Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Urdu
whisper
urdu
mozilla-foundation/common_voice_17_0
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use kingabzpro/whisper-base-urdu-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kingabzpro/whisper-base-urdu-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kingabzpro/whisper-base-urdu-full")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("kingabzpro/whisper-base-urdu-full") model = AutoModelForSpeechSeq2Seq.from_pretrained("kingabzpro/whisper-base-urdu-full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the common_voice_17_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5551
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- Wer: 41.6169
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## Usage
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# Whisper Base Urdu ASR Model
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This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the common_voice_17_0 dataset.
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## Usage
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