533 lines
24 KiB
Python
533 lines
24 KiB
Python
import html
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import json
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import os
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import sys
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import threading
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import time
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import warnings
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warnings.filterwarnings("ignore", category=FutureWarning)
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warnings.filterwarnings("ignore", category=UserWarning)
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import pandas as pd
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current_dir = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(current_dir)
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sys.path.append(os.path.join(current_dir, "indextts"))
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import argparse
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parser = argparse.ArgumentParser(
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description="IndexTTS WebUI",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add_argument("--verbose", action="store_true", default=False, help="Enable verbose mode")
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parser.add_argument("--port", type=int, default=7860, help="Port to run the web UI on")
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parser.add_argument("--host", type=str, default="0.0.0.0", help="Host to run the web UI on")
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parser.add_argument("--model_dir", type=str, default="./checkpoints", help="Model checkpoints directory")
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parser.add_argument("--fp16", action="store_true", default=False, help="Use FP16 for inference if available")
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parser.add_argument("--deepspeed", action="store_true", default=False, help="Use DeepSpeed to accelerate if available")
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parser.add_argument("--cuda_kernel", action="store_true", default=False, help="Use CUDA kernel for inference if available")
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parser.add_argument("--gui_seg_tokens", type=int, default=120, help="GUI: Max tokens per generation segment")
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cmd_args = parser.parse_args()
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if not os.path.exists(cmd_args.model_dir):
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print(f"Model directory {cmd_args.model_dir} does not exist. Please download the model first.")
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sys.exit(1)
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for file in [
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"bpe.model",
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"gpt.pth",
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"config.yaml",
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"s2mel.pth",
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"wav2vec2bert_stats.pt"
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]:
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file_path = os.path.join(cmd_args.model_dir, file)
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if not os.path.exists(file_path):
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print(f"Required file {file_path} does not exist. Please download it.")
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sys.exit(1)
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import gradio as gr
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from indextts.infer_v2 import IndexTTS2
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from tools.i18n.i18n import I18nAuto
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i18n = I18nAuto(language="Auto")
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MODE = 'local'
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tts = IndexTTS2(model_dir=cmd_args.model_dir,
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cfg_path=os.path.join(cmd_args.model_dir, "config.yaml"),
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use_fp16=cmd_args.fp16,
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use_deepspeed=cmd_args.deepspeed,
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use_cuda_kernel=cmd_args.cuda_kernel,
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)
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# 支持的语言列表
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LANGUAGES = {
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"中文": "zh_CN",
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"English": "en_US"
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}
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EMO_CHOICES_ALL = [i18n("与音色参考音频相同"),
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i18n("使用情感参考音频"),
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i18n("使用情感向量控制"),
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i18n("使用情感描述文本控制")]
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EMO_CHOICES_OFFICIAL = EMO_CHOICES_ALL[:-1] # skip experimental features
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os.makedirs("outputs/tasks",exist_ok=True)
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os.makedirs("prompts",exist_ok=True)
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MAX_LENGTH_TO_USE_SPEED = 70
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example_cases = []
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with open("examples/cases.jsonl", "r", encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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example = json.loads(line)
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if example.get("emo_audio",None):
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emo_audio_path = os.path.join("examples",example["emo_audio"])
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else:
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emo_audio_path = None
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example_cases.append([os.path.join("examples", example.get("prompt_audio", "sample_prompt.wav")),
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EMO_CHOICES_ALL[example.get("emo_mode",0)],
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example.get("text"),
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emo_audio_path,
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example.get("emo_weight",1.0),
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example.get("emo_text",""),
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example.get("emo_vec_1",0),
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example.get("emo_vec_2",0),
