README.md

metadata

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

FutureProofGlitch - Whisper Small - Fine Tuned on Lectures

This model is a fine-tuned version of 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