metadata
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
base_model: openai/whisper-tiny
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: 25.86151801007235
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: 15.97
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: 18.48
name: WER
pipeline_tag: automatic-speech-recognition
Whisper Tiny en
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6106
- Wer: 25.8615
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 |
|---|---|---|---|---|
| 0.4769 | 1.0974 | 1000 | 0.6212 | 26.6080 |
| 0.346 | 3.0922 | 2000 | 0.6184 | 26.1229 |
| 0.3654 | 5.087 | 3000 | 0.6130 | 26.0782 |
| 0.2858 | 7.0818 | 4000 | 0.6196 | 26.2060 |
| 0.3308 | 9.0766 | 5000 | 0.6106 | 25.8615 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1