---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- arrow
metrics:
- wer
model-index:
- name: enenlhet-whisper-model
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: arrow
      type: arrow
      config: default
      split: validation
      args: default
    metrics:
    - type: wer
      value: 1.341365
      name: Wer
---

# enenlhet-whisper-model

The model in this repository was developed by Samuel J. Huskey in partial fulfillment of a seed funding grant from the [University of Oklahoma](https://ou.edu/)'s [Data Institute for Societal Challenges](https://www.ou.edu/disc) (DISC). The project's title is "AI for Cost-Effective Research Workflows When Funding is Scarce" (co-PI's: Samuel J. Huskey, Raina Heaton, and Caroline T. Schroeder). Varun Sayapaneni, Research Informatics Specialist at [OU Libraries](https://libraries.ou.edu/), contributed valuable insights and made important contributions to this project.

See the code repository at <https://github.com/sjhuskey/enenlhet> for more information.

## Model description

- **Developed by:** [Samuel J. Huskey](https://sjhuskey.info/)
- **Funded by:** [University of Oklahoma Data Institute for Societal Challenges](https://www.ou.edu/disc)
- **Model type:** Automatic Speech Recognition
- **License:** [Apache license 2.0](https://www.apache.org/licenses/LICENSE-2.0.html)
- **Finetuned from model:** [openai/whisper-small](https://huggingface.co/openai/whisper-small)
- **Dataset:** [sjhuskey/enenlhet-whisper-dataset](https://huggingface.co/datasets/sjhuskey/enenlhet-whisper-dataset)

## Uses

To provide first-pass transcriptions of field recordings of Enenlhet speakers.

## Training and evaluation data

The training and evaluation data consist of `.wav` files of Enenlhet speakers and `.eaf` files contains transcriptions of those files.

The raw data was preprocessed with the script at <https://github.com/sjhuskey/enenlhet/blob/main/python/whisper-prepare-dataset.py>, which
segments the `.eaf` files and normalizes and segments the audio files.

The dataset was split on a 90-5-5 basis into train, test, and validation sets.

The model was trained on Colab with a version of the Jupyter notebook at <https://github.com/sjhuskey/enenlhet/blob/main/python/whisper_train_colab.ipynb>, which was based on the tutorial at <https://huggingface.co/blog/fine-tune-whisper#prepare-environment>.

## Training procedure

### Training hyperparameters

```
per_device_train_batch_size=16,
gradient_accumulation_steps=2,
learning_rate=1.25e-5,
predict_with_generate=False,
generation_max_length=80,
warmup_steps=500,
fp16=True,
eval_strategy="epoch",
num_train_epochs=15,
save_strategy="epoch",
save_total_limit=15,
logging_steps=10,
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
greater_is_better=False
```

### Training results

| Epoch | Training Loss | Validation Loss | Wer      | 
|-------|---------------|-----------------|----------| 
| 1     | 3.994700      | 3.676985        | 1.591700 | 
| 2     | 2.001900      | 1.887706        | 1.597055 | 
| 3     | 1.541100      | 1.465359        | 1.804552 | 
| 4     | 0.967300      | 1.218838        | 1.504685 | 
| 5     | 0.529700      | 0.959480        | 1.105756 | 
| 6     | 0.281000      | 0.960863        | 1.733601 | 
| 7     | 0.122700      | 0.968978        | 1.437751 | 
| 8     | 0.065600      | 0.992428        | 1.341365 | 


### Environmental Impacts

Output of CodeCarbon for one fine-tuning run:

```
timestamp: 2025-08-29T22:13:49
duration: 3332.9584745599996
emissions: 0.0696567052363746
emissions_rate: 2.089936186365788e-05
cpu_power: 42.5
gpu_power: 50.06983315793876
ram_power: 38.0
cpu_energy: 0.0390040343676826
gpu_energy: 0.074261856353882
ram_energy: 0.034693371266176
energy_consumed: 0.1479592619877408
os: Linux-6.1.123+-x86_64-with-glibc2.35
python_version: 3.12.11
codecarbon_version: 3.0.4
cpu_count: 12
cpu_model: Intel(R) Xeon(R) CPU @ 2.20GHz
gpu_count: 1
gpu_model: 1 x NVIDIA A100-SXM4-40GB
ram_total_size: 83.4760627746582
tracking_mode: machine
on_cloud: N
pue: 1.0
```