HierSpeech_TTS / inference_vc.py
Sang-Hoon Lee
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import os
import torch
import argparse
import numpy as np
from scipy.io.wavfile import write
import torchaudio
import utils
from Mels_preprocess import MelSpectrogramFixed
from torch.nn import functional as F
from hierspeechpp_speechsynthesizer import (
SynthesizerTrn, Wav2vec2
)
from ttv_v1.text import text_to_sequence
from ttv_v1.t2w2v_transformer import SynthesizerTrn as Text2W2V
from speechsr24k.speechsr import SynthesizerTrn as SpeechSR24
from speechsr48k.speechsr import SynthesizerTrn as SpeechSR48
from denoiser.generator import MPNet
from denoiser.infer import denoise
import amfm_decompy.basic_tools as basic
import amfm_decompy.pYAAPT as pYAAPT
seed = 1111
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
def get_yaapt_f0(audio, rate=16000, interp=False):
frame_length = 20.0
to_pad = int(frame_length / 1000 * rate) // 2
f0s = []
for y in audio.astype(np.float64):
y_pad = np.pad(y.squeeze(), (to_pad, to_pad), "constant", constant_values=0)
signal = basic.SignalObj(y_pad, rate)
pitch = pYAAPT.yaapt(signal, **{'frame_length': frame_length, 'frame_space': 5.0, 'nccf_thresh1': 0.25,
'tda_frame_length': 25.0, 'f0_max':1100})
if interp:
f0s += [pitch.samp_interp[None, None, :]]
else:
f0s += [pitch.samp_values[None, None, :]]
f0 = np.vstack(f0s)
return f0
def load_text(fp):
with open(fp, 'r') as f:
filelist = [line.strip() for line in f.readlines()]
return filelist
def load_checkpoint(filepath, device):
print(filepath)
assert os.path.isfile(filepath)
print("Loading '{}'".format(filepath))
checkpoint_dict = torch.load(filepath, map_location=device)
print("Complete.")
return checkpoint_dict
def get_param_num(model):
num_param = sum(param.numel() for param in model.parameters())
return num_param
def intersperse(lst, item):
result = [item] * (len(lst) * 2 + 1)
result[1::2] = lst
return result
def add_blank_token(text):
text_norm = intersperse(text, 0)
text_norm = torch.LongTensor(text_norm)
return text_norm
def VC(a, hierspeech):
net_g, speechsr, denoiser, mel_fn, w2v = hierspeech
os.makedirs(a.output_dir, exist_ok=True)
source_audio, sample_rate = torchaudio.load(a.source_speech)
if sample_rate != 16000:
source_audio = torchaudio.functional.resample(source_audio, sample_rate, 16000, resampling_method="kaiser_window")
p = (source_audio.shape[-1] // 1280 + 1) * 1280 - source_audio.shape[-1]
source_audio = torch.nn.functional.pad(source_audio, (0, p), mode='constant').data
file_name_s = os.path.splitext(os.path.basename(a.source_speech))[0]
try:
f0 = get_yaapt_f0(source_audio.numpy())
except:
f0 = np.zeros((1, 1, source_audio.shape[-1] // 80))
f0 = f0.astype(np.float32)
f0 = f0.squeeze(0)
ii = f0 != 0
f0[ii] = (f0[ii] - f0[ii].mean()) / f0[ii].std()
y_pad = F.pad(source_audio, (40, 40), "reflect")
x_w2v = w2v(y_pad.cuda())
x_length = torch.LongTensor([x_w2v.size(2)]).to(device)
# Prompt load
target_audio, sample_rate = torchaudio.load(a.target_speech)
# support only single channel
target_audio = target_audio[:1,:]
# Resampling
if sample_rate != 16000:
target_audio = torchaudio.functional.resample(target_audio, sample_rate, 16000, resampling_method="kaiser_window")
if a.scale_norm == 'prompt':
prompt_audio_max = torch.max(target_audio.abs())
try:
t_f0 = get_yaapt_f0(target_audio.numpy())
except:
t_f0 = np.zeros((1, 1, target_audio.shape[-1] // 80))
t_f0 = t_f0.astype(np.float32)
t_f0 = t_f0.squeeze(0)
j = t_f0 != 0
f0[ii] = ((f0[ii] * t_f0[j].std()) + t_f0[j].mean()).clip(min=0)
denorm_f0 = torch.log(torch.FloatTensor(f0+1).cuda())
# We utilize a hop size of 320 but denoiser uses a hop size of 400 so we utilize a hop size of 1600
ori_prompt_len = target_audio.shape[-1]
p = (ori_prompt_len // 1600 + 1) * 1600 - ori_prompt_len
target_audio = torch.nn.functional.pad(target_audio, (0, p), mode='constant').data
file_name_t = os.path.splitext(os.path.basename(a.target_speech))[0]
# If you have a memory issue during denosing the prompt, try to denoise the prompt with cpu before TTS
# We will have a plan to replace a memory-efficient denoiser
if a.denoise_ratio == 0:
target_audio = torch.cat([target_audio.cuda(), target_audio.cuda()], dim=0)
else:
with torch.no_grad():
denoised_audio = denoise(target_audio.squeeze(0).cuda(), denoiser, hps_denoiser)
target_audio = torch.cat([target_audio.cuda(), denoised_audio[:,:target_audio.shape[-1]]], dim=0)
target_audio = target_audio[:,:ori_prompt_len] # 20231108 We found that large size of padding decreases a performance so we remove the paddings after denosing.
