metadata

library_name: peft
language:
  - en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - wft
  - whisper
  - automatic-speech-recognition
  - audio
  - speech
  - generated_from_trainer
datasets:
  - hf-internal-testing/librispeech_asr_dummy
metrics:
  - wer
model-index:
  - name: wft-test-model
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: hf-internal-testing/librispeech_asr_dummy
          type: hf-internal-testing/librispeech_asr_dummy
        metrics:
          - type: wer
            value: 5.905511811023622
            name: Wer

wft-test-model

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

Loss: 0.1185
Wer: 5.9055
Cer: 83.2386
Decode Time: 0.5299
Wer Time: 0.0047
Cer Time: 0.0030

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: 0.0005
train_batch_size: 4
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: 50
training_steps: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer	Decode Time	Wer Time	Cer Time
2.4079	0.1	10	1.9885	312.2047	119.2472	0.5334	0.0169	0.0041
1.2303	1.01	20	1.1646	258.2677	100.0	0.5213	1.4057	0.0046
0.8667	1.11	30	0.8100	37.7953	52.3438	0.5008	0.0396	0.0045
0.4517	2.02	40	0.6337	40.9449	73.7926	0.5137	0.0217	0.0030
0.4352	2.12	50	0.4493	16.5354	88.1392	0.5203	0.0054	0.0032
0.2341	3.03	60	0.2922	8.2677	97.5852	0.5434	0.0060	0.0032
0.2233	3.13	70	0.2026	9.0551	83.5227	0.5359	0.0063	0.0033
0.1098	4.04	80	0.1665	5.9055	83.9489	0.5316	0.0056	0.0029
0.0678	4.14	90	0.1279	7.0866	81.1080	0.5388	0.0079	0.0038
0.078	5.05	100	0.1185	5.9055	83.2386	0.5299	0.0047	0.0030

Framework versions

PEFT 0.13.2
Transformers 4.45.2
Pytorch 2.5.0
Datasets 3.0.2
Tokenizers 0.20.1