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---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-us_en_bs128
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train[450:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3417945690672963
---
<!-- 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-us_en_bs128
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: 0.8372
- Wer Ortho: 0.3399
- Wer: 0.3418
## 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: 128
- eval_batch_size: 128
- seed: 42
- 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: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|
| 0.1633 | 6.25 | 25 | 0.5503 | 0.3177 | 0.3164 |
| 0.0027 | 12.5 | 50 | 0.6676 | 0.3288 | 0.3294 |
| 0.0011 | 18.75 | 75 | 0.7095 | 0.3134 | 0.3182 |
| 0.0012 | 25.0 | 100 | 0.7296 | 0.3196 | 0.3176 |
| 0.0014 | 31.25 | 125 | 0.7460 | 0.3541 | 0.3583 |
| 0.005 | 37.5 | 150 | 0.7059 | 0.4405 | 0.4610 |
| 0.0009 | 43.75 | 175 | 0.7803 | 0.3924 | 0.3961 |
| 0.0004 | 50.0 | 200 | 0.7996 | 0.3455 | 0.3512 |
| 0.0001 | 56.25 | 225 | 0.8074 | 0.3411 | 0.3442 |
| 0.0001 | 62.5 | 250 | 0.8146 | 0.3424 | 0.3459 |
| 0.0001 | 68.75 | 275 | 0.8197 | 0.3430 | 0.3459 |
| 0.0001 | 75.0 | 300 | 0.8239 | 0.3399 | 0.3424 |
| 0.0001 | 81.25 | 325 | 0.8274 | 0.3374 | 0.3400 |
| 0.0001 | 87.5 | 350 | 0.8303 | 0.3356 | 0.3383 |
| 0.0001 | 93.75 | 375 | 0.8324 | 0.3368 | 0.3400 |
| 0.0001 | 100.0 | 400 | 0.8341 | 0.3368 | 0.3388 |
| 0.0001 | 106.25 | 425 | 0.8354 | 0.3405 | 0.3424 |
| 0.0001 | 112.5 | 450 | 0.8364 | 0.3399 | 0.3418 |
| 0.0001 | 118.75 | 475 | 0.8371 | 0.3399 | 0.3418 |
| 0.0001 | 125.0 | 500 | 0.8372 | 0.3399 | 0.3418 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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