Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Portuguese
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use jlondonobo/whisper-large-v2-pt-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jlondonobo/whisper-large-v2-pt-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jlondonobo/whisper-large-v2-pt-v3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("jlondonobo/whisper-large-v2-pt-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("jlondonobo/whisper-large-v2-pt-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Whisper Large Portuguese
This model is a fine-tuned version of openai/whisper-large-v2 on the mozilla-foundation/common_voice_11_0 pt dataset. It achieves the following results on the evaluation set:
- Loss: 0.1503
- Wer: 4.8385
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-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- training_steps: 1500
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1526 | 0.33 | 500 | 0.1588 | 4.9074 |
| 0.1046 | 1.3 | 1000 | 0.1510 | 4.8806 |
| 0.079 | 2.28 | 1500 | 0.1503 | 4.8385 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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Evaluation results
- Wer on mozilla-foundation/common_voice_11_0 pttest set self-reported4.839