metadata
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: 0.4328507693708459
wav2vec2-base-timit-fine-tuned
This model is a fine-tuned version of facebook/wav2vec2-base on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:
- Loss: 0.4233
- Wer: 0.4329
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: 64
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|
| 3.158 | 1.7241 | 100 | 3.6803 | 1.0 |
| 2.9744 | 3.4483 | 200 | 3.1165 | 1.0 |
| 2.9266 | 5.1724 | 300 | 3.0175 | 1.0 |
| 2.1336 | 6.8966 | 400 | 2.2135 | 1.0117 |
| 1.0119 | 8.6207 | 500 | 1.0227 | 0.8251 |
| 0.4995 | 10.3448 | 600 | 0.7700 | 0.6574 |
| 0.3233 | 12.0690 | 700 | 0.4970 | 0.5241 |
| 0.2452 | 13.7931 | 800 | 0.4585 | 0.4908 |
| 0.181 | 15.5172 | 900 | 0.4626 | 0.4814 |
| 0.1419 | 17.2414 | 1000 | 0.4917 | 0.4775 |
| 0.1175 | 18.9655 | 1100 | 0.4279 | 0.4359 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0a0+gitcd033a1
- Datasets 2.19.1
- Tokenizers 0.19.1