mirror of https://github.com/coqui-ai/TTS.git
163 lines
6.7 KiB
Python
163 lines
6.7 KiB
Python
import io
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import os
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import numpy as np
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import torch
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import sys
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import numpy as np
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import torch
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from models.tacotron import Tacotron
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from utils.audio import AudioProcessor
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from utils.generic_utils import load_config, setup_model
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from utils.text import phoneme_to_sequence, phonemes, symbols, text_to_sequence, sequence_to_phoneme
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import re
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alphabets= "([A-Za-z])"
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prefixes = "(Mr|St|Mrs|Ms|Dr)[.]"
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suffixes = "(Inc|Ltd|Jr|Sr|Co)"
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starters = "(Mr|Mrs|Ms|Dr|He\s|She\s|It\s|They\s|Their\s|Our\s|We\s|But\s|However\s|That\s|This\s|Wherever)"
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acronyms = "([A-Z][.][A-Z][.](?:[A-Z][.])?)"
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websites = "[.](com|net|org|io|gov)"
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from models.tacotron import Tacotron
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from utils.audio import AudioProcessor
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from utils.generic_utils import load_config
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from utils.text import phoneme_to_sequence, phonemes, symbols, text_to_sequence
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class Synthesizer(object):
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def __init__(self, config):
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self.wavernn = None
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self.config = config
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self.use_cuda = config.use_cuda
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if self.use_cuda:
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assert torch.cuda.is_available(), "CUDA is not availabe on this machine."
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self.load_tts(self.config.tts_path, self.config.tts_file, self.config.tts_config, config.use_cuda)
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if self.config.wavernn_lib_path:
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self.load_wavernn(config.wavernn_lib_path, config.wavernn_path, config.wavernn_file, config.wavernn_config, config.use_cuda)
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def load_tts(self, model_path, model_file, model_config, use_cuda):
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tts_config = os.path.join(model_path, model_config)
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self.model_file = os.path.join(model_path, model_file)
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print(" > Loading TTS model ...")
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print(" | > model config: ", tts_config)
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print(" | > model file: ", model_file)
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self.tts_config = load_config(tts_config)
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self.use_phonemes = self.tts_config.use_phonemes
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self.ap = AudioProcessor(**self.tts_config.audio)
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if self.use_phonemes:
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self.input_size = len(phonemes)
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self.input_adapter = lambda sen: phoneme_to_sequence(sen, [self.tts_config.text_cleaner], self.tts_config.phoneme_language, self.tts_config.enable_eos_bos_chars)
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else:
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self.input_size = len(symbols)
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self.input_adapter = lambda sen: text_to_sequence(sen, [self.tts_config.text_cleaner])
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self.tts_model = setup_model(self.input_size, self.tts_config)
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# load model state
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if use_cuda:
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cp = torch.load(self.model_file)
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else:
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cp = torch.load(self.model_file, map_location=lambda storage, loc: storage)
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# load the model
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self.tts_model.load_state_dict(cp['model'])
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if use_cuda:
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self.tts_model.cuda()
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self.tts_model.eval()
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self.tts_model.decoder.max_decoder_steps = 3000
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def load_wavernn(self, lib_path, model_path, model_file, model_config, use_cuda):
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sys.path.append(lib_path) # set this if TTS is not installed globally
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from WaveRNN.models.wavernn import Model
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wavernn_config = os.path.join(model_path, model_config)
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model_file = os.path.join(model_path, model_file)
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print(" > Loading WaveRNN model ...")
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print(" | > model config: ", wavernn_config)
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print(" | > model file: ", model_file)
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self.wavernn_config = load_config(wavernn_config)
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self.wavernn = Model(
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rnn_dims=512,
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fc_dims=512,
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mode=self.wavernn_config.mode,
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pad=2,
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upsample_factors=self.wavernn_config.upsample_factors, # set this depending on dataset
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feat_dims=80,
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compute_dims=128,
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res_out_dims=128,
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res_blocks=10,
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hop_length=self.ap.hop_length,
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sample_rate=self.ap.sample_rate,
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).cuda()
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check = torch.load(model_file)
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self.wavernn.load_state_dict(check['model'])
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if use_cuda:
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self.wavernn.cuda()
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self.wavernn.eval()
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def save_wav(self, wav, path):
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# wav *= 32767 / max(1e-8, np.max(np.abs(wav)))
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wav = np.array(wav)
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self.ap.save_wav(wav, path)
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def split_into_sentences(self, text):
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text = " " + text + " "
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text = text.replace("\n"," ")
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text = re.sub(prefixes,"\\1<prd>",text)
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text = re.sub(websites,"<prd>\\1",text)
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if "Ph.D" in text: text = text.replace("Ph.D.","Ph<prd>D<prd>")
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text = re.sub("\s" + alphabets + "[.] "," \\1<prd> ",text)
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text = re.sub(acronyms+" "+starters,"\\1<stop> \\2",text)
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text = re.sub(alphabets + "[.]" + alphabets + "[.]" + alphabets + "[.]","\\1<prd>\\2<prd>\\3<prd>",text)
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text = re.sub(alphabets + "[.]" + alphabets + "[.]","\\1<prd>\\2<prd>",text)
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text = re.sub(" "+suffixes+"[.] "+starters," \\1<stop> \\2",text)
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text = re.sub(" "+suffixes+"[.]"," \\1<prd>",text)
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text = re.sub(" " + alphabets + "[.]"," \\1<prd>",text)
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if "”" in text: text = text.replace(".”","”.")
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if "\"" in text: text = text.replace(".\"","\".")
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if "!" in text: text = text.replace("!\"","\"!")
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if "?" in text: text = text.replace("?\"","\"?")
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text = text.replace(".",".<stop>")
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text = text.replace("?","?<stop>")
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text = text.replace("!","!<stop>")
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text = text.replace("<prd>",".")
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sentences = text.split("<stop>")
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sentences = sentences[:-1]
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sentences = [s.strip() for s in sentences]
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return sentences
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def tts(self, text):
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wavs = []
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sens = self.split_into_sentences(text)
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if len(sens) == 0:
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sens = [text+'.']
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for sen in sens:
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if len(sen) < 3:
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continue
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sen = sen.strip()
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print(sen)
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seq = np.array(self.input_adapter(sen))
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text_hat = sequence_to_phoneme(seq)
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print(text_hat)
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chars_var = torch.from_numpy(seq).unsqueeze(0).long()
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if self.use_cuda:
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chars_var = chars_var.cuda()
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decoder_out, postnet_out, alignments, stop_tokens = self.tts_model.inference(
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chars_var)
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postnet_out = postnet_out[0].data.cpu().numpy()
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if self.tts_config.model == "Tacotron":
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wav = self.ap.inv_spectrogram(postnet_out.T)
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elif self.tts_config.model == "Tacotron2":
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if self.wavernn:
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wav = self.wavernn.generate(torch.FloatTensor(postnet_out.T).unsqueeze(0).cuda(), batched=self.config.is_wavernn_batched, target=11000, overlap=550)
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else:
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wav = self.ap.inv_mel_spectrogram(postnet_out.T)
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wavs += list(wav)
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wavs += [0] * 10000
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out = io.BytesIO()
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self.save_wav(wavs, out)
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return out
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