Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Uzbek
whisper
Generated from Trainer
Instructions to use jmshd/whisper-uz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jmshd/whisper-uz with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jmshd/whisper-uz")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("jmshd/whisper-uz") model = AutoModelForSpeechSeq2Seq.from_pretrained("jmshd/whisper-uz", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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results: []
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datasets:
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- mozilla-foundation/common_voice_17_0
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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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# Whisper base uz - Jamshid Ahmadov
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This model is a fine-tuned version of
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It achieves the following results on the evaluation set:
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- Loss: 0.1652
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- Wer: 14.0135
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results: []
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datasets:
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- mozilla-foundation/common_voice_17_0
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- DavronSherbaev/uzbekvoice
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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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# Whisper base uz - Jamshid Ahmadov
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This model is a fine-tuned version of Whisper Base on an Common Voice dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1652
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- Wer: 14.0135
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