Moshi: a speech-text foundation model for real-time dialogue
Abstract
We introduce Moshi, a speech-text foundation model and full-duplex spoken dialogue framework. Current systems for spoken dialogue rely on pipelines of independent components, namely voice activity detection, speech recognition, textual dialogue and text-to-speech. Such frameworks cannot emulate the experience of real conversations. First, their complexity induces a latency of several seconds between interactions. Second, text being the intermediate modality for dialogue, non-linguistic information that modifies meaning -- such as emotion or non-speech sounds -- is lost in the interaction. Finally, they rely on a segmentation into speaker turns, which does not take into account overlapping speech, interruptions and interjections. Moshi solves these independent issues altogether by casting spoken dialogue as speech-to-speech generation. Starting from a text language model backbone, Moshi generates speech as tokens from the residual quantizer of a neural audio codec, while modeling separately its own speech and that of the user into parallel streams. This allows for the removal of explicit speaker turns, and the modeling of arbitrary conversational dynamics. We moreover extend the hierarchical semantic-to-acoustic token generation of previous work to first predict time-aligned text tokens as a prefix to audio tokens. Not only this "Inner Monologue" method significantly improves the linguistic quality of generated speech, but we also illustrate how it can provide streaming speech recognition and text-to-speech. Our resulting model is the first real-time full-duplex spoken large language model, with a theoretical latency of 160ms, 200ms in practice, and is available at https://github.com/kyutai-labs/moshi.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents Moshi, a novel speech-text foundation model that enables real-time, full-duplex spoken dialogue, addressing critical limitations in existing systems. The technical contributions, including a multi-stream architecture and innovative token generation methods, mark a significant advancement in the field of conversational AI, with the potential to enhance user experience in voice interactions.
Methodology
The methodology presented in this paper is innovative, as it introduces Moshi, a speech-text foundation model that operates in a full-duplex manner, allowing for real-time dialogue without the traditional latency associated with multi-component systems. The authors effectively address key limitations of existing spoken dialogue systems by leveraging a multi-stream architecture that integrates both semantic and acoustic token generation. The "Inner Monologue" method, which predicts time-aligned text tokens before audio tokens, is a significant advancement that enhances the linguistic quality of generated speech. The hierarchical modeling approach and the use of a neural audio codec for generating speech tokens are well-justified and contribute to the overall robustness of the model.
Results & evaluation
The experimental evaluation is thorough, with the authors reporting results across multiple axes, including text understanding, speech intelligibility, audio quality, and spoken question answering. The use of extensive datasets, including 7 million hours of audio and high-quality text data, strengthens the validity of the results. The paper also includes ablation studies that demonstrate the effectiveness of various components of the model, which adds credibility to the findings. However, the lack of comparative benchmarks against other state-of-the-art systems in real-time dialogue could have further solidified the claims of superiority.
Reproducibility
The paper provides a detailed description of the architecture, training datasets, and experimental setup, which aids in reproducibility. The inclusion of a GitHub repository with the model's implementation is a positive aspect, allowing other researchers to access and potentially replicate the work. However, the paper could benefit from more explicit instructions on the setup and execution of experiments, as well as the specific configurations used during training.
Limitations
One limitation of the study is the reliance on a single actor's voice for the TTS system, which may limit the diversity of generated speech. Additionally, while the model shows promise in real-time dialogue, the evaluation primarily focuses on English, which raises questions about its generalizability to other languages and dialects. The model's performance in noisy environments or with overlapping speech from multiple speakers also warrants further investigation.
Broader impact
The development of Moshi has significant implications for the field of conversational AI, particularly in enhancing the naturalness and fluidity of human-computer interactions. By addressing key challenges in spoken dialogue systems, such as latency and the loss of non-linguistic information, this work could pave the way for more intuitive and effective voice interfaces in various applications, from virtual assistants to customer service bots. The potential for real-time, full-duplex communication opens new avenues for user engagement and interaction design. The paper presents Moshi, a novel speech-text foundation model that enables real-time, full-duplex spoken dialogue, addressing critical limitations in existing systems. The technical contributions, including a multi-stream architecture and innovative token generation methods, mark a significant advancement in the field of conversational AI, with the potential to enhance user experience in voice interactions.
Curator note
Défossez et al., Kyutai; first real-time full-duplex speech LLM; simultaneous listening and speaking
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