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command:
- python3
- ${program}
- --do_train
- --do_eval
- --use_scan
- --gradient_checkpointing
- --overwrite_output_dir
- --predict_with_generate
- --freeze_encoder
- --streaming
- --use_auth_token
- --compilation_cache
- --return_timestamps
- ${args}
method: grid
metric:
goal: minimize
name: eval/wer
parameters:
model_name_or_path:
value: distil-whisper/large-32-2
teacher_model_name_or_path:
value: openai/whisper-large-v2
train_dataset_name:
value: librispeech_asr-timestamped+librispeech_asr-timestamped+librispeech_asr-timestamped+common_voice_13_0-timestamped+voxpopuli-timestamped+ami-ihm-timestamped+ami-sdm-timestamped+peoples_speech-clean-timestamped+tedlium-timestamped+switchboard-data+gigaspeech-l-timestamped+spgispeech-timestamped
train_dataset_config_name:
value: all+all+all+en+en+ihm+sdm+clean+release3+all+l+L
train_split_name:
value: train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train
train_dataset_samples:
value: 2.9+10.4+14.9+89+18.2+10.9+10.9+288+26.8+371.2+226.6+192.7
timestamp_probability:
values:
- 0.0
- 0.2
- 0.4
- 0.6
- 0.8
- 1.0
round_timestamps:
values:
- True
- False
eval_dataset_name:
value: "distil-whisper/gigaspeech-l"
eval_dataset_config_name:
value: "l"
cache_dir:
value: /home/sanchitgandhi/.cache
dataset_cache_dir:
value: /home/sanchitgandhi/.cache
output_dir:
value: ./
per_device_train_batch_size:
value: 64
dtype:
value: bfloat16
learning_rate:
value: 1e-4
lr_scheduler_type:
value: constant_with_warmup
warmup_steps:
value: 50
max_steps:
value: 2500
save_steps:
value: 2501 # don't save checkpoints during sweep
dataloader_num_workers:
value: 48
logging_steps:
value: 25
wer_threshold:
value: 10
program: run_distillation.py
project: distil-whisper-sweeps
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