diff --git a/app_local.py b/app_local.py deleted file mode 100644 index 1503b9d..0000000 --- a/app_local.py +++ /dev/null @@ -1,530 +0,0 @@ -import os -import sys -import logging -import numpy as np -import torch -import gradio as gr -from typing import Optional, Tuple -from funasr import AutoModel -from pathlib import Path - -os.environ["TOKENIZERS_PARALLELISM"] = "false" -if os.environ.get("HF_REPO_ID", "").strip() == "": - os.environ["HF_REPO_ID"] = "openbmb/VoxCPM2" - -import voxcpm - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(levelname)s - %(message)s", - handlers=[logging.StreamHandler(sys.stdout)], -) -logger = logging.getLogger(__name__) - -# ---------- Inline i18n (en + zh-CN only) ---------- - -_USAGE_INSTRUCTIONS_EN = ( - "**VoxCPM2 — Three Modes of Speech Generation:**\n\n" - "🎨 **Voice Design** — Create a brand-new voice \n" - "No reference audio required. Describe the desired voice characteristics " - "(gender, age, tone, emotion, pace …) in **Control Instruction**, and VoxCPM2 " - "will craft a unique voice from your description alone.\n\n" - "🎛️ **Controllable Cloning** — Clone a voice with optional style guidance \n" - "Upload a reference audio clip, then use **Control Instruction** to steer " - "emotion, speaking pace, and overall style while preserving the original timbre.\n\n" - "🎙️ **Ultimate Cloning** — Reproduce every vocal nuance through audio continuation \n" - "Turn on **Ultimate Cloning Mode** and provide (or auto-transcribe) the reference audio's transcript. " - "The model treats the reference clip as a spoken prefix and seamlessly **continues** from it, faithfully preserving every vocal detail." - "Note: This mode will disable Control Instruction." -) - -_EXAMPLES_FOOTER_EN = ( - "---\n" - "**💡 Voice Description Examples:** \n" - "Try the following Control Instructions to explore different voices: \n\n" - "**Example 1 — Gentle & Melancholic Girl** \n" - '`Control Instruction`: *"A young girl with a soft, sweet voice. ' - 'Speaks slowly with a melancholic, slightly tsundere tone."* \n' - '`Target Text`: *"I never asked you to stay… It\'s not like I care or anything. ' - 'But… why does it still hurt so much now that you\'re gone?"* \n\n' - "**Example 2 — Laid-Back Surfer Dude** \n" - '`Control Instruction`: *"Relaxed young male voice, slightly nasal, ' - 'lazy drawl, very casual and chill."* \n' - '`Target Text`: *"Dude, did you see that set? The waves out there are totally gnarly today. ' - "Just catching barrels all morning — it's like, totally righteous, you know what I mean?\"*" -) - -_USAGE_INSTRUCTIONS_ZH = ( - "**VoxCPM2 — 三种语音生成方式：**\n\n" - "🎨 **声音设计（Voice Design）** \n" - "无需参考音频。在 **Control Instruction** 中描述目标音色特征" - "（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" - "🎛️ **可控克隆（Controllable Cloning）** \n" - "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，" - "在保留原始音色的基础上灵活控制说话风格。\n\n" - "🎙️ **极致克隆（Ultimate Cloning）** \n" - "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）。" - "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。" - "注意：该模式与可控克隆模式互斥，将禁用Control Instruction。\n\n" -) - -_EXAMPLES_FOOTER_ZH = ( - "---\n" - "**💡 声音描述示例（中英文均可）：** \n\n" - "**示例 1 — 深宫太后** \n" - '`Control Instruction`: *"中老年女性，声音低沉阴冷，语速缓慢而有力，' - '字字深思熟虑，带有深不可测的城府与威慑感。"* \n' - '`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' - "**示例 2 — 暴躁驾校教练** \n" - '`Control Instruction`: *"暴躁的中年男声，语速快，充满无奈和愤怒"* \n' - '`Target Text`: *"踩离合！踩刹车啊！你往哪儿开呢？前面是树你看不见吗？' - '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' - "---\n" - "**🗣️ 方言生成指南：** \n" - "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，" - "并在 **Control Instruction** 中描述方言特征。 \n\n" - "**示例 — 广东话** \n" - '`Control Instruction`: *"粤语，中年男性，语气平淡"* \n' - '✅ 正确（粤语表达）：*"伙計，唔該一個A餐，凍奶茶少甜！"* \n' - '❌ 错误（普通话原文）：*"伙计，麻烦来一个A餐，冻奶茶少甜！"* \n\n' - "**示例 — 河南话** \n" - '`Control Instruction`: *"河南话，接地气的大叔"* \n' - '✅ 正确（河南话表达）：*"恁这是弄啥嘞？晌午吃啥饭？"* \n' - '❌ 错误（普通话原文）：*"你这是在干什么呢？中午吃什么饭？"* \n\n' - "🤖 **小技巧：** 不知道方言怎么写？可以用豆包、DeepSeek、Kimi 等 AI 助手" - "将普通话翻译为方言文本，再粘贴到 Target Text 中即可。 \n\n" -) - -_I18N_TRANSLATIONS = { - "en": { - "reference_audio_label": "🎤 Reference Audio (optional — upload for cloning)", - "show_prompt_text_label": "🎙️ Ultimate Cloning Mode (transcript-guided cloning)", - "show_prompt_text_info": "Auto-transcribes reference audio for every vocal nuance reproduced. Control Instruction will be disabled when active.", - "prompt_text_label": "Transcript of Reference Audio (auto-filled via ASR, editable)", - "prompt_text_placeholder": "The transcript of your reference audio will appear here …", - "control_label": "🎛️ Control Instruction (optional — supports Chinese & English)", - "control_placeholder": "e.g. A warm young woman / 年轻女性，温柔甜美 / Excited and fast-paced", - "target_text_label": "✍️ Target Text — the content to speak", - "generate_btn": "🔊 Generate Speech", - "generated_audio_label": "Generated Audio", - "advanced_settings_title": "⚙️ Advanced Settings", - "ref_denoise_label": "Reference audio enhancement", - "ref_denoise_info": "Apply ZipEnhancer denoising to the reference audio before cloning", - "normalize_label": "Text normalization", - "normalize_info": "Normalize numbers, dates, and abbreviations via wetext", - "cfg_label": "CFG (guidance scale)", - "cfg_info": "Higher → closer to the prompt / reference; lower → more creative variation", - "dit_steps_label": "LocDiT flow-matching steps", - "dit_steps_info": "LocDiT flow-matching steps — more steps → maybe better audio quality, but slower", - "usage_instructions": _USAGE_INSTRUCTIONS_EN, - "examples_footer": _EXAMPLES_FOOTER_EN, - }, - "zh-CN": { - "reference_audio_label": "🎤 参考音频（可选 — 上传后用于克隆）", - "show_prompt_text_label": "🎙️ 极致克隆模式（基于文本引导的极致克隆）", - "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", - "prompt_text_label": "参考音频内容文本（ASR 自动填充，可手动编辑）", - "prompt_text_placeholder": "参考音频的文字内容将自动识别并显示在此处 …", - "control_label": "🎛️ Control Instruction（可选 — 支持中英文描述）", - "control_placeholder": "如：年轻女性，温柔甜美 / A warm young woman / 暴躁老哥，语速飞快", - "target_text_label": "✍️ Target Text — 要合成的目标文本", - "generate_btn": "🔊 开始生成", - "generated_audio_label": "生成结果", - "advanced_settings_title": "⚙️ 高级设置", - "ref_denoise_label": "参考音频降噪增强", - "ref_denoise_info": "克隆前使用 ZipEnhancer 对参考音频进行降噪处理", - "normalize_label": "文本规范化", - "normalize_info": "自动规范化数字、日期及缩写（基于 wetext）", - "cfg_label": "CFG（引导强度）", - "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", - "dit_steps_label": "LocDiT 流匹配迭代步数", - "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", - "usage_instructions": _USAGE_INSTRUCTIONS_ZH, - "examples_footer": _EXAMPLES_FOOTER_ZH, - }, - "zh-Hans": None, # alias, filled below - "zh": None, # alias, filled below -} -_I18N_TRANSLATIONS["zh-Hans"] = _I18N_TRANSLATIONS["zh-CN"] -_I18N_TRANSLATIONS["zh"] = _I18N_TRANSLATIONS["zh-CN"] - -for _d in _I18N_TRANSLATIONS.values(): - if _d is not None: - for _k, _v in _I18N_TRANSLATIONS["en"].items(): - _d.setdefault(_k, _v) - -I18N = gr.I18n(**_I18N_TRANSLATIONS) - -DEFAULT_TARGET_TEXT = ( - "VoxCPM2 is a creative multilingual TTS model from ModelBest, " - "designed to generate highly realistic speech." -) - -_CUSTOM_CSS = """ -.logo-container { - text-align: center; - margin: 0.5rem 0 1rem 0; -} -.logo-container img { - height: 80px; - width: auto; - max-width: 200px; - display: inline-block; -} - -/* Toggle switch style */ -.switch-toggle { - padding: 8px 12px; - border-radius: 8px; - background: var(--block-background-fill); -} -.switch-toggle input[type="checkbox"] { - appearance: none; - -webkit-appearance: none; - width: 44px; - height: 24px; - background: #ccc; - border-radius: 12px; - position: relative; - cursor: pointer; - transition: background 0.3s ease; - flex-shrink: 0; -} -.switch-toggle input[type="checkbox"]::after { - content: ""; - position: absolute; - top: 2px; - left: 2px; - width: 20px; - height: 20px; - background: white; - border-radius: 50%; - transition: transform 0.3s ease; - box-shadow: 0 1px 3px rgba(0,0,0,0.2); -} -.switch-toggle input[type="checkbox"]:checked { - background: var(--color-accent); -} -.switch-toggle input[type="checkbox"]:checked::after { - transform: translateX(20px); -} -""" - -_APP_THEME = gr.themes.Soft( - primary_hue="blue", - secondary_hue="gray", - neutral_hue="slate", - font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"], -) - - -# ---------- Model ---------- - -class VoxCPMDemo: - def __init__(self, model_dir: Optional[str] = None) -> None: - self.device = "cuda" if torch.cuda.is_available() else "cpu" - logger.info(f"Running on device: {self.device}") - - self.asr_model_id = "iic/SenseVoiceSmall" - self.asr_model: Optional[AutoModel] = AutoModel( - model=self.asr_model_id, - disable_update=True, - log_level="DEBUG", - device="cuda:0" if self.device == "cuda" else "cpu", - ) - - self.voxcpm_model: Optional[voxcpm.VoxCPM] = None - self.explicit_model_dir = model_dir - - def _resolve_model_dir(self) -> str: - if self.explicit_model_dir and os.path.isdir(self.explicit_model_dir): - return self.explicit_model_dir - env_model_dir = os.environ.get("VOXCPM_MODEL_DIR", "").strip() - if env_model_dir and os.path.isdir(env_model_dir): - return env_model_dir - repo_id = os.environ.get("HF_REPO_ID", "").strip() - if len(repo_id) > 0: - target_dir = os.path.join("models", repo_id.replace("/", "__")) - if not os.path.isdir(target_dir): - try: - from huggingface_hub import snapshot_download - os.makedirs(target_dir, exist_ok=True) - logger.info(f"Downloading model from HF repo '{repo_id}' to '{target_dir}' ...") - snapshot_download(repo_id=repo_id, local_dir=target_dir, local_dir_use_symlinks=False) - except Exception as e: - logger.warning(f"HF download failed: {e}. Falling back to 'models'.") - return "models" - return target_dir - return "models" - - def get_or_load_voxcpm(self) -> voxcpm.VoxCPM: - if self.voxcpm_model is not None: - return self.voxcpm_model - logger.info("Model not loaded, initializing...") - model_dir = self._resolve_model_dir() - logger.info(f"Using model dir: {model_dir}") - self.voxcpm_model = voxcpm.VoxCPM(voxcpm_model_path=model_dir, optimize=True) - logger.info("Model loaded successfully.") - return self.voxcpm_model - - def prompt_wav_recognition(self, prompt_wav: Optional[str]) -> str: - if prompt_wav is None: - return "" - res = self.asr_model.generate(input=prompt_wav, language="auto", use_itn=True) - return res[0]["text"].split("|>")[-1] - - def _build_generate_kwargs( - self, - *, - final_text: str, - audio_path: Optional[str], - prompt_text_clean: Optional[str], - cfg_value_input: float, - do_normalize: bool, - denoise: bool, - inference_timesteps: int = 10, - ) -> dict: - generate_kwargs = dict( - text=final_text, - reference_wav_path=audio_path, - cfg_value=float(cfg_value_input), - inference_timesteps=inference_timesteps, - 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( - '
' - 'VoxCPM Logo' - "
" - ) - - 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)