Delete webui.py

webui.py

@@ -1,485 +0,0 @@
-# flake8: noqa: E402
-import os
-import logging
-import re_matching
-from tools.sentence import split_by_language
-
-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 utils
-from infer import infer, latest_version, get_net_g, infer_multilang
-import gradio as gr
-import webbrowser
-import numpy as np
-from config import config
-from tools.translate import translate
-import librosa
-
-net_g = None
-
-device = config.webui_config.device
-if device == "mps":
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-
-
-def generate_audio(
-    slices,
-    sdp_ratio,
-    noise_scale,
-    noise_scale_w,
-    length_scale,
-    speaker,
-    language,
-    reference_audio,
-    emotion,
-    skip_start=False,
-    skip_end=False,
-):
-    audio_list = []
-    # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
-    with torch.no_grad():
-        for idx, piece in enumerate(slices):
-            skip_start = (idx != 0) and skip_start
-            skip_end = (idx != len(slices) - 1) and skip_end
-            audio = infer(
-                piece,
-                reference_audio=reference_audio,
-                emotion=emotion,
-                sdp_ratio=sdp_ratio,
-                noise_scale=noise_scale,
-                noise_scale_w=noise_scale_w,
-                length_scale=length_scale,
-                sid=speaker,
-                language=language,
-                hps=hps,
-                net_g=net_g,
-                device=device,
-                skip_start=skip_start,
-                skip_end=skip_end,
-            )
-            audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
-            audio_list.append(audio16bit)
-            # audio_list.append(silence)  # 将静音添加到列表中
-    return audio_list
-
-
-def generate_audio_multilang(
-    slices,
-    sdp_ratio,
-    noise_scale,
-    noise_scale_w,
-    length_scale,
-    speaker,
-    language,
-    reference_audio,
-    emotion,
-    skip_start=False,
-    skip_end=False,
-):
-    audio_list = []
-    # silence = np.zeros(hps.data.sampling_rate // 2, dtype=np.int16)
-    with torch.no_grad():
-        for idx, piece in enumerate(slices):
-            skip_start = (idx != 0) and skip_start
-            skip_end = (idx != len(slices) - 1) and skip_end
-            audio = infer_multilang(
-                piece,
-                reference_audio=reference_audio,
-                emotion=emotion,
-                sdp_ratio=sdp_ratio,
-                noise_scale=noise_scale,
-                noise_scale_w=noise_scale_w,
-                length_scale=length_scale,
-                sid=speaker,
-                language=language[idx],
-                hps=hps,
-                net_g=net_g,
-                device=device,
-                skip_start=skip_start,
-                skip_end=skip_end,
-            )
-            audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
-            audio_list.append(audio16bit)
-            # audio_list.append(silence)  # 将静音添加到列表中
-    return audio_list
-
-
-def tts_split(
-    text: str,
-    speaker,
-    sdp_ratio,
-    noise_scale,
-    noise_scale_w,
-    length_scale,
-    language,
-    cut_by_sent,
-    interval_between_para,
-    interval_between_sent,
-    reference_audio,
-    emotion,
-):
-    if language == "mix":
-        return ("invalid", None)
-    while text.find("\n\n") != -1:
-        text = text.replace("\n\n", "\n")
-    para_list = re_matching.cut_para(text)
-    audio_list = []
-    if not cut_by_sent:
-        for idx, p in enumerate(para_list):
-            skip_start = idx != 0
-            skip_end = idx != len(para_list) - 1
-            audio = infer(
-                p,
-                reference_audio=reference_audio,
-                emotion=emotion,
-                sdp_ratio=sdp_ratio,
-                noise_scale=noise_scale,
-                noise_scale_w=noise_scale_w,
-                length_scale=length_scale,
-                sid=speaker,
-                language=language,
-                hps=hps,
-                net_g=net_g,
-                device=device,
-                skip_start=skip_start,
-                skip_end=skip_end,
-            )
-            audio16bit = gr.processing_utils.convert_to_16_bit_wav(audio)
-            audio_list.append(audio16bit)
-            silence = np.zeros((int)(44100 * interval_between_para), dtype=np.int16)
-            audio_list.append(silence)
-    else:
-        for idx, p in enumerate(para_list):
-            skip_start = idx != 0
-            skip_end = idx != len(para_list) - 1
-            audio_list_sent = []
-            sent_list = re_matching.cut_sent(p)
-            for idx, s in enumerate(sent_list):
-                skip_start = (idx != 0) and skip_start
-                skip_end = (idx != len(sent_list) - 1) and skip_end
-                audio = infer(
-                    s,
-                    reference_audio=reference_audio,
-                    emotion=emotion,
-                    sdp_ratio=sdp_ratio,
-                    noise_scale=noise_scale,
-                    noise_scale_w=noise_scale_w,
-                    length_scale=length_scale,
-                    sid=speaker,
-                    language=language,
-                    hps=hps,
-                    net_g=net_g,
-                    device=device,
-                    skip_start=skip_start,
-                    skip_end=skip_end,
-                )
-                audio_list_sent.append(audio)
-                silence = np.zeros((int)(44100 * interval_between_sent))
-                audio_list_sent.append(silence)
