MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer

## Abstract

Abstract The recent large-scale text-to-speech (TTS) systems are usually grouped as autoregressive and non-autoregressive systems. The autoregressive systems implicitly model duration but exhibit certain deficiencies in robustness and lack of duration controllability. Non-autoregressive systems require explicit alignment information between text and speech during training and predict durations for linguistic units (e.g. phone), which may compromise their naturalness.
In this paper, we introduce Mask ed G enerative C odec T ransformer (MaskGCT), a fully non-autoregressive TTS model that eliminates the need for explicit alignment information between text and speech supervision, as well as phone-level duration prediction . MaskGCT is a two-stage model: in the first stage, the model uses text to predict semantic tokens extracted from a speech self-supervised learning (SSL) model, and in the second stage, the model predicts acoustic tokens conditioned on these semantic tokens. MaskGCT follows the mask-and-predict learning paradigm. During training, MaskGCT learns to predict masked semantic or acoustic tokens based on given conditions and prompts. During inference, the model generates tokens of a specified length in a parallel manner.
Experiments with 100K hours of in-the-wild speech demonstrate that MaskGCT outperforms the current state-of-the-art zero-shot TTS systems in terms of quality, similarity, and intelligibility. Audio samples are available at https://maskgct.github.io/ . We release our code and model checkpoints at https://github.com/open-mmlab/Amphion/blob/main/models/tts/maskgct .

## 1 Introduction

In recent years, large-scale zero-shot text-to-speech (TTS) systems Kharitonov et al. (); Wang et al. (); Łajszczak et al. (); Kim et al. (); Peng et al. (); Anastassiou et al. (); Shen et al. (); Ju et al. (); Le et al. (); Jiang et al. () have achieved significant improvements by scaling data and model sizes, including both autoregressive (AR) Kharitonov et al. (); Wang et al. (); Łajszczak et al. (); Kim et al. (); Peng et al. (); Anastassiou et al. () and non-autoregressive (NAR) models Shen et al. (); Ju et al. (); Le et al. (); Jiang et al. (). However, both AR-based and NAR-based systems still exhibit some shortcomings. In particular, AR-based TTS systems typically quantize speech into discrete tokens and then use decoder-only models to autoregressively generate these tokens, which offer diverse prosody but also suffer from problems such as poor robustness and slow inference speed. NAR-based models, typically based on diffusion Ju et al. (); Shen et al. (), flow matching Le et al. (), or GAN Jiang et al. (), require explicit text and speech alignment information as well as the prediction of phone-level duration, resulting in a complex pipeline and producing more standardized but less diverse speech.

Recently, masked generative transformers, a class of generative models, have achieved significant results in the fields of image Chang et al. (, ); Li et al. (), video Yu et al. (, ), and audio Garcia et al. (); Li et al. (); Ziv et al. () generation, demonstrating potential comparable to or superior to autoregressive models or diffusion models. These models employ a mask-and-predict training paradigm and utilize iterative parallel decoding during inference. Some previous works have attempted to introduce masked generative models into the field of TTS. SoundStorm Borsos et al. () was the first attempt to use a masked generative transformer to predict multi-layer acoustic tokens extracted from SoundStream, conditioned on speech semantic tokens; however, it needs to receive the semantic tokens of an AR model as input. Thus, SoundStorm is more of an acoustic model that converts semantic tokens into acoustic tokens and does not fully utilize the powerful generative potential of masked generative models. NaturalSpeech 3 Ju et al. () decomposes speech into discrete token sequences representing different attributes through special designs and generates tokens representing different attributes through masked generative models. However, it still needs speech-text alignment supervision and phone-level duration prediction.

In this work, we propose MaskGCT, a fully non-autoregressive model for text-to-speech synthesis that uses masked generative transformers without requiring text-speech alignment supervision and phone-level duration prediction. MaskGCT is a two-stage system, both stages are trained using the mask-and-predict learning paradigm.
The first stage, the text-to-semantic (T2S) model, predicts masked semantic tokens with in-context learning, using text token sequences and prompt speech semantic token sequences as the prefix, without explicit duration prediction. The second stage, the semantic-to-acoustic (S2A) model, utilizes semantic tokens to predict masked acoustic tokens extracted from an RVQ-based speech codec with prompt acoustic tokens.
During inference, MaskGCT can generate semantic tokens of various specified lengths with a few iteration steps given a sequence of text.
In addition, we train a VQ-VAE Van Den Oord et al. () to quantize speech self-supervised learning embedding, rather than using k-means to extract semantic tokens that is common in previous work. This approach minimizes the information loss of semantic features even with a single codebook.
We also explore the scalability of our methods beyond the zero-shot TTS task, such as speech translation (cross-lingual dubbing), speech content editing, voice conversion, and emotion control, demonstrating the potential of MaskGCT as a foundational model for speech generation. Table 1 shows a comparison between MaskGCT and some previous works.

**Table 1: A comparison between MaskGCT and existing systems. “Model” stands for modeling method and “Rep.” stands for the representation used. MaskGCT uses masked generative modeling for acoustic and semantic tokens (“A.” stands for acoustic, “S.” stands for semantic, “F.” stands for factorized tokens used in NaturalSpeech 3). MaskGCT implicitly models duration (“Imp. Dur.”) and allows flexible control over the total length of generated speech (“Len. Ctrl”). MaskGCT supports various speech generation tasks.**
| System | Model | Rep. | Imp. Dur. | Len. Ctrl. | ZS TTS | CL TTS | Dubbing | Edit |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| VALL-E | Autoregressive | A. Tokens | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ |
| NaturalSpeech 2 | Diffusion | A. Features | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| VoiceBox | Diffusion | A. Features | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ |
| VoiceCraft | Autoregressive | A. Tokens | ✓ | ✗ | ✓ | ✗ | ✗ | ✓ |
| NaturalSpeech 3 | Masked Generative | F. Tokens | ✗ | ✗ | ✓ | ✗ | ✗ | ✓ |
| MaskGCT | Masked Generative | S.&A. Tokens | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |

Our experiments demonstrate that MaskGCT has achieved performance comparable to or superior to that of existing models in terms of speech quality, similarity, prosody, and intelligibility. Specifically, (1) It achieves comparable or better quality and naturalness than the ground truth speech across three benchmarks (LibriSpeech, SeedTTS test-en, and SeedTTS test-zh) in terms of CMOS. (2) It achieves human-level similarity between the generated speech and the prompt speech, with improvements of +0.017, -0.002, and +0.027 in SIM-O and +0.28, +0.32 and +0.25 in SMOS for LibriSpeech, SeedTTS test-en, and SeedTTS test-zh, respectively. (3) It achieves comparable intelligibility in terms of WER across the three benchmarks and demonstrates stability within a reasonable range of speech duration, which also indicates the diversity and controllability of the generated speech.

In summary, we propose a non-autoregressive zero-shot TTS system based on masked generative transformers and introduce a speech discrete semantic representation by training a VQ-VAE on speech self-supervised representations. Our system achieves human-level similarity, naturalness, and intelligibility by scaling data to 100K hours of in-the-wild speech, while also demonstrating high flexibility, diversity, and controllability. We investigate the scalability of our system across various tasks, including cross-lingual dubbing, voice conversion, emotion control, and speech content editing, utilizing zero-shot learning or post-training methods. This showcases the potential of our system as a foundational model for speech generation.

## 2 Related Work

Large-scale TTS. Traditional TTS systems Ren et al. (, ); Tan et al. (); Wang et al. (); Kim et al. () are trained to generate speech from a single speaker or multiple speakers using hours of high-quality transcribed training data. Modern large-scale TTS systems Kharitonov et al. (); Wang et al. (); Łajszczak et al. (); Kim et al. (); Peng et al. (); Anastassiou et al. () aim to achieve zero-shot TTS (synthesizing speech for unseen speakers with speech prompts) by scaling both the model and data size. These systems can be mainly divided into AR-based and NAR-based categories.
For AR-based systems:
SpearTTS Kharitonov et al. () utilizes three AR models to predict semantic tokens from text, coarse-grained acoustic tokens from semantic tokens, and fine-grained acoustic tokens from coarse-grained tokens.
VALL-E Wang et al. () predicts the first layer of acoustic tokens extracted from EnCodec Défossez et al. () using an AR codec language model, and the final layers with a NAR model.
VoiceCraft Peng et al. () employs a single AR model to predict multi-layer acoustic tokens in a delayed pattern Copet et al. ().
BASETTS Łajszczak et al. () predicts novel speech codes extracted from WavLM features and uses a GAN model for waveform reconstruction.
For NAR-based systems:
NaturalSpeech 2 Shen et al. () employs latent diffusion to predict the latent representations from a codec model Zeghidour et al. (). VoiceBox Le et al. () uses flow matching and in-context learning to predict mel-spectrograms.
MegaTTS Jiang et al. () utilizes a GAN to predict mel-spectrograms, while an AR model predicts phone-level prosody codes.
NaturalSpeech 3 Ju et al. () employs a unified framework based on discrete diffusion models to predict discrete representations of different speech attributes. However, these NAR systems need to predict phoneme-level duration, leading to a complex pipeline and more standardized generative results. SimpleSpeech Yang et al. (), DiTTo-TTS Lee et al. (), and E2 TTS Eskimez et al. () are also NAR-based models that do not require precise alignment information between text and speech, nor do they predict phoneme-level duration. We discuss these concurrent works in Appendix K.

