Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Min Nan Chinese
whisper
Generated from Trainer
Instructions to use linshoufan/linshoufan-whisper-small-nan-tw-pinyin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use linshoufan/linshoufan-whisper-small-nan-tw-pinyin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="linshoufan/linshoufan-whisper-small-nan-tw-pinyin")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("linshoufan/linshoufan-whisper-small-nan-tw-pinyin") model = AutoModelForSpeechSeq2Seq.from_pretrained("linshoufan/linshoufan-whisper-small-nan-tw-pinyin", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Whisper Small Taiwanese
This model is a fine-tuned version of openai/whisper-small on the Common Voice and 16.1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6744
- Cer: 22.0296
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: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.2594 | 2.99 | 1000 | 0.5468 | 22.6648 |
| 0.0255 | 5.99 | 2000 | 0.6744 | 22.0296 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for linshoufan/linshoufan-whisper-small-nan-tw-pinyin
Base model
openai/whisper-small