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Neural Audio Codecs: How to Get Audio into LLMs
Integrating audio into Large Language Models (LLMs) has been significantly advanced through the development of neural audio codecs. These codecs convert audio signals into discrete tokens, enabling LLMs to process and generate audio data effectively. Recent research has focused on enhancing the efficiency, quality, and versatility of these codecs.
Key Approaches and Technologies
SemantiCodec: Ultra-Low Bitrate Semantic Compression
SemantiCodec compresses diverse audio types—including speech, general sounds, and music—into fewer than a hundred tokens per second without compromising quality. It employs a dual-encoder architecture:
- Semantic encoder: Uses a self-supervised Audio Masked Autoencoder (AudioMAE)
- Acoustic encoder: Captures additional audio details
- Causal convolutional codec: Encodes audio into a hierarchy of coarse-to-fine tokens using residual vector quantization
- Transformer language model: Predicts fine tokens from coarse ones, allowing transmission of fewer codes
- FSQ encodes inherent redundancy
- Maintains performance despite bit-level perturbations
- Superior robustness compared to traditional Residual Vector Quantization (RVQ) codecs under similar conditions
- Operates at low frame rate of 21.5 frames per second
- Bitrate of 1.89 kbps
- Accelerates inference times by approximately threefold
- Maintains high audio quality and intelligibility
- LLM-Codec: Translates audio into textual space by representing audio tokens with words or sub-words from the LLM vocabulary
- Modality reduction: Reduces modality heterogeneity, allowing LLMs to process audio as a "foreign language"
- Task versatility: Supports speech emotion classification, text-to-speech generation, and other audio tasks
- Audio Encoding: Raw audio signals are processed through neural codec encoders
- Tokenization: Continuous audio features are converted into discrete tokens
- Quantization: Vector quantization techniques compress the representation
- Integration: Audio tokens are combined with text tokens in the LLM input sequence
- Processing: LLMs learn to understand and generate audio patterns alongside text
- Efficiency: Ultra-low bitrates enable practical integration with LLMs
- Quality: Maintains high audio quality despite compression
- Versatility: Supports multiple audio types (speech, music, environmental sounds)
- Robustness: Resilient to transmission errors and noisy conditions
- Scalability: Compatible with existing LLM architectures and training methods
- Even lower bitrates while maintaining quality
- Better cross-modal understanding between audio and text
- Improved few-shot learning capabilities
- Enhanced robustness for real-world applications
- Integration with multimodal LLMs for comprehensive audio-text understanding
This design supports ultra-low bit rates between 0.31 kbps and 1.40 kbps, facilitating efficient audio processing within LLMs.
LMCodec: Causal Transformer-Based Codec
LMCodec is a causal neural speech codec that delivers high-quality audio at very low bitrates. Its architecture features:
Subjective tests indicate that LMCodec's quality is comparable to reference codecs operating at higher bitrates.
NeuCodec: Finite Scalar Quantization
NeuCodec introduces a Finite Scalar Quantization (FSQ)-based neural audio codec that emphasizes robustness in transmission through noisy channels. Key features:
Low Frame-rate Speech Codec (LFSC)
LFSC is specifically designed to enhance the efficiency of LLM-based text-to-speech models:
UniAudio 1.5: LLM-Driven Audio Codec
UniAudio 1.5 presents a cross-modal in-context learning approach that enables frozen LLMs to perform various audio tasks in a few-shot manner without parameter updates. It introduces:
Technical Implementation Process
The typical workflow for getting audio into LLMs involves:
Advantages of Neural Audio Codecs
Future Directions
The field continues to evolve with research focusing on:
These advancements underscore the critical role of neural audio codecs in bridging audio data with LLMs, facilitating efficient and high-quality audio processing and generation across various applications.
The prompt for this was: Neural audio codecs: how to get audio into LLMs
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