import librosa import tensorflow as tf from tensorflow.keras.models import model_from_json import soundfile as sf import numpy as np import os import scipy from scipy.io import wavfile import gradio as gr def audio_to_audio_frame_stack(sound_data, frame_length, hop_length_frame): """This function take an audio and split into several frame in a numpy matrix of size (nb_frame,frame_length)""" sequence_sample_length = sound_data.shape[0] sound_data_list = [sound_data[start:start + frame_length] for start in range( 0, sequence_sample_length - frame_length + 1, hop_length_frame)] # get sliding windows sound_data_array = np.vstack(sound_data_list) return sound_data_array def audio_files_to_numpy(audio_dir, list_audio_files, sample_rate, frame_length, hop_length_frame, min_duration): """This function take audio files of a directory and merge them in a numpy matrix of size (nb_frame,frame_length) for a sliding window of size hop_length_frame""" list_sound_array = [] for file in list_audio_files: # open the audio file y, sr = librosa.load(os.path.join(audio_dir, file), sr=sample_rate) total_duration = librosa.get_duration(y=y, sr=sr) if (total_duration >= min_duration): list_sound_array.append(audio_to_audio_frame_stack( y, frame_length, hop_length_frame)) else: print( f"The following file {os.path.join(audio_dir,file)} is below the min duration") return np.vstack(list_sound_array) def blend_noise_randomly(voice, noise, nb_samples, frame_length): """This function takes as input numpy arrays representing frames of voice sounds, noise sounds and the number of frames to be created and return numpy arrays with voice randomly blend with noise""" prod_voice = np.zeros((nb_samples, frame_length)) prod_noise = np.zeros((nb_samples, frame_length)) prod_noisy_voice = np.zeros((nb_samples, frame_length)) for i in range(nb_samples): id_voice = np.random.randint(0, voice.shape[0]) id_noise = np.random.randint(0, noise.shape[0]) level_noise = np.random.uniform(0.2, 0.8) prod_voice[i, :] = voice[id_voice, :] prod_noise[i, :] = level_noise * noise[id_noise, :] prod_noisy_voice[i, :] = prod_voice[i, :] + prod_noise[i, :] return prod_voice, prod_noise, prod_noisy_voice def audio_to_magnitude_db_and_phase(n_fft, hop_length_fft, audio): """This function takes an audio and convert into spectrogram, it returns the magnitude in dB and the phase""" stftaudio = librosa.stft(audio, n_fft=n_fft, hop_length=hop_length_fft) stftaudio_magnitude, stftaudio_phase = librosa.magphase(stftaudio) stftaudio_magnitude_db = librosa.amplitude_to_db( stftaudio_magnitude, ref=np.max) return stftaudio_magnitude_db, stftaudio_phase def numpy_audio_to_matrix_spectrogram(numpy_audio, dim_square_spec, n_fft, hop_length_fft): """This function takes as input a numpy audi of size (nb_frame,frame_length), and return a numpy containing the matrix spectrogram for amplitude in dB and phase. It will have the size (nb_frame,dim_square_spec,dim_square_spec)""" nb_audio = numpy_audio.shape[0] m_mag_db = np.zeros((nb_audio, dim_square_spec, dim_square_spec)) m_phase = np.zeros((nb_audio, dim_square_spec, dim_square_spec), dtype=complex) for i in range(nb_audio): m_mag_db[i, :, :], m_phase[i, :, :] = audio_to_magnitude_db_and_phase( n_fft, hop_length_fft, numpy_audio[i]) return m_mag_db, m_phase def magnitude_db_and_phase_to_audio(frame_length, hop_length_fft, stftaudio_magnitude_db, stftaudio_phase): """This functions reverts a spectrogram to an audio""" stftaudio_magnitude_rev = librosa.db_to_amplitude(stftaudio_magnitude_db, ref=1.0) # taking magnitude and phase of audio audio_reverse_stft = stftaudio_magnitude_rev * stftaudio_phase audio_reconstruct = librosa.core.istft(audio_reverse_stft, hop_length=hop_length_fft, length=frame_length) return audio_reconstruct def matrix_spectrogram_to_numpy_audio(m_mag_db, m_phase, frame_length, hop_length_fft) : """This functions reverts the matrix spectrograms to numpy audio""" list_audio = [] nb_spec = m_mag_db.shape[0] for i in range(nb_spec): audio_reconstruct = magnitude_db_and_phase_to_audio(frame_length, hop_length_fft, m_mag_db[i], m_phase[i]) list_audio.append(audio_reconstruct) return np.vstack(list_audio) def scaled_in(matrix_spec): "global scaling apply to noisy voice spectrograms (scale between -1 and 1)" matrix_spec = (matrix_spec + 46)/50 return matrix_spec def scaled_ou(matrix_spec): "global scaling apply to noise models spectrograms (scale between -1 and 1)" matrix_spec = (matrix_spec -6 )/82 return matrix_spec def inv_scaled_in(matrix_spec): "inverse global scaling apply to noisy voices spectrograms" matrix_spec = matrix_spec * 50 - 46 return matrix_spec def inv_scaled_ou(matrix_spec): "inverse global scaling apply to noise models spectrograms" matrix_spec = matrix_spec * 82 + 6 return matrix_spec def prediction(weights_path, name_model, audio_dir_prediction, dir_save_prediction, audio_input_prediction, audio_output_prediction, sample_rate, min_duration, frame_length, hop_length_frame, n_fft, hop_length_fft): """ This function takes as input pretrained weights, noisy voice sound to denoise, predict the denoise sound and save it to disk. """ # load json and create model json_file = open(weights_path+'/'+name_model+'.json', 'r') loaded_model_json = json_file.read() json_file.close() loaded_model = model_from_json(loaded_model_json) # load weights into new model loaded_model.load_weights(weights_path+'/'+name_model+'.h5') print("Loaded model from disk") # Extracting noise and voice from folder and convert to numpy audio = audio_files_to_numpy(audio_dir_prediction, audio_input_prediction, sample_rate, frame_length, hop_length_frame, min_duration) # audio = audioData #Dimensions of squared spectrogram dim_square_spec = int(n_fft / 2) + 1 print(dim_square_spec) # Create Amplitude and phase of the sounds m_amp_db_audio, m_pha_audio = numpy_audio_to_matrix_spectrogram( audio, dim_square_spec, n_fft, hop_length_fft) #global scaling to have distribution -1/1 X_in = scaled_in(m_amp_db_audio) #Reshape for prediction X_in = X_in.reshape(X_in.shape[0],X_in.shape[1],X_in.shape[2],1) #Prediction using loaded network X_pred = loaded_model.predict(X_in) #Rescale back the noise model inv_sca_X_pred = inv_scaled_ou(X_pred) #Remove noise model from noisy speech X_denoise = m_amp_db_audio - inv_sca_X_pred[:,:,:,0] #Reconstruct audio from denoised spectrogram and phase print(X_denoise.shape) print(m_pha_audio.shape) print(frame_length) print(hop_length_fft) audio_denoise_recons = matrix_spectrogram_to_numpy_audio(X_denoise, m_pha_audio, frame_length, hop_length_fft) #Number of frames nb_samples = audio_denoise_recons.shape[0] #Save all frames in one file denoise_long = audio_denoise_recons.reshape(1, nb_samples * frame_length)*10 # librosa.output.write_wav(dir_save_prediction + audio_output_prediction, denoise_long[0, :], sample_rate) print(audio_output_prediction) sf.write(audio_output_prediction , denoise_long[0, :], sample_rate) def denoise_audio(audioName): sr, data = audioName sf.write("temp.wav",data, sr) testNo = "temp" audio_dir_prediction = os.path.abspath("/")+ str(testNo) +".wav" sample_rate, data = audioName[0], audioName[1] len_data = len(data) # holds length of the numpy array t = len_data / sample_rate # returns duration but in floats print("t:",t) weights_path = os.path.abspath("./") name_model = "model_unet" audio_dir_prediction = os.path.abspath("./") dir_save_prediction = os.path.abspath("./") audio_output_prediction = "test.wav" audio_input_prediction = ["temp.wav"] sample_rate = 8000 min_duration = t frame_length = 8064 hop_length_frame = 8064 n_fft = 255 hop_length_fft = 63 dim_square_spec = int(n_fft / 2) + 1 prediction(weights_path, name_model, audio_dir_prediction, dir_save_prediction, audio_input_prediction, audio_output_prediction, sample_rate, min_duration, frame_length, hop_length_frame, n_fft, hop_length_fft) print(audio_output_prediction) return audio_output_prediction examples = [ [os.path.abspath("crowdNoise.wav")], [os.path.abspath("CrowdNoise2.wav")], [os.path.abspath("whiteNoise.wav")] ] iface = gr.Interface(fn = denoise_audio, inputs = 'audio', outputs = 'audio', title = 'audio to denoised Audio Application', description = 'A simple application to denoise audio speech usinf UNet deep learning model. Upload your own audio, or click one of the examples to load them.', article = '''

All you need to do is to upload the pdf file and hit submit, then wait for compiling. After that click on Play/Pause for listing to the audio. The audio is saved in a wav format.

''', examples=examples ) iface.launch()