Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Russian
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("internalhell/whisper_small_ru_model_trainer_2")
model = AutoModelForSpeechSeq2Seq.from_pretrained("internalhell/whisper_small_ru_model_trainer_2")Quick Links
Whisper Small ru - slowlydoor
This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1989
- Wer: 16.6769
- Cer: 4.3640
- Ser: 59.1591
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: 4
- seed: 42
- 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
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Ser |
|---|---|---|---|---|---|---|
| 0.2176 | 0.1516 | 500 | 0.2575 | 21.0009 | 5.4512 | 69.0581 |
| 0.2146 | 0.3032 | 1000 | 0.2395 | 19.7826 | 5.2221 | 66.5785 |
| 0.1817 | 0.4548 | 1500 | 0.2264 | 18.5724 | 4.7800 | 64.4320 |
| 0.1862 | 0.6064 | 2000 | 0.2140 | 18.2088 | 4.7904 | 62.3542 |
| 0.1618 | 0.7580 | 2500 | 0.2049 | 17.0765 | 4.3953 | 60.4234 |
| 0.1597 | 0.9096 | 3000 | 0.1989 | 16.6769 | 4.3640 | 59.1591 |
Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
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Model tree for internalhell/whisper_small_ru_model_trainer_2
Base model
openai/whisper-smallDataset used to train internalhell/whisper_small_ru_model_trainer_2
Evaluation results
- Wer on Common Voice 17.0self-reported16.677
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="internalhell/whisper_small_ru_model_trainer_2")