# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("Dandan0K/xls_1b_decoding_fr_decoding_test")
model = AutoModelForCTC.from_pretrained("Dandan0K/xls_1b_decoding_fr_decoding_test")Quick Links
xls_1b_decoding_fr_decoding_test
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 40
- num_epochs: 30
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.43.0.dev0
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Dandan0K/xls_1b_decoding_fr_decoding_test
Base model
facebook/wav2vec2-xls-r-300m
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Dandan0K/xls_1b_decoding_fr_decoding_test")