Delete webui.py

webui.py

@@ -1,392 +0,0 @@
-import spaces
-import json
-import os
-import sys
-import threading
-import time
-
-import warnings
-
-import numpy as np
-
-warnings.filterwarnings("ignore", category=FutureWarning)
-warnings.filterwarnings("ignore", category=UserWarning)
-
-import pandas as pd
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(current_dir)
-sys.path.append(os.path.join(current_dir, "indextts"))
-
-import argparse
-parser = argparse.ArgumentParser(
-    description="IndexTTS WebUI",
-    formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-)
-parser.add_argument("--verbose", action="store_true", default=False, help="Enable verbose mode")
-parser.add_argument("--port", type=int, default=7860, help="Port to run the web UI on")
-parser.add_argument("--host", type=str, default="0.0.0.0", help="Host to run the web UI on")
-parser.add_argument("--model_dir", type=str, default="./checkpoints", help="Model checkpoints directory")
-parser.add_argument("--fp16", action="store_true", default=False, help="Use FP16 for inference if available")
-parser.add_argument("--deepspeed", action="store_true", default=False, help="Use DeepSpeed to accelerate if available")
-parser.add_argument("--cuda_kernel", action="store_true", default=False, help="Use CUDA kernel for inference if available")
-parser.add_argument("--gui_seg_tokens", type=int, default=120, help="GUI: Max tokens per generation segment")
-cmd_args = parser.parse_args()
-
-from tools.download_files import download_model_from_huggingface
-download_model_from_huggingface(os.path.join(current_dir,"checkpoints"),
-                                os.path.join(current_dir, "checkpoints","hf_cache"))
-
-import gradio as gr
-from indextts.infer_v2 import IndexTTS2
-from tools.i18n.i18n import I18nAuto
-
-i18n = I18nAuto(language="Auto")
-MODE = 'local'
-tts = IndexTTS2(model_dir=cmd_args.model_dir,
-                cfg_path=os.path.join(cmd_args.model_dir, "config.yaml"),
-                use_fp16=cmd_args.fp16,
-                use_deepspeed=cmd_args.deepspeed,
-                use_cuda_kernel=cmd_args.cuda_kernel,
-                )
-# 支持的语言列表
-LANGUAGES = {
-    "中文": "zh_CN",
-    "English": "en_US"
-}
-EMO_CHOICES = [i18n("与音色参考音频相同"),
-                i18n("使用情感参考音频"),
-                i18n("使用情感向量控制"),
-                i18n("使用情感描述文本控制")]
-EMO_CHOICES_BASE = EMO_CHOICES[:3]  # 基础选项
-EMO_CHOICES_EXPERIMENTAL = EMO_CHOICES  # 全部选项（包括文本描述）
-
-os.makedirs("outputs/tasks",exist_ok=True)
-os.makedirs("prompts",exist_ok=True)
-
-MAX_LENGTH_TO_USE_SPEED = 70
-with open("examples/cases.jsonl", "r", encoding="utf-8") as f:
-    example_cases = []
-    for line in f:
-        line = line.strip()
-        if not line:
-            continue
-        example = json.loads(line)
-        if example.get("emo_audio",None):
-            emo_audio_path = os.path.join("examples",example["emo_audio"])
-        else:
-            emo_audio_path = None
-        example_cases.append([os.path.join("examples", example.get("prompt_audio", "sample_prompt.wav")),
-                              EMO_CHOICES[example.get("emo_mode",0)],
-                              example.get("text"),
-                             emo_audio_path,
-                             example.get("emo_weight",1.0),
-                             example.get("emo_text",""),
-                             example.get("emo_vec_1",0),
-                             example.get("emo_vec_2",0),
-                             example.get("emo_vec_3",0),
-                             example.get("emo_vec_4",0),
-                             example.get("emo_vec_5",0),
-                             example.get("emo_vec_6",0),
-                             example.get("emo_vec_7",0),
-                             example.get("emo_vec_8",0),
-                             example.get("emo_text") is not None]
-                             )
-
-def normalize_emo_vec(emo_vec):
-    # emotion factors for better user experience
-    k_vec = [0.75,0.70,0.80,0.80,0.75,0.75,0.55,0.45]
-    tmp = np.array(k_vec) * np.array(emo_vec)
-    if np.sum(tmp) > 0.8:
-        tmp = tmp * 0.8/ np.sum(tmp)
-    return tmp.tolist()
-
-@spaces.GPU
-def gen_single(emo_control_method,prompt, text,
-               emo_ref_path, emo_weight,
-               vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8,
-               emo_text,emo_random,
-               max_text_tokens_per_segment=120,
-                *args, progress=gr.Progress()):
-    output_path = None
-    if not output_path:
