metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- multilingual_librispeech
metrics:
- wer
model-index:
- name: openai/whisper-large-v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: multilingual_librispeech
type: multilingual_librispeech
config: french
split: test
args: french
metrics:
- name: Wer
type: wer
value: 4.561620226935377
openai/whisper-large-v2
This model is a fine-tuned version of openai/whisper-large-v2 on the multilingual_librispeech dataset. It achieves the following results on the evaluation set:
- Loss: 0.0903
- Wer: 4.5616
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1303 | 0.25 | 1000 | 0.1219 | 6.3618 |
| 0.0751 | 0.5 | 2000 | 0.1033 | 5.3905 |
| 0.0613 | 0.75 | 3000 | 0.0970 | 4.9193 |
| 0.0796 | 1.0 | 4000 | 0.0903 | 4.5616 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2