Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Vietnamese
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Scrya/whisper-medium-vi-augmented")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Scrya/whisper-medium-vi-augmented")Quick Links
Whisper Medium VI - Multi - Augmented
This model is a fine-tuned version of openai/whisper-medium on the following datasets:
It achieves the following results on the evaluation set:
- Loss: 0.3696
- Wer: 16.6594
- Cer: 7.7625
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
Training:
- mozilla-foundation/common_voice_11_0 (train+validation)
- google/fleurs (train+validation)
- vivos (train)
Evaluation:
- mozilla-foundation/common_voice_11_0 (test)
- google/fleurs (test)
- vivos (test)
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- 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 | Cer |
|---|---|---|---|---|---|
| 0.1992 | 1.8 | 1000 | 0.2726 | 17.4929 | 8.2562 |
| 0.0402 | 3.6 | 2000 | 0.3317 | 17.4929 | 8.2588 |
| 0.0073 | 5.4 | 3000 | 0.3429 | 17.6793 | 8.8913 |
| 0.0014 | 7.19 | 4000 | 0.3599 | 19.0283 | 9.5103 |
| 0.0006 | 8.99 | 5000 | 0.3696 | 16.6594 | 7.7625 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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Datasets used to train Scrya/whisper-medium-vi-augmented
Evaluation results
- WER on mozilla-foundation/common_voice_11_0test set self-reported16.630
- CER on mozilla-foundation/common_voice_11_0test set self-reported7.740
- WER on google/fleurstest set self-reported9.040
- CER on google/fleurstest set self-reported4.810
- WER on vivostest set self-reported8.530
- CER on vivostest set self-reported3.670
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Scrya/whisper-medium-vi-augmented")