Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Nepali
whisper
ne-NP-asr-leaderboard
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("sunilregmi/whispNEP")
model = AutoModelForSpeechSeq2Seq.from_pretrained("sunilregmi/whispNEP")Quick Links
whisper-Nepali-ASR Sunil Regmi
This model is a fine-tuned version of sunilregmi/whisper_small_ne_NP on the Openslr54 data-cleaned by spktsagar/openslr-nepali-asr-cleaned 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 500
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sunilregmi/whispNEP")