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farsipal/whisper-lg-el-intlv-xs-2

sourceHugging Faceapache-2.0updated 4y agoView on Hugging Face
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whisper-lg-el-intlv-xs-2

This model is a fine-tuned version of farsipal/whisper-lg-el-intlv-xs on the mozilla-foundation/commonvoice110,google/fleurs el,elgr dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.2872
  • —Wer: 9.5004

Model description

The model was trained on two interleaved datasets for transcription in the Greek language.

Intended uses & limitations

Transcription in the Greek language

Training and evaluation data

Training was performed on two interleaved datasets. Testing was performed on common voice 11.0 (el) test only.

Training procedure

                --model_name_or_path   'farsipal/whisper-lg-el-intlv-xs' \
                --model_revision   main \
                --do_train   True \
                --do_eval   True \
                --use_auth_token   False \
                --freeze_feature_encoder   False \
                --freeze_encoder   False \
                --model_index_name   'whisper-lg-el-intlv-xs-2' \
                --dataset_name 'mozilla-foundation/common_voice_11_0,google/fleurs' \
                --dataset_config_name 'el,el_gr' \
                --train_split_name  'train+validation,train+validation' \
                --eval_split_name   'test,-' \
                --text_column_name  'sentence,transcription' \
                --audio_column_name 'audio,audio' \
                --streaming   False \
                --max_duration_in_seconds   30 \
                --do_lower_case   False \
                --do_remove_punctuation   False \
                --do_normalize_eval   True \
                --language   greek \
                --task transcribe \
                --shuffle_buffer_size   500 \
                --output_dir   './data/finetuningRuns/whisper-lg-el-intlv-xs-2' \
                --overwrite_output_dir   True \
                --per_device_train_batch_size   8 \
                --gradient_accumulation_steps  4 \
                --learning_rate   3.5e-6 \
                --dropout         0.15 \
                --attention_dropout 0.05 \
                --warmup_steps   500 \
                --max_steps   5000 \
                --eval_steps   1000 \
                --gradient_checkpointing   True \
                --cache_dir   '~/.cache' \
                --fp16   True \
                --evaluation_strategy   steps \
                --per_device_eval_batch_size   8 \
                --predict_with_generate   True \
                --generation_max_length   225 \
                --save_steps   1000 \
                --logging_steps   25 \
                --report_to   tensorboard \
                --load_best_model_at_end   True \
                --metric_for_best_model   wer \
                --greater_is_better   False \
                --push_to_hub   False  \
                --dataloader_num_workers 6

Training hyperparameters

The following hyperparameters were used during training:

  • —learning_rate: 3.5e-06
  • —trainbatchsize: 8
  • —evalbatchsize: 8
  • —seed: 42
  • —gradientaccumulationsteps: 4
  • —totaltrainbatch_size: 32
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —lrschedulerwarmup_steps: 500
  • —training_steps: 5000
  • —mixedprecisiontraining: Native AMP

Training results

Training LossEpochStepValidation LossWer
0.08132.4910000.214710.8284
0.03794.9820000.243910.0111
0.01957.4630000.27679.8811
0.01269.9540000.28729.5004
0.010312.4450000.30219.6954

Framework versions

  • —Transformers 4.26.0.dev0
  • —Pytorch 1.13.0+cu117
  • —Datasets 2.8.1.dev0
  • —Tokenizers 0.13.2