Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use 1aurent/whisper-tiny-minds14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 1aurent/whisper-tiny-minds14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="1aurent/whisper-tiny-minds14")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("1aurent/whisper-tiny-minds14") model = AutoModelForSpeechSeq2Seq.from_pretrained("1aurent/whisper-tiny-minds14") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6457
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- Wer Ortho:
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- Wer:
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
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| 0.0007 | 17.86 | 500 | 0.6457 |
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### Framework versions
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6457
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- Wer Ortho: 0.357187
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- Wer: 0.353011
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|
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| 0.0007 | 17.86 | 500 | 0.6457 | 0.357187 | 0.353011 |
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### Framework versions
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