Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Urdu
wav2vec2
hf-asr-leaderboard
robust-speech-event
Eval Results (legacy)
Instructions to use kingabzpro/wav2vec2-60-urdu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kingabzpro/wav2vec2-60-urdu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kingabzpro/wav2vec2-60-urdu")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("kingabzpro/wav2vec2-60-urdu") model = AutoModelForCTC.from_pretrained("kingabzpro/wav2vec2-60-urdu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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mixed_precision_training: Native AMP
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# wav2vec2-large-xlsr-53-urdu
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This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-urdu-urm-60](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-urdu-urm-60) on the common_voice dataset.
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**🔴Check out my latest URDU ASR model with `25 WER` here ->** [kingabzpro/whisper-large-v3-turbo-urdu](https://huggingface.co/kingabzpro/whisper-large-v3-turbo-urdu)
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# wav2vec2-large-xlsr-53-urdu
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This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-urdu-urm-60](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-urdu-urm-60) on the common_voice dataset.
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