Instructions to use readerbench/whisper-ro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use readerbench/whisper-ro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="readerbench/whisper-ro")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("readerbench/whisper-ro") model = AutoModelForSpeechSeq2Seq.from_pretrained("readerbench/whisper-ro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - automatic-speech-recognition | |
| - whisper | |
| - romanian | |
| datasets: | |
| - readerbench/echo | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: whisper-ro | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: Echo | |
| type: readerbench/echo | |
| config: ro | |
| metrics: | |
| - name: WER | |
| type: wer | |
| value: 0.08668345828147764 | |
| # whisper-ro | |
| This model is a fine-tuned version of | |
| [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [Echo | |
| dataset](https://huggingface.co/datasets/readerbench/echo), a large open-source | |
| Romanian dataset. | |
| | Name | Small | Large-v2 | Fine-tuned small <br/><small>(this model)</small> | | |
| |:------------:|:-----:|:--------:|:-------------------------------------------------:| | |
| | Common Voice | 33.2 | 15.8 | 12.2 | | |
| | FLEURS | 29.8 | 14.4 | 10.9 | | |
| | VoxPopuli | 28.6 | 14.4 | 9.4 | | |
| | Echo | >100 | >100 | 8.6 | | |
| | RSC | 38.6 | 28.5 | 5.4 | | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - `learning_rate`: 1e-05 | |
| - `train_batch_size`: 128 | |
| - `eval_batch_size`: 128 | |
| - `seed`: 42 | |
| - `distributed_type`: multi-GPU | |
| - `num_devices`: 2 | |
| - `total_train_batch_size`: 256 | |
| - `total_eval_batch_size`: 256 | |
| - `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - `lr_scheduler_type`: linear | |
| - `lr_scheduler_warmup_steps`: 500 | |
| - `num_epochs`: 20.0 | |
| - `mixed_precision_training`: Native AMP | |