Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Hausa
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use ibrahimchristopher/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibrahimchristopher/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ibrahimchristopher/whisper-small-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ibrahimchristopher/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("ibrahimchristopher/whisper-small-dv") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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metrics:
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
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It achieves the following results on the evaluation set:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
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### Framework versions
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metrics:
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- name: Wer
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type: wer
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value: 45.91914569031274
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6920
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- Wer Ortho: 48.8189
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- Wer: 45.9191
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
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| 0.0754 | 3.1847 | 500 | 0.6920 | 48.8189 | 45.9191 |
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### Framework versions
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