transformers tensorboard safetensors whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer ro dataset:mozilla-foundation/common_voice_16_1 base_model:openai/whisper-small license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- ro
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Small Ro - Sarbu Vlad
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.1
      type: mozilla-foundation/common_voice_16_1
      args: 'config: ro, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 18.664730616813383
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Small Ro - Sarbu Vlad

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2920
- Wer: 18.6647

## 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: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 96
- total_eval_batch_size: 48
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1437        | 3.91  | 500  | 0.2167          | 20.5100 |
| 0.0268        | 7.81  | 1000 | 0.2202          | 18.6557 |
| 0.008         | 11.72 | 1500 | 0.2478          | 18.6829 |
| 0.0037        | 15.62 | 2000 | 0.2644          | 18.6708 |
| 0.0024        | 19.53 | 2500 | 0.2761          | 18.6405 |
| 0.0018        | 23.44 | 3000 | 0.2844          | 18.6859 |
| 0.0016        | 27.34 | 3500 | 0.2900          | 18.6799 |
| 0.0014        | 31.25 | 4000 | 0.2920          | 18.6647 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.1
