app.py

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, "")
    # --------------------vvv CHANGES START HERE vvv--------------------
    # max_s = 10  # limit to 10 seconds # <--- COMMENT OUT OR DELETE THIS LINE

    if speech_upl is not None:
        sample, meta = load_audio(speech_upl, sr)
        # max_len = max_s * sr # <--- COMMENT OUT OR DELETE THIS LINE
        # if sample.shape[-1] > max_len: # <--- COMMENT OUT OR DELETE THIS LINE
        #     start = torch.randint(0, sample.shape[-1] - max_len, ()).item() # <--- COMMENT OUT OR DELETE
        #     sample = sample[..., start : start + max_len] # <--- COMMENT OUT OR DELETE
    else:
        sample, meta = load_audio("samples/p232_013_clean.wav", sr)
        # sample = sample[..., : max_s * sr] # <--- COMMENT OUT OR DELETE THIS LINE
    # --------------------^^^ CHANGES END HERE ^^^--------------------

    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"),
            ]
    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)