Malasar ASR Resources
Collection
5 items • Updated
Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Malasar
whisper
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("vrclc/Malasar_small_DTF")
model = AutoModelForSpeechSeq2Seq.from_pretrained("vrclc/Malasar_small_DTF")Quick Links
leenag/Malasar_Luke
This model is a fine-tuned version of openai/whisper-small on the Spoken Bible Corpus: Malasar dataset. It achieves the following results on the evaluation set:
- Loss: 0.6211
- Wer: 60.1371
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: 32
- 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: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1612 | 11.3636 | 250 | 0.3185 | 64.0206 |
| 0.0114 | 22.7273 | 500 | 0.4682 | 65.7339 |
| 0.0016 | 34.0909 | 750 | 0.5380 | 59.9657 |
| 0.0004 | 45.4545 | 1000 | 0.5761 | 59.8515 |
| 0.0003 | 56.8182 | 1250 | 0.5969 | 59.6802 |
| 0.0002 | 68.1818 | 1500 | 0.6104 | 60.3655 |
| 0.0002 | 79.5455 | 1750 | 0.6181 | 60.1942 |
| 0.0002 | 90.9091 | 2000 | 0.6211 | 60.1371 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.19.1
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Model tree for vrclc/Malasar_small_DTF
Base model
openai/whisper-small
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vrclc/Malasar_small_DTF")