Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| import argparse | |
| import torch | |
| import commons | |
| import utils | |
| from models import ( | |
| SynthesizerTrn, ) | |
| from text.symbols import symbol_len, lang_to_dict | |
| # we use Kyubyong/g2p for demo instead of our internal g2p | |
| # https://github.com/Kyubyong/g2p | |
| from g2p_en import G2p | |
| import re | |
| _symbol_to_id = lang_to_dict("en_US") | |
| class GradioApp: | |
| def __init__(self, args): | |
| self.hps = utils.get_hparams_from_file(args.config) | |
| self.device = "cpu" | |
| self.net_g = SynthesizerTrn(symbol_len(self.hps.data.languages), | |
| self.hps.data.filter_length // 2 + 1, | |
| self.hps.train.segment_size // | |
| self.hps.data.hop_length, | |
| midi_start=-5, | |
| midi_end=75, | |
| octave_range=24, | |
| n_speakers=len(self.hps.data.speakers), | |
| **self.hps.model).to(self.device) | |
| _ = self.net_g.eval() | |
| _ = utils.load_checkpoint(args.checkpoint_path, model_g=self.net_g) | |
| self.g2p = G2p() | |
| self.interface = self._gradio_interface() | |
| def get_phoneme(self, text): | |
| phones = [re.sub("[0-9]", "", p) for p in self.g2p(text)] | |
| tone = [0 for p in phones] | |
| if self.hps.data.add_blank: | |
| text_norm = [_symbol_to_id[symbol] for symbol in phones] | |
| text_norm = commons.intersperse(text_norm, 0) | |
| tone = commons.intersperse(tone, 0) | |
| else: | |
| text_norm = phones | |
| text_norm = torch.LongTensor(text_norm) | |
| tone = torch.LongTensor(tone) | |
| return text_norm, tone, phones | |
| def inference(self, text, speaker_id_val, seed, scope_shift, duration): | |
| torch.manual_seed(seed) | |
| text_norm, tone, phones = self.get_phoneme(text) | |
| x_tst = text_norm.to(self.device).unsqueeze(0) | |
| t_tst = tone.to(self.device).unsqueeze(0) | |
| x_tst_lengths = torch.LongTensor([text_norm.size(0)]).to(self.device) | |
| speaker_id = torch.LongTensor([speaker_id_val]).to(self.device) | |
| decoder_inputs,*_ = self.net_g.infer_pre_decoder( | |
| x_tst, | |
| t_tst, | |
| x_tst_lengths, | |
| sid=speaker_id, | |
| noise_scale=0.667, | |
| noise_scale_w=0.8, | |
| length_scale=duration, | |
| scope_shift=scope_shift) | |
| audio = self.net_g.infer_decode_chunk( | |
| decoder_inputs, sid=speaker_id)[0, 0].data.cpu().float().numpy() | |
| del decoder_inputs, | |
| return phones, (self.hps.data.sampling_rate, audio) | |
| def _gradio_interface(self): | |
| title = "PITS Demo" | |
| inputs = [ | |
| gr.Textbox(label="Text (150 words limitation)", | |
| value="This is demo page.", | |
| elem_id="tts-input"), | |
| gr.Dropdown(list(self.hps.data.speakers), | |
| value="p225", | |
| label="Speaker Identity", | |
| type="index"), | |
| gr.Slider(0, 65536, step=1, label="random seed"), | |
| gr.Slider(-15, 15, value=0, step=1, label="scope-shift"), | |
| gr.Slider(0.5, 2., value=1., step=0.1, | |
| label="duration multiplier"), | |
| ] | |
| outputs = [ | |
| gr.Textbox(label="Phonemes"), | |
| gr.Audio(type="numpy", label="Output audio") | |
| ] | |
| description = "Welcome to the Gradio demo for PITS: Variational Pitch Inference without Fundamental Frequency for End-to-End Pitch-controllable TTS.\n In this demo, we utilize an open-source G2P library (g2p_en) with stress removing, instead of our internal G2P.\n You can fix the latent z by controlling random seed.\n You can shift the pitch scope, but please note that this is opposite to pitch-shift. In addition, it is cropped from fixed z so please check pitch-controllability by comparing with normal synthesis.\n Thank you for trying out our PITS demo!" | |
| article = "Github:https://github.com/anonymous-pits/pits \n Our current preprint contains several errors. Please wait for next update." | |
| examples = [["This is a demo page of the PITS."],["I love hugging face."]] | |
| return gr.Interface( | |
| fn=self.inference, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| ) | |
| def launch(self): | |
| return self.interface.launch(share=True) | |
| def parsearg(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('-c', | |
| '--config', | |
| type=str, | |
| default="./configs/config_en.yaml", | |
| help='Path to configuration file') | |
| parser.add_argument('-m', | |
| '--model', | |
| type=str, | |
| default='PITS', | |
| help='Model name') | |
| parser.add_argument('-r', | |
| '--checkpoint_path', | |
| type=str, | |
| default='./logs/pits_vctk_AD_3000.pth', | |
| help='Path to checkpoint for resume') | |
| parser.add_argument('-f', | |
| '--force_resume', | |
| type=str, | |
| help='Path to checkpoint for force resume') | |
| parser.add_argument('-d', | |
| '--dir', | |
| type=str, | |
| default='/DATA/audio/pits_samples', | |
| help='root dir') | |
| args = parser.parse_args() | |
| return args | |
| if __name__ == "__main__": | |
| args = parsearg() | |
| app = GradioApp(args) | |
| app.launch() | |