baiiiiiiiiii / asr-trainer

one script for xls-r/xlsr/whisper/hubert alpaca fine-tuning

one script for xls-r/xlsr/whisper fine-tuning

script modify from https://huggingface.co/blog/fine-tune-xlsr-wav2vec2

• fix large memory usage during eval metric calculation
• add cer and wer for evaluation

install requirement

pip install -r requirements.txt

example usage - common voice

python -m torch.distributed.launch --nproc_per_node=2 \
train.py \
--train_subset zh-TW \
--train_split train \
--test_split validation \
--tokenize_config voidful/wav2vec2-large-xlsr-53-tw-gpt \
--model_config facebook/wav2vec2-xls-r-300m \
--batch 10 \
--group_by_length \
--max_input_length_in_sec 20

python -m torch.distributed.launch --nproc_per_node=2 \
train.py \
--train_subset zh-TW \
--model_config openai/whisper-base \
--batch 10 \
--group_by_length \
--max_input_length_in_sec 20

example usage - custom set

custom data format data.csv:

path,text
/xxx/2.wav,被你拒絕而記仇
/xxx/4.wav,電影界的人
/xxx/7.wav,其實我最近在想

python -m torch.distributed.launch --nproc_per_node=2 \
--custom_set ./data.csv \
--tokenize_config voidful/wav2vec2-large-xlsr-53-tw-gpt \
--model_config facebook/wav2vec2-xls-r-300m \
--batch 3 \
--grad_accum 15 \
--max_input_length_in_sec 15 \
--eval_step 10000

python -m torch.distributed.launch --nproc_per_node=2 \
train.py --tokenize_config facebook/hubert-large-ls960-ft \
--model_config ntu-spml/distilhubert \
--group_by_length \
--train_set librispeech_asr \
--train_subset all \
--train_split train.clean.100+train.clean.360+train.other.500 \
--test_split test.other \
--learning_rate 0.0003 \
--batch 30 \
--logging_steps 10 \
--eval_steps 60 \
--epoch 150 \
--use_auth_token True \
--output_dir ./model_sweep \
--overwrite_output_dir

Train whisper on custom dataset

python train.py --tokenize_config openai/whisper-base \
--model_config openai/whisper-base \
--group_by_length \
--custom_set_train cm_train_unified.csv \
--custom_set_test cm_dev_unified.csv \
--output_dir ./whisper-custom \
--overwrite_output_dir

sweep usage

python -m wandb sweep ./sweep_xxx.yaml
python -m wandb agent xxxxxxxxxxxxxx

test command

python train.py --tokenize_config facebook/hubert-large-ls960-ft --model_config ntu-spml/distilhubert --group_by_length --train_set hf-internal-testing/librispeech_asr_dummy --train_split validation --train_subset clean --test_split validation --test_subset clean --learning_rate 0.0003 --batch 30 --logging_steps 10 --eval_steps 60 --epoch 150 --use_auth_token True --output_dir ./model_test --overwrite_output_dir --batch 3

python train.py --tokenize_config openai/whisper-tiny --model_config openai/whisper-tiny --group_by_length --train_set hf-internal-testing/librispeech_asr_dummy --train_split validation --train_subset clean --test_split validation --test_subset clean --learning_rate 0.0003 --batch 30 --logging_steps 10 --eval_steps 60 --epoch 150 --use_auth_token True --output_dir ./model_test --overwrite_output_dir --batch 3