# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("MBMMurad/wav2vec2_murad")
model = AutoModelForCTC.from_pretrained("MBMMurad/wav2vec2_murad")Quick Links
wav2vec2_murad
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.2006
- eval_wer: 0.2084
- eval_runtime: 556.4634
- eval_samples_per_second: 8.985
- eval_steps_per_second: 0.562
- epoch: 12.32
- step: 28800
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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 30
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.21.1
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MBMMurad/wav2vec2_murad")