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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: repo_name
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: timit_asr
type: timit_asr
config: clean
split: None
args: clean
metrics:
- type: wer
value: 0.22107366825167116
name: Wer
---
<!-- 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. -->
# repo_name
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: 0.5351
- Wer: 0.2211
## 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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 3.5252 | 1.0040 | 500 | 1.6991 | 0.9701 |
| 0.854 | 2.0080 | 1000 | 0.5187 | 0.4025 |
| 0.4211 | 3.0120 | 1500 | 0.4289 | 0.3326 |
| 0.2871 | 4.0161 | 2000 | 0.3947 | 0.2896 |
| 0.2266 | 5.0201 | 2500 | 0.4034 | 0.2881 |
| 0.1789 | 6.0241 | 3000 | 0.4833 | 0.2926 |
| 0.1638 | 7.0281 | 3500 | 0.4342 | 0.2776 |
| 0.15 | 8.0321 | 4000 | 0.4643 | 0.2750 |
| 0.1251 | 9.0361 | 4500 | 0.4449 | 0.2642 |
| 0.1064 | 10.0402 | 5000 | 0.4785 | 0.2578 |
| 0.0986 | 11.0442 | 5500 | 0.4480 | 0.2627 |
| 0.0883 | 12.0482 | 6000 | 0.4876 | 0.2603 |
| 0.0784 | 13.0522 | 6500 | 0.5100 | 0.2519 |
| 0.0721 | 14.0562 | 7000 | 0.4795 | 0.2536 |
| 0.0696 | 15.0602 | 7500 | 0.4797 | 0.2456 |
| 0.0598 | 16.0643 | 8000 | 0.5064 | 0.2410 |
| 0.0575 | 17.0683 | 8500 | 0.5075 | 0.2362 |
| 0.0508 | 18.0723 | 9000 | 0.5062 | 0.2420 |
| 0.048 | 19.0763 | 9500 | 0.5078 | 0.2397 |
| 0.0402 | 20.0803 | 10000 | 0.5511 | 0.2341 |
| 0.0429 | 21.0843 | 10500 | 0.4835 | 0.2330 |
| 0.0362 | 22.0884 | 11000 | 0.5800 | 0.2308 |
| 0.0333 | 23.0924 | 11500 | 0.5250 | 0.2306 |
| 0.0285 | 24.0964 | 12000 | 0.5242 | 0.2288 |
| 0.0296 | 25.1004 | 12500 | 0.4995 | 0.2238 |
| 0.0264 | 26.1044 | 13000 | 0.5296 | 0.2236 |
| 0.0245 | 27.1084 | 13500 | 0.5530 | 0.2233 |
| 0.0214 | 28.1124 | 14000 | 0.5376 | 0.2209 |
| 0.0214 | 29.1165 | 14500 | 0.5351 | 0.2211 |
### Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0+cu126
- Datasets 2.21.0
- Tokenizers 0.22.1
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