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# parameters to set
model_cfg:
init_from_hub_path: openai/whisper-large-v2
# lang: None
# apply_spec_augment: True
# mask_time_prob: 0.05
# mask_feature_prob: 0.05
# mask_time_length: 40
# mask_feature_length: 30
# mask_time_min_masks: 2
# mask_feature_min_masks: 2
data_cfg:
data_root: ~/corpora/
train_manif: ~/corpora/data_manifests/ASR/PUBLIC_KIDS_TRAIN_v4_deduped.csv
val_manif: # small private dataset of classroom speech, only affects training if load_best_model_at_end: True
test_manif: # small private dataset of classroom speech, doesn't affect training
experiment_cfg:
OUT_DIR: train/whisat/save/publicKS_LoRA_int8
use_lora: True
use_int8: True
train_cfg:
training_args:
output_dir: !ref <experiment_cfg[OUT_DIR]>
per_device_train_batch_size: 32 # 64
learning_rate: 0.0001 # 1e-5 orig, 1e-3 lora
warmup_steps: 50 # 500 orig 50 lora
num_train_epochs: 1
fp16: True # True
evaluation_strategy: steps # or epochs
per_device_eval_batch_size: 4
predict_with_generate: True
generation_max_length: 112
save_steps: 500
eval_steps: 500
eval_accumulation_steps: 2
logging_steps: 25
report_to:
- tensorboard
load_best_model_at_end: False
metric_for_best_model: wer
greater_is_better: False
push_to_hub: False
remove_unused_columns: False # required as the PeftModel forward doesn't have the signature of the wrapped model's forward
label_names:
- labels
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