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import json |
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import os |
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import re |
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import librosa |
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import numpy as np |
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import torch |
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from torch import no_grad, LongTensor |
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import commons |
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import utils |
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import gradio as gr |
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from models import SynthesizerTrn |
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from text import text_to_sequence, _clean_text |
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from mel_processing import spectrogram_torch |
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limitation = os.getenv("SYSTEM") == "spaces" |
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def get_text(text, hps, is_phoneme): |
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text_norm = text_to_sequence(text, hps.symbols, [] if is_phoneme else hps.data.text_cleaners) |
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if hps.data.add_blank: |
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text_norm = commons.intersperse(text_norm, 0) |
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text_norm = LongTensor(text_norm) |
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return text_norm |
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def create_tts_fn(model, hps, speaker_ids): |
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def tts_fn(text, speaker, speed, is_phoneme): |
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if limitation: |
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text_len = len(text) |
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max_len = 100 |
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if is_phoneme: |
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max_len *= 3 |
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else: |
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if len(hps.data.text_cleaners) > 0 and hps.data.text_cleaners[0] == "zh_ja_mixture_cleaners": |
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text_len = len(re.sub("(\[ZH\]|\[JA\])", "", text)) |
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if text_len > max_len: |
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return "Error: Text is too long", None |
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speaker_id = speaker_ids[speaker] |
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stn_tst = get_text(text, hps, is_phoneme) |
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with no_grad(): |
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x_tst = stn_tst.unsqueeze(0) |
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x_tst_lengths = LongTensor([stn_tst.size(0)]) |
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sid = LongTensor([speaker_id]) |
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audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, |
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length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy() |
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del stn_tst, x_tst, x_tst_lengths, sid |
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return "Success", (hps.data.sampling_rate, audio) |
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return tts_fn |
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def create_to_phoneme_fn(hps): |
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def to_phoneme_fn(text): |
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return _clean_text(text, hps.data.text_cleaners) if text != "" else "" |
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return to_phoneme_fn |
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css = """ |
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#advanced-btn { |
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color: white; |
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border-color: black; |
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background: black; |
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font-size: .7rem !important; |
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line-height: 19px; |
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margin-top: 24px; |
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margin-bottom: 12px; |
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padding: 2px 8px; |
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border-radius: 14px !important; |
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} |
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#advanced-options { |
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display: none; |
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margin-bottom: 20px; |
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} |
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""" |
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if __name__ == '__main__': |
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models_tts = [] |
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name = 'η½ζ΄²γγγ(γγ«γ’γ«) TTS' |
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lang = 'ζ₯ζ¬θͺ (Japanese)' |
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example = 'γγγΏγΉ γγγΏγγ₯γ γ¨γ γͺγ γγ’ γγγΏγΉγ' |
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config_path = f"saved_model/config.json" |
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model_path = f"saved_model/model.pth" |
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cover_path = f"saved_model/cover.png" |
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hps = utils.get_hparams_from_file(config_path) |
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model = SynthesizerTrn( |
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len(hps.symbols), |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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**hps.model) |
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utils.load_checkpoint(model_path, model, None) |
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model.eval() |
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speaker_ids = [0] |
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speakers = [name] |
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t = 'vits' |
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models_tts.append((name, cover_path, speakers, lang, example, |
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hps.symbols, create_tts_fn(model, hps, speaker_ids), |
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create_to_phoneme_fn(hps))) |
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app = gr.Blocks(css=css) |
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with app: |
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gr.Markdown("# Blue Archive Azusa TTS Using Vits Model\n" |
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"\n\n") |
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for i, (name, cover_path, speakers, lang, example, symbols, tts_fn, |
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to_phoneme_fn) in enumerate(models_tts): |
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with gr.Column(): |
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gr.Markdown(f"## {name}\n\n" |
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f"\n\n" |
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f"lang: {lang}") |
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tts_input1 = gr.TextArea(label="Text (100 words limitation)", value=example, |
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elem_id=f"tts-input{i}") |
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tts_input2 = gr.Dropdown(label="Speaker", choices=speakers, |
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type="index", value=speakers[0]) |
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tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.1, maximum=2, step=0.1) |
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with gr.Accordion(label="Advanced Options", open=False): |
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phoneme_input = gr.Checkbox(value=False, label="Phoneme input") |
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to_phoneme_btn = gr.Button("Covert text to phoneme") |
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phoneme_list = gr.Dataset(label="Phoneme list", components=[tts_input1], |
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samples=[[x] for x in symbols], |
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elem_id=f"phoneme-list{i}") |
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phoneme_list_json = gr.Json(value=symbols, visible=False) |
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tts_submit = gr.Button("Generate", variant="primary") |
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tts_output1 = gr.Textbox(label="Output Message") |
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tts_output2 = gr.Audio(label="Output Audio") |
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tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, phoneme_input], |
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[tts_output1, tts_output2]) |
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to_phoneme_btn.click(to_phoneme_fn, [tts_input1], [tts_input1]) |
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phoneme_list.click(None, [phoneme_list, phoneme_list_json], [], |
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_js=f""" |
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(i,phonemes) => {{ |
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let root = document.querySelector("body > gradio-app"); |
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if (root.shadowRoot != null) |
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root = root.shadowRoot; |
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let text_input = root.querySelector("#tts-input{i}").querySelector("textarea"); |
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let startPos = text_input.selectionStart; |
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let endPos = text_input.selectionEnd; |
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let oldTxt = text_input.value; |
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let result = oldTxt.substring(0, startPos) + phonemes[i] + oldTxt.substring(endPos); |
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text_input.value = result; |
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let x = window.scrollX, y = window.scrollY; |
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text_input.focus(); |
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text_input.selectionStart = startPos + phonemes[i].length; |
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text_input.selectionEnd = startPos + phonemes[i].length; |
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text_input.blur(); |
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window.scrollTo(x, y); |
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return []; |
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}}""") |
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app.queue(concurrency_count=3).launch(show_api=False) |
