metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: openai/whisper-large-v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: lv
split: test
args: lv
metrics:
- name: Wer
type: wer
value: 27.504743833017077
openai/whisper-large-v2
This model is a fine-tuned version of openai/whisper-large-v2 on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3198
- Wer: 27.5047
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: 3e-07
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- training_steps: 1500
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
0.5148 | 3.01 | 200 | 0.4189 | 39.3454 |
0.3041 | 6.03 | 400 | 0.3335 | 29.5731 |
0.1961 | 9.04 | 600 | 0.3186 | 27.7799 |
0.2579 | 13.01 | 800 | 0.3167 | 27.5712 |
0.2034 | 16.03 | 1000 | 0.3179 | 27.4763 |
0.1478 | 19.04 | 1200 | 0.3193 | 27.5237 |
0.2169 | 23.01 | 1400 | 0.3198 | 27.5047 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 2.0.0.dev20221218+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2