From TOWER to SPIRE: Adding the Speech Modality to a Text-Only LLM

↗ arXiv ↗ Hugging Face

TL;DR

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Key Takeaways

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Why does it matter?

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This paper is important because it provides a recipe to add speech to LLMs, enabling multimodal capabilities, which is a growing field. It offers insights into the trade-offs and best practices for integrating speech w/o compromising existing text capabilities. The reproducible pipeline and open-source models facilitate further research and development in multilingual speech processing and multimodal LLMs.

Visual Insights

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🔼 This figure illustrates the training pipeline for the two main variants of the SPIRE model: SpireBase and SpireFull. SpireBase undergoes continued pre-training (CPT) using a mixture of text data from the TOWER model and English speech data transcribed using HuBERT-based k-means clustering into discrete speech units (DSUs). SpireFull then takes the SpireBase model and further fine-tunes it through instruction tuning (IT) using additional data including task-specific datasets for machine translation (MT), automatic speech recognition (ASR), and speech translation (ST). The figure visually depicts the data flow and processing steps involved in building both models.

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Figure 1: Illustration of the model training method for SpireBase and SpireFull.

🔼 This table details the different prompt formats used during the training process of the SPIRE model. It shows how the input (speech or text) and desired output (transcript or translation) are formatted for different tasks and training stages (Continued Pretraining (CPT) and Instruction Tuning (IT)). The table is crucial for understanding the specifics of the multi-modal training approach and how speech is integrated with the existing text-based training data.

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Table 1: Prompt formats used at different training stages.

In-depth insights

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LLM Speech Boost

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While “LLM Speech Boost” isn’t a direct heading in the paper, the study inherently explores methods to enhance LLMs using speech data. Key areas involve modality integration, specifically adding speech to text-only LLMs. The core idea revolves around leveraging speech discretization and continued pre-training to adapt existing LLMs. A critical aspect is the pursuit of a balance, retaining the original LLM’s textual capabilities while successfully integrating speech processing. The research aims to achieve superior ASR and ST performance without sacrificing MT capabilities. This suggests exploring strategies for efficient transfer learning and adaptation of existing models to incorporate speech data effectively. Ultimately, the goal is to improve the LLM’s performance on speech-related tasks while preserving or even enhancing its existing capabilities.

Discrete Speech

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The concept of discrete speech is crucial in bridging the gap between continuous audio signals and language models. Instead of directly feeding raw audio into models, discrete speech involves converting the audio into a sequence of discrete units. This is often achieved through techniques like vector quantization or clustering of acoustic features. The benefits include reduced computational complexity, easier integration with text-based models, and potentially improved robustness to noise. However, a key challenge is finding the right level of granularity for these discrete units. Too coarse, and information is lost; too fine, and the sequence becomes unwieldy. Recent research explores various methods for learning these discrete units in a self-supervised manner, allowing models to capture the underlying structure of speech without explicit phonetic labels.

CPT & IT Synergy

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Continued Pre-training (CPT) followed by Instruction Tuning (IT) emerges as a powerful paradigm for adapting Large Language Models (LLMs) to new modalities, like speech, while preserving existing capabilities. CPT allows the model to learn fundamental representations of the new modality by treating discrete speech units as an additional language. IT then refines the model’s understanding by exposing it to task-specific instructions, guiding it to perform ASR, ST and translation-related functions. The synergy lies in the staged approach: CPT provides the foundation, and IT hones the model’s skills.

Text Task Kept

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Maintaining performance on text-based tasks is a crucial aspect when augmenting Large Language Models (LLMs) with speech processing capabilities. The central concern is ensuring that the integration of speech data does not degrade the LLM’s original strengths in understanding, generating, and manipulating text. This involves careful design of the training regime, including the selection of speech data, the method of integrating speech features, and the balancing of training objectives. If not done correctly, the adaptation process can lead to catastrophic forgetting or a shift in the model’s capabilities, resulting in diminished performance on its original text-based tasks like translation, summarization, and question answering. It’s important to implement strategies that prioritize textual performance while facilitating the acquisition of new speech-related skills.

Multiling. Path

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Considering a ‘Multiling. Path’ for an LLM entails several crucial steps. It begins with data acquisition, requiring vast amounts of text and speech data in multiple languages, ensuring diversity and balanced representation. Models like mHuBERT are pivotal here. Next, model architecture must accommodate multilingual processing, which is typically achieved through shared embeddings or language-specific adapters. The choice of training strategy is key; options include joint training, transfer learning, and curriculum learning, each impacting performance differently. A critical aspect is evaluation, which necessitates benchmarks that accurately assess cross-lingual understanding and generation, mitigating bias. Finally, it’s necessary to investigate methods of model alignment, to guarantee that the model displays equivalent levels of competence and generates consistent behavior across all supported languages.

More visual insights

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Full paper

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