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: italian
split: test
args: italian
metrics:
- name: Wer
type: wer
value: 8.335297167365791
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: 0.2051
- Wer: 8.3353
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
- 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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1115 | 1.02 | 1000 | 0.2116 | 9.4217 |
| 0.0867 | 2.03 | 2000 | 0.1964 | 9.7823 |
| 0.0447 | 3.05 | 3000 | 0.2001 | 9.6409 |
| 0.0426 | 4.07 | 4000 | 0.2051 | 8.3353 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2