Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Polish
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("ambind/whisper-tiny-pl")
model = AutoModelForSpeechSeq2Seq.from_pretrained("ambind/whisper-tiny-pl")Quick Links
Whisper tiny pl
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.7001
- Wer: 45.1027
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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 2
- training_steps: 2020
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0369 | 1.2878 | 2000 | 0.7001 | 45.1027 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
- Downloads last month
- 2
Model tree for ambind/whisper-tiny-pl
Base model
openai/whisper-tinyEvaluation results
- Wer on Common Voice 11.0self-reported45.103
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ambind/whisper-tiny-pl")