DeepFilterNet2 / app.py
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import glob
import math
import os
import tempfile
import time
from typing import List, Optional, Tuple, Union
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import torch
from loguru import logger
from PIL import Image
from torch import Tensor
from torchaudio.backend.common import AudioMetaData
from df import config
from df.enhance import enhance, init_df, load_audio, save_audio
from df.io import resample
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model, df, _ = init_df("./DeepFilterNet2", config_allow_defaults=True)
model = model.to(device=device).eval()
fig_noisy: plt.Figure
fig_enh: plt.Figure
ax_noisy: plt.Axes
ax_enh: plt.Axes
fig_noisy, ax_noisy = plt.subplots(figsize=(15.2, 4))
fig_noisy.set_tight_layout(True)
fig_enh, ax_enh = plt.subplots(figsize=(15.2, 4))
fig_enh.set_tight_layout(True)
NOISES = {
"None": None,
"Kitchen": "samples/dkitchen.wav",
"Living Room": "samples/dliving.wav",
"River": "samples/nriver.wav",
"Cafe": "samples/scafe.wav",
}
def mix_at_snr(clean, noise, snr, eps=1e-10):
"""Mix clean and noise signal at a given SNR.
Args:
clean: 1D Tensor with the clean signal to mix.
noise: 1D Tensor of shape.
snr: Signal to noise ratio.
Returns:
clean: 1D Tensor with gain changed according to the snr.
noise: 1D Tensor with the combined noise channels.
mix: 1D Tensor with added clean and noise signals.
"""
clean = torch.as_tensor(clean).mean(0, keepdim=True)
noise = torch.as_tensor(noise).mean(0, keepdim=True)
if noise.shape[1] < clean.shape[1]:
noise = noise.repeat((1, int(math.ceil(clean.shape[1] / noise.shape[1]))))
max_start = int(noise.shape[1] - clean.shape[1])
start = torch.randint(0, max_start, ()).item() if max_start > 0 else 0
logger.debug(f"start: {start}, {clean.shape}")
noise = noise[:, start : start + clean.shape[1]]
E_speech = torch.mean(clean.pow(2)) + eps
E_noise = torch.mean(noise.pow(2))
K = torch.sqrt((E_noise / E_speech) * 10 ** (snr / 10) + eps)
noise = noise / K
mixture = clean + noise
logger.debug("mixture: {mixture.shape}")
assert torch.isfinite(mixture).all()
max_m = mixture.abs().max()
if max_m > 1:
logger.warning(f"Clipping detected during mixing. Reducing gain by {1/max_m}")
clean, noise, mixture = clean / max_m, noise / max_m, mixture / max_m
return clean, noise, mixture
def load_audio_gradio(
audio_or_file: Union[None, str, Tuple[int, np.ndarray]], sr: int
) -> Optional[Tuple[Tensor, AudioMetaData]]:
if audio_or_file is None:
return None
if isinstance(audio_or_file, str):
if audio_or_file.lower() == "none":
return None
# First try default format
audio, meta = load_audio(audio_or_file, sr)
else:
meta = AudioMetaData(-1, -1, -1, -1, "")
assert isinstance(audio_or_file, (tuple, list))
meta.sample_rate, audio_np = audio_or_file
# Gradio documentation says, the shape is [samples, 2], but apparently sometimes its not.
audio_np = audio_np.reshape(audio_np.shape[0], -1).T
if audio_np.dtype == np.int16:
audio_np = (audio_np / (1 << 15)).astype(np.float32)
elif audio_np.dtype == np.int32:
audio_np = (audio_np / (1 << 31)).astype(np.float32)
audio = resample(torch.from_numpy(audio_np), meta.sample_rate, sr)
return audio, meta
def demo_fn(speech_upl: str, noise_type: str, snr: int, mic_input: Optional[str] = None):
if mic_input:
speech_upl = mic_input
sr = config("sr", 48000, int, section="df")
logger.info(f"Got parameters speech_upl: {speech_upl}, noise: {noise_type}, snr: {snr}")
snr = int(snr)
noise_fn = NOISES[noise_type]
meta = AudioMetaData(-1, -1, -1, -1, "")
max_s = 10 # limit to 10 seconds
if speech_upl is not None:
sample, meta = load_audio(speech_upl, sr)
max_len = max_s * sr
if sample.shape[-1] > max_len:
start = torch.randint(0, sample.shape[-1] - max_len, ()).item()
sample = sample[..., start : start + max_len]
else:
sample, meta = load_audio("samples/p232_013_clean.wav", sr)
sample = sample[..., : max_s * sr]
if sample.dim() > 1 and sample.shape[0] > 1:
assert (
sample.shape[1] > sample.shape[0]
), f"Expecting channels first, but got {sample.shape}"
sample = sample.mean(dim=0, keepdim=True)
logger.info(f"Loaded sample with shape {sample.shape}")
if noise_fn is not None:
noise, _ = load_audio(noise_fn, sr) # type: ignore
logger.info(f"Loaded noise with shape {noise.shape}")
_, _, sample = mix_at_snr(sample, noise, snr)
logger.info("Start denoising audio")
enhanced = enhance(model, df, sample)
logger.info("Denoising finished")
lim = torch.linspace(0.0, 1.0, int(sr * 0.15)).unsqueeze(0)
lim = torch.cat((lim, torch.ones(1, enhanced.shape[1] - lim.shape[1])), dim=1)
enhanced = enhanced * lim
if meta.sample_rate != sr:
enhanced = resample(enhanced, sr, meta.sample_rate)
sample = resample(sample, sr, meta.sample_rate)
sr = meta.sample_rate
noisy_wav = tempfile.NamedTemporaryFile(suffix="noisy.wav", delete=False).name
save_audio(noisy_wav, sample, sr)
enhanced_wav = tempfile.NamedTemporaryFile(suffix="enhanced.wav", delete=False).name
save_audio(enhanced_wav, enhanced, sr)
logger.info(f"saved audios: {noisy_wav}, {enhanced_wav}")
ax_noisy.clear()
ax_enh.clear()
noisy_im = spec_im(sample, sr=sr, figure=fig_noisy, ax=ax_noisy)
enh_im = spec_im(enhanced, sr=sr, figure=fig_enh, ax=ax_enh)
filter = [speech_upl, noisy_wav, enhanced_wav]
if mic_input is not None and mic_input != "":
filter.append(mic_input)
cleanup_tmp(filter)
return noisy_wav, noisy_im, enhanced_wav, enh_im
def specshow(
spec,
ax=None,
title=None,
xlabel=None,
ylabel=None,
sr=48000,
n_fft=None,
hop=None,
t=None,
f=None,
vmin=-100,
vmax=0,
xlim=None,
ylim=None,
cmap="inferno",
):
"""Plots a spectrogram of shape [F, T]"""
spec_np = spec.cpu().numpy() if isinstance(spec, torch.Tensor) else spec
if ax is not None:
set_title = ax.set_title
set_xlabel = ax.set_xlabel
set_ylabel = ax.set_ylabel
set_xlim = ax.set_xlim
set_ylim = ax.set_ylim
else:
ax = plt
set_title = plt.title
set_xlabel = plt.xlabel
set_ylabel = plt.ylabel
set_xlim = plt.xlim
set_ylim = plt.ylim
if n_fft is None:
if spec.shape[0] % 2 == 0:
n_fft = spec.shape[0] * 2
else:
n_fft = (spec.shape[0] - 1) * 2
hop = hop or n_fft // 4
if t is None:
t = np.arange(0, spec_np.shape[-1]) * hop / sr
if f is None:
f = np.arange(0, spec_np.shape[0]) * sr // 2 / (n_fft // 2) / 1000
im = ax.pcolormesh(
t, f, spec_np, rasterized=True, shading="auto", vmin=vmin, vmax=vmax, cmap=cmap
)
