Learned pooling of speech KV caches from an intermediate LLM layer compresses speech to text-level length while matching or exceeding the uncompressed baseline on ASR and entity recognition, with 1.49–2× decoding speedup.
Librispeech: an asr corpus based on public domain audio books
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PoDAR disentangles audio signal power from semantic content in latents using power augmentation and consistency objectives, yielding 2x faster convergence and gains of 0.055 speaker similarity and 0.22 UTMOS when applied to Stable Audio VAE with F5-TTS.
citing papers explorer
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Compress the Cache, Not the Speech Embedding: KV Compression for Efficient Speech LLMs
Learned pooling of speech KV caches from an intermediate LLM layer compresses speech to text-level length while matching or exceeding the uncompressed baseline on ASR and entity recognition, with 1.49–2× decoding speedup.
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PoDAR: Power-Disentangled Audio Representation for Generative Modeling
PoDAR disentangles audio signal power from semantic content in latents using power augmentation and consistency objectives, yielding 2x faster convergence and gains of 0.055 speaker similarity and 0.22 UTMOS when applied to Stable Audio VAE with F5-TTS.