Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
phongdtd/youtube_casual_audio
Generated from Trainer
Instructions to use phongdtd/fb-youtube-vi-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phongdtd/fb-youtube-vi-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="phongdtd/fb-youtube-vi-large")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("phongdtd/fb-youtube-vi-large") model = AutoModelForCTC.from_pretrained("phongdtd/fb-youtube-vi-large") - Notebooks
- Google Colab
- Kaggle
fb-youtube-vi-large
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the PHONGDTD/YOUTUBE_CASUAL_AUDIO - NA 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 8
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 25.0
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
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