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---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
metrics:
- wer
model-index:
- name: wav2vec2-base-timit-fine-tuned
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: TIMIT_ASR - NA
type: timit_asr
config: clean
split: test
args: 'Config: na, Training split: train, Eval split: test'
metrics:
- name: Wer
type: wer
value: 0.41728125284530637
---
<!-- 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-fine-tuned
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4275
- Wer: 0.4173
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 3.1618 | 0.8621 | 100 | 3.1117 | 1.0 |
| 2.9798 | 1.7241 | 200 | 2.9736 | 1.0 |
| 2.9144 | 2.5862 | 300 | 2.9075 | 1.0 |
| 2.1714 | 3.4483 | 400 | 2.0945 | 1.0325 |
| 1.1579 | 4.3103 | 500 | 1.0451 | 0.8299 |
| 0.6087 | 5.1724 | 600 | 0.6754 | 0.6441 |
| 0.481 | 6.0345 | 700 | 0.5275 | 0.5761 |
| 0.3072 | 6.8966 | 800 | 0.4836 | 0.5264 |
| 0.332 | 7.7586 | 900 | 0.4403 | 0.5234 |
| 0.1876 | 8.6207 | 1000 | 0.4758 | 0.5222 |
| 0.2232 | 9.4828 | 1100 | 0.4508 | 0.4892 |
| 0.1332 | 10.3448 | 1200 | 0.4394 | 0.4740 |
| 0.1085 | 11.2069 | 1300 | 0.4466 | 0.4621 |
| 0.098 | 12.0690 | 1400 | 0.4230 | 0.4493 |
| 0.1219 | 12.9310 | 1500 | 0.4180 | 0.4460 |
| 0.1021 | 13.7931 | 1600 | 0.4179 | 0.4406 |
| 0.0741 | 14.6552 | 1700 | 0.4113 | 0.4309 |
| 0.0896 | 15.5172 | 1800 | 0.4392 | 0.4308 |
| 0.0492 | 16.3793 | 1900 | 0.4202 | 0.4313 |
| 0.0759 | 17.2414 | 2000 | 0.4348 | 0.4207 |
| 0.0406 | 18.1034 | 2100 | 0.4419 | 0.4205 |
| 0.074 | 18.9655 | 2200 | 0.4306 | 0.4200 |
| 0.0378 | 19.8276 | 2300 | 0.4273 | 0.4173 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0.post300
- Datasets 2.19.1
- Tokenizers 0.19.1
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