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README.md

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+---
+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