Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Instructions to use Makkoen/whisper-large-v3-cit-do01-wd0-lr3e-06-FULL4c 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-FULL4c 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-FULL4c")# 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-FULL4c") model = AutoModelForSpeechSeq2Seq.from_pretrained("Makkoen/whisper-large-v3-cit-do01-wd0-lr3e-06-FULL4c", device_map="auto") - Notebooks
- Google Colab
- Kaggle
./4585
This model is a fine-tuned version of openai/whisper-large-v3 on the 4585 FULL-2024-09-26 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4883
- Wer Ortho: 27.5525
- Wer: 19.6598
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 |
|---|---|---|---|---|---|
| 0.7769 | 0.7752 | 200 | 0.5669 | 31.2018 | 22.9917 |
| 0.5359 | 1.5504 | 400 | 0.5151 | 29.0481 | 20.9467 |
| 0.4524 | 2.3256 | 600 | 0.4949 | 28.1973 | 20.0166 |
| 0.3889 | 3.1008 | 800 | 0.4895 | 27.6788 | 19.6471 |
| 0.3431 | 3.8760 | 1000 | 0.4841 | 27.4063 | 19.4368 |
| 0.3196 | 4.6512 | 1200 | 0.4883 | 27.5525 | 19.6598 |
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-FULL4c
Base model
openai/whisper-large-v3