readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model_index:
    name: wav2vec-english-speech-emotion-recognition
  ---
  # Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0
  The model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) for a Speech Emotion Recognition (SER) task.
  
  Several datasets were used the fine-tune the original model:  
  - Surrey Audio-Visual Expressed Emotion [(SAVEE)](http://kahlan.eps.surrey.ac.uk/savee/Database.html) - 480 audio files from 4 male actors
  - Ryerson Audio-Visual Database of Emotional Speech and Song [(RAVDESS)](https://zenodo.org/record/1188976) - 1440 audio files from 24 professional actors (12 female, 12 male)
  - Toronto emotional speech set [(TESS)](https://tspace.library.utoronto.ca/handle/1807/24487) - 2800 audio files from 2 female actors
  
  7 labels/emotions were used as classification labels
  ```python
  emotions = ['angry' 'disgust' 'fear' 'happy' 'neutral' 'sad' 'surprise']
  ```
  It achieves the following results on the evaluation set:
  - Loss: 0.104075
  - Accuracy: 0.97463
  ## Model description
  More information needed
  ## Intended uses & limitations
  More information needed
  ## Training and evaluation data
  More information needed
  ## Training procedure
  ### Training hyperparameters
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 4
  - eval_batch_size: 4
  - eval_steps: 500
  - seed: 42
  - gradient_accumulation_steps: 2
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - num_epochs: 4
  - max_steps=7500
  - save_steps: 1500
  
  ### Training results
  | Step | Training Loss | Validation Loss | Accuracy |
  | ---- | ------------- | --------------- | -------- |
  | 500  | 1.8124        | 1.365212        | 0.486258 |
  | 1000 | 0.8872        | 0.773145        | 0.79704  |
  | 1500 | 0.7035        | 0.574954        | 0.852008 |
  | 2000 | 0.6879        | 1.286738        | 0.775899 |
  | 2500 | 0.6498        | 0.697455        | 0.832981 |
  | 3000 | 0.5696        | 0.33724         | 0.892178 |
  | 3500 | 0.4218        | 0.307072        | 0.911205 |
  | 4000 | 0.3088        | 0.374443        | 0.930233 |
  | 4500 | 0.2688        | 0.260444        | 0.936575 |
  | 5000 | 0.2973        | 0.302985        | 0.92389  |
  | 5500 | 0.1765        | 0.165439        | 0.961945 |
  | 6000 | 0.1475        | 0.170199        | 0.961945 |
  | 6500 | 0.1274        | 0.15531         | 0.966173 |
  | 7000 | 0.0699        | 0.103882        | 0.976744 |
  | 7500 | 0.083         | 0.104075        | 0.97463  |