CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models

• URL: http://arxiv.org/abs/2412.10117v3
• Date: Wed, 25 Dec 2024 11:54:03 GMT
• Title: CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models
• Authors: Zhihao Du, Yuxuan Wang, Qian Chen, Xian Shi, Xiang Lv, Tianyu Zhao, Zhifu Gao, Yexin Yang, Changfeng Gao, Hui Wang, Fan Yu, Huadai Liu, Zhengyan Sheng, Yue Gu, Chong Deng, Wen Wang, Shiliang Zhang, Zhijie Yan, Jingren Zhou,
• Abstract summary: We present an improved streaming speech synthesis model, CosyVoice 2. Specifically, we introduce finite-scalar quantization to improve codebook utilization of speech tokens. We develop a chunk-aware causal flow matching model to support various synthesis scenarios.
• Score: 74.80386066714229
• License:
• Abstract: In our previous work, we introduced CosyVoice, a multilingual speech synthesis model based on supervised discrete speech tokens. By employing progressive semantic decoding with two popular generative models, language models (LMs) and Flow Matching, CosyVoice demonstrated high prosody naturalness, content consistency, and speaker similarity in speech in-context learning. Recently, significant progress has been made in multi-modal large language models (LLMs), where the response latency and real-time factor of speech synthesis play a crucial role in the interactive experience. Therefore, in this report, we present an improved streaming speech synthesis model, CosyVoice 2, which incorporates comprehensive and systematic optimizations. Specifically, we introduce finite-scalar quantization to improve the codebook utilization of speech tokens. For the text-speech LM, we streamline the model architecture to allow direct use of a pre-trained LLM as the backbone. In addition, we develop a chunk-aware causal flow matching model to support various synthesis scenarios, enabling both streaming and non-streaming synthesis within a single model. By training on a large-scale multilingual dataset, CosyVoice 2 achieves human-parity naturalness, minimal response latency, and virtually lossless synthesis quality in the streaming mode. We invite readers to listen to the demos at https://funaudiollm.github.io/cosyvoice2.

Related papers

• Moshi: a speech-text foundation model for real-time dialogue [78.88479749811376]
  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.
  arXiv  Detail & Related papers  (2024-09-17T17:55:39Z)
• Language Model Can Listen While Speaking [17.584201137311286]
  Listen-while-speaking language model (LSLM) is an end-to-end system equipped with both listening and speaking channels. Our results highlight LSLM's capability to achieve duplex communication with minimal impact on existing systems.
  arXiv  Detail & Related papers  (2024-08-05T16:47:22Z)
• FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs [63.8261207950923]
  FunAudioLLM is a model family designed to enhance natural voice interactions between humans and large language models (LLMs) At its core are two innovative models: SenseVoice, which handles multilingual speech recognition, emotion recognition, and audio event detection; and CosyVoice, which facilitates natural speech generation with control over multiple languages, timbre, speaking style, and speaker identity. The models related to SenseVoice and CosyVoice have been open-sourced on Modelscope and Huggingface, along with the corresponding training, inference, and fine-tuning codes released on GitHub.
  arXiv  Detail & Related papers  (2024-07-04T16:49:02Z)
• SeamlessExpressiveLM: Speech Language Model for Expressive Speech-to-Speech Translation with Chain-of-Thought [12.54786997634534]
  This work proposes SeamlessExpressiveLM, a single speech language model for expressive S2ST. We decompose the complex source-to-target speech mapping into intermediate generation steps with chain-of-thought prompting. The model is first guided to translate target semantic content and then transfer the speaker style to multi-stream acoustic units.
  arXiv  Detail & Related papers  (2024-05-30T18:28:31Z)
• TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation [97.54885207518946]
  We introduce a novel model framework TransVIP that leverages diverse datasets in a cascade fashion. We propose two separated encoders to preserve the speaker's voice characteristics and isochrony from the source speech during the translation process. Our experiments on the French-English language pair demonstrate that our model outperforms the current state-of-the-art speech-to-speech translation model.
  arXiv  Detail & Related papers  (2024-05-28T04:11:37Z)
• HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech Synthesis [39.892633589217326]
  Large language models (LLM)-based speech synthesis has been widely adopted in zero-shot speech synthesis. This paper proposes HierSpeech++, a fast and strong zero-shot speech synthesizer for text-to-speech (TTS) and voice conversion (VC)
  arXiv  Detail & Related papers  (2023-11-21T09:07:11Z)
• On decoder-only architecture for speech-to-text and large language model integration [59.49886892602309]
  Speech-LLaMA is a novel approach that effectively incorporates acoustic information into text-based large language models. We conduct experiments on multilingual speech-to-text translation tasks and demonstrate a significant improvement over strong baselines.
  arXiv  Detail & Related papers  (2023-07-08T06:47:58Z)
• How Generative Spoken Language Modeling Encodes Noisy Speech: Investigation from Phonetics to Syntactics [33.070158866023]
  generative spoken language modeling (GSLM) involves using learned symbols derived from data rather than phonemes for speech analysis and synthesis. This paper presents the findings of GSLM's encoding and decoding effectiveness at the spoken-language and speech levels.
  arXiv  Detail & Related papers  (2023-06-01T14:07:19Z)
• NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers [90.83782600932567]
  We develop NaturalSpeech 2, a TTS system that leverages a neural audio predictor with residual vectorizers to get the quantized latent vectors. We scale NaturalSpeech 2 to large-scale datasets with 44K hours of speech and singing data and evaluate its voice quality on unseen speakers. NaturalSpeech 2 outperforms previous TTS systems by a large margin in terms of prosody/timbre similarity, synthesis, and voice quality in a zero-shot setting.
  arXiv  Detail & Related papers  (2023-04-18T16:31:59Z)
• One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech [3.42658286826597]
  We introduce an approach to multilingual speech synthesis which uses the meta-learning concept of contextual parameter generation. Our model is shown to effectively share information across languages and according to a subjective evaluation test, it produces more natural and accurate code-switching speech than the baselines.
  arXiv  Detail & Related papers  (2020-08-03T10:43:30Z)

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.