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license: mit |
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datasets: |
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- mozilla-foundation/common_voice_13_0 |
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Sure, here's a more detailed and visually appealing model card for your `whisper-small-dv` model: |
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# Whisper Small DV Model |
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## Model Description |
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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. |
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## Training |
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The model was trained using the PyTorch framework and the Transformers library. Training metrics and visualizations can be viewed on TensorBoard. |
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## Performance |
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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. |
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## Usage |
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The model can be used for any ASR task. To use the model, you can load it using the Transformers library: |
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```python |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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# Load the model |
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model = Wav2Vec2ForCTC.from_pretrained("Ryukijano/whisper-small-dv") |
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processor = Wav2Vec2Processor.from_pretrained("Ryukijano/whisper-small-dv") |
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# Use the model for ASR |
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inputs = processor("path_to_audio_file", return_tensors="pt", padding=True) |
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logits = model(inputs.input_values).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.decode(predicted_ids[0]) |
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``` |
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## License |
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This model is released under the MIT license. |
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