import json import os import re import librosa import numpy as np import torch from torch import no_grad, LongTensor import commons import utils import gradio as gr from models import SynthesizerTrn from text import text_to_sequence, _clean_text from mel_processing import spectrogram_torch from text.symbols import symbols limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces device = 'cpu' 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 = LongTensor(text_norm) return text_norm def create_tts_fn(model, hps, speaker_ids): def tts_fn(text, speaker, speed, is_phoneme): print(speaker, text) if limitation: text_len = len(text) max_len = 500 if is_phoneme: max_len *= 3 else: if len(hps.data.text_cleaners) > 0 and hps.data.text_cleaners[0] == "zh_ja_mixture_cleaners": text_len = len(re.sub("(\[ZH\]|\[JA\])", "", text)) if text_len > max_len: return "Error: Text is too long", None speaker_id = speaker_ids[speaker] stn_tst = get_text(text, hps) 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_to_phoneme_fn(hps): def to_phoneme_fn(text): return _clean_text(text, hps.data.text_cleaners) if text != "" else "" return to_phoneme_fn css = """ #advanced-btn { color: white; border-color: black; background: black; font-size: .7rem !important; line-height: 19px; margin-top: 24px; margin-bottom: 12px; padding: 2px 8px; border-radius: 14px !important; } #advanced-options { display: none; margin-bottom: 20px; } """ if __name__ == '__main__': models_tts = [] models_vc = [] models_soft_vc = [] name = 'BlueArchiveTTS' lang = '日本語 (Japanese)' example = '先生、何をお手伝いしましょうか？' config_path = f"saved_model/config.json" model_path = f"saved_model/model.pth" cover_path = f"saved_model/cover.png" hps = utils.get_hparams_from_file(config_path) if "use_mel_posterior_encoder" in hps.model.keys() and hps.model.use_mel_posterior_encoder == True: print("Using mel posterior encoder for VITS2") posterior_channels = 80 # vits2 hps.data.use_mel_posterior_encoder = True else: print("Using lin posterior encoder for VITS1") posterior_channels = hps.data.filter_length // 2 + 1 hps.data.use_mel_posterior_encoder = False model = SynthesizerTrn( len(symbols), posterior_channels, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, #- >0 for multi speaker **hps.model) utils.load_checkpoint(model_path, model, None) model.eval() 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 = 'vits' models_tts.append((name, cover_path, speakers, lang, example, symbols, create_tts_fn(model, hps, speaker_ids), create_to_phoneme_fn(hps))) app = gr.Blocks(css=css) with app: gr.Markdown("# BlueArchiveTTS Using VITS2 Model\n\n" "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=ORI-Muchim.BlueArchiveTTS)\n\n") with gr.Tabs(): with gr.TabItem("TTS"): with gr.Tabs(): for i, (name, cover_path, speakers, lang, example, symbols, tts_fn, to_phoneme_fn) in enumerate(models_tts): with gr.TabItem(f"BlueArchive"): with gr.Column(): gr.Markdown(f"## {name}\n\n" f"![cover](file/{cover_path})\n\n" f"lang: {lang}") tts_input1 = gr.TextArea(label="Text (500 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=0.1, maximum=2, step=0.1) # with gr.Accordion(label="Advanced Options", open=False): # phoneme_input = gr.Checkbox(value=False, label="Phoneme input") # to_phoneme_btn = gr.Button("Covert text to phoneme") # phoneme_list = gr.Dataset(label="Phoneme list", components=[tts_input1], # samples=[[x] for x in symbols], # elem_id=f"phoneme-list{i}") # phoneme_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") tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3], [tts_output1, tts_output2])=f""" (i,phonemes) => {{ 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) + phonemes[i] + oldTxt.substring(endPos); text_input.value = result; let x = window.scrollX, y = window.scrollY; text_input.focus(); text_input.selectionStart = startPos + phonemes[i].length; text_input.selectionEnd = startPos + phonemes[i].length; text_input.blur(); window.scrollTo(x, y); return []; }}""" app.queue(concurrency_count=3).launch(show_api=False)