Masked Generative Model. Masked generative transformers, a class of generative models, achieve significant results and demonstrate potential comparable to or superior to that of autoregressive models or diffusion models in the fields of image Chang et al. (, ); Lezama et al. (); Li et al. (), video Yu et al. (, ), and audio Borsos et al. (); Garcia et al. (); Li et al. (); Ziv et al. () generation. MaskGIT Chang et al. () is the first work to use masked generative models for both unconditional and conditional image generation. Subsequently, Muse Chang et al. () leverages rich text to achieve high-quality and diverse text-to-image generation within the same framework. MAGVIT-v2 Yu et al. () employs masked generative models with novel lookup-free quantization, outperforming diffusion models in image and video generation. Recently, some efforts have been made to adapt masked generative models to the field of audio. SoundStorm Borsos et al. () takes in the semantic tokens from AudioLM and utilizes this generative paradigm to generate tokens for a neural audio codec Zeghidour et al. (). VampNet Garcia et al. () and MAGNeT Ziv et al. () apply masked generative models for music and audio generation, while MaskSR Li et al. () extends these models for speech restoration.

Discrete Speech Representation. Speech representation is a crucial aspect of speech generation. Early works Ren et al. (); Wang et al. () typically utilized mel-spectrograms as the modeling target. Recently, some large-scale TTS systems Wang et al. (); Ju et al. () have shifted to using discrete speech representations. Discrete speech representation can be primarily divided into two types: semantic discrete representation and acoustic discrete representation(^1^11We give a more detailed discussion about the definitions of “semantic” and “acoustic” in Appendix B.). Semantic discrete representations are mainly extracted from various speech SSL models Chung et al. (); Hsu et al. (); Chen et al. () using quantization methods such as k-means. Acoustic discrete representations, on the other hand, are usually obtained by training a VQ-GAN model Van Den Oord et al. () with the goal of waveform reconstruction, as seen in speech codecs Défossez et al. (); Zeghidour et al. (); Kumar et al. (). Semantic discrete representation typically shows a stronger correlation with text, whereas acoustic discrete representation more effectively reconstructs audio. Consequently, some two-stage TTS models predict both semantic and acoustic tokens. FACodec Ju et al. () is a novel speech codec that disentangles speech into subspaces of different attributes, including content, prosody, timbre, and acoustic details.

## 3 Method

### 3.1 Background: Non-Autoregressive Masked Generative Transformer

Given a discrete representation sequence $\mathbf{X}$ of some data, we define $\mathbf{X}_{t}=\mathbf{X}\odot\mathbf{M}_{t}$ as the process of masking a subset of tokens in $\mathbf{X}$ with the corresponding binary mask $\mathbf{M}_{t}=[m_{t,i}]_{i=1}^{N}$. Specifically, this involves replacing $x_{i}$ with a special [MASK] token if $m_{t,i}=1$, and otherwise leaving $x_{i}$ unmasked if $m_{t,i}=0$. Here, each $m_{t,i}$ is independently and identically distributed according to a Bernoulli distribution with parameter $\gamma(t)$, where $\gamma(t)\in(0,1]$ represents a mask schedule function (for example, $\gamma(t)=\sin(\frac{\pi t}{2T}),t\in(0,T]$). We denote $\mathbf{X}_{0}=\mathbf{X}$. The non-autoregressive masked generative transformers are trained to predict the masked tokens based on the unmasked tokens and a condition $\mathbf{C}$. This prediction is modeled as $p_{\theta}(\mathbf{X}_{0}|\mathbf{X}_{t},\mathbf{C})$. The parameters $\theta$ are optimized to minimize the negative log-likelihood of the masked tokens:

$$ $\displaystyle\mathcal{L}_{\text{mask}}$ $\displaystyle=\mathop{\mathbb{E}}\limits_{\mathbf{X}\in\mathcal{D},t\in\left[0 ,T\right]}-\sum_{i=1}^{N}m_{t,i}\cdot\log(p_{\theta}(x_{i}|\mathbf{X}_{t}, \mathbf{C})).$ $$

At the inference stage, we decode the tokens in parallel through iterative decoding. We start with a fully masked sequence $\mathbf{X}_{T}$. Assuming the total number of decoding steps is $S$, for each step $i$ from 1 to $S$, we first sample $\mathbf{\hat{X}}_{0}$ from $p_{\theta}(\mathbf{X}_{0}|\mathbf{X}_{T-(i-1)\cdot\frac{T}{S}},\mathbf{C})$. Then, we sample $\lfloor N\cdot\gamma(T-i\cdot\frac{T}{S})\rfloor$ tokens based on the confidence score to remask, resulting in $\mathbf{X}_{T-i\cdot\frac{T}{S}}$, where $N$ is the total number of tokens in $\mathbf{X}$. The confidence score for $\hat{x}_{i}$ in $\mathbf{\hat{X}}_{0}$ is assigned to $p_{\theta}(\mathbf{X}_{0}|\mathbf{X}_{T-(i-1)\cdot\frac{T}{S}},\mathbf{C})$ if $x_{T-(i-1)\cdot\frac{T}{S},i}$ is a [MASK] token; otherwise, we set the confidence score of $\hat{x}_{i}$ to $1$, indicating that tokens already unmasked in $\mathbf{X}_{T-(i-1)\cdot\frac{T}{S}}$ will not be remasked. Particularly, we choose $\lfloor N\cdot\gamma(T-i\cdot\frac{T}{S})\rfloor$ tokens with the lowest confidence scores to be masked.

The masked generative modeling paradigm was first introduced in Chang et al. (), and subsequent work such as Lezama et al. () has further explored it under the perspective of discrete diffusion.

### 3.2 Model Overview

An overview of the MaskGCT framework is presented in Figure 1.
Following Betker (); Borsos et al. (); Wang et al. (), MaskGCT is a two-stage TTS system. The first stage uses text to predict speech semantic representation tokens, which contain most information of content and partial information of prosody. The second stage model is trained to learn more acoustic information. Unlike previous works Betker (); Wang et al. (); Borsos et al. (); Kharitonov et al. () use an autoregressive model for the first stage, MaskGCT utilizes the non-autoregressive masked generative modeling paradigm for both the two stages without text-speech alignment supervision and phone-level duration prediction: (1) For the first stage model, we trained a model to learn $p_{\theta_{\text{s1}}}(\mathbf{S}|\mathbf{S}_{t},(\mathbf{S}^{p},\mathbf{P}))$, where $\mathbf{S}$ is the speech semantic representation token sequence obtained from a speech semantic representation codec (we introduce in 3.2.1), $\mathbf{S}^{p}$ is the prompt semantic token sequence, and $\mathbf{P}$ is the text token sequence. $\mathbf{S}^{p}$ and $\mathbf{P}$ are the condition for the first stage model. (2) The second stage model is trained to learn $p_{\theta_{\text{s2}}}(\mathbf{A}|\mathbf{A}_{t},(\mathbf{A}^{p},\mathbf{S}))$, where $\mathbf{A}$ is the multi-layer acoustic token sequence from a speech acoustic codec like Zeghidour et al. (); Défossez et al. (). Our second stage model is similar to SoundStorm Borsos et al. (). We give more details about the four parts in the following sections.

Figure: Figure 1: An overview of the proposed two-stage MaskGCT framework. It consists of four main components: (1) a speech semantic representation codec converts speech to semantic tokens; (2) a text-to-semantic model predicts semantic tokens with text and prompt semantic tokens; (3) a semantic-to-acoustic model predicts acoustic tokens conditioned on semantic tokens; (4) a speech acoustic codec reconstructs waveform from acoustic tokens.
Refer to caption: x1.png

#### 3.2.1 Speech Semantic Representation Codec

Discrete speech representations can be divided into semantic tokens and acoustic tokens. Generally, semantic tokens are obtained by discretizing features from speech self-supervised learning (SSL). Previous two-stage, large-scale TTS systems Betker (); Borsos et al. (); Kharitonov et al. () typically first use text to predict semantic tokens, and then employ another model to predict acoustic tokens or features. This is because semantic tokens have a stronger correlation with text or phonemes, which makes predicting them more straightforward than directly predicting acoustic tokens. Commonly, previous works have used k-means to discretize semantic features to obtain semantic tokens; however, this method can lead to a loss of information. This loss may complicate the accurate reconstruction of high-quality speech or the precise prediction of acoustic tokens, especially for tonally rich languages. For example, our early experiments demonstrate the challenges of accurately predicting acoustic tokens to achieve proper prosody for Chinese using semantic tokens obtained via k-means. Therefore, we need to discretize semantic representation features while minimizing information loss. Inspired by Huang et al. (), we train a VQ-VAE model to learn a vector quantization codebook that reconstructs speech semantic representations from a speech SSL model. For a speech semantic representation sequence $\mathbf{S}\in\mathbb{R}^{T\times d}$, the vector quantizer quantizes the output of the encoder $\mathcal{E}(\mathbf{S})$ to $\mathbf{E}$, and the decoder reconstructs $\mathbf{E}$ back to $\hat{\mathbf{S}}$. We optimize the encoder and the decoder using a reconstruction loss between $\mathbf{S}$ and $\hat{\mathbf{S}}$, employ codebook loss to optimize the codebook and use commitment loss to optimize the encoder with the straight-through method Van Den Oord et al. (). The total loss for training the semantic representation codec can be written as:

$$ $\displaystyle\mathcal{L}_{\text{total}}$ $\displaystyle=\frac{1}{Td}(\lambda_{\text{rec}}\cdot||\mathbf{S}-\hat{\mathbf{ S}}||_{1}+\lambda_{\text{codebook}}\cdot||\text{sg}(\mathcal{E}(\mathbf{S}))- \mathbf{E}||_{2}+\lambda_{\text{commit}}\cdot||\text{sg}(\mathbf{E})-\mathcal{ E}(\mathbf{S})||_{2}).$ $$

where sg means stop-gradient.

