9 Commits

Author	SHA1	Message	Date
MasterPhooey	94903783cb	blackwell/wham & chim datasets	2026-03-09 19:48:35 -05:00
MasterPhooey	747822e856	wsl2 fix	2026-02-01 08:45:12 -06:00
MasterPhooey	423bbd15f5	tweaks	2026-01-21 06:03:57 -06:00
Tater Totterson	8669ccdfd4	Improve CUDA/XLA setup and CPU fallback	2026-01-20 20:47:58 -06:00
MasterPhooey	d5e8d187a1	personal samples	2026-01-17 23:01:50 -06:00
MasterPhooey	2b9aa95903	personal samples	2026-01-17 20:24:43 -06:00
MasterPhooey	c52f92d3c9	cli + web recorder ui	2026-01-17 16:23:24 -06:00
MasterPhooey	5bc0f12a7f	cli + web recorder ui	2026-01-17 01:23:51 -06:00
George Joseph	cb81f7f02d	Train from the command line ... The files in the `cli` directory allow you to train wake words from the command line without needing to use the Jupyter notebook or a web browser. Basically, the logic from the notebook has been placed in separate shell scripts and python files wrapped by 3 high-level scripts that do the following: * setup_python_venv: Creates a Python virtual environment with all the packages needed to train. The venv is created in the container's /data directory and is therefore stored on the host, not in the container's root docker volume. * setup_training_datasets: Downloads, extracts and converts the MIT RIR, FMA, Audioset and Negative training reference datasets. Also stored in /data. * train_wake_word: Generates the wake word samples, augments them with the audio from the training datasets, and finally runs the microwakeword training. The resulting model tflite and json files are placed in the /data/output directory. See the README.md file for much more information.	2025-12-28 12:48:51 -07:00