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| # import gradio as gr | |
| # gr.Interface.load("models/ulysses115/pmvoice").launch() | |
| import argparse | |
| import json | |
| import os | |
| import re | |
| import tempfile | |
| import librosa | |
| import numpy as np | |
| import torch | |
| from torch import no_grad, LongTensor | |
| import commons | |
| import utils | |
| import gradio as gr | |
| import gradio.utils as gr_utils | |
| import gradio.processing_utils as gr_processing_utils | |
| from models import SynthesizerTrn | |
| from text.symbols import symbols | |
| from text import text_to_sequence, _clean_text | |
| from mel_processing import spectrogram_torch | |
| limitation = False#os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces | |
| def audio_postprocess(self, y): | |
| if y is None: | |
| return None | |
| if gr_utils.validate_url(y): | |
| file = gr_processing_utils.download_to_file(y, dir=self.temp_dir) | |
| elif isinstance(y, tuple): | |
| sample_rate, data = y | |
| file = tempfile.NamedTemporaryFile( | |
| suffix=".wav", dir=self.temp_dir, delete=False | |
| ) | |
| gr_processing_utils.audio_to_file(sample_rate, data, file.name) | |
| else: | |
| file = gr_processing_utils.create_tmp_copy_of_file(y, dir=self.temp_dir) | |
| return gr_processing_utils.encode_url_or_file_to_base64(file.name) | |
| gr.Audio.postprocess = audio_postprocess | |
| def get_text(text, hps): | |
| text_norm = text_to_sequence(text, hps.data.text_cleaners) | |
| if hps.data.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = torch.LongTensor(text_norm) | |
| return text_norm | |
| def create_tts_fn(model, hps, speaker_ids): | |
| def tts_fn(text, speaker, speed, is_symbol): | |
| if limitation: | |
| text_len = len(re.sub("\[([A-Z]{2})\]", "", text)) | |
| max_len = 150 | |
| if is_symbol: | |
| max_len *= 3 | |
| if text_len > max_len: | |
| return "Error: Text is too long", None | |
| speaker_id = speaker_ids[speaker] | |
| stn_tst = get_text(text, hps, is_symbol) | |
| 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 | |
| def create_to_symbol_fn(hps): | |
| def to_symbol_fn(is_symbol_input, input_text, temp_text): | |
| return (_clean_text(input_text, hps.data.text_cleaners), input_text) if is_symbol_input \ | |
| else (temp_text, temp_text) | |
| return to_symbol_fn | |
| download_audio_js = """ | |
| () =>{{ | |
| let root = document.querySelector("body > gradio-app"); | |
| if (root.shadowRoot != null) | |
| root = root.shadowRoot; | |
| let audio = root.querySelector("#{audio_id}").querySelector("audio"); | |
| if (audio == undefined) | |
| return; | |
| audio = audio.src; | |
| let oA = document.createElement("a"); | |
| oA.download = Math.floor(Math.random()*100000000)+'.wav'; | |
| oA.href = audio; | |
| document.body.appendChild(oA); | |
| oA.click(); | |
| oA.remove(); | |
| }} | |
| """ | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--device', type=str, default='cpu') | |
| parser.add_argument("--share", action="store_true", default=False, help="share gradio app") | |
| args = parser.parse_args() | |
| device = torch.device(args.device) | |
| models_tts = [] | |
| with open("save_model/info.json", "r", encoding="utf-8") as f: | |
| models_info = json.load(f) | |
| for i, info in models_info.items(): | |
| name = info["title"] | |
| author = info["author"] | |
| lang = info["lang"] | |
| example = info["example"] | |
| config_path = f"config.json" | |
| model_path = f"G_1434000.pth" | |
| cover = info["cover"] | |
| cover_path = cover | |
| hps = utils.get_hparams_from_file(config_path) | |
| model = SynthesizerTrn( | |
| len(symbols), | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| **hps.model) | |
| utils.load_checkpoint(model_path, model, None) | |
| model.eval().to(device) | |
| speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"] | |
| speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"] | |
| t = info["type"] | |
| if t == "vits": | |
| models_tts.append((name, author, cover_path, speakers, lang, example, | |
| symbols, create_tts_fn(model, hps, speaker_ids), | |
| create_to_symbol_fn(hps))) | |
| app = gr.Blocks() | |
| with app: | |
| for i, (name, author, cover_path, speakers, lang, example, symbols, tts_fn, | |
| to_symbol_fn) in enumerate(models_tts): | |
| with gr.TabItem(f"model{i}"): | |
| with gr.Column(): | |
| tts_input1 = gr.TextArea(label="Text (150 words limitation)", value=example, | |
| elem_id=f"tts-input{i}") | |
| tts_input2 = gr.Dropdown(label="Speaker", choices=speakers, | |
| type="index", value=speakers[0]) | |
| tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.5, maximum=2, step=0.1) | |
| with gr.Accordion(label="Advanced Options", open=False): | |
| temp_text_var = gr.Variable() | |
| symbol_input = gr.Checkbox(value=False, label="Symbol input") | |
| symbol_list = gr.Dataset(label="Symbol list", components=[tts_input1], | |
| samples=[[x] for x in symbols], | |
| elem_id=f"symbol-list{i}") | |
| symbol_list_json = gr.Json(value=symbols, visible=False) | |
| tts_submit = gr.Button("Generate", variant="primary") | |
| tts_output1 = gr.Textbox(label="Output Message") | |
| tts_output2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio{i}") | |
| download = gr.Button("Download Audio") | |
| download.click(None, [], [], _js=download_audio_js.format(audio_id=f"tts-audio{i}")) | |
| tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, symbol_input], | |
| [tts_output1, tts_output2]) | |
| symbol_input.change(to_symbol_fn, | |
| [symbol_input, tts_input1, temp_text_var], | |
| [tts_input1, temp_text_var]) | |
| symbol_list.click(None, [symbol_list, symbol_list_json], [], | |
| _js=f""" | |
| (i,symbols) => {{ | |
| let root = document.querySelector("body > gradio-app"); | |
| if (root.shadowRoot != null) | |
| root = root.shadowRoot; | |
| let text_input = root.querySelector("#tts-input{i}").querySelector("textarea"); | |
| let startPos = text_input.selectionStart; | |
| let endPos = text_input.selectionEnd; | |
| let oldTxt = text_input.value; | |
| let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos); | |
| text_input.value = result; | |
| let x = window.scrollX, y = window.scrollY; | |
| text_input.focus(); | |
| text_input.selectionStart = startPos + symbols[i].length; | |
| text_input.selectionEnd = startPos + symbols[i].length; | |
| text_input.blur(); | |
| window.scrollTo(x, y); | |
| return []; | |
| }}""") | |
| app.queue(concurrency_count=3).launch(show_api=True, share=args.share) |