In detail, we utilize the hidden states from the 17th layer of W2v-BERT 2.0 Chung et al. () as the semantic features for our speech encoder. The encoder and decoder are composed of multiple ConvNext Liu et al. () blocks. Following the methods of improved VQ-GAN Yu et al. () and DAC Kumar et al. (), we use factorized codes to project the output of the encoder into a low-dimensional latent variable space. The codebook contains 8,192 entries, each of dimension 8. Further details about the model architecture are provided in Appendix A.4.

#### 3.2.2 Text-to-Semantic Model

Based on the previous discussion, we employ a non-autoregressive masked generative transformer to train a text-to-semantic (T2S) model, instead of using an autoregressive model or any text-to-speech alignment information. During training, we randomly extract a portion of the prefix of the semantic token sequence as the prompt, denoted as $\mathbf{S}^{p}$. We then concatenate the text token sequence $\mathbf{P}$ with $\mathbf{S}^{p}$ to form the condition. We simply add $(\mathbf{P},\mathbf{S}^{p})$ as the prefix sequence to the input masked semantic token sequence $\mathbf{S}_{t}$ to leverage the in-context learning ability of language models. We use a Llama-style Touvron et al. () transformer as the backbone of our model, incorporating gated linear units with GELU Hendrycks and Gimpel () activation, rotation position encoding Su et al. (), etc., but replacing causal attention with bidirectional attention. We also use adaptive RMSNorm Zhang and Sennrich (), which accepts the time step $t$ as the condition.

During inference, we generate the target semantic token sequence of any specified length conditioned on the text and the prompt semantic token sequence. In this paper, we also train a flow matching Lipman et al. () based duration prediction model to predict the total duration conditioned on the text and prompt speech duration, leveraging in-context learning. More details can be found in Appendix A.5.

#### 3.2.3 Semantic-to-Acoustic Model

We also train a semantic-to-acoustic (S2A) model using a masked generative codec transformer conditioned on the semantic tokens. Our semantic-to-acoustic model is based on SoundStorm  Borsos et al. (), which generates multi-layer acoustic token sequences. Given $N$ layers of the acoustic token sequence $\mathbf{A}^{1:N}$, during training, we select one layer $j$ from $1$ to $N$. We denote the $j$th layer of the acoustic token sequence as $A^{j}$. Following the previous discussion, we mask $A^{j}$ at the timestep $t$ to get $\mathbf{A}^{j}_{t}$. The model is then trained to predict $\mathbf{A}^{j}$ conditioned on the prompt $\mathbf{A}^{p}$, the corresponding semantic token sequence $\mathbf{S}$, and all the layers smaller than $j$ of the acoustic tokens. This can be formulated as $p_{\theta_{\text{s2a}}}(\mathbf{A}^{j}|\mathbf{A}^{j}_{t},(\mathbf{A}^{p},
\mathbf{S},\mathbf{A}^{1:j-1}))$.
We sample $j$ according to a linear schedule $p(j)=1-\frac{2j}{N(N+1)}$. For the input of the S2A model, since the number of frames in the semantic token sequence is equal to the sum of the frames in the prompt acoustic sequence and the target acoustic sequence, we simply sum the embeddings of the semantic tokens and the embeddings of the acoustic tokens from layer $1$ to $j$.
During inference, we generate tokens for each layer from coarse to fine, using iterative parallel decoding within each layer. Figure 2 shows a simplified training diagram of the T2S and S2A models.

Figure: Figure 2: An overview of training diagram of the T2S (left) and S2A (right) models. The T2S model is trained to predict masked semantic tokens with text and prompt semantic tokens as the prefix. The S2A model is trained to predict masked acoustic tokens of a random layer conditioned on prompt acoustic tokens, semantic tokens, and acoustic tokens of the previous layers.
Refer to caption: x2.png

#### 3.2.4 Speech Acoustic Codec

Speech acoustic codec is trained to quantize speech waveform to multi-layer discrete tokens while aiming to preserve all the information of the speech as soon as possible. We follow the residual vector quantization (RVQ) method to compress the 24K sampling rate speech waveform into discrete tokens of 12 layers. The codebook size of each layer is 1,024 and the codebook dimension is 8. The model architectures, discriminators, and training losses follow DAC Kumar et al. (), except that we use the Vocos Siuzdak () architecture as the decoder for more efficient training and inference. Figure 5 shows the comparison between the semantic codec and acoustic codec.

### 3.3 Other Applications

MaskGCT can accomplish tasks beyond zero-shot TTS, such as duration-controllable speech translation (cross-lingual dubbing), emotion control, speech content editing, and voice conversion with simple modifications or the assistance of external tools, demonstrating the potential of MaskGCT as a foundational model for speech generation. We provide more details in Appendix F,  G,  H,  I.

## 4 Experiments and Results

### 4.1 Experimental Settings

Datasets.
We use the Emilia He et al. () dataset to train our models. Emilia is a multilingual and diverse in-the-wild speech dataset designed for large-scale speech generation. In this work, we use English and Chinese data from Emilia, each with 50K hours of speech (totaling 100K hours).
We evaluate our zero-shot TTS models with three benchmarks: (1) LibriSpeech Panayotov et al. () test-clean, a widely used test set for English zero-shot TTS.
(2) SeedTTS test-en, a test set introduced in Seed-TTS Anastassiou et al. () of samples extracted from English public corpora, includes 1,000 samples from the Common Voice dataset Ardila et al. ().
(3) SeedTTS test-zh, a test set introduced in Seed-TTS of samples extracted from Chinese
public corpora, includes 2,000 samples from the DiDiSpeech dataset Guo et al. (). We also scale the training dataset to six languages to support multilingual zero-shot TTS. We provide additional experimental details and evaluation results about multilingual zero-shot TTS in Appendix E.

Evaluation Metrics. We use both objective and subjective metrics to evaluate our models. For the objective metrics, we evaluate speaker similarity (SIM-O), robustness (WER), and speech quality (FSD). Specifically, for speaker similarity, we compute the cosine similarity between the WavLM TDNN(^2^22[https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification](https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification)) Chen et al. () speaker embedding of generated samples and the prompt. For Word Error Rate (WER), we use a HuBERT-based(^3^33[https://huggingface.co/facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft)) ASR model for LibriSpeech test-clean, Whisper-large-v3 for Seed-TTS test-en, and Paraformer-zh for Seed-TTS test-zh, following previous works. For speech quality, we use Fréchet Speech Distance (FSD) with self-supervised wav2vec 2.0 Baevski et al. () features, following Le et al. (). For the subjective metrics, comparative mean option score (CMOS) and similarity mean option score (SMOS) are used to evaluate naturalness and similarity, respectively. CMOS is on a scale of -3 to 3, and SMOS is on a scale of 1 to 5.

Baseline. We compare our models with state-of-the-art zero-shot TTS systems, including NaturalSpeech 3 Ju et al. (), VALL-E Wang et al. (), VoiceBox Le et al. (), VoiceCraft Peng et al. (), XTTS-v2 Casanova et al. (), and CosyVoice Du et al. (). More details of each model can be found in Appendix D. We also train an AR-based T2S model to replace the T2S part of MaskGCT, we term it as AR + SoundStorm.

Training. We train all models on 8 NVIDIA A100 80GB GPUs. We train two T2S models of different sizes (denoted as T2S-Base and T2S-large). For more details about the model architecture, please refer to Appendix A.1. We report the metrics of T2S-large by default, and you can find a comparison of model sizes in Section 4.4. We also compare two different methods of text tokenization:
Grapheme-to-Phoneme (G2P) Bernard and Titeux () and Byte Pair Encoding (BPE) Gage (). See more details of the two methods in Appendix A.6. We report the metrics of G2P by default. We optimize these models with the AdamW Loshchilov and Hutter () optimizer with a learning rate of 1e-4 and 32K warmup steps, following the inverse square root learning schedule. We use the classifier-free guidance Ho and Salimans (), during training for both the T2S and S2A models, we drop the prompt with a probability of 0.15. See more details about classifier-free guidance and classifier-free guidance rescale in Appendix C.

