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surport voxcpm2 cli

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刘鑫
2026-04-01 21:15:55 +08:00
parent 42c428164c
commit addee2c550
8 changed files with 1642 additions and 375 deletions
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app.py
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@@ -1,9 +1,9 @@
import os
import sys
import logging
import numpy as np
import torch
import gradio as gr
import spaces # noqa: F401
from typing import Optional, Tuple
from funasr import AutoModel
from pathlib import Path
@@ -14,130 +14,150 @@ if os.environ.get("HF_REPO_ID", "").strip() == "":
import voxcpm
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
handlers=[logging.StreamHandler(sys.stdout)],
)
logger = logging.getLogger(__name__)
class VoxCPMDemo:
def __init__(self) -> None:
self.device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"🚀 Running on device: {self.device}", file=sys.stderr)
# ---------- Inline i18n (en + zh-CN only) ----------
# ASR model for prompt text recognition
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",
)
# TTS model (lazy init)
self.voxcpm_model: Optional[voxcpm.VoxCPM] = None
self.default_local_model_dir = "/Users/xinliu/Downloads/VoxCPM2-0.5B-newaudiovae-6hz-0316"
# ---------- Model helpers ----------
def _resolve_model_dir(self) -> str:
"""
Resolve model directory:
1) Use local checkpoint directory if exists
2) If HF_REPO_ID env is set, download into models/{repo}
3) Fallback to 'models'
"""
if os.path.isdir(self.default_local_model_dir):
return self.default_local_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 # type: ignore
os.makedirs(target_dir, exist_ok=True)
print(f"Downloading model from HF repo '{repo_id}' to '{target_dir}' ...", file=sys.stderr)
snapshot_download(repo_id=repo_id, local_dir=target_dir, local_dir_use_symlinks=False)
except Exception as e:
print(f"Warning: HF download failed: {e}. Falling back to 'data'.", file=sys.stderr)
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
print("Model not loaded, initializing...", file=sys.stderr)
model_dir = self._resolve_model_dir()
print(f"Using model dir: {model_dir}", file=sys.stderr)
self.voxcpm_model = voxcpm.VoxCPM(voxcpm_model_path=model_dir, optimize=False)
print("Model loaded successfully.", file=sys.stderr)
return self.voxcpm_model
# ---------- Functional endpoints ----------
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)
text = res[0]["text"].split("|>")[-1]
return text
def generate_tts_audio(
self,
text_input: str,
control_instruction: str = "",
reference_wav_path_input: Optional[str] = None,
cfg_value_input: float = 2.0,
inference_timesteps_input: int = 10,
do_normalize: bool = True,
denoise: bool = True,
) -> Tuple[int, np.ndarray]:
"""
Generate speech from text using VoxCPM.
- If reference_wav provided: Prompt isolation mode (voice cloning)
- If no reference_wav: Voice design mode (use control_instruction to describe voice)
Returns (sample_rate, waveform_numpy)
"""
current_model = self.get_or_load_voxcpm()
text = (text_input or "").strip()
if len(text) == 0:
raise ValueError("Please input text to synthesize.")
# 处理 control instruction
control = (control_instruction or "").strip()
if control:
final_text = f"({control}){text}"
else:
final_text = text
reference_wav_path = reference_wav_path_input if reference_wav_path_input else None
# 判断模式
if reference_wav_path:
print(f"[Prompt Isolation Mode] reference_wav: {reference_wav_path}", file=sys.stderr)
else:
print(f"[Voice Design Mode] control: {control[:50] if control else 'None'}...", file=sys.stderr)
print(f"Generating audio for text: '{final_text[:80]}...'", file=sys.stderr)
wav = current_model.generate(
text=final_text,
reference_wav_path=reference_wav_path,
cfg_value=float(cfg_value_input),
inference_timesteps=int(inference_timesteps_input),
normalize=do_normalize,
denoise=denoise,
)
return (current_model.tts_model.sample_rate, wav)
# ---------- UI Builders ----------
THEME = gr.themes.Soft(
primary_hue="blue",
secondary_hue="gray",
neutral_hue="slate",
font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"],
_USAGE_INSTRUCTIONS_EN = (
"**Usage Instructions:**\n\n"
"🎨 **Voice Design** — Create a voice from scratch \n"
"No reference audio needed. Simply describe the desired gender, tone, and emotion "
"in Control Instruction, and VoxCPM will generate a unique voice for you.\n\n"
"🎛️ **Controllable Voice Cloning** — Clone with style control \n"
"Upload reference audio and use Control Instruction to guide speed, emotion, style, and more.\n\n"
"🎙️ **Hi-Fi Cloning** — Maximum voice similarity \n"
"For the best cloning quality, enable and provide the reference audio transcript "
"to reproduce the original voice as closely as possible."
