96 lines
4.1 KiB
Python
96 lines
4.1 KiB
Python
import torch
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import argparse
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import commons
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import utils
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from models import SynthesizerTrn
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from text.symbols import symbols
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from text import cleaned_text_to_sequence, get_bert
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from text.cleaner import clean_text
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import gradio as gr
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import webbrowser
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def get_text(text, language_str, hps):
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norm_text, phone, tone, word2ph = clean_text(text, language_str)
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phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
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if hps.data.add_blank:
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phone = commons.intersperse(phone, 0)
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tone = commons.intersperse(tone, 0)
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language = commons.intersperse(language, 0)
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for i in range(len(word2ph)):
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word2ph[i] = word2ph[i] * 2
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word2ph[0] += 1
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bert = get_bert(norm_text, word2ph, language_str)
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assert bert.shape[-1] == len(phone)
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phone = torch.LongTensor(phone)
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tone = torch.LongTensor(tone)
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language = torch.LongTensor(language)
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return bert, phone, tone, language
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def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
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bert, phones, tones, lang_ids = get_text(text, "ZH", hps,)
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with torch.no_grad():
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x_tst=phones.to(device).unsqueeze(0)
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tones=tones.to(device).unsqueeze(0)
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lang_ids=lang_ids.to(device).unsqueeze(0)
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bert = bert.to(device).unsqueeze(0)
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x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
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speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
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audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
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, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
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return audio
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def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
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with torch.no_grad():
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audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker)
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return "Success", (hps.data.sampling_rate, audio)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("-m", "--model", default="./logs/as/G_8000.pth", help="path of your model")
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parser.add_argument("-c", "--config", default="./configs/config.json", help="path of your config file")
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parser.add_argument("--share", default=False, help="make link public")
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args = parser.parse_args()
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hps = utils.get_hparams_from_file(args.config)
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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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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n_speakers=hps.data.n_speakers,
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**hps.model).to(device)
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_ = net_g.eval()
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_ = utils.load_checkpoint(args.model, net_g, None,skip_optimizer=True)
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speaker_ids = hps.data.spk2id
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speakers = list(speaker_ids.keys())
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app = gr.Blocks()
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with app:
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with gr.Row():
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with gr.Column():
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text = gr.TextArea(label="Text", placeholder="Input Text Here",
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value="吃葡萄不吐葡萄皮，不吃葡萄倒吐葡萄皮。")
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speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker')
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sdp_ratio = gr.Slider(minimum=0.1, maximum=2, value=0.2, step=0.1, label='SDP Ratio')
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noise_scale = gr.Slider(minimum=0.1, maximum=2, value=0.5, step=0.1, label='Noise Scale')
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noise_scale_w = gr.Slider(minimum=0.1, maximum=2, value=0.6, step=0.1, label='Noise Scale W')
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length_scale = gr.Slider(minimum=0.1, maximum=2, value=1.2, step=0.1, label='Length Scale')
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btn = gr.Button("Generate!", variant="primary")
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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(label="Output Audio")
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btn.click(tts_fn,
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inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
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outputs=[text_output, audio_output])
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webbrowser.open("http://127.0.0.1:7860")
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app.launch(share=args.share)
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