---
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
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# 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: 0.0526
- Accuracy: 0.9925
- F1: 0.9925
- Recall: 0.9925
- Precision: 0.9927
- Mcc: 0.9907
- Auc: 0.9994

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
| 0.2855        | 1.0   | 200  | 0.1316          | 0.9675   | 0.9676 | 0.9675 | 0.9687    | 0.9596 | 0.9977 |
| 0.0865        | 2.0   | 400  | 0.0724          | 0.9825   | 0.9825 | 0.9825 | 0.9833    | 0.9783 | 0.9999 |
| 0.0536        | 3.0   | 600  | 0.0436          | 0.9925   | 0.9925 | 0.9925 | 0.9926    | 0.9906 | 0.9981 |
| 0.1822        | 4.0   | 800  | 0.0470          | 0.99     | 0.9900 | 0.99   | 0.9901    | 0.9875 | 0.9999 |
| 0.0343        | 5.0   | 1000 | 0.1409          | 0.975    | 0.9751 | 0.975  | 0.9765    | 0.9691 | 0.9997 |
| 0.1201        | 6.0   | 1200 | 0.1419          | 0.9675   | 0.9677 | 0.9675 | 0.9696    | 0.9598 | 0.9975 |
| 0.102         | 7.0   | 1400 | 0.1631          | 0.9725   | 0.9727 | 0.9725 | 0.9746    | 0.9661 | 0.9967 |
| 0.0105        | 8.0   | 1600 | 0.0528          | 0.9925   | 0.9925 | 0.9925 | 0.9927    | 0.9907 | 0.9987 |
| 0.0039        | 9.0   | 1800 | 0.0708          | 0.9875   | 0.9875 | 0.9875 | 0.9880    | 0.9845 | 0.9984 |
| 0.0058        | 10.0  | 2000 | 0.0526          | 0.9925   | 0.9925 | 0.9925 | 0.9927    | 0.9907 | 0.9994 |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1