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| import argparse | |
| import json | |
| import os | |
| import re | |
| import tempfile | |
| import logging | |
| logging.getLogger('numba').setLevel(logging.WARNING) | |
| 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 ONNXVITS_infer import SynthesizerTrn | |
| from text import text_to_sequence, _clean_text | |
| from text.symbols import symbols | |
| from mel_processing import spectrogram_torch | |
| import psutil | |
| from datetime import datetime | |
| 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) | |
| language_marks = { | |
| "日本語": "[JA]", | |
| "简体中文": "[ZH]", | |
| "English": "[EN]", | |
| "Mix": "", | |
| } | |
| gr.Audio.postprocess = audio_postprocess | |
| limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces | |
| def create_tts_fn(model, hps, speaker_ids): | |
| def tts_fn(text, speaker, language, 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 | |
| if language is not None: | |
| text = language_marks[language] + text + language_marks[language] | |
| speaker_id = speaker_ids[speaker] | |
| stn_tst = get_text(text, hps, is_symbol) | |
| with no_grad(): | |
| x_tst = stn_tst.unsqueeze(0) | |
| x_tst_lengths = LongTensor([stn_tst.size(0)]) | |
| sid = LongTensor([speaker_id]) | |
| 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_vc_fn(model, hps, speaker_ids): | |
| def vc_fn(original_speaker, target_speaker, input_audio): | |
| if input_audio is None: | |
| return "You need to upload an audio", None | |
| sampling_rate, audio = input_audio | |
| duration = audio.shape[0] / sampling_rate | |
| if limitation and duration > 30: | |
| return "Error: Audio is too long", None | |
| original_speaker_id = speaker_ids[original_speaker] | |
| target_speaker_id = speaker_ids[target_speaker] | |
| audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) | |
| if len(audio.shape) > 1: | |
| audio = librosa.to_mono(audio.transpose(1, 0)) | |
| if sampling_rate != hps.data.sampling_rate: | |
| audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate) | |
| with no_grad(): | |
| y = torch.FloatTensor(audio) | |
| y = y.unsqueeze(0) | |
| spec = spectrogram_torch(y, hps.data.filter_length, | |
| hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, | |
| center=False).to(device) | |
| spec_lengths = LongTensor([spec.size(-1)]).to(device) | |
| sid_src = LongTensor([original_speaker_id]).to(device) | |
| sid_tgt = LongTensor([target_speaker_id]).to(device) | |
| audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][ | |
| 0, 0].data.cpu().float().numpy() | |
| del y, spec, spec_lengths, sid_src, sid_tgt | |
| return "Success", (hps.data.sampling_rate, audio) | |
| return vc_fn | |
| def get_text(text, hps, is_symbol): | |
| text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners) | |
| if hps.data.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = LongTensor(text_norm) | |
| return text_norm | |
| 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(); | |
| }} | |
| """ | |
| models_tts = [] | |
| models_vc = [] | |
| models_info = [ | |
| { | |
| "title": "Japanese", | |
| "languages": ["日本語"], | |
| "description": "", | |
| "model_path": "./pretrained_models/G_1153000.pth", | |
| "config_path": "./configs/uma87.json", | |
| "examples": [['お疲れ様です，トレーナーさん。', 'Silence Suzuka', '日本語', 1, False], | |
| ['張り切っていこう！', 'Kitasan Black', '日本語', 1, False], | |
| ['何でこんなに慣れでんのよ，私のほが先に好きだっだのに。', 'Grass Wonder', '日本語', 1, False], | |
| ['授業中に出しだら，学校生活終わるですわ。', 'Mejiro Mcqueen', '日本語', 1, False], | |
| ['お帰りなさい，お兄様！', 'Rice Shower', '日本語', 1, False], | |
| ['私の処女をもらっでください！', 'Rice Shower', '日本語', 1, False]] | |
| }, | |
| { | |
| "title": "Japanese", | |
| "languages": ['日本語', '简体中文', 'English', 'Mix'], | |
| "description": "", | |
| "model_path": "./pretrained_models/G_1396000.pth", | |
| "config_path": "./configs/uma_trilingual.json", | |
| "examples": [['你好，训练员先生，很高兴见到你。', '草上飞 Grass Wonder (Umamusume Pretty Derby)', '简体中文', 1, False], | |
| ['To be honest, I have no idea what to say as examples.', '派蒙 Paimon (Genshin Impact)', 'English', 1, False], | |
