---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-medium
tags:
- stuttered-speech
- speech-recognition
- asr
- whisper
- disfluency
- generated_from_trainer
datasets:
- arielcerdap/TimeStamped
metrics:
- wer
model-index:
- name: Whisper Medium 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: 14.050889439315469
---

<!-- 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 Medium Optimized for Stuttered Speech

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the TimeStamped dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9889
- Wer: 14.0509
- Wer Ortho: 7.9350
- Cer: 7.9188

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|:---------:|:-------:|
| 1.4886        | 5.8187  | 500  | 1.6803          | 13.3866 | 6.8710    | 6.8779  |
| 1.4379        | 11.6316 | 1000 | 1.7141          | 11.9117 | 7.1422    | 7.1376  |
| 1.4115        | 17.4444 | 1500 | 1.7555          | 11.5740 | 6.3332    | 6.3308  |
| 1.4093        | 23.2573 | 2000 | 1.7849          | 13.2177 | 7.8678    | 7.8655  |
| 1.4068        | 29.0702 | 2500 | 1.8295          | 12.0243 | 6.7643    | 6.7527  |
| 1.4178        | 34.8889 | 3000 | 1.8251          | 16.7530 | 10.8906   | 10.8790 |
| 1.4169        | 40.7018 | 3500 | 1.8499          | 12.3959 | 6.8872    | 6.8802  |
| 1.4005        | 46.5146 | 4000 | 1.8905          | 13.2740 | 7.4482    | 7.4366  |
| 1.3999        | 52.3275 | 4500 | 1.9124          | 13.3191 | 7.4946    | 7.4830  |
| 1.3999        | 58.1404 | 5000 | 1.9269          | 13.9045 | 7.8376    | 7.8237  |
| 1.4135        | 63.9591 | 5500 | 1.9485          | 13.8257 | 7.8585    | 7.8446  |
| 1.4133        | 69.7719 | 6000 | 1.9668          | 13.8820 | 7.8446    | 7.8284  |
| 1.4132        | 75.5848 | 6500 | 1.9779          | 13.9833 | 7.8863    | 7.8724  |
| 1.3989        | 81.3977 | 7000 | 1.9854          | 13.9946 | 7.8770    | 7.8608  |
| 1.3989        | 87.2105 | 7500 | 1.9884          | 14.0396 | 7.9280    | 7.9118  |
| 1.3989        | 93.0234 | 8000 | 1.9889          | 14.0509 | 7.9350    | 7.9188  |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1