].onnxModelFile
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isONNX = (
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True if self.settings.modelSlots[slot].onnxModelFile is not None else False
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)
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# モデルのロード
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if isONNX:
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print("[Voice Changer] Loading ONNX Model...")
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self.next_onnx_session = ModelWrapper(onnxModelFile)
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@ -214,8 +219,36 @@ class RVC:
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self.next_net_g = net_g
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self.next_onnx_session = None
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# Indexのロード
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print("[Voice Changer] Loading index...")
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self.next_feature_file = self.settings.modelSlots[slot].featureFile
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self.next_index_file = self.settings.modelSlots[slot].indexFile
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if (
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self.settings.modelSlots[slot].featureFile is not None
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and self.settings.modelSlots[slot].indexFile is not None
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):
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if (
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os.path.exists(self.settings.modelSlots[slot].featureFile) is True
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and os.path.exists(self.settings.modelSlots[slot].indexFile) is True
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):
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try:
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self.next_index = faiss.read_index(
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self.settings.modelSlots[slot].indexFile
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)
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self.next_feature = np.load(
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self.settings.modelSlots[slot].featureFile
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)
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except:
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print("[Voice Changer] load index failed. Use no index.")
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traceback.print_exc()
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self.next_index = self.next_feature = None
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else:
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print("[Voice Changer] Index file is not found. Use no index.")
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self.next_index = self.next_feature = None
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else:
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self.next_index = self.next_feature = None
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self.next_trans = self.settings.modelSlots[slot].defaultTrans
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self.next_samplingRate = self.settings.modelSlots[slot].samplingRate
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self.next_framework = (
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@ -232,6 +265,8 @@ class RVC:
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self.onnx_session = self.next_onnx_session
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self.feature_file = self.next_feature_file
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self.index_file = self.next_index_file
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self.feature = self.next_feature
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self.index = self.next_index
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self.settings.tran = self.next_trans
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self.settings.framework = self.next_framework
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self.settings.modelSamplingRate = self.next_samplingRate
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@ -436,14 +471,10 @@ class RVC:
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repeat *= self.settings.rvcQuality # 0 or 3
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vc = VC(self.settings.modelSamplingRate, dev, self.is_half, repeat)
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sid = 0
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times = [0, 0, 0]
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f0_up_key = self.settings.tran
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f0_method = self.settings.f0Detector
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file_index = self.index_file if self.index_file is not None else ""
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file_big_npy = self.feature_file if self.feature_file is not None else ""
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index_rate = self.settings.indexRatio
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if_f0 = 1 if self.settings.modelSlots[self.currentSlot].f0 else 0
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f0_file = None
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embChannels = self.settings.modelSlots[self.currentSlot].embChannels
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audio_out = vc.pipeline(
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@ -451,14 +482,12 @@ class RVC:
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self.net_g,
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sid,
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audio,
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times,
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f0_up_key,
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f0_method,
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file_index,
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file_big_npy,
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self.index,
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self.feature,
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index_rate,
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if_f0,
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f0_file=f0_file,
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silence_front=self.settings.extraConvertSize
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/ self.settings.modelSamplingRate,
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embChannels=embChannels,
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@ -1,16 +1,10 @@
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import numpy as np
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import parselmouth
