Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Instructions to use Makkoen/whisper-large-v3-cit-do01-wd0-lr3e-06-steps1200-FULL4test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Makkoen/whisper-large-v3-cit-do01-wd0-lr3e-06-steps1200-FULL4test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Makkoen/whisper-large-v3-cit-do01-wd0-lr3e-06-steps1200-FULL4test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Makkoen/whisper-large-v3-cit-do01-wd0-lr3e-06-steps1200-FULL4test") model = AutoModelForSpeechSeq2Seq.from_pretrained("Makkoen/whisper-large-v3-cit-do01-wd0-lr3e-06-steps1200-FULL4test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
./4528
This model is a fine-tuned version of openai/whisper-large-v3 on the 4528 FULL-2024-10-24 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5039
- Wer Ortho: 28.2453
- Wer: 20.5359
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: 3e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- training_steps: 1200
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.7791 | 0.7851 | 200 | 0.5708 | 32.0818 | 24.2128 |
| 0.5319 | 1.5702 | 400 | 0.5254 | 30.1601 | 22.5438 |
| 0.4597 | 2.3553 | 600 | 0.5083 | 28.4089 | 21.1357 |
| 0.3953 | 3.1403 | 800 | 0.5049 | 28.2658 | 20.6467 |
| 0.3522 | 3.9254 | 1000 | 0.4995 | 28.1840 | 20.4577 |
| 0.3191 | 4.7105 | 1200 | 0.5039 | 28.2453 | 20.5359 |
Framework versions
- Transformers 4.45.1
- Pytorch 1.13.1+cu117
- Datasets 3.0.1
- Tokenizers 0.20.0
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Model tree for Makkoen/whisper-large-v3-cit-do01-wd0-lr3e-06-steps1200-FULL4test
Base model
openai/whisper-large-v3