-        output_path = os.path.join("outputs", f"spk_{int(time.time())}.wav")
-    # set gradio progress
-    tts.gr_progress = progress
-    do_sample, top_p, top_k, temperature, \
-        length_penalty, num_beams, repetition_penalty, max_mel_tokens = args
-    kwargs = {
-        "do_sample": bool(do_sample),
-        "top_p": float(top_p),
-        "top_k": int(top_k) if int(top_k) > 0 else None,
-        "temperature": float(temperature),
-        "length_penalty": float(length_penalty),
-        "num_beams": num_beams,
-        "repetition_penalty": float(repetition_penalty),
-        "max_mel_tokens": int(max_mel_tokens),
-        # "typical_sampling": bool(typical_sampling),
-        # "typical_mass": float(typical_mass),
-    }
-    if type(emo_control_method) is not int:
-        emo_control_method = emo_control_method.value
-    if emo_control_method == 0:  # emotion from speaker
-        emo_ref_path = None  # remove external reference audio
-    if emo_control_method == 1:  # emotion from reference audio
-        # normalize emo_alpha for better user experience
-        emo_weight = emo_weight * 0.8
-        pass
-    if emo_control_method == 2:  # emotion from custom vectors
-        vec = [vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8]
-        vec = normalize_emo_vec(vec)
-    else:
-        # don't use the emotion vector inputs for the other modes
-        vec = None
-
-    if emo_text == "":
-        # erase empty emotion descriptions; `infer()` will then automatically use the main prompt
-        emo_text = None
-
-    print(f"Emo control mode:{emo_control_method},weight:{emo_weight},vec:{vec}")
-    output = tts.infer(spk_audio_prompt=prompt, text=text,
-                       output_path=output_path,
-                       emo_audio_prompt=emo_ref_path, emo_alpha=emo_weight,
-                       emo_vector=vec,
-                       use_emo_text=(emo_control_method==3), emo_text=emo_text,use_random=emo_random,
-                       verbose=cmd_args.verbose,
-                       max_text_tokens_per_segment=int(max_text_tokens_per_segment),
-                       **kwargs)
-    return gr.update(value=output,visible=True)
-
-def update_prompt_audio():
-    update_button = gr.update(interactive=True)
-    return update_button
-
-with gr.Blocks(title="IndexTTS Demo") as demo:
-    mutex = threading.Lock()
-    gr.HTML('''
-    <h2><center>IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech</h2>
-<p align="center">
-<a href='https://arxiv.org/abs/2506.21619'><img src='https://img.shields.io/badge/ArXiv-2506.21619-red'></a>
-</p>
-    ''')
-
-    with gr.Tab(i18n("音频生成")):
-        with gr.Row():
-            os.makedirs("prompts",exist_ok=True)
-            prompt_audio = gr.Audio(label=i18n("音色参考音频"),key="prompt_audio",
-                                    sources=["upload","microphone"],type="filepath")
-            prompt_list = os.listdir("prompts")
-            default = ''
-            if prompt_list:
-                default = prompt_list[0]
-            with gr.Column():
-                input_text_single = gr.TextArea(label=i18n("文本"),key="input_text_single", placeholder=i18n("请输入目标文本"), info=f"{i18n('当前模型版本')}{tts.model_version or '1.0'}")
-                gen_button = gr.Button(i18n("生成语音"), key="gen_button",interactive=True)
-            output_audio = gr.Audio(label=i18n("生成结果"), visible=True,key="output_audio")
-        experimental_checkbox = gr.Checkbox(label=i18n("显示实验功能"),value=False)
-        with gr.Accordion(i18n("功能设置")):
-            # 情感控制选项部分
-            with gr.Row():
-                emo_control_method = gr.Radio(
-                    choices=EMO_CHOICES_BASE,
-                    type="index",
-                    value=EMO_CHOICES_BASE[0],label=i18n("情感控制方式"))
-        # 情感参考音频部分
-        with gr.Group(visible=False) as emotion_reference_group:
-            with gr.Row():
-                emo_upload = gr.Audio(label=i18n("上传情感参考音频"), type="filepath")
-
-        # 情感随机采样
-        with gr.Row(visible=False) as emotion_randomize_group:
-            emo_random = gr.Checkbox(label=i18n("情感随机采样"), value=False)
-
-        # 情感向量控制部分
-        with gr.Group(visible=False) as emotion_vector_group:
-            with gr.Row():
-                with gr.Column():
-                    vec1 = gr.Slider(label=i18n("喜"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
-                    vec2 = gr.Slider(label=i18n("怒"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
-                    vec3 = gr.Slider(label=i18n("哀"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
