---
library_name: transformers
license: apache-2.0
datasets:
- sjhuskey/enenlhet-wav2vec2-dataset
metrics:
- wer
base_model:
- facebook/wav2vec2-large-xlsr-53
model-index:
- name: enenlhet-wav2vec2
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: sjhuskey/enenlhet-wav2vec2-dataset
      type: arrow
      config: default
      split: validation
      args: default
    metrics:
    - type: wer
      value: 0.59
      name: Wer
    - type: cer
      value: 0.14
      name: Cer
---

# enenlhet-wav2vec2

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:** [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
- **Dataset:** [sjhuskey/enenlhet-wav2vec2-dataset](https://huggingface.co/datasets/sjhuskey/enenlhet-wav2vec2-dataset)

## Uses

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

## Training Details

### Training 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/wav2vec2-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/wav2vec2_train_colab.ipynb>, 
which was based on the tutorial at <https://huggingface.co/blog/fine-tune-w2v2-bert>.

### Training Hyperparameters

```
group_by_length=True,
per_device_train_batch_size=16,
gradient_accumulation_steps=2,
eval_strategy="epoch",
num_train_epochs=60,
save_strategy="epoch",
fp16=True,
logging_steps=10,
learning_rate=5e-5,
warmup_ratio=0.1,
save_total_limit=20,
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
greater_is_better=False,
```

### Training Results

| Epoch  | Training Loss  | Validation Loss  | Wer       | Cer      | 
|--------|----------------|------------------|-----------|----------| 
| 1      | 9.739200       | 7.026410         | 1.000000  | 1.000000 | 
| 2      | 3.333400       | 3.148107         | 1.000000  | 1.000000 | 
| 3      | 2.852200       | 2.819278         | 1.000000  | 1.000000 | 
| 4      | 2.849000       | 2.760045         | 1.000000  | 1.000000 | 
| 5      | 2.836700       | 2.586708         | 1.000000  | 1.000000 | 
| 6      | 1.626700       | 1.224432         | 1.005532  | 0.351696 | 
| 7      | 1.113500       | 0.854202         | 0.961893  | 0.275788 | 
| 8      | 0.998200       | 0.699204         | 0.900430  | 0.245716 | 
| 9      | 0.935100       | 0.623386         | 0.849416  | 0.219500 | 
| 10     | 0.837600       | 0.543402         | 0.823602  | 0.208105 | 
| 11     | 0.674800       | 0.503909         | 0.779348  | 0.196282 | 
| 12     | 0.609300       | 0.462162         | 0.759066  | 0.185829 | 
| 13     | 0.587600       | 0.444128         | 0.755993  | 0.186515 | 
| 14     | 0.635400       | 0.423765         | 0.738783  | 0.180346 | 
| 15     | 0.547000       | 0.415437         | 0.726490  | 0.175463 | 
| 16     | 0.499700       | 0.370264         | 0.697603  | 0.165353 | 
| 17     | 0.537900       | 0.353593         | 0.685925  | 0.160898 | 
| 18     | 0.503400       | 0.344040         | 0.675476  | 0.159527 | 
| 19     | 0.454500       | 0.335758         | 0.659496  | 0.155072 | 
| 20     | 0.473700       | 0.334173         | 0.655808  | 0.155329 | 
| 21     | 0.436400       | 0.326168         | 0.647818  | 0.151731 | 
| 22     | 0.402600       | 0.306144         | 0.629994  | 0.147104 | 
| 23     | 0.394500       | 0.311927         | 0.618316  | 0.145905 | 
| 24     | 0.413800       | 0.297368         | 0.622618  | 0.142392 | 
| 25     | 0.379200       | 0.288900         | 0.607253  | 0.141107 | 
| 26     | 0.343500       | 0.285305         | 0.596189  | 0.139051 | 
| 27     | 0.343700       | 0.276769         | 0.591272  | 0.137080 | 
| 28     | 0.337600       | 0.274651         | 0.586355  | 0.136223 | 

### Environmental Impact

CodeCarbon's log for one fine-tuning run:

```
timestamp: 2025-08-29T17:14:37
duration: 3772.417071911
emissions: 0.0891595389213348
emissions_rate: 2.3634592151861177e-05
cpu_power: 42.5
gpu_power: 56.70040165244587
ram_power: 38.0
cpu_energy: 0.0444480260015881
gpu_energy: 0.105260611708422
ram_energy: 0.039677000516805
energy_consumed: 0.1893856382268154
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
```