---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: wav2vec2-base-lang-id
  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-lang-id

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6572
- Accuracy: 0.5802

## 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: 4
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.5548        | 1.0   | 173  | 3.6911          | 0.0659   |
| 2.9779        | 2.0   | 346  | 3.0769          | 0.2024   |
| 2.4706        | 3.0   | 519  | 2.6689          | 0.2964   |
| 2.0118        | 4.0   | 692  | 2.3199          | 0.4234   |
| 1.6745        | 5.0   | 865  | 2.1130          | 0.4650   |
| 1.3753        | 6.0   | 1038 | 1.9451          | 0.5156   |
| 1.1559        | 7.0   | 1211 | 1.8114          | 0.5442   |
| 1.0208        | 8.0   | 1384 | 1.7058          | 0.5652   |
| 0.9342        | 9.0   | 1557 | 1.6644          | 0.5759   |
| 0.9008        | 10.0  | 1730 | 1.6572          | 0.5802   |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.11.0+cu113
- Datasets 2.6.1
- Tokenizers 0.12.1