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: 6.035531759552202
---

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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: 0.0758
- Wer: 6.0355

## 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: 600
- training_steps: 6000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7452        | 0.22  | 250  | 0.6060          | 16.6525 |
| 0.1399        | 0.43  | 500  | 0.1351          | 13.3305 |
| 0.1444        | 0.65  | 750  | 0.1168          | 11.9676 |
| 0.1259        | 0.86  | 1000 | 0.1078          | 11.8277 |
| 0.0636        | 1.08  | 1250 | 0.0948          | 9.8382  |
| 0.0704        | 1.3   | 1500 | 0.0905          | 9.5461  |
| 0.0633        | 1.51  | 1750 | 0.0898          | 9.9385  |
| 0.0611        | 1.73  | 2000 | 0.0833          | 8.8464  |
| 0.0516        | 1.94  | 2250 | 0.0797          | 8.5757  |
| 0.0259        | 2.16  | 2500 | 0.0783          | 7.8517  |
| 0.0204        | 2.38  | 2750 | 0.0806          | 8.0646  |
| 0.024         | 2.59  | 3000 | 0.0775          | 7.6783  |
| 0.0203        | 2.81  | 3250 | 0.0767          | 7.4379  |
| 0.0129        | 3.03  | 3500 | 0.0761          | 7.1824  |
| 0.0109        | 3.24  | 3750 | 0.0790          | 7.4075  |
| 0.0096        | 3.46  | 4000 | 0.0768          | 7.3223  |
| 0.0104        | 3.67  | 4250 | 0.0766          | 6.9330  |
| 0.0101        | 3.89  | 4500 | 0.0722          | 6.3945  |
| 0.0045        | 4.11  | 4750 | 0.0770          | 6.3641  |
| 0.0052        | 4.32  | 5000 | 0.0763          | 6.3397  |
| 0.0051        | 4.54  | 5250 | 0.0761          | 6.2272  |
| 0.0038        | 4.75  | 5500 | 0.0756          | 6.1420  |
| 0.0036        | 4.97  | 5750 | 0.0751          | 6.0629  |
| 0.0025        | 5.19  | 6000 | 0.0758          | 6.0355  |


### Framework versions

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