Instructions to use esb/conformer-rnnt-ami with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use esb/conformer-rnnt-ami with NeMo:
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- Notebooks
- Google Colab
- Kaggle
| 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 |