metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- voxpopuli
metrics:
- wer
base_model: openai/whisper-large-v2
model-index:
- name: whisper-large-v2-german
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: voxpopuli
type: voxpopuli
config: de
split: test
args: de
metrics:
- type: wer
value: 0.12201852946974177
name: Wer
whisper-large-v2-german
This model is a fine-tuned version of openai/whisper-large-v2 on the voxpopuli dataset. It achieves the following results on the evaluation set:
- Loss: 0.2841
- Wer Ortho: 0.1517
- Wer: 0.1220
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-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.2616 | 1.0 | 1679 | 0.2695 | 0.1601 | 0.1303 |
| 0.1801 | 2.0 | 3358 | 0.2690 | 0.1554 | 0.1235 |
| 0.1185 | 3.0 | 5037 | 0.2841 | 0.1517 | 0.1220 |
Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3