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---
language:
- ro
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- iulik-pisik/horoscop_neti
- iulik-pisik/audio_vreme
metrics:
- wer
model-index:
- name: Whisper Small - 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: 8.51
pipeline_tag: automatic-speech-recognition
---
# Whisper Small - finetuned on weather and horoscope
This model is a fine-tuned version of [openai/whisper-small](openai/whisper-small) on the Vreme ProTV and Horoscop Neti datasets.
It achieves the following results on the evaluation set:
- Loss: 0.0004
- Wer: 8.51
## Model description
This is a fine-tuned version of the Whisper Small 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 Small 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 | 0.0332 | 9.1945 |
| 7.69 | 2000 | 0.0035 | 10.845 |
| 11.54 | 3000 | 0.0005 | 8.4679 |
| 15.38 | 4000 | 0.0004 | 8.5127 |
### Framework versions
- Transformers 4.39.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |