DeepFilterNet2 / app.py
Hendrik Schroeter
Disable audio recording for now
f81803d
raw
history blame
9.88 kB
import math
import tempfile
from typing import Optional, Tuple, Union
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 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.utils 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, 5))
fig_noisy.set_tight_layout(True)
fig_enh, ax_enh = plt.subplots(figsize=(15.2, 5))
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_rec: Union[str, Tuple[int, np.ndarray]], speech_upl: str, noise_type: str, snr: int
):
sr = config("sr", 48000, int, section="df")
logger.info(
f"Got parameters speech_rec: {speech_rec}, speech_upl: {speech_upl}, noise: {noise_type}"
)
noise_fn = NOISES[noise_type]
meta = AudioMetaData(-1, -1, -1, -1, "")
max_s = 10 # limit to 10 seconds
if speech_rec is None and speech_upl is None:
sample, meta = load_audio("samples/p232_013_clean.wav", sr)
elif 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:
tmp = load_audio_gradio(speech_rec, sr)
assert tmp is not None
sample, meta = tmp
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_fn = tempfile.NamedTemporaryFile(suffix="noisy.wav", delete=False).name
save_audio(noisy_fn, sample, 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}")
ax_noisy.clear()
ax_enh.clear()
return (
noisy_fn,
spec_figure(sample, sr=sr, figure=fig_noisy, ax=ax_noisy),
enhanced_fn,
spec_figure(enhanced, sr=sr, figure=fig_enh, ax=ax_enh),
)
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(
# label="Record your own voice",
# source="microphone",
# type="numpy",
# ),
gradio.inputs.Audio(
label="Upload audio sample",
source="upload",
type="filepath",
),
gradio.inputs.Dropdown(
label="Add background noise",
choices=list(NOISES.keys()),
default="None",
),
gradio.inputs.Dropdown(
label="Noise Level (SNR)",
choices=[-5, 0, 10, 20],
default=10,
),
]
outputs = [
gradio.outputs.Audio(label="Noisy audio"),
gradio.outputs.Image(type="plot", label="Noisy spectrogram"),
gradio.outputs.Audio(label="Enhanced audio"),
gradio.outputs.Image(type="plot", label="Enhanced spectrogram"),
]
description = "This demo denoises audio files using DeepFilterNet. Try it with your own voice!"
iface = gradio.Interface(
fn=demo_fn,
title="DeepFilterNet2 Demo",
inputs=inputs,
outputs=outputs,
description=description,
allow_flagging="never",
article=markdown.markdown(open("usage.md").read()),
)
iface.launch(debug=True)