whisper-sm-el-frzEnc-xlate

Whisper Small - Greek (el)

This model is a fine-tuned version of openai/whisper-small on the mozilla-foundation/common_voice_11_0 el dataset for translation from Greek to English. It achieves the following results on the evaluation set:

• Loss: 0.4642
• Wer: 25.6965

Model description

This model was finetuned with the encoder frozen. Only the decoder weights have been changed by this training run.

Intended uses & limitations

The purpose of this model was to understand how the freezing of a part of the model might affect learning, in an effort to assess the feasibility of enabling adapters.

Training and evaluation data

The training was performed by streaming interleaved train+eval spits of the greek (el) subset of mozilla-foundation/common_voice_11_0 (el). The test set was similarly used for validation.

Training procedure

Fine-tuning was performed on a lambdalabs laptop equipped with an NVIDIA GeForce RTX 3080 Laptop GPU (16GB).

The script used to perform the training run_speech_recognition_seq2seq_streaming.py is included in the files of this space with the following arguments:

                --model_name_or_path   "openai/whisper-small"
                --model_revision       "main"
                --do_train             True
                --do_eval              True
                --use_auth_token       False
                --freeze_encoder       True
                --model_index_name     "Whisper Small - Greek (el)"
                --dataset_name         "mozilla-foundation/common_voice_11_0"
                --dataset_config_name  "el"
                --audio_column_name    "audio"
                --text_column_name     "sentence"
                --max_duration_in_seconds 30
                --train_split_name    "train+validation"
                --eval_split_name      "test"
                --do_lower_case         False
                --do_remove_punctuation False
                --do_normalize_eval    True
                --language             "greek"
                --task                  "translate"
                --shuffle_buffer_size   500
                --output_dir             "./data/finetuningRuns/whisper-sm-el-frzEnc-xlate"
                --per_device_train_batch_size 16
                --gradient_accumulation_steps 4 
                --learning_rate          1e-5
                --warmup_steps           500
                --max_steps              5000
                --gradient_checkpointing True
                --fp16                   True
                --evaluation_strategy    "steps"
                --per_device_eval_batch_size 8
                --predict_with_generate  True
                --generation_max_length  225
                --save_steps             1000
                --eval_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
                --overwrite_output_dir    True

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 5000
• mixed_precision_training: Native AMP

Training results

Upon completion of training the best model was reloaded and tested with the following results extracted from the stdout log:

***** eval metrics *****
  epoch                   =       94.0
  eval_loss               =     0.4642
  eval_runtime            = 0:19:54.59
  eval_samples_per_second =       1.42
  eval_steps_per_second   =      0.177
  eval_wer                =    25.6965

Framework versions

• Transformers 4.26.0.dev0
• Pytorch 1.13.0
• Datasets 2.7.1.dev0
• Tokenizers 0.12.1