# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("DazMashaly/output")
model = AutoModelForSpeechSeq2Seq.from_pretrained("DazMashaly/output")Quick Links
output
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 5.5136
- eval_wer: 1.0
- eval_cer: 1.0
- eval_runtime: 702.4432
- eval_samples_per_second: 3.131
- eval_steps_per_second: 0.026
- epoch: 2.0
- step: 818
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: 124
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DazMashaly/output")