metadata

language:
  - it
license: apache-2.0
tags:
  - generated_from_trainer
base_model: openai/whisper-medium
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
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
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: facebook/multilingual_librispeech
          type: facebook/multilingual_librispeech
          config: italian
          split: test
        metrics:
          - type: wer
            value: 17.25
            name: WER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: facebook/voxpopuli
          type: facebook/voxpopuli
          config: it
          split: test
        metrics:
          - type: wer
            value: 18.75
            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