Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
./train_dataset.py
Generated from Trainer
Instructions to use yesj1234/enko_xlsr_100p_run3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yesj1234/enko_xlsr_100p_run3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="yesj1234/enko_xlsr_100p_run3")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("yesj1234/enko_xlsr_100p_run3") model = AutoModelForCTC.from_pretrained("yesj1234/enko_xlsr_100p_run3") - Notebooks
- Google Colab
- Kaggle
en_xlsr_100p_run2
This model is a fine-tuned version of en_xlsr_100p_run2 on the ./TRAIN_DATASET.PY - 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: 3.6254652680427316e-07
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
Training results
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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