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| # app.py | |
| import torch | |
| import spaces | |
| import gradio as gr | |
| from diffusers import AudioLDM2Pipeline | |
| import scipy.io.wavfile as wavfile | |
| import tempfile | |
| import os | |
| _pipe = None | |
| # 设置缓存（只需这三行，永久生效） | |
| os.environ["HF_HOME"] = "/tmp/huggingface" | |
| os.makedirs("/tmp/huggingface", exist_ok=True) | |
| def get_pipeline(): | |
| """只在 CPU 上懒加载和缓存模型，避免在主进程中过早初始化 CUDA。""" | |
| global _pipe | |
| if _pipe is None: | |
| _pipe = AudioLDM2Pipeline.from_pretrained( | |
| "cvssp/audioldm2", | |
| torch_dtype=torch.float16, | |
| revision="0f5395520e81196e2edb657c0ea85aac026b0599" | |
| ) | |
| _pipe.enable_attention_slicing() | |
| _pipe.vae.enable_slicing() | |
| print("模型加载完毕，后续调用秒响应！") | |
| return _pipe | |
| # 生成函数（所有 CUDA 相关操作必须放在 @spaces.GPU 内） | |
| def text_to_audio( | |
| prompt: str, | |
| negative_prompt: str = "", | |
| duration: float = 5.0, | |
| guidance_scale: float = 3.5, | |
| num_inference_steps: int = 100, | |
| num_waveforms: int = 1, | |
| seed: int = -1, | |
| ): | |
| # 在 GPU 进程中获取模型并移动到 CUDA | |
| pipe = get_pipeline().to("cuda") | |
| generator = None if seed == -1 else torch.Generator(device="cuda").manual_seed(seed) | |
| with torch.autocast(device_type="cuda"): | |
| audio = pipe( | |
| prompt, | |
| negative_prompt=negative_prompt or None, | |
| num_inference_steps=num_inference_steps, | |
| audio_length_in_s=duration, | |
| num_waveforms_per_prompt=num_waveforms, | |
| guidance_scale=guidance_scale, | |
| generator=generator, | |
| ).audios[0] | |
| tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav", dir="/tmp") | |
| wavfile.write(tmp_file.name, rate=16000, data=audio) | |
| return tmp_file.name | |
| # ==================== Gradio 界面 ==================== | |
| css = """ | |
| .gradio-container {max-width: 900px !important; margin: auto !important;} | |
| footer {display: none !important;} | |
| """ | |
| with gr.Blocks(title="AudioLDM2-Large Text-to-Audio") as demo: | |
| gr.HTML(f"<style>{css}</style>") | |
| gr.Markdown(""" | |
| # AudioLDM2-Large | |
| 文本生成音频模型 | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| prompt = gr.Textbox( | |
| label="描述你想要的音频（越详细越好）", | |
| placeholder="例如：A dog barking angrily on a busy city street with car horns", | |
| lines=3 | |
| ) | |
| negative = gr.Textbox( | |
| label="负面提示（可选）", | |
| placeholder="low quality, noise, distortion, echo", | |
| value="low quality,music,noise", | |
| lines=3 | |
| ) | |
| with gr.Row(): | |
| duration = gr.Slider(2.0, 120.0, value=30, step=0.5, label="时长（秒）") | |
| steps = gr.Slider(50, 200, value=100, step=25, label="采样步数（越高越精细但越慢）") | |
| with gr.Row(): | |
| guidance = gr.Slider(1.0, 10.0, value=3.5, step=0.5, label="引导尺度（Guidance Scale）") | |
| num = gr.Slider(1, 4, value=1, step=1, label="生成数量（同时生成多个候选）") | |
| seed = gr.Number(value=-1, label="随机种子（相同种子+相同提示 = 可复现，填 -1 随机）") | |
| btn = gr.Button("Generate Audio 🎵", variant="primary", size="lg") | |
| with gr.Column(scale=1): | |
| output_audio = gr.Audio(label="生成的音频", type="filepath", interactive=False) | |
| btn.click( | |
| fn=text_to_audio, | |
| inputs=[prompt, negative, duration, guidance, steps, num, seed], | |
| outputs=output_audio, | |
| show_progress=True | |
| ) | |
| gr.Examples( | |
| examples=[ | |
| ["A beautiful piano melody with soft strings in the background", "", 8.0], | |
| ["Thunderstorm with heavy rain and strong wind blowing through trees", "", 7.0], | |
| ["A cat meowing and then purring while being petted", "", 5.0], | |
| ["80s synthwave music with retro drums and electric guitar solo", "", 10.0], | |
| ["Fire crackling in a cozy fireplace on a winter night", "", 6.0], | |
| ], | |
| inputs=[prompt, negative, duration], | |
| label="点击示例一键生成" | |
| ) | |
| gr.Markdown(""" | |
| ### Tips | |
| - 生成一次大约需要 20～60 秒（取决于步数和时长） | |
| - 推荐 200 步 + Guidance 3.5～4.5 获得最佳质量 | |
| """) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() |