---
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: 0.1896
- Accuracy: 0.96
- F1: 0.9601
- Recall: 0.96
- Precision: 0.9606
- Mcc: 0.9501
- Auc: 0.9939

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
| 1.5599        | 1.0   | 200  | 1.5446          | 0.415    | 0.3951 | 0.4150 | 0.6762    | 0.3213 | 0.8445 |
| 1.1707        | 2.0   | 400  | 1.0171          | 0.7575   | 0.7502 | 0.7575 | 0.7665    | 0.7023 | 0.9487 |
| 0.7857        | 3.0   | 600  | 0.7125          | 0.8375   | 0.8311 | 0.8375 | 0.8453    | 0.8008 | 0.9667 |
| 0.5713        | 4.0   | 800  | 0.5097          | 0.88     | 0.8794 | 0.8800 | 0.8929    | 0.8536 | 0.9874 |
| 0.4225        | 5.0   | 1000 | 0.3919          | 0.9075   | 0.9076 | 0.9075 | 0.9116    | 0.8853 | 0.9894 |
| 0.5846        | 6.0   | 1200 | 0.3119          | 0.9325   | 0.9327 | 0.9325 | 0.9355    | 0.9163 | 0.9883 |
| 0.3004        | 7.0   | 1400 | 0.2308          | 0.9475   | 0.9477 | 0.9475 | 0.9487    | 0.9346 | 0.9925 |
| 0.3011        | 8.0   | 1600 | 0.1974          | 0.955    | 0.9551 | 0.9550 | 0.9557    | 0.9439 | 0.9940 |
| 0.138         | 9.0   | 1800 | 0.1851          | 0.96     | 0.9601 | 0.96   | 0.9606    | 0.9501 | 0.9932 |
| 0.1582        | 10.0  | 2000 | 0.1896          | 0.96     | 0.9601 | 0.96   | 0.9606    | 0.9501 | 0.9939 |


### Framework versions

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