-            if (interval_between_para - interval_between_sent) > 0:
-                silence = np.zeros(
-                    (int)(44100 * (interval_between_para - interval_between_sent))
-                )
-                audio_list_sent.append(silence)
-            audio16bit = gr.processing_utils.convert_to_16_bit_wav(
-                np.concatenate(audio_list_sent)
-            )  # 对完整句子做音量归一
-            audio_list.append(audio16bit)
-    audio_concat = np.concatenate(audio_list)
-    return ("Success", (44100, audio_concat))
-
-
-def tts_fn(
-    text: str,
-    speaker,
-    sdp_ratio,
-    noise_scale,
-    noise_scale_w,
-    length_scale,
-    language,
-    reference_audio,
-    emotion,
-):
-    audio_list = []
-    if language == "mix":
-        bool_valid, str_valid = re_matching.validate_text(text)
-        if not bool_valid:
-            return str_valid, (
-                hps.data.sampling_rate,
-                np.concatenate([np.zeros(hps.data.sampling_rate // 2)]),
-            )
-        result = []
-        for slice in re_matching.text_matching(text):
-            _speaker = slice.pop()
-            temp_contant = []
-            temp_lang = []
-            for lang, content in slice:
-                if "|" in content:
-                    temp = []
-                    temp_ = []
-                    for i in content.split("|"):
-                        if i != "":
-                            temp.append([i])
-                            temp_.append([lang])
-                        else:
-                            temp.append([])
-                            temp_.append([])
-                    temp_contant += temp
-                    temp_lang += temp_
-                else:
-                    if len(temp_contant) == 0:
-                        temp_contant.append([])
-                        temp_lang.append([])
-                    temp_contant[-1].append(content)
-                    temp_lang[-1].append(lang)
-            for i, j in zip(temp_lang, temp_contant):
-                result.append([*zip(i, j), _speaker])
-        for i, one in enumerate(result):
-            skip_start = i != 0
-            skip_end = i != len(result) - 1
-            _speaker = one.pop()
-            idx = 0
-            while idx < len(one):
-                text_to_generate = []
-                lang_to_generate = []
-                while True:
-                    lang, content = one[idx]
-                    temp_text = [content]
-                    if len(text_to_generate) > 0:
-                        text_to_generate[-1] += [temp_text.pop(0)]
-                        lang_to_generate[-1] += [lang]
-                    if len(temp_text) > 0:
-                        text_to_generate += [[i] for i in temp_text]
-                        lang_to_generate += [[lang]] * len(temp_text)
-                    if idx + 1 < len(one):
-                        idx += 1
-                    else:
-                        break
-                skip_start = (idx != 0) and skip_start
-                skip_end = (idx != len(one) - 1) and skip_end
-                print(text_to_generate, lang_to_generate)
-                audio_list.extend(
-                    generate_audio_multilang(
-                        text_to_generate,
-                        sdp_ratio,
-                        noise_scale,
-                        noise_scale_w,
-                        length_scale,
-                        _speaker,
-                        lang_to_generate,
-                        reference_audio,
-                        emotion,
-                        skip_start,
-                        skip_end,
-                    )
-                )
-                idx += 1
-    elif language.lower() == "auto":
-        for idx, slice in enumerate(text.split("|")):
-            if slice == "":
-                continue
-            skip_start = idx != 0
-            skip_end = idx != len(text.split("|")) - 1
-            sentences_list = split_by_language(
-                slice, target_languages=["zh", "ja", "en"]
-            )
-            idx = 0
-            while idx < len(sentences_list):
-                text_to_generate = []
-                lang_to_generate = []
-                while True:
-                    content, lang = sentences_list[idx]
-                    temp_text = [content]
-                    lang = lang.upper()
-                    if lang == "JA":
-                        lang = "JP"
-                    if len(text_to_generate) > 0:
-                        text_to_generate[-1] += [temp_text.pop(0)]
-                        lang_to_generate[-1] += [lang]
-                    if len(temp_text) > 0:
-                        text_to_generate += [[i] for i in temp_text]
-                        lang_to_generate += [[lang]] * len(temp_text)
-                    if idx + 1 < len(sentences_list):
-                        idx += 1
-                    else:
-                        break
-                skip_start = (idx != 0) and skip_start
-                skip_end = (idx != len(sentences_list) - 1) and skip_end
-                print(text_to_generate, lang_to_generate)
-                audio_list.extend(
-                    generate_audio_multilang(
-                        text_to_generate,
-                        sdp_ratio,
-                        noise_scale,
-                        noise_scale_w,
-                        length_scale,
-                        speaker,
-                        lang_to_generate,
-                        reference_audio,
-                        emotion,
-                        skip_start,
