---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
datasets:
- audiofolder
model-index:
- name: wav2vec2-base-960h_SER_merged_dataset
  results: []
---

<!-- 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-base-960h_SER_merged_dataset

This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on a merged dataset of RAVDESS,  CREMA, SAVEE, and TESS.
It achieves the following results on the evaluation set:

- eval_accuracy: 0.7480
- eval_loss: 0.7940
- eval_weighted_f1: 0.7438
- eval_micro_f1: 0.7480
- eval_macro_f1: 0.7559
- eval_weighted_recall: 0.7480
- eval_micro_recall: 0.7480
- eval_macro_recall: 0.7622
- eval_weighted_precision: 0.7518
- eval_micro_precision: 0.7480
- eval_macro_precision: 0.7609
- eval_runtime: 82.1913
- eval_samples_per_second: 29.602
- eval_steps_per_second: 0.937
- epoch: 14.0
- step: 1078

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0