Inference. For the T2S model, we use 50 steps as the default total inference steps. The classifier-free guidance scale and the classifier-free guidance rescale factor Lin et al. () are set to 2.5 and 0.75, respectively. For sampling, we use a top-k of 20, with the sampling temperature annealing from 1.5 to 0. We add Gumbel noise to token confidences when determining the remasking process, following Chang et al. ().
For the S2A model, we use $[40,16,1,1,1,1,1,1,1,1,1,1]$ steps for acoustic RVQ layers by default, we find the S2A model can also perform well with fewer inference steps of $[10,1,1,1,1,1,1,1,1,1,1,1]$ (see Appendix A.3). We use the same sampling strategy as the T2S model, except that we use greedy sampling instead of top-k sampling if the inference step is 1.

### 4.2 Zero-Shot TTS

**Table 2: Evaluation results for MaskGCT and the baseline methods on LibriSpeech test-clean, SeedTTS test-en, SeedTTS test-zh. The boldface denotes the best result, the underline denotes the second best. gt length denotes the result obtained by using ground truth total speech length. The results in ‘()’ means the result is the best one selected from five random samples (rerank 5).**
| System | SIM-O $\uparrow$ | WER $\downarrow$ | FSD $\downarrow$ | SMOS $\uparrow$ | CMOS $\uparrow$ |
| --- | --- | --- | --- | --- | --- |
| LibriSpeech test-clean |  |  |  |  |  |
| Ground Truth | 0.68 | 1.94 | - | 4.05${}_{\scriptscriptstyle\pm\text{0.12}}$ | 0.00 |
| VALL-E Wang et al. () | 0.50 | 5.90 | - | 3.47${}_{\scriptscriptstyle\pm\text{0.26}}$ | -0.52${}_{\scriptscriptstyle\pm\text{0.22}}$ |
| VoiceBox Le et al. () | 0.64 | 2.03 | 0.762 | 3.80${}_{\scriptscriptstyle\pm\text{0.17}}$ | -0.41${}_{\scriptscriptstyle\pm\text{0.13}}$ |
| NaturalSpeech 3 Ju et al. () | 0.67 | 1.94 | 0.786 | 4.26${}_{\scriptscriptstyle\pm\text{0.10}}$ | 0.16${}_{\scriptscriptstyle\pm\text{0.14}}$ |
| VoiceCraft Peng et al. () | 0.45 | 4.68 | 0.981 | 3.52${}_{\scriptscriptstyle\pm\text{0.21}}$ | -0.33${}_{\scriptscriptstyle\pm\text{0.16}}$ |
| XTTS-v2 Casanova et al. () | 0.51 | 4.20 | 0.945 | 3.02${}_{\scriptscriptstyle\pm\text{0.22}}$ | -0.98${}_{\scriptscriptstyle\pm\text{0.19}}$ |
| MaskGCT | 0.687(0.723) | 2.634(1.976) | 0.886 | 4.27${}_{\scriptscriptstyle\pm\text{0.14}}$ | 0.10${}_{\scriptscriptstyle\pm\text{0.16}}$ |
| MaskGCT (gt length) | 0.697 | 2.012 | 0.746 | 4.33${}_{\scriptscriptstyle\pm\text{0.11}}$ | 0.13${}_{\scriptscriptstyle\pm\text{0.13}}$ |
| SeedTTS test-en |  |  |  |  |  |
| Ground Truth | 0.730 | 2.143 | - | 3.92${}_{\scriptscriptstyle\pm\text{0.15}}$ | 0.00 |
| CosyVoice Du et al. () | 0.643 | 4.079 | 0.316 | 3.52${}_{\scriptscriptstyle\pm\text{0.17}}$ | -0.41${}_{\scriptscriptstyle\pm\text{0.18}}$ |
| XTTS-v2 Casanova et al. () | 0.463 | 3.248 | 0.484 | 3.15${}_{\scriptscriptstyle\pm\text{0.22}}$ | -0.86${}_{\scriptscriptstyle\pm\text{0.19}}$ |
| VoiceCraft Peng et al. () | 0.470 | 7.556 | 0.226 | 3.18${}_{\scriptscriptstyle\pm\text{0.20}}$ | -1.08${}_{\scriptscriptstyle\pm\text{0.15}}$ |
| MaskGCT | 0.717(0.760) | 2.623(1.283) | 0.188 | 4.24${}_{\scriptscriptstyle\pm\text{0.12}}$ | 0.03${}_{\scriptscriptstyle\pm\text{0.14}}$ |
| MaskGCT (gt length) | 0.728 | 2.466 | 0.159 | 4.13${}_{\scriptscriptstyle\pm\text{0.17}}$ | 0.12${}_{\scriptscriptstyle\pm\text{0.15}}$ |
| SeedTTS test-zh |  |  |  |  |  |
| Ground Truth | 0.750 | 1.254 | - | 3.86${}_{\scriptscriptstyle\pm\text{0.17}}$ | 0.00 |
| CosyVoice Du et al. () | 0.750 | 4.089 | 0.276 | 3.54${}_{\scriptscriptstyle\pm\text{0.12}}$ | -0.45${}_{\scriptscriptstyle\pm\text{0.15}}$ |
| XTTS-v2 Casanova et al. () | 0.635 | 2.876 | 0.413 | 2.95${}_{\scriptscriptstyle\pm\text{0.18}}$ | -0.81${}_{\scriptscriptstyle\pm\text{0.22}}$ |
| MaskGCT | 0.774(0.805) | 2.273(0.843) | 0.106 | 4.09${}_{\scriptscriptstyle\pm\text{0.12}}$ | 0.05${}_{\scriptscriptstyle\pm\text{0.17}}$ |
| MaskGCT (gt length) | 0.777 | 2.183 | 0.101 | 4.11${}_{\scriptscriptstyle\pm\text{0.12}}$ | 0.08${}_{\scriptscriptstyle\pm\text{0.18}}$ |

In this section, we show the main results of zero-shot TTS: we show comparison results with SOTA baselines in Section 4.2.1; we compare MaskGCT with replacing T2S model to an AR model in Section 4.2.2; We present the performance of MaskGCT across varying speech tempos in Section 4.2.3. Additionally, we present the results of zero-shot TTS for speech style imitation in Section 4.3, multilingual zero-shot TTS in Appendix E, and cross-lingual speech translation (dubbing) in Appendix F.

#### 4.2.1 Comparison with Baselines

We compare MaskGCT with baselines in terms of similarity, robustness, and generation quality. The main results are shown in Table 2. MaskGCT demonstrates excellent performance on all metrics and achieves human-level similarity, naturalness, and intelligibility.
In similarity, MaskGCT’s SIM-O and SMOS both outperform the best baseline, whether assessed using the total length of ground truth or the predicted total duration (0.67$\rightarrow$0.687 in LibriSpeech, 0.643$\rightarrow$0.717 in SeedTTS test-en, 0.75$\rightarrow$0.774 in SeedTTS test-zh for SIM-O; +0.01 in LibriSpeech, +0.72 in SeedTTS test-en, +0.55 in SeedTTS test-zh for SMOS). When compared with human recordings, MaskGCT achieves human-level similarity across all three test sets (+0.017, -0.002, and +0.027 for SIM-O respectively in the three test sets, and +0.28, +0.32, and +0.25 for SMOS respectively in the three test sets).
In robustness, MaskGCT likewise results nearly on par with ground truth (with 2.634, 2.623, 2.273 WER on LibriSpeech, SeedTTS test-en, and SeedTTS test-zh, respectively), exhibiting enhanced robustness compared to AR-based models and performing on par or better than NAR-based models such as VoiceBox and NaturalSpeech 3, without relying on phone-level duration predictions.
In generation quality, MaskGCT achieves +0.10, +0.03, and +0.05 CMOS across the three test sets when compared with human recordings, indicating that MaskGCT attains human-level naturalness on these test sets. We also observe that MaskGCT exhibits excellent performance when using both ground truth total duration and predicted total duration, indicating the robustness of MaskGCT within a reasonable range of total speech duration and the capability of our total duration predictor to yield appropriate durations.

