Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Bengali
whisper
hf-asr-leaderboard
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("sakib131/whisper-tiny-bn")
model = AutoModelForSpeechSeq2Seq.from_pretrained("sakib131/whisper-tiny-bn")Quick Links
Whisper Small Bn - Sakib Chowdhury
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 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: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 80000
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
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Model tree for sakib131/whisper-tiny-bn
Base model
openai/whisper-tiny
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sakib131/whisper-tiny-bn")