Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Romanian
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use iulik-pisik/horoscop_vreme_tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iulik-pisik/horoscop_vreme_tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="iulik-pisik/horoscop_vreme_tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("iulik-pisik/horoscop_vreme_tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("iulik-pisik/horoscop_vreme_tiny") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - ro | |
| license: apache-2.0 | |
| base_model: iulik-pisik/horoscope_model_tiny | |
| tags: | |
| - hf-asr-leaderboard | |
| - generated_from_trainer | |
| datasets: | |
| - iulik-pisik/audio_vreme | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Horoscope Model Tiny - finetuned on weather | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: Vreme ProTV | |
| type: iulik-pisik/audio_vreme | |
| config: default | |
| split: test | |
| args: 'config: ro, split: test' | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 13.995067817509248 | |
| <!-- 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. --> | |
| # Horoscope Model Tiny - finetuned on weather | |
| This model is a fine-tuned version of [iulik-pisik/horoscope_model_tiny](https://huggingface.co/iulik-pisik/horoscope_model_tiny) on the Vreme ProTV dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2868 | |
| - Wer: 13.9951 | |
| ## Model description | |
| 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - training_steps: 4000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:| | |
| | 0.0443 | 6.02 | 1000 | 0.1949 | 15.5569 | | |
| | 0.0023 | 12.05 | 2000 | 0.2601 | 14.1389 | | |
| | 0.0008 | 18.07 | 3000 | 0.2798 | 14.0362 | | |
| | 0.0006 | 24.1 | 4000 | 0.2868 | 13.9951 | | |
| ### Framework versions | |
| - Transformers 4.39.2 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 | |