# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("tensorops/whisper-th-small-combined")
model = AutoModelForSpeechSeq2Seq.from_pretrained("tensorops/whisper-th-small-combined")Quick Links
whisper-th-small-combined
This model was trained from scratch 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 1000
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.1.0a0+4136153
- Datasets 2.13.1
- 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="tensorops/whisper-th-small-combined")