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Update app.py
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app.py
CHANGED
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# coding=utf-8
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import os
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import re
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import utils
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import commons
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import json
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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
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from torch import no_grad, LongTensor
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import logging
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logging.getLogger('numba').setLevel(logging.WARNING)
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hps_ms = utils.get_hparams_from_file(r'config/config.json')
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def get_text(text, hps):
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text_norm, clean_text = text_to_sequence(text, hps.symbols, 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, clean_text
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def create_tts_fn(net_g_ms, speaker_id):
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def tts_fn(text, language, noise_scale, noise_scale_w, length_scale):
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text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
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text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
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max_len = 150
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if text_len > max_len:
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return "Error: Text is too long", None
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if language == 0:
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text = f"[ZH]{text}[ZH]"
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elif language == 1:
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text = f"[JA]{text}[JA]"
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else:
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text = f"{text}"
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stn_tst, clean_text = get_text(text, hps_ms)
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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 = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
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length_scale=length_scale)[0][0, 0].data.float().numpy()
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return "Success", (22050, audio)
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return tts_fn
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def change_lang(language):
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if language == 0:
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return 0.6, 0.668, 1.2
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else:
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return 0.6, 0.668, 1
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download_audio_js = """
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() =>{{
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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 audio = root.querySelector("#tts-audio").querySelector("audio");
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let text = root.querySelector("#input-text").querySelector("textarea");
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if (audio == undefined)
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return;
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text = text.value;
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if (text == undefined)
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text = Math.floor(Math.random()*100000000);
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audio = audio.src;
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let oA = document.createElement("a");
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oA.download = text.substr(0, 20)+'.wav';
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oA.href = audio;
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document.body.appendChild(oA);
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oA.click();
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oA.remove();
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}}
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"""
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if __name__ == '__main__':
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models = []
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with open("pretrained_models/info.json", "r", encoding="utf-8") as f:
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models_info = json.load(f)
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for i, info in models_info.items():
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net_g_ms = SynthesizerTrn(
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len(hps_ms.symbols),
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hps_ms.data.filter_length // 2 + 1,
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hps_ms.train.segment_size // hps_ms.data.hop_length,
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n_speakers=hps_ms.data.n_speakers,
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**hps_ms.model)
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_ = net_g_ms.eval()
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sid = info['sid']
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name_en = info['name_en']
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name_zh = info['name_zh']
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title = info['title']
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cover = f"pretrained_models/{i}/{info['cover']}"
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utils.load_checkpoint(f'pretrained_models/{i}/{i}.pth', net_g_ms, None)
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models.append((sid, name_en, name_zh, title, cover, net_g_ms, create_tts_fn(net_g_ms, sid)))
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with gr.Blocks() as app:
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gr.Markdown(
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"# <center> vits-models\n"
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"
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)
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'<
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)
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'<
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)
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lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
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app.queue(concurrency_count=1).launch()
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# coding=utf-8
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import os
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import re
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import utils
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import commons
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import json
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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
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from torch import no_grad, LongTensor
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import logging
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logging.getLogger('numba').setLevel(logging.WARNING)
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hps_ms = utils.get_hparams_from_file(r'config/config.json')
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def get_text(text, hps):
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text_norm, clean_text = text_to_sequence(text, hps.symbols, 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, clean_text
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def create_tts_fn(net_g_ms, speaker_id):
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def tts_fn(text, language, noise_scale, noise_scale_w, length_scale):
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text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
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text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
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max_len = 150
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if text_len > max_len:
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return "Error: Text is too long", None
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if language == 0:
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text = f"[ZH]{text}[ZH]"
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elif language == 1:
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text = f"[JA]{text}[JA]"
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else:
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text = f"{text}"
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stn_tst, clean_text = get_text(text, hps_ms)
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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 = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
