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| | license: mit
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| | ---
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| | # Model Card for SW2V-120k
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| | *Reconstruct! Don't Encode: Self-Supervised Representation Reconstruction Loss for High-Intelligibility and Low-Latency Streaming Neural Audio Codec*
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| | 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)
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| | - **GitHub Repository:** [https://github.com/jhcodec843/jhcodec](https://github.com/jhcodec843/jhcodec)
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| | - **Demo:** [https://jhcodec843.github.io/jhcodec/](https://jhcodec843.github.io/jhcodec/)
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| | - **License:** MIT
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| |
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| | ## Model Details
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| | ### Model Description
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| | To enhance noise robustness for future applications, we incorporated noise augmentation during SW2V training.
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| | To ensure the performance Flash-Attention is required.
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| | ## Uses
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| | 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.
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| | ### Intended Use
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| | - Real-time low-latency audio codecs for speech-to-speech models
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| | - Research into neural codecs and generative modeling
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| | - Preprocessing for downstream speech and audio ML models
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| | ### Out-of-Scope Use
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| | - Any malicious, deceptive, or privacy-violating applications
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| | ## How to Get Started with JHCodec
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| | For programmatic usage, please refer to the [GitHub repository](https://github.com/jhcodec843/jhcodec) for installation, API documentation, and practical examples.
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| | ## Training Details
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| | Please refer to the GitHub repository README.
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| | ## Authors
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| | Anonymous, Submitted to Interspeech2026
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