Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use chattoe/whisper-tiny_to_portuguese_accent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chattoe/whisper-tiny_to_portuguese_accent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="chattoe/whisper-tiny_to_portuguese_accent")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("chattoe/whisper-tiny_to_portuguese_accent") model = AutoModelForSpeechSeq2Seq.from_pretrained("chattoe/whisper-tiny_to_portuguese_accent") - Notebooks
- Google Colab
- Kaggle
Whisper tiny Portuguese
This model is a fine-tuned version of openai/whisper-tiny on the Portuguese English dataset. It achieves the following results on the evaluation set:
- Loss: 0.2541
- Wer: 12.7120
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: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 2000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2351 | 0.9217 | 1000 | 0.2626 | 14.7996 |
| 0.1044 | 1.8433 | 2000 | 0.2541 | 12.7120 |
Framework versions
- Transformers 5.3.0.dev0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
- Downloads last month
- 2
Model tree for chattoe/whisper-tiny_to_portuguese_accent
Base model
openai/whisper-tinySpace using chattoe/whisper-tiny_to_portuguese_accent 1
Evaluation results
- Wer on Portuguese Englishself-reported12.712