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---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-en-US
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.296010296010296
---
<!-- 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. -->
# whisper-tiny-en-US
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4990
- Wer Ortho: 0.2965
- Wer: 0.2960
## 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: 5e-07
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 100
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|
| 0.0244 | 0.8969 | 50 | 0.5282 | 0.3012 | 0.3005 |
| 0.0178 | 1.7937 | 100 | 0.5213 | 0.2985 | 0.2986 |
| 0.0171 | 2.6906 | 150 | 0.5147 | 0.2979 | 0.2967 |
| 0.0121 | 3.5874 | 200 | 0.5092 | 0.2925 | 0.2915 |
| 0.0071 | 4.4843 | 250 | 0.5057 | 0.3072 | 0.3069 |
| 0.0073 | 5.3812 | 300 | 0.5034 | 0.2945 | 0.2941 |
| 0.003 | 6.2780 | 350 | 0.5014 | 0.2945 | 0.2934 |
| 0.0036 | 7.1749 | 400 | 0.5003 | 0.2972 | 0.2967 |
| 0.0034 | 8.0717 | 450 | 0.4997 | 0.2965 | 0.2960 |
| 0.0034 | 8.9686 | 500 | 0.4990 | 0.2965 | 0.2960 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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