metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: openai/whisper-large-v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fleurs
type: fleurs
config: ps_af
split: test
args: ps_af
metrics:
- name: Wer
type: wer
value: 55.92064476131432
openai/whisper-large-v2
This model is a fine-tuned version of openai/whisper-large-v2 on the fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 1.0077
- Wer: 55.9206
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-07
- train_batch_size: 8
- eval_batch_size: 4
- 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: 50
- training_steps: 700
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.2281 | 16.59 | 100 | 1.0951 | 69.3118 |
| 0.7529 | 33.3 | 200 | 0.8693 | 57.5635 |
| 0.5372 | 49.89 | 300 | 0.8399 | 54.7350 |
| 0.4398 | 66.59 | 400 | 0.8623 | 54.0685 |
| 0.3244 | 83.3 | 500 | 0.9098 | 54.7505 |
| 0.238 | 99.89 | 600 | 0.9607 | 55.3782 |
| 0.2014 | 116.59 | 700 | 1.0077 | 55.9206 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2