Automatic Speech Recognition
Transformers
PyTorch
Hindi
wav2vec2
common_voice
Generated from Trainer
Instructions to use Priyajay/xls-r-ab-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Priyajay/xls-r-ab-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Priyajay/xls-r-ab-test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Priyajay/xls-r-ab-test") model = AutoModelForCTC.from_pretrained("Priyajay/xls-r-ab-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Priyajay/xls-r-ab-test: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
-
https://huggingface.co/Priyajay/xls-r-ab-test/resolve/main/README.md
- Command line
-
hf download hf://Priyajay/xls-r-ab-test/README.md
-
curl -L -o README.md https://huggingface.co/Priyajay/xls-r-ab-test/resolve/main/README.md
1.31 kB
metadata
language:
- hi
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []
This model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - HI dataset. It achieves the following results on the evaluation set:
- Loss: 248.1278
- Wer: 1.0
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2.0
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0