Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Marathi
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("PolyChirag/Marathi_ASR_using_Whisper_Small")
model = AutoModelForSpeechSeq2Seq.from_pretrained("PolyChirag/Marathi_ASR_using_Whisper_Small")Quick Links
Whisper Small MR - Chirag Brahme
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:
- Loss: 0.2701
- Wer: 45.6837
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1621 | 2.0325 | 500 | 0.2701 | 45.6837 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for PolyChirag/Marathi_ASR_using_Whisper_Small
Base model
openai/whisper-smallSpace using PolyChirag/Marathi_ASR_using_Whisper_Small 1
Evaluation results
- Wer on Common Voice 11.0self-reported45.684
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="PolyChirag/Marathi_ASR_using_Whisper_Small")