| ['授業中に出しだら，学校生活終わるですわ。', '綾地 寧々 Ayachi Nene (Sanoba Witch)', '日本語', 1, False]] | |
| } | |
| ] | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--share", action="store_true", default=False, help="share gradio app") | |
| args = parser.parse_args() | |
| for info in models_info: | |
| name = info['title'] | |
| lang = info['languages'] | |
| examples = info['examples'] | |
| config_path = info['config_path'] | |
| model_path = info['model_path'] | |
| hps = utils.get_hparams_from_file(config_path) | |
| model = SynthesizerTrn( | |
| len(hps.symbols), | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| n_speakers=hps.data.n_speakers, | |
| **hps.model) | |
| utils.load_checkpoint(model_path, model, None) | |
| model.eval() | |
| speaker_ids = hps.speakers | |
| speakers = list(hps.speakers.keys()) | |
| models_tts.append((name, speakers, lang, example, | |
| hps.symbols, create_tts_fn(model, hps, speaker_ids), | |
| create_to_symbol_fn(hps))) | |
| models_vc.append((name, speakers, create_vc_fn(model, hps, speaker_ids))) | |
| app = gr.Blocks() | |
| with app: | |
| gr.Markdown("# English & Chinese & Japanese Anime TTS\n\n" | |
| "\n\n" | |
| "Including Japanese TTS & Trilingual TTS, speakers are all anime characters. 包含一个纯日语TTS和一个中日英三语TTS模型，主要为二次元角色。" | |
| "If you have any suggestions or bug reports, feel free to open discussion in [Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions).\n\n" | |
| "若有bug反馈或建议，请在[Community](https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer/discussions)下开启一个新的Discussion。 \n\n" | |
| ) | |
| with gr.Tabs(): | |
| with gr.TabItem("TTS"): | |
| with gr.Tabs(): | |
| for i, (name, speakers, lang, example, symbols, tts_fn, to_symbol_fn) in enumerate(models_tts): | |
| with gr.TabItem(name): | |
| with gr.Row(): | |
| with gr.Column(): | |
| textbox = gr.TextArea(label="Text", placeholder="Type your sentence here (Maximum 150 words)", value="こんにちわ。", elem_id=f"tts-input") | |
| with gr.Accordion(label="Phoneme Input", open=False): | |
| temp_text_var = gr.Variable() | |
| symbol_input = gr.Checkbox(value=False, label="Symbol input") | |
| symbol_list = gr.Dataset(label="Symbol list", components=[textbox], | |
| samples=[[x] for x in symbols], | |
| elem_id=f"symbol-list") | |
| symbol_list_json = gr.Json(value=symbols, visible=False) | |
| symbol_input.change(to_symbol_fn, | |
| [symbol_input, textbox, temp_text_var], | |
| [textbox, temp_text_var]) | |
| symbol_list.click(None, [symbol_list, symbol_list_json], textbox, | |
| _js=f""" | |
| (i, symbols, text) => {{ | |
| let root = document.querySelector("body > gradio-app"); | |
| if (root.shadowRoot != null) | |
| root = root.shadowRoot; | |
| let text_input = root.querySelector("#tts-input").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); | |
| text = text_input.value; | |
| return text; | |
| }}""") | |
| # select character | |
| char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character') | |
| language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language') | |
| duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1, label='时长 Duration') | |
| with gr.Column(): | |
| text_output = gr.Textbox(label="Message") | |
| audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio") | |
| btn = gr.Button("Generate!") | |
| download = gr.Button("Download Audio") | |
| download.click(None, [], [], _js=download_audio_js.format(audio_id="tts-audio")) | |
| if len(lang) == 1: | |
| btn.click(tts_fn, inputs=[textbox, char_dropdown, None, duration_slider, symbol_input], | |
| outputs=[text_output, audio_output]) | |
| else: | |
| btn.click(tts_fn, inputs=[textbox, char_dropdown, language_dropdown, duration_slider, symbol_input], | |
| outputs=[text_output, audio_output]) | |
| gr.Examples( | |
| examples=example, | |
| inputs=[textbox, char_dropdown, language_dropdown, | |
| duration_slider, symbol_input], | |
| outputs=[text_output, audio_output], | |
| fn=tts_fn | |
| ) | |
| app.queue(concurrency_count=3).launch(show_api=False, share=args.share) |