---
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: 0.8298611111111112
    - name: Precision
      type: precision
      value: 0.8453025128787324
    - name: Recall
      type: recall
      value: 0.8298611111111112
    - name: F1
      type: f1
      value: 0.8329568451751053
---

<!-- 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: 0.5624
- Accuracy: 0.8299
- Precision: 0.8453
- Recall: 0.8299
- F1: 0.8330

## 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: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.9765        | 1.0   | 288  | 1.9102          | 0.3090   | 0.3203    | 0.3090 | 0.1941 |
| 1.4803        | 2.0   | 576  | 1.4590          | 0.5660   | 0.5493    | 0.5660 | 0.4811 |
| 1.1625        | 3.0   | 864  | 1.2308          | 0.6215   | 0.6299    | 0.6215 | 0.5936 |
| 0.8354        | 4.0   | 1152 | 0.7821          | 0.7222   | 0.7555    | 0.7222 | 0.6869 |
| 0.2066        | 5.0   | 1440 | 0.7910          | 0.7708   | 0.8373    | 0.7708 | 0.7881 |
| 0.6335        | 6.0   | 1728 | 0.5624          | 0.8299   | 0.8453    | 0.8299 | 0.8330 |


### Framework versions

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