Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Nepali
whisper
hf-asr-leaderboard
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("Pranjal12345/whisper-small-ne-pranjal")
model = AutoModelForMultimodalLM.from_pretrained("Pranjal12345/whisper-small-ne-pranjal")Quick Links
Whisper Small Nepali - Pranjal Khadka
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0001
- eval_wer: 0.0
- eval_runtime: 5.3111
- eval_samples_per_second: 0.753
- eval_steps_per_second: 0.188
- epoch: 76.92
- step: 1000
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: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- 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="Pranjal12345/whisper-small-ne-pranjal")