Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Thai
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use fruk19/C_ASR_MID with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fruk19/C_ASR_MID with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fruk19/C_ASR_MID")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("fruk19/C_ASR_MID") model = AutoModelForSpeechSeq2Seq.from_pretrained("fruk19/C_ASR_MID") - Notebooks
- Google Colab
- Kaggle
South_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: 0.0347
- Wer: 3.7677
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: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0304 | 2.0 | 6000 | 0.0440 | 5.5648 |
| 0.0061 | 4.0 | 12000 | 0.0358 | 4.1532 |
| 0.0007 | 6.0 | 18000 | 0.0347 | 3.7677 |
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/C_ASR_MID
Base model
openai/whisper-smallEvaluation results
- Wer on aicookcookself-reported3.768