def test_reduce_cafe_noise(): # load data wav_loc = "assets/coffe-1_2020-03-08_170854361.wav" rate, data = wavfile.read(wav_loc) data = int16_to_float32(data) noise_loc = "assets/coffe-1x_2020-03-08_170932616.wav" noise_rate, noise_data = wavfile.read(noise_loc) noise_data = int16_to_float32(noise_data) # add noise snr = 2 # signal to noise ratio noise_clip = noise_data / snr audio_clip_cafe = data # reduce noise reduced_noise = nr.reduce_noise(audio_clip=audio_clip_cafe, noise_clip=noise_clip, verbose=True) return float32_to_int16(reduced_noise)
def test_reduce_cafe_noise(): # load data wav_loc = "assets/fish.wav" rate, data = wavfile.read(wav_loc) data = int16_to_float32(data) noise_loc = "assets/cafe_short.wav" noise_rate, noise_data = wavfile.read(noise_loc) noise_data = int16_to_float32(noise_data) # add noise snr = 2 # signal to noise ratio noise_clip = noise_data / snr audio_clip_cafe = data + noise_clip # reduce noise reduced_noise = nrv1.reduce_noise(audio_clip=audio_clip_cafe, noise_clip=noise_clip, verbose=True) return float32_to_int16(reduced_noise)
for i in data: if i > 0: break else: lag = lag + 1 cnt = 0 for i in data: cnt = cnt + 1 if abs(i) > 0: if cnt < 100000: noise_list.append(i) else: break #picking up noise clip data from lag+3000 to lag+15000 and creating noise profile from noise clip noise_clip = data[lag + 3000:lag + 15000] video_len = len(data) / rate reduced = float32_to_int16( nr.reduce_noise(audio_clip=data, noise_clip=noise_clip, prop_decrease=0.8)) from scipy.io.wavfile import write write("v1_ss8.wav", rate, reduced) cmd = 'ffmpeg -y -i v1_ss8.wav -r 30 -i v1_ss_V_8.h264 -filter:a aresample=async=1 -c:a flac -c:v copy v1_ss_V_8.mkv' subprocess.call(cmd, shell=True) # "Muxing Done print('Done')