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import hashlib
import json
import logging
import os
import time
from pathlib import Path
import io
import librosa
import maad
import numpy as np
from inference import slicer
import parselmouth
import soundfile
import torch
import torchaudio
from hubert import hubert_model
import utils
from models import SynthesizerTrn
logging.getLogger('numba').setLevel(logging.WARNING)
logging.getLogger('matplotlib').setLevel(logging.WARNING)
def resize2d_f0(x, target_len):
source = np.array(x)
source[source < 0.001] = np.nan
target = np.interp(np.arange(0, len(source) * target_len, len(source)) / target_len, np.arange(0, len(source)),
source)
res = np.nan_to_num(target)
return res
def get_f0(x, p_len,f0_up_key=0):
time_step = 160 / 16000 * 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)
f0 = parselmouth.Sound(x, 16000).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)
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, f0
def clean_pitch(input_pitch):
num_nan = np.sum(input_pitch == 1)
if num_nan / len(input_pitch) > 0.9:
input_pitch[input_pitch != 1] = 1
return input_pitch
def plt_pitch(input_pitch):
input_pitch = input_pitch.astype(float)
input_pitch[input_pitch == 1] = np.nan
return input_pitch
def f0_to_pitch(ff):
f0_pitch = 69 + 12 * np.log2(ff / 440)
return f0_pitch
def fill_a_to_b(a, b):
if len(a) < len(b):
for _ in range(0, len(b) - len(a)):
a.append(a[0])
def mkdir(paths: list):
for path in paths:
if not os.path.exists(path):
os.mkdir(path)
class VitsSvc(object):
def __init__(self):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.SVCVITS = None
self.hps = None
self.speakers = None
self.hubert_soft = utils.get_hubert_model()
def set_device(self, device):
self.device = torch.device(device)
self.hubert_soft.to(self.device)
if self.SVCVITS != None:
self.SVCVITS.to(self.device)
def loadCheckpoint(self, path):
self.hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
self.SVCVITS = SynthesizerTrn(
self.hps.data.filter_length // 2 + 1,
self.hps.train.segment_size // self.hps.data.hop_length,
**self.hps.model)
_ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", self.SVCVITS, None)
_ = self.SVCVITS.eval().to(self.device)
self.speakers = self.hps.spk
def get_units(self, source, sr):
source = source.unsqueeze(0).to(self.device)
with torch.inference_mode():
units = self.hubert_soft.units(source)
return units
def get_unit_pitch(self, in_path, tran):
source, sr = torchaudio.load(in_path)
source = torchaudio.functional.resample(source, sr, 16000)
if len(source.shape) == 2 and source.shape[1] >= 2:
source = torch.mean(source, dim=0).unsqueeze(0)
soft = self.get_units(source, sr).squeeze(0).cpu().numpy()
f0_coarse, f0 = get_f0(source.cpu().numpy()[0], soft.shape[0]*2, tran)
return soft, f0
def infer(self, speaker_id, tran, raw_path):
speaker_id = self.speakers[speaker_id]
sid = torch.LongTensor([int(speaker_id)]).to(self.device).unsqueeze(0)
soft, pitch = self.get_unit_pitch(raw_path, tran)
f0 = torch.FloatTensor(clean_pitch(pitch)).unsqueeze(0).to(self.device)
stn_tst = torch.FloatTensor(soft)
with torch.no_grad():
x_tst = stn_tst.unsqueeze(0).to(self.device)
x_tst = torch.repeat_interleave(x_tst, repeats=2, dim=1).transpose(1, 2)
audio = self.SVCVITS.infer(x_tst, f0=f0, g=sid)[0,0].data.float()
return audio, audio.shape[-1]
def inference(self,srcaudio,chara,tran,slice_db):
sampling_rate, audio = srcaudio
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
if sampling_rate != 16000:
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
soundfile.write("tmpwav.wav", audio, 16000, format="wav")
chunks = slicer.cut("tmpwav.wav", db_thresh=slice_db)
audio_data, audio_sr = slicer.chunks2audio("tmpwav.wav", chunks)
audio = []
for (slice_tag, data) in audio_data:
length = int(np.ceil(len(data) / audio_sr * self.hps.data.sampling_rate))
raw_path = io.BytesIO()
soundfile.write(raw_path, data, audio_sr, format="wav")
raw_path.seek(0)
if slice_tag:
_audio = np.zeros(length)
else:
out_audio, out_sr = self.infer(chara, tran, raw_path)
_audio = out_audio.cpu().numpy()
audio.extend(list(_audio))
audio = (np.array(audio) * 32768.0).astype('int16')
return (self.hps.data.sampling_rate,audio)
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