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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- timit_asr
model-index:
- name: wav2vec2-base_phoneme-timit_english_timit-4k_001
results: []
language:
- en
metrics:
- wer
library_name: transformers
pipeline_tag: automatic-speech-recognition
---
<!-- 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_phoneme-timit_english_timit-4k_001
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the timit dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6361
- Per: 0.1195
## Model description
The wav2vec 2.0 base model is pre-trained on 960 hours of the LibriSpeech dataset.
- 12 Transformer blocks (Each block: 768 dimensions & 8 attention heads)
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Per |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.2193 | 3.46 | 1000 | 3.5945 | 0.9617 |
| 1.5174 | 6.92 | 2000 | 0.5574 | 0.1665 |
| 0.5246 | 10.38 | 3000 | 0.4228 | 0.1503 |
| 0.3915 | 13.84 | 4000 | 0.4276 | 0.1512 |
| 0.3293 | 17.3 | 5000 | 0.4656 | 0.1517 |
| 0.2757 | 20.76 | 6000 | 0.4719 | 0.1486 |
| 0.209 | 24.22 | 7000 | 0.5314 | 0.1478 |
| 0.1589 | 27.68 | 8000 | 0.6102 | 0.1484 |
| 0.1207 | 31.14 | 9000 | 0.6449 | 0.1484 |
| 0.0951 | 34.6 | 10000 | 0.6579 | 0.1471 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1
- Datasets 2.18.0
- Tokenizers 0.13.3 |