chore: remove accidentally committed app_local.py
Made-with: Cursor
This commit is contained in:
刘鑫
-530
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import os
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import sys
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import logging
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import numpy as np
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import torch
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import gradio as gr
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from typing import Optional, Tuple
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from funasr import AutoModel
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from pathlib import Path
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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if os.environ.get("HF_REPO_ID", "").strip() == "":
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os.environ["HF_REPO_ID"] = "openbmb/VoxCPM2"
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import voxcpm
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s - %(levelname)s - %(message)s",
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handlers=[logging.StreamHandler(sys.stdout)],
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)
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logger = logging.getLogger(__name__)
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# ---------- Inline i18n (en + zh-CN only) ----------
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_USAGE_INSTRUCTIONS_EN = (
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"**VoxCPM2 — Three Modes of Speech Generation:**\n\n"
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"🎨 **Voice Design** — Create a brand-new voice \n"
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"No reference audio required. Describe the desired voice characteristics "
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"(gender, age, tone, emotion, pace …) in **Control Instruction**, and VoxCPM2 "
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"will craft a unique voice from your description alone.\n\n"
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"🎛️ **Controllable Cloning** — Clone a voice with optional style guidance \n"
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"Upload a reference audio clip, then use **Control Instruction** to steer "
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"emotion, speaking pace, and overall style while preserving the original timbre.\n\n"
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"🎙️ **Ultimate Cloning** — Reproduce every vocal nuance through audio continuation \n"
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"Turn on **Ultimate Cloning Mode** and provide (or auto-transcribe) the reference audio's transcript. "
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"The model treats the reference clip as a spoken prefix and seamlessly **continues** from it, faithfully preserving every vocal detail."
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"Note: This mode will disable Control Instruction."
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)
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_EXAMPLES_FOOTER_EN = (
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"---\n"
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"**💡 Voice Description Examples:** \n"
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"Try the following Control Instructions to explore different voices: \n\n"
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"**Example 1 — Gentle & Melancholic Girl** \n"
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'`Control Instruction`: *"A young girl with a soft, sweet voice. '
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'Speaks slowly with a melancholic, slightly tsundere tone."* \n'
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'`Target Text`: *"I never asked you to stay… It\'s not like I care or anything. '
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'But… why does it still hurt so much now that you\'re gone?"* \n\n'
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"**Example 2 — Laid-Back Surfer Dude** \n"
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'`Control Instruction`: *"Relaxed young male voice, slightly nasal, '
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'lazy drawl, very casual and chill."* \n'
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'`Target Text`: *"Dude, did you see that set? The waves out there are totally gnarly today. '
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"Just catching barrels all morning — it's like, totally righteous, you know what I mean?\"*"
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)
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_USAGE_INSTRUCTIONS_ZH = (
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"**VoxCPM2 — 三种语音生成方式：**\n\n"
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"🎨 **声音设计（Voice Design）** \n"
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"无需参考音频。在 **Control Instruction** 中描述目标音色特征"
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"（性别、年龄、语气、情绪、语速等），VoxCPM2 即可为你从零创造独一无二的声音。\n\n"
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"🎛️ **可控克隆（Controllable Cloning）** \n"
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"上传参考音频，同时可选地使用 **Control Instruction** 来指定情绪、语速、风格等表达方式，"
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"在保留原始音色的基础上灵活控制说话风格。\n\n"
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"🎙️ **极致克隆（Ultimate Cloning）** \n"
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"开启 **极致克隆模式** 并提供参考音频的文字内容（可自动识别）。"
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"模型会将参考音频视为已说出的前文，以**音频续写**的方式完整还原参考音频中的所有声音细节。"
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"注意：该模式与可控克隆模式互斥，将禁用Control Instruction。\n\n"
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)
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_EXAMPLES_FOOTER_ZH = (
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"---\n"
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"**💡 声音描述示例（中英文均可）：** \n\n"
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"**示例 1 — 深宫太后** \n"
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'`Control Instruction`: *"中老年女性，声音低沉阴冷，语速缓慢而有力，'
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'字字深思熟虑，带有深不可测的城府与威慑感。"* \n'
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'`Target Text`: *"哀家在这深宫待了四十年，什么风浪没见过？你以为瞒得过哀家？"* \n\n'
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"**示例 2 — 暴躁驾校教练** \n"
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'`Control Instruction`: *"暴躁的中年男声，语速快，充满无奈和愤怒"* \n'
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'`Target Text`: *"踩离合！踩刹车啊！你往哪儿开呢？前面是树你看不见吗？'
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'我教了你八百遍了，打死方向盘！你是不是想把车给我开到沟里去？"* \n\n'
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"---\n"
