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(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") # Cleanup some old wav files if os.path.exists("/tmp"): for f in glob.glob("/tmp/*"): is_old = (time.time() - os.path.getmtime(f)) / 3600 > hours_keep if is_old and (filt not in f for filt in filter): 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) 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)