metadata

language:
  - sn
license: apache-2.0
library_name: peft
tags:
  - whisper-event and peft-lora
  - generated_from_trainer
base_model: openai/whisper-tiny
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Tiny Sn - Bright Chirindo
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Google Fleurs
          type: google/fleurs
          config: sn_zw
          split: None
          args: sn_zw
        metrics:
          - type: wer
            value: 95.27619047619048
            name: Wer

Whisper Tiny Sn - Bright Chirindo

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

Loss: 1.9909
Wer: 95.2762

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: 8
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: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.087	3.0164	1000	2.4026	109.4095
1.8305	6.0328	2000	2.1613	101.2419
1.7145	9.0492	3000	2.0536	99.8705
1.6314	13.0044	4000	2.0050	99.0095
1.665	16.0208	5000	1.9909	95.2762

Framework versions

PEFT 0.10.1.dev0
Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.2.dev0
Tokenizers 0.19.1