| import os | |
| import numpy as np | |
| import torch | |
| from torch import no_grad, LongTensor | |
| import argparse | |
| import commons | |
| from mel_processing import spectrogram_torch | |
| import utils | |
| from models import SynthesizerTrn | |
| import gradio as gr | |
| import librosa | |
| import webbrowser | |
| import time | |
| import commons | |
| import utils | |
| from models import SynthesizerTrn | |
| from text.symbols import symbols | |
| from text import cleaned_text_to_sequence,_symbol_to_id, get_bert | |
| from text.cleaner import clean_text | |
| from scipy.io import wavfile | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| import logging | |
| logging.getLogger("PIL").setLevel(logging.WARNING) | |
| logging.getLogger("urllib3").setLevel(logging.WARNING) | |
| logging.getLogger("markdown_it").setLevel(logging.WARNING) | |
| logging.getLogger("httpx").setLevel(logging.WARNING) | |
| logging.getLogger("asyncio").setLevel(logging.WARNING) | |
| language_marks = { | |
| "简体中文": "[ZH]", | |
| } | |
| lang = ['简体中文'] | |
| def get_text(text, language_str, hps): | |
| norm_text, phone, tone, word2ph = clean_text(text, language_str) | |
| print([f"{p}{t}" for p, t in zip(phone, tone)]) | |
| phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str) | |
| if hps.data.add_blank: | |
| phone = commons.intersperse(phone, 0) | |
| tone = commons.intersperse(tone, 0) | |
| language = commons.intersperse(language, 0) | |
| for i in range(len(word2ph)): | |
| word2ph[i] = word2ph[i] * 2 | |
| word2ph[0] += 1 | |
| bert = get_bert(norm_text, word2ph, language_str) | |
| assert bert.shape[-1] == len(phone) | |
| phone = torch.LongTensor(phone) | |
| tone = torch.LongTensor(tone) | |
| language = torch.LongTensor(language) | |
| return bert, phone, tone, language | |
| ''' | |
| def create_tts_fn(model, hps, speaker_ids): | |
| def tts_fn(text, speaker, language, speed): | |
| if language is not None: | |
| text = language_marks[language] + text + language_marks[language] | |
| speaker_id = speaker_ids[speaker] | |
| stn_tst = get_text(text, hps, False) | |
| with no_grad(): | |
| x_tst = stn_tst.unsqueeze(0).to(device) | |
| x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device) | |
| sid = LongTensor([speaker_id]).to(device) | |
| audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, | |
| length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy() | |
| del stn_tst, x_tst, x_tst_lengths, sid | |
| return "Success", (hps.data.sampling_rate, audio) | |
| return tts_fn | |
| ''' | |
| dev='cuda' | |
| def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid): | |
| bert, phones, tones, lang_ids = get_text(text,"ZH", hps,) | |
| print(sid) | |
| with torch.no_grad(): | |
| x_tst=phones.to(dev).unsqueeze(0) | |
| tones=tones.to(dev).unsqueeze(0) | |
| lang_ids=lang_ids.to(dev).unsqueeze(0) | |
| bert = bert.to(dev).unsqueeze(0) | |
| x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev) | |
| del phones | |
| speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev) | |
| audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio | |
| , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy() | |
| del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers | |
| return "Success",(hps.data.sampling_rate, audio) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--model_dir", default="./G_latest.pth", help="directory to your fine-tuned model") | |
| parser.add_argument("--config_dir", default="./configs\config.json", help="directory to your model config file") | |
| parser.add_argument("--share", default=False, help="make link public (used in colab)") | |
| args = parser.parse_args() | |
| hps = utils.get_hparams_from_file(args.config_dir) | |
| net_g = SynthesizerTrn( | |
| len(symbols), | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| n_speakers=hps.data.n_speakers, | |
| **hps.model).to(dev) | |
| _ = net_g.eval() | |
| _ = utils.load_checkpoint(args.model_dir, net_g, None,skip_optimizer=True) | |
| speaker_ids = hps.data.spk2id | |
| speakers = list(hps.data.spk2id.keys()) | |
| #inf = infer(net_g, hps, speaker_ids) | |
| app = gr.Blocks() | |
| with app: | |
| with gr.Tab("Text-to-Speech"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| textbox = gr.TextArea(label="Text", | |
| placeholder="Type your sentence here", | |
| value="生活就像海洋，只有意志坚强的人，才能到达彼岸。", elem_id=f"tts-input") | |
| # select character | |
| char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character') | |
| language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language') | |
| sdp_ratio = gr.Slider(minimum=0.1, maximum=0.9, value=0.2, step=0.1, | |
| label='SDP/DP混合比-语调方差') | |
| noise_scale = gr.Slider(minimum=0.1, maximum=1.5, value=0.5, step=0.1, | |
| label='noise/感情变化') | |
| noise_scale_w = gr.Slider(minimum=0.1, maximum=1.4, value=0.9, step=0.1, | |
| label='noisew/音节发音长度变化') | |
| length_scale = gr.Slider(minimum=0.1, maximum=2, value=1.0, step=0.1, | |
| label='length/语速') | |
| with gr.Column(): | |
| text_output = gr.Textbox(label="Message") | |
| audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio") | |
| btn = gr.Button("Generate!") | |
| btn.click(infer, | |
| inputs=[textbox,sdp_ratio,noise_scale,noise_scale_w,length_scale,char_dropdown], | |
| outputs=[text_output, audio_output]) | |
| webbrowser.open("http://127.0.0.1:7860") | |
| app.launch(share=args.share) | |