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ttrain.bash
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ttrain.bash
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#!/bin/bash -l
#SBATCH -N 1
#SBATCH -c 12
#SBATCH -p gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --constraint=a100@80g
#SBATCH --time=08:00:00
#SBATCH --output=log/%j.output
#SBATCH --error=log/%j.error
nvidia-smi
module load anaconda3/2022.05
module load ffmpeg/20190305
source activate /work/van-speech-nlp/jindaznb/visenv/
cd /work/van-speech-nlp/jindaznb/jslpnb/mllm_expriments/Whispering-LLaMA
# 43210104 43271154
# speaker_id='M05' && option='M' \
# && base_path='/work/van-speech-nlp/jindaznb/jslpnb/mllm_expriments/Whispering-LLaMA' \
# && python training/WL-S_M_train.py --lr 1e-3 \
# --option $option \
# --d 1 \
# --pretrained_path "${base_path}/weights/alpaca.pth" \
# --tokenizer_path "${base_path}/weights/tokenizer.model" \
# --data_path "${base_path}/Inference/gs_inferences/single_phoneme/torgo_${speaker_id}_large-v2_phoneme" \
# --adapter_path "${base_path}/runs/WL_${option}_0.001_torgo_${speaker_id}_large-v2_phoneme" \
# --dataset_name "torgo_${speaker_id}_large-v2_phoneme" \
# --num_epochs 20 \
# --input_batch_size 32
#
# speaker_id='M04' && option='M' \
# && base_path='/work/van-speech-nlp/jindaznb/jslpnb/mllm_expriments/Whispering-LLaMA' \
# && python training/WL-S_M_train.py --lr 1e-3 \
# --option $option \
# --d 1 \
# --pretrained_path "${base_path}/weights/alpaca.pth" \
# --tokenizer_path "${base_path}/weights/tokenizer.model" \
# --data_path "${base_path}/Inference/gs_inferences/single_phoneme/torgo_${speaker_id}_large-v2_phoneme" \
# --adapter_path "${base_path}/runs/WL_${option}_0.001_torgo_${speaker_id}_large-v2_phoneme" \
# --dataset_name "torgo_${speaker_id}_large-v2_phoneme" \
# --num_epochs 20 \
# --input_batch_size 32
# 43281905
# speaker_id='M03' && option='M' \
# && base_path='/work/van-speech-nlp/jindaznb/jslpnb/mllm_expriments/Whispering-LLaMA' \
# && python training/WL-S_M_train.py --lr 1e-3 \
# --option $option \
# --d 1 \
# --pretrained_path "${base_path}/weights/alpaca.pth" \
# --tokenizer_path "${base_path}/weights/tokenizer.model" \
# --data_path "${base_path}/Inference/gs_inferences/single_phoneme/torgo_${speaker_id}_large-v2_phoneme" \
# --adapter_path "${base_path}/runs/WL_${option}_0.001_torgo_${speaker_id}_large-v2_phoneme" \
# --dataset_name "torgo_${speaker_id}_large-v2_phoneme" \
# --num_epochs 20 \
# --input_batch_size 32
# 43163037
# speaker_id='F01' && option='M' \
# && base_path='/work/van-speech-nlp/jindaznb/jslpnb/mllm_expriments/Whispering-LLaMA' \
# && python training/WL-S_M_train.py --lr 1e-3 \
# --option $option \
# --d 1 \
# --pretrained_path "${base_path}/weights/alpaca.pth" \
# --tokenizer_path "${base_path}/weights/tokenizer.model" \
# --data_path "${base_path}/Inference/gs_inferences/large-v2_hypo/torgo_${speaker_id}_large-v2" \
# --dataset_name "torgo_${speaker_id}_large-v2" \
# --adapter_path "${base_path}/runs/WL_${option}_0.001_torgo_${speaker_id}_large-v2" \
# --num_epochs 20
# 43163038
# speaker_id='F03' && option='M' \
# && base_path='/work/van-speech-nlp/jindaznb/jslpnb/mllm_expriments/Whispering-LLaMA' \
# && python training/WL-S_M_train.py --lr 1e-3 \
# --option $option \
# --d 1 \
# --pretrained_path "${base_path}/weights/alpaca.pth" \
# --tokenizer_path "${base_path}/weights/tokenizer.model" \
# --data_path "${base_path}/Inference/gs_inferences/large-v2_hypo/torgo_${speaker_id}_large-v2" \
# --dataset_name "torgo_${speaker_id}_large-v2" \
# --adapter_path "${base_path}/runs/WL_${option}_0.001_torgo_${speaker_id}_large-v2" \
# --num_epochs 20
# 43163040
# speaker_id='F04' && option='M' \
# && base_path='/work/van-speech-nlp/jindaznb/jslpnb/mllm_expriments/Whispering-LLaMA' \
# && python training/WL-S_M_train.py --lr 1e-3 \
# --option $option \
# --d 1 \
# --pretrained_path "${base_path}/weights/alpaca.pth" \
# --tokenizer_path "${base_path}/weights/tokenizer.model" \
# --data_path "${base_path}/Inference/gs_inferences/large-v2_hypo/torgo_${speaker_id}_large-v2" \
# --dataset_name "torgo_${speaker_id}_large-v2" \
# --adapter_path "${base_path}/runs/WL_${option}_0.001_torgo_${speaker_id}_large-v2" \
# --num_epochs 20
# 43111952
# speaker_id='F03' && python training/WL-S_M_train.py --lr 1e-3 \
# --option 'M' \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/large-v2_hypo/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43110608
# speaker_id='F01' && python training/WL-S_M_train.py --lr 1e-3 \
# --option 'M' \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/large-v2_hypo/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
## 43110607
# speaker_id='M05' && python training/WL-S_M_train.py --lr 1e-3 \
# --option 'M' \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/large-v2_hypo/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# speaker_id='M04' && python training/WL-S_M_train.py --lr 1e-3 \
# --option 'M' \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/large-v2_hypo/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
#\ --adapter_path "/work/van-speech-nlp/jindaznb/jslpnb/mllm_expriments/Whispering-LLaMA/runs/WL_M_0.001_torgo_M05_large-v2"
