---
license: mit
datasets:
- mozilla-foundation/common_voice_13_0
---
Sure, here's a more detailed and visually appealing model card for your `whisper-small-dv` model:

---

# Whisper Small DV Model

![Model Banner](https://uploads-ssl.webflow.com/614c82ed388d53640613982e/63eb5ebedd3a9a738e22a03f_open%20ai%20whisper.jpg)

## Model Description

The `whisper-small-dv` model is an advanced Automatic Speech Recognition (ASR) model, trained on the extensive [Mozilla Common Voice 13.0](https://commonvoice.mozilla.org/en/datasets) dataset. This model is capable of transcribing spoken language into written text with high accuracy, making it a valuable tool for a wide range of applications, from transcription services to voice assistants.

## Training

The model was trained using the PyTorch framework and the Transformers library. Training metrics and visualizations can be viewed on TensorBoard.

## Performance

The model's performance was evaluated on a held-out test set. The evaluation metrics and results can be found in the "Eval Results" section.

## Usage

The model can be used for any ASR task. To use the model, you can load it using the Transformers library:

```python
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

# Load the model
model = Wav2Vec2ForCTC.from_pretrained("Ryukijano/whisper-small-dv")
processor = Wav2Vec2Processor.from_pretrained("Ryukijano/whisper-small-dv")

# Use the model for ASR
inputs = processor("path_to_audio_file", return_tensors="pt", padding=True)
logits = model(inputs.input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.decode(predicted_ids[0])
```

## License

This model is released under the MIT license.

---

P