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
Commit ·
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Parent(s): e6f5514
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README.md
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---
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license: apache-2.0
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tags:
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- robust-speech-event
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datasets:
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- common_voice
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model-index:
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- name: wav2vec2-large-xlsr-53-urdu
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results:
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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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---
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language:
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- ur
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license: apache-2.0
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tags:
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- automatic-speech-recognition
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- robust-speech-event
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datasets:
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- common_voice
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metrics:
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- wer
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model-index:
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- name: wav2vec2-large-xlsr-53-urdu
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results:
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- task:
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type: automatic-speech-recognition # Required. Example: automatic-speech-recognition
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name: Urdu Speech Recognition # Optional. Example: Speech Recognition
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dataset:
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type: common_voice # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
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name: ur # Required. Example: Common Voice zh-CN
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args: ur # Optional. Example: zh-CN
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metrics:
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- type: wer # Required. Example: wer
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value: 100 # Required. Example: 20.90
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name: Test WER # Optional. Example: Test WER
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args:
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- learning_rate: 0.0003
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- train_batch_size: 16
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 30
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- mixed_precision_training: Native AMP # Optional. Example for BLEU: max_order
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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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