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#!/bin/bash
FAIRSEQ= # Setup your fairseq directory
config_dir=${FAIRSEQ}/examples/mr_hubert/config
config_name=mr_hubert_base_librispeech
# Prepared Data Directory
data_dir=librispeech
# -- data_dir
# -- test.tsv
# -- test.ltr
# -- dict.ltr.txt
exp_dir=exp # Target experiments directory (where you have your pre-trained model with checkpoint_best.pt)
ratios="[1, 2]" # Default label rate ratios
_opts=
# If use slurm, uncomment this line and modify the job submission at
# _opts="${_opts} hydra/launcher=submitit_slurm +hydra.launcher.partition=${your_slurm_partition} +run=submitit_reg"
# If want to set additional experiment tag, uncomment this line
# _opts="${_opts} hydra.sweep.subdir=${your_experiment_tag}"
# If use un-normalized audio, uncomment this line
# _opts="${_opts} task.normalize=false"
PYTHONPATH=${FAIRSEQ}
python examples/speech_recognition/new/infer.py \
--config-dir ${config_dir} \
--config-name infer_multires \
${_opts} \
task.data=${data_dir} \
task.label_rate_ratios='${ratios}' \
common_eval.results_path=${exp_dir} \
common_eval.path=${exp_dir}/checkpoint_best.pt \
dataset.max_tokens=2000000 \
dataset.gen_subset=test \
dataset.skip_invalid_size_inputs_valid_test=true
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