-                    vec4 = gr.Slider(label=i18n("惧"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
-                with gr.Column():
-                    vec5 = gr.Slider(label=i18n("厌恶"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
-                    vec6 = gr.Slider(label=i18n("低落"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
-                    vec7 = gr.Slider(label=i18n("惊喜"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
-                    vec8 = gr.Slider(label=i18n("平静"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
-
-        with gr.Group(visible=False) as emo_text_group:
-            with gr.Row():
-                emo_text = gr.Textbox(label=i18n("情感描述文本"),
-                                      placeholder=i18n("请输入情绪描述（或留空以自动使用目标文本作为情绪描述）"),
-                                      value="",
-                                      info=i18n("例如：委屈巴巴、危险在悄悄逼近"))
-
-
-        with gr.Row(visible=False) as emo_weight_group:
-            emo_weight = gr.Slider(label=i18n("情感权重"), minimum=0.0, maximum=1.0, value=0.8, step=0.01)
-
-        with gr.Accordion(i18n("高级生成参数设置"), open=False,visible=False) as advanced_settings_group:
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.Markdown(f"**{i18n('GPT2 采样设置')}** _{i18n('参数会影响音频多样性和生成速度详见')} [Generation strategies](https://huggingface.co/docs/transformers/main/en/generation_strategies)._")
-                    with gr.Row():
-                        do_sample = gr.Checkbox(label="do_sample", value=True, info=i18n("是否进行采样"))
-                        temperature = gr.Slider(label="temperature", minimum=0.1, maximum=2.0, value=0.8, step=0.1)
-                    with gr.Row():
-                        top_p = gr.Slider(label="top_p", minimum=0.0, maximum=1.0, value=0.8, step=0.01)
-                        top_k = gr.Slider(label="top_k", minimum=0, maximum=100, value=30, step=1)
-                        num_beams = gr.Slider(label="num_beams", value=3, minimum=1, maximum=10, step=1)
-                    with gr.Row():
-                        repetition_penalty = gr.Number(label="repetition_penalty", precision=None, value=10.0, minimum=0.1, maximum=20.0, step=0.1)
-                        length_penalty = gr.Number(label="length_penalty", precision=None, value=0.0, minimum=-2.0, maximum=2.0, step=0.1)
-                    max_mel_tokens = gr.Slider(label="max_mel_tokens", value=1500, minimum=50, maximum=tts.cfg.gpt.max_mel_tokens, step=10, info=i18n("生成Token最大数量，过小导致音频被截断"), key="max_mel_tokens")
-                    # with gr.Row():
-                    #     typical_sampling = gr.Checkbox(label="typical_sampling", value=False, info="不建议使用")
-                    #     typical_mass = gr.Slider(label="typical_mass", value=0.9, minimum=0.0, maximum=1.0, step=0.1)
-                with gr.Column(scale=2):
-                    gr.Markdown(f'**{i18n("分句设置")}** _{i18n("参数会影响音频质量和生成速度")}_')
-                    with gr.Row():
-                        initial_value = max(20, min(tts.cfg.gpt.max_text_tokens, cmd_args.gui_seg_tokens))
-                        max_text_tokens_per_segment = gr.Slider(
-                            label=i18n("分句最大Token数"), value=initial_value, minimum=20, maximum=tts.cfg.gpt.max_text_tokens, step=2, key="max_text_tokens_per_segment",
-                            info=i18n("建议80~200之间，值越大，分句越长；值越小，分句越碎；过小过大都可能导致音频质量不高"),
-                        )
-                    with gr.Accordion(i18n("预览分句结果"), open=True) as segments_settings:
-                        segments_preview = gr.Dataframe(
-                            headers=[i18n("序号"), i18n("分句内容"), i18n("Token数")],
-                            key="segments_preview",
-                            wrap=True,
-                        )
-            advanced_params = [
-                do_sample, top_p, top_k, temperature,
-                length_penalty, num_beams, repetition_penalty, max_mel_tokens,
-                # typical_sampling, typical_mass,
-            ]
-        
-        if len(example_cases) > 2:
-            example_table = gr.Examples(
-                examples=example_cases[:-2],
-                examples_per_page=20,
-                inputs=[prompt_audio,
-                        emo_control_method,
-                        input_text_single,
-                        emo_upload,
-                        emo_weight,
-                        emo_text,
-                        vec1,vec2,vec3,vec4,vec5,vec6,vec7,vec8,experimental_checkbox]
-            )
-        elif len(example_cases) > 0:
-            example_table = gr.Examples(