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example.get("emo_vec_3",0),
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example.get("emo_vec_4",0),
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example.get("emo_vec_5",0),
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example.get("emo_vec_6",0),
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example.get("emo_vec_7",0),
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example.get("emo_vec_8",0),
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])
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def get_example_cases(include_experimental = False):
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if include_experimental:
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return example_cases # show every example
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# exclude emotion control mode 3 (emotion from text description)
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return [x for x in example_cases if x[1] != EMO_CHOICES_ALL[3]]
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def format_glossary_markdown():
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"""将词汇表转换为Markdown表格格式"""
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if not tts.normalizer.term_glossary:
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return i18n("暂无术语")
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lines = [f"| {i18n('术语')} | {i18n('中文读法')} | {i18n('英文读法')} |"]
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lines.append("|---|---|---|")
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for term, reading in tts.normalizer.term_glossary.items():
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zh = reading.get("zh", "") if isinstance(reading, dict) else reading
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en = reading.get("en", "") if isinstance(reading, dict) else reading
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lines.append(f"| {term} | {zh} | {en} |")
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return "\n".join(lines)
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def gen_single(emo_control_method,prompt, text,
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emo_ref_path, emo_weight,
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vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8,
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emo_text,emo_random,
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max_text_tokens_per_segment=120,
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*args, progress=gr.Progress()):
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output_path = None
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if not output_path:
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output_path = os.path.join("outputs", f"spk_{int(time.time())}.wav")
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# set gradio progress
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tts.gr_progress = progress
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do_sample, top_p, top_k, temperature, \
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length_penalty, num_beams, repetition_penalty, max_mel_tokens = args
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kwargs = {
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"do_sample": bool(do_sample),
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"top_p": float(top_p),
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"top_k": int(top_k) if int(top_k) > 0 else None,
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"temperature": float(temperature),
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"length_penalty": float(length_penalty),
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"num_beams": num_beams,
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"repetition_penalty": float(repetition_penalty),
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"max_mel_tokens": int(max_mel_tokens),
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# "typical_sampling": bool(typical_sampling),
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# "typical_mass": float(typical_mass),
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}
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if type(emo_control_method) is not int:
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emo_control_method = emo_control_method.value
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if emo_control_method == 0: # emotion from speaker
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emo_ref_path = None # remove external reference audio
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if emo_control_method == 1: # emotion from reference audio
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pass
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if emo_control_method == 2: # emotion from custom vectors
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vec = [vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8]
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vec = tts.normalize_emo_vec(vec, apply_bias=True)
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else:
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# don't use the emotion vector inputs for the other modes
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vec = None
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if emo_text == "":
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# erase empty emotion descriptions; `infer()` will then automatically use the main prompt
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emo_text = None
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print(f"Emo control mode:{emo_control_method},weight:{emo_weight},vec:{vec}")
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output = tts.infer(spk_audio_prompt=prompt, text=text,
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output_path=output_path,
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emo_audio_prompt=emo_ref_path, emo_alpha=emo_weight,
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emo_vector=vec,
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use_emo_text=(emo_control_method==3), emo_text=emo_text,use_random=emo_random,
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verbose=cmd_args.verbose,
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max_text_tokens_per_segment=int(max_text_tokens_per_segment),
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**kwargs)
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return gr.update(value=output,visible=True)
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def update_prompt_audio():
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update_button = gr.update(interactive=True)
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return update_button
