Upload webui.py

webui.py

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+# Fix for asyncio event loop errors on Hugging Face Spaces
+import asyncio
+import sys
+
+# Set event loop policy for compatibility
+if sys.platform == 'linux':
+    try:
+        import uvloop
+        asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
+    except ImportError:
+        pass
+
+import spaces
+import json
+import os
+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"))
+
+# Simplified config for Hugging Face Spaces (no command-line args)
+class Args:
+    verbose = False
+    port = 7860
+    host = "0.0.0.0"
+    model_dir = "./checkpoints"
+    fp16 = False
+    deepspeed = False
+    cuda_kernel = False
+    gui_seg_tokens = 120
+
+cmd_args = 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)