Delete webui.py

webui.py

@@ -1,130 +0,0 @@
-import sys, os
-
-if sys.platform == "darwin":
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-
-import logging
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s")
-
-logger = logging.getLogger(__name__)
-
-import torch
-import argparse
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-import gradio as gr
-import webbrowser
-
-
-net_g = None
-
-
-def get_text(text, language_str, hps):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    bert = get_bert(norm_text, word2ph, language_str)
-    del word2ph
-
-    assert bert.shape[-1] == len(phone)
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-
-    return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
-    global net_g
-    bert, phones, tones, lang_ids = get_text(text, "ZH", hps)
-    with torch.no_grad():
-        x_tst=phones.to(device).unsqueeze(0)
-        tones=tones.to(device).unsqueeze(0)
-        lang_ids=lang_ids.to(device).unsqueeze(0)
-        bert = bert.to(device).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
-        del phones
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
-        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, sdp_ratio=sdp_ratio
-                           , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
-        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
-        return audio
-
-def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
-    with torch.no_grad():
-        audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker)
-    return "Success", (hps.data.sampling_rate, audio)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-m", "--model", default="./logs/as/G_8000.pth", help="path of your model")
-    parser.add_argument("-c", "--config", default="./configs/config.json", help="path of your config file")
-    parser.add_argument("--share", default=False, help="make link public")
-    parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log")
-
-    args = parser.parse_args()
-    if args.debug:
-        logger.info("Enable DEBUG-LEVEL log")
-        logging.basicConfig(level=logging.DEBUG)
-    hps = utils.get_hparams_from_file(args.config)
-
-    device = (
-        "cuda:0"
-        if torch.cuda.is_available()
-        else (
-            "mps"
-            if sys.platform == "darwin" and torch.backends.mps.is_available()
-            else "cpu"
-        )
-    )
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model).to(device)
-    _ = net_g.eval()
-
-    _ = utils.load_checkpoint(args.model, net_g, None, skip_optimizer=True)
-
-    speaker_ids = hps.data.spk2id
-    speakers = list(speaker_ids.keys())
-    with gr.Blocks() as app:
-        with gr.Row():
-            with gr.Column():
-                text = gr.TextArea(label="Text", placeholder="Input Text Here",
-                                      value="吃葡萄不吐葡萄皮，不吃葡萄倒吐葡萄皮。")
-                speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker')
-                sdp_ratio = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.1, label='SDP Ratio')
-                noise_scale = gr.Slider(minimum=0.1, maximum=2, value=0.6, step=0.1, label='Noise Scale')
-                noise_scale_w = gr.Slider(minimum=0.1, maximum=2, value=0.8, step=0.1, label='Noise Scale W')
-                length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.1, label='Length Scale')
-                btn = gr.Button("Generate!", variant="primary")
-            with gr.Column():
-                text_output = gr.Textbox(label="Message")
-                audio_output = gr.Audio(label="Output Audio")
-
-        btn.click(tts_fn,
-                inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
-                outputs=[text_output, audio_output])
-    
-    webbrowser.open("http://127.0.0.1:7860")
-    app.launch(share=args.share)