---
library_name: transformers
language:
- en
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- base_model:adapter:openai/whisper-large-v3-turbo
- lora
- transformers
datasets:
- ntnu-smil/ami-1s-ft
metrics:
- wer
model-index:
- name: whisper-large-v3-turbo-ami-1
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: ntnu-smil/ami-1s-ft
      type: ntnu-smil/ami-1s-ft
    metrics:
    - type: wer
      value: 40.4099560761347
      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-ami-1

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the ntnu-smil/ami-1s-ft dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4985
- Wer: 40.4100
- Cer: 34.3391
- Decode Runtime: 0.0102
- Wer Runtime: 0.0078
- Cer Runtime: 0.0077

## 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-06
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     | Cer     | Decode Runtime | Wer Runtime | Cer Runtime |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:--------------:|:-----------:|:-----------:|
| No log        | 0     | 0    | 3.9428          | 53.6237 | 39.2308 | 0.0108         | 0.0079      | 0.0071      |
| 0.4259        | 0.1   | 200  | 3.3403          | 36.4934 | 30.1718 | 0.0097         | 0.0080      | 0.0074      |
| 0.1836        | 0.2   | 400  | 3.3032          | 37.2255 | 32.0015 | 0.0097         | 0.0079      | 0.0072      |
| 0.333         | 0.3   | 600  | 3.3810          | 38.5798 | 32.6363 | 0.0110         | 0.0084      | 0.0072      |
| 0.1716        | 0.4   | 800  | 3.4782          | 39.2020 | 33.0769 | 0.0096         | 0.0081      | 0.0069      |
| 0.1919        | 0.5   | 1000 | 3.4548          | 39.1654 | 33.6146 | 0.0097         | 0.0079      | 0.0071      |
| 0.2053        | 0.6   | 1200 | 3.4784          | 39.6413 | 33.5848 | 0.0101         | 0.0084      | 0.0072      |
| 0.126         | 0.7   | 1400 | 3.5044          | 40.3734 | 34.0553 | 0.0100         | 0.0083      | 0.0073      |
| 0.331         | 0.8   | 1600 | 3.4866          | 40.0439 | 34.0777 | 0.0100         | 0.0084      | 0.0072      |
| 0.1882        | 0.9   | 1800 | 3.4982          | 40.0805 | 34.1001 | 0.0100         | 0.0079      | 0.0075      |
| 0.1013        | 1.0   | 2000 | 3.4985          | 40.4100 | 34.3391 | 0.0102         | 0.0078      | 0.0077      |


### Framework versions

- PEFT 0.17.0
- Transformers 4.54.1
- Pytorch 2.8.0.dev20250319+cu128
- Datasets 3.6.0
- Tokenizers 0.21.4