---
library_name: peft
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
model-index:
- name: whisper-large-v3-turbo-verbatim-3-lora
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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:
- eval_loss: 0.1998
- eval_wer: 9.9454
- eval_cer: 4.1038
- eval_decode_runtime: 108.1653
- eval_wer_runtime: 0.0730
- eval_cer_runtime: 0.0960
- eval_runtime: 182.769
- eval_samples_per_second: 10.352
- eval_steps_per_second: 0.328
- epoch: 0.1
- step: 100

## 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

### Framework versions

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