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-medium license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- ro
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Whisper Medium 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: 12.10333666091902
---

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

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1719
- Wer: 12.1033

## 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: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 48
- 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: 250
- training_steps: 2500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1563        | 0.98  | 250  | 0.1542          | 14.6716 |
| 0.0933        | 1.96  | 500  | 0.1306          | 13.0714 |
| 0.0428        | 2.94  | 750  | 0.1298          | 11.8886 |
| 0.0243        | 3.92  | 1000 | 0.1353          | 12.0096 |
| 0.0147        | 4.9   | 1250 | 0.1433          | 12.1064 |
| 0.0083        | 5.88  | 1500 | 0.1572          | 12.2606 |
| 0.0052        | 6.86  | 1750 | 0.1591          | 12.3090 |
| 0.0037        | 7.84  | 2000 | 0.1665          | 12.0307 |
| 0.0026        | 8.82  | 2250 | 0.1708          | 12.0549 |
| 0.0021        | 9.8   | 2500 | 0.1719          | 12.1033 |


### Framework versions

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