| 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" |
|
|
|
|
| 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 = 200 |
| 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, 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 = 'HutaoTTS' |
| lang = 'ํ๊ตญ์ด (Korean)' |
| example = '์ ๋ ์์๋น์ ๋น์ฃผ, ํธ๋์์!' |
| 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, |
| hps.symbols, create_tts_fn(model, hps, speaker_ids), |
| create_to_phoneme_fn(hps))) |
| |
| app = gr.Blocks(css=css) |
|
|
| with app: |
| gr.Markdown("# Genshin Impact Hutao TTS Using Vits Model\n" |
| "\n\n") |
| |
| for i, (name, cover_path, speakers, lang, example, symbols, 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="Text (100 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, phoneme_input], |
| [tts_output1, tts_output2]) |
| to_phoneme_btn.click(to_phoneme_fn, [tts_input1], [tts_input1]) |
| phoneme_list.click(None, [phoneme_list, phoneme_list_json], [], |
| _js=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) |
|
|
