Instructions to use Sjdan/switch_high_x_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sjdan/switch_high_x_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sjdan/switch_high_x_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Sjdan/switch_high_x_2") model = AutoModelForCTC.from_pretrained("Sjdan/switch_high_x_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Sjdan/switch_high_x_2: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
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https://huggingface.co/Sjdan/switch_high_x_2/resolve/main/README.md
- Command line
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hf download hf://Sjdan/switch_high_x_2/README.md
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curl -L -o README.md https://huggingface.co/Sjdan/switch_high_x_2/resolve/main/README.md
1.09 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: switch_high_2
results: []
switch_high_2
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3