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README.md

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 license: mit
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+# Model Card for SW2V
+
+*Reconstruct! Don't Encode: Self-Supervised Representation Reconstruction Loss for High-Intelligibility and Low-Latency Streaming Neural Audio Codec*
+
+SW2V is a pure Transformer decoder based speech representation model. This model is trained via distillation of [W2V-Bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0)
+
+- **GitHub Repository:** [https://github.com/jhcodec843/jhcodec](https://github.com/jhcodec843/jhcodec)
+- **Demo:** [https://jhcodec843.github.io/jhcodec/](https://jhcodec843.github.io/jhcodec/)
+- **License:** MIT
+
+## Model Details
+
+### Model Description
+
+This is corresponding to the paper's SW2V model (60k).
+To ensure the performance Flash-Attention is required.
+
+## Uses
+
+JHCodec can be used for research and practical applications that require lossy audio compression. It is particularly well-suited for streaming speech, compressing large audio datasets, and serving as a neural front-end for speech recognition or synthesis pipelines.
+
+### Intended Use
+
+- Real-time low-latency audio codecs for speech-to-speech models
+- Research into neural codecs and generative modeling
+- Preprocessing for downstream speech and audio ML models
+
+### Out-of-Scope Use
+
+- Any malicious, deceptive, or privacy-violating applications
+
+## How to Get Started with JHCodec
+
+For programmatic usage, please refer to the [GitHub repository](https://github.com/jhcodec843/jhcodec) for installation, API documentation, and practical examples.
+
+## Training Details
+
+Please refer to the GitHub repository README.
+
+## Authors
+
+Anonymous, Submitted to Interspeech2026
+

sw2v_60000.pt

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