transformers tensorboard safetensors whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer ro dataset:iulik-pisik/horoscop_neti base_model:openai/whisper-base license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- ro
license: apache-2.0
base_model: openai/whisper-base
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- iulik-pisik/horoscop_neti
metrics:
- wer
model-index:
- name: Whisper Base Romanian - Horoscop Neti
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Horoscop Neti
      type: iulik-pisik/horoscop_neti
      config: default
      split: None
      args: 'config: ro, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 17.035775127768314
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Base Romanian - Horoscop Neti

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Horoscop Neti dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3282
- Wer: 17.0358

## 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: 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0142        | 9.71  | 1000 | 0.2707          | 18.0336 |
| 0.0012        | 19.42 | 2000 | 0.3073          | 17.0601 |
| 0.0007        | 29.13 | 3000 | 0.3225          | 17.0114 |
| 0.0005        | 38.83 | 4000 | 0.3282          | 17.0358 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