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"**🗣️ 方言生成指南：** \n"
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"要生成地道的方言语音，请在 **Target Text** 中直接使用方言词汇和句式，"
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"并在 **Control Instruction** 中描述方言特征。 \n\n"
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"**示例 — 广东话** \n"
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'`Control Instruction`: *"粤语，中年男性，语气平淡"* \n'
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'✅ 正确（粤语表达）：*"伙計，唔該一個A餐，凍奶茶少甜！"* \n'
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'❌ 错误（普通话原文）：*"伙计，麻烦来一个A餐，冻奶茶少甜！"* \n\n'
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"**示例 — 河南话** \n"
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'`Control Instruction`: *"河南话，接地气的大叔"* \n'
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'✅ 正确（河南话表达）：*"恁这是弄啥嘞？晌午吃啥饭？"* \n'
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'❌ 错误（普通话原文）：*"你这是在干什么呢？中午吃什么饭？"* \n\n'
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"🤖 **小技巧：** 不知道方言怎么写？可以用豆包、DeepSeek、Kimi 等 AI 助手"
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"将普通话翻译为方言文本，再粘贴到 Target Text 中即可。 \n\n"
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)
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_I18N_TRANSLATIONS = {
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"en": {
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"reference_audio_label": "🎤 Reference Audio (optional — upload for cloning)",
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"show_prompt_text_label": "🎙️ Ultimate Cloning Mode (transcript-guided cloning)",
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"show_prompt_text_info": "Auto-transcribes reference audio for every vocal nuance reproduced. Control Instruction will be disabled when active.",
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"prompt_text_label": "Transcript of Reference Audio (auto-filled via ASR, editable)",
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"prompt_text_placeholder": "The transcript of your reference audio will appear here …",
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"control_label": "🎛️ Control Instruction (optional — supports Chinese & English)",
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"control_placeholder": "e.g. A warm young woman / 年轻女性，温柔甜美 / Excited and fast-paced",
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"target_text_label": "✍️ Target Text — the content to speak",
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"generate_btn": "🔊 Generate Speech",
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"generated_audio_label": "Generated Audio",
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"advanced_settings_title": "⚙️ Advanced Settings",
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"ref_denoise_label": "Reference audio enhancement",
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"ref_denoise_info": "Apply ZipEnhancer denoising to the reference audio before cloning",
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"normalize_label": "Text normalization",
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"normalize_info": "Normalize numbers, dates, and abbreviations via wetext",
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"cfg_label": "CFG (guidance scale)",
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"cfg_info": "Higher → closer to the prompt / reference; lower → more creative variation",
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"dit_steps_label": "LocDiT flow-matching steps",
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"dit_steps_info": "LocDiT flow-matching steps — more steps → maybe better audio quality, but slower",
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"usage_instructions": _USAGE_INSTRUCTIONS_EN,
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"examples_footer": _EXAMPLES_FOOTER_EN,
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},
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"zh-CN": {
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"reference_audio_label": "🎤 参考音频（可选 — 上传后用于克隆）",
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"show_prompt_text_label": "🎙️ 极致克隆模式（基于文本引导的极致克隆）",
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"show_prompt_text_info": "自动识别参考音频文本，完整还原音色、节奏、情感等全部声音细节。开启后 Control Instruction 将暂时禁用",
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"prompt_text_label": "参考音频内容文本（ASR 自动填充，可手动编辑）",
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"prompt_text_placeholder": "参考音频的文字内容将自动识别并显示在此处 …",
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"control_label": "🎛️ Control Instruction（可选 — 支持中英文描述）",
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"control_placeholder": "如：年轻女性，温柔甜美 / A warm young woman / 暴躁老哥，语速飞快",
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"target_text_label": "✍️ Target Text — 要合成的目标文本",
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"generate_btn": "🔊 开始生成",
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"generated_audio_label": "生成结果",
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"advanced_settings_title": "⚙️ 高级设置",
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"ref_denoise_label": "参考音频降噪增强",
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"ref_denoise_info": "克隆前使用 ZipEnhancer 对参考音频进行降噪处理",
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"normalize_label": "文本规范化",
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"normalize_info": "自动规范化数字、日期及缩写（基于 wetext）",
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"cfg_label": "CFG（引导强度）",
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"cfg_info": "数值越高 → 越贴合提示/参考音色；数值越低 → 生成风格更自由",
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"dit_steps_label": "LocDiT 流匹配迭代步数",
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"dit_steps_info": "LocDiT 流匹配生成迭代步数 — 步数越多 → 可能生成更好的音频质量，但速度变慢",
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"usage_instructions": _USAGE_INSTRUCTIONS_ZH,
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"examples_footer": _EXAMPLES_FOOTER_ZH,
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},
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"zh-Hans": None, # alias, filled below
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"zh": None, # alias, filled below
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}
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_I18N_TRANSLATIONS["zh-Hans"] = _I18N_TRANSLATIONS["zh-CN"]
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_I18N_TRANSLATIONS["zh"] = _I18N_TRANSLATIONS["zh-CN"]
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for _d in _I18N_TRANSLATIONS.values():
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if _d is not None:
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for _k, _v in _I18N_TRANSLATIONS["en"].items():
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_d.setdefault(_k, _v)