)
CSS = """
_EXAMPLES_FOOTER_EN = (
"---\n"
"**Voice Description Examples:** \n"
"You can describe it like this: \n"
"【Example 1: Melancholic/Tsundere Female】 \n"
'Control Instruction: "A young beautiful girl with a sweet voice, '
'tsundere tone, slow speaking pace, and a touch of sadness." \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: Lazy/Casual Male】 \n"
'Control Instruction: "Lazy and drawling male voice, nasal, '
'very relaxed and casual." \n'
'Target Text: "Dude, did you see that set? The waves out there are totally gnarly today, bro. '
"Just catching barrels all morning. It's like, totally righteous, you know what I mean?\""
)
_USAGE_INSTRUCTIONS_ZH = (
"**使用说明：**\n\n"
"🎨 **Voice Design — 声音定制** \n"
"无需上传参考音频，只需在 Control Instruction 中描述你想要的性别、音色和情绪，"
"VoxCPM 即可凭空为你生成专属音色。\n\n"
"🎛️ **Controllable Voice Cloning — 可控音色克隆** \n"
"支持上传参考音频，并可以给instruction文本来指导控制语速、情绪、风格等表现。\n\n"
"🎙️ **Hi-Fi Cloning — 高保真克隆** \n"
"追求最佳克隆效果，启用并上传参考音频文本来最大程度克隆原始音色。\n\n"
)
_EXAMPLES_FOOTER_ZH = (
"---\n"
"**声音描述示例：** \n"
"你可以这样输入（中英文均可）： \n"
"【示例1：深宫太后】 \n"
'`Control Instruction`: `"中老年女性，声音低沉阴冷，语速慢而有力，'
'每个字都像是深思熟虑后说出，带有深不可测的城府和威胁感。"` \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：暴躁男声】 \n"
'`Control Instruction`: `"暴躁的中年男声，语速较快，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'
"💡 **方言生成特别说明：** \n"
'当前版本若要生成纯正的方言，请务必在"Target Text"中直接输入方言专属的词汇和表达，'
"并配合方言的音色描述。 \n\n"
"【示例一：广东话】 \n"
'`Control Instruction`: `"广东话，中年男性，语气平淡"` \n'
"✅ 正确的 `Target Text`（使用粤语表达）："
'`"伙計，唔該一個A餐，凍奶茶少甜！"` \n'
"❌ 错误的 `Target Text`（使用普通话）："
'`"伙计，麻烦来一个A餐，冻奶茶少甜！"` \n\n'
"【示例二：河南话】 \n"
'`Control Instruction`: `"河南话，接地气的大叔"` \n'
"✅ 正确的 `Target Text`（使用河南话表达）："
'`"恁这是弄啥嘞？晌午吃啥饭？"` \n'
"❌ 错误的 `Target Text`（使用普通话）："
'`"你这是在干什么呢？中午吃什么饭？"` \n\n'
"🤖 **实用小技巧：不知道怎么写地道的方言？** \n"
"您可以先在 豆包、DeepSeek、Kimi 等 AI 助手中输入普通话，"
"让它们帮你翻译成方言文本，然后再复制粘贴到 `Target Text` 中直接使用！ \n\n"
"📢 **研发小贴士：** \n"
'我们正在努力优化 AI！后续版本将支持"输入普通话文本，一键生成方言口音"的功能，敬请期待！'
)
_I18N_TRANSLATIONS = {
"en": {
"reference_audio_label": "Reference Audio (optional — for cloning)",
"show_prompt_text_label": "Enable Prompt Text (improves voice similarity)",
"show_prompt_text_info": "Uses the ASR transcript of reference audio for higher cloning fidelity. Control Instruction will be disabled.",
"prompt_text_label": "Prompt Text (auto-filled by ASR, editable)",
"prompt_text_placeholder": "The transcript of your reference audio will appear here...",
"control_label": "Control Instruction (optional, only support English and Chinese)",
"control_placeholder": "e.g. 年轻女性，温柔甜美 / sadly / an excited young man",
"target_text_label": "Target Text",
"generate_btn": "Generate Speech",
"generated_audio_label": "Generated Audio",
"advanced_settings_title": "Advanced Settings",
"ref_denoise_label": "Reference audio enhancement",
"ref_denoise_info": "Denoise reference audio with ZipEnhancer",
"normalize_label": "Text normalization",
"normalize_info": "Normalize input text with wetext",
"cfg_label": "CFG (guidance scale)",
"cfg_info": "Higher = stronger prompt adherence; lower = more variation",
"usage_instructions": _USAGE_INSTRUCTIONS_EN,
"examples_footer": _EXAMPLES_FOOTER_EN,
},
"zh-CN": {
"reference_audio_label": "参考音频（可选 - 用于克隆）",
"show_prompt_text_label": "启用 Prompt Text（提升音色还原度）",
"show_prompt_text_info": "使用参考音频的文本内容提升克隆相似度，开启后 Control Instruction 将被禁用",
"prompt_text_label": "Prompt Text（ASR 自动填充，可编辑）",
"prompt_text_placeholder": "参考音频的文本内容将自动识别到这里...",
"control_label": "Control Instruction（可选，仅支持中文和英文）",
"control_placeholder": "如：年轻女性，温柔甜美 / sadly / an excited young man",
"target_text_label": "Target Text（要合成的文本）",
"generate_btn": "开始生成",
"generated_audio_label": "生成音频",
"advanced_settings_title": "高级设置",
"ref_denoise_label": "参考音频降噪增强",
"ref_denoise_info": "使用 ZipEnhancer 对参考音频进行降噪",
"normalize_label": "文本规范化",
"normalize_info": "使用 wetext 对输入文本进行规范化处理",
"cfg_label": "CFG Value（引导强度）",
"cfg_info": "数值越高，越贴合提示要求；数值越低，变化空间越大",
"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 = (
"VoxCPM is an innovative end-to-end TTS model from ModelBest, "
"designed to generate highly realistic speech."
