Automatic Speech Recognition
Transformers
PyTorch
Japanese
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use hoangvanvietanh/model_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hoangvanvietanh/model_trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hoangvanvietanh/model_trained")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hoangvanvietanh/model_trained") model = AutoModelForSpeechSeq2Seq.from_pretrained("hoangvanvietanh/model_trained") - Notebooks
- Google Colab
- Kaggle
PXAudio Whisper For 363ebm
This model is a fine-tuned version of openai/whisper-small on the ja 0.1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Wer: 100.0
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: 16
- eval_batch_size: 8
- 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: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0 | 1000.0 | 1000 | 0.0000 | 100.0 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.13.3
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Model tree for hoangvanvietanh/model_trained
Base model
openai/whisper-smallEvaluation results
- Wer on ja 0.1self-reported100.000