Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Instructions to use navin-kumar-j/whisper-base-en-w-pcd-10-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use navin-kumar-j/whisper-base-en-w-pcd-10-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="navin-kumar-j/whisper-base-en-w-pcd-10-2", device_map="auto")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("navin-kumar-j/whisper-base-en-w-pcd-10-2") model = AutoModelForSpeechSeq2Seq.from_pretrained("navin-kumar-j/whisper-base-en-w-pcd-10-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.
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- Wer: 0.7863
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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### Framework versions
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This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.3819
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- Wer: 0.7863
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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| 36.1644 | 0.9091 | 40 | 24.3079 | 1.0 |
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| 5.8058 | 1.8182 | 80 | 5.1572 | 0.8473 |
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| 2.9137 | 2.7273 | 120 | 2.4350 | 0.8779 |
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| 1.8303 | 3.6364 | 160 | 1.6639 | 0.8702 |
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| 1.3301 | 4.5455 | 200 | 1.3819 | 0.7863 |
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### Framework versions
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