Instructions to use reproductionguru/voicetest5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reproductionguru/voicetest5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="reproductionguru/voicetest5")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("reproductionguru/voicetest5") model = AutoModelForSpeechSeq2Seq.from_pretrained("reproductionguru/voicetest5") - Notebooks
- Google Colab
- Kaggle
base
This model is a fine-tuned version of openai/whisper-small on the tutorial Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6918
- Wer: 91.5799
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: 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: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.6216 | 0.64 | 1000 | 0.6918 | 91.5799 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for reproductionguru/voicetest5
Base model
openai/whisper-small