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1#!/usr/bin/env bash
2CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
3 --config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
4 --model_name_or_path="stt_en_conformer_transducer_xlarge" \
5 --dataset_name="esc-benchmark/esc-datasets" \
6 --tokenizer_path="tokenizer" \
7 --vocab_size="1024" \
8 --max_steps="100000" \
9 --dataset_config_name="librispeech" \
10 --output_dir="./" \
11 --run_name="conformer-rnnt-librispeech" \
12 --wandb_project="rnnt" \
13 --per_device_train_batch_size="8" \
14 --per_device_eval_batch_size="4" \
15 --logging_steps="50" \
16 --learning_rate="1e-4" \
17 --warmup_steps="500" \
18 --save_strategy="steps" \
19 --save_steps="20000" \
20 --evaluation_strategy="steps" \
21 --eval_steps="20000" \
22 --report_to="wandb" \
23 --preprocessing_num_workers="4" \
24 --fused_batch_size="4" \
25 --length_column_name="input_lengths" \
26 --fuse_loss_wer \
27 --group_by_length \
28 --overwrite_output_dir \
29 --do_train \
30 --do_eval \
31 --do_predict \
32 --use_auth_token
33