trg_mel = mel_fn(target_audio.cuda())
trg_length = torch.LongTensor([trg_mel.size(2)]).to(device)
trg_length2 = torch.cat([trg_length,trg_length], dim=0)
with torch.no_grad():
## Hierarchical Speech Synthesizer (W2V, F0 --> 16k Audio)
converted_audio = \
net_g.voice_conversion_noise_control(x_w2v, x_length, trg_mel, trg_length2, denorm_f0, noise_scale=a.noise_scale_vc, denoise_ratio=a.denoise_ratio)
## SpeechSR (Optional) (16k Audio --> 24k or 48k Audio)
if a.output_sr == 48000 or 24000:
converted_audio = speechsr(converted_audio)
converted_audio = converted_audio.squeeze()
if a.scale_norm == 'prompt':
converted_audio = converted_audio / (torch.abs(converted_audio).max()) * 32767.0 * prompt_audio_max
else:
converted_audio = converted_audio / (torch.abs(converted_audio).max()) * 32767.0 * 0.999
converted_audio = converted_audio.cpu().numpy().astype('int16')
file_name2 = "{}.wav".format(file_name_s+"_to_"+file_name_t)
output_file = os.path.join(a.output_dir, file_name2)
if a.output_sr == 48000:
write(output_file, 48000, converted_audio)
elif a.output_sr == 24000:
write(output_file, 24000, converted_audio)
else:
write(output_file, 16000, converted_audio)
def model_load(a):
mel_fn = MelSpectrogramFixed(
sample_rate=hps.data.sampling_rate,
n_fft=hps.data.filter_length,
win_length=hps.data.win_length,
hop_length=hps.data.hop_length,
f_min=hps.data.mel_fmin,
f_max=hps.data.mel_fmax,
n_mels=hps.data.n_mel_channels,
window_fn=torch.hann_window
).cuda()
w2v = Wav2vec2().cuda()
net_g = SynthesizerTrn(hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model).cuda()
net_g.load_state_dict(torch.load(a.ckpt))
_ = net_g.eval()
if a.output_sr == 48000:
speechsr = SpeechSR48(h_sr48.data.n_mel_channels,
h_sr48.train.segment_size // h_sr48.data.hop_length,
**h_sr48.model).cuda()
utils.load_checkpoint(a.ckpt_sr48, speechsr, None)
speechsr.eval()
elif a.output_sr == 24000:
speechsr = SpeechSR24(h_sr.data.n_mel_channels,
h_sr.train.segment_size // h_sr.data.hop_length,
**h_sr.model).cuda()
utils.load_checkpoint(a.ckpt_sr, speechsr, None)
speechsr.eval()
else:
speechsr = None
denoiser = MPNet(hps_denoiser).cuda()
state_dict = load_checkpoint(a.denoiser_ckpt, device)
denoiser.load_state_dict(state_dict['generator'])
denoiser.eval()
return net_g, speechsr, denoiser, mel_fn, w2v
def inference(a):
hierspeech = model_load(a)
VC(a, hierspeech)
def main():
print('Initializing Inference Process..')
parser = argparse.ArgumentParser()
parser.add_argument('--source_speech', default='example/reference_2.wav')
parser.add_argument('--target_speech', default='example/reference_1.wav')
parser.add_argument('--output_dir', default='output')
parser.add_argument('--ckpt', default='./logs/hierspeechpp_eng_kor/hierspeechpp_v2_ckpt.pth')
parser.add_argument('--ckpt_sr', type=str, default='./speechsr24k/G_340000.pth')
parser.add_argument('--ckpt_sr48', type=str, default='./speechsr48k/G_100000.pth')
parser.add_argument('--denoiser_ckpt', type=str, default='denoiser/g_best')
parser.add_argument('--scale_norm', type=str, default='max')
parser.add_argument('--output_sr', type=float, default=48000)
parser.add_argument('--noise_scale_ttv', type=float,
default=0.333)
parser.add_argument('--noise_scale_vc', type=float,
default=0.333)
parser.add_argument('--denoise_ratio', type=float,
default=0.8)
a = parser.parse_args()
global device, hps, h_sr,h_sr48, hps_denoiser
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
hps = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt)[0], 'config.json'))
h_sr = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_sr)[0], 'config.json') )
h_sr48 = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_sr48)[0], 'config.json') )
hps_denoiser = utils.get_hparams_from_file(os.path.join(os.path.split(a.denoiser_ckpt)[0], 'config.json'))
inference(a)
if __name__ == '__main__':
main()