Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Urdu
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use stackintel/whisper-small-ur with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stackintel/whisper-small-ur with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="stackintel/whisper-small-ur")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("stackintel/whisper-small-ur") model = AutoModelForSpeechSeq2Seq.from_pretrained("stackintel/whisper-small-ur", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from stackintel/whisper-small-ur: direct link, hf CLI and curl.
- Browser
- Download file 1.22 kB
-
https://huggingface.co/stackintel/whisper-small-ur/resolve/main/README.md
- Command line
-
hf download hf://stackintel/whisper-small-ur/README.md
-
curl -L -o README.md https://huggingface.co/stackintel/whisper-small-ur/resolve/main/README.md
1.22 kB
metadata
language:
- ur
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_1122_0
model-index:
- name: Whisper Small Urdu - Stack Intel
results: []
Whisper Small Urdu - Stack Intel
This model is a fine-tuned version of openai/whisper-small on the Common Voice 1122.0 dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2