---
library_name: transformers
language:
- en
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- stuttered-speech
- speech-recognition
- asr
- whisper
- disfluency
- fluencybank
- generated_from_trainer
datasets:
- arielcerdap/TimeStamped-Splits
metrics:
- wer
model-index:
- name: "Whisper fine-tuned on FluencyBank \u2014 openai/whisper-large-v3-turbo"
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: FluencyBank Timestamped
      type: arielcerdap/TimeStamped-Splits
      args: 'split: test, target: verbatim'
    metrics:
    - name: Wer
      type: wer
      value: 9.97582948802461
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper fine-tuned on FluencyBank — openai/whisper-large-v3-turbo

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the FluencyBank Timestamped dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8510
- Wer: 9.9758
- Cer: 5.8628

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 2500
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch    | Step | Validation Loss | Wer     | Cer    |
|:-------------:|:--------:|:----:|:---------------:|:-------:|:------:|
| 1.4697        | 11.6279  | 250  | 1.7083          | 12.7005 | 6.4682 |
| 1.4366        | 23.2558  | 500  | 1.7474          | 9.8879  | 5.6989 |
| 1.4266        | 34.8837  | 750  | 1.7590          | 9.9978  | 5.8992 |
| 1.4248        | 46.5116  | 1000 | 1.7597          | 10.2395 | 6.0267 |
| 1.4195        | 58.1395  | 1250 | 1.8063          | 9.6902  | 5.6580 |
| 1.4183        | 69.7674  | 1500 | 1.8249          | 9.9978  | 5.8492 |
| 1.4176        | 81.3953  | 1750 | 1.8405          | 9.9319  | 5.8309 |
| 1.4173        | 93.0233  | 2000 | 1.8477          | 9.9978  | 5.8901 |
| 1.4172        | 104.6512 | 2250 | 1.8514          | 9.9539  | 5.8810 |
| 1.4172        | 116.2791 | 2500 | 1.8510          | 9.9758  | 5.8628 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.20.3