FastSLM: Hierarchical Temporal Abstraction for Efficient Long-Form Speech Adaptation
📰 ArXiv cs.AI
Learn how FastSLM's Hierarchical Temporal Abstraction enables efficient long-form speech adaptation in multimodal large language models
Action Steps
- Apply Hierarchical Temporal Abstraction to compress audio inputs
- Use FastSLM architecture to reduce token explosion in long-form speech
- Configure HTA to progressively distill non-overlapping information
- Test FastSLM on various speech datasets to evaluate efficiency
- Compare results with other token compression methods to assess performance
Who Needs to Know This
Researchers and engineers working on multimodal large language models can benefit from this approach to improve speech adaptation efficiency
Key Insight
💡 Hierarchical Temporal Abstraction can efficiently compress audio inputs without losing fine-grained acoustic cues
Share This
🗣️ Improve long-form speech adaptation with FastSLM's Hierarchical Temporal Abstraction! 🚀
Key Takeaways
Learn how FastSLM's Hierarchical Temporal Abstraction enables efficient long-form speech adaptation in multimodal large language models
Full Article
Title: FastSLM: Hierarchical Temporal Abstraction for Efficient Long-Form Speech Adaptation
Abstract:
arXiv:2601.06199v3 Announce Type: replace-cross Abstract: Scaling Multimodal Large Language Models (MLLMs) to long-form speech is bottlenecked by the explosive growth of input tokens. Unlike images or videos, audio lacks overlapping information, making extreme 1-token compression highly susceptible to the loss of fine-grained acoustic cues. To overcome this, we propose FastSLM, a token-efficient architecture featuring the Hierarchical Temporal Abstractor (HTA). HTA progressively distills non-ove
Abstract:
arXiv:2601.06199v3 Announce Type: replace-cross Abstract: Scaling Multimodal Large Language Models (MLLMs) to long-form speech is bottlenecked by the explosive growth of input tokens. Unlike images or videos, audio lacks overlapping information, making extreme 1-token compression highly susceptible to the loss of fine-grained acoustic cues. To overcome this, we propose FastSLM, a token-efficient architecture featuring the Hierarchical Temporal Abstractor (HTA). HTA progressively distills non-ove
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