metadata
license: mit
base_model: distil-whisper/distil-large-v3
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: distil-whisper/distil-large-v3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_16_1
type: common_voice_16_1
config: hi
split: test
args: hi
metrics:
- name: Wer
type: wer
value: 0.3297535347291973
distil-whisper/distil-large-v3
This model is a fine-tuned version of distil-whisper/distil-large-v3 on the common_voice_16_1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6148
- Wer: 0.3298
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.125 | 4.5 | 1000 | 0.4658 | 0.4300 |
| 0.0412 | 9.01 | 2000 | 0.5247 | 0.3960 |
| 0.0077 | 13.51 | 3000 | 0.5476 | 0.3535 |
| 0.0007 | 18.02 | 4000 | 0.5731 | 0.3398 |
| 0.0001 | 22.52 | 5000 | 0.6148 | 0.3298 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1