Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
Abstract
We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called Vall-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than continuous signal regression as in previous work. During the pre-training stage, we scale up the TTS training data to 60K hours of English speech which is hundreds of times larger than existing systems. Vall-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech with only a 3-second enrolled recording of an unseen speaker as an acoustic prompt. Experiment results show that Vall-E significantly outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity. In addition, we find Vall-E could preserve the speaker's emotion and acoustic environment of the acoustic prompt in synthesis. See https://aka.ms/valle for demos of our work.
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 introduces Vall-E, a language model-based TTS framework that leverages large-scale data and in-context learning to achieve state-of-the-art performance in zero-shot scenarios. This work significantly advances the field of speech synthesis by demonstrating the feasibility of using discrete audio representations and large datasets to enhance TTS capabilities, paving the way for future research and applications in personalized and adaptive speech technologies.
Methodology
The paper presents a novel approach to TTS by framing it as a conditional language modeling task, utilizing a neural codec model for discrete audio representation. This methodology diverges from traditional continuous signal regression techniques, allowing for significant scalability and robustness in synthesizing speech from unseen speakers. The use of a large dataset (60K hours) enhances the model's generalization capabilities, and the integration of in-context learning demonstrates a forward-thinking approach to TTS synthesis. The combination of autoregressive and non-autoregressive models is well-justified, balancing quality and efficiency.
Results & evaluation
The experiments are comprehensive, utilizing both objective metrics (CMOS, SMOS, WER) and subjective human evaluations to assess performance against state-of-the-art systems. The results indicate significant improvements in speech naturalness and speaker similarity, with quantitative metrics supporting the qualitative claims. The use of diverse datasets and unseen speakers in evaluations strengthens the validity of the findings.
Reproducibility
The paper provides sufficient details regarding the model architecture, training procedures, and dataset preparation, which should allow for reproducibility. However, the lack of a publicly available code repository limits the ease of reproduction for independent researchers. The authors could enhance reproducibility by sharing their trained models and code.
Limitations
The paper acknowledges several limitations, including synthesis robustness issues, particularly with word clarity and alignment errors. Additionally, the model's performance may vary with different accents and speaking styles, indicating a need for more diverse training data. These limitations suggest that while the model is a significant advancement, further refinement is necessary for broader applicability.
Broader impact
The ability of the model to synthesize speech that closely resembles unseen speakers raises ethical considerations regarding potential misuse, such as voice impersonation. The authors recognize these risks and suggest the development of detection models to mitigate them. The implications for accessibility and personalized applications in TTS are substantial, potentially transforming user interactions with technology. The paper introduces Vall-E, a language model-based TTS framework that leverages large-scale data and in-context learning to achieve state-of-the-art performance in zero-shot scenarios. This work significantly advances the field of speech synthesis by demonstrating the feasibility of using discrete audio representations and large datasets to enhance TTS capabilities, paving the way for future research and applications in personalized and adaptive speech technologies.
Curator note
Wang et al., Microsoft; 3-second voice cloning using EnCodec tokens + language model
Discussion
Talks & videos
Other indexed descriptions from the archive
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers (VALL-E)
Abstract
We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called Vall-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than continuous signal regression as in previous work. During the pre-training stage, we scale up the TTS training data to 60K hours of English speech which is hundreds of times larger than existing systems. Vall-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech with only a 3-second enrolled recording of an unseen speaker as an acoustic prompt. Experiment results show that Vall-E significantly outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity. In addition, we find Vall-E could preserve the speaker's emotion and acoustic environment of the acoustic prompt in synthesis. See https://aka.ms/valle for demos of our work.
AI research summary
The paper introduces Vall-E, a language model-based TTS framework that leverages large-scale data and in-context learning to achieve state-of-the-art performance in zero-shot scenarios. This work significantly advances the field of speech synthesis by demonstrating the feasibility of using discrete audio representations and large datasets to enhance TTS capabilities, paving the way for future research and applications in personalized and adaptive speech technologies.
AI methodology analysis
The paper presents a novel approach to TTS by framing it as a conditional language modeling task, utilizing a neural codec model for discrete audio representation. This methodology diverges from traditional continuous signal regression techniques, allowing for significant scalability and robustness in synthesizing speech from unseen speakers. The use of a large dataset (60K hours) enhances the model's generalization capabilities, and the integration of in-context learning demonstrates a forward-thinking approach to TTS synthesis. The combination of autoregressive and non-autoregressive models is well-justified, balancing quality and efficiency.
AI results analysis
The experiments are comprehensive, utilizing both objective metrics (CMOS, SMOS, WER) and subjective human evaluations to assess performance against state-of-the-art systems. The results indicate significant improvements in speech naturalness and speaker similarity, with quantitative metrics supporting the qualitative claims. The use of diverse datasets and unseen speakers in evaluations strengthens the validity of the findings.
AI reproducibility analysis
The paper provides sufficient details regarding the model architecture, training procedures, and dataset preparation, which should allow for reproducibility. However, the lack of a publicly available code repository limits the ease of reproduction for independent researchers. The authors could enhance reproducibility by sharing their trained models and code.
AI limitations analysis
The paper acknowledges several limitations, including synthesis robustness issues, particularly with word clarity and alignment errors. Additionally, the model's performance may vary with different accents and speaking styles, indicating a need for more diverse training data. These limitations suggest that while the model is a significant advancement, further refinement is necessary for broader applicability.
AI broader impact analysis
The ability of the model to synthesize speech that closely resembles unseen speakers raises ethical considerations regarding potential misuse, such as voice impersonation. The authors recognize these risks and suggest the development of detection models to mitigate them. The implications for accessibility and personalized applications in TTS are substantial, potentially transforming user interactions with technology. The paper introduces Vall-E, a language model-based TTS framework that leverages large-scale data and in-context learning to achieve state-of-the-art performance in zero-shot scenarios. This work significantly advances the field of speech synthesis by demonstrating the feasibility of using discrete audio representations and large datasets to enhance TTS capabilities, paving the way for future research and applications in personalized and adaptive speech technologies.
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