# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("hts98/demo_model")
model = AutoModelForSpeechSeq2Seq.from_pretrained("hts98/demo_model")Quick Links
demo_model
This model is a fine-tuned version of hts98/whisper-medium-1113 on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.5144
- eval_wer: 100.0055
- eval_runtime: 67.4062
- eval_samples_per_second: 3.397
- eval_steps_per_second: 0.341
- epoch: 3.04
- step: 280
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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- training_steps: 500
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hts98/demo_model")