-                examples=example_cases,
-                examples_per_page=20,
-                inputs=[prompt_audio,
-                        emo_control_method,
-                        input_text_single,
-                        emo_upload,
-                        emo_weight,
-                        emo_text,
-                        vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8, experimental_checkbox]
-            )
-
-    def on_input_text_change(text, max_text_tokens_per_segment):
-        if text and len(text) > 0:
-            text_tokens_list = tts.tokenizer.tokenize(text)
-
-            segments = tts.tokenizer.split_segments(text_tokens_list, max_text_tokens_per_segment=int(max_text_tokens_per_segment))
-            data = []
-            for i, s in enumerate(segments):
-                segment_str = ''.join(s)
-                tokens_count = len(s)
-                data.append([i, segment_str, tokens_count])
-            return {
-                segments_preview: gr.update(value=data, visible=True, type="array"),
-            }
-        else:
-            df = pd.DataFrame([], columns=[i18n("序号"), i18n("分句内容"), i18n("Token数")])
-            return {
-                segments_preview: gr.update(value=df),
-            }
-
-    def on_method_select(emo_control_method):
-        if emo_control_method == 1:  # emotion reference audio
-            return (gr.update(visible=True),
-                    gr.update(visible=False),
-                    gr.update(visible=False),
-                    gr.update(visible=False),
-                    gr.update(visible=True)
-                    )
-        elif emo_control_method == 2:  # emotion vectors
-            return (gr.update(visible=False),
-                    gr.update(visible=True),
-                    gr.update(visible=True),
-                    gr.update(visible=False),
-                    gr.update(visible=False)
-                    )
-        elif emo_control_method == 3:  # emotion text description
-            return (gr.update(visible=False),
-                    gr.update(visible=True),
-                    gr.update(visible=False),
-                    gr.update(visible=True),
-                    gr.update(visible=True)
-                    )
-        else:  # 0: same as speaker voice
-            return (gr.update(visible=False),
-                    gr.update(visible=False),
-                    gr.update(visible=False),
-                    gr.update(visible=False),
-                    gr.update(visible=False)
-                    )
-
-    def on_experimental_change(is_exp):
-        # 切换情感控制选项
-        # 第三个返回值实际没有起作用
-        if is_exp:
-            return gr.update(choices=EMO_CHOICES_EXPERIMENTAL, value=EMO_CHOICES_EXPERIMENTAL[0]), gr.update(visible=True),gr.update(value=example_cases)
-        else:
-            return gr.update(choices=EMO_CHOICES_BASE, value=EMO_CHOICES_BASE[0]), gr.update(visible=False),gr.update(value=example_cases[:-2])
-
-    emo_control_method.select(on_method_select,
-        inputs=[emo_control_method],
-        outputs=[emotion_reference_group,
-                 emotion_randomize_group,
-                 emotion_vector_group,
-                 emo_text_group,
-                 emo_weight_group]
-    )
-
-    input_text_single.change(
-        on_input_text_change,
-        inputs=[input_text_single, max_text_tokens_per_segment],
-        outputs=[segments_preview]
-    )
-
-    experimental_checkbox.change(
-        on_experimental_change,
-        inputs=[experimental_checkbox],
-        outputs=[emo_control_method, advanced_settings_group,example_table.dataset]  # 高级参数Accordion
-    )
-
-    max_text_tokens_per_segment.change(
-        on_input_text_change,
-        inputs=[input_text_single, max_text_tokens_per_segment],
-        outputs=[segments_preview]
-    )
-
-    prompt_audio.upload(update_prompt_audio,
-                         inputs=[],
-                         outputs=[gen_button])
-
-    gen_button.click(gen_single,
-                     inputs=[emo_control_method,prompt_audio, input_text_single, emo_upload, emo_weight,
-                            vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8,
-                             emo_text,emo_random,
-                             max_text_tokens_per_segment,
-                             *advanced_params,
-                     ],
-                     outputs=[output_audio])
-
-
-
-if __name__ == "__main__":
-    demo.queue(20)
-    demo.launch(server_name=cmd_args.host, server_port=cmd_args.port)