**Table 3: Comparison results of the evaluation of MaskGCT and AR+SoundStorm. AR+SoundStorm can be regarded as replacing the T2S MaskGCT with the AR T2S model.**
| System | SIM-O $\uparrow$ | WER $\downarrow$ | FSD $\downarrow$ | SMOS $\uparrow$ | CMOS $\uparrow$ |
| --- | --- | --- | --- | --- | --- |
| LibriSpeech test-clean |  |  |  |  |  |
| AR + SoundStorm | 0.672 | 3.267 | 0.998 | 4.20${}_{\scriptscriptstyle\pm\text{0.17}}$ | -0.02${}_{\scriptscriptstyle\pm\text{0.20}}$ |
| MaskGCT | 0.687 | 2.634 | 0.886 | 4.27${}_{\scriptscriptstyle\pm\text{0.14}}$ | 0.10${}_{\scriptscriptstyle\pm\text{0.16}}$ |
| SeedTTS test-en |  |  |  |  |  |
| AR + SoundStorm | 0.683 | 2.846 | 0.323 | 4.03${}_{\scriptscriptstyle\pm\text{0.23}}$ | -0.05${}_{\scriptscriptstyle\pm\text{0.22}}$ |
| MaskGCT | 0.717 | 2.623 | 0.188 | 4.24${}_{\scriptscriptstyle\pm\text{0.12}}$ | 0.03${}_{\scriptscriptstyle\pm\text{0.14}}$ |
| SeedTTS test-zh |  |  |  |  |  |
| AR + SoundStorm | 0.747 | 3.865 | 0.238 | 3.78${}_{\scriptscriptstyle\pm\text{0.23}}$ | -0.32${}_{\scriptscriptstyle\pm\text{0.19}}$ |
| MaskGCT | 0.774 | 2.273 | 0.106 | 4.09${}_{\scriptscriptstyle\pm\text{0.12}}$ | 0.05${}_{\scriptscriptstyle\pm\text{0.17}}$ |

#### 4.2.2 Autoregressive vs. Masked Generative Models

We compare MaskGCT to replacing T2S MaskGCT with an AR T2S model (which we call AR + SoundStorm). Table 3 shows the performance of these two models on all three test sets. MaskGCT demonstrates improved similarity, robustness, and CMOS (+0.12 on LibriSpeech test-clean, +0.08 on SeedTTS test-en, and +0.37 on SeedTTS test-zh) across all three test sets. We also conduct comparisons on more challenging hard cases (such as repeating words, and tongue twisters, which are often considered as samples where TTS systems are prone to hallucinations). MaskGCT exhibits a more pronounced robustness advantage in these scenarios. See details in Appendix J. In addition, compared to AR-based models, MaskGCT offers the capability to control the total duration of the generated speech, along with fewer inference steps, requiring only 25 to 50 steps for T2S models to achieve optimal results for speeches of any length. Conversely, the inference steps for AR-based models increase linearly with the length of the speech.

#### 4.2.3 Duration Length Analysis

Figure: Figure 3: WER vs. Total Duration Multiplier.

We analyze the robustness of the generated results of MaskGCT under different changes in total duration length (which can also be regarded as changes in speech tempo). The results are shown in Figure 3. We explore the results of multiplying the ground truth total duration by 0.7 to 1.3. The results show that the lowest WER is achieved at a total duration multiplier of 1.0, indicating that the models perform best when the speech is played at its natural speed. When the multiplier is 0.9 or 1.1, the model is still able to achieve a WER very close to the best. When the multiplier is 0.7 or 1.3, the WER is slightly higher but still within a reasonable range. This shows that our model can generate reasonable and accurate content at different speech tempos.

### 4.3 Speech Style Imitation

Zero-shot TTS endeavors to learn how to speak, including voice timbre and style, from prompt speech. Previous works utilized SIM-O to measure the similarity between generated speech and reference speech; however, SIM-O primarily assesses the similarity in voice timbre. In addition to evaluating the model’s zero-shot cloning ability through timbre similarity metrics, we also explored MaskGCT’s capability to clone overall style from two more expressive and stylized dimensions: accent and emotion. We randomly sampled a portion of data from the L2-ARCTIC Zhao et al. () accent corpus and the ESD Zhou et al. () emotion corpus to construct our accent and emotion evaluation datasets. Additionally, we introduce supplementary metrics to assess the model’s performance. For accent imitation, we employ SIM-Accent, to measure the similarity in accent between the generated speech and reference speech. The calculation process is analogous to SIM-O, but we utilize CommonAccent(^4^44[https://huggingface.co/Jzuluaga/accent-id-commonaccent_ecapa](https://huggingface.co/Jzuluaga/accent-id-commonaccent_ecapa)) ; Qian et al. () to derive the accent representation features of the speech. We also incorporate a subjective evaluation metric, Accent SMOS, which is similar to SMOS but focuses on accent rather than timbre. For emotion, we introduce Emotion SIM (with emotion2vec(^5^55[https://github.com/ddlBoJack/emotion2vec](https://github.com/ddlBoJack/emotion2vec)) Ma et al. () to extract features) and Emotion SMOS.

Our experiments demonstrate that MaskGCT exhibits powerful style cloning capabilities. For accent imitation, MaskGCT achieves the highest SIM-O of 0.717, close to the ground truth of 0.747. It also maintains a competitive WER of 6.382 and the best Accent SIM of 0.645. Additionally, MaskGCT leads in CMOS of 0.23, SMOS of 4.24, and Accent SMOS of 4.38. For emotion imitation, MaskGCT achieves the highest SIM-O of 0.600. It also attains a competitive WER of 12.502 and a strong Emotion SIM of 0.822. Furthermore, MaskGCT leads in all subjective metrics with CMOS of -0.31, SMOS of 4.07, and Emotion SMOS of 3.76, indicating natural and pleasant emotion imitation.

**Table 4: Evaluation results for MaskGCT and the baseline methods on accent imitation.**
| System | SIM-O $\uparrow$ | WER $\downarrow$ | Accent SIM $\uparrow$ | CMOS $\uparrow$ | SMOS $\uparrow$ | Accent SMOS $\uparrow$ |
| --- | --- | --- | --- | --- | --- | --- |
| Accent Corpus L2-Arctit |  |  |  |  |  |  |
| Ground Truth | 0.747 | 10.903 | 0.633 | 0.00 | - | - |
| VALL-E | 0.403 | 10.721 | 0.485 | -1.04${}_{\scriptscriptstyle\pm\text{0.50}}$ | 3.12${}_{\scriptscriptstyle\pm\text{0.41}}$ | 2.77${}_{\scriptscriptstyle\pm\text{0.45}}$ |
| CosyVoice | 0.653 | 6.660 | 0.640 | 0.10${}_{\scriptscriptstyle\pm\text{0.19}}$ | 4.23${}_{\scriptscriptstyle\pm\text{0.18}}$ | 3.99${}_{\scriptscriptstyle\pm\text{0.23}}$ |
| VoiceBox | 0.475 | 6.181 | 0.575 | -0.55${}_{\scriptscriptstyle\pm\text{0.22}}$ | 3.93${}_{\scriptscriptstyle\pm\text{0.25}}$ | 3.49${}_{\scriptscriptstyle\pm\text{0.29}}$ |
| VoiceCraft | 0.438 | 10.072 | 0.517 | -0.39${}_{\scriptscriptstyle\pm\text{0.22}}$ | 3.51${}_{\scriptscriptstyle\pm\text{0.33}}$ | 3.29${}_{\scriptscriptstyle\pm\text{0.28}}$ |
| MaskGCT | 0.717 | 6.382 | 0.645 | 0.23${}_{\scriptscriptstyle\pm\text{0.17}}$ | 4.24${}_{\scriptscriptstyle\pm\text{0.16}}$ | 4.38${}_{\scriptscriptstyle\pm\text{0.25}}$ |

**Table 5: Evaluation results for MaskGCT and the baseline methods on emotion imitation.**
| System | SIM-O $\uparrow$ | WER $\downarrow$ | Emotion SIM $\uparrow$ | CMOS $\uparrow$ | SMOS $\uparrow$ | Emotion SMOS $\uparrow$ |
| --- | --- | --- | --- | --- | --- | --- |
| Emotion Corpus ESD |  |  |  |  |  |  |
| Ground Truth | 0.673 | 11.792 | 0.936 | 0.00 | - | - |
| VALL-E | 0.396 | 15.731 | 0.735 | -1.43${}_{\scriptscriptstyle\pm\text{0.33}}$ | 2.52${}_{\scriptscriptstyle\pm\text{0.38}}$ | 2.63${}_{\scriptscriptstyle\pm\text{0.36}}$ |
| CosyVoice | 0.575 | 10.139 | 0.839 | -0.45${}_{\scriptscriptstyle\pm\text{0.18}}$ | 3.98${}_{\scriptscriptstyle\pm\text{0.19}}$ | 3.66${}_{\scriptscriptstyle\pm\text{0.19}}$ |
| VoiceBox | 0.451 | 12.647 | 0.811 | -0.65${}_{\scriptscriptstyle\pm\text{0.20}}$ | 3.81${}_{\scriptscriptstyle\pm\text{0.16}}$ | 3.61${}_{\scriptscriptstyle\pm\text{0.19}}$ |
| VoiceCraft | 0.345 | 16.042 | 0.788 | -0.60${}_{\scriptscriptstyle\pm\text{0.24}}$ | 3.42${}_{\scriptscriptstyle\pm\text{0.31}}$ | 3.52${}_{\scriptscriptstyle\pm\text{0.25}}$ |
| MaskGCT | 0.600 | 12.502 | 0.822 | -0.31${}_{\scriptscriptstyle\pm\text{0.17}}$ | 4.07${}_{\scriptscriptstyle\pm\text{0.16}}$ | 3.76${}_{\scriptscriptstyle\pm\text{0.25}}$ |

### 4.4 Ablation Study

Inference Timesteps. We explore the impact of inference steps of the T2S model on the results, ranging from 5 steps to 75 steps.
Initially, SIM increases significantly and stabilizes after 25 steps. For test-zh, it rises from 0.761 at 5 steps to 0.771 at 75 steps, and for test-en, from 0.696 to 0.715. SIM peaks around 25 steps.
WER improves more dramatically, especially up to 25 steps. For test-zh, it drops from 10.19 at 5 steps to 2.507 at 25 steps, and for test-en, from 8.096 to 2.346. Both SIM and WER show minimal changes beyond 25 steps.
These findings suggest that SIM can be optimized with around 10 steps, while achieving the lowest WER requires approximately 25 steps. Beyond this, both metrics show minimal changes, indicating that further increases in steps do not yield substantial improvements.
Therefore, for practical applications, 25 inference steps may be considered optimal for balancing SIM and WER, ensuring efficient and effective performance. See more details in Appendix A.2.

