metadata
language:
- it
license: apache-2.0
tags:
- generated_from_trainer
base_model: openai/whisper-small
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Small it
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: it
split: test
args: it
metrics:
- type: wer
value: 8.644182992734926
name: Wer
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: it_it
split: test
metrics:
- type: wer
value: 6.69
name: WER
Whisper Small it
This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1960
- Wer: 8.6442
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: 64
- eval_batch_size: 8
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2184 | 0.3460 | 1000 | 0.2458 | 11.3839 |
| 0.1863 | 0.6920 | 2000 | 0.2186 | 10.1784 |
| 0.1138 | 1.0381 | 3000 | 0.2049 | 9.1252 |
| 0.1184 | 1.3841 | 4000 | 0.1996 | 8.9385 |
| 0.1189 | 1.7301 | 5000 | 0.1960 | 8.6442 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1