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import argparse
import parselmouth
import soundfile
import librosa
import torch
import numpy as np
import cluster
import utils
import json
from inference.infer_tool import Svc
import logging
logging.getLogger('numba').setLevel(logging.WARNING)
sample_rate = 44100
hop_len = 512
def getc(audio_data, hmodel, spk):
devive = torch.device("cuda" if torch.cuda.is_available() else "cpu")
wav, sr = audio_data
wav = librosa.resample(wav,sr, 16000)
wav = torch.from_numpy(wav).unsqueeze(0).to(devive)
c = utils.get_vec_units(hmodel, wav).cpu().squeeze(0)
c = utils.repeat_expand_2d(c, int((wav.shape[1] * sample_rate / 16000) // hop_len)).numpy()
c = cluster.get_cluster_center_result(c.transpose(), spk)
return c
def get_f0(audio_data, p_len=None, f0_up_key=0):
x, sr = audio_data
x = librosa.resample(x,sr, sample_rate)
if p_len is None:
p_len = x.shape[0] // hop_len
else:
assert abs(p_len - x.shape[0] // hop_len) < 3, (p_len, x.shape)
time_step = hop_len / sample_rate * 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, sample_rate).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
hmodel = utils.load_vec_model()
def infer(spk, raw, model, trans=0):
audio_data = librosa.load(raw, sr=None)
c = getc(audio_data, hmodel, spk)
_, f0 = get_f0(audio_data, c.shape[0], trans)
# tgt = raw.replace("raw", "results").replace(".wav", f'_{spk}_{trans}_{step}step.wav')
c = torch.from_numpy(c).float().unsqueeze(0).transpose(1, 2)
f0 = torch.from_numpy(f0).float().unsqueeze(0)
res = model.infer(spk, c, f0)
# soundfile.write(tgt, res[0].cpu().numpy(), 44100)
return res
if __name__ == '__main__':
step = 34000
model_path = f"logs/G_{step}.pth"
config_path = "configs/config.json"
svc_model = Svc(model_path, config_path)
raw = 'raw/君の知らない物語.wav'
spk = "taffy"
infer(spk, raw, svc_model)
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