---
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: distilhubert-timit
  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. -->

# distilhubert-timit

This model is a fine-tuned version of [anton-l/distilhubert](https://huggingface.co/anton-l/distilhubert) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3688
- Wer: 0.6818

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.2247        | 0.69  | 100  | 3.8607          | 1.0    |
| 2.9444        | 1.38  | 200  | 2.9509          | 1.0    |
| 2.8858        | 2.07  | 300  | 2.8446          | 1.0    |
| 2.2804        | 2.76  | 400  | 2.1985          | 1.0014 |
| 1.505         | 3.45  | 500  | 1.4972          | 0.9609 |
| 1.06          | 4.14  | 600  | 1.2014          | 0.8058 |
| 1.0166        | 4.83  | 700  | 1.0605          | 0.7536 |
| 0.966         | 5.52  | 800  | 0.9963          | 0.7101 |
| 0.6857        | 6.21  | 900  | 0.9443          | 0.6898 |
| 0.5859        | 6.9   | 1000 | 0.9043          | 0.6796 |
| 0.6812        | 7.59  | 1100 | 0.9095          | 0.6716 |
| 0.6088        | 8.28  | 1200 | 0.9422          | 0.6677 |
| 0.4162        | 8.97  | 1300 | 0.9548          | 0.6657 |
| 0.3411        | 9.66  | 1400 | 0.9901          | 0.6689 |
| 0.3323        | 10.34 | 1500 | 0.9996          | 0.6638 |
| 0.431         | 11.03 | 1600 | 1.0521          | 0.6708 |
| 0.2029        | 11.72 | 1700 | 1.0946          | 0.6793 |
| 0.1424        | 12.41 | 1800 | 1.1288          | 0.6712 |
| 0.1922        | 13.1  | 1900 | 1.1456          | 0.6740 |
| 0.326         | 13.79 | 2000 | 1.2077          | 0.6915 |
| 0.0892        | 14.48 | 2100 | 1.2525          | 0.6796 |
| 0.0769        | 15.17 | 2200 | 1.2313          | 0.6736 |
| 0.0927        | 15.86 | 2300 | 1.3001          | 0.6864 |
| 0.232         | 16.55 | 2400 | 1.3490          | 0.6963 |
| 0.0485        | 17.24 | 2500 | 1.3268          | 0.6763 |
| 0.0487        | 17.93 | 2600 | 1.3376          | 0.6780 |
| 0.0607        | 18.62 | 2700 | 1.3701          | 0.6895 |
| 0.1618        | 19.31 | 2800 | 1.3657          | 0.6796 |
| 0.0415        | 20.0  | 2900 | 1.3688          | 0.6818 |


### Framework versions

- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3