# coding=utf-8 import time import os import gradio as gr import utils import argparse import commons from models import SynthesizerTrn from text import text_to_sequence import torch from torch import no_grad, LongTensor import webbrowser import logging logging.getLogger('numba').setLevel(logging.WARNING) limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces def get_text(text, hps): text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners) if hps.data.add_blank: text_norm = commons.intersperse(text_norm, 0) text_norm = LongTensor(text_norm) return text_norm, clean_text def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale): print(f'________________{speaker_id}') parser = argparse.ArgumentParser() parser.add_argument('--device', type=str, default='cpu') parser.add_argument('--api', action="store_true", default=True) parser.add_argument("--share", action="store_true", default=False, help="share gradio app") parser.add_argument("--colab", action="store_true", default=False, help="share gradio app") args = parser.parse_args() device = torch.device(args.device) hps_ms = utils.get_hparams_from_file(r'./model/config.json') net_g_ms = SynthesizerTrn( len(hps_ms.symbols), hps_ms.data.filter_length // 2 + 1, hps_ms.train.segment_size // hps_ms.data.hop_length, n_speakers=hps_ms.data.n_speakers, **hps_ms.model) _ = net_g_ms.eval().to(device) speakers = hps_ms.speakers model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None) start = time.perf_counter() if not len(text): return "输入文本不能为空！", None, None text = text.replace('\n', ' ').replace('\r', '').replace(" ", "") if len(text) > 100 and limitation: return f"输入文字过长！{len(text)}>100", None, None if language == 0: text = f"[ZH]{text}[ZH]" elif language == 1: text = f"[JA]{text}[JA]" else: text = f"{text}" stn_tst, clean_text = get_text(text, hps_ms) with no_grad(): x_tst = stn_tst.unsqueeze(0).to(device) x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device) speaker_id = LongTensor([speaker_id]).to(device) audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0, 0].data.cpu().float().numpy() return "生成成功!",(22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s" def search_speaker(search_value): hps_ms = utils.get_hparams_from_file(r'./model/config.json') speakers = hps_ms.speakers for s in speakers: if search_value == s: return s for s in speakers: if search_value in s: return s def change_lang(language): if language == 0: return 0.6, 0.668, 1.2 else: return 0.6, 0.668, 1.1 download_audio_js = """ () =>{{ let root = document.querySelector("body > gradio-app"); if (root.shadowRoot != null) root = root.shadowRoot; let audio = root.querySelector("#tts-audio").querySelector("audio"); let text = root.querySelector("#input-text").querySelector("textarea"); if (audio == undefined) return; text = text.value; if (text == undefined) text = Math.floor(Math.random()*100000000); audio = audio.src; let oA = document.createElement("a"); oA.download = text.substr(0, 20)+'.wav'; oA.href = audio; document.body.appendChild(oA); oA.click(); oA.remove(); }} """ if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--device', type=str, default='cpu') parser.add_argument('--api', action="store_true", default=True) parser.add_argument("--share", action="store_true", default=False, help="share gradio app") parser.add_argument("--colab", action="store_true", default=False, help="share gradio app") args = parser.parse_args() device = torch.device(args.device) hps_ms = utils.get_hparams_from_file(r'./model/config.json') net_g_ms = SynthesizerTrn( len(hps_ms.symbols), hps_ms.data.filter_length // 2 + 1, hps_ms.train.segment_size // hps_ms.data.hop_length, n_speakers=hps_ms.data.n_speakers, **hps_ms.model) _ = net_g_ms.eval().to(device) speakers = hps_ms.speakers model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None) with gr.Blocks() as app: gr.Markdown( "#
VITS语音在线合成demo\n" "#
严禁将模型用于任何商业项目，否则后果自负\n" "
主要有赛马娘，原神中文，原神日语，崩坏3的音色
" '
结果有随机性，语调可能很奇怪，可多次生成取最佳效果
' '
标点符号会影响生成的结果
' ) with gr.Tabs(): with gr.TabItem("vits"): with gr.Row(): with gr.Column(): input_text = gr.Textbox(label="Text (100 words limitation) " if limitation else "Text", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text") lang = gr.Dropdown(label="Language", 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）"], type="index", value="中文") btn = gr.Button(value="Submit") with gr.Row(): search = gr.Textbox(label="Search Speaker", lines=1) btn2 = gr.Button(value="Search") sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228]) with gr.Row(): ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True) nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True) ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True) with gr.Column(): o1 = gr.Textbox(label="Output Message") o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio") o3 = gr.Textbox(label="Extra Info") download = gr.Button("Download Audio") btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3],api_name='voice') download.click(None, [], [], _js=download_audio_js.format()) btn2.click(search_speaker, inputs=[search], outputs=[sid]) lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls]) with gr.TabItem("可用人物一览"): gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index") if args.colab: webbrowser.open("http://127.0.0.1:7860") app.queue(concurrency_count=3, api_open=True).launch(show_api=True)