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 @torch.no_grad() 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()