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---
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
|