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import torch
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import pdb
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from time import time as ttime
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import torch.nn.functional as F
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from config import x_pad, x_query, x_center, x_max
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import scipy.signal as signal
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import pyworld
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import os
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import traceback
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import faiss
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# from .const import RVC_MODEL_TYPE_NORMAL, RVC_MODEL_TYPE_PITCHLESS, RVC_MODEL_TYPE_WEBUI_256_NORMAL, RVC_MODEL_TYPE_WEBUI_768_NORMAL, RVC_MODEL_TYPE_WEBUI_256_PITCHLESS, RVC_MODEL_TYPE_WEBUI_768_PITCHLESS
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from .const import RVC_MODEL_TYPE_RVC, RVC_MODEL_TYPE_WEBUI
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@ -20,7 +14,6 @@ class VC(object):
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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.t_pad2 = self.t_pad * 2
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self.t_query = self.sr * x_query # 查询切点前后查询时间
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self.t_center = self.sr * x_center # 查询切点位置
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self.t_max = self.sr * x_max # 免查询时长阈值
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@ -28,26 +21,34 @@ class VC(object):
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self.is_half = is_half
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def get_f0(self, audio, p_len, f0_up_key, f0_method, inp_f0=None, silence_front=0):
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n_frames = int(len(audio) // self.window) + 1
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start_frame = int(silence_front * self.sr / self.window)
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real_silence_front = start_frame * self.window / self.sr
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audio = audio[int(np.round(real_silence_front * self.sr)):]
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audio = audio[int(np.round(real_silence_front * self.sr)) :]
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time_step = self.window / self.sr * 1000
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f0_min = 50
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f0_max = 1100
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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if (f0_method == "pm"):
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f0 = parselmouth.Sound(audio, self.sr).to_pitch_ac(
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time_step=time_step / 1000, voicing_threshold=0.6,
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pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
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if f0_method == "pm":
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f0 = (
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parselmouth.Sound(audio, self.sr)
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.to_pitch_ac(
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time_step=time_step / 1000,
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voicing_threshold=0.6,
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pitch_floor=f0_min,
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pitch_ceiling=f0_max,
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)
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.selected_array["frequency"]
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)
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pad_size = (p_len - len(f0) + 1) // 2
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if (pad_size > 0 or p_len - len(f0) - pad_size > 0):
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f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode='constant')
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elif (f0_method == "harvest"):
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if pad_size > 0 or p_len - len(f0) - pad_size > 0:
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f0 = np.pad(
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f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
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)
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elif f0_method == "harvest":
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f0, t = pyworld.harvest(
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audio.astype(np.double),
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fs=self.sr,
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@ -57,36 +58,65 @@ class VC(object):
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f0 = pyworld.stonemask(audio.astype(np.double), f0, t, self.sr)
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f0 = signal.medfilt(f0, 3)
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f0 = np.pad(f0.astype('float'), (start_frame, n_frames - len(f0) - start_frame))
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f0 = np.pad(
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f0.astype("float"), (start_frame, n_frames - len(f0) - start_frame)
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)
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else:
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print("[Voice Changer] invalid f0 detector, use pm.", f0_method)
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f0 = parselmouth.Sound(audio, self.sr).to_pitch_ac(
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time_step=time_step / 1000, voicing_threshold=0.6,
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pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
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f0 = (
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parselmouth.Sound(audio, self.sr)
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.to_pitch_ac(
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time_step=time_step / 1000,
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voicing_threshold=0.6,
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pitch_floor=f0_min,
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pitch_ceiling=f0_max,
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)
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.selected_array["frequency"]
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)
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pad_size = (p_len - len(f0) + 1) // 2
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if (pad_size > 0 or p_len - len(f0) - pad_size > 0):
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f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode='constant')
