---
language:
- ro
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- VladS159/common_voice_17_0_romanian_speech_synthesis
metrics:
- wer
model-index:
- name: Whisper Medium Ro - Sarbu Vlad - 3 gpus
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17.0 + Romanian speech synthesis
      type: VladS159/common_voice_17_0_romanian_speech_synthesis
      args: 'config: ro, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 6.359842831470257
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Medium Ro - Sarbu Vlad - 3 gpus

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 17.0 + Romanian speech synthesis dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0878
- Wer: 6.3598

## 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: 5e-06
- 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: 1000
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.2346        | 0.42  | 500   | 0.1671          | 15.7382 |
| 0.1441        | 0.85  | 1000  | 0.1183          | 12.1318 |
| 0.0887        | 1.27  | 1500  | 0.1026          | 10.3012 |
| 0.0821        | 1.7   | 2000  | 0.0926          | 9.8230  |
| 0.0403        | 2.12  | 2500  | 0.0871          | 9.1590  |
| 0.0394        | 2.55  | 3000  | 0.0914          | 8.8788  |
| 0.0381        | 2.97  | 3500  | 0.0790          | 8.2909  |
| 0.0178        | 3.4   | 4000  | 0.0825          | 7.7579  |
| 0.0185        | 3.82  | 4500  | 0.0776          | 7.4929  |
| 0.0102        | 4.25  | 5000  | 0.0818          | 7.4350  |
| 0.0105        | 4.67  | 5500  | 0.0803          | 6.9599  |
| 0.0054        | 5.1   | 6000  | 0.0830          | 6.9264  |
| 0.0046        | 5.52  | 6500  | 0.0827          | 6.6949  |
| 0.005         | 5.95  | 7000  | 0.0831          | 6.7101  |
| 0.0043        | 6.37  | 7500  | 0.0840          | 6.6462  |
| 0.003         | 6.8   | 8000  | 0.0845          | 6.5274  |
| 0.0017        | 7.22  | 8500  | 0.0875          | 6.5121  |
| 0.0015        | 7.65  | 9000  | 0.0867          | 6.4025  |
| 0.0012        | 8.07  | 9500  | 0.0875          | 6.3568  |
| 0.001         | 8.5   | 10000 | 0.0878          | 6.3598  |


### Framework versions

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