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synthesizer.py
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synthesizer.py
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# import io
# import os
# import re
# import librosa
# import argparse
# import numpy as np
# from glob import glob
# from tqdm import tqdm
# import tensorflow as tf
# from functools import partial
# from hparams import hparams
# from models import create_model, get_most_recent_checkpoint
# from audio import save_audio, inv_spectrogram, inv_preemphasis, \
# inv_spectrogram_tensorflow
# from utils import plot, PARAMS_NAME, load_json, load_hparams, \
# add_prefix, add_postfix, get_time, parallel_run, makedirs, str2bool
# from text.korean import tokenize
# from text import text_to_sequence, sequence_to_text
import sys
sys.path.append('waveglow/')
import numpy as np
import torch
from hparams import create_hparams
from model import Tacotron2
from layers import TacotronSTFT
from train import load_model
from text import text_to_sequence
from utils import load_wav_to_torch
from scipy.io.wavfile import write
import os
import time
import librosa
# from sklearn.manifold import TSNE
# import matplotlib
# matplotlib.use("Agg")
# import matplotlib.pylab as plt
# %matplotlib inline
# import IPython.display as ipd
from tqdm import tqdm
class Synthesizer(object):
def __init__(self):
super().__init__()
self.hparams = create_hparams()
self.hparams.sampling_rate = 16000
self.hparams.max_decoder_steps = 600
self.stft = TacotronSTFT(
self.hparams.filter_length, self.hparams.hop_length, self.hparams.win_length,
self.hparams.n_mel_channels, self.hparams.sampling_rate, self.hparams.mel_fmin,
self.hparams.mel_fmax)
def load_mel(self, path):
audio, sampling_rate = load_wav_to_torch(path)
if sampling_rate != self.hparams.sampling_rate:
raise ValueError("{} SR doesn't match target {} SR".format(
sampling_rate, self.stft.sampling_rate))
audio_norm = audio / self.hparams.max_wav_value
audio_norm = audio_norm.unsqueeze(0)
audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
melspec = self.stft.mel_spectrogram(audio_norm)
melspec = melspec.cuda()
return melspec
# def close(self):
# tf.reset_default_graph()
# self.sess.close()
def load(self, checkpoint_path, waveglow_path):
self.model = load_model(self.hparams)
self.model.load_state_dict(torch.load(checkpoint_path)['state_dict'])
_ = self.model.eval()
self.waveglow = torch.load(waveglow_path)['model']
self.waveglow.cuda()
path = './web/static/uploads/koemo_spk_emo_all_test.txt'
with open(path, encoding='utf-8') as f:
filepaths_and_text = [line.strip().split("|") for line in f]
base_path = os.path.dirname(checkpoint_path)
data_path = os.path.basename(checkpoint_path) + '_' + path.rsplit('_', 1)[1].split('.')[0] + '.npz'
npz_path = os.path.join(base_path, data_path)
if os.path.exists(npz_path):
d = np.load(npz_path)
zs = d['zs']
emotions = d['emotions']
else:
emotions = []
zs = []
for audio_path, _, _, emotion in tqdm(filepaths_and_text):
melspec = self.load_mel(audio_path)
_, _, _, z = self.model.vae_gst(melspec)
zs.append(z.cpu().data)
emotions.append(int(emotion))
emotions = np.array(emotions) # list이면 안됨 -> ndarray
zs = torch.cat(zs, dim=0).data.numpy()
d = {'zs':zs, 'emotions':emotions}
np.savez(npz_path, **d)
self.neu = np.mean(zs[emotions==0,:], axis=0)
self.sad = np.mean(zs[emotions==1,:], axis=0)
self.ang = np.mean(zs[emotions==2,:], axis=0)
self.hap = np.mean(zs[emotions==3,:], axis=0)
def synthesize(self, text, path, condition_on_ref, ref_audio, ratios):
print(ratios)
sequence = np.array(text_to_sequence(text, ['korean_cleaners']))[None, :]
sequence = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()
inputs = self.model.parse_input(sequence)
transcript_embedded_inputs = self.model.transcript_embedding(inputs).transpose(1,2)
transcript_outputs = self.model.encoder.inference(transcript_embedded_inputs)
print(condition_on_ref)
if condition_on_ref:
#ref_audio = '/data1/jinhan/KoreanEmotionSpeech/wav/hap/hap_00000001.wav'
ref_audio_mel = self.load_mel(ref_audio)
latent_vector, _, _, _ = self.model.vae_gst(ref_audio_mel)
latent_vector = latent_vector.unsqueeze(1).expand_as(transcript_outputs)
else: # condition on emotion ratio
latent_vector = ratios[0] * self.neu + ratios[1] * self.sad + \
ratios[2] * self.hap + ratios[3] * self.ang
latent_vector = torch.FloatTensor(latent_vector).cuda()
latent_vector = self.model.vae_gst.fc3(latent_vector)
encoder_outputs = transcript_outputs + latent_vector
decoder_input = self.model.decoder.get_go_frame(encoder_outputs)
self.model.decoder.initialize_decoder_states(encoder_outputs, mask=None)
mel_outputs, gate_outputs, alignments = [], [], []
while True:
decoder_input = self.model.decoder.prenet(decoder_input)
mel_output, gate_output, alignment = self.model.decoder.decode(decoder_input)
mel_outputs += [mel_output]
gate_outputs += [gate_output]
alignments += [alignment]
if torch.sigmoid(gate_output.data) > self.hparams.gate_threshold:
# print(torch.sigmoid(gate_output.data), gate_output.data)
break
if len(mel_outputs) == self.hparams.max_decoder_steps:
print("Warning! Reached max decoder steps")
break
decoder_input = mel_output
mel_outputs, gate_outputs, alignments = self.model.decoder.parse_decoder_outputs(
mel_outputs, gate_outputs, alignments)
mel_outputs_postnet = self.model.postnet(mel_outputs)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
# print(mel_outputs_postnet.shape)
with torch.no_grad():
synth = self.waveglow.infer(mel_outputs, sigma=0.666)
# return synth[0].data.cpu().numpy()
# path = add_postfix(path, idx)
# print(path)
librosa.output.write_wav(path, synth[0].data.cpu().numpy(), 16000)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--load_path', required=True)
parser.add_argument('--sample_path', default="samples")
parser.add_argument('--text', required=True)
parser.add_argument('--num_speakers', default=1, type=int)
parser.add_argument('--speaker_id', default=0, type=int)
parser.add_argument('--checkpoint_step', default=None, type=int)
parser.add_argument('--is_korean', default=True, type=str2bool)
config = parser.parse_args()
makedirs(config.sample_path)
synthesizer = Synthesizer()
synthesizer.load(config.load_path, config.num_speakers, config.checkpoint_step)
audio = synthesizer.synthesize(
texts=[config.text],
base_path=config.sample_path,
speaker_ids=[config.speaker_id],
attention_trim=False,
isKorean=config.is_korean)[0]