forked from Verssae/flask-tacotron2-tts-web-app
/
text2speech.py
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/
text2speech.py
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import os
import numpy as np
import sys
import time
import argparse
import torch
import matplotlib.pyplot as plt
from scipy.io.wavfile import write
from hparams import create_hparams
from model import Tacotron2
from text import text_to_sequence
sys.path.append('waveglow/')
from waveglow.mel2samp import MAX_WAV_VALUE
from denoiser import Denoiser
import json
class T2S:
def __init__(self, lang):
self.language = lang
self.hparams = create_hparams()
self.hparams.sampling_rate = 22050
with open('config.json', 'r') as f:
self.config = json.load(f)
self.waveglow_path = self.config.get('model').get('waveglow')
self.waveglow = torch.load(self.waveglow_path)['model']
self.waveglow.cuda().eval().half()
for m in self.waveglow.modules():
if 'Conv' in str(type(m)):
setattr(m, 'padding_mode', 'zeros')
for k in self.waveglow.convinv:
k.float()
self.denoiser = Denoiser(self.waveglow)
self.update_model(lang)
def load_model(self):
model = Tacotron2(self.hparams).cuda()
if self.hparams.fp16_run:
model.decoder.attention_layer.score_mask_value = finfo('float16').min
if self.hparams.distributed_run:
model = apply_gradient_allreduce(model)
return model
def tts(self, text, filename=None):
if not filename:
filename = str(time.time())
sequence = np.array(text_to_sequence(text, [self.cleaner]))[None, :]
sequence = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()
mel_outputs, mel, _, alignments = self.model.inference(sequence)
with torch.no_grad():
audio = self.waveglow.infer(mel, sigma=0.666)
audio = audio * MAX_WAV_VALUE
# audio = self.denoiser(audio, strength=0.01)[:, 0]
audio = audio.squeeze()
audio = audio.cpu().numpy()
audio = audio.astype('int16')
audio_path =f"{filename}.wav"
save_path = os.path.join('wavs',audio_path)
write(save_path, self.hparams.sampling_rate, audio)
print("audio saved at: {}".format(save_path))
return audio_path
def update_model(self, lang):
if lang == 'en':
self.checkpoint_path = self.config.get('model').get('en')
self.cleaner = 'english_cleaners'
self.language = lang
else:
self.checkpoint_path = self.config.get('model').get('kr')
self.cleaner = 'transliteration_cleaners'
self.language = lang
self.model = self.load_model()
self.model.load_state_dict(torch.load(self.checkpoint_path)['state_dict'])
_ = self.model.cuda().eval().half()
return self