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Update app.py
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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 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 SpeechSR24
from speechsr48k.speechsr import SynthesizerTrn as SpeechSR48
from denoiser.generator import MPNet
from denoiser.infer import denoise
import gradio as gr
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,
prompt,
ttv_temperature,
vc_temperature,
duratuion_temperature,
duratuion_length,
denoise_ratio,
random_seed):
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
np.random.seed(random_seed)
text_len = len(text)
if text_len > 200:
raise gr.Error("Text length limited to 200 characters for this demo. Current text length is " + str(text_len))
else:
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
# sample_rate, audio = prompt
# audio = torch.FloatTensor([audio]).cuda()
# if audio.shape[0] != 1:
# audio = audio[:1,:]
# audio = audio / 32768
audio, sample_rate = torchaudio.load(prompt)
# support only single channel
if audio.shape[0] != 1:
audio = audio[:1,:]
# Resampling
if sample_rate != 16000:
audio = torchaudio.functional.resample(audio, sample_rate, 16000, resampling_method="kaiser_window")
# 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
# 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 denoise == 0:
audio = torch.cat([audio.cuda(), audio.cuda()], dim=0)
else:
with torch.no_grad():
if ori_prompt_len > 80000:
denoised_audio = []
for i in range((ori_prompt_len//80000)):
denoised_audio.append(denoise(audio.squeeze(0).cuda()[i*80000:(i+1)*80000], denoiser, hps_denoiser))
denoised_audio.append(denoise(audio.squeeze(0).cuda()[(i+1)*80000:], denoiser, hps_denoiser))
denoised_audio = torch.cat(denoised_audio, dim=1)
else:
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.
if audio.shape[-1]<48000:
audio = torch.cat([audio,audio,audio,audio,audio], dim=1)
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=ttv_temperature, noise_scale_w=duratuion_temperature,
length_scale=duratuion_length, denoise_ratio=denoise_ratio)
src_length = torch.LongTensor([w2v_x.size(2)]).cuda()
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=vc_temperature, denoise_ratio=denoise_ratio)
converted_audio = speechsr(converted_audio)
converted_audio = converted_audio.squeeze()
converted_audio = converted_audio / (torch.abs(converted_audio).max()) * 32767.0 * 0.999
converted_audio = converted_audio.cpu().numpy().astype('int16')
write('output.wav', 48000, converted_audio)
return 'output.wav'
def main():
print('Initializing Inference Process..')
parser = argparse.ArgumentParser()
parser.add_argument('--input_prompt', default='example/steve-jobs-2005.wav')
parser.add_argument('--input_txt', default='example/abstract.txt')
parser.add_argument('--output_dir', default='output')
parser.add_argument('--ckpt', default='./logs/hierspeechpp_eng_kor/hierspeechpp_v1.1_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)
parser.add_argument('--duration_ratio', type=float,
default=0.8)
parser.add_argument('--seed', type=int,
default=1111)
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'))
global mel_fn, net_g, text2w2v, speechsr, denoiser
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()
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()
denoiser = MPNet(hps_denoiser).cuda()
state_dict = load_checkpoint(a.denoiser_ckpt, device)
denoiser.load_state_dict(state_dict['generator'])
denoiser.eval()
demo_play = gr.Interface(fn = tts,
inputs = [gr.Textbox(max_lines=6, label="Input Text", value="HierSpeech is a zero shot speech synthesis model, which can generate high-quality audio", info="Up to 200 characters"),
gr.Audio(type='filepath', value="./example/3_rick_gt.wav"),
gr.Slider(0,1,0.333),
gr.Slider(0,1,0.333),
gr.Slider(0,1,1.0),
gr.Slider(0.5,2,1.0),
gr.Slider(0,1,0),
gr.Slider(0,9999,1111)],
outputs = 'audio',
title = 'HierSpeech++',
description = '''<div>
<p style="text-align: left"> HierSpeech++ is a zero-shot speech synthesis model.</p>
<p style="text-align: left"> Our model is trained with LibriTTS dataset so this model only supports english. We will release a multi-lingual HierSpeech++ soon.</p>
<p style="text-align: left"> <a href="https://sh-lee-prml.github.io/HierSpeechpp-demo/">[Demo Page]</a> <a href="https://github.com/sh-lee-prml/HierSpeechpp">[Source Code]</a></p>
<p style="text-align: left"> HuggingFace provides us with a community GPU grant. Thanks 😊 </p>
</div>''',
examples=[["HierSpeech is a zero shot speech synthesis model, which can generate high-quality audio", "./example/3_rick_gt.wav", 0.333,0.333, 1.0, 1.0, 0, 1111],
["HierSpeech is a zero shot speech synthesis model, which can generate high-quality audio", "./example/dog.wav", 0.333,0.667, 1.0, 1.0, 0, 1790],
["HierSpeech is a zero shot speech synthesis model, which can generate high-quality audio", "./example/ex01_whisper_00359.wav", 0.333,0.333, 1.0, 1.0, 0, 1111],
["하앜! It's too cold today", "./example/openai_tts_sample.mp3", 0.333,0.333, 1.0, 1.0, 0, 1111],
["Hi there, I'm your new voice clone. Try your best to upload quality audio", "./example/female.wav", 0.333,0.333, 1.0, 1.0, 0, 1111],
["Hello I'm HierSpeech++", "./example/reference_1.wav", 0.333,0.333, 1.0, 1.0, 0, 1111],
]
)
demo_play.launch()
if __name__ == '__main__':
main()