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def create_warning_message(warning_text):
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return gr.HTML(f"<div style=\"padding: 0.5em 0.8em; border-radius: 0.5em; background: #ffa87d; color: #000; font-weight: bold\">{html.escape(warning_text)}</div>")
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def create_experimental_warning_message():
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return create_warning_message(i18n('提示：此功能为实验版，结果尚不稳定，我们正在持续优化中。'))
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with gr.Blocks(title="IndexTTS Demo") as demo:
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mutex = threading.Lock()
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gr.HTML('''
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<h2><center>IndexTTS2: A Breakthrough in Emotionally Expressive and Duration-Controlled Auto-Regressive Zero-Shot Text-to-Speech</h2>
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<p align="center">
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<a href='https://arxiv.org/abs/2506.21619'><img src='https://img.shields.io/badge/ArXiv-2506.21619-red'></a>
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</p>
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''')
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with gr.Tab(i18n("音频生成")):
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with gr.Row():
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os.makedirs("prompts",exist_ok=True)
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prompt_audio = gr.Audio(label=i18n("音色参考音频"),key="prompt_audio",
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sources=["upload","microphone"],type="filepath")
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prompt_list = os.listdir("prompts")
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default = ''
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if prompt_list:
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default = prompt_list[0]
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with gr.Column():
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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'}")
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gen_button = gr.Button(i18n("生成语音"), key="gen_button",interactive=True)
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output_audio = gr.Audio(label=i18n("生成结果"), visible=True,key="output_audio")
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with gr.Row():
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experimental_checkbox = gr.Checkbox(label=i18n("显示实验功能"), value=False)
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glossary_checkbox = gr.Checkbox(label=i18n("开启术语词汇读音"), value=tts.normalizer.enable_glossary)
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with gr.Accordion(i18n("功能设置")):
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# 情感控制选项部分
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with gr.Row():
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emo_control_method = gr.Radio(
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choices=EMO_CHOICES_OFFICIAL,
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type="index",
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value=EMO_CHOICES_OFFICIAL[0],label=i18n("情感控制方式"))
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# we MUST have an extra, INVISIBLE list of *all* emotion control
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# methods so that gr.Dataset() can fetch ALL control mode labels!
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# otherwise, the gr.Dataset()'s experimental labels would be empty!
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emo_control_method_all = gr.Radio(
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choices=EMO_CHOICES_ALL,
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type="index",
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value=EMO_CHOICES_ALL[0], label=i18n("情感控制方式"),
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visible=False) # do not render
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# 情感参考音频部分
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with gr.Group(visible=False) as emotion_reference_group:
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with gr.Row():
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emo_upload = gr.Audio(label=i18n("上传情感参考音频"), type="filepath")
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# 情感随机采样
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with gr.Row(visible=False) as emotion_randomize_group:
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emo_random = gr.Checkbox(label=i18n("情感随机采样"), value=False)
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# 情感向量控制部分
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with gr.Group(visible=False) as emotion_vector_group:
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with gr.Row():
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with gr.Column():
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vec1 = gr.Slider(label=i18n("喜"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
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vec2 = gr.Slider(label=i18n("怒"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
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vec3 = gr.Slider(label=i18n("哀"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
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vec4 = gr.Slider(label=i18n("惧"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
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with gr.Column():
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vec5 = gr.Slider(label=i18n("厌恶"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
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vec6 = gr.Slider(label=i18n("低落"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
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vec7 = gr.Slider(label=i18n("惊喜"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
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vec8 = gr.Slider(label=i18n("平静"), minimum=0.0, maximum=1.0, value=0.0, step=0.05)
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with gr.Group(visible=False) as emo_text_group:
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create_experimental_warning_message()
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with gr.Row():
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emo_text = gr.Textbox(label=i18n("情感描述文本"),
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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）"),
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value="",
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info=i18n("例如：委屈巴巴、危险在悄悄逼近"))
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with gr.Row(visible=False) as emo_weight_group:
