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command:
- python3
- ${program}
- --use_auth_token
- --do_eval
- --group_by_length
- --overwrite_output_dir
- --fp16
- --do_lower_case
- --do_eval
- --do_train
- --fuse_loss_wer
- ${args}
method: grid
metric:
goal: minimize
name: train/train_loss
parameters:
config_path:
value: conf/conformer_transducer_bpe_xlarge.yaml
dataset_config_name:
value: clean
dataset_name:
value: librispeech_asr
max_steps:
value: 50
model_name_or_path:
value: stt_en_conformer_transducer_xlarge
output_dir:
value: ./sweep_output_dir
gradient_accumulation_steps:
values:
- 1
- 2
per_device_train_batch_size:
values:
- 8
- 16
fused_batch_size:
values:
- 4
- 8
- 16
per_device_eval_batch_size:
value: 4
preprocessing_num_workers:
value: 1
train_split_name:
value: train.100[:500]
eval_split_name:
value: validation[:100]
tokenizer_path:
value: tokenizer
vocab_size:
value: 1024
wandb_project:
value: rnnt-debug
logging_steps:
value: 5
program: run_speech_recognition_rnnt.py
project: rnnt-debug |