mirror of
https://github.com/index-tts/index-tts.git
synced 2025-11-25 19:37:47 +08:00
05a8ae45e5
- A recent change made DeepSpeed optional (off by default), but the code was still trying to load DeepSpeed even when `use_deepspeed = False`. This means users would still have a big startup slowdown and a lot of error messages if their DeepSpeed module isn't working (usually because it's not able to compile itself on their machines). - We now only load DeepSpeed if the user requested it. - Translated the DeepSpeed error message to English, since all other errors in the same function were already English.
351 lines
16 KiB
Python
351 lines
16 KiB
Python
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("--use_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.use_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 = [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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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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with open("examples/cases.jsonl", "r", encoding="utf-8") as f:
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example_cases = []
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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[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 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:
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emo_ref_path = None
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emo_weight = 1.0
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if emo_control_method == 1:
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emo_weight = emo_weight
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if emo_control_method == 2:
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vec = [vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8]
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vec_sum = sum([vec1, vec2, vec3, vec4, vec5, vec6, vec7, vec8])
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if vec_sum > 1.5:
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gr.Warning(i18n("情感向量之和不能超过1.5，请调整后重试。"))
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return
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else:
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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},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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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.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,
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type="index",
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value=EMO_CHOICES[0],label=i18n("情感控制方式"))
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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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with gr.Row():
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emo_weight = gr.Slider(label=i18n("情感权重"), minimum=0.0, maximum=1.6, value=0.8, step=0.01)
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# 情感随机采样
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with gr.Row():
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emo_random = gr.Checkbox(label=i18n("情感随机采样"),value=False,visible=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=1.4, value=0.0, step=0.05)
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vec2 = gr.Slider(label=i18n("怒"), minimum=0.0, maximum=1.4, value=0.0, step=0.05)
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vec3 = gr.Slider(label=i18n("哀"), minimum=0.0, maximum=1.4, value=0.0, step=0.05)
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vec4 = gr.Slider(label=i18n("惧"), minimum=0.0, maximum=1.4, 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=1.4, value=0.0, step=0.05)
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vec6 = gr.Slider(label=i18n("低落"), minimum=0.0, maximum=1.4, value=0.0, step=0.05)
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vec7 = gr.Slider(label=i18n("惊喜"), minimum=0.0, maximum=1.4, value=0.0, step=0.05)
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vec8 = gr.Slider(label=i18n("平静"), minimum=0.0, maximum=1.4, value=0.0, step=0.05)
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with gr.Group(visible=False) as emo_text_group:
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with gr.Row():
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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：高兴，愤怒，悲伤等"))
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with gr.Accordion(i18n("高级生成参数设置"), open=False):
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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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if len(example_cases) > 0:
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gr.Examples(
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examples=example_cases,
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examples_per_page=20,
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inputs=[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:
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text_tokens_list = tts.tokenizer.tokenize(text)
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segments = tts.tokenizer.split_segments(text_tokens_list, max_text_tokens_per_segment=int(max_text_tokens_per_segment))
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data = []
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for i, s in enumerate(segments):
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segment_str = ''.join(s)
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tokens_count = len(s)
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data.append([i, segment_str, tokens_count])
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return {
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segments_preview: gr.update(value=data, visible=True, type="array"),
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}
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else:
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df = pd.DataFrame([], columns=[i18n("序号"), i18n("分句内容"), i18n("Token数")])
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return {
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segments_preview: gr.update(value=df),
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}
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def on_method_select(emo_control_method):
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if emo_control_method == 1:
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return (gr.update(visible=True),
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gr.update(visible=False),
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gr.update(visible=False),
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gr.update(visible=False)
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)
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elif emo_control_method == 2:
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return (gr.update(visible=False),
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gr.update(visible=True),
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gr.update(visible=True),
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gr.update(visible=False)
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)
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elif emo_control_method == 3:
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return (gr.update(visible=False),
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gr.update(visible=True),
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gr.update(visible=False),
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gr.update(visible=True)
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)
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else:
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return (gr.update(visible=False),
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gr.update(visible=False),
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gr.update(visible=False),
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gr.update(visible=False)
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)
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emo_control_method.select(on_method_select,
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inputs=[emo_control_method],
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outputs=[emotion_reference_group,
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emo_random,
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emotion_vector_group,
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emo_text_group]
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)
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input_text_single.change(
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on_input_text_change,
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inputs=[input_text_single, max_text_tokens_per_segment],
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outputs=[segments_preview]
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)
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max_text_tokens_per_segment.change(
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on_input_text_change,
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inputs=[input_text_single, max_text_tokens_per_segment],
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outputs=[segments_preview]
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)
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prompt_audio.upload(update_prompt_audio,
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inputs=[],
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outputs=[gen_button])
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gen_button.click(gen_single,
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inputs=[emo_control_method,prompt_audio, input_text_single, emo_upload, 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,
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*advanced_params,
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],
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outputs=[output_audio])
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if __name__ == "__main__":
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demo.queue(20)
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demo.launch(server_name=cmd_args.host, server_port=cmd_args.port)
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