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width=\"400\"/\u003e\n\u003c/p\u003e\n\nAutomatic Chord Recognition tools\n\n## About\n\n`autochord` is:\n\n\u0026#10004; a [Python library](https://pypi.org/project/autochord/) for automatic chord recognition (using TensorFlow)\n\n\u0026#10004; a [Javascript app](https://cjbayron.github.io/autochord/) for visualization of chord transcriptions:\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/cjbayron/autochord/blob/main/images/autochord-js.png\" width=\"900\"/\u003e\n\u003c/p\u003e\n\n## Library Usage\n\nTo install library, run:\n```\n$ pip install autochord\n```\n\n`autochord` provides a very simple API for performing chord recognition:\n```\nimport autochord\nautochord.recognize('audio.wav', lab_fn='chords.lab')\n# This gives out a list of tuples in the format:\n#  (chord start, chord end, chord name)\n# e.g.\n# [(0.0, 5.944308390022676, 'D:maj'),\n#  (5.944308390022676, 7.476825396825397, 'C:maj'),\n#  (7.476825396825397, 18.250884353741498, 'D:maj'),\n#  (18.250884353741498, 19.736961451247165, 'C:maj')\n#  ...\n#  (160.49632653061224, 162.30748299319728, 'N')]\n```\n\nUnder the hood `autochord.recognize()` runs the NNLS-Chroma VAMP plugin to extract chroma features from the audio, and feeds it to a Bi-LSTM-CRF model in TensorFlow to recognize the chords. Currently, the model can recognize 25 chord classes: the 12 major triads, 12 minor triads, and no-chord ('N').\n\nOPTIONALLY, you may dump the chords in a `.lab` file by using the `lab_fn` parameter. The output file follows the MIREX chord label format.\n\nUpon import `autochord` takes care of setting up the VAMP plugin and downloading the pre-trained chord recognition model.\n\nThe measured test accuracy of the TensorFlow model is 67.33%. That may be enough for some songs, but we can explore in the future how to further improve this.\n\n### Supported Environments\n\n- Library has been tested to work out-of-the-box for **Python 3** setup in **Ubuntu** (18.04).\n- **Windows** is unsupported (see [issue](https://github.com/cjbayron/autochord/issues/1#issuecomment-1257176042))\n- **OSX** _can_ be supported with [some tweaks](https://github.com/cjbayron/autochord/issues/2#issue-1583722364)\n\n## App Usage\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/cjbayron/autochord/blob/main/images/autochord-js-ui.png\" width=\"600\"/\u003e\n\u003c/p\u003e\n\nThe app is pretty straightforward: you need to load a song, then you can upload a LAB file to visualize its chord labels. You may use the `autochord` Python library for generating this file. Optionally, you may load another LAB file for comparison (e.g. ground-truth labels, LAB file from another model's prediction).\n\n## Future Improvements\n\n- Integrate everything into a full chord recognition app! For that we need to:\n\t- convert VAMP plugin to JS module\n\t- model conversion to TensorFlow.js (as of writing, some CRF operations are not supported by TFJS yet)\n\t- converting all other Python functions to JS equivalent\n- Experimenting with other approaches to improve chord recognition accuracy\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcjbayron%2Fautochord","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcjbayron%2Fautochord","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcjbayron%2Fautochord/lists"}