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---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- timit_asr
metrics:
- wer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: timit_asr
type: timit_asr
config: clean
split: test
args: clean
metrics:
- name: Wer
type: wer
value: 0.3367100820067535
---
<!-- 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. -->
# wav2vec2-base-timit-demo-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the timit_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4634
- Wer: 0.3367
## 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: 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_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6019 | 1.0 | 500 | 2.4586 | 1.0 |
| 0.9594 | 2.01 | 1000 | 0.5023 | 0.5122 |
| 0.4324 | 3.01 | 1500 | 0.4808 | 0.4703 |
| 0.2991 | 4.02 | 2000 | 0.4098 | 0.4208 |
| 0.2257 | 5.02 | 2500 | 0.4883 | 0.4264 |
| 0.18 | 6.02 | 3000 | 0.4441 | 0.3914 |
| 0.1524 | 7.03 | 3500 | 0.4360 | 0.3869 |
| 0.1315 | 8.03 | 4000 | 0.4448 | 0.3783 |
| 0.1101 | 9.04 | 4500 | 0.4570 | 0.3704 |
| 0.1017 | 10.04 | 5000 | 0.4252 | 0.3680 |
| 0.0863 | 11.04 | 5500 | 0.4492 | 0.3606 |
| 0.0798 | 12.05 | 6000 | 0.4241 | 0.3604 |
| 0.0688 | 13.05 | 6500 | 0.4585 | 0.3535 |
| 0.0608 | 14.06 | 7000 | 0.4491 | 0.3488 |
| 0.0524 | 15.06 | 7500 | 0.4550 | 0.3456 |
| 0.0502 | 16.06 | 8000 | 0.4570 | 0.3453 |
| 0.0458 | 17.07 | 8500 | 0.4680 | 0.3421 |
| 0.0395 | 18.07 | 9000 | 0.4663 | 0.3390 |
| 0.0352 | 19.08 | 9500 | 0.4634 | 0.3367 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 1.18.3
- Tokenizers 0.15.2
|