Moshi: a speech-text foundation model for real-time dialogue

• URL: http://arxiv.org/abs/2410.00037v2
• Date: Wed, 2 Oct 2024 09:11:45 GMT
• Title: Moshi: a speech-text foundation model for real-time dialogue
• Authors: Alexandre Défossez, Laurent Mazaré, Manu Orsini, Amélie Royer, Patrick Pérez, Hervé Jégou, Edouard Grave, Neil Zeghidour,
• Abstract summary: Current systems for spoken dialogue rely on pipelines independent voice activity detection and text-to-speech. We show how Moshi Moshi can provide streaming speech recognition and text-to-speech. Our resulting model is first real-time full spoken large language model modality.
• Score: 78.88479749811376
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Scaling Speech-Text Pre-training with Synthetic Interleaved Data [31.77653849518526]
  Speech language models (SpeechLMs) accept speech input and produce speech output, allowing for more natural human-computer interaction. Traditional approaches for developing SpeechLMs are constrained by the limited availability of unsupervised speech data and parallel speech-text data. We propose a novel approach to scaling speech-text pre-training by leveraging large-scale synthetic interleaved data derived from text corpora.
  arXiv  Detail & Related papers  (2024-11-26T17:19:09Z)
• IntrinsicVoice: Empowering LLMs with Intrinsic Real-time Voice Interaction Abilities [55.11130688075417]
  We introduce IntrinsicVoic,e an LLM designed with intrinsic real-time voice interaction capabilities. Our novelty architecture, GroupFormer, can reduce speech sequences to lengths comparable to text sequences. We construct a multi-turn speech-to-speech dialogue dataset named method-500k which includes nearly 500k turns of speech-to-speech dialogues.
  arXiv  Detail & Related papers  (2024-10-09T05:04:31Z)
• Style-Talker: Finetuning Audio Language Model and Style-Based Text-to-Speech Model for Fast Spoken Dialogue Generation [16.724603503894166]
  Style-Talker is an innovative framework that fine-tunes an audio LLM alongside a style-based TTS model for fast spoken dialog generation. Our experimental results show that Style-Talker significantly outperforms the conventional cascade and speech-to-speech baselines in terms of both dialogue naturalness and coherence.
  arXiv  Detail & Related papers  (2024-08-13T04:35:11Z)
• CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations [97.75037148056367]
  CoVoMix is a novel model for zero-shot, human-like, multi-speaker, multi-round dialogue speech generation. We devise a comprehensive set of metrics for measuring the effectiveness of dialogue modeling and generation.
  arXiv  Detail & Related papers  (2024-04-10T02:32:58Z)
• Towards human-like spoken dialogue generation between AI agents from written dialogue [8.4989907582951]
  This study proposes CHATS - CHatty Agents Text-to-Speech - a discrete token-based system designed to generate spoken dialogues based on written dialogues. Our system can generate speech for both the speaker side and the listener side simultaneously, using only the transcription from the speaker side.
  arXiv  Detail & Related papers  (2023-10-02T11:03:20Z)
• SpeechX: Neural Codec Language Model as a Versatile Speech Transformer [57.82364057872905]
  SpeechX is a versatile speech generation model capable of zero-shot TTS and various speech transformation tasks. Experimental results show SpeechX's efficacy in various tasks, including zero-shot TTS, noise suppression, target speaker extraction, speech removal, and speech editing with or without background noise.
  arXiv  Detail & Related papers  (2023-08-14T01:01:19Z)
• token2vec: A Joint Self-Supervised Pre-training Framework Using Unpaired Speech and Text [65.04385919645395]
  token2vec is a novel joint pre-training framework for unpaired speech and text based on discrete representations of speech. Experiments show that token2vec is significantly superior to various speech-only pre-training baselines, with up to 17.7% relative WER reduction.
  arXiv  Detail & Related papers  (2022-10-30T06:38:19Z)
• SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data [100.46303484627045]
  We propose a cross-modal Speech and Language Model (SpeechLM) to align speech and text pre-training with a pre-defined unified representation. Specifically, we introduce two alternative discrete tokenizers to bridge the speech and text modalities. We evaluate SpeechLM on various spoken language processing tasks including speech recognition, speech translation, and universal representation evaluation framework SUPERB.
  arXiv  Detail & Related papers  (2022-09-30T09:12:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.