metadata
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
base_model: openai/whisper-base
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Medium en
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: en
split: test
args: en
metrics:
- type: wer
value: 19.814275123347905
name: Wer
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: en_us
split: test
metrics:
- type: wer
value: 14
name: WER
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: facebook/voxpopuli
type: facebook/voxpopuli
config: en
split: test
metrics:
- type: wer
value: 13.25
name: WER
pipeline_tag: automatic-speech-recognition
Whisper Base en
This model is a fine-tuned version of openai/whisper-base on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5062
- Wer: 19.8143
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: 64
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3654 | 1.0974 | 1000 | 0.5075 | 20.6605 |
| 0.2314 | 3.0922 | 2000 | 0.5117 | 20.1370 |
| 0.261 | 5.087 | 3000 | 0.5058 | 20.1230 |
| 0.1793 | 7.0818 | 4000 | 0.5196 | 20.5831 |
| 0.2344 | 9.0766 | 5000 | 0.5062 | 19.8143 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1