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Monophonic resampling

The following code block demonstrates how to resample an audio signal.

We use librosa for loading the audio, but this is purely for ease of demonstration. resampy does not depend on librosa.

import librosa
import resampy

# Load in librosa's example audio file at its native sampling rate
x, sr_orig = librosa.load(librosa.util.example_audio_file(), sr=None)

# x is now a 1-d numpy array, with `sr_orig` audio samples per second

# We can resample this to any sampling rate we like, say 16000 Hz
y_low = resampy.resample(x, sr_orig, 16000)

# That's it!

Stereo and multi-dimensional data

The previous example operates on monophonic signals, but resampy also supports stereo resampling, as demonstrated below.

import librosa
import resampy

# Load in librosa's example audio file at its native sampling rate.
# This time, also disable the stereo->mono downmixing
x, sr_orig = librosa.load(librosa.util.example_audio_file(), sr=None, mono=False)

# x is now a 2-d numpy array, with `sr_orig` audio samples per second
# The first dimension of x indexes the channels, the second dimension indexes
# samples.
# x[0] is the left channel, x[1] is the right channel.

# We can again resample.  By default, resample assumes the last index is time.
y_low = resampy.resample(x, sr_orig, 16000)

# To be more explicit, provide a target axis
y_low = resampy.resample(x, sr_orig, 16000, axis=1)

The next block illustrates resampling along an arbitrary dimension.

import numpy as np
import resampy

# Generate 4-dimensional white noise.  The third axis (axis=2) will index time.
sr_orig = 22050
x = np.random.randn(10, 3, sr_orig * 5, 2)

# x is now a 10-by-3-by-(5*22050)-by-2 tensor of data.

# We can resample along the time axis as follows
y_low = resampy.resample(x, sr_orig, 11025, axis=2)

# y_low is now a 10-by-3-(5*11025)-by-2 tensor of data

Advanced filtering

resampy allows you to control the design of the filters used in resampling operations.

import numpy as np
import scipy.signal
import librosa
import resampy

# Load in some audio
x, sr_orig = librosa.load(librosa.util.example_audio_file(), sr=None, mono=False)

# Resample to 22050Hz using a Hann-windowed sinc-filter
y = resampy.resample(x, sr_orig, sr_new, filter='sinc_window', window=scipy.signal.hann)

# Or a shorter sinc-filter than the default (num_zeros=64)
y = resampy.resample(x, sr_orig, sr_new, filter='sinc_window', num_zeros=32)

# Or use the pre-built high-quality filter
y = resampy.resample(x, sr_orig, sr_new, filter='kaiser_best')

# Or use the pre-built fast filter
y = resampy.resample(x, sr_orig, sr_new, filter='kaiser_fast')

Benchmarking

Benchmarking resampy is relatively simple, using ipython's %timeit magic. The following example demonstrates resampling a monophonic signal of 400000 samples from 22.05 KHz to 16 KHz using both resampy and scipy.signal.resample.

In [1]: import numpy as np

In [2]: import scipy

In [3]: import resampy

In [4]: x = np.random.randn(400000)

In [5]: sr_in, sr_out = 22050, 16000

In [6]: %timeit resampy.resample(x, sr_in, sr_out, axis=-1)
1 loop, best of 3: 199 ms per loop

In [7]: %timeit scipy.signal.resample(x,
   ...:                               int(x.shape[-1] * sr_out / float(sr_in)),
   ...:                               axis=-1)
1 loop, best of 3: 6min 5s per loop