Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use RitchieP/verbalex-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RitchieP/verbalex-zh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="RitchieP/verbalex-zh")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("RitchieP/verbalex-zh") model = AutoModelForSpeechSeq2Seq.from_pretrained("RitchieP/verbalex-zh") - Notebooks
- Google Colab
- Kaggle
verbalex-zh
This model is a fine-tuned version of openai/whisper-small on the verba_lex_voice dataset. It achieves the following results on the evaluation set:
- Loss: 0.1147
- Wer: 4.6706
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: 3000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0025 | 5.0505 | 1000 | 0.1035 | 8.5071 |
| 0.0002 | 10.1010 | 2000 | 0.1130 | 4.7540 |
| 0.0002 | 15.1515 | 3000 | 0.1147 | 4.6706 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.19.1
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Model tree for RitchieP/verbalex-zh
Base model
openai/whisper-smallEvaluation results
- Wer on verba_lex_voicetest set self-reported4.671