Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Uzbek
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use mrmuminov/whisper-tiny-uz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrmuminov/whisper-tiny-uz with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mrmuminov/whisper-tiny-uz")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mrmuminov/whisper-tiny-uz") model = AutoModelForSpeechSeq2Seq.from_pretrained("mrmuminov/whisper-tiny-uz", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| language: | |
| - uz | |
| license: apache-2.0 | |
| base_model: openai/whisper-tiny | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - mozilla-foundation/common_voice_16_1 | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Whisper Tiny UZ - Bahriddin Muminov | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: Common Voice 16.1 | |
| type: mozilla-foundation/common_voice_16_1 | |
| config: uz | |
| split: test | |
| args: 'config: uz, split: test' | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 50.19916836282356 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # Whisper Tiny UZ - Bahriddin Muminov | |
| This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 16.1 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5943 | |
| - Wer: 50.1992 | |
| ## 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: 16 | |
| - eval_batch_size: 8 | |
| - 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 | |
| - lr_scheduler_warmup_steps: 1000 | |
| - training_steps: 10000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:------:|:-----:|:---------------:|:-------:| | |
| | 0.6528 | 0.0352 | 2000 | 0.8591 | 62.8798 | | |
| | 0.5201 | 0.0704 | 4000 | 0.7052 | 56.4951 | | |
| | 0.4258 | 0.1056 | 6000 | 0.6407 | 53.7817 | | |
| | 0.4136 | 0.1408 | 8000 | 0.6057 | 50.5588 | | |
| | 0.4164 | 0.1760 | 10000 | 0.5943 | 50.1992 | | |
| ### Framework versions | |
| - Transformers 4.49.0 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.4.1 | |
| - Tokenizers 0.21.1 | |