readme: ---
  language:
  - el
  license: apache-2.0
  tags:
  - whisper-event
  - generated_from_trainer
  datasets:
  - mozilla-foundation/common_voice_11_0
  metrics:
  - wer
  model-index:
  - name: whisper-sm-el-xs
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: mozilla-foundation/common_voice_11_0 el
        type: mozilla-foundation/common_voice_11_0
        config: el
        split: test
        args: el
      metrics:
      - name: Wer
        type: wer
        value: 20.63521545319465
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # Whisper-Small (el) for Transcription
  
  This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 el dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4805
  - Wer: 20.6352
  
  ## Model description
  
  This model is trained for transcription on the Greek subset on mozilla-foundation/common_voice_11_0 interleaved splits train+eval
  
  ## Intended uses & limitations
  
  This is part of the Whisper Finetuning Event (December 2022)
  
  ## Training and evaluation data
  
  Training used interleaved splits: train + evaluation. 
  Evaluation was done on the test split.
  Data was streamed from Hugging Face's Hub.
  
  ## Training procedure
  
  The script used has been uploaded in the files of this space
  The command to run it was:
  ```
  python ./run_speech_recognition_seq2seq_streaming.py \
                  --model_name_or_path   "openai/whisper-small" \
                  --model_revision       "main" \
                  --do_train             True \
                  --do_eval              True \
                  --use_auth_token       False \
                  --freeze_encoder       False \
                  --model_index_name     "whisper-sm-el-xs" \
                  --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                  "transcribe" \
                  --shuffle_buffer_size   500 \
                  --output_dir             "./data/finetuningRuns/whisper-sm-el-xs" \
                  --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
  
  | Training Loss | Epoch | Step | Validation Loss | Wer     |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|
  | 0.0024        | 18.01 | 1000 | 0.4246          | 21.0438 |
  | 0.0003        | 37.01 | 2000 | 0.4805          | 20.6352 |
  | 0.0001        | 56.01 | 3000 | 0.5102          | 20.8395 |
  | 0.0001        | 75.0  | 4000 | 0.5296          | 21.0717 |
  | 0.0001        | 94.0  | 5000 | 0.5375          | 21.0253 |
  
  Here is the summary from the log of the run:
  
  ```
  ***** train metrics *****
    epoch                    =        94.0
    train_loss               =      0.0222
    train_runtime            = 23:06:13.19
    train_samples_per_second =       3.847
    train_steps_per_second   =        0.06
  12/08/2022 11:20:17 - INFO - __main__ - *** Evaluate ***
  
  ***** eval metrics *****
    epoch                   =       94.0
    eval_loss               =     0.48