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: cs
split: test
args: cs
metrics:
- name: Wer
type: wer
value: 8.473140457818456
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: 0.2913
- Wer: 8.4731
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: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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 |
|---|---|---|---|---|
| 0.0022 | 24.39 | 1000 | 0.2181 | 8.7807 |
| 0.0002 | 48.77 | 2000 | 0.2563 | 8.3774 |
| 0.0001 | 73.17 | 3000 | 0.2756 | 8.4510 |
| 0.0001 | 97.55 | 4000 | 0.2871 | 8.4823 |
| 0.0001 | 121.94 | 5000 | 0.2913 | 8.4731 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2