metadata

language:
  - ro
license: apache-2.0
base_model: openai/whisper-base
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - iulik-pisik/horoscop_neti
  - iulik-pisik/audio_vreme
metrics:
  - wer
model-index:
  - name: Whisper Base - finetuned on weather and horoscope
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Vreme ProTV and Horoscop Neti
          type: iulik-pisik/audio_vreme
          config: default
          split: test
          args: 'config: ro, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 13.61
pipeline_tag: automatic-speech-recognition

Whisper Base - finetuned on weather and horoscope

This model is a fine-tuned version of openai/whisper-base on the Vreme ProTV and Horoscop Neti datasets. It achieves the following results on the evaluation set:

Loss: 0.0016
Wer: 13.61

Model description

This is a fine-tuned version of the Whisper Base model, specifically adapted for Romanian language Automatic Speech Recognition (ASR) in the domains of weather forecasts and horoscopes. The model has been trained on two custom datasets to improve its performance in transcribing Romanian speech in these specific contexts.

Training procedure

The model was fine-tuned using transfer learning techniques on the pre-trained Whisper Base model. Two custom datasets were used: audio recordings of weather forecasts and horoscopes in Romanian.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 16
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: 3000
mixed_precision_training: Native AMP

Training results

Epoch	Step	Validation Loss	WER
3.85	1000	0.0784	14.2716
7.69	2000	0.0124	14.1371
11.54	3000	0.0022	13.6796
15.38	4000	0.0016	13.6168

Framework versions

Transformers 4.39.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2