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(config_allow_defaults=True) 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() 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 mix_and_denoise( speech_rec: Union[str, Tuple[int, np.ndarray]], speech_upl: str, noise_fn: 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_fn}, snr: {snr}" ) if noise_fn is None: noise_fn = "samples/dkitchen.wav" meta = AudioMetaData(-1, -1, -1, -1, "") max_s = 10 # limit to 10 seconds if speech_rec is None and speech_upl is None: speech, meta = load_audio("samples/p232_013_clean.wav", sr) elif speech_upl is not None: speech, meta = load_audio(speech_upl, sr) else: tmp = load_audio_gradio(speech_rec, sr) assert tmp is not None speech, meta = tmp if speech.dim() > 1 and speech.shape[0] > 1: assert ( speech.shape[1] > speech.shape[0] ), f"Expecting channels first, but got {speech.shape}" speech = speech.mean(dim=0, keepdim=True) speech = speech[..., : max_s * sr] logger.info(f"Loaded speech with shape {speech.shape}") noise, _ = load_audio(noise_fn, sr) # type: ignore 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="numpy", 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, debug=True)