An open-source dataset for multiple purposes, such as speaker localization/tracking, dereverberation, enhancement, separation, and recognition.
Project description
MixSim is an open-source multipurpose speech mixture simulator that covers speaker localization/tracking, dereverberation, enhancement, separation, and recognition tasks.
Documentation
See documentation for more details.
A Simple Example
First, install MixSim using:
pip install -U mixsim
mixsim --help
You can use the mixsim
command to run the simulator:
mixsim \
--seed 1 \
--sample_rate 16000 \
--clean.fpath_file /path/to/clean.txt \
--noisy.fpath_file /path/to/noisy.txt
...
Use an additional configuration file to specify the parameters:
mixsim --config_file /path/to/config.toml
Use package reference to access the simulator:
from mixsim import Mixer, Writer
mixture_list = Mixer(clean_file_list, noise_file_list, snr_list, output_members=["n_mix_y_rvb", "s_y", "s_transcript"])
file_writer = Writer(output_list = mixture_list)
file_writer.write()
Contributing
For guidance on setting up a development environment and how to contribute to MixSim, see Contributing to MixSim.
License
MixSim is released under the MIT license.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file mixsim-0.3.0.tar.gz
.
File metadata
- Download URL: mixsim-0.3.0.tar.gz
- Upload date:
- Size: 1.6 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | f118956b54f92c432e702f4afbe7eb23199a430cdc277aff301fd084bc8eee5b |
|
MD5 | 011716519487708a572e2a39a0c1f321 |
|
BLAKE2b-256 | 744dd480558b76bc57e4086381eafde92665d8b782b1a2af4d2163453164baa3 |
File details
Details for the file mixsim-0.3.0-py3-none-any.whl
.
File metadata
- Download URL: mixsim-0.3.0-py3-none-any.whl
- Upload date:
- Size: 778.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2116e40d43dc7b49d1d3e38f27e24d611c82a12e67d2bb2d879043f11d95ea9c |
|
MD5 | 72372ac505282300f106f7ec8b590a81 |
|
BLAKE2b-256 | 6b5fa6332b63250cbfe2316dfaf5128869c4a1082214ba3e9e000a5301ac85af |