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d932265 cd06905 d932265 cd06905 d932265 ea4ccbd a42ebc7 d932265 a42ebc7 d932265 e8b8f2a cd06905 0dddc64 d932265 ea4ccbd d932265 cd06905 0dddc64 d932265 0dddc64 d932265 e8b8f2a d932265 0dddc64 d932265 0dddc64 d932265 e8b8f2a d932265 cd06905 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | #!/usr/bin/env python3
"""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)
# 混回原声：mix=1 为纯降噪结果，调低可找回被吃掉的细节
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)
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