---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_silero_mini_model
  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. -->

# my_awesome_silero_mini_model

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5030
- Accuracy: 0.74

## 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: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6843        | 1.0    | 38   | 0.6689          | 0.615    |
| 0.6019        | 2.0    | 76   | 0.6146          | 0.635    |
| 0.5591        | 3.0    | 114  | 0.5583          | 0.68     |
| 0.5717        | 4.0    | 152  | 0.5293          | 0.73     |
| 0.4943        | 5.0    | 190  | 0.5587          | 0.7      |
| 0.5018        | 6.0    | 228  | 0.5269          | 0.725    |
| 0.4932        | 7.0    | 266  | 0.5162          | 0.695    |
| 0.465         | 8.0    | 304  | 0.5303          | 0.72     |
| 0.4476        | 9.0    | 342  | 0.5220          | 0.735    |
| 0.4509        | 9.7467 | 370  | 0.5030          | 0.74     |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.1.0+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0