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I18N = gr.I18n(**_I18N_TRANSLATIONS)
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DEFAULT_TARGET_TEXT = (
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"VoxCPM2 is a creative multilingual TTS model from ModelBest, "
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"designed to generate highly realistic speech."
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)
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_CUSTOM_CSS = """
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.logo-container {
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text-align: center;
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margin: 0.5rem 0 1rem 0;
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}
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.logo-container img {
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height: 80px;
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width: auto;
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max-width: 200px;
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display: inline-block;
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}
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/* Toggle switch style */
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.switch-toggle {
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padding: 8px 12px;
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border-radius: 8px;
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background: var(--block-background-fill);
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}
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.switch-toggle input[type="checkbox"] {
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appearance: none;
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-webkit-appearance: none;
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width: 44px;
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height: 24px;
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background: #ccc;
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border-radius: 12px;
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position: relative;
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cursor: pointer;
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transition: background 0.3s ease;
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flex-shrink: 0;
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}
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.switch-toggle input[type="checkbox"]::after {
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content: "";
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position: absolute;
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top: 2px;
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left: 2px;
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width: 20px;
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height: 20px;
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background: white;
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border-radius: 50%;
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transition: transform 0.3s ease;
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box-shadow: 0 1px 3px rgba(0,0,0,0.2);
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}
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.switch-toggle input[type="checkbox"]:checked {
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background: var(--color-accent);
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}
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.switch-toggle input[type="checkbox"]:checked::after {
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transform: translateX(20px);
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}
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"""
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_APP_THEME = gr.themes.Soft(
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primary_hue="blue",
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secondary_hue="gray",
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neutral_hue="slate",
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font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"],
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)
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# ---------- Model ----------
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class VoxCPMDemo:
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def __init__(self, model_dir: Optional[str] = None) -> None:
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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logger.info(f"Running on device: {self.device}")
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self.asr_model_id = "iic/SenseVoiceSmall"
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self.asr_model: Optional[AutoModel] = AutoModel(
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model=self.asr_model_id,
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disable_update=True,
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log_level="DEBUG",
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device="cuda:0" if self.device == "cuda" else "cpu",
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)
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self.voxcpm_model: Optional[voxcpm.VoxCPM] = None
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self.explicit_model_dir = model_dir
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def _resolve_model_dir(self) -> str:
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if self.explicit_model_dir and os.path.isdir(self.explicit_model_dir):
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return self.explicit_model_dir
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env_model_dir = os.environ.get("VOXCPM_MODEL_DIR", "").strip()
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if env_model_dir and os.path.isdir(env_model_dir):
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return env_model_dir
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repo_id = os.environ.get("HF_REPO_ID", "").strip()
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if len(repo_id) > 0:
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target_dir = os.path.join("models", repo_id.replace("/", "__"))
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if not os.path.isdir(target_dir):
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try:
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from huggingface_hub import snapshot_download
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os.makedirs(target_dir, exist_ok=True)
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logger.info(f"Downloading model from HF repo '{repo_id}' to '{target_dir}' ...")
