Always activate the ALICE conda environment before usage. To do this, run:
$ conda activate ALICE
To process your .wav files containing the audio of interest, run:
$ ./run_ALICE.sh <data_location>
where <data_location> = folder of .wavs, path to a .wav, or path to a .txt file with a list of .wav paths, one per row.
For GPU use during diarization , use
$ ./run_ALICE.sh <data_location> gpu
Note that the use of GPU will speed up diarization substantially, but this will require CUDA toolkit and a compatible GPU.
After the processing is complete, linguistic unit estimates for each input audio file will be written to ALICE_output.txt inside ALICE main directory. Diarization outputs will be written to diarization_output.rttm inside the same directory.
In addition, utterance-level outputs for detected adult speech can be found from ALICE_output_utterances.txt, where each row corresponds to one utterance detected by the diarizer together with its estimated phoneme, syllable, and word counts. Timestamps appended to the filenames are of form <onset_time_in_ms x 10> _ <offset_time_in_ms x 10>, as measured from the beginning of each audio file. For instance, <filename>_0000062740_0000096150.wav stands for an utterance in <filename.wav> that started at 6.274 seconds and ended at 9.615 seconds.
NOTE: utterance-level unit count estimates are not meant to be precise at short time-scales, but they can be used to create aggregate measures of unit counts for desired time windows shorter than the full (e.g., daylong) recordings. At 2 minutes of audio, correlation with "real" linguistic unit counts should be around r = 0.75-0.90 depending on the data and language (see the ALICE paper for evaluation).
When done, deactivate the environment with
$ conda deactivate
Notes:
- ALICE will require empty hard disk space equal to approx. the size of the .wavs to be processed.