Model Size. We compare the performance differences of T2S models with varying model sizes. The result is shown in Table 6. We observe that the large model outperforms the base model across all metrics, albeit not significantly. We suggest that our system can achieve good performance with just the setting of the base model when using 100K hours of data. In the future, we will explore more comprehensive scaling laws for both model size and data scaling.

**Table 6: Comparison results between T2S-Large and T2S-Base.**
| System | SIM-O $\uparrow$ | WER $\downarrow$ | FSD $\downarrow$ | #Parameters |
| --- | --- | --- | --- | --- |
| SeedTTS test-en |  |  |  |  |
| T2S-Base | 0.714 | 2.514 | 0.189 | 315M |
| T2S-Large | 0.728 | 2.466 | 0.159 | 695M |
| SeedTTS test-zh |  |  |  |  |
| T2S-Base | 0.769 | 2.216 | 0.123 | 315M |
| T2S-Large | 0.777 | 2.183 | 0.101 | 695M |

Text Tokenizer. We compare two text tokenization methods: Grapheme-to-Phoneme (G2P) and Byte Pair Encoding (BPE). See more details in Appendix A.6.

## 5 Conclusion

In this paper, we present MaskGCT, a large-scale zero-shot TTS system that leverages fully non-autoregressive masked generative codec transformers while not requiring text-speech alignment supervision
and phone-level duration prediction. MaskGCT achieves high-quality text-to-speech synthesis using text to predict semantic tokens extracted from a
speech self-supervised learning (SSL) model, and then predicting acoustic tokens conditioned on these semantic tokens.
Our experiments demonstrate that MaskGCT outperforms the state-of-the-art TTS system on speech quality, similarity, and intelligibility with scaled model size and training data, and MaskGCT can control the total duration of generated speech. We also explore the scalability of MaskGCT in tasks such as speech translation, voice conversion, emotion control, and speech content editing, demonstrating the potential of MaskGCT as a foundational model for speech generation.

## Appendix A Details of MaskGCT

### A.1 Model Architecture

We use a Llama-style Touvron et al. [] Transformer architecture as the backbone of our model, incorporating gated linear units with GELU Hendrycks and Gimpel [] activation (SwiGLU), rotation position encoding Su et al. [], etc., but replacing causal attention with bidirectional attention. We also use adaptive RMSNorm Zhang and Sennrich [], which accepts the time step $t$ as the condition. Table 7 presents the key hyperparameters of the models.

**Table 7: Overview of the key hyperparameters of MaskGCT.**
|  | T2S-Base | T2S-Large | S2A |
| --- | --- | --- | --- |
| Layers | 16 | 16 | 16 |
| Model Dimension | 1,024 | 1,536 | 1,024 |
| FFN Dimension | 4,096 | 6,144 | 4,096 |
| Attention Heads | 16 | 16 | 16 |
| Attention Type | Bidirectional | Bidirectional | Bidirectional |
| Activation Function | SwiGLU | - | - |
| Positional Embeddings | RoPE ($\theta$ = 10,000) | - | - |
| Number of Parameters | 315M | 695M | 353M |

### A.2 Inference Steps for the T2S model

Figure 4 shows the relationship between inference steps and metrics SIM and WER for SeedTTS test-zh (left) and test-en (right).
Initially, SIM increases significantly, stabilizing after 25 steps. For test-zh, SIM rises from 0.761 at 5 steps to 0.771 at 75 steps, and for test-en, from 0.696 to 0.715. SIM reaches high values with just 10 steps but peaks around 25 steps.
WER improves more dramatically, especially up to 25 steps. For test-zh, WER drops from 10.19 at 5 steps to 2.507 at 25 steps, and for test-en, from 8.096 to 2.346. Both SIM and WER show minimal changes beyond 25 steps.
These findings indicate that while SIM metrics can be sufficiently optimized with around 10 inference steps, achieving the lowest WER values requires approximately 25 inference steps. Beyond this threshold, both SIM and WER metrics exhibit minimal changes, implying that further increases in inference steps do not yield substantial improvements in these performance metrics. Therefore, for practical applications, 25 inference steps may be considered optimal for balancing SIM and WER, ensuring efficient and effective performance.

Figure: Figure 4: Inference Steps vs. SIM and WER. The results on the left are for SeedTTS test-zh, and the results on the right are for SeedTTS test-en. In this ablation study, we utilize the ground truth speech length.

### A.3 Inference Steps for the S2A model

The S2A model generates tokens layer by layer during inference. Since the acoustic codec follows an RVQ structure, we can view the S2A inference as a process from coarse to fine. We also use more iterations in the initial layers, as the first few layers carry more information. By default, we use inference steps of $[40,16,1,1,1,1,1,1,1,1,1,1]$ for each layer, however, we find that the S2A model can also perform well with fewer steps, such as $[10,1,1,1,1,1,1,1,1,1,1,1]$, with only a very slight performance loss.

**Table 8: Evaluation results of different inference steps for the S2A model.**
| Inference Steps | SIM-O $\uparrow$ | WER $\downarrow$ | FSD $\downarrow$ |
| --- | --- | --- | --- |
| SeedTTS test-en |  |  |  |
| $[10,1,1,1,1,1,1,1,1,1,1,1]$ | 0.709 | 2.796 | 0.164 |
| $[40,16,1,1,1,1,1,1,1,1,1,1]$ | 0.728 | 2.466 | 0.159 |
| SeedTTS test-zh |  |  |  |
| $[10,1,1,1,1,1,1,1,1,1,1,1]$ | 0.766 | 2.268 | 0.111 |
| $[40,16,1,1,1,1,1,1,1,1,1,1]$ | 0.777 | 2.183 | 0.101 |

### A.4 Details of Semantic and Acoustic Codec

For semantic codec, we train a VQ-VAE model using the hidden features from the 17th layer of W2v-BERT 2.0, incorporating factorized codec Lezama et al. [] technology. The original hidden dimension of 1,024 is projected into a lower-dimensional space for quantization. The codebook size is set to 8,192, with a codebook dimension of 8. We employ only the $\mathcal{L}_{1}$ loss as the reconstruction target, optimizing the codebook with codebook loss and commitment loss. The input features are normalized to have a mean of 0 and a variance of 1, based on the statistics of the training dataset. The encoder and the decoder are each composed of 12 mirrored ConvNext blocks, featuring a kernel size of 7 and a hidden size of 384.

Figure: Figure 5: An overview of the semantic codec (left) and acoustic codec (right). The semantic codec is trained to quantize semantic features with a single codebook and reconstruct semantic features. The acoustic codec is trained to quantize and reconstruct the speech waveform using RVQ, with time and spectral discriminators to enhance the reconstruction quality further.
Refer to caption: x4.png

For acoustic codec, the basic architecture of the encoder follows Kumar et al. [] and the decoder follows Siuzdak []. The Vocos-based decoder can model amplitude and phase, enabling waveform generation through inverse STFT transformation without requiring upsampling. The number of RVQ layers, codebook size, and codebook dimension are set to 12, 8,192, and 8, respectively. We utilize the multi-scale mel-reconstruction loss Kumar et al. [] $\mathcal{L}_{\text{rec}}$, for the
adversarial loss $\mathcal{L}_{\text{adv}}$, we employ both the multi-period discriminator (MPD) and the multi-band multi-scale STFT discriminator, as proposed by Lee et al. [], Kumar et al. []. Additionally, we incorporate the relative feature matching loss $\mathcal{L}_{\text{feat}}$. For codebook learning, we use the codebook loss $\mathcal{L}_{\text{codebook}}$ and the commitment loss $\mathcal{L}_{\text{commit}}$ from VQ-VAE. We set $\lambda_{\text{rec}}=10.0$, $\lambda_{\text{adv}}=2.0$, $\lambda_{\text{feat}}=2.0$, $\lambda_{\text{codebook}}=1.0$, $\lambda_{\text{commit}}=0.25$ as coefficients for balancing each loss terms. Figure 5 shows the overview of the semantic codec and acoustic codec, Table 9 presents the detailed model configurations of semantic codec and acoustic codec.

