Monolithic python wrapper for libsamplerate based on pybind11 and NumPy

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Project description

python-samplerate-ledfx

Note: This is a fork of the original python-samplerate maintained by the LedFx team.

Why this fork exists:

The original python-samplerate project is sporadically active

We need Python 3.14 support with pre-built wheels on PyPI

We require the latest fixes and improvements from the main branch of python-samplerate

LedFx depends on python-samplerate and needs a reliable, up-to-date release

All credit for python-samplerate goes to the original authors. This fork exists solely to provide maintained releases for projects that depend on python-samplerate.

Original project: https://github.com/tuxu/python-samplerate
This fork: https://github.com/LedFx/python-samplerate-ledfx

This is a wrapper around Erik de Castro Lopo's libsamplerate (aka Secret Rabbit Code) for high-quality sample rate conversion.

It implements all three APIs available in libsamplerate:

Simple API: for resampling a large chunk of data with a single library call
Full API: for obtaining the resampled signal from successive chunks of data
Callback API: like Full API, but input samples are provided by a callback function

The libsamplerate library is statically built together with the python bindings using pybind11.

Installation

$ pip install samplerate-ledfx

Binary wheels of samplerate-ledfx are available. A C++ 14 or above compiler is required to build the package.

Usage

import numpy as np
import samplerate

# Synthesize data
fs = 1000.
t = np.arange(fs * 2) / fs
input_data = np.sin(2 * np.pi * 5 * t)

# Simple API
ratio = 1.5
converter = 'sinc_best'  # or 'sinc_fastest', ...
output_data_simple = samplerate.resample(input_data, ratio, converter)

# Full API
resampler = samplerate.Resampler(converter, channels=1)
output_data_full = resampler.process(input_data, ratio, end_of_input=True)

# The result is the same for both APIs.
assert np.allclose(output_data_simple, output_data_full)

# See `samplerate.CallbackResampler` for the Callback API, or
# `examples/play_modulation.py` for an example.

# Callback API Example
def producer():
    # Generate data in chunks
    for i in range(10):
        yield np.random.uniform(-1, 1, 1024).astype(np.float32)
    yield None # Signal end of stream

data_iter = producer()
callback = lambda: next(data_iter)

resampler = samplerate.CallbackResampler(callback, ratio, converter)
output_chunks = []
while True:
    # Read chunks of resampled data
    chunk = resampler.read(512) 
    if chunk.shape[0] == 0:
        break
    output_chunks.append(chunk)

Performance Tips

To get the maximum performance from samplerate:

Use np.float32: The underlying libsamplerate library operates on 32-bit floats. Passing np.float64 (default numpy float) or integer arrays triggers an implicit copy and cast, which can be expensive.

# Fast (no copy)
data = np.zeros(1000, dtype=np.float32)
samplerate.resample(data, 1.5)

# Slower (implicit copy + cast)
data = np.zeros(1000, dtype=np.float64) 
samplerate.resample(data, 1.5)

Use C-Contiguous Arrays: Ensure your input arrays are C-contiguous (row-major). Non-contiguous arrays (e.g., column slices) will also trigger a copy.
Adjust GIL Threshold: If you are processing many small chunks in a multi-threaded application, the default "auto" GIL release threshold (1000 frames) might be too high or too low. You can tune it:
```
# Release GIL even for small chunks (e.g. > 100 frames)
samplerate.set_gil_release_threshold(100)
```

Multi-threading and GIL Control

All resampling methods support a release_gil parameter that controls Python's Global Interpreter Lock (GIL) during resampling operations. This is useful for optimizing performance in different scenarios:

import samplerate

# Default: "auto" mode - releases GIL only for large data (>= 1000 frames)
# Balances single-threaded performance with multi-threading capability
# The threshold is configurable: samplerate.set_gil_release_threshold(2000)
output = samplerate.resample(input_data, ratio)

# Force GIL release - best for multi-threaded applications
# Allows other Python threads to run during resampling
output = samplerate.resample(input_data, ratio, release_gil=True)

# Disable GIL release - best for single-threaded applications with small data
# Avoids the ~1-5µs overhead of GIL release/acquire
output = samplerate.resample(input_data, ratio, release_gil=False)

The same parameter is available on Resampler.process() and CallbackResampler.read():

resampler = samplerate.Resampler('sinc_best', channels=1)
output = resampler.process(input_data, ratio, release_gil=True)

License

This project is licensed under the MIT license.

As of version 0.1.9, libsamplerate is licensed under the 2-clause BSD license.

Project details

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Release history Release notifications | RSS feed

This version

0.2.6

Dec 1, 2025

0.2.5

Nov 27, 2025

0.2.4

Nov 27, 2025

0.2.3

Nov 19, 2025

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samplerate-ledfx 0.2.6

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BLAKE2b-256	`7af1b75dc13eb7b1e2dc21eba54e66e2f668576c2fba73f761c1f82b33561446`

Algorithm	Hash digest
SHA256	`4776a41ac38c2c69a405c15113fac9edb4e844743e0f7fdc09f3b30b82931258`
MD5	`1027a19781442dea70bfe7f9eb073103`
BLAKE2b-256	`64b5e81e63dc10602f9e0fd00f745726f3afe66645a9f11c5bbb7a62f94bc75b`

Algorithm	Hash digest
SHA256	`01c9cf0578e4e51ba8c286f829247e6263c7fcbb51663e1503f6d35b6b658e9c`
MD5	`c0acd88a3c672ca72e012c0ddba749ba`
BLAKE2b-256	`f1c2908862494d493c9a662093b954acccf946c0bd89490fbf37171af852a521`

Algorithm	Hash digest
SHA256	`e232903ed3a9d69f6217fb7cc7950d6ff44f464ddbd52feba19f9d2245ed329c`
MD5	`d16ee03352bb8eb921089086f34b24cf`
BLAKE2b-256	`42a92f6b0faca47d32dd8ef8737370cd32e47618f81bd1da5a7e7a185cf3f76e`