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snapshot_download(repo_id=repo_id, local_dir=target_dir, local_dir_use_symlinks=False)
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except Exception as e:
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logger.warning(f"HF download failed: {e}. Falling back to 'models'.")
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return "models"
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return target_dir
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return "models"
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def get_or_load_voxcpm(self) -> voxcpm.VoxCPM:
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if self.voxcpm_model is not None:
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return self.voxcpm_model
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logger.info("Model not loaded, initializing...")
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model_dir = self._resolve_model_dir()
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logger.info(f"Using model dir: {model_dir}")
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self.voxcpm_model = voxcpm.VoxCPM(voxcpm_model_path=model_dir, optimize=True)
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logger.info("Model loaded successfully.")
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return self.voxcpm_model
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def prompt_wav_recognition(self, prompt_wav: Optional[str]) -> str:
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if prompt_wav is None:
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return ""
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res = self.asr_model.generate(input=prompt_wav, language="auto", use_itn=True)
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return res[0]["text"].split("|>")[-1]
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def _build_generate_kwargs(
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self,
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*,
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final_text: str,
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audio_path: Optional[str],
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prompt_text_clean: Optional[str],
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cfg_value_input: float,
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do_normalize: bool,
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denoise: bool,
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inference_timesteps: int = 10,
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) -> dict:
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generate_kwargs = dict(
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text=final_text,
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reference_wav_path=audio_path,
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cfg_value=float(cfg_value_input),
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inference_timesteps=inference_timesteps,
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normalize=do_normalize,
|
|
||||||
denoise=denoise,
|
|
||||||
)
|
|
||||||
if prompt_text_clean and audio_path:
|
|
||||||
generate_kwargs["prompt_wav_path"] = audio_path
|
|
||||||
generate_kwargs["prompt_text"] = prompt_text_clean
|
|
||||||
return generate_kwargs
|
|
||||||
|
|
||||||
def generate_tts_audio(
|
|
||||||
self,
|
|
||||||
text_input: str,
|
|
||||||
control_instruction: str = "",
|
|
||||||
reference_wav_path_input: Optional[str] = None,
|
|
||||||
prompt_text: str = "",
|
|
||||||
cfg_value_input: float = 2.0,
|
|
||||||
do_normalize: bool = True,
|
|
||||||
denoise: bool = True,
|
|
||||||
inference_timesteps: int = 10,
|
|
||||||
) -> Tuple[int, np.ndarray]:
|
|
||||||
current_model = self.get_or_load_voxcpm()
|
|
||||||
|
|
||||||
text = (text_input or "").strip()
|
|
||||||
if len(text) == 0:
|
|
||||||
raise ValueError("Please input text to synthesize.")
|
|
||||||
|
|
||||||
control = (control_instruction or "").strip()
|
|
||||||
final_text = f"({control}){text}" if control else text
|
|
||||||
|
|
||||||
audio_path = reference_wav_path_input if reference_wav_path_input else None
|
|
||||||
prompt_text_clean = (prompt_text or "").strip() or None
|
|
||||||
|
|
||||||
if audio_path and prompt_text_clean:
|
|
||||||
logger.info(f"[Voice Cloning] prompt_wav + prompt_text + reference_wav")
|
|
||||||
elif audio_path:
|
|
||||||
logger.info(f"[Voice Control] reference_wav only")
|
|
||||||
else:
|
|
||||||
logger.info(f"[Voice Design] control: {control[:50] if control else 'None'}...")
|
|
||||||
|
|
||||||
logger.info(f"Generating audio for text: '{final_text[:80]}...'")
