---
license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-english
tags:
- generated_from_trainer
datasets:
- narad/ravdess
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: wav2vec2-large-xlsr-53-english-finetuned-ravdess
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: RAVDESS
      type: narad/ravdess
      config: all
      split: train
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7152777777777778
    - name: Precision
      type: precision
      value: 0.7360657858765911
    - name: Recall
      type: recall
      value: 0.7152777777777778
    - name: F1
      type: f1
      value: 0.6891900402765098
---

<!-- 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. -->

# wav2vec2-large-xlsr-53-english-finetuned-ravdess

This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the RAVDESS dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0013
- Accuracy: 0.7153
- Precision: 0.7361
- Recall: 0.7153
- F1: 0.6892

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.9323        | 1.0   | 288  | 1.9023          | 0.2917   | 0.4800    | 0.2917 | 0.2042 |
| 1.4114        | 2.0   | 576  | 1.2845          | 0.6111   | 0.7423    | 0.6111 | 0.5283 |
| 0.938         | 3.0   | 864  | 1.0013          | 0.7153   | 0.7361    | 0.7153 | 0.6892 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1