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 underlyinglibsampleratelibrary operates on 32-bit floats. Passingnp.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)
Thread safety: separate Resampler / CallbackResampler objects can run in parallel threads. A single object holds stream state, so using it from two threads at once raises RuntimeError rather than corrupting that state. While the GIL is released, the input array is read without it: don't modify it from another thread during the call.
See also
- scikits.samplerate implements only the Simple API and uses Cython for extern calls. The resample function of scikits.samplerate and this package share the same function signature for compatiblity.
- resampy: sample rate conversion in Python + Cython.
License
This project is licensed under the MIT license.
As of version 0.1.9, libsamplerate is licensed under the 2-clause BSD license.
Metadata
Release files for samplerate-ledfx 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| samplerate_ledfx-0.3.0.tar.gz | 57.6 kB | Details |
Built distributions (wheels)
Total release size: 30.2 MB
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