README.md

---
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 Small - Greek (el)
  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: 25.696508172362552
---

<!-- 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 - Greek (el)

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 
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

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0032        | 18.01 | 1000 | 0.4642          | 25.6965 |
| 0.0006        | 37.01 | 2000 | 0.5369          | 26.4395 |
| 0.0003        | 56.01 | 3000 | 0.5703          | 26.3187 |
| 0.0002        | 75.0  | 4000 | 0.5913          | 26.4302 |
| 0.0001        | 94.0  | 5000 | 0.5996          | 26.4952 |

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