| language: | |
| - en | |
| tags: | |
| - esb | |
| datasets: | |
| - esb/datasets | |
| - edinburghcstr/ami | |
| To reproduce this run, first install NVIDIA NeMo according to the [official instructions](https://github.com/NVIDIA/NeMo#installation), then execute: | |
| ```python | |
| #!/usr/bin/env bash | |
| CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \ | |
| --config_path="conf/conformer_transducer_bpe_xlarge.yaml" \ | |
| --model_name_or_path="stt_en_conformer_transducer_xlarge" \ | |
| --dataset_name="esb/datasets" \ | |
| --tokenizer_path="tokenizer" \ | |
| --vocab_size="1024" \ | |
| --max_steps="100000" \ | |
| --dataset_config_name="ami" \ | |
| --output_dir="./" \ | |
| --run_name="conformer-rnnt-ami" \ | |
| --wandb_project="rnnt" \ | |
| --per_device_train_batch_size="8" \ | |
| --per_device_eval_batch_size="4" \ | |
| --logging_steps="50" \ | |
| --learning_rate="1e-4" \ | |
| --warmup_steps="500" \ | |
| --save_strategy="steps" \ | |
| --save_steps="20000" \ | |
| --evaluation_strategy="steps" \ | |
| --eval_steps="20000" \ | |
| --report_to="wandb" \ | |
| --preprocessing_num_workers="4" \ | |
| --fused_batch_size="4" \ | |
| --length_column_name="input_lengths" \ | |
| --fuse_loss_wer \ | |
| --group_by_length \ | |
| --overwrite_output_dir \ | |
| --do_train \ | |
| --do_eval \ | |
| --do_predict \ | |
| --use_auth_token | |
| ``` | |