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length_scale=length_scale)[0][0, 0].data.float().numpy()
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return "Success", (22050, audio)
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return tts_fn
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def change_lang(language):
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if language == 0:
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return 0.6, 0.668, 1.2
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else:
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return 0.6, 0.668, 1
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download_audio_js = """
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() =>{{
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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 audio = root.querySelector("#tts-audio").querySelector("audio");
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let text = root.querySelector("#input-text").querySelector("textarea");
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if (audio == undefined)
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return;
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text = text.value;
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if (text == undefined)
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text = Math.floor(Math.random()*100000000);
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audio = audio.src;
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let oA = document.createElement("a");
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oA.download = text.substr(0, 20)+'.wav';
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oA.href = audio;
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document.body.appendChild(oA);
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oA.click();
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oA.remove();
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}}
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"""
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if __name__ == '__main__':
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models = []
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with open("pretrained_models/info.json", "r", encoding="utf-8") as f:
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models_info = json.load(f)
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for i, info in models_info.items():
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net_g_ms = SynthesizerTrn(
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len(hps_ms.symbols),
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hps_ms.data.filter_length // 2 + 1,
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hps_ms.train.segment_size // hps_ms.data.hop_length,
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n_speakers=hps_ms.data.n_speakers,
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**hps_ms.model)
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_ = net_g_ms.eval()
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sid = info['sid']
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name_en = info['name_en']
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name_zh = info['name_zh']
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title = info['title']
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cover = f"pretrained_models/{i}/{info['cover']}"
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utils.load_checkpoint(f'pretrained_models/{i}/{i}.pth', net_g_ms, None)
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models.append((sid, name_en, name_zh, title, cover, net_g_ms, create_tts_fn(net_g_ms, sid)))
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with gr.Blocks() as app:
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gr.Markdown(
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"# <center> vits-models\n"
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"![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.vits-models)\n\n"
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)
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with gr.Tabs():
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with gr.TabItem("EN"):
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for (sid, name_en, name_zh, title, cover, net_g_ms, tts_fn) in models:
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with gr.TabItem(name_en):
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with gr.Row():
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gr.Markdown(
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'<div align="center">'
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f'<a><strong>{title}</strong></a>'
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f'<img width="300px" src="file/{cover}">' if cover else ""
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'</div>'
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)
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with gr.Row():
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with gr.Column():
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input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value="先生。今日も全力であなたをアシストしますね。", elem_id=f"input-text")
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lang = gr.Dropdown(label="Language", choices=["Chinese", "Japanese", "Mix(wrap the Chinese text with [ZH][ZH], wrap the Japanese text with [JA][JA])"],
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type="index", value="Japanese")
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btn = gr.Button(value="Generate")
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with gr.Row():
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ns = gr.Slider(label="noise_scale", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
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nsw = gr.Slider(label="noise_scale_w", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
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ls = gr.Slider(label="length_scale", minimum=0.1, maximum=2.0, step=0.1, value=1, interactive=True)
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with gr.Column():
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o1 = gr.Textbox(label="Output Message")
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o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
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download = gr.Button("Download Audio")
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btn.click(tts_fn, inputs=[input_text, lang, ns, nsw, ls], outputs=[o1, o2])
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download.click(None, [], [], _js=download_audio_js.format())
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lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
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with gr.TabItem("中文"):
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for (sid, name_en, name_zh, title, cover, net_g_ms, tts_fn) in models:
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with gr.TabItem(name_zh):
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with gr.Row():
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gr.Markdown(
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'<div align="center">'
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f'<a><strong>{title}</strong></a>'
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f'<img src="file/{cover}">' if cover else ""
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'</div>'
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)
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with gr.Row():
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with gr.Column():
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input_text = gr.Textbox(label="文本 (100字上限)", lines=5, value="先生。今日も全力であなたをアシストしますね。", elem_id=f"input-text")
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lang = gr.Dropdown(label="语言", choices=["中文", "日语", "中日混合(中文用[ZH][ZH]包裹起来,日文用[JA][JA]包裹起来)"],
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type="index", value="日语")
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btn = gr.Button(value="生成")
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with gr.Row():
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ns = gr.Slider(label="控制感情变化程度", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
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nsw = gr.Slider(label="控制音素发音长度", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
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ls = gr.Slider(label="控制整体语速", minimum=0.1, maximum=2.0, step=0.1, value=1, interactive=True)
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with gr.Column():
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o1 = gr.Textbox(label="输出信息")
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o2 = gr.Audio(label="输出音频", elem_id=f"tts-audio")
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download = gr.Button("下载音频")
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btn.click(tts_fn, inputs=[input_text, lang, ns, nsw, ls], outputs=[o1, o2])
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download.click(None, [], [], _js=download_audio_js.format())
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lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
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app.queue(concurrency_count=1).launch()
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