The implementation of wav2vec2.0 for SLR
- copy how2sign from Royal
scp -J karahan@193.140.195.142 karahan@193.140.195.17:/ssd1/karahan/How2Sign/H2S_train.h5 /home/kara-nlp/Documents/Repositories/Thesis/SLT/Datasets/How2Sign/Mediapipe/
scp -J karahan@193.140.195.142 karahan@193.140.195.17:/ssd1/karahan/How2Sign/H2S_val.h5 /home/kara-nlp/Documents/Repositories/Thesis/SLT/Datasets/How2Sign/Mediapipe/
scp -J karahan@193.140.195.142 karahan@193.140.195.17:/ssd1/karahan/How2Sign/H2S_test.h5 /home/kara-nlp/Documents/Repositories/Thesis/SLT/Datasets/How2Sign/Mediapipe/
accelerate launch sign2vec/train/run_sign2vec_pretraining.py \
--dataset_name="YoutubeASL" \
--tags sign2vec base v0.0 single_cue dev \
--datasets "train" "test" \
--dataset_path="/ssd2/karahan/YASL/pose" \
--model_config_file="experimental/configs/sign2vec_pretraining_config.yaml" \
--output_dir="./sign2vec-base-v0.0" \
--max_train_steps="20000" \
--num_warmup_steps="32000" \
--gradient_accumulation_steps="8" \
--learning_rate="0.005" \
--weight_decay="0.01" \
--max_duration_in_seconds="20.0" \
--min_duration_in_seconds="2.0" \
--logging_steps="1" \
--saving_steps="10000" \
--per_device_train_batch_size="8" \
--per_device_eval_batch_size="8" \
--adam_beta1="0.9" \
--adam_beta2="0.98" \
--adam_epsilon="1e-06" \
--gradient_checkpointing \
--mask_time_prob="0.65" \
--mask_time_length="10" \
--push_to_hub In this training, you can either utilize raw pose to train your modals or you can use pretrained sign2vec model for training.
The parameters in the pre-training and fine-tuning are coming from original YoutubeASL paper to replicate the experimental setup.
In either case, you are file structure should look like this.
YASL
|---yasl.train.csv
|---yasl.dev.csv
|---yasl.test.csv
|---mae
| |--- yasl_mae_0.h5
| |--- ....
|---sign2vec
| |--- yasl_sign2vec_0.h5
| |--- ....
|---pose
| |--- yasl_pose_0.h5
| |--- ....python3 -m sign2vec.train.run_finetuning --annotation_file='/home/kara-nlp/Documents/Repositories/Thesis/SLT/Datasets/YASL' \
--metadata_file='/home/kara-nlp/Documents/Repositories/Thesis/SLT/Datasets/YASL/keypoints/' \
--dataset_type='yasl' \
--model_id="google-t5/t5-base" \
--max_training_step="20000" \
--per_device_train_batch_size="32" \
--per_device_eval_batch_size=2
--gradient_accumulation_steps=4 \
--eval_steps=2 \
--learning_rate=0.001 \
--max_sequence_length=256 \
--max_token_length=128 \
--model_name=h2s-pose-no-norm
--skip_frames \
--logging_steps=1 \
--eval_steps="1000"python3 -m sign2vec.train.run_finetuning --dataset_dir=/ssd1/karahan/How2Sign \
--modality="pose" \
--model_id="google-t5/t5-base" \
--max_training_step="20000" \
--learning_rate="0.001" \
--max_sequence_length="256" \
--max_token_length="128" \
--per_device_train_batch_size="32" \
--gradient_accumulation_steps="4" \
--skip_frames \
--per_device_eval_batch_size="2" \
--logging_steps="10" \
--eval_steps="1000" \
--model_name=h2s-pose-no-norm