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emo_weight = gr.Slider(label=i18n("情感权重"), minimum=0.0, maximum=1.0, value=0.65, step=0.01)
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# 术语词汇表管理
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with gr.Accordion(i18n("自定义术语词汇读音"), open=False, visible=tts.normalizer.enable_glossary) as glossary_accordion:
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gr.Markdown(i18n("自定义个别专业术语的读音"))
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with gr.Row():
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with gr.Column(scale=1):
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glossary_term = gr.Textbox(
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label=i18n("术语"),
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placeholder="IndexTTS2",
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)
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glossary_reading_zh = gr.Textbox(
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label=i18n("中文读法"),
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placeholder="Index T-T-S 二",
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)
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glossary_reading_en = gr.Textbox(
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label=i18n("英文读法"),
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placeholder="Index T-T-S two",
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)
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btn_add_term = gr.Button(i18n("添加术语"), scale=1)
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with gr.Column(scale=2):
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glossary_table = gr.Markdown(
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value=format_glossary_markdown()
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)
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with gr.Accordion(i18n("高级生成参数设置"), open=False, visible=True) as advanced_settings_group:
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with gr.Row():
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with gr.Column(scale=1):
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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)._")
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with gr.Row():
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do_sample = gr.Checkbox(label="do_sample", value=True, info=i18n("是否进行采样"))
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temperature = gr.Slider(label="temperature", minimum=0.1, maximum=2.0, value=0.8, step=0.1)
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with gr.Row():
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top_p = gr.Slider(label="top_p", minimum=0.0, maximum=1.0, value=0.8, step=0.01)
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top_k = gr.Slider(label="top_k", minimum=0, maximum=100, value=30, step=1)
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num_beams = gr.Slider(label="num_beams", value=3, minimum=1, maximum=10, step=1)
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with gr.Row():
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repetition_penalty = gr.Number(label="repetition_penalty", precision=None, value=10.0, minimum=0.1, maximum=20.0, step=0.1)
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length_penalty = gr.Number(label="length_penalty", precision=None, value=0.0, minimum=-2.0, maximum=2.0, step=0.1)
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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")
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# with gr.Row():
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# typical_sampling = gr.Checkbox(label="typical_sampling", value=False, info="不建议使用")
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# typical_mass = gr.Slider(label="typical_mass", value=0.9, minimum=0.0, maximum=1.0, step=0.1)
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with gr.Column(scale=2):
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gr.Markdown(f'**{i18n("分句设置")}** _{i18n("参数会影响音频质量和生成速度")}_')
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with gr.Row():
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initial_value = max(20, min(tts.cfg.gpt.max_text_tokens, cmd_args.gui_seg_tokens))
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max_text_tokens_per_segment = gr.Slider(
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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",
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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"),
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)
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with gr.Accordion(i18n("预览分句结果"), open=True) as segments_settings:
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segments_preview = gr.Dataframe(
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headers=[i18n("序号"), i18n("分句内容"), i18n("Token数")],
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key="segments_preview",
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wrap=True,
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)
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advanced_params = [
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do_sample, top_p, top_k, temperature,
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length_penalty, num_beams, repetition_penalty, max_mel_tokens,
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# typical_sampling, typical_mass,
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]
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# we must use `gr.Dataset` to support dynamic UI rewrites, since `gr.Examples`
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# binds tightly to UI and always restores the initial state of all components,
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# such as the list of available choices in emo_control_method.
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example_table = gr.Dataset(label="Examples",
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samples_per_page=20,
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samples=get_example_cases(include_experimental=False),
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type="values",
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# these components are NOT "connected". it just reads the column labels/available
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# states from them, so we MUST link to the "all options" versions of all components,
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# such as `emo_control_method_all` (to be able to see EXPERIMENTAL text labels)!
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components=[prompt_audio,
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emo_control_method_all, # important: support all mode labels!
