import io
import sys
import time
import numpy as np
import torch
import pysbd
from TTS.utils.audio import AudioProcessor
from TTS.utils.io import load_config
from TTS.tts.utils.generic_utils import setup_model
from TTS.tts.utils.speakers import load_speaker_mapping
from TTS.vocoder.utils.generic_utils import setup_generator
# pylint: disable=unused-wildcard-import
# pylint: disable=wildcard-import
from TTS.tts.utils.synthesis import *
from TTS.tts.utils.text import make_symbols, phonemes, symbols
class Synthesizer(object):
def __init__(self, config):
self.wavernn = None
self.vocoder_model = None
self.config = config
print(config)
self.seg = self.get_segmenter("en")
self.use_cuda = self.config.use_cuda
if self.use_cuda:
assert torch.cuda.is_available(), "CUDA is not availabe on this machine."
self.load_tts(self.config.tts_checkpoint, self.config.tts_config,
self.config.use_cuda)
if self.config.vocoder_checkpoint:
self.load_vocoder(self.config.vocoder_checkpoint, self.config.vocoder_config, self.config.use_cuda)
if self.config.wavernn_lib_path:
self.load_wavernn(self.config.wavernn_lib_path, self.config.wavernn_checkpoint,
self.config.wavernn_config, self.config.use_cuda)
@staticmethod
def get_segmenter(lang):
return pysbd.Segmenter(language=lang, clean=True)
def load_tts(self, tts_checkpoint, tts_config, use_cuda):
# pylint: disable=global-statement
global symbols, phonemes
print(" > Loading TTS model ...")
print(" | > model config: ", tts_config)
print(" | > checkpoint file: ", tts_checkpoint)
self.tts_config = load_config(tts_config)
self.use_phonemes = self.tts_config.use_phonemes
self.ap = AudioProcessor(**self.tts_config.audio)
if 'characters' in self.tts_config.keys():
symbols, phonemes = make_symbols(**self.tts_config.characters)
if self.use_phonemes:
self.input_size = len(phonemes)
else:
self.input_size = len(symbols)
# TODO: fix this for multi-speaker model - load speakers
if self.config.tts_speakers is not None:
self.tts_speakers = load_speaker_mapping(self.config.tts_speakers)
num_speakers = len(self.tts_speakers)
else:
num_speakers = 0
self.tts_model = setup_model(self.input_size, num_speakers=num_speakers, c=self.tts_config)
# load model state
cp = torch.load(tts_checkpoint, map_location=torch.device('cpu'))
# load the model
self.tts_model.load_state_dict(cp['model'])
if use_cuda:
self.tts_model.cuda()
self.tts_model.eval()
self.tts_model.decoder.max_decoder_steps = 3000
if 'r' in cp:
self.tts_model.decoder.set_r(cp['r'])
print(f" > model reduction factor: {cp['r']}")
def load_vocoder(self, model_file, model_config, use_cuda):
self.vocoder_config = load_config(model_config)
self.vocoder_model = setup_generator(self.vocoder_config)
self.vocoder_model.load_state_dict(torch.load(model_file, map_location="cpu")["model"])
self.vocoder_model.remove_weight_norm()
self.vocoder_model.inference_padding = 0
self.vocoder_config = load_config(model_config)
if use_cuda:
self.vocoder_model.cuda()
self.vocoder_model.eval()
def load_wavernn(self, lib_path, model_file, model_config, use_cuda):
# TODO: set a function in wavernn code base for model setup and call it here.
sys.path.append(lib_path) # set this if WaveRNN is not installed globally
#pylint: disable=import-outside-toplevel
from WaveRNN.models.wavernn import Model
print(" > Loading WaveRNN model ...")
print(" | > model config: ", model_config)
print(" | > model file: ", model_file)
self.wavernn_config = load_config(model_config)
# This is the default architecture we use for our models.
# You might need to update it
self.wavernn = Model(
rnn_dims=512,
fc_dims=512,
mode=self.wavernn_config.mode,
mulaw=self.wavernn_config.mulaw,
pad=self.wavernn_config.pad,
use_aux_net=self.wavernn_config.use_aux_net,
use_upsample_net=self.wavernn_config.use_upsample_net,
upsample_factors=self.wavernn_config.upsample_factors,
feat_dims=80,
compute_dims=128,
res_out_dims=128,
res_blocks=10,
hop_length=self.ap.hop_length,
sample_rate=self.ap.sample_rate,
).cuda()
check = torch.load(model_file, map_location="cpu")
self.wavernn.load_state_dict(check['model'])
if use_cuda:
self.wavernn.cuda()
self.wavernn.eval()
def save_wav(self, wav, path):
# wav *= 32767 / max(1e-8, np.max(np.abs(wav)))
wav = np.array(wav)
self.ap.save_wav(wav, path)
def split_into_sentences(self, text):
return self.seg.segment(text)
def tts(self, text, speaker_id=None):
start_time = time.time()
wavs = []
sens = self.split_into_sentences(text)
print(sens)
speaker_id = id_to_torch(speaker_id)
if speaker_id is not None and self.use_cuda:
speaker_id = speaker_id.cuda()
for sen in sens:
# preprocess the given text
inputs = text_to_seqvec(sen, self.tts_config)
inputs = numpy_to_torch(inputs, torch.long, cuda=self.use_cuda)
inputs = inputs.unsqueeze(0)
# synthesize voice
_, postnet_output, _, _ = run_model_torch(self.tts_model, inputs, self.tts_config, False, speaker_id, None)
if self.vocoder_model:
# use native vocoder model
vocoder_input = postnet_output[0].transpose(0, 1).unsqueeze(0)
wav = self.vocoder_model.inference(vocoder_input)
if self.use_cuda:
wav = wav.cpu().numpy()
else:
wav = wav.numpy()
wav = wav.flatten()
elif self.wavernn:
# use 3rd paty wavernn
vocoder_input = None
if self.tts_config.model == "Tacotron":
vocoder_input = torch.FloatTensor(self.ap.out_linear_to_mel(linear_spec=postnet_output.T).T).T.unsqueeze(0)
else:
vocoder_input = postnet_output[0].transpose(0, 1).unsqueeze(0)
if self.use_cuda:
vocoder_input.cuda()
wav = self.wavernn.generate(vocoder_input, batched=self.config.is_wavernn_batched, target=11000, overlap=550)
else:
# use GL
if self.use_cuda:
postnet_output = postnet_output[0].cpu()
else:
postnet_output = postnet_output[0]
postnet_output = postnet_output.numpy()
wav = inv_spectrogram(postnet_output, self.ap, self.tts_config)
# trim silence
wav = trim_silence(wav, self.ap)
wavs += list(wav)
wavs += [0] * 10000
out = io.BytesIO()
self.save_wav(wavs, out)
# compute stats
process_time = time.time() - start_time
audio_time = len(wavs) / self.tts_config.audio['sample_rate']
print(f" > Processing time: {process_time}")
print(f" > Real-time factor: {process_time / audio_time}")
return out