Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Italian
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use matteocirca/whisper-small-it-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matteocirca/whisper-small-it-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="matteocirca/whisper-small-it-2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("matteocirca/whisper-small-it-2") model = AutoModelForSpeechSeq2Seq.from_pretrained("matteocirca/whisper-small-it-2") - Notebooks
- Google Colab
- Kaggle
Whisper Small Italian 2
This model is a fine-tuned version of openai/whisper-small on the type: common_voice_11_0, name: Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2438
- Wer: 56.0495
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0976 | 1.03 | 800 | 0.2438 | 56.0495 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for matteocirca/whisper-small-it-2
Base model
openai/whisper-small