Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("rngzhi/cs3264-project")
model = AutoModelForSpeechSeq2Seq.from_pretrained("rngzhi/cs3264-project")Quick Links
Whipser Small - Singlish
This model is a fine-tuned version of openai/whisper-small on the National Speech Corpus(partial) dataset. It achieves the following results on the evaluation set:
- Loss: 0.2020
- Wer: 5.3795
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
- gradient_accumulation_steps: 2
- 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: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0068 | 5.01 | 500 | 0.1508 | 5.4137 |
| 0.001 | 11.01 | 1000 | 0.1691 | 5.0832 |
| 0.0003 | 16.02 | 1500 | 0.1769 | 5.1060 |
| 0.0006 | 22.01 | 2000 | 0.1840 | 5.0946 |
| 0.0005 | 28.0 | 2500 | 0.1891 | 5.1174 |
| 0.0003 | 33.02 | 3000 | 0.1933 | 5.2086 |
| 0.0005 | 39.01 | 3500 | 0.1962 | 5.2997 |
| 0.0002 | 45.0 | 4000 | 0.1991 | 5.3339 |
| 0.0002 | 50.02 | 4500 | 0.2010 | 5.3681 |
| 0.0003 | 56.01 | 5000 | 0.2020 | 5.3795 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.1.dev0
- Tokenizers 0.15.2
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Model tree for rngzhi/cs3264-project
Base model
openai/whisper-smallDataset used to train rngzhi/cs3264-project
Space using rngzhi/cs3264-project 1
Evaluation results
- Wer on National Speech Corpus(partial)self-reported5.380
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rngzhi/cs3264-project")