Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("GCYY/whisper-tiny-asr")
model = AutoModelForSpeechSeq2Seq.from_pretrained("GCYY/whisper-tiny-asr")Quick Links
whisper-tiny-asr
This model is a fine-tuned version of openai/whisper-tiny on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6674
- Wer Ortho: 0.3590
- Wer: 0.3377
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: 64
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 1.3109 | 14.29 | 50 | 0.6984 | 0.4084 | 0.3684 |
| 0.0461 | 28.57 | 100 | 0.6674 | 0.3590 | 0.3377 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for GCYY/whisper-tiny-asr
Base model
openai/whisper-tinyDataset used to train GCYY/whisper-tiny-asr
Evaluation results
- Wer on PolyAI/minds14self-reported0.338
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="GCYY/whisper-tiny-asr")