# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("cportoca/Quechua_Project_Whisper")
model = AutoModelForSpeechSeq2Seq.from_pretrained("cportoca/Quechua_Project_Whisper")Quick Links
Quechua_Project_Whisper
This model is a fine-tuned version of openai/whisper-tiny on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.0580
- Wer: 1442.0775
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: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 5.2233 | 0.3388 | 1000 | 5.3550 | 1952.6512 |
| 4.3345 | 0.6775 | 2000 | 4.7009 | 1775.3798 |
| 3.849 | 1.0163 | 3000 | 4.2552 | 1539.1008 |
| 3.2258 | 1.3550 | 4000 | 4.0580 | 1442.0775 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for cportoca/Quechua_Project_Whisper
Base model
openai/whisper-tiny
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cportoca/Quechua_Project_Whisper")