Audio-to-Audio
PyTorch
audio
speech
speech-language-models
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@@ -24,7 +24,7 @@ The distilled model makes the following adjustments to the model:
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  * Swap the notoriuously slow [BigCodec](https://arxiv.org/abs/2409.05377) acoustic encoder for the [SQCodec](https://arxiv.org/abs/2504.04949) acoustic encoder (70m → 36m)
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  * Swap the [w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) semantic encoder for [DistilHuBERT](https://huggingface.co/ntu-spml/distilhubert) (600m → 21m)
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- Our work largely based on extending the work of [X-Codec2.0](https://huggingface.co/HKUSTAudio/xcodec2) and [SQCodec](https://arxiv.org/abs/2504.04949).
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  - **Developed by:** Neuphonic
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  - **Model type:** Neural Audio Codec
 
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  * Swap the notoriuously slow [BigCodec](https://arxiv.org/abs/2409.05377) acoustic encoder for the [SQCodec](https://arxiv.org/abs/2504.04949) acoustic encoder (70m → 36m)
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  * Swap the [w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) semantic encoder for [DistilHuBERT](https://huggingface.co/ntu-spml/distilhubert) (600m → 21m)
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+ Our work is largely based on extending the work of [X-Codec2.0](https://huggingface.co/HKUSTAudio/xcodec2) and [SQCodec](https://arxiv.org/abs/2504.04949).
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  - **Developed by:** Neuphonic
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  - **Model type:** Neural Audio Codec