Audio Tokenization: Methods and Trade-offs

• Audio tokenization is the process of converting continuous audio signals into discrete tokens that capture both semantic and acoustic features.
• It employs techniques such as k-means clustering for semantic tokens and residual vector quantization for acoustic tokens to balance reconstruction fidelity with structural integrity.
• This approach enables transformer-based language models to perform tasks like speech synthesis, captioning, and multimodal integration effectively.

Audio tokenization refers to the process of mapping continuous audio signals into sequences of discrete tokens suitable for downstream modeling with language-model architectures. This approach underpins a growing body of work aiming to bridge the methodological gap between conventional audio processing and the token-centric paradigm prevalent in natural language and multimodal LLMs. Audio tokens enable the application of autoregressive sequence models, facilitate multi-modal integration, and support efficient compression and generation across diverse audio domains such as speech, music, and general sounds.

1. Discrete Audio Tokenization: Principles and Process

Audio tokenization transforms a raw audio sample $x \in \mathbb{R}^T$ into a sequence of discrete units via a pre-trained, frozen tokenizer. There are two principal token categories:

• Semantic Tokens: Derived by quantizing activations (typically via k-means clustering) from an intermediate layer of a self-supervised masked LLM (e.g., w2v-BERT). These tokens capture high-level, long-range syntactic or semantic structure (e.g., phonetic content, linguistic meaning) but are not typically invertible to high-fidelity audio waveforms.
• Acoustic Tokens: Produced by neural audio codecs (e.g., SoundStream) using residual vector quantization (RVQ) on encoder outputs. These tokens excel at preserving fine acoustic details for high-quality synthesis but may not efficiently represent long-term structure.

The trade-off between these representations is foundational: codebooks trained for reconstruction quality (acoustic tokenizers) yield high-fidelity outputs (ViSQOL, PESQ metrics) but lack strong phonetic discriminability, while semantic tokens provide a compressed, structure-rich representation at the expense of reconstructibility (Borsos et al., 2022).

To reconcile these attributes, hybrid frameworks (such as AudioLM) employ a hierarchical strategy: semantic tokens scaffold long-term structure, and acoustic tokens "flesh out" details for reconstruction. This duality underlies most modern audio tokenization pipelines.

2. Tokenization Architectures and Quantization Methodologies

Tokenization Architectures

A general pipeline consists of:

1. Encoder $f_e(x)$: Maps the input waveform to latent features.
2. Quantizer $Q(z)$: Converts latents to discrete tokens, $q$.
3. Decoder $f_d(q)$: Optionally reconstructs the original signal from tokens.

Quantization Techniques

• K-means Clustering (for semantic tokens): $q_t = \arg\min_k \|z_t - c_k\|^2$
• Residual Vector Quantization (RVQ):
  • For $M$ quantizers, sequentially quantize current residual: $q_t^{(m)} = \arg\min_k \|r_t^{(m)} - c_k^{(m)}\|^2$ with $r_t^{(m+1)} = r_t^{(m)} - c_{q_t^{(m)}}^{(m)}$
  • Final token sequence concatenates the indices from each RVQ stage.
• Masking and BPE: Hierarchical tokenization via Byte Pair Encoding reduces sequence length and exposure bias, balancing per-token error and sequence compression (Dekel et al., 8 Jun 2024).

Recent innovations incorporate learnable query tokens (ALMTokenizer (Yang et al., 14 Apr 2025)), masked autoencoder objectives for semantic enhancement, and autoregressive losses to model context across segments, striking new balances between bitrate and semantic fidelity.

3. Tokenized Audio Modeling and LLM Integration

Audio tokenization enables the recasting of audio generation as next-token prediction—a formulation directly compatible with transformer-based LLMs. Specifically, tokenized audio is modeled as:

$$P(h_1, h_2, \ldots, h_{T'}) = \prod_{t=1}^{T'} P(h_t | h_{<t})$$

where $h$ denotes either semantic or acoustic tokens. Hierarchical models, such as AudioLM, sequence modeling first over semantic tokens and then condition acoustic token generation on their outputs (Borsos et al., 2022). Approaches such as UniAudio generalize this further, performing foundation model pre-training across $165\mathrm{K}$ hours of audio with large-scale multi-scale transformers that manage the increased sequence length induced by high-rate token streams (Yang et al., 2023).

Audio tokens allow for multimodal integration. For example, AudioToken adapts text-conditioned diffusion models for audio-to-image generation by projecting audio embeddings into the text latent space, leveraging attention mechanisms and small trainable projection networks (Yariv et al., 2023).

4. Benchmarks, Trade-offs, and Task Performance

Tokenization strategies introduce multiple trade-offs evident in reconstruction and downstream task performance:

• Reconstruction vs. Structure: Acoustic tokens (SoundStream, DAC, EnCodec) offer high-fidelity synthesis (STFT loss, PESQ, STOI) but poorer results in ASR or structured tasks due to weak long-range dependencies (Puvvada et al., 2023, Shechtman et al., 10 Oct 2024).
• Semantic Tokens for Discriminative Tasks: On discriminative tasks such as ASR, emotion recognition, and intent classification, semantic tokens (e.g., discrete HuBERT, WavLM) outperform codec tokens; they consistently achieve lower WER and dWER even at low bitrates (Mousavi et al., 20 Jun 2024, Mousavi et al., 15 Jun 2024).
• Speaker and Paralinguistic Details: Compression-based acoustic tokens are superior for tasks requiring fine acoustic detail (speaker verification, speech enhancement), as confirmed by higher cosine similarity preservation and subjective quality (Libera et al., 17 Jul 2025, Mousavi et al., 20 Jun 2024).
• Compression Efficiency: RVQ-based codecs achieve up to 20× data reduction compared to mel-spectrograms, with minimal impact on ASR and speaker recognition accuracy (Puvvada et al., 2023, Shechtman et al., 10 Oct 2024).

