webui.py

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
from omegaconf import OmegaConf
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
# Respect HF cache env vars; set defaults if missing
os.environ.setdefault("HF_HOME", os.path.join(current_dir, "checkpoints", "hf_cache"))
os.environ.setdefault("TRANSFORMERS_CACHE", os.path.join(current_dir, "checkpoints", "hf_cache"))
os.environ.setdefault("TORCH_HOME", os.path.join(current_dir, "checkpoints", "hf_cache"))

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
from indextts.text.phonemizer import phonemize
from indextts.text.lang_detect import detect_language
from indextts.text.indic_normalizer import normalize_indic_unicode
from indextts.text.hindi_phonemizer import hindi_phonemize
from indextts.utils.front import TextNormalizer, TextTokenizer

i18n = I18nAuto(language="Auto")
MODE = 'local'

# Load config for UI controls without instantiating heavy models
cfg_path = os.path.join(cmd_args.model_dir, "config.yaml")
if os.path.exists(cfg_path):
    cfg = OmegaConf.load(cfg_path)
else:
    # fallback minimal config
    cfg = OmegaConf.create({"gpt": {"max_mel_tokens": 1500}})

# Initialize light tokenizer for UI token counting
try:
    bpe_path = os.path.join(cmd_args.model_dir, cfg.dataset["bpe_model"]) if hasattr(cfg, "dataset") else os.path.join(cmd_args.model_dir, "bpe.model")
    normalizer_ui = TextNormalizer()
    normalizer_ui.load()
    ui_tokenizer = TextTokenizer(bpe_path, normalizer_ui)
except Exception:
    ui_tokenizer = None

# Lazy-loaded heavy TTS model
_GLOBAL_TTS = None
def get_tts():
    global _GLOBAL_TTS
    if _GLOBAL_TTS is None:
        _GLOBAL_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,
                                )
    return _GLOBAL_TTS
# 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")
    # lazy-load heavy model and set gradio progress
    tts = get_tts()
    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}")

    # Language detection and phonemization routing
    try:
        lang = detect_language(text)
    except Exception:
        lang = "en"

    if lang == "hi":
        # For Hindi, don't pre-normalize to Devanagari here; let the
        # TextNormalizer (attached to the tokenizer) normalize and
        # transliterate to Latin/ITRANS so the SentencePiece tokenizer
        # can produce known tokens.
        text_for_infer = text
    else:
        text_for_infer = text

    output = tts.infer(spk_audio_prompt=prompt, text=text_for_infer,
                       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():
                # show model version only if model loaded
                model_version_info = ''
                try:
                    model_version_info = get_tts().model_version or '1.0'
                except Exception:
                    model_version_info = 'unknown'
                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')}{model_version_info}")
                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=cfg.gpt.max_mel_tokens if hasattr(cfg, 'gpt') else 1500, 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(getattr(cfg.gpt, 'max_text_tokens', cmd_args.gui_seg_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=getattr(cfg.gpt, 'max_text_tokens', cmd_args.gui_seg_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:
            try:
                lang = detect_language(text)
            except Exception:
                lang = "en"

            if lang == "hi":
                try:
                    # Use detailed Hindi phoneme representation for UI preview
                    # with vowel length, aspiration, and nasalization preserved
                    from indextts.text.hindi_phonemizer import hindi_to_phoneme

                    token_input = hindi_to_phoneme(text)
                except Exception:
                    token_input = text
            else:
                token_input = text

            # use lightweight tokenizer for UI if available to avoid heavy model init
            tokenizer_use = ui_tokenizer if ui_tokenizer is not None else get_tts().tokenizer
            text_tokens_list = tokenizer_use.tokenize(token_input)

            segments = tokenizer_use.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)