transformers tensorboard safetensors wav2vec2 automatic-speech-recognition generated_from_trainer base_model:facebook/wav2vec2-base license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-timit-demo-google-colab
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5328
- Wer: 0.2175

## 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.4608        | 1.0   | 500   | 1.4610          | 0.9290 |
| 0.796         | 2.01  | 1000  | 0.5283          | 0.4217 |
| 0.4219        | 3.01  | 1500  | 0.4277          | 0.3353 |
| 0.2919        | 4.02  | 2000  | 0.4154          | 0.3102 |
| 0.2263        | 5.02  | 2500  | 0.4096          | 0.2954 |
| 0.1885        | 6.02  | 3000  | 0.4274          | 0.2944 |
| 0.1595        | 7.03  | 3500  | 0.4529          | 0.2681 |
| 0.1371        | 8.03  | 4000  | 0.4309          | 0.2721 |
| 0.1218        | 9.04  | 4500  | 0.4574          | 0.2629 |
| 0.1118        | 10.04 | 5000  | 0.5396          | 0.2605 |
| 0.0988        | 11.04 | 5500  | 0.5031          | 0.2683 |
| 0.0944        | 12.05 | 6000  | 0.5040          | 0.2595 |
| 0.0781        | 13.05 | 6500  | 0.4909          | 0.2611 |
| 0.0714        | 14.06 | 7000  | 0.4740          | 0.2635 |
| 0.0648        | 15.06 | 7500  | 0.4613          | 0.2509 |
| 0.0628        | 16.06 | 8000  | 0.4731          | 0.2508 |
| 0.0539        | 17.07 | 8500  | 0.5100          | 0.2448 |
| 0.0542        | 18.07 | 9000  | 0.5048          | 0.2507 |
| 0.0453        | 19.08 | 9500  | 0.5290          | 0.2466 |
| 0.0446        | 20.08 | 10000 | 0.5482          | 0.2398 |
| 0.0405        | 21.08 | 10500 | 0.5768          | 0.2422 |
| 0.0368        | 22.09 | 11000 | 0.5848          | 0.2403 |
| 0.0349        | 23.09 | 11500 | 0.5469          | 0.2321 |
| 0.0326        | 24.1  | 12000 | 0.5618          | 0.2294 |
| 0.028         | 25.1  | 12500 | 0.5590          | 0.2297 |
| 0.0254        | 26.1  | 13000 | 0.5531          | 0.2291 |
| 0.0258        | 27.11 | 13500 | 0.5302          | 0.2215 |
| 0.0244        | 28.11 | 14000 | 0.5388          | 0.2188 |
| 0.0195        | 29.12 | 14500 | 0.5328          | 0.2175 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 1.18.3
- Tokenizers 0.15.0
