Parses wave files chunks in order to extract metadata and audio. Also provides an option to write a bunch of given chunks back into a coherent file.
pip install wave-chunk-parser
from wave_chunk_parser.chunks import RiffChunk
with open("file.wav", "rb") as file:
riff_chunk = RiffChunk.from_file(file)
From there you can access the sub chunks from riff_chunk.sub_chunks. The data chunk uses a numpy array to hold the vectors of audio samples.
Format (fmt) and data chunks are critical. Cart chunk is optional but provides those markers we need for handling broadcast audio.
You will need to build the chunks individually and supply them in a list of:
chunks = [FormatChunk, DataChunk, CartChunk]
The format chunk must come before the data chunk (we need to know how to en/decode it). Cart chunk can appear anywhere in the list or even not exist at all.
To get a blob for writing to disk (or wherever), simply:
riff_chunk = RiffChunk(chunks)
blob = riff_chunk.to_bytes()
The to_bytes function exists on every chunk type. So, if you particularly desire, you can turn a standalone format chunk into a blob.
We need openblas to make Numpy work on macOS:
pip uninstall numpy
brew install openblas
OPENBLAS="$(brew --prefix openblas)" pip install numpy --no-cache-dir
To build the package for deployment:
poetry run python setup.py bdist_wheel
poetry run twine upload --repository testpypi dist/*
poetry run twine upload --repository pypi dist/*