|
|
||||||
generate_kwargs = self._build_generate_kwargs(
|
|
||||||
final_text=final_text,
|
|
||||||
audio_path=audio_path,
|
|
||||||
prompt_text_clean=prompt_text_clean,
|
|
||||||
cfg_value_input=cfg_value_input,
|
|
||||||
do_normalize=do_normalize,
|
|
||||||
denoise=denoise,
|
|
||||||
inference_timesteps=inference_timesteps,
|
|
||||||
)
|
|
||||||
wav = current_model.generate(**generate_kwargs)
|
|
||||||
return (current_model.tts_model.sample_rate, wav)
|
|
||||||
|
|
||||||
|
|
||||||
# ---------- UI ----------
|
|
||||||
|
|
||||||
def create_demo_interface(demo: VoxCPMDemo):
|
|
||||||
gr.set_static_paths(paths=[Path.cwd().absolute() / "assets"])
|
|
||||||
|
|
||||||
def _generate(
|
|
||||||
text: str,
|
|
||||||
control_instruction: str,
|
|
||||||
ref_wav: Optional[str],
|
|
||||||
use_prompt_text: bool,
|
|
||||||
prompt_text_value: str,
|
|
||||||
cfg_value: float,
|
|
||||||
do_normalize: bool,
|
|
||||||
denoise: bool,
|
|
||||||
dit_steps: int,
|
|
||||||
):
|
|
||||||
actual_prompt_text = prompt_text_value.strip() if use_prompt_text else ""
|
|
||||||
actual_control = "" if use_prompt_text else control_instruction
|
|
||||||
sr, wav_np = demo.generate_tts_audio(
|
|
||||||
text_input=text,
|
|
||||||
control_instruction=actual_control,
|
|
||||||
reference_wav_path_input=ref_wav,
|
|
||||||
prompt_text=actual_prompt_text,
|
|
||||||
cfg_value_input=cfg_value,
|
|
||||||
do_normalize=do_normalize,
|
|
||||||
denoise=denoise,
|
|
||||||
inference_timesteps=int(dit_steps),
|
|
||||||
)
|
|
||||||
return (sr, wav_np)
|
|
||||||
|
|
||||||
def _on_toggle_instant(checked):
|
|
||||||
"""Instant UI toggle — no ASR, no blocking."""
|
|
||||||
if checked:
|
|
||||||
return (
|
|
||||||
gr.update(visible=True, value="", placeholder="Recognizing reference audio..."),
|
|
||||||
gr.update(visible=False),
|
|
||||||
)
|
|
||||||
return (
|
|
||||||
gr.update(visible=False),
|
|
||||||
gr.update(visible=True, interactive=True),
|
|
||||||
)
|
|
||||||
|
|
||||||
def _run_asr_if_needed(checked, audio_path):
|
|
||||||
"""Run ASR after the UI has updated. Only when toggled ON."""
|
|
||||||
if not checked or not audio_path:
|
|
||||||
return gr.update()
|
|
||||||
try:
|
|
||||||
logger.info("Running ASR on reference audio...")
|
|
||||||
asr_text = demo.prompt_wav_recognition(audio_path)
|
|
||||||
logger.info(f"ASR result: {asr_text[:60]}...")
|
|
||||||
return gr.update(value=asr_text)
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(f"ASR recognition failed: {e}")
|
|
||||||
return gr.update(value="")
|
|
||||||
|
|
||||||
with gr.Blocks() as interface:
|
|
||||||
gr.HTML(
|
|
||||||
'<div class="logo-container">'
|
|
||||||
'<img src="/gradio_api/file=assets/voxcpm_logo.png" alt="VoxCPM Logo">'
|
|
||||||
"</div>"
|
|
||||||
)
|
|
||||||
|
|
||||||
gr.Markdown(I18N("usage_instructions"))
|
|
||||||
|
|
||||||
with gr.Row():
|
|
||||||
with gr.Column():
|
|
||||||
reference_wav = gr.Audio(
|
|
||||||
sources=["upload", "microphone"],
|
|
||||||
type="filepath",
|
|
||||||
label=I18N("reference_audio_label"),
|
|
||||||
)
|
|
||||||
show_prompt_text = gr.Checkbox(
|
|
||||||
value=False,
|
|
||||||
label=I18N("show_prompt_text_label"),
|
|
||||||
info=I18N("show_prompt_text_info"),
|
|
||||||
elem_classes=["switch-toggle"],
|
|
||||||
)
|
|
||||||
prompt_text = gr.Textbox(
|
|
||||||
value="",
|
|
||||||
label=I18N("prompt_text_label"),
|
|
||||||
