---
library_name: transformers
language:
- en
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- wft
- whisper
- automatic-speech-recognition
- audio
- speech
- generated_from_trainer
datasets:
- JacobLinCool/ami-disfluent
metrics:
- wer
model-index:
- name: whisper-large-v3-turbo-verbatim-3-lora
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: JacobLinCool/ami-disfluent
      type: JacobLinCool/ami-disfluent
    metrics:
    - type: wer
      value: 7.726913698959442
      name: Wer
---

<!-- 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-large-v3-turbo-verbatim-3-lora

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the JacobLinCool/ami-disfluent dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1459
- Wer: 7.7269
- Cer: 3.2519
- Decode Runtime: 111.0004
- Wer Runtime: 0.0705
- Cer Runtime: 0.0932

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- 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: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     | Cer     | Decode Runtime | Wer Runtime | Cer Runtime |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:--------------:|:-----------:|:-----------:|
| No log        | 0     | 0    | 2.2169          | 32.7209 | 17.9205 | 106.5404       | 0.0825      | 0.1203      |
| 0.1681        | 0.1   | 100  | 0.1998          | 9.9454  | 4.1038  | 108.1653       | 0.0730      | 0.0960      |
| 0.1025        | 0.2   | 200  | 0.1693          | 8.6885  | 3.7458  | 109.6779       | 0.0707      | 0.0957      |
| 0.2508        | 0.3   | 300  | 0.1590          | 8.3897  | 3.4931  | 110.3209       | 0.0716      | 0.0947      |
| 0.1446        | 1.088 | 400  | 0.1571          | 8.2626  | 3.4939  | 110.1930       | 0.0718      | 0.0951      |
| 0.1833        | 1.188 | 500  | 0.1505          | 8.0463  | 3.4298  | 110.3821       | 0.0709      | 0.0950      |
| 0.1409        | 1.288 | 600  | 0.1489          | 7.9948  | 3.3401  | 110.6880       | 0.0709      | 0.0939      |
| 0.1184        | 2.076 | 700  | 0.1492          | 7.9124  | 3.3181  | 110.6153       | 0.0728      | 0.0946      |
| 0.1737        | 2.176 | 800  | 0.1468          | 7.8128  | 3.2583  | 110.7120       | 0.0714      | 0.0947      |
| 0.1522        | 2.276 | 900  | 0.1462          | 7.7887  | 3.2604  | 110.7694       | 0.0710      | 0.0937      |
| 0.1077        | 3.064 | 1000 | 0.1459          | 7.7269  | 3.2519  | 111.0004       | 0.0705      | 0.0932      |


### Framework versions

- PEFT 0.14.0
- Transformers 4.48.0
- Pytorch 2.4.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0