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---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- stuttered-speech
- speech-recognition
- asr
- whisper
- disfluency
- generated_from_trainer
datasets:
- arielcerdap/TimeStamped
metrics:
- wer
model-index:
- name: Whisper Small Optimized for Stuttered Speech
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: TimeStamped
type: arielcerdap/TimeStamped
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 15.852285521278992
---
<!-- 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 Small Optimized for Stuttered Speech
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the TimeStamped dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0628
- Wer: 15.8523
- Wer Ortho: 9.0384
- Cer: 9.0083
## 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: 8e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 8000
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Wer Ortho | Cer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|:---------:|:-------:|
| 1.5552 | 5.8187 | 500 | 1.7178 | 23.1142 | 12.5690 | 12.5620 |
| 1.4449 | 11.6316 | 1000 | 1.7658 | 14.3774 | 9.1288 | 9.1103 |
| 1.4477 | 17.4444 | 1500 | 1.8778 | 18.3517 | 13.5472 | 13.5171 |
| 1.4132 | 23.2573 | 2000 | 1.8607 | 13.6005 | 7.7101 | 7.6846 |
| 1.4065 | 29.0702 | 2500 | 1.8845 | 14.4112 | 8.2271 | 8.1993 |
| 1.4182 | 34.8889 | 3000 | 1.9307 | 14.4112 | 7.9953 | 7.9675 |
| 1.4177 | 40.7018 | 3500 | 1.9481 | 17.6649 | 10.8814 | 10.8535 |
| 1.4036 | 46.5146 | 4000 | 1.9508 | 15.2105 | 8.5331 | 8.5076 |
| 1.4012 | 52.3275 | 4500 | 1.9831 | 15.4695 | 8.7324 | 8.7069 |
| 1.4005 | 58.1404 | 5000 | 2.0116 | 15.6046 | 8.8252 | 8.7973 |
| 1.4143 | 63.9591 | 5500 | 2.0306 | 15.6609 | 8.9318 | 8.9040 |
| 1.4141 | 69.7719 | 6000 | 2.0445 | 15.7172 | 8.9573 | 8.9295 |
| 1.414 | 75.5848 | 6500 | 2.0525 | 15.8410 | 9.0083 | 8.9805 |
| 1.3998 | 81.3977 | 7000 | 2.0598 | 15.8523 | 9.0361 | 9.0060 |
| 1.3997 | 87.2105 | 7500 | 2.0625 | 15.8635 | 9.0454 | 9.0153 |
| 1.3997 | 93.0234 | 8000 | 2.0628 | 15.8523 | 9.0384 | 9.0083 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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