---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: unispeech-sat-base-ft
  results: []
datasets:
- mazkooleg/0-9up_google_speech_commands_augmented_raw
---

<!-- 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-ft

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: 0.0123
- Accuracy: 0.9979

## 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: 32
- eval_batch_size: 32
- seed: 42
- 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: 5

### Training results

| Training Loss | Epoch | Step  | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.0916        | 1.0   | 8558  | 0.9958   | 0.0174          |
| 0.0592        | 2.0   | 17116 | 0.9979   | 0.0123          |
| 0.0725        | 3.0   | 25674 | 0.9958   | 0.0196          |
| 0.0585        | 4.0   | 34232 | 0.9955   | 0.0213          |
| 0.0472        | 5.0   | 42790 | 0.9958   | 0.0212          |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.11.0
- Datasets 2.10.1
- Tokenizers 0.12.1