# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Eimhin03/outout_model_29Jan26_TestingGeneralisation")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Eimhin03/outout_model_29Jan26_TestingGeneralisation")Quick Links
outout_model_29Jan26_TestingGeneralisation
This model is a fine-tuned version of Eimhin03/outout_model 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 0.0020 | 1 | 3.8299 | 101.7722 |
Framework versions
- Transformers 5.0.1.dev0
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Eimhin03/outout_model_29Jan26_TestingGeneralisation")