Evaluating a CTC model

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wav2vec 2.0 Evaluate CTC model run after fine-tuning python examples/speech_recognition/infer.py /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw --task audio_finetuning
--nbest 1 --path /path/to/model --gen-subset $subset --results-path /path/to/save/results/for/sclite --w2l-decoder kenlm
--lm-model /path/to/kenlm.bin --lm-weight 2 --word-score -1 --sil-weight 0 --criterion ctc --labels ltr --max-tokens 4000000
--post-process letter I would like to ask what /checkpoint/abaevski/data/speech/libri/10h/wav2vec/raw stands for; What does the path in --lm-model /path/to/kenlm.bin represent; What does --word-score -1 represent; What does --sil-weight 0 represent; What does --criterion ctc stand for; What does --labels ltr stand for; What does --post-process represent;