Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Belarusian
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use ales/whisper-tiny-be-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ales/whisper-tiny-be-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ales/whisper-tiny-be-test")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ales/whisper-tiny-be-test") model = AutoModelForSpeechSeq2Seq.from_pretrained("ales/whisper-tiny-be-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
upd readme
Browse files
README.md
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# Whisper Tiny Belarusian
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 be dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4388
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# Whisper Tiny Belarusian
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Repo to test model training
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 be dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4388
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