DeepFilterNet / app.py
Hendrik Schroeter
use logger
e20c50f unverified
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import math
import tempfile
import gradio
import gradio.inputs
import gradio.outputs
import markdown
import matplotlib.pyplot as plt
import numpy as np
import torch
from loguru import logger
from df import config
from df.enhance import enhance, init_df, load_audio, save_audio
from df.utils import resample
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model, df, _ = init_df()
model = model.to(device=device).eval()
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()
print("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
print("mixture:", mixture.shape)
assert torch.isfinite(mixture).all()
max_m = mixture.abs().max()
if max_m > 1:
print(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 mix_and_denoise(speech_rec, speech_upl, noise, snr):
sr = config("sr", 48000, int, section="df")
logger.info(f"Got parameters speech_rec: {speech_rec}, speech_upl: {speech_upl}, noise: {noise}, snr: {snr}")
if noise is None:
noise = "samples/dkitchen.wav"
sp_kwargs = {}
if speech_rec is None or "none" in speech_rec:
speech_file = "samples/p232_013_clean.wav"
if speech_upl is not None and "none" not in speech_upl:
speech_file = speech_upl
else:
speech_file = speech_rec
sp_kwargs = {"frame_offset": 4800}
try:
speech, meta = load_audio(speech_file, sr, **sp_kwargs)
except RuntimeError as e:
logger.error("Could not load audio: " + str(e))
import os
print(os.path.getsize(speech_file))
print(os.path.getmtime(speech_file))
print(os.path.getctime(speech_file))
raise e
logger.info(f"Loaded speech with shape {speech.shape}")
noise, _ = load_audio(noise, sr)
if meta.sample_rate != sr:
# Low pass filter by resampling
noise = resample(resample(noise, sr, meta.sample_rate), meta.sample_rate, sr)
logger.info(f"Loaded noise with shape {noise.shape}")
speech, noise, noisy = mix_at_snr(speech, noise, snr)
logger.info("Start denoising audio")
enhanced = enhance(model, df, noisy)
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)
noisy = resample(noisy, sr, meta.sample_rate)
sr = meta.sample_rate
noisy_fn = tempfile.NamedTemporaryFile(suffix="noisy.wav", delete=False).name
save_audio(noisy_fn, noisy, sr)
enhanced_fn = tempfile.NamedTemporaryFile(suffix="enhanced.wav", delete=False).name
save_audio(enhanced_fn, enhanced, sr)
logger.info(f"saved audios: {noisy_fn}, {enhanced_fn}")
return (
noisy_fn,
spec_figure(noisy, sr=sr),
enhanced_fn,
spec_figure(enhanced, sr=sr),
)
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_figure(
audio: torch.Tensor,
figsize=(15, 5),
colorbar=False,
colorbar_format=None,
figure=None,
return_im=False,
labels=True,
**kwargs,
) -> plt.Figure:
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)
if return_im:
return im
return figure
inputs = [
gradio.inputs.Audio(
source="microphone",
type="filepath",
optional=True,
label="Record your own voice",
),
gradio.inputs.Audio(
source="upload",
type="filepath",
optional=True,
label="Alternative: Upload speech sample",
),
gradio.inputs.Audio(
source="upload", type="filepath", optional=True, label="Upload noise sample"
),
gradio.inputs.Slider(minimum=-10, maximum=40, step=5, default=10), # SNR
]
examples = [
[
"none",
"samples/p232_013_clean.wav",
"samples/dkitchen.wav",
10,
],
[
"none",
"samples/p232_019_clean.wav",
"samples/dliving.wav",
10,
],
]
outputs = [
gradio.outputs.Audio(label="Noisy"),
gradio.outputs.Image(type="plot"),
gradio.outputs.Audio(label="Enhanced"),
gradio.outputs.Image(type="plot"),
]
description = "This demo denoises audio files using DeepFilterNet. Try it with your own voice!"
iface = gradio.Interface(
fn=mix_and_denoise,
title="DeepFilterNet Demo",
inputs=inputs,
outputs=outputs,
examples=examples,
description=description,
layout="horizontal",
allow_flagging="never",
article=markdown.markdown(open("usage.md").read()),
)
iface.launch(cache_examples=False)