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 hierspeechpp_speechsynthesizer import (
SynthesizerTrn
)
from ttv_v1.text import text_to_sequence
from ttv_v1.t2w2v_transformer import SynthesizerTrn as Text2W2V
from speechsr24k.speechsr import SynthesizerTrn as AudioSR
from speechsr48k.speechsr import SynthesizerTrn as AudioSR48
from denoiser.generator import MPNet
from denoiser.infer import denoise
seed = 1111
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
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 tts(text, a, hierspeech):
net_g, text2w2v, audiosr, denoiser, mel_fn = hierspeech
os.makedirs(a.output_dir, exist_ok=True)
text = text_to_sequence(str(text), ["english_cleaners2"])
token = add_blank_token(text).unsqueeze(0).cuda()
token_length = torch.LongTensor([token.size(-1)]).cuda()
# Prompt load
audio, sample_rate = torchaudio.load(a.input_prompt)
# support only single channel
audio = audio[:1,:]
# Resampling
if sample_rate != 16000:
audio = torchaudio.functional.resample(audio, sample_rate, 16000, resampling_method="kaiser_window")
if a.scale_norm == 'prompt':
prompt_audio_max = torch.max(audio.abs())
# 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 = audio.shape[-1]
p = (ori_prompt_len // 1600 + 1) * 1600 - ori_prompt_len
audio = torch.nn.functional.pad(audio, (0, p), mode='constant').data
file_name = os.path.splitext(os.path.basename(a.input_prompt))[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:
audio = torch.cat([audio.cuda(), audio.cuda()], dim=0)
else:
with torch.no_grad():
denoised_audio = denoise(audio.squeeze(0).cuda(), denoiser, hps_denoiser)
audio = torch.cat([audio.cuda(), denoised_audio[:,:audio.shape[-1]]], dim=0)
audio = audio[:,:ori_prompt_len] # 20231108 We found that large size of padding decreases a performance so we remove the paddings after denosing.
src_mel = mel_fn(audio.cuda())
src_length = torch.LongTensor([src_mel.size(2)]).to(device)
src_length2 = torch.cat([src_length,src_length], dim=0)
## TTV (Text --> W2V, F0)
with torch.no_grad():
w2v_x, pitch = text2w2v.infer_noise_control(token, token_length, src_mel, src_length2, noise_scale=a.noise_scale_ttv, denoise_ratio=a.denoise_ratio)
src_length = torch.LongTensor([w2v_x.size(2)]).cuda()
## Pitch Clipping
pitch[pitch<torch.log(torch.tensor([55]).cuda())] = 0
## Hierarchical Speech Synthesizer (W2V, F0 --> 16k Audio)
converted_audio = \
net_g.voice_conversion_noise_control(w2v_x, src_length, src_mel, src_length2, pitch, 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 = audiosr(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)
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()
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()
text2w2v = Text2W2V(hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps_t2w2v.model).cuda()
text2w2v.load_state_dict(torch.load(a.ckpt_text2w2v))
text2w2v.eval()
if a.output_sr == 48000:
audiosr = AudioSR48(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, audiosr, None)
audiosr.eval()
elif a.output_sr == 24000:
audiosr = AudioSR(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, audiosr, None)
audiosr.eval()
else:
audiosr = 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, text2w2v, audiosr, denoiser, mel_fn
def inference(a):
hierspeech = model_load(a)
# Input Text
text = load_text(a.input_txt)
# text = "hello I'm hierspeech"
tts(text, a, hierspeech)
def main():
print('Initializing Inference Process..')
parser = argparse.ArgumentParser()
parser.add_argument('--input_prompt', default='example/reference_4.wav')
parser.add_argument('--input_txt', default='example/reference_4.txt')
parser.add_argument('--output_dir', default='output')
parser.add_argument('--ckpt', default='./logs/hierspeechpp_eng_kor/hierspeechpp_v2_ckpt.pth')
parser.add_argument('--ckpt_text2w2v', '-ct', help='text2w2v checkpoint path', default='./logs/ttv_libritts_v1/ttv_lt960_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, hps_t2w2v,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'))
hps_t2w2v = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_text2w2v)[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()