metadata
language:
- ro
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Medium Ro - Sarbu Vlad
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
args: 'config: ro, split: test'
metrics:
- name: Wer
type: wer
value: 12.10333666091902
Whisper Medium Ro - Sarbu Vlad
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1719
- Wer: 12.1033
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: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 48
- total_eval_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 2500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1563 | 0.98 | 250 | 0.1542 | 14.6716 |
| 0.0933 | 1.96 | 500 | 0.1306 | 13.0714 |
| 0.0428 | 2.94 | 750 | 0.1298 | 11.8886 |
| 0.0243 | 3.92 | 1000 | 0.1353 | 12.0096 |
| 0.0147 | 4.9 | 1250 | 0.1433 | 12.1064 |
| 0.0083 | 5.88 | 1500 | 0.1572 | 12.2606 |
| 0.0052 | 6.86 | 1750 | 0.1591 | 12.3090 |
| 0.0037 | 7.84 | 2000 | 0.1665 | 12.0307 |
| 0.0026 | 8.82 | 2250 | 0.1708 | 12.0549 |
| 0.0021 | 9.8 | 2500 | 0.1719 | 12.1033 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.1