textless-lib: a Library for Textless Spoken Language Processing (2202.07359v1)

Abstract: Textless spoken language processing research aims to extend the applicability of standard NLP toolset onto spoken language and languages with few or no textual resources. In this paper, we introduce textless-lib, a PyTorch-based library aimed to facilitate research in this research area. We describe the building blocks that the library provides and demonstrate its usability by discuss three different use-case examples: (i) speaker probing, (ii) speech resynthesis and compression, and (iii) speech continuation. We believe that textless-lib substantially simplifies research the textless setting and will be handful not only for speech researchers but also for the NLP community at large. The code, documentation, and pre-trained models are available at https://github.com/facebookresearch/textlesslib/ .

• The paper introduces a novel library that models spoken language from raw audio without relying on textual representations.
• It details a comprehensive pipeline including speech-to-unit, unit-to-speech, and unit-to-unit modules using techniques like HuBERT and Tacotron2.
• The framework supports advanced research in low-resource languages, offering practical examples in speaker probing, speech resynthesis, and speech continuation.

Textless Spoken Language Processing: An Overview of textless-lib

The paper "textless-lib: a Library for Textless Spoken Language Processing" introduces a novel PyTorch-based library designed to facilitate research in spoken language processing without text. This area of research focuses on leveraging self-supervised learning methods to model languages directly from raw audio, bypassing the need for text-based resources such as lexicons or transcriptions. The primary motivation is to address challenges faced by languages with limited textual resources and to capture the rich prosodic and paralinguistic features often lost in text representation.

Key Components of textless-lib

The development of textless-lib is grounded on the integration of advancements in speech encoding, LLMing, and speech synthesis. The library provides researchers with the tools to model spoken language by converting audio to discrete units, modeling these units, and ultimately converting them back to audio.

1. Speech to Units (S2U): The library includes modules for encoding speech into discrete representations, referred to as "pseudo-text". This involves using models like HuBERT and CPC for self-supervised dense representation, followed by quantization using pre-trained k-means models.
2. Units to Speech (U2S): To synthesize speech from unit sequences, the library offers Tacotron2 for generating mel-spectrograms and WaveGlow for time-domain reconstruction. This setup provides an efficient pipeline for translating "pseudo-text" back into human-understandable speech.
3. Unit to Units (U2U): The library also accommodates the modeling of unit sequences through traditional NLP architectures, enabling tasks like speech continuation and emotion conversion.

Functionality and Usability

The textless-lib library is user-friendly, providing pre-trained models for dense representations, pitch extraction, quantizers, and vocoders. It aims to democratize access to speech processing research by offering simple APIs and well-structured examples. These examples illustrate core functionalities, such as speaker probing, speech resynthesis, and speech continuation.

Speaker Probing

The library includes a probing example that assesses whether different representation models encase speaker-specific information. The results reveal that continuous representations like HuBERT and CPC facilitate speaker identification, whereas quantization introduces speaker variance reduction, a desirable trait in certain applications.

Speech Resynthesis

Through its resynthesis capabilities, the library demonstrates potential in extreme speech compression scenarios. The discreteness of the units allows for effective compression without significant loss of information, as evidenced by strong Word Error Rate (WER) results relative to data bitrate.

Speech Continuation

Textless-lib replicates the Generative Spoken LLMing (GSLM) pipeline to perform speech continuation tasks. The ability to generate coherent continuations from a given speech prompt showcases the underlying LLM's strength.

Implications and Future Directions

The introduction of textless-lib marks a significant step toward expanding the reach of NLP toolsets into spoken language processing for low-resource languages. By providing a simplified framework, this library can attract broader participation from the NLP community, fostering new research avenues in areas such as code-switching, emotion detection, and audio-based human-computer interaction.

Future enhancements to the library may involve incorporating state-of-the-art models for speech encoding and synthesis, optimizing performance, and expanding the library's capabilities to support additional textless processing tasks like translation. Moreover, the ability to train and fine-tune components within textless-lib could further empower researchers to customize and adapt the library to specific linguistic contexts.

In conclusion, textless-lib offers a robust foundation for advancing textless spoken language processing, bridging the gap for numerous languages in accessing digital transformation through modern AI technologies.