đ Whisper medium AT
This model is a fine - tuned version of openai/whisper - medium, which is used for automatic speech recognition on the AT_ENT dataset, achieving good results in evaluation.
đ Quick Start
This model is a fine - tuned version of openai/whisper - medium on the AT_ENT dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2100
- Wer: 66.8297
đ Documentation
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: 32
- 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: 500
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss |
Epoch |
Step |
Validation Loss |
Wer |
| No log |
1.0 |
111 |
1.0527 |
63.9374 |
| No log |
2.0 |
222 |
1.0961 |
65.8796 |
| No log |
3.0 |
333 |
1.1626 |
67.5283 |
| No log |
4.0 |
444 |
1.2100 |
66.8297 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
đ License
The model is licensed under the apache - 2.0 license.
đĻ Model Information
| Property |
Details |
| Base Model |
openai/whisper - medium |
| Datasets |
AT_ENT |
| Language |
aeb |
| Library Name |
transformers |
| Metrics |
wer |
| Tags |
generated_from_trainer |
| Model Name |
Whisper medium AT |
| Task Type |
automatic - speech - recognition |
| Evaluation Dataset |
AT_ENT (config: aeb, split: test) |
| Wer on Evaluation Set |
66.82967723906664 |
%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
