transformers tensorboard safetensors whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer ro dataset:VladS159/common_voice_16_1_romanian_speech_synthesis 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:
- VladS159/common_voice_16_1_romanian_speech_synthesis
metrics:
- wer
model-index:
- name: Whisper Medium Ro - Sarbu Vlad - multi gpu - 3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.1 + Romanian speech synthesis
      type: VladS159/common_voice_16_1_romanian_speech_synthesis
      args: 'config: ro, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 6.5648576295935746
---

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# Whisper Medium Ro - Sarbu Vlad - multi gpu - 3

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 16.1 + Romanian speech synthesis dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0743
- Wer: 6.5649

## 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: 10
- eval_batch_size: 10
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 30
- total_eval_batch_size: 30
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 450
- training_steps: 4500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2982        | 0.22  | 250  | 0.1790          | 15.9224 |
| 0.1365        | 0.43  | 500  | 0.1313          | 12.7038 |
| 0.1381        | 0.65  | 750  | 0.1126          | 11.4201 |
| 0.1207        | 0.86  | 1000 | 0.1037          | 11.1432 |
| 0.0579        | 1.08  | 1250 | 0.0931          | 9.6404  |
| 0.0665        | 1.3   | 1500 | 0.0929          | 9.4822  |
| 0.0572        | 1.51  | 1750 | 0.0875          | 9.4457  |
| 0.0556        | 1.73  | 2000 | 0.0825          | 8.6122  |
| 0.0458        | 1.94  | 2250 | 0.0778          | 8.2836  |
| 0.0243        | 2.16  | 2500 | 0.0786          | 7.9095  |
| 0.0197        | 2.38  | 2750 | 0.0795          | 7.8578  |
| 0.0229        | 2.59  | 3000 | 0.0758          | 7.4714  |
| 0.0175        | 2.81  | 3250 | 0.0755          | 7.3497  |
| 0.0109        | 3.03  | 3500 | 0.0751          | 7.0759  |
| 0.0098        | 3.24  | 3750 | 0.0773          | 7.1094  |
| 0.0081        | 3.46  | 4000 | 0.0748          | 6.7778  |
| 0.0087        | 3.67  | 4250 | 0.0754          | 6.6774  |
| 0.0086        | 3.89  | 4500 | 0.0743          | 6.5649  |


### Framework versions

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