)
_CUSTOM_CSS = """
.logo-container {
text-align: center;
margin: 0.5rem 0 1rem 0;
@@ -148,165 +168,314 @@ CSS = """
max-width: 200px;
display: inline-block;
}
/* Bold accordion labels */
#acc_quick > .label-wrap,
#acc_tips > .label-wrap,
#acc_quick > .label-wrap > span,
#acc_tips > .label-wrap > span,
#acc_quick summary,
#acc_tips summary {
font-weight: 600 !important;
font-size: 1.1em !important;
/* Toggle switch style */
.switch-toggle {
padding: 8px 12px;
border-radius: 8px;
background: var(--block-background-fill);
}
/* Bold labels for specific checkboxes */
#chk_denoise label,
#chk_denoise span,
#chk_normalize label,
#chk_normalize span {
font-weight: 600;
.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,
) -> dict:
generate_kwargs = dict(
text=final_text,
reference_wav_path=audio_path,
cfg_value=float(cfg_value_input),
inference_timesteps=10,
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,
) -> 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,
)
wav = current_model.generate(**generate_kwargs)
return (current_model.tts_model.sample_rate, wav)
# ---------- UI ----------
def create_demo_interface(demo: VoxCPMDemo):
"""Build the Gradio UI for VoxCPM demo."""
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,
):
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,
)
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>',
padding=True,
'<div class="logo-container">'
'<img src="/gradio_api/file=assets/voxcpm_logo.png" alt="VoxCPM Logo">'
"</div>"
)
# Quick Start
with gr.Accordion("📋 Quick Start Guide ｜快速入门", open=False, elem_id="acc_quick"):
gr.Markdown("""
### How to Use ｜使用说明
1. **(Optional) Provide a Voice Prompt** - Upload or record an audio clip to provide the desired voice characteristics for synthesis.
**（可选）提供参考声音** - 上传或录制一段音频，为声音合成提供音色、语调和情感等个性化特征
2. **(Optional) Enter prompt text** - If you provided a voice prompt, enter the corresponding transcript here (auto-recognition available).
**（可选项）输入参考文本** - 如果提供了参考语音，请输入其对应的文本内容（支持自动识别）。
3. **Enter target text** - Type the text you want the model to speak.
**输入目标文本** - 输入您希望模型朗读的文字内容。
4. **Generate Speech** - Click the "Generate" button to create your audio.
**生成语音** - 点击"生成"按钮，即可为您创造出音频。
""")
gr.Markdown(I18N("usage_instructions"))
# Pro Tips
with gr.Accordion("💡 Pro Tips ｜使用建议", open=False, elem_id="acc_tips"):
gr.Markdown("""
### Prompt Speech Enhancement｜参考语音降噪
- **Enable** to remove background noise for a clean voice, with an external ZipEnhancer component. However, this will limit the audio sampling rate to 16kHz, restricting the cloning quality ceiling.
**启用**：通过 ZipEnhancer 组件消除背景噪音，但会将音频采样率限制在16kHz，限制克隆上限。
- **Disable** to preserve the original audio's all information, including background atmosphere, and support audio cloning up to 44.1kHz sampling rate.
**禁用**：保留原始音频的全部信息，包括背景环境声，最高支持44.1kHz的音频复刻。
### Text Normalization｜文本正则化
- **Enable** to process general text with an external WeTextProcessing component.
