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from models import SynthesizerTrn |
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from vits_pinyin import VITS_PinYin |
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from text import cleaned_text_to_sequence |
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from text.symbols import symbols |
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import gradio as gr |
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import utils |
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import torch |
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import argparse |
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import os |
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import re |
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import logging |
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logging.getLogger('numba').setLevel(logging.WARNING) |
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limitation = os.getenv("SYSTEM") == "spaces" |
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def create_calback(net_g: SynthesizerTrn, tts_front: VITS_PinYin): |
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def tts_calback(text, dur_scale): |
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if limitation: |
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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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phonemes, char_embeds = tts_front.chinese_to_phonemes(text) |
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input_ids = cleaned_text_to_sequence(phonemes) |
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with torch.no_grad(): |
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x_tst = torch.LongTensor(input_ids).unsqueeze(0).to(device) |
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x_tst_lengths = torch.LongTensor([len(input_ids)]).to(device) |
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x_tst_prosody = torch.FloatTensor( |
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char_embeds).unsqueeze(0).to(device) |
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audio = net_g.infer(x_tst, x_tst_lengths, x_tst_prosody, noise_scale=0.5, |
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length_scale=dur_scale)[0][0, 0].data.cpu().float().numpy() |
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del x_tst, x_tst_lengths, x_tst_prosody |
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return "Success", (16000, audio) |
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return tts_calback |
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example = [['天空呈现的透心的蓝，像极了当年。总在这样的时候，透过窗棂，心，在天空里无尽的游弋！柔柔的，浓浓的，痴痴的风，牵引起心底灵动的思潮；情愫悠悠，思情绵绵，风里默坐，红尘中的浅醉，诗词中的优柔，任那自在飞花轻似梦的情怀，裁一束霓衣，织就清浅淡薄的安寂。', 1], |
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['风的影子翻阅过淡蓝色的信笺，柔和的文字浅浅地漫过我安静的眸，一如几朵悠闲的云儿，忽而氤氲成汽，忽而修饰成花，铅华洗尽后的透彻和靓丽，爽爽朗朗，轻轻盈盈', 1], |
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['时光仿佛有穿越到了从前，在你诗情画意的眼波中，在你舒适浪漫的暇思里，我如风中的思绪徜徉广阔天际，仿佛一片沾染了快乐的羽毛，在云环影绕颤动里浸润着风的呼吸，风的诗韵，那清新的耳语，那婉约的甜蜜，那恬淡的温馨，将一腔情澜染得愈发的缠绵。', 1],] |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--share", action="store_true", |
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default=False, help="share gradio app") |
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args = parser.parse_args() |
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device = torch.device("cpu") |
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tts_front = VITS_PinYin("./bert", device) |
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hps = utils.get_hparams_from_file("./configs/bert_vits.json") |
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net_g = SynthesizerTrn( |
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len(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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**hps.model) |
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model_path = "vits_bert_model.pth" |
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utils.load_model(model_path, net_g) |
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net_g.eval() |
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net_g.to(device) |
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tts_calback = create_calback(net_g, tts_front) |
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app = gr.Blocks() |
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with app: |
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gr.Markdown("# Best TTS based on BERT and VITS with some Natural Speech Features Of Microsoft\n\n" |
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"code : github.com/PlayVoice/vits_chinese\n\n" |
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"1, Hidden prosody embedding from BERT，get natural pauses in grammar\n\n" |
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"2, Infer loss from NaturalSpeech，get less sound error\n\n" |
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"3, Framework of VITS，get high audio quality\n\n" |
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"<video id='video' controls='' preload='yes'>\n\n" |
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"<source id='mp4' src='https://user-images.githubusercontent.com/16432329/220678182-4775dec8-9229-4578-870f-2eebc3a5d660.mp4' type='video/mp4'>\n\n" |
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"</videos>\n\n" |
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) |
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with gr.Tabs(): |
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with gr.TabItem("TTS"): |
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with gr.Row(): |
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with gr.Column(): |
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textbox = gr.TextArea(label="Text", |
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placeholder="Type your sentence here (Maximum 150 words)", |
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value="中文语音合成", elem_id=f"tts-input") |
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duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1, |
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label='速度 Speed') |
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with gr.Column(): |
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text_output = gr.Textbox(label="Message") |
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audio_output = gr.Audio( |
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label="Output Audio", elem_id="tts-audio") |
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btn = gr.Button("Generate!") |
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btn.click(tts_calback, |
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inputs=[textbox, duration_slider], |
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outputs=[text_output, audio_output]) |
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gr.Examples( |
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examples=example, |
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inputs=[textbox, duration_slider], |
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outputs=[text_output, audio_output], |
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fn=tts_calback |
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) |
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app.queue(concurrency_count=3).launch(show_api=False, share=args.share) |
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