# 43097452
# speaker_id='M01' && python training/WL-S_M_train-Copy1.py --lr 1e-3 \
# --option 'S' \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/large-v2_hypo/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43100449
# speaker_id='M02' && python training/WL-S_M_train-Copy1.py --lr 1e-3 \
# --option 'S' \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/large-v2_hypo/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43100450
# speaker_id='M03' && python training/WL-S_M_train-Copy1.py --lr 1e-3 \
# --option 'S' \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/large-v2_hypo/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43095348
# speaker_id='F01' && python training/WL-S_M_train-Copy1.py --lr 1e-3 \
# --option 'M' \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/large-v2_hypo/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# S: 43094923
# 43077610
# speaker_id='M01' && python training/WL-S_M_train.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43077614
# speaker_id='M02' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43077615
# speaker_id='M03' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43074028
# speaker_id='M04' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43074027
# speaker_id='M05' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43074029
# speaker_id='F04' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43074030
# speaker_id='F03' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43066918
# speaker_id='M01' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43066919
# speaker_id='M02' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
# 43066920
# speaker_id='M03' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}_large-v2" --dataset_name "torgo_${speaker_id}_large-v2"
#
# python 1_finetuned_whisper_gen_json_torch_pt_train.py --speaker_id M02
# speaker_id='M02' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/finetuned_torgo_${speaker_id}" --dataset_name "torgo_${speaker_id}_finetuned_whisper"
# 42977426
# python 1_finetuned_whisper_gen_json_torch_pt_train.py --speaker_id M03
# speaker_id='M03' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/finetuned_torgo_${speaker_id}" --dataset_name "torgo_${speaker_id}_finetuned_whisper"
# 42981874
# python 1_finetuned_whisper_gen_json_torch_pt_train.py --speaker_id M04
# speaker_id='M04' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/finetuned_torgo_${speaker_id}" --dataset_name "torgo_${speaker_id}_finetuned_whisper"
# 42981906
# python 1_finetuned_whisper_gen_json_torch_pt_train.py --speaker_id M05 && speaker_id='M05' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/finetuned_torgo_${speaker_id}" --dataset_name "torgo_${speaker_id}_finetuned_whisper"
# train adpater with giga_17 dataset
# python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/17" \
# --dataset_name "giga17"
# 42867478
# speaker_id='F01' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}" --dataset_name "torgo_${speaker_id}"
# speaker_id='F03' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}" --dataset_name "torgo_${speaker_id}"
# 42867479
# speaker_id='F04' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}" --dataset_name "torgo_${speaker_id}"
# 42884604
# speaker_id='M01' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}" --dataset_name "torgo_${speaker_id}"
# 42867483
# speaker_id='M02' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}" --dataset_name "torgo_${speaker_id}"
# 42884605
# speaker_id='M03' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}" --dataset_name "torgo_${speaker_id}"
# 42867486
# speaker_id='M04' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}" --dataset_name "torgo_${speaker_id}"
# 42896850
# speaker_id='M05' && python training/WL-S_train-Copy1.py --lr 1e-3 \
# --d 1 \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data_path "Inference/gs_inferences/torgo_${speaker_id}" --dataset_name "torgo_${speaker_id}"
# 42892049
# debug M05 running with whisper-en
# python 3_prepare_for_torgo_baseline.py --speaker_id M05
# python 4_output_feature_pt.py --speaker_id M05
# speaker_id='M05' && python Inference/WL-S_inference.py \
# --pretrained_path 'weights/alpaca.pth' \
# --tokenizer_path 'weights/tokenizer.model' \
# --data "Inference/gs_inferences/torgo_${speaker_id}_test.pt" \
# --save_dir "runs/Inference/${speaker_id}" \
# --root 'runs/WL_S_0.001_giga17'
# python 3_hypothesis_whisper_finetuned.py --speaker_id M01 --model_name "finetuned_whisper_output/model/torgo_tiny_finetune_F01"