Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Thai
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("fruk19/N_ASR_SMALL")
model = AutoModelForSpeechSeq2Seq.from_pretrained("fruk19/N_ASR_SMALL")Quick Links
North_asri
This model is a fine-tuned version of openai/whisper-small on the aicookcook dataset. It achieves the following results on the evaluation set:
- Loss: 0.0764
- Wer: 5.7726
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0486 | 2.0 | 6000 | 0.0722 | 9.8591 |
| 0.0125 | 4.0 | 12000 | 0.0682 | 6.9130 |
| 0.0038 | 6.0 | 18000 | 0.0722 | 6.3537 |
| 0.0019 | 8.0 | 24000 | 0.0752 | 5.9627 |
| 0.0001 | 10.0 | 30000 | 0.0764 | 5.7726 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.0.1+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for fruk19/N_ASR_SMALL
Base model
openai/whisper-smallEvaluation results
- Wer on aicookcookself-reported5.773
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fruk19/N_ASR_SMALL")