metadata
language:
- de
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Mona Speech Model (Trained on ICU Data)
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Mona Speech
type: mozilla-foundation/common_voice_11_0
config: de
split: test
args: 'config: de, split: test'
metrics:
- name: Wer
type: wer
value: 114.52938535018846
Mona Speech Model (Trained on ICU Data)
This model is a fine-tuned version of openai/whisper-small on the Mona Speech dataset. It achieves the following results on the evaluation set:
- Loss: 0.6949
- Wer: 114.5294
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: 16
- 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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0001 | 31.25 | 1000 | 0.6152 | 109.7314 |
| 0.0001 | 62.5 | 2000 | 0.6619 | 111.6657 |
| 0.0 | 93.75 | 3000 | 0.6838 | 114.1096 |
| 0.0 | 125.0 | 4000 | 0.6949 | 114.5294 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2