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Merge pull request #54 from SuperKogito/last-tweaks-for-v0.2.1
fix docs & version and update accordingly
2 parents 3b9b51d + afbe42e commit 3bd21a2

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‎CHANGELOG.md

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# Changelog
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## v0.3.0
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- improve code based on Joss reviewers recommendations.
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- add type hinting.
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- fix documentation typos.
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## v0.2.0
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- add spectrogram implementations.
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- add predefined filter banks input to the spafe features functions for faster batch processing.

‎CONTRIBUTING.md

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@@ -39,17 +39,17 @@ The preferred way to contribute to spafe is to fork the [main repository](https:
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5. Start making changes.
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\`\``diff
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- Please never work directly on the `master` branch!\`\`\`
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```diff
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-> Please never work directly on the `master` branch!
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```
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6. Once you are done, make sure to format the code using black to fit spafe's codestyle.
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\`\``black spafe/\`\`\`
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```black spafe/```
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7. Make sure that the tests succeed and have enough coverage.
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\`\``pytest -x --cov-report term-missing --cov=spafe spafe/tests/test_*.py \`\`\`
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```pytest -x --cov-report term-missing --cov=spafe spafe/tests/test_*.py ```
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8. Use Git for the to do the version controlling of this copy. When you're done editing, you know the drill `add`, `commit` then `push`:
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import spafe
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import platform
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print(platform.platform())
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print("Python", sys.version)
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print("NumPy", numpy.__version__)

‎docs/source/_static/switcher.json

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[
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{
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"name": "dev",
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"version": "dev",
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"url": "https://superkogito.github.io/spafe/v0.3.0/index.html"
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},
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{
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"name": "v0.3.0",
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"version": "0.3.0",
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"url": "https://superkogito.github.io/spafe/v0.3.0/index.html"
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},
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{
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"name": "v0.2.0",
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"version": "0.2.0",

‎docs/source/conf.py

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html_favicon = "_static/favicon_io/favicon.ico"
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# The short X.Y version
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version = "0.2.0"
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version = "0.3.0"
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# The full version, including alpha/beta/rc tags
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release = "0.2.0"
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release = "0.3.0"
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# -- General configuration ---------------------------------------------------
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‎paper/paper.md

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---
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# Summary
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This paper describes version 0.2.0 of spafe: a python package for audio features extraction based on the Numpy [@numpy:2020] and Scipy [@scipy:2019] libraries.
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This paper describes version 0.3.0 of spafe: a python package for audio features extraction based on the Numpy [@numpy:2020] and Scipy [@scipy:2019] libraries.
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Spafe implements various features extraction techniques that can be used to solve a wide variety of recognition and classification tasks (speaker verification, spoken emotion recognition, spoken language identification etc.).
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The paper provides a brief overview of the library’s structure, theory and functionalities.
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‎paper/paper.pdf

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‎spafe/features/bfcc.py

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from scipy.io.wavfile import read
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute bark spectrogram
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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute bfccs

‎spafe/features/cqcc.py

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from scipy.io.wavfile import read
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute spectrogram
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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute cqccs

‎spafe/features/gfcc.py

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from scipy.io.wavfile import read
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute erb spectrogram
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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute mfccs and mfes

‎spafe/features/lfcc.py

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from scipy.io.wavfile import read
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute spectrogram
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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute lfccs

‎spafe/features/lpc.py

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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute lpcs
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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute lpccs

‎spafe/features/mfcc.py

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from scipy.io.wavfile import read
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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mSpec, _ = mel_spectrogram(sig,
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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute mfccs and mfes
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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute mfccs and mfes

‎spafe/features/msrcc.py

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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute msrccs

‎spafe/features/ngcc.py

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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute ngccs

‎spafe/features/pncc.py

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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute pnccs

‎spafe/features/psrcc.py

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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute psrccs

‎spafe/features/rplp.py

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from spafe.utils.vis import show_features
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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‎spafe/features/spfeats.py

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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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fs, sig = read(fpath)
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# compute erb spectrogram

‎spafe/frequencies/dominant_frequencies.py

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win_hop = 0.010
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# read audio
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fpath = "../../../data/test.wav"
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fpath = "../../../tests/data/test.wav"
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win_type: WindowType="hamming")
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win_type="hamming")
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# compute FFT, Magnitude, Power spectra
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fourrier_transform = np.absolute(np.fft.fft(sig, nfft))

‎spafe/frequencies/fundamental_frequencies.py

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# read audio
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fpath = "../../../tests/data/test.wav"
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duration = len(sig) / fs
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harmonic_threshold = 0.85

‎spafe/version.py

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For a copy, see <https://github.com/SuperKogito/spafe/blob/master/LICENSE>.
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"""
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__version__ = "0.2.0"
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__version__ = "0.3.0"

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