if title is not None:
set_title(title)
if xlabel is not None:
set_xlabel(xlabel)
if ylabel is not None:
set_ylabel(ylabel)
if xlim is not None:
set_xlim(xlim)
if ylim is not None:
set_ylim(ylim)
return im
def spec_im(
audio: torch.Tensor,
figsize=(15, 5),
colorbar=False,
colorbar_format=None,
figure=None,
labels=True,
**kwargs,
) -> Image:
audio = torch.as_tensor(audio)
if labels:
kwargs.setdefault("xlabel", "Time [s]")
kwargs.setdefault("ylabel", "Frequency [Hz]")
n_fft = kwargs.setdefault("n_fft", 1024)
hop = kwargs.setdefault("hop", 512)
w = torch.hann_window(n_fft, device=audio.device)
spec = torch.stft(audio, n_fft, hop, window=w, return_complex=False)
spec = spec.div_(w.pow(2).sum())
spec = torch.view_as_complex(spec).abs().clamp_min(1e-12).log10().mul(10)
kwargs.setdefault("vmax", max(0.0, spec.max().item()))
if figure is None:
figure = plt.figure(figsize=figsize)
figure.set_tight_layout(True)
if spec.dim() > 2:
spec = spec.squeeze(0)
im = specshow(spec, **kwargs)
if colorbar:
ckwargs = {}
if "ax" in kwargs:
if colorbar_format is None:
if kwargs.get("vmin", None) is not None or kwargs.get("vmax", None) is not None:
colorbar_format = "%+2.0f dB"
ckwargs = {"ax": kwargs["ax"]}
plt.colorbar(im, format=colorbar_format, **ckwargs)
figure.canvas.draw()
return Image.frombytes("RGB", figure.canvas.get_width_height(), figure.canvas.tostring_rgb())
def cleanup_tmp(filter: List[str] = [], hours_keep=2):
filter.append("p232")
logger.info(f"Filter: {filter}")
# Cleanup some old wav files
if os.path.exists("/tmp"):
for f in glob.glob("/tmp/*"):
print(f"Got file {f}")
is_old = (time.time() - os.path.getmtime(f)) / 3600 > hours_keep
filtered = any(filt in f for filt in filter if filt is not None)
if is_old and not filtered:
try:
os.remove(f)
logger.info(f"Removed file {f}")
except Exception as e:
logger.warning(f"failed to remove file {f}: {e}")
def toggle(choice):
if choice == "mic":
return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
else:
return gr.update(visible=False, value=None), gr.update(visible=True, value=None)
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown(
"""
## DeepFilterNet2 Demo\
This demo denoises audio files using DeepFilterNet. Try it with your own voice!
"""
)
with gr.Row():
with gr.Column():
radio = gr.Radio(
["mic", "file"], value="file", label="How would you like to upload your audio?"
)
mic_input = gr.Mic(label="Input", type="filepath", visible=False)
audio_file = gr.Audio(type="filepath", label="Input", visible=True)
inputs = [
audio_file,
gr.Dropdown(
label="Add background noise",
choices=list(NOISES.keys()),
value="None",
),
gr.Dropdown(
label="Noise Level (SNR)",
choices=["-5", "0", "10", "20"],
value="10",
),
mic_input,
]
btn = gr.Button("Generate")
with gr.Column():
outputs = [
# gr.Video(type="filepath", label="Noisy audio"),
gr.Audio(type="filepath", label="Noisy audio"),
gr.Image(label="Noisy spectrogram"),
# gr.Video(type="filepath", label="Enhanced audio"),
gr.Audio(type="filepath", label="Enhanced audio"),
gr.Image(label="Enhanced spectrogram"),
]
#Gowdamn - Added api_name = 'denoise'
btn.click(fn=demo_fn, inputs=inputs, outputs=outputs,api_name='denoise')
radio.change(toggle, radio, [mic_input, audio_file])
gr.Examples(
[
["./samples/p232_013_clean.wav", "Kitchen", "10"],
["./samples/p232_013_clean.wav", "Cafe", "10"],
["./samples/p232_019_clean.wav", "Cafe", "10"],
["./samples/p232_019_clean.wav", "River", "10"],
],
fn=demo_fn,
inputs=inputs,
outputs=outputs,
cache_examples=True,
),
gr.Markdown(open("usage.md").read())
cleanup_tmp()
demo.launch(enable_queue=True)