Automatic Speech Recognition
Transformers
PyTorch
Armenian
whisper
whisper-event
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("arampacha/whisper-tiny-hy")
model = AutoModelForSpeechSeq2Seq.from_pretrained("arampacha/whisper-tiny-hy")Quick Links
whisper-tiny-hy
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- eval_loss: 1.0292
- eval_wer: 0.7789
- eval_runtime: 157.9679
- eval_samples_per_second: 2.513
- eval_steps_per_second: 0.317
- epoch: 53.32
- step: 800
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 2000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arampacha/whisper-tiny-hy")