Hugging Face's logo

akera
/
whisper-medium-ach-only

Automatic Speech Recognition
Transformers
Safetensors
whisper
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
whisper-medium-ach-only / README.md
akera's picture
akera
Upload tokenizer
e28f3a1 verified
preview code
|
raw
history blame contribute delete
No virus
3.17 kB
metadata
license: apache-2.0
tags:
  - generated_from_trainer
base_model: openai/whisper-medium
datasets:
  - generator
metrics:
  - wer
model-index:
  - name: whisper-medium-ach-only
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: generator
          type: generator
          config: default
          split: train
          args: default
        metrics:
          - type: wer
            value: 21.152030217186024
            name: Wer

Visualize in Weights & Biases

whisper-medium-ach-only

This model is a fine-tuned version of openai/whisper-medium on the generator dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3942
  • Wer: 21.1520

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: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 4000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.1662 0.05 200 0.7062 47.0255
0.6014 1.0248 400 0.4606 32.9556
0.5638 1.0748 600 0.4021 27.2899
0.3677 2.0495 800 0.3736 24.3626
0.2711 3.0242 1000 0.3648 23.5127
0.2862 3.0743 1200 0.3402 23.7016
0.2023 4.049 1400 0.3665 22.4740
0.1166 5.0237 1600 0.4023 23.6072
0.1089 5.0738 1800 0.3871 22.5685
0.0859 6.0485 2000 0.3837 25.6846
0.0557 7.0232 2200 0.3942 21.1520
0.0572 7.0732 2400 0.3805 22.0963
0.0469 8.048 2600 0.3995 23.6072
0.0308 9.0228 2800 0.4057 21.5297
0.0288 9.0727 3000 0.3999 21.1520
0.0222 10.0475 3200 0.4121 21.7186
0.0239 11.0222 3400 0.4162 21.9075
0.024 11.0723 3600 0.4154 21.9075
0.0219 12.047 3800 0.4186 21.3409
0.0133 13.0218 4000 0.4173 21.1520

Framework versions

  • Transformers 4.41.0.dev0
  • Pytorch 2.2.0
  • Datasets 2.16.1
  • Tokenizers 0.19.1