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| 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 | |
| limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces | |
| max_length = 100 | |
| def get_text(text, hps, is_phoneme): | |
| text_norm = text_to_sequence(text, hps.symbols, [] if is_phoneme 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_tts_fn(model, hps, speaker_ids): | |
| def tts_fn(text, speaker, speed, is_phoneme): | |
| if limitation: | |
| text_len = len(text) | |
| max_len = max_length | |
| if text_len > max_len: | |
| return "Error: Text is too long", None | |
| speaker_id = speaker_ids[speaker] | |
| stn_tst = get_text(text, hps, is_phoneme) | |
| 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 = [] | |
| name = 'ε°ι³₯ιγγ·γ(γγ«γ’γ«) TTS' | |
| lang = 'ζ₯ζ¬θͺ (Japanese)' | |
| example = 'γγ γγΎγΌγ' | |
| 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) | |
| 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 = [0] | |
| speakers = [name] | |
| t = 'vits' | |
| models_tts.append((name, cover_path, speakers, lang, example, | |
| create_tts_fn(model, hps, speaker_ids), | |
| create_to_phoneme_fn(hps))) | |
| app = gr.Blocks(css=css) | |
| with app: | |
| gr.Markdown("# BlueArchive Hoshino TTS Using Vits Model\n" | |
| "\n\n") | |
| for i, (name, cover_path, speakers, lang, example, tts_fn, to_phoneme_fn) in enumerate(models_tts): | |
| with gr.Column(): | |
| gr.Markdown(f"## {name}\n\n" | |
| f"\n\n" | |
| f"lang: {lang}") | |
| tts_input1 = gr.TextArea(label=f"Text ({max_length} 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=0.9, minimum=0.5, maximum=2, step=0.1) | |
| 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]) | |
| app.queue(concurrency_count=3).launch(show_api=False) | |