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library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- wer
model-index:
- name: misiker/trainer_output
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: minds14
config: en-US
split: train[:500]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.9748427672955975
---
<!-- 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. -->
# misiker/trainer_output
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 18.8318
- Wer: 0.9748
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use adamw_torch 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: 40
- training_steps: 80
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.8 | 20 | 37.9601 | 1.6562 |
| 41.3656 | 1.6 | 40 | 20.2900 | 0.9755 |
| 18.9017 | 2.4 | 60 | 10.7917 | 0.9734 |
| 18.9017 | 3.2 | 80 | 11.6330 | 0.9734 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cpu
- Datasets 3.6.0
- Tokenizers 0.21.1
|