**Table 9: The detailed model configurations of semantic codec and acoustic codec.**
|  | Semantic Codec | Acoustic Codec |
| --- | --- | --- |
| Input | W2v-BERT 2.0 hidden | Waveform |
| Sample Rate | 16K | 24K |
| Hopsize | 320 | 480 |
| Number of (R)VQ Blocks | 1 | 12 |
| Codebook size | 8,192 | 1,024 |
| Codebook Dimension | 8 | 8 |
| Decoder Hidden Dimension | 384 | 512 |
| Decoder Kernel Size | 7 | 7 |
| Number of Decoder Blocks | 12 | 30 |
| Number of Parameters | 44M | 170M |

### A.5 Details of Duration Predictor

MaskGCT requires specifying the target speech duration during inference, so we train a flow matching Lipman et al. [], Liu et al. [] based duration predictor to obtain the total duration of the target audio by summing the phone-level duration. Note that we do not need to actually use the phone-level durations but only use them to make a reasonable estimate of the total duration, leaving other total duration predictor methods for future works to explore. The duration predictor has a similar Transformer architecture to MaskGCT, with 12 layers, 12 attention heads, and a hidden size of 768. We also adapt in-context learning and classifier-free guidance for the duration predictor. During training, we randomly select a prefix segment of the phoneme sequence and its corresponding duration as a prompt, which is not added with noise. At the same time, we use a probability of 0.15 to drop the prompt. We model the duration in the log domain using flow matching. We denote $x_{1}$ as a random variable of $\log(\text{duration}+1)$, $x_{0}$ as a randomly sampled Gaussian noise, then $v_{\theta}(x_{t},t)=x_{t}=(1-t)x_{0}+tx_{1}$, where the timestep $t\in[0,1]$. The loss function of the duration predictor is $\mathbb{E}_{t,x_{1}}(v_{\theta}(x_{t},t)-(x_{1}-x_{0}))^{2}$. In the inference stage, we use a midpoint ODE solver to generate the target from randomly sampled Gaussian noise with a total of 4 steps. We pretrain a duration aligner (between phoneme and W2v-BERT 2.0 semantic feature) based on monotonic alignment search (MAS) Kim et al. [] to get the ground truth duration for each phoneme.

### A.6 Text Tokenizer

**Table 10: G2P vs. BPE.**
|  | SIM-O $\uparrow$ | WER $\downarrow$ |
| --- | --- | --- |
| SeedTTS test-en |  |  |
| G2P | 0.728 | 2.466 |
| BPE | 0.711 | 4.036 |
| SeedTTS test-zh |  |  |
| G2P | 0.777 | 2.183 |
| BPE | 0.769 | 1.921 |

We consider two text tokenization methods: Grapheme-to-Phoneme (G2P) and Byte Pair Encoding (BPE). For G2P, we employ phonemize(^6^66[https://github.com/bootphon/phonemizer](https://github.com/bootphon/phonemizer)) for English and a combination of jieba(^7^77[https://github.com/fxsjy/jieba](https://github.com/fxsjy/jieba)) and pypinyin(^8^88[https://github.com/mozillazg/python-pinyin](https://github.com/mozillazg/python-pinyin)) for Chinese. For BPE, we utilize the BPE method and vocabulary from Whisper(^9^99[https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/tokenization_whisper.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/tokenization_whisper.py)), with a vocabulary size exceeding 30,000. Table 10 shows the comparison results of MaskGCT using the two different text tokenization methods. The results indicate that G2P outperforms BPE in English with a higher SIM-O of 0.728 compared to 0.711 and a lower WER of 2.466 versus 4.036. Conversely, in Chinese, G2P maintains a slightly higher SIM-O (0.777 vs. 0.769) but BPE achieves a lower WER (1.921 vs. 2.338). These findings suggest that while G2P is superior in preserving text similarity and reducing errors in English, BPE is more effective in minimizing WER in Chinese. We hypothesize that the reason might be that the Chinese G2P system we used still has deficiencies in handling polyphonic characters. In contrast, BPE can learn different pronunciations for the same character based on context.

## Appendix B Discussion about Semantic and Acoustic Definitions

In this paper, we refer to the speech representation extracted from the speech self-supervised learning (SSL) model as the semantic feature. The discrete tokens obtained through the discretization of these semantic features (using k-means or vector quantization are termed semantic tokens. Similarly, we define the representations from melspectrogram, neural speech codecs, or speech VAE as acoustic features, and their discrete counterparts are called acoustic tokens. This terminology was first introduced in Borsos et al. [] and has since been adopted by many subsequent works Borsos et al. [], Guo et al. [], Ju et al. [], Huang et al. [], Zhang et al. [].
It is important to note that this is not a strictly rigorous definition. Generally, we consider semantic features or tokens to contain more prominent linguistic information and exhibit stronger correlations with phonemes or text. One measure of this is the phonetic discriminability in terms of the ABX error rate. In this paper, the W2v-BERT 2.0 features we use have a phonetic discriminability within less than 5 on the LibriSpeech dev-clean dataset, whereas acoustic features, for example, Encodec latent features, score above 20 on this metric.
However, it is worth noting that semantic features or tokens not only contain semantic information but also include prosodic and timbre aspects. In fact, we suggest that for certain two-stage zero-shot TTS systems, excessive loss of information in semantic tokens can degrade the performance of the second stage, where semantic-to-acoustic conversion occurs. Therefore, finding a speech representation that is more suitable for speech generation remains a challenging problem.

## Appendix C Classifier-Free Guidance

We adopt the classifier-free guidance Ho and Salimans [] technique for both the T2S model and the S2A model. We also introduce classifier-free guidance with rescaling, following Lin et al. []. In the training stage, we randomly drop the prompt with a probability of 0.15 to model the probability distribution $p_{\theta}(\mathbf{X})$ without the prompt. During inference, we compute the output embedding $g^{\text{cfg}}_{\theta}(\mathbf{X}|\mathbf{X}^{p})=g_{\theta}(\mathbf{X}|
\mathbf{X}^{p})+w_{\text{cfg}}\cdot(g_{\theta}(\mathbf{X}|\mathbf{X}^{p})-g_{
\theta}(\mathbf{X}))$ of the last layer of the model, where $w_{\text{cfg}}$ is the classifier-free guidance scale, then we compute the rescale embedding $g^{\text{rescale}}_{\theta}(\mathbf{X}|\mathbf{X}^{p})=g^{\text{cfg}}_{\theta}
(\mathbf{X}|\mathbf{X}^{p})\times\text{std}(g_{\theta}(\mathbf{X}|\mathbf{X}^{
p}))/\text{std}(g^{\text{cfg}}_{\theta}(\mathbf{X}|\mathbf{X}^{p}))$, the final output embedding is computed as $w_{\text{rescale}}\cdot g^{\text{rescale}}_{\theta}(\mathbf{X}|\mathbf{X}^{p})
+(1-w_{\text{rescale}})\cdot g^{\text{cfg}}_{\theta}(\mathbf{X}|\mathbf{X}^{p})$. In our paper, $w_{\text{cfg}}$ and $w_{\text{rescale}}$ are set as 2.5 and 0.75 by default.

## Appendix D Evaluation Baselines

VALL-E Wang et al. []. A large-scale TTS system uses an autoregressive and an additional non-autoregressive model to predict discrete tokens from a neural speech codec Défossez et al. []. We reproduce VALL-E with Amphion toolkit Zhang et al. [] and Librilight Kahn et al. [] dataset.

NaturalSpeech 3 Ju et al. []. A non-autoregressive model large-scale TTS systems with factorized speech codec for speech decoupling representation and factorized diffusion Models for speech generation. It achieves human-level naturalness on the LibriSpeech test set. We report the scores of LibriSpeech test-clean obtained from Ju et al. [] and ask for the generated samples for subjective evaluation.

VoiceBox Le et al. []. A non-autoregressive model large-scale multi-task speech generation model based on flow matching Lipman et al. []. We report the scores of LibriSpeech test-clean obtained from Ju et al. [] and ask for the generated samples for subjective evaluation.

XTTS-v2 Casanova et al. []. An open-source multilingual TTS model that supports 16 languages. It is also based on an autoregressive model. We use the official code and pre-trained checkpoint(^10^1010[https://huggingface.co/coqui/XTTS-v2](https://huggingface.co/coqui/XTTS-v2)).

VoiceCraft Peng et al. []. A token-infilling neural codec language model for text editing and text-to-speech. It predicts multi-layer tokens in a delay pattern. We use the official code and pre-trained checkpoint(^11^1111[https://huggingface.co/pyp1/VoiceCraft/blob/main/830M_TTSEnhanced.pth](https://huggingface.co/pyp1/VoiceCraft/blob/main/830M_TTSEnhanced.pth)).

CosyVoice Du et al. []. A two-stage large-scale TTS system. The first stage is an autoregressive model and the second stage is a diffusion model. It is trained on 170,000 hours of multilingual speech data. We use the official code and pre-trained checkpoint(^12^1212[https://huggingface.co/model-scope/CosyVoice-300M](https://huggingface.co/model-scope/CosyVoice-300M)).

