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command:
- python3
- ${program}
- --use_auth_token
- --group_by_length
- --overwrite_output_dir
- --fp16
- --do_lower_case
- --do_eval
- --do_train
- --fuse_loss_wer
- ${args}
method: random
metric:
goal: minimize
name: eval/wer
parameters:
config_path:
value: conf/conformer_transducer_bpe_xlarge.yaml
dataset_config_name:
value: release3
dataset_name:
value: LIUM/tedlium
eval_split_name:
value: validation
evaluation_strategy:
value: steps
eval_steps:
value: 2000
fused_batch_size:
value: 8
learning_rate:
values:
- 1e-1
- 3e-2
- 1e-2
- 3e-3
- 1e-3
- 3e-4
- 1e-4
logging_steps:
value: 25
model_name_or_path:
value: stt_en_conformer_transducer_xlarge
max_steps:
value: 8000
output_dir:
value: ./sweep_output_dir
per_device_eval_batch_size:
value: 4
per_device_train_batch_size:
value: 8
preprocessing_num_workers:
value: 4
save_strategy:
value: "no"
tokenizer_path:
value: tokenizer
train_split_name:
value: train
vocab_size:
value: 1024
warmup_steps:
value: 500
wandb_project:
value: rnnt-debug-tedlium
freeze_encoder:
values:
- true
- false
add_adapter:
values:
- true
- false
unfreeze_encoder:
values:
- true
- false
length_column_name:
value: input_lengths
program: run_speech_recognition_rnnt.py
project: rnnt-debug-tedlium |