---
base_model: microsoft/unispeech-sat-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: unispeech-sat-base-finetuned-common_voice
  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. -->

# unispeech-sat-base-finetuned-common_voice

This model is a fine-tuned version of [microsoft/unispeech-sat-base](https://huggingface.co./microsoft/unispeech-sat-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0481
- Accuracy: 0.9925
- F1: 0.9925
- Recall: 0.9925
- Precision: 0.9928
- Mcc: 0.9907
- Auc: 0.9999

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision | Mcc    | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
| 1.5302        | 1.0   | 50   | 1.4495          | 0.56     | 0.5047 | 0.5600 | 0.6655    | 0.4723 | 0.8635 |
| 1.1592        | 2.0   | 100  | 0.9831          | 0.7125   | 0.6783 | 0.7125 | 0.7985    | 0.6723 | 0.9633 |
| 0.7313        | 3.0   | 150  | 0.5535          | 0.9425   | 0.9428 | 0.9425 | 0.9455    | 0.9287 | 0.9926 |
| 0.4431        | 4.0   | 200  | 0.2633          | 0.965    | 0.9651 | 0.9650 | 0.9676    | 0.9569 | 0.9976 |
| 0.2353        | 5.0   | 250  | 0.1310          | 0.985    | 0.9850 | 0.985  | 0.9856    | 0.9814 | 0.9998 |
| 0.1846        | 6.0   | 300  | 0.1136          | 0.9775   | 0.9775 | 0.9775 | 0.9783    | 0.9721 | 0.9978 |
| 0.1464        | 7.0   | 350  | 0.0714          | 0.9875   | 0.9875 | 0.9875 | 0.9878    | 0.9844 | 1.0000 |
| 0.1016        | 8.0   | 400  | 0.0592          | 0.99     | 0.9900 | 0.99   | 0.9902    | 0.9876 | 0.9999 |
| 0.057         | 9.0   | 450  | 0.0466          | 0.9925   | 0.9925 | 0.9925 | 0.9928    | 0.9907 | 0.9999 |
| 0.068         | 10.0  | 500  | 0.0481          | 0.9925   | 0.9925 | 0.9925 | 0.9928    | 0.9907 | 0.9999 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1