---
language:
- en
license: apache-2.0
base_model: futureProofGlitch/whisper-small-v2
tags:
- generated_from_trainer
datasets:
- futureProofGlitch/Lectures-test-V1
metrics:
- wer
model-index:
- name: FutureProofGlitch - Whisper Small - Fine Tuned on Lectures
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: TBK's Treasured Lectures
      type: futureProofGlitch/Lectures-test-V1
    metrics:
    - name: Wer
      type: wer
      value: 0.056233149313133904
---

<!-- 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. -->

# FutureProofGlitch - Whisper Small - Fine Tuned on Lectures

This model is a fine-tuned version of [futureProofGlitch/whisper-small-v2](https://huggingface.co/futureProofGlitch/whisper-small-v2) on the TBK's Treasured Lectures dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3574
- Wer Ortho: 0.1834
- Wer: 0.0562

## 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: 1.1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 10
- training_steps: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| No log        | 0.21  | 25   | 0.8342          | 0.2377    | 0.0939 |
| 3.0694        | 0.42  | 50   | 0.4413          | 0.2100    | 0.0651 |
| 3.0694        | 0.64  | 75   | 0.3754          | 0.1859    | 0.0557 |
| 0.3126        | 0.85  | 100  | 0.3574          | 0.1834    | 0.0562 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2