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input_text_single,
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emo_upload,
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emo_weight,
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emo_text,
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vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8]
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)
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def on_example_click(example):
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print(f"Example clicked: ({len(example)} values) = {example!r}")
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return (
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gr.update(value=example[0]),
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gr.update(value=example[1]),
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gr.update(value=example[2]),
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gr.update(value=example[3]),
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gr.update(value=example[4]),
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gr.update(value=example[5]),
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gr.update(value=example[6]),
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gr.update(value=example[7]),
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gr.update(value=example[8]),
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gr.update(value=example[9]),
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gr.update(value=example[10]),
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gr.update(value=example[11]),
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gr.update(value=example[12]),
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gr.update(value=example[13]),
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)
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# click() event works on both desktop and mobile UI
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example_table.click(on_example_click,
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inputs=[example_table],
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outputs=[prompt_audio,
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emo_control_method,
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input_text_single,
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emo_upload,
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emo_weight,
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emo_text,
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vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8]
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)
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def on_input_text_change(text, max_text_tokens_per_segment):
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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),
|
||
}
|
||
|
||
# 术语词汇表事件处理函数
|
||
def on_add_glossary_term(term, reading_zh, reading_en):
|
||
"""添加术语到词汇表并自动保存"""
|
||
if not term:
|
||
gr.Warning(i18n("请输入术语"))
|
||
return gr.update()
|
||
|
||
if not reading_zh and not reading_en:
|
||
gr.Warning(i18n("请至少输入一种读法"))
|
||
return gr.update()
|
||
|
||
|
||
# 构建读法数据
|
||
if reading_zh and reading_en:
|
||
reading = {"zh": reading_zh, "en": reading_en}
|
||
elif reading_zh:
|
||
reading = {"zh": reading_zh}
|
||
elif reading_en:
|
||
reading = {"en": reading_en}
|
||
else:
|
||
reading = reading_zh or reading_en
|
||
|
||
# 添加到词汇表
|
||
tts.normalizer.term_glossary[term] = reading
|
||
|
||
# 自动保存到文件
|
||
tts.normalizer.save_glossary_to_yaml(tts.glossary_path)
|
||
|
||
# 更新Markdown表格
|
||
return gr.update(value=format_glossary_markdown())
|
||
|
||
|
||
def on_method_change(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=True)
|
||
)
|
||
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)
|
||
)
|
||
|
||
emo_control_method.change(on_method_change,
|
||
inputs=[emo_control_method],
|
||
outputs=[emotion_reference_group,
|
||
emotion_randomize_group,
|
||
emotion_vector_group,
|
||
emo_text_group,
|
||
emo_weight_group]
|
||
)
|
||
|
||
def on_experimental_change(is_experimental, current_mode_index):
|
||
# 切换情感控制选项
|
||
new_choices = EMO_CHOICES_ALL if is_experimental else EMO_CHOICES_OFFICIAL
|
||
# if their current mode selection doesn't exist in new choices, reset to 0.
|
||
# we don't verify that OLD index means the same in NEW list, since we KNOW it does.
|
||
new_index = current_mode_index if current_mode_index < len(new_choices) else 0
|
||
|
||
return (
|
||
gr.update(choices=new_choices, value=new_choices[new_index]),
|
||
gr.update(samples=get_example_cases(include_experimental=is_experimental)),
|
||
)
|
||
|
||
experimental_checkbox.change(
|
||
on_experimental_change,
|
||
inputs=[experimental_checkbox, emo_control_method],
|
||
outputs=[emo_control_method, example_table]
|
||
)
|
||
|
||
def on_glossary_checkbox_change(is_enabled):
|
||
"""控制术语词汇表的可见性"""
|
||
tts.normalizer.enable_glossary = is_enabled
|
||
return gr.update(visible=is_enabled)
|
||
|
||
glossary_checkbox.change(
|
||
on_glossary_checkbox_change,
|
||
inputs=[glossary_checkbox],
|
||
outputs=[glossary_accordion]
|
||
)
|
||
|
||
input_text_single.change(
|
||
on_input_text_change,
|
||
inputs=[input_text_single, max_text_tokens_per_segment],
|
||
outputs=[segments_preview]
|
||
)
|
||
|
||
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])
|
||
|
||
# 术语词汇表事件绑定
|
||
btn_add_term.click(
|
||
on_add_glossary_term,
|
||
inputs=[glossary_term, glossary_reading_zh, glossary_reading_en],
|
||
outputs=[glossary_table]
|
||
)
|
||
|
||
|
||
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)
|