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---
language:
- vi
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium Vietnamese
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0 vi
type: mozilla-foundation/common_voice_11_0
config: vi
split: test
args: vi
metrics:
- type: wer
value: 15.492494795661225
name: Wer
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: vi_vn
split: test
metrics:
- type: wer
value: 19.55
name: WER
---
<!-- 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 Vietnamese
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 vi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7136
- Wer: 15.4925
## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0001 | 124.0 | 1000 | 0.7136 | 15.4925 |
| 0.0001 | 249.0 | 2000 | 0.8532 | 17.0045 |
| 0.0 | 374.0 | 3000 | 0.9251 | 19.0972 |
| 0.0 | 499.0 | 4000 | 0.9787 | 21.5953 |
| 0.0 | 624.0 | 5000 | 0.9921 | 21.4638 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.12.1
|