The DASB and AudioCodecBench frameworks systematically compare tokenizers along axes including reconstruction quality, index (ID) stability, transformer perplexity, and downstream probe task scores, providing a multidimensional understanding of strengths and limitations (Mousavi et al., 20 Jun 2024, Wang et al., 2 Sep 2025).

Tokenizer Type	Strengths	Weaknesses
Semantic (SSL+K-means)	Structure, linguistic tasks	Limited reconstructibility
Acoustic (Codec, RVQ)	Fidelity, paralinguistics	Weak structure, longer tokens
Hybrid/Query-based	Balanced, adaptive bitrate	Complexity, retrain overhead

For highly compressed regimes (<1 kbps), advanced tokenizers (LongCat-Audio-Codec, RVQGAN) achieve low-latency, streaming, and intelligibility suitable for conversational LLM systems (Shechtman et al., 10 Oct 2024, Zhao et al., 17 Oct 2025).

5. Applications and Domains

Audio tokenization extends beyond speech to encompass music, sound events, and cross-modal generation:

• Speech and Textless Synthesis: Enables prompt-based speech continuation without transcripts, textless TTS, and speaker-aware prosodic synthesis (Borsos et al., 2022, Yang et al., 2023).
• Instrument and Timbre Modeling: MIDI-to-audio and timbre-interpolated generation via CLAP/TokenSynth demonstrate flexible and high-fidelity music synthesis (Kim et al., 13 Feb 2025).
• Automated Audio Captioning: Semantically rich tokenization (RepCodec, BEATs-RVQ, supervised ARTs) is critical for generating captions describing complex soundscapes, outperforming waveform-oriented tokens (EnCodec) for semantic tasks (Tian et al., 21 May 2025, Takeuchi et al., 1 Jun 2025).
• Separation and Enhancement: Discrete tokens support multi-task learning for separation and refinement (TokenSplit), as well as advancements in AR-based speech enhancement (Erdogan et al., 2023, Libera et al., 17 Jul 2025).
• Multimodal LLMs: Instruction-tuned datasets (Audio-FLAN) facilitate unified LLMs that reason over tokenized audio, supporting cross-domain reasoning and zero-shot transfer (Xue et al., 23 Feb 2025, Mehta et al., 28 Mar 2025).

Tokenization schemes must be tailored to domain needs: while semantic tokens excel in ASR and captioning, acoustic tokens are preferred for enhancement and speaker-sensitive tasks. Hybrid and hierarchical approaches are emerging as a means to achieve broad coverage.

6. Technical Challenges and Future Directions

Several open challenges persist:

• Fidelity vs. Structure Trade-off: Balancing acoustic detail with semantic richness remains non-trivial. The loss of fine-grained information during discretization underlies a persistent performance gap with continuous representations on certain generative tasks (Mousavi et al., 20 Jun 2024, Wang et al., 2 Sep 2025).
• Evaluation Standardization: Recent benchmarks address the prior lack of unified cross-task, cross-domain metrics, but further disentanglement of vocoder quality from LM performance is required (Mousavi et al., 12 Jun 2025).
• Task/Domain Adaptivity: Directions include layer-aware token selection (attention-based selectors), task-specific loss weighting, and joint optimization for both generative fidelity and discriminative utility (Mousavi et al., 15 Jun 2024, Yang et al., 14 Apr 2025).
• Linguistic and Underresourced Settings: For low-resource or diagrammatic languages, linguistically informed (phonemic) tokenization strategies yield substantive improvements in ASR compared to naively orthographic schemes (Daul et al., 7 Oct 2025). A plausible implication is that future tokenization pipelines for documentation of underresourced languages should default to linguistically grounded, phonemic tokens.

Anticipated developments involve scaling training, hybrid and hierarchical tokenization, and broader multimodal integration with language and vision models, supported by open-source codecs and public token databases (Zhao et al., 17 Oct 2025, Mousavi et al., 12 Jun 2025).

7. Summary Table: Audio Token Types and Properties

Type	Source	Optimized For	Typical Tasks	Key Limitation
Semantic Tokens	SSL (e.g., HuBERT, WavLM) + K-means	Structure, semantics	ASR, captioning, KWS	Poor waveform reconstructibility
Acoustic Tokens	Encoder–Decoder Codec (RVQ, DAC, EnCodec)	Synthesis fidelity	TTS, enhancement, speaker ID	Poor semantic/long-term content
Hybrid/Hierarchical	Combined SSL + Codec	Structure + fidelity	Generation, LLM input	Training complexity, recombination
Query/MAE/AR enhanced	Transformers + MAE/AR Loss	Adaptive bitrate, semantics	Foundation models	Extra compute/memory overhead

These frameworks and their technical trade-offs define the current landscape of audio tokenization research, anchoring ongoing work towards seamless and efficient integration of audio modalities into modern LLMs and multimodal AI systems.