metadata
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: openai/whisper-large-v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: pt
split: test
args: pt
metrics:
- name: Wer
type: wer
value: 5.875201261788191
openai/whisper-large-v2
This model is a fine-tuned version of openai/whisper-large-v2 on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4680
- Wer: 5.8752
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-06
- train_batch_size: 32
- eval_batch_size: 16
- 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: 20000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0874 | 3.53 | 1000 | 0.1593 | 4.9765 |
| 0.0318 | 7.05 | 2000 | 0.2263 | 5.4365 |
| 0.0121 | 10.58 | 3000 | 0.2966 | 5.5630 |
| 0.005 | 14.11 | 4000 | 0.3400 | 5.6123 |
| 0.0036 | 17.64 | 5000 | 0.3554 | 5.6600 |
| 0.0034 | 21.16 | 6000 | 0.3640 | 5.6370 |
| 0.0021 | 24.69 | 7000 | 0.3714 | 5.6485 |
| 0.0016 | 28.22 | 8000 | 0.3962 | 5.6255 |
| 0.0013 | 31.75 | 9000 | 0.3960 | 5.6731 |
| 0.0009 | 35.27 | 10000 | 0.4107 | 5.7027 |
| 0.0008 | 38.8 | 11000 | 0.3981 | 5.9869 |
| 0.0006 | 42.33 | 12000 | 0.4097 | 5.7010 |
| 0.0005 | 45.86 | 13000 | 0.4226 | 5.8144 |
| 0.0004 | 49.38 | 14000 | 0.4330 | 5.8259 |
| 0.0004 | 52.91 | 15000 | 0.4415 | 5.7914 |
| 0.0003 | 56.44 | 16000 | 0.4490 | 5.7848 |
| 0.0003 | 59.96 | 17000 | 0.4553 | 5.8013 |
| 0.0002 | 63.49 | 18000 | 0.4625 | 5.7963 |
| 0.0002 | 67.02 | 19000 | 0.4663 | 5.8522 |
| 0.0002 | 70.55 | 20000 | 0.4680 | 5.8752 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.15.1