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update voxcpm2

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This commit is contained in:
刘鑫
2026-03-31 11:50:37 +08:00
parent 23ed7ffeee
commit d9cf376e16
36 changed files with 8163 additions and 834 deletions
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app.py
+146 -87
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@@ -2,14 +2,15 @@ import os
import sys
import numpy as np
import torch
import gradio as gr
import spaces
import gradio as gr
import spaces # noqa: F401
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/VoxCPM1.5"
os.environ["HF_REPO_ID"] = "openbmb/VoxCPM2"
import voxcpm
@@ -24,13 +25,13 @@ class VoxCPMDemo:
self.asr_model: Optional[AutoModel] = AutoModel(
model=self.asr_model_id,
disable_update=True,
log_level='DEBUG',
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 = "./models/VoxCPM1.5"
self.default_local_model_dir = "/Users/xinliu/Downloads/VoxCPM2-0.5B-newaudiovae-6hz-0316"
# ---------- Model helpers ----------
def _resolve_model_dir(self) -> str:
@@ -49,6 +50,7 @@ class VoxCPMDemo:
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)
@@ -64,7 +66,7 @@ class VoxCPMDemo:
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)
self.voxcpm_model = voxcpm.VoxCPM(voxcpm_model_path=model_dir, optimize=False)
print("Model loaded successfully.", file=sys.stderr)
return self.voxcpm_model
@@ -73,21 +75,24 @@ class VoxCPMDemo:
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]
text = res[0]["text"].split("|>")[-1]
return text
def generate_tts_audio(
self,
text_input: str,
prompt_wav_path_input: Optional[str] = None,
prompt_text_input: Optional[str] = None,
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; optional reference audio for voice style guidance.
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()
@@ -96,14 +101,25 @@ class VoxCPMDemo:
if len(text) == 0:
raise ValueError("Please input text to synthesize.")
prompt_wav_path = prompt_wav_path_input if prompt_wav_path_input else None
prompt_text = prompt_text_input if prompt_text_input else None
# 处理 control instruction
control = (control_instruction or "").strip()
if control:
final_text = f"({control}){text}"
else:
final_text = text
print(f"Generating audio for text: '{text[:60]}...'", file=sys.stderr)
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=text,
prompt_text=prompt_text,
prompt_wav_path=prompt_wav_path,
text=final_text,
reference_wav_path=reference_wav_path,
cfg_value=float(cfg_value_input),
inference_timesteps=int(inference_timesteps_input),
normalize=do_normalize,
@@ -114,46 +130,53 @@ class VoxCPMDemo:
# ---------- UI Builders ----------
THEME = gr.themes.Soft(
primary_hue="blue",
secondary_hue="gray",
neutral_hue="slate",
font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"],
)
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;
}
/* 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;
}
/* Bold labels for specific checkboxes */
#chk_denoise label,
#chk_denoise span,
#chk_normalize label,
#chk_normalize span {
font-weight: 600;
}
"""
def create_demo_interface(demo: VoxCPMDemo):
"""Build the Gradio UI for VoxCPM demo."""
# static assets (logo path)
gr.set_static_paths(paths=[Path.cwd().absolute()/"assets"])
gr.set_static_paths(paths=[Path.cwd().absolute() / "assets"])
with gr.Blocks(
theme=gr.themes.Soft(
primary_hue="blue",
secondary_hue="gray",
neutral_hue="slate",
font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"]
),
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;
}
/* Bold accordion labels */
#acc_quick details > summary,
#acc_tips details > summary {
font-weight: 600 !important;
font-size: 1.1em !important;
}
/* Bold labels for specific checkboxes */
#chk_denoise label,
#chk_denoise span,
#chk_normalize label,
#chk_normalize span {
font-weight: 600;
}
"""
) as interface:
# Header logo
gr.HTML('<div class="logo-container"><img src="/gradio_api/file=assets/voxcpm_logo.png" alt="VoxCPM Logo"></div>')
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,
)
# Quick Start
with gr.Accordion("📋 Quick Start Guide ｜快速入门", open=False, elem_id="acc_quick"):
@@ -200,34 +223,56 @@ def create_demo_interface(demo: VoxCPMDemo):
# Main controls
with gr.Row():
with gr.Column():
prompt_wav = gr.Audio(
sources=["upload", 'microphone'],
# 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="Prompt Speech (Optional, or let VoxCPM improvise)",
value="./examples/example.wav",
label="Reference Audio (Optional)",
)
DoDenoisePromptAudio = gr.Checkbox(
value=False,
label="Prompt Speech Enhancement",
label="Reference Audio Enhancement",
elem_id="chk_denoise",
info="We use ZipEnhancer model to denoise the prompt audio."
info="Use ZipEnhancer to denoise the reference audio",
)
with gr.Row():
prompt_text = gr.Textbox(
value="Just by listening a few minutes a day, you'll be able to eliminate negative thoughts by conditioning your mind to be more positive.",
label="Prompt Text",
placeholder="Please enter the prompt text. Automatic recognition is supported, and you can correct the results yourself..."
)
run_btn = gr.Button("Generate Speech", variant="primary")
# 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*",
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",
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.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 values increase adherence to prompt, lower values allow more creativity"
info="Higher = more adherence to prompt; Lower = more creativity",
)
inference_timesteps = gr.Slider(
minimum=4,
@@ -235,41 +280,55 @@ def create_demo_interface(demo: VoxCPMDemo):
value=10,
step=1,
label="Inference Timesteps",
info="Number of inference timesteps for generation (higher values may improve quality but slower)"
info="Higher = better quality but slower",
)
with gr.Row():
text = gr.Textbox(
value="VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly realistic speech.",
label="Target Text",
)
with gr.Row():
DoNormalizeText = gr.Checkbox(
value=False,
label="Text Normalization",
elem_id="chk_normalize",
info="We use wetext library to normalize the input text."
)
audio_output = gr.Audio(label="Output Audio")
gr.Markdown("### 🔈 Output")
audio_output = gr.Audio(label="Generated Audio")
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,
inputs=[text, prompt_wav, prompt_text, cfg_value, inference_timesteps, DoNormalizeText, DoDenoisePromptAudio],
inputs=[
text,
control_instruction,
reference_wav,
cfg_value,
inference_timesteps,
DoNormalizeText,
DoDenoisePromptAudio,
],
outputs=[audio_output],
show_progress=True,
api_name="generate",
)
prompt_wav.change(fn=demo.prompt_wav_recognition, inputs=[prompt_wav], outputs=[prompt_text])
return interface
def run_demo(server_name: str = "localhost", server_port: int = 7860, show_error: bool = True):
def run_demo(server_name: str = "0.0.0.0", server_port: int = 7869, show_error: bool = True):
demo = VoxCPMDemo()
interface = create_demo_interface(demo)
# Recommended to enable queue on Spaces for better throughput
interface.queue(max_size=10, default_concurrency_limit=1).launch(server_name=server_name, server_port=server_port, show_error=show_error)
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,
)
if __name__ == "__main__":
run_demo()
run_demo()