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import os |
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import re |
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import json |
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import torch |
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import librosa |
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import soundfile |
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import torchaudio |
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import numpy as np |
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import torch.nn as nn |
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from tqdm import tqdm |
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from . import utils |
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from . import commons |
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from .models import SynthesizerTrn |
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from .split_utils import split_sentence |
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from .mel_processing import spectrogram_torch, spectrogram_torch_conv |
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from .download_utils import load_or_download_config, load_or_download_model |
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class TTS(nn.Module): |
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def __init__(self, |
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language, |
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device='cuda:0'): |
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super().__init__() |
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if 'cuda' in device: |
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assert torch.cuda.is_available() |
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hps = load_or_download_config(language) |
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num_languages = hps.num_languages |
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num_tones = hps.num_tones |
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symbols = hps.symbols |
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model = SynthesizerTrn( |
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len(symbols), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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num_tones=num_tones, |
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num_languages=num_languages, |
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**hps.model, |
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).to(device) |
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model.eval() |
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self.model = model |
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self.symbol_to_id = {s: i for i, s in enumerate(symbols)} |
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self.hps = hps |
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self.device = device |
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checkpoint_dict = load_or_download_model(language, device) |
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self.model.load_state_dict(checkpoint_dict['model'], strict=True) |
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language = language.split('_')[0] |
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self.language = 'ZH_MIX_EN' if language == 'ZH' else language |
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@staticmethod |
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def audio_numpy_concat(segment_data_list, sr, speed=1.): |
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audio_segments = [] |
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for segment_data in segment_data_list: |
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audio_segments += segment_data.reshape(-1).tolist() |
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audio_segments += [0] * int((sr * 0.05) / speed) |
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audio_segments = np.array(audio_segments).astype(np.float32) |
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return audio_segments |
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@staticmethod |
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def split_sentences_into_pieces(text, language): |
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texts = split_sentence(text, language_str=language) |
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return texts |
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def tts_to_file(self, text, speaker_id, output_path=None, sdp_ratio=0.2, noise_scale=0.6, noise_scale_w=0.8, speed=1.0, pbar=None, format=None, position=None): |
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language = self.language |
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texts = self.split_sentences_into_pieces(text, language) |
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audio_list = [] |
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tx = texts |
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if pbar: |
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tx = pbar(texts) |
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else: |
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if position: |
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tx = tqdm(texts, position=position) |
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else: |
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tx = tqdm(texts) |
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for t in tx: |
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if language in ['EN', 'ZH_MIX_EN']: |
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t = re.sub(r'([a-z])([A-Z])', r'\1 \2', t) |
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device = self.device |
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bert, ja_bert, phones, tones, lang_ids = utils.get_text_for_tts_infer(t, language, self.hps, device, self.symbol_to_id) |
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with torch.no_grad(): |
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x_tst = phones.to(device).unsqueeze(0) |
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tones = tones.to(device).unsqueeze(0) |
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lang_ids = lang_ids.to(device).unsqueeze(0) |
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bert = bert.to(device).unsqueeze(0) |
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ja_bert = ja_bert.to(device).unsqueeze(0) |
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device) |
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del phones |
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speakers = torch.LongTensor([speaker_id]).to(device) |
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audio = self.model.infer( |
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x_tst, |
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x_tst_lengths, |
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speakers, |
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tones, |
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lang_ids, |
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bert, |
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ja_bert, |
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sdp_ratio=sdp_ratio, |
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noise_scale=noise_scale, |
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noise_scale_w=noise_scale_w, |
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length_scale=1. / speed, |
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)[0][0, 0].data.cpu().float().numpy() |
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del x_tst, tones, lang_ids, bert, ja_bert, x_tst_lengths, speakers |
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audio_list.append(audio) |
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torch.cuda.empty_cache() |
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audio = self.audio_numpy_concat(audio_list, sr=self.hps.data.sampling_rate, speed=speed) |
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if output_path is None: |
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return audio |
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else: |
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soundfile.write(output_path, audio, self.hps.data.sampling_rate, format=format) |
