|
--- |
|
language: |
|
- ro |
|
license: apache-2.0 |
|
base_model: openai/whisper-tiny |
|
tags: |
|
- hf-asr-leaderboard |
|
- generated_from_trainer |
|
datasets: |
|
- iulik-pisik/horoscop_neti |
|
- iulik-pisik/audio_vreme |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: Whisper Tiny - 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: 17.14 |
|
pipeline_tag: automatic-speech-recognition |
|
--- |
|
|
|
|
|
|
|
|
|
|
|
# Whisper Tiny - finetuned on weather and horoscope |
|
This model is a fine-tuned version of [openai/whisper-tiny](openai/whisper-tiny) on the Vreme ProTV and Horoscop Neti datasets. |
|
It achieves the following results on the evaluation set: |
|
|
|
- Loss: 0.0053 |
|
- Wer: 17.14 |
|
|
|
|
|
## Model description |
|
|
|
This is a fine-tuned version of the Whisper Tiny 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 Tiny 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 | |
|
|:-----:|:----:|:---------------:|:-------:| |
|
| 3.75 | 1000 | 0.1388 | 18.7298 | |
|
| 7.69 | 2000 | 0.036 | 17.5637 | |
|
| 11.63 | 3000 | 0.0089 | 17.3574 | |
|
| 15.38 | 4000 | 0.0053 | 17.1421 | |
|
|
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