**启用**：使用 WeTextProcessing 组件，可支持常见文本的正则化处理。
- **Disable** to use VoxCPM's native text understanding ability. For example, it supports phonemes input (For Chinese, phonemes are converted using pinyin, {ni3}{hao3}; For English, phonemes are converted using CMUDict, {HH AH0 L OW1}), try it!
**禁用**：将使用 VoxCPM 内置的文本理解能力。如，支持音素输入（如中文转拼音：{ni3}{hao3}；英文转CMUDict：{HH AH0 L OW1}）和公式符号合成，尝试一下！
### CFG Value｜CFG 值
- **Lower CFG** if the voice prompt sounds strained or expressive, or instability occurs with long text input.
**调低**：如果提示语音听起来不自然或过于夸张，或者长文本输入出现稳定性问题。
- **Higher CFG** for better adherence to the prompt speech style or input text, or instability occurs with too short text input.
**调高**：为更好地贴合提示音频的风格或输入文本， 或者极短文本输入出现稳定性问题。
### Inference Timesteps｜推理时间步
- **Lower** for faster synthesis speed.
**调低**：合成速度更快。
- **Higher** for better synthesis quality.
**调高**：合成质量更佳。
""")
# Main controls
with gr.Row():
with gr.Column():
# 1. Reference Audio
# gr.Markdown("### 🎤 Reference Audio (Optional)")
# gr.Markdown("*提供参考音频进行音色克隆；不提供则使用 Voice Design 模式*")
reference_wav = gr.Audio(
sources=["upload", "microphone"],
type="filepath",
label="Reference Audio (Optional)",
label=I18N("reference_audio_label"),
)
DoDenoisePromptAudio = gr.Checkbox(
show_prompt_text = gr.Checkbox(
value=False,
label="Reference Audio Enhancement",
elem_id="chk_denoise",
info="Use ZipEnhancer to denoise the reference audio",
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,
)
# 2. Control Instruction
# gr.Markdown("### 🎛️ Control Instruction (Optional)")
# gr.Markdown("*描述声音风格、情感等，格式：`(instruction) text`*")
control_instruction = gr.Textbox(
value="",
label="Control Instruction",
placeholder="*描述声音风格、情感等，格式：`(instruction) text`，例如：年轻女性，温柔甜美 / 悲伤地说 / an excited young man*",
label=I18N("control_label"),
placeholder=I18N("control_placeholder"),
lines=2,
)
# 3. Target Text
# gr.Markdown("### 📝 Target Text")
text = gr.Textbox(
value="VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly realistic speech.",
label="Target Text",
value=DEFAULT_TARGET_TEXT,
label=I18N("target_text_label"),
lines=3,
)
DoNormalizeText = gr.Checkbox(
value=False,
label="Text Normalization",
elem_id="chk_normalize",
info="Use wetext library to normalize the input text",
)
run_btn = gr.Button("🔊 Generate Speech", variant="primary", size="lg")
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"),
)
run_btn = gr.Button(I18N("generate_btn"), variant="primary", size="lg")
with gr.Column():
gr.Markdown("### ⚙️ Generation Settings")
cfg_value = gr.Slider(
minimum=1.0,
maximum=3.0,
value=2.0,
step=0.1,
label="CFG Value (Guidance Scale)",
info="Higher = more adherence to prompt; Lower = more creativity",
)
inference_timesteps = gr.Slider(
minimum=4,
maximum=30,
value=10,
step=1,
label="Inference Timesteps",
info="Higher = better quality but slower",
)
audio_output = gr.Audio(label=I18N("generated_audio_label"))
gr.Markdown(I18N("examples_footer"))
gr.Markdown("### 🔈 Output")
audio_output = gr.Audio(label="Generated Audio")
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],
)
gr.Markdown("""
---
**模式说明 / Mode Info:**
- **有 Reference Audio** → Prompt 隔离模式（音色克隆）
- **无 Reference Audio** → Voice Design 模式（用 Control Instruction 描述声音）
**Control Instruction 示例：**
- `年轻女性，温柔甜美`
- `悲伤地说`
- `an excited young man`
""")
# Wiring
run_btn.click(
fn=demo.generate_tts_audio,
fn=_generate,
inputs=[
text,
control_instruction,
reference_wav,
show_prompt_text,
prompt_text,
cfg_value,
inference_timesteps,
DoNormalizeText,
DoDenoisePromptAudio,
],
@@ -317,18 +486,28 @@ def create_demo_interface(demo: VoxCPMDemo):
return interface
def run_demo(server_name: str = "0.0.0.0", server_port: int = 7869, show_error: bool = True):
demo = VoxCPMDemo()
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,
theme=THEME,
css=CSS,
i18n=I18N,
theme=_APP_THEME,
css=_CUSTOM_CSS,
)
if __name__ == "__main__":
run_demo()
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)