command: - python3 - ${program} - --do_train - --do_eval - --use_scan - --gradient_checkpointing - --overwrite_output_dir - --predict_with_generate - --freeze_encoder - --streaming - --use_auth_token - ${args} method: grid metric: goal: minimize name: gigaspeech-l/validation/wer parameters: model_name_or_path: values: - distil-whisper/large-32-16 - distil-whisper/large-32-8 - distil-whisper/large-32-4 - 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+librispeech_asr-prompted+librispeech_asr-prompted+librispeech_asr-prompted+tedlium-prompted train_dataset_config_name: value: all+all+all+en+en+ihm+sdm+clean+release3+all+l+all+all+all+release3 train_split_name: value: train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train.clean.100+train.clean.360+train.other.500+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+2.9+10.4+14.9+26.8 eval_dataset_name: value: librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs eval_dataset_config_name: value: all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us eval_split_name: value: validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation eval_text_column_name: value: text+text+text+text+text+text+text+text+text+text+text+text+transcription cache_dir: value: /home/sanchitgandhi/.cache dataset_cache_dir: value: /home/sanchitgandhi/.cache output_dir: value: ./ per_device_train_batch_size: value: 64 per_device_eval_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: 10000 save_steps: value: 10001 # 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