-                        skip_end,
-                    )
-                )
-                idx += 1
-    else:
-        audio_list.extend(
-            generate_audio(
-                text.split("|"),
-                sdp_ratio,
-                noise_scale,
-                noise_scale_w,
-                length_scale,
-                speaker,
-                language,
-                reference_audio,
-                emotion,
-            )
-        )
-
-    audio_concat = np.concatenate(audio_list)
-    return "Success", (hps.data.sampling_rate, audio_concat)
-
-
-if __name__ == "__main__":
-    if config.webui_config.debug:
-        logger.info("Enable DEBUG-LEVEL log")
-        logging.basicConfig(level=logging.DEBUG)
-    hps = utils.get_hparams_from_file(config.webui_config.config_path)
-    # 若config.json中未指定版本则默认为最新版本
-    version = hps.version if hasattr(hps, "version") else latest_version
-    net_g = get_net_g(
-        model_path=config.webui_config.model, version=version, device=device, hps=hps
-    )
-    speaker_ids = hps.data.spk2id
-    speakers = list(speaker_ids.keys())
-    languages = ["ZH", "JP", "EN", "mix", "auto"]
-    with gr.Blocks() as app:
-        with gr.Row():
-            with gr.Column():
-                text = gr.TextArea(
-                    label="输入文本内容",
-                    placeholder="""
-                    如果你选择语言为\'mix\'，必须按照格式输入，否则报错:
-                        格式举例(zh是中文，jp是日语，不区分大小写；说话人举例:gongzi):
-                         [说话人1]<zh>你好，こんにちは！ <jp>こんにちは，世界。
-                         [说话人2]<zh>你好吗？<jp>元気ですか？
-                         [说话人3]<zh>谢谢。<jp>どういたしまして。
-                         ...
-                    另外，所有的语言选项都可以用'|'分割长段实现分句生成。
-                    """,
-                )
-                trans = gr.Button("中翻日", variant="primary")
-                slicer = gr.Button("快速切分", variant="primary")
-                speaker = gr.Dropdown(
-                    choices=speakers, value=speakers[0], label="Speaker"
-                )
-                emotion = gr.Slider(
-                    minimum=0, maximum=9, value=0, step=1, label="Emotion"
-                )
-                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"
-                )
-                noise_scale_w = gr.Slider(
-                    minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise_W"
-                )
-                length_scale = gr.Slider(
-                    minimum=0.1, maximum=2, value=1.0, step=0.1, label="Length"
-                )
-                language = gr.Dropdown(
-                    choices=languages, value=languages[0], label="Language"
-                )
-                btn = gr.Button("生成音频！", variant="primary")
-            with gr.Column():
-                with gr.Row():
-                    with gr.Column():
-                        interval_between_sent = gr.Slider(
-                            minimum=0,
-                            maximum=5,
-                            value=0.2,
-                            step=0.1,
-                            label="句间停顿(秒)，勾选按句切分才生效",
-                        )
-                        interval_between_para = gr.Slider(
-                            minimum=0,
-                            maximum=10,
-                            value=1,
-                            step=0.1,
-                            label="段间停顿(秒)，需要大于句间停顿才有效",
-                        )
-                        opt_cut_by_sent = gr.Checkbox(
-                            label="按句切分    在按段落切分的基础上再按句子切分文本"
-                        )
-                        slicer = gr.Button("切分生成", variant="primary")
-                text_output = gr.Textbox(label="状态信息")
-                audio_output = gr.Audio(label="输出音频")
-                # explain_image = gr.Image(
-                #     label="参数解释信息",
-                #     show_label=True,
-                #     show_share_button=False,
-                #     show_download_button=False,
-                #     value=os.path.abspath("./img/参数说明.png"),
-                # )
-                reference_text = gr.Markdown(value="## 情感参考音频（WAV 格式）：用于生成语音的情感参考。")
-                reference_audio = gr.Audio(label="情感参考音频（WAV 格式）", type="filepath")
-        btn.click(
-            tts_fn,
-            inputs=[
-                text,
-                speaker,
-                sdp_ratio,
-                noise_scale,
-                noise_scale_w,
-                length_scale,
-                language,
-                reference_audio,
-                emotion,
-            ],
-            outputs=[text_output, audio_output],
-        )
-
-        trans.click(
-            translate,
-            inputs=[text],
-            outputs=[text],
-        )
-        slicer.click(
-            tts_split,
-            inputs=[
-                text,
-                speaker,
-                sdp_ratio,
-                noise_scale,
-                noise_scale_w,
-                length_scale,
-                language,
-                opt_cut_by_sent,
-                interval_between_para,
-                interval_between_sent,
-                reference_audio,
-                emotion,
-            ],
-            outputs=[text_output, audio_output],
-        )
-
-        reference_audio.upload(
-            lambda x: librosa.load(x, 16000)[::-1],
-            inputs=[reference_audio],
-            outputs=[reference_audio],
-        )
-    print("推理页面已开启!")
-    webbrowser.open(f"http://127.0.0.1:{config.webui_config.port}")
-    app.launch(share=config.webui_config.share, server_port=config.webui_config.port)