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)
def test_reduce_generated_noise():
# load data
wav_loc = "assets/fish.wav"
rate, data = wavfile.read(wav_loc)
data = int16_to_float32(data)
# add noise
noise_len = 2 # seconds
noise = (band_limited_noise(
min_freq=2000, max_freq=12000, samples=len(data), samplerate=rate) *
10)
noise_clip = noise[:rate * noise_len]
audio_clip_band_limited = data + noise
return nrv1.reduce_noise(audio_clip=audio_clip_band_limited,
noise_clip=noise_clip,
verbose=True)
type=str,
help='Noisy File',
required=True)
parser.add_argument('--output_file',
'-o',
type=str,
help='Denoised file',
required=True)
parser.add_argument('--threshold',
'-t',
type=float,
help='Threshold',
default=1.5)
args = parser.parse_args()
r, y = load_wav(args.input_file)
y = int16_to_float32(y)
noise = find_some_background_noise(r, y)
wavfile.write(f'{args.output_file}.noise_section.wav', r, noise)
reduced_noise = nr.reduce_noise(
n_std_thresh=args.threshold,
n_fft=1024,
win_length=1024,
audio_clip=y,
noise_clip=noise,
use_tensorflow=False,
verbose=False,
)
wavfile.write(args.output_file, r, np.array(reduced_noise))