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| import torch | |
| import torchaudio | |
| import torchaudio.functional as F | |
| from torchaudio.utils import download_asset | |
| from pesq import pesq | |
| from pystoi import stoi | |
| import mir_eval | |
| from pydub import AudioSegment | |
| import matplotlib.pyplot as plt | |
| import streamlit as st | |
| from helper import plot_spectrogram,plot_mask,si_snr,generate_mixture,evaluate,get_irms | |
| target_snr=3 | |
| #parameters for STFT | |
| N_FFT = 1024 | |
| N_HOP = 256 | |
| stft = torchaudio.transforms.Spectrogram( | |
| n_fft=N_FFT, | |
| hop_length=N_HOP, | |
| power=None, | |
| ) | |
| istft = torchaudio.transforms.InverseSpectrogram(n_fft=N_FFT, hop_length=N_HOP) | |
| #defining a psd transform | |
| psd_transform = torchaudio.transforms.PSD() | |
| mvdr_transform = torchaudio.transforms.SoudenMVDR() | |
| #defining the reference microphone | |
| REFERENCE_CHANNEL = 0 | |
| #creating a random noise for better calculations | |
| SAMPLE_NOISE = download_asset("tutorial-assets/mvdr/noise.wav") | |
| waveform_noise, sr2 = torchaudio.load(SAMPLE_NOISE) | |
| waveform_noise = waveform_noise.to(torch.float32) | |
| stft_noise = stft(waveform_noise) | |
| def ui(): | |
| st.title("Speech Enhancer") | |
| st.markdown("Made by Vageesh") | |
| #making an audio developer uploader: | |
| audio_file = st.file_uploader("Upload an audio file in wav format", type=[ "wav"]) | |
| if audio_file is not None: | |
| waveform_clean,sr=torchaudio.load(audio_file) | |
| waveform_clean = waveform_clean.to(torch.float32) | |
| stft_clean = stft(waveform_clean) | |
| st.text("Your uploaded audio") | |
| st.audio(audio_file) | |
| #creating a mixture of our audio file and the noise file | |
| waveform_mix = generate_mixture(waveform_clean, waveform_noise, target_snr) | |
| #making the files into torch double format | |
| waveform_mix = waveform_mix.to(torch.float32) | |
| #computing STFT | |
| stft_mix = stft(waveform_mix) | |
| #plotting the spectogram | |
| spec_img=plot_spectrogram(stft_mix) | |
| # st.image(spec_img) | |
| #showing mixed audio in streamlit | |
| torchaudio.save("./waveform_mix.wav", waveform_mix, sr) | |
| st.text("The noise mixed audio") | |
| st.audio("./waveform_mix.wav") | |
| #getting the irms | |
| irm_speech, irm_noise = get_irms(stft_clean, stft_noise) | |
| #getting the psd speech | |
| psd_speech = psd_transform(stft_mix, irm_speech) | |
| psd_noise = psd_transform(stft_mix, irm_noise) | |
| stft_souden = mvdr_transform(stft_mix, psd_speech, psd_noise, reference_channel=REFERENCE_CHANNEL) | |
| waveform_souden = istft(stft_souden, length=waveform_mix.shape[-1]) | |
| #plotting the cleaned audio and hearing it | |
| spec_clean_img=plot_spectrogram(stft_souden) | |
| waveform_souden = waveform_souden.reshape(1, -1) | |
| # st.image(spec_clean_img) | |
| torchaudio.save("./waveform_souden.wav", waveform_souden, sr) | |
| st.text("The cleaned Audio") | |
| st.audio("./waveform_souden.wav") | |
| if __name__=="__main__": | |
| ui() | |