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if pad_size > 0 or p_len - len(f0) - pad_size > 0:
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f0 = np.pad(
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f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
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)
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f0 *= pow(2, f0_up_key / 12)
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# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
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tf0 = self.sr // self.window # 每秒f0点数
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if (inp_f0 is not None):
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delta_t = np.round((inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1).astype("int16")
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replace_f0 = np.interp(list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1])
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shape = f0[x_pad * tf0:x_pad * tf0 + len(replace_f0)].shape[0]
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f0[x_pad * tf0:x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
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if inp_f0 is not None:
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delta_t = np.round(
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(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
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).astype("int16")
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replace_f0 = np.interp(
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list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
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)
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shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
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f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
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# with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
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f0bak = f0.copy()
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f0_mel = 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
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f0_mel_max - f0_mel_min
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) + 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > 255] = 255
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f0_coarse = np.rint(f0_mel).astype(np.int)
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return f0_coarse, f0bak # 1-0
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def vc(self, model, net_g, sid, audio0, pitch, pitchf, times, index, big_npy, index_rate, embChannels=256): # ,file_index,file_big_npy
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def vc(
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self,
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model,
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net_g,
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sid,
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audio0,
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pitch,
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pitchf,
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index,
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big_npy,
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index_rate,
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embChannels=256,
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): # ,file_index,file_big_npy
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feats = torch.from_numpy(audio0)
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if (self.is_half == True):
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if self.is_half == True:
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feats = feats.half()
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else:
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feats = feats.float()
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@ -107,7 +137,6 @@ class VC(object):
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"padding_mask": padding_mask,
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}
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t0 = ttime()
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with torch.no_grad():
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logits = model.extract_features(**inputs)
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if embChannels == 256:
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@ -115,82 +144,121 @@ class VC(object):
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else:
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feats = logits[0]
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if (isinstance(index, type(None)) == False and isinstance(big_npy, type(None)) == False and index_rate != 0):
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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 == True):
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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 == True):
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if self.is_half is True:
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npy = npy.astype("float16")
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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 = (
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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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t1 = ttime()
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p_len = audio0.shape[0] // self.window
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if (feats.shape[1] < p_len):
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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 != None and pitchf != None):
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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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with torch.no_grad():
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if pitch != None:
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audio1 = (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
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if pitch is not None:
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audio1 = (
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(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
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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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else:
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if hasattr(net_g, "infer_pitchless"):
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audio1 = (net_g.infer_pitchless(feats, p_len, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
