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---
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 |