placeholder=I18N("prompt_text_placeholder"),
|
|
||||||
lines=2,
|
|
||||||
visible=False,
|
|
||||||
)
|
|
||||||
control_instruction = gr.Textbox(
|
|
||||||
value="",
|
|
||||||
label=I18N("control_label"),
|
|
||||||
placeholder=I18N("control_placeholder"),
|
|
||||||
lines=2,
|
|
||||||
)
|
|
||||||
text = gr.Textbox(
|
|
||||||
value=DEFAULT_TARGET_TEXT,
|
|
||||||
label=I18N("target_text_label"),
|
|
||||||
lines=3,
|
|
||||||
)
|
|
||||||
|
|
||||||
with gr.Accordion(I18N("advanced_settings_title"), open=False):
|
|
||||||
DoDenoisePromptAudio = gr.Checkbox(
|
|
||||||
value=False,
|
|
||||||
label=I18N("ref_denoise_label"),
|
|
||||||
elem_classes=["switch-toggle"],
|
|
||||||
info=I18N("ref_denoise_info"),
|
|
||||||
)
|
|
||||||
DoNormalizeText = gr.Checkbox(
|
|
||||||
value=False,
|
|
||||||
label=I18N("normalize_label"),
|
|
||||||
elem_classes=["switch-toggle"],
|
|
||||||
info=I18N("normalize_info"),
|
|
||||||
)
|
|
||||||
cfg_value = gr.Slider(
|
|
||||||
minimum=1.0,
|
|
||||||
maximum=3.0,
|
|
||||||
value=2.0,
|
|
||||||
step=0.1,
|
|
||||||
label=I18N("cfg_label"),
|
|
||||||
info=I18N("cfg_info"),
|
|
||||||
)
|
|
||||||
dit_steps = gr.Slider(
|
|
||||||
minimum=1,
|
|
||||||
maximum=50,
|
|
||||||
value=10,
|
|
||||||
step=1,
|
|
||||||
label=I18N("dit_steps_label"),
|
|
||||||
info=I18N("dit_steps_info"),
|
|
||||||
)
|
|
||||||
|
|
||||||
run_btn = gr.Button(I18N("generate_btn"), variant="primary", size="lg")
|
|
||||||
|
|
||||||
with gr.Column():
|
|
||||||
audio_output = gr.Audio(label=I18N("generated_audio_label"))
|
|
||||||
gr.Markdown(I18N("examples_footer"))
|
|
||||||
|
|
||||||
show_prompt_text.change(
|
|
||||||
fn=_on_toggle_instant,
|
|
||||||
inputs=[show_prompt_text],
|
|
||||||
outputs=[prompt_text, control_instruction],
|
|
||||||
).then(
|
|
||||||
fn=_run_asr_if_needed,
|
|
||||||
inputs=[show_prompt_text, reference_wav],
|
|
||||||
outputs=[prompt_text],
|
|
||||||
)
|
|
||||||
|
|
||||||
run_btn.click(
|
|
||||||
fn=_generate,
|
|
||||||
inputs=[
|
|
||||||
text,
|
|
||||||
control_instruction,
|
|
||||||
reference_wav,
|
|
||||||
show_prompt_text,
|
|
||||||
prompt_text,
|
|
||||||
cfg_value,
|
|
||||||
DoNormalizeText,
|
|
||||||
DoDenoisePromptAudio,
|
|
||||||
dit_steps,
|
|
||||||
],
|
|
||||||
outputs=[audio_output],
|
|
||||||
show_progress=True,
|
|
||||||
api_name="generate",
|
|
||||||
)
|
|
||||||
|
|
||||||
return interface
|
|
||||||
|
|
||||||
def run_demo(
|
|
||||||
server_name: str = "0.0.0.0",
|
|
||||||
server_port: int = 8808,
|
|
||||||
show_error: bool = True,
|
|
||||||
model_dir: Optional[str] = None,
|
|
||||||
):
|
|
||||||
demo = VoxCPMDemo(model_dir=model_dir)
|
|
||||||
interface = create_demo_interface(demo)
|
|
||||||
interface.queue(max_size=10, default_concurrency_limit=1).launch(
|
|
||||||
server_name=server_name,
|
|
||||||
server_port=server_port,
|
|
||||||
show_error=show_error,
|
|
||||||
i18n=I18N,
|
|
||||||
theme=_APP_THEME,
|
|
||||||
css=_CUSTOM_CSS,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
import argparse
|
|
||||||
parser = argparse.ArgumentParser()
|
|
||||||
parser.add_argument("--model-dir", type=str, default=None, help="Path to VoxCPM2 checkpoint directory")
|
|
||||||
parser.add_argument("--port", type=int, default=8808, help="Server port")
|
|
||||||
args = parser.parse_args()
|
|
||||||
run_demo(model_dir=args.model_dir, server_port=args.port)
|
|
||||||
Reference in New Issue
Block a user