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audio1 = (
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(net_g.infer_pitchless(feats, p_len, sid)[0][0, 0] * 32768)
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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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else:
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audio1 = (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
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# audio1 = (net_g.infer(feats, p_len, None, pitchf, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
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audio1 = (
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(net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
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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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del feats, p_len, padding_mask
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torch.cuda.empty_cache()
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t2 = ttime()
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times[0] += (t1 - t0)
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times[2] += (t2 - t1)
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return audio1
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def pipeline(self, model, net_g, sid, audio, times, f0_up_key, f0_method, file_index, file_big_npy, index_rate, if_f0, f0_file=None, silence_front=0, embChannels=256):
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if (file_big_npy != "" and file_index != "" and os.path.exists(file_big_npy) == True and os.path.exists(file_index) == True and index_rate != 0):
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try:
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index = faiss.read_index(file_index)
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big_npy = np.load(file_big_npy)
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except:
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traceback.print_exc()
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index = big_npy = None
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else:
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index = big_npy = None
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audio_opt = []
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t = None
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t1 = ttime()
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audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode='reflect')
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def pipeline(
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self,
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embedder,
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model,
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sid,
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audio,
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f0_up_key,
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f0_method,
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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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inp_f0 = None
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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 = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0, silence_front=silence_front)
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if if_f0 == 1:
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pitch, pitchf = self.get_f0(
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audio_pad,
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p_len,
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f0_up_key,
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f0_method,
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inp_f0,
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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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pitchf = torch.tensor(
|
||||
pitchf, device=self.device, dtype=torch.float
|
||||
).unsqueeze(0)
|
||||
|
||||
t2 = ttime()
|
||||
times[1] += (t2 - t1)
|
||||
if self.t_pad_tgt == 0:
|
||||
audio_opt.append(self.vc(model, net_g, sid, audio_pad[t:], pitch[:, t // self.window:]if t is not None else pitch,
|
||||
pitchf[:, t // self.window:]if t is not None else pitchf, times, index, big_npy, index_rate, embChannels))
|
||||
else:
|
||||
audio_opt.append(self.vc(model, net_g, sid, audio_pad[t:], pitch[:, t // self.window:]if t is not None else pitch,
|
||||
pitchf[:, t // self.window:]if t is not None else pitchf, times, index, big_npy, index_rate, embChannels)[self.t_pad_tgt:-self.t_pad_tgt])
|
||||
output = self.vc(
|
||||
embedder,
|
||||
model,
|
||||
sid,
|
||||
audio_pad,
|
||||
pitch,
|
||||
pitchf,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
embChannels,
|
||||
)
|
||||
if self.t_pad_tgt != 0:
|
||||
offset = self.t_pad_tgt
|
||||
end = -1 * self.t_pad_tgt
|
||||
output = output[offset:end]
|
||||
|
||||
audio_opt = np.concatenate(audio_opt)
|
||||
del pitch, pitchf, sid
|
||||
torch.cuda.empty_cache()
|
||||
return audio_opt
|
||||
return output
|
||||
|
||||
315
server/voice_changer/RVC/custom_vc_infer_pipeline_backup.py
Normal file
315
server/voice_changer/RVC/custom_vc_infer_pipeline_backup.py
Normal file
@ -0,0 +1,315 @@
|
||||
import numpy as np
|
||||
import parselmouth
|
||||
import torch
|
||||
import pdb
|
||||
from time import time as ttime
|
||||
import torch.nn.functional as F
|
||||
from config import x_pad, x_query, x_center, x_max
|
||||
import scipy.signal as signal
|
||||
import pyworld
|
||||
import os
|
||||
import traceback
|
||||
import faiss
|
||||
from .const import RVC_MODEL_TYPE_RVC, RVC_MODEL_TYPE_WEBUI
|
||||
|
||||
|
||||
class VC(object):
|
||||
def __init__(self, tgt_sr, device, is_half, x_pad):
|
||||
self.sr = 16000 # hubert输入采样率
|
||||
self.window = 160 # 每帧点数
|
||||
self.t_pad = self.sr * x_pad # 每条前后pad时间
|
||||
self.t_pad_tgt = tgt_sr * x_pad
|
||||
self.t_pad2 = self.t_pad * 2
|
||||
self.t_query = self.sr * x_query # 查询切点前后查询时间
|
||||
self.t_center = self.sr * x_center # 查询切点位置
|
||||
self.t_max = self.sr * x_max # 免查询时长阈值
|
||||
self.device = device
|
||||
self.is_half = is_half
|
||||
|
||||
def get_f0(self, audio, p_len, f0_up_key, f0_method, inp_f0=None, silence_front=0):
|
||||
n_frames = int(len(audio) // self.window) + 1
|
||||
start_frame = int(silence_front * self.sr / self.window)
|
||||
real_silence_front = start_frame * self.window / self.sr
|
||||
|
||||
audio = audio[int(np.round(real_silence_front * self.sr)) :]
|
||||
|
||||
time_step = self.window / self.sr * 1000
|
||||
f0_min = 50
|
||||
f0_max = 1100
|
||||
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
|
||||
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
|
||||
if f0_method == "pm":
|
||||
f0 = (
|
||||
parselmouth.Sound(audio, self.sr)
|
||||
.to_pitch_ac(
|
||||
time_step=time_step / 1000,
|
||||
