Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Pashto
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("afaaaak/whisper-base-ps")
model = AutoModelForSpeechSeq2Seq.from_pretrained("afaaaak/whisper-base-ps")Quick Links
Whisper Base Ps - ZFA
This model is a fine-tuned version of openai/whisper-base on the Common Voice 20.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6856
- Wer: 54.0707
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: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.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: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2602 | 5.9199 | 1000 | 0.6856 | 54.0707 |
Framework versions
- Transformers 4.53.2
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.2
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Model tree for afaaaak/whisper-base-ps
Base model
openai/whisper-baseDataset used to train afaaaak/whisper-base-ps
Evaluation results
- Wer on Common Voice 20.0self-reported54.071
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="afaaaak/whisper-base-ps")