Instructions to use zainulhakim/240719-wav2vec2-ASR-Chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zainulhakim/240719-wav2vec2-ASR-Chinese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="zainulhakim/240719-wav2vec2-ASR-Chinese")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("zainulhakim/240719-wav2vec2-ASR-Chinese") model = AutoModelForCTC.from_pretrained("zainulhakim/240719-wav2vec2-ASR-Chinese") - Notebooks
- Google Colab
- Kaggle
240719-wav2vec2-ASR-Chinese
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9113
- Wer: 0.2543
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: 3e-05
- train_batch_size: 5
- 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
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 6.25 | 100 | 0.9113 | 0.2543 |
| No log | 12.5 | 200 | 1.0939 | 0.2622 |
| No log | 18.75 | 300 | 1.0345 | 0.2754 |
| No log | 25.0 | 400 | 1.2808 | 0.3083 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
- Downloads last month
- 11