470 lines
21 KiB
Python
470 lines
21 KiB
Python
import html
|
||
import os
|
||
import sys
|
||
import threading
|
||
import time
|
||
|
||
import warnings
|
||
|
||
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()
|
||
|
||
if not os.path.exists(cmd_args.model_dir):
|
||
print(f"Model directory {cmd_args.model_dir} does not exist. Please download the model first.")
|
||
sys.exit(1)
|
||
|
||
for file in [
|
||
"bpe.model",
|
||
"gpt.pth",
|
||
"config.yaml",
|
||
"s2mel.pth",
|
||
"wav2vec2bert_stats.pt"
|
||
]:
|
||
file_path = os.path.join(cmd_args.model_dir, file)
|
||
if not os.path.exists(file_path):
|
||
print(f"Required file {file_path} does not exist. Please download it.")
|
||
sys.exit(1)
|
||
|
||
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_ALL = [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_OFFICIAL = EMO_CHOICES_ALL[:-1] # skip experimental features
|
||
|
||
os.makedirs("outputs/tasks",exist_ok=True)
|
||
os.makedirs("prompts",exist_ok=True)
|
||
|
||
MAX_LENGTH_TO_USE_SPEED = 70
|
||
|
||
def format_glossary_markdown():
|
||
"""将词汇表转换为Markdown表格格式"""
|
||
if not tts.normalizer.term_glossary:
|
||
return i18n("暂无术语")
|
||
|
||
lines = [f"| {i18n('术语')} | {i18n('中文读法')} | {i18n('英文读法')} |"]
|
||
lines.append("|---|---|---|")
|
||
|
||
for term, reading in tts.normalizer.term_glossary.items():
|
||
zh = reading.get("zh", "") if isinstance(reading, dict) else reading
|
||
en = reading.get("en", "") if isinstance(reading, dict) else reading
|
||
lines.append(f"| {term} | {zh} | {en} |")
|
||
|
||
return "\n".join(lines)
|
||
|
||
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
|
||
pass
|
||
if emo_control_method == 2: # emotion from custom vectors
|
||
vec = [vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8]
|
||
vec = tts.normalize_emo_vec(vec, apply_bias=True)
|
||
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
|
||
|
||
def create_warning_message(warning_text):
|
||
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>")
|
||
|
||
def create_experimental_warning_message():
|
||
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。'))
|
||
|
||
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")
|
||
|
||
with gr.Row():
|
||
experimental_checkbox = gr.Checkbox(label=i18n("显示实验功能"), value=False)
|
||
glossary_checkbox = gr.Checkbox(label=i18n("开启术语词汇读音"), value=tts.normalizer.enable_glossary)
|
||
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_OFFICIAL,
|
||
type="index",
|
||
value=EMO_CHOICES_OFFICIAL[0],label=i18n("情感控制方式"))
|
||
# we MUST have an extra, INVISIBLE list of *all* emotion control
|
||
# methods so that gr.Dataset() can fetch ALL control mode labels!
|
||
# otherwise, the gr.Dataset()'s experimental labels would be empty!
|
||
emo_control_method_all = gr.Radio(
|
||
choices=EMO_CHOICES_ALL,
|
||
type="index",
|
||
value=EMO_CHOICES_ALL[0], label=i18n("情感控制方式"),
|
||
visible=False) # do not render
|
||
# 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:
|
||
create_experimental_warning_message()
|
||
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=0.65, step=0.01)
|
||
|
||
# 术语词汇表管理
|
||
with gr.Accordion(i18n("自定义术语词汇读音"), open=False, visible=tts.normalizer.enable_glossary) as glossary_accordion:
|
||
gr.Markdown(i18n("自定义个别专业术语的读音"))
|
||
with gr.Row():
|
||
with gr.Column(scale=1):
|
||
glossary_term = gr.Textbox(
|
||
label=i18n("术语"),
|
||
placeholder="IndexTTS2",
|
||
)
|
||
glossary_reading_zh = gr.Textbox(
|
||
label=i18n("中文读法"),
|
||
placeholder="Index T-T-S 二",
|
||
)
|
||
glossary_reading_en = gr.Textbox(
|
||
label=i18n("英文读法"),
|
||
placeholder="Index T-T-S two",
|
||
)
|
||
btn_add_term = gr.Button(i18n("添加术语"), scale=1)
|
||
with gr.Column(scale=2):
|
||
glossary_table = gr.Markdown(
|
||
value=format_glossary_markdown()
|
||
)
|
||
|
||
with gr.Accordion(i18n("高级生成参数设置"), open=False, visible=True) 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,
|
||
]
|
||
|
||
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),
|
||
}
|
||
|
||
# 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"""
|
||
term = term.rstrip()
|
||
reading_zh = reading_zh.rstrip()
|
||
reading_en = reading_en.rstrip()
|
||
|
||
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
|
||
try:
|
||
tts.normalizer.save_glossary_to_yaml(tts.glossary_path)
|
||
gr.Info(i18n("词汇表已更新"), duration=1)
|
||
except Exception as e:
|
||
gr.Error(i18n("保存词汇表时出错"))
|
||
print(f"Error details: {e}")
|
||
return gr.update()
|
||
|
||
# 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])
|
||
|
||
experimental_checkbox.change(
|
||
on_experimental_change,
|
||
inputs=[experimental_checkbox, emo_control_method],
|
||
outputs=[emo_control_method]
|
||
)
|
||
|
||
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])
|
||
|
||
def on_demo_load():
|
||
"""页面加载时重新加载glossary数据"""
|
||
try:
|
||
tts.normalizer.load_glossary_from_yaml(tts.glossary_path)
|
||
except Exception as e:
|
||
gr.Error(i18n("加载词汇表时出错"))
|
||
print(f"Failed to reload glossary on page load: {e}")
|
||
return gr.update(value=format_glossary_markdown())
|
||
|
||
# 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]
|
||
)
|
||
|
||
# 页面加载时重新加载glossary
|
||
demo.load(
|
||
on_demo_load,
|
||
inputs=[],
|
||
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)
|