voicing_threshold=0.6,
|
||||
pitch_floor=f0_min,
|
||||
pitch_ceiling=f0_max,
|
||||
)
|
||||
.selected_array["frequency"]
|
||||
)
|
||||
pad_size = (p_len - len(f0) + 1) // 2
|
||||
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
|
||||
f0 = np.pad(
|
||||
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
|
||||
)
|
||||
elif f0_method == "harvest":
|
||||
f0, t = pyworld.harvest(
|
||||
audio.astype(np.double),
|
||||
fs=self.sr,
|
||||
f0_ceil=f0_max,
|
||||
frame_period=10,
|
||||
)
|
||||
f0 = pyworld.stonemask(audio.astype(np.double), f0, t, self.sr)
|
||||
f0 = signal.medfilt(f0, 3)
|
||||
|
||||
f0 = np.pad(
|
||||
f0.astype("float"), (start_frame, n_frames - len(f0) - start_frame)
|
||||
)
|
||||
else:
|
||||
print("[Voice Changer] invalid f0 detector, use pm.", f0_method)
|
||||
f0 = (
|
||||
parselmouth.Sound(audio, self.sr)
|
||||
.to_pitch_ac(
|
||||
time_step=time_step / 1000,
|
||||
voicing_threshold=0.6,
|
||||
pitch_floor=f0_min,
|
||||
pitch_ceiling=f0_max,
|
||||
)
|
||||
.selected_array["frequency"]
|
||||
)
|
||||
pad_size = (p_len - len(f0) + 1) // 2
|
||||
if pad_size > 0 or p_len - len(f0) - pad_size > 0:
|
||||
f0 = np.pad(
|
||||
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
|
||||
)
|
||||
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
|
||||
tf0 = self.sr // self.window # 每秒f0点数
|
||||
if inp_f0 is not None:
|
||||
delta_t = np.round(
|
||||
(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
|
||||
).astype("int16")
|
||||
replace_f0 = np.interp(
|
||||
list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
|
||||
)
|
||||
shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
|
||||
f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
|
||||
# with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
|
||||
f0bak = f0.copy()
|
||||
f0_mel = 1127 * np.log(1 + f0 / 700)
|
||||
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
|
||||
f0_mel_max - f0_mel_min
|
||||
) + 1
|
||||
f0_mel[f0_mel <= 1] = 1
|
||||
f0_mel[f0_mel > 255] = 255
|
||||
f0_coarse = np.rint(f0_mel).astype(np.int)
|
||||
return f0_coarse, f0bak # 1-0
|
||||
|
||||
def vc(
|
||||
self,
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio0,
|
||||
pitch,
|
||||
pitchf,
|
||||
times,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
embChannels=256,
|
||||
): # ,file_index,file_big_npy
|
||||
feats = torch.from_numpy(audio0)
|
||||
if self.is_half == True:
|
||||
feats = feats.half()
|
||||
else:
|
||||
feats = feats.float()
|
||||
if feats.dim() == 2: # double channels
|
||||
feats = feats.mean(-1)
|
||||
assert feats.dim() == 1, feats.dim()
|
||||
feats = feats.view(1, -1)
|
||||
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
|
||||
if embChannels == 256:
|
||||
inputs = {
|
||||
"source": feats.to(self.device),
|
||||
"padding_mask": padding_mask,
|
||||
"output_layer": 9, # layer 9
|
||||
}
|
||||
else:
|
||||
inputs = {
|
||||
"source": feats.to(self.device),
|
||||
"padding_mask": padding_mask,
|
||||
}
|
||||
|
||||
t0 = ttime()
|
||||
with torch.no_grad():
|
||||
logits = model.extract_features(**inputs)
|
||||
if embChannels == 256:
|
||||
feats = model.final_proj(logits[0])
|
||||
else:
|
||||
feats = logits[0]
|
||||
|
||||
if (
|
||||
isinstance(index, type(None)) == False
|
||||
and isinstance(big_npy, type(None)) == False
|
||||
and index_rate != 0
|
||||
):
|
||||
npy = feats[0].cpu().numpy()
|
||||
if self.is_half == True:
|
||||
npy = npy.astype("float32")
|
||||
D, I = index.search(npy, 1)
|
||||
npy = big_npy[I.squeeze()]
|
||||
if self.is_half == True:
|
||||
npy = npy.astype("float16")
|
||||
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)
|
||||
|
||||
t1 = ttime()
|
||||
p_len = audio0.shape[0] // self.window
|
||||
if feats.shape[1] < p_len:
|
||||
p_len = feats.shape[1]
|
||||
if pitch != None and pitchf != None:
|
||||
pitch = pitch[:, :p_len]
|
||||
pitchf = pitchf[:, :p_len]
|
||||
p_len = torch.tensor([p_len], device=self.device).long()
|
||||
|
||||
with torch.no_grad():
|
||||
if pitch != None:
|
||||
audio1 = (
|
||||
(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
|
||||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
.astype(np.int16)
|
||||
)
|
||||
else:
|
||||
if hasattr(net_g, "infer_pitchless"):
|
||||
audio1 = (
|
||||
(net_g.infer_pitchless(feats, p_len, sid)[0][0, 0] * 32768)
|
||||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
.astype(np.int16)
|
||||
)
|
||||
else:
|
||||
audio1 = (
|
||||
(net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
|
||||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
.astype(np.int16)
|
||||
)
|
||||
|
||||
# audio1 = (net_g.infer(feats, p_len, None, pitchf, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
|
||||
|
||||
del feats, p_len, padding_mask
|
||||
torch.cuda.empty_cache()
|
||||
t2 = ttime()
|
||||
times[0] += t1 - t0
|
||||
times[2] += t2 - t1
|
||||
return audio1
|
||||
|
||||
def pipeline(
|
||||
self,
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio,
|
||||
times,
|
||||
f0_up_key,
|
||||
f0_method,
|
||||
file_index,
|
||||
file_big_npy,
|
||||
index_rate,
|
||||
if_f0,
|
||||
f0_file=None,
|
||||
silence_front=0,
|
||||
embChannels=256,
|
||||
):
|
||||
if (
|
||||
file_big_npy != ""
|
||||
and file_index != ""
|
||||
and os.path.exists(file_big_npy) == True
|
||||
and os.path.exists(file_index) == True
|
||||
and index_rate != 0
|
||||
):
|
||||
try:
|
||||
index = faiss.read_index(file_index)
|
||||
big_npy = np.load(file_big_npy)
|
||||
except:
|
||||
traceback.print_exc()
|
||||
index = big_npy = None
|
||||
else:
|
||||
index = big_npy = None
|
||||
|
||||
audio_opt = []
|
||||
t = None
|
||||
t1 = ttime()
|
||||
audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
|
||||
p_len = audio_pad.shape[0] // self.window
|
||||
inp_f0 = None
|
||||
|
||||
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
|
||||
pitch, pitchf = None, None
|
||||
if if_f0 == 1:
|
||||
pitch, pitchf = self.get_f0(
|
||||
audio_pad,
|
||||
p_len,
|
||||
f0_up_key,
|
||||
f0_method,
|
||||
inp_f0,
|
||||
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)
|
||||
|
||||
t2 = ttime()
|
||||
times[1] += t2 - t1
|
||||
if self.t_pad_tgt == 0:
|
||||
audio_opt.append(
|
||||
self.vc(
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio_pad[t:],
|
||||
pitch[:, t // self.window :] if t is not None else pitch,
|
||||
pitchf[:, t // self.window :] if t is not None else pitchf,
|
||||
times,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
embChannels,
|
||||
)
|
||||
)
|
||||
else:
|
||||
audio_opt.append(
|
||||
self.vc(
|
||||
model,
|
||||
net_g,
|
||||
sid,
|
||||
audio_pad[t:],
|
||||
pitch[:, t // self.window :] if t is not None else pitch,
|
||||
pitchf[:, t // self.window :] if t is not None else pitchf,
|
||||
times,
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
embChannels,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
|
||||
audio_opt = np.concatenate(audio_opt)
|
||||
del pitch, pitchf, sid
|
||||
torch.cuda.empty_cache()
|
||||
return audio_opt
|
||||
Loading…
Reference in New Issue
Block a user