metadata
base_model: openai/whisper-medium
datasets:
- mozilla-foundation/common_voice_17_0
language:
- it
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Medium 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: 5.709779804285139
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: 4.47
name: WER
Whisper Medium it
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1462
- Wer: 5.7098
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: 32
- 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 |
|---|---|---|---|---|
| 0.1755 | 0.1730 | 1000 | 0.1974 | 8.0595 |
| 0.157 | 0.3461 | 2000 | 0.1776 | 7.1199 |
| 0.1287 | 0.5191 | 3000 | 0.1622 | 6.5201 |
| 0.1287 | 0.6922 | 4000 | 0.1521 | 5.9863 |
| 0.1168 | 0.8652 | 5000 | 0.1462 | 5.7098 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1