audio-denoiser 0.1.0

A Python library for (speech) audio denoising.

This Python library reduces substantial background noise

in audio files containing speech. It uses a machine

learning model (38m parameters) trained to handle

different types of ambient noise.

Installation

pip install audio-denoiser

Usage

Basic:

from audio_denoiser.AudioDenoiser import AudioDenoiser



denoiser = AudioDenoiser()

in_audio_file = '/content/input-audio-with-noise.wav'

out_audio_file = '/content/output-denoised-audio.wav'

denoiser.process_audio_file(in_audio_file, out_audio_file)

With additional options:

from audio_denoiser.AudioDenoiser import AudioDenoiser

import torch

import torchaudio



# Use the "soundfile" audio backend, used in training.

torchaudio.set_audio_backend("soundfile")

# Use a CUDA device for inference if available

device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')

denoiser = AudioDenoiser(device=device)

in_audio_file = '/content/input-audio-with-noise.wav'

out_audio_file = '/content/output-denoised-audio.wav'

auto_scale = True # Recommended for low-volume input audio

denoiser.process_audio_file(in_audio_file, out_audio_file, auto_scale=auto_scale)

You can also provide your own waveform tensor:

from audio_denoiser.AudioDenoiser import AudioDenoiser



noisy_waveform, sample_rate = torchaudio.load('/content/input-audio-with-noise.wav')

denoiser = AudioDenoiser()

denoised_waveform = denoiser.process_waveform(noisy_waveform, sample_rate, auto_scale=False)

print('Tensor shape: ', denoised_waveform.shape)