Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Turkish
whisper
hf-asr-leaderboard
Generated from Trainer
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("Mehtap/whisper-base")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Mehtap/whisper-base")Quick Links
Base Turkish Whisper (BTW)
This model is a fine-tuned version of openai/whisper-base on the Ermetal Meetings dataset. It achieves the following results on the evaluation set:
- Loss: 0.0009
- Wer: 0.0
- Cer: 0.0
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 1.8786 | 6.63 | 100 | 1.3510 | 0.7866 | 0.6649 |
| 0.4559 | 13.32 | 200 | 0.3395 | 0.3590 | 0.2157 |
| 0.0793 | 19.95 | 300 | 0.0564 | 0.0996 | 0.0531 |
| 0.0137 | 26.63 | 400 | 0.0120 | 0.0017 | 0.0017 |
| 0.0042 | 33.32 | 500 | 0.0032 | 0.0 | 0.0 |
| 0.0021 | 39.95 | 600 | 0.0018 | 0.0 | 0.0 |
| 0.0014 | 46.63 | 700 | 0.0013 | 0.0 | 0.0 |
| 0.0012 | 53.32 | 800 | 0.0011 | 0.0 | 0.0 |
| 0.001 | 59.95 | 900 | 0.0010 | 0.0 | 0.0 |
| 0.001 | 66.63 | 1000 | 0.0009 | 0.0 | 0.0 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.9.1+cu111
- Datasets 2.7.1
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Mehtap/whisper-base")