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---
library_name: transformers
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: None
args: clean
metrics:
- name: Wer
type: wer
value: 0.3354696437185583
---
<!-- 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: 0.4743
- Wer: 0.3355
## 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: 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4394 | 4.0 | 500 | 1.2662 | 0.8530 |
| 0.5192 | 8.0 | 1000 | 0.4308 | 0.4176 |
| 0.1896 | 12.0 | 1500 | 0.4249 | 0.3656 |
| 0.1158 | 16.0 | 2000 | 0.4405 | 0.3583 |
| 0.0791 | 20.0 | 2500 | 0.4949 | 0.3481 |
| 0.0578 | 24.0 | 3000 | 0.4895 | 0.3448 |
| 0.0462 | 28.0 | 3500 | 0.4743 | 0.3355 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.1.2
- Datasets 3.0.1
- Tokenizers 0.20.0
|