Delete app.py

app.py

@@ -1,213 +0,0 @@
-# flake8: noqa: E402
-
-import sys, os
-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
-import numpy as np
-
-net_g = None
-
-if sys.platform == "darwin" and torch.backends.mps.is_available():
-    device = "mps"
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-else:
-    device = "cuda"
-
-
-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, device)
-    del word2ph
-    assert bert.shape[-1] == len(phone), phone
-
-    if language_str == "ZH":
-        bert = bert
-        ja_bert = torch.zeros(768, len(phone))
-    elif language_str == "JP":
-        ja_bert = bert
-        bert = torch.zeros(1024, len(phone))
-    else:
-        bert = torch.zeros(1024, len(phone))
-        ja_bert = torch.zeros(768, len(phone))
-
-    assert bert.shape[-1] == len(
-        phone
-    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-    return bert, ja_bert, phone, tone, language
-
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
-    global net_g
-    bert, ja_bert, phones, tones, lang_ids = get_text(text, language, 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)
-        ja_bert = ja_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,
-                ja_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
-        torch.cuda.empty_cache()
-        return audio
-
-
-def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language):
-    slices = text.split("|")
-    audio_list = []
-    with torch.no_grad():
-        for slice in slices:
-            audio = infer(slice, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker, language=language)
-            audio_list.append(audio)
-            silence = np.zeros(hps.data.sampling_rate)  # 生成1秒的静音
-            audio_list.append(silence)  # 将静音添加到列表中
-    audio_concat = np.concatenate(audio_list)
-    return "Success", (hps.data.sampling_rate, audio_concat)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-m", "--model", default="./model/G_10000.pth", help="path of your model"
-    )
-    parser.add_argument(
-        "-c",
-        "--config",
-        default="./model/1118config.json",
-        help="path of your config file",
-    )
-    parser.add_argument(
-        "--share", default=False, help="make link public", action="store_true"
-    )
-    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())
-    languages = ["ZH", "JP"]
-    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"
-                )
-                language = gr.Dropdown(
-                    choices=languages, value=languages[0], label="Language"
-                )
-                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,
-                language,
-            ],
-            outputs=[text_output, audio_output],
-        )
-
-    webbrowser.open("http://127.0.0.1:7860")
-    app.launch(share=args.share)