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command:
- python3
- ${program}
- --do_train
- --do_eval
- --use_scan
- --gradient_checkpointing
- --overwrite_output_dir
- --predict_with_generate
- ${args}
method: random
metric:
goal: minimize
name: eval/wer
parameters:
model_name_or_path:
value: distil-whisper/large-32-2
dataset_name:
value: distil-whisper/librispeech_asr
dataset_config_name:
value: all
train_split_name:
value: train.clean.100+train.clean.360+train.other.500
eval_split_name:
value: validation.clean
text_column_name:
value: whisper_transcript
cache_dir:
value: /home/sanchitgandhi/cache
dataset_cache_dir:
value: /home/sanchitgandhi/cache
output_dir:
value: ./
per_device_train_batch_size:
value: 32
per_device_eval_batch_size:
value: 16
dtype:
value: bfloat16
learning_rate:
distribution: log_uniform
max: -6.91
min: -11.51
warmup_steps:
value 500
num_train_epochs:
value: 1
preprocessing_num_workers:
value: 16
dataloader_num_workers:
value: 16
logging_steps:
value: 25
freeze_encoder:
values:
- True
- False
program: run_finetuning.py
project: distil-whisper
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