command: - python3 - ${program} - --do_train - --do_eval - --gradient_checkpointing - --overwrite_output_dir - --predict_with_generate - --streaming - --use_auth_token - --use_scan - ${args} method: grid metric: goal: minimize name: eval/wer parameters: model_name_or_path: value: distil-whisper/large-16-2 teacher_model_name_or_path: value: openai/whisper-large-v2 train_dataset_name: value: librispeech_asr+librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech 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: 100+360+500+2300+450+90+90+12000+450+3600+2500+5000 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: 32 per_device_eval_batch_size: value: 64 dtype: value: bfloat16 learning_rate: value: 0.0001 lr_scheduler_type: value: constant_with_warmup warmup_steps: value: 50 max_steps: value: 2500 eval_steps: value: 2500 save_steps: value: 2001 # don't save checkpoints during sweep dataloader_num_workers: value: 16 logging_steps: value: 5 wer_threshold: value: 10 mse_weight: values: - 0.0 - 0.3 - 1 - 3 program: run_distillation.py project: distil-whisper-sweeps