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command: |
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- python3 |
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- ${program} |
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- --use_auth_token |
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- --do_eval |
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- --group_by_length |
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- --overwrite_output_dir |
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- --fp16 |
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- --do_lower_case |
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- --do_eval |
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- --do_train |
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- --fuse_loss_wer |
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- ${args} |
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method: grid |
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metric: |
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goal: minimize |
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name: train/loss |
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parameters: |
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config_path: |
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value: conf/conformer_transducer_bpe_xlarge.yaml |
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dataset_config_name: |
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value: clean |
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dataset_name: |
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value: librispeech_asr |
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max_steps: |
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value: 50 |
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model_name_or_path: |
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value: stt_en_conformer_transducer_xlarge |
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output_dir: |
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value: ./sweep_output_dir |
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per_device_train_batch_size: |
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value: 8 |
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fused_batch_size: |
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value: 8 |
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per_device_eval_batch_size: |
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value: 4 |
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gradient_accumulation_steps: |
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value: 1 |
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preprocessing_num_workers: |
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value: 1 |
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train_split_name: |
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value: train.100 |
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eval_split_name: |
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value: validation |
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tokenizer_path: |
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value: tokenizer |
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vocab_size: |
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value: 1024 |
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learning_rate: |
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values: |
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- 3e-4 |
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- 1e-4 |
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- 3e-5 |
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- 1e-5 |
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num_train_epochs: |
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value: 3 |
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program: run_speech_recognition_rnnt.py |
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project: rnnt-debug |
