| |
| """音频降噪增强：DeepFilterNet2 人声增强。 |
| |
| 上传带噪音频（口播、讲座录音等），输出降噪后的 wav。 |
| 模型在首次请求时加载并缓存，之后常驻内存。 |
| """ |
| import shutil |
| import subprocess |
| import threading |
| import uuid |
|
|
| import gradio as gr |
|
|
| _lock = threading.Lock() |
| _model = None |
| _df_state = None |
|
|
|
|
| def get_model(): |
| global _model, _df_state |
| if _model is None: |
| with _lock: |
| if _model is None: |
| from df.enhance import init_df |
|
|
| _model, _df_state, _ = init_df() |
| return _model, _df_state |
|
|
|
|
| def to_wav(audio_path): |
| """把任意格式（m4a/mp3/flac 等）经 ffmpeg 转成 48k 单声道 wav。 |
| |
| df 自带的 torchaudio 2.0 sox 后端解不了 m4a/AAC，这是报错的根源。 |
| 若容器里没有 ffmpeg，则原样返回，让 load_audio 自己试。 |
| """ |
| if shutil.which("ffmpeg") is None: |
| return audio_path |
| out = f"/tmp/conv_{uuid.uuid4().hex}.wav" |
| r = subprocess.run( |
| ["ffmpeg", "-y", "-v", "error", "-i", audio_path, "-ar", "48000", "-ac", "1", out], |
| capture_output=True, |
| text=True, |
| ) |
| if r.returncode != 0: |
| raise gr.Error(f"音频转码失败：{r.stderr.strip()[-300:]}") |
| return out |
|
|
|
|
| def denoise(audio_path, atten_lim, mix): |
| if not audio_path: |
| return None |
| from df.enhance import enhance, load_audio, save_audio |
|
|
| model, df_state = get_model() |
| audio, _ = load_audio(to_wav(audio_path), sr=df_state.sr()) |
| lim = None if atten_lim == 0 else atten_lim |
| enhanced = enhance(model, df_state, audio, atten_lim_db=lim) |
| |
| n = min(enhanced.shape[-1], audio.shape[-1]) |
| out_audio = enhanced[..., :n] * mix + audio[..., :n] * (1.0 - mix) |
| out = "/tmp/enhanced.wav" |
| save_audio(out, out_audio, df_state.sr()) |
| return out |
|
|
|
|
| with gr.Blocks(title="音频降噪增强") as demo: |
| gr.Markdown( |
| "# 🎧 音频降噪增强\n" |
| "基于 DeepFilterNet2 的人声降噪：上传带底噪、环境声的录音，输出干净版 wav（48kHz）。\n\n" |
| "适合口播、讲座这类以人声为主的素材。CPU 推理，长音频请按时长耐心等。" |
| ) |
| inp = gr.Audio(label="原始音频", type="filepath") |
| atten = gr.Slider( |
| 0, 40, value=20, step=1, |
| label="最大衰减上限 (dB)", |
| info="限制模型对每个频点的抑制强度：数值越小越温和、损失越小；0 = 不限制（最强降噪）。默认 20。", |
| ) |
| mix = gr.Slider( |
| 0, 1, value=1.0, step=0.05, |
| label="降噪结果占比", |
| info="1 = 纯降噪结果；调低可按比例混回原声，找回被吃掉的细节（相声里的笑声、掌声也适用）。", |
| ) |
| btn = gr.Button("开始降噪", variant="primary") |
| out = gr.Audio(label="降噪后", type="filepath") |
| btn.click(denoise, inputs=[inp, atten, mix], outputs=out) |
|
|
|
|
| if __name__ == "__main__": |
| demo.launch(show_error=True) |
|
|