## Appendix E Multilingual Zero-Shot TTS

We validate the effectiveness of MaskGCT across four additional languages beyond Chinese and English, specifically Japanese, Korean, German, and French. On the foundation of our existing training data, we expand by 2,500 hours of Japanese, 7,400 hours of Korean, 6,900 hours of German, and 8,200 hours of French. We collect these data using the data collection pipeline proposed by He et al. []. For evaluation, we use the test sets provided in He et al. []. We still employ SIM-O and WER as evaluation metrics, with Whisper-medium(^13^1313[ttps://huggingface.co/openai/whisper-medium](ttps://huggingface.co/openai/whisper-medium)) serving as the ASR model for WER assessment. We utilize XTTS-v2 and the two models proposed in He et al. []: Emilia-AR and Emilia-NAR as comparative baselines. Table 11 shows the results. MaskGCT demonstrates significant improvements over the baselines, with the exception of WER in Japanese. It is noteworthy that we only retrained our text-to-semantic model using the expanded data, without retraining the tokenizers and semantic-to-acoustic models. We believe that further enhancements in our model’s performance can be achieved if all components are retrained on the expanded data.

**Table 11: Evaluation results for MaskGCT and baseline methods on the test sets for Japanese, Korean, German, and French.**
| System | Ja | Ko | Fr | De |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| WER | SIM-O | WER | SIM-O | WER | SIM-O | WER | SIM-O |  |
| Emilia-AR | 3.6 | 0.625 | 10.9 | 0.681 | 8.2 | 0.589 | 6.8 | 0.680 |
| Emilia-NAR | 10.8 | 0.562 | 15.2 | 0.608 | 17.5 | 0.550 | 13.3 | 0.633 |
| XTTS-v2 | 2.981 | 0.579 | 12.45 | 0.617 | 6.898 | 0.531 | 9.168 | 0.569 |
| MaskGCT | 3.903 | 0.678 | 9.417 | 0.732 | 5.598 | 0.667 | 5.126 | 0.745 |

## Appendix F Duration-Controllable Speech Translation

The goal of the speech translation task is to translate speech from one language to another while preserving the original semantic, timbre, and prosody.
In some scenarios, we also need to ensure that the total duration remains relatively unchanged, such as in cross-lingual dubbing.
Our model can achieve this seamlessly, with the ability to control the total duration and, through in-context learning, use the pre-translation speech as a prompt to maintain the timbre and prosody.
To quantify the capabilities of our model, we randomly select 200 samples from SeedTTS test-zh and 200 samples from SeedTTS test-en. Additionally, we sample 200 examples for each language of Japanese, Korean, German, and French from each of the test sets provided in He et al. [].
Subsequently, we utilize GPT4o-mini Achiam et al. [] to translate each sample into one of the other five languages, using the translated text as the target text. We use the duration of prompt speech as the duration of target speech. This process yields 30 sets of test data. Table 12 shows the results of the 30 sets of experiments.
We observe that MaskGCT maintains a good level of speaker similarity across translations between the six languages. Both “X to En” and “En to X” generally perform well, characterized by relatively low WER values and moderate SIM-O scores. “X to Ja” also achieve low WER values. However, for languages other than English, “X to Zh”, “X to De”, and “X to Fr” exhibit higher WER values. We hypothesize that the primary reasons for this include the difficulty in maintaining accurate pronunciation while preserving the same duration before and after translation, as well as the limited training data for Fr and De. Achieving more robust cross-lingual translation remains a focus for future work.
We also show some examples of speech translation in our demo page.

**Table 12: Evaluation results in cross-lingual speech translation with consistent total duration.**
|  | Zh | En | Ja | Ko | De | Fr |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| WER | SIM-O | WER | SIM-O | WER | SIM-O | WER | SIM-O | WER | SIM-O | WER | SIM-O |  |
| Zh | - | - | 7.466 | 0.678 | 7.864 | 0.720 | 9.751 | 0.736 | 25.54 | 0.724 | 16.21 | 0.687 |
| En | 7.411 | 0.535 | - | - | 5.870 | 0.544 | 12.18 | 0.543 | 12.43 | 0.579 | 17.48 | 0.590 |
| Ja | 13.93 | 0.647 | 7.387 | 0.642 | - | - | 10.98 | 0.703 | 12.85 | 0.649 | 14.61 | 0.645 |
| Ko | 31.30 | 0.734 | 14.61 | 0.697 | 12.79 | 0.749 | - | - | 26.58 | 0.722 | 33.96 | 0.712 |
| De | 19.54 | 0.714 | 5.148 | 0.740 | 6.072 | 0.678 | 12.02 | 0.667 | - | - | 14.53 | 0.672 |
| Fr | 32.84 | 0.672 | 12.17 | 0.682 | 6.076 | 0.640 | 12.07 | 0.582 | 21.65 | 0.682 | - | - |

## Appendix G Post-Training for Emotion Control

MaskGCT can unlock more extensive capabilities with post-training. We take emotion control as an example. After being pretrained on a large-scale dataset, we fine-tune the T2S model by adding an additional emotion label as a prefix to the original input sequence. We use an emotion dataset, ESD Zhou et al. [], which consists of 350 parallel utterances with an average duration of 2.9 seconds spoken by 10 native English and 10 native Mandarin speakers, to fine-tune our model. The experimental results show that MaskGCT can unlock emotion control capabilities for zero-shot in-context learning scenarios. For the construction of the train and test datasets, we selected one male and one female speaker each from native English and native Mandarin backgrounds, resulting in a total of four speakers for the test dataset. The remaining 16 speakers were allocated to the training dataset. For the 350 parallel Chinese utterances, we randomly chose 22 utterances for the test set, with the remaining utterances designated for training. Similarly, for the 350 parallel English utterances, we randomly selected 21 utterances for the test set, with the rest used for training. To assess the consistency between the generated audio and the target emotion label, we trained an emotion classification model using the constructed train dataset. This model achieved a classification accuracy of 72% on the test dataset. We show some examples in our demo page.

## Appendix H Speech Content Editing

Based on the mask-and-predict mechanism, our text-to-semantic model supports zero-shot speech content editing with the assistance of a text-speech aligner. By using the aligner, we can identify the editing boundary of the original semantic token sequence, mask the portion that needs to be edited, and then predict the masked semantic tokens using the edited text and the unmasked semantic tokens. However, we have observed that our system is not very robust in editing tasks. A possible conjecture is that we need to adopt a training paradigm better suited for editing tasks, such as fill-in-mask Le et al. [], Du et al. []. We show some examples in our demo page.

## Appendix I Voice Conversion

MaskGCT supports zero-shot voice conversion by fine-tuning the S2A with a modified training strategy. The zero-shot voice conversion task aims to alter the source speech to sound like that of a target speaker using a reference speech from the target speaker, without changing the semantic content. We can directly use the semantic tokens $\mathbf{S}_{\text{src}}$ extracted from the source speech and the prompt acoustic tokens $\mathbf{A}_{\text{ref}}$ extracted from the reference speech to predict the target acoustic tokens $\mathbf{A}_{\text{tgt}}$. Since $\mathbf{S}_{\text{src}}$ may retain some timbre information, we perform timbral perturbation on the semantic features input to the semantic codec encoder. Specifically, we apply timbral perturbation to the input mel-spectrogram features of the W2v-BERT 2.0 model, following the method outlined in FreeVC Li et al. []. We fine-tune our S2A model using this training strategy. We show some examples in our demo page.

## Appendix J Hard Cases Evaluation

We evaluate the performance of MaskGCT on some hard cases (SeedTTS test-hard), which refer to instances where large-scale TTS models, particularly those AR-based models, often exhibit hallucinations. These cases include phrases with repeating words, tongue twisters, and other complex linguistic structures. Examples of such cases include: “the great greek grape growers grow great greek grapes”, “How many cookies could a good cook cook If a good cook could cook cookies? A good cook could cook as much cookies as a good cook who could cook cookies”, and “ thought a thought. But the thought I thought wasn’t the thought I thought I thought. If the thought I thought I thought had been the thought I thought, I wouldn’t have thought so much”.

**Table 13: The evaluation results of MaskGCT and AR + SoundStorm on SeedTTS test-hard.**
| System | SIM-O $\uparrow$ | WER $\downarrow$ |
| --- | --- | --- |
| SeedTTS test-hard |  |  |
| AR + SoundStorm | 0.692 | 34.16 |
| AR + SoundStorm (rank 5) | 0.739 | 17.05 |
| MaskGCT | 0.748 | 10.27 |
| MaskGCT (rank 5) | 0.776 | 6.258 |

## Appendix K Discussion about Concurrent Works

SimpleSpeech Yang et al. [], DiTTo-TTS Lee et al. [], and E2 TTS Eskimez et al. [] are also NAR-based models that do not necessitate precise alignment information between text and speech, nor do they forecast phoneme-level duration. These are concurrent works with MaskGCT. The three models all employ diffusion modeling on speech representations within continuous spaces. SimpleSpeech models the latent representation of a wav codec based on finite scalar quantization (FSQ) Mentzer et al. [], DiTTo-TTS utilizes the latent representation of a wav codec based on residual vector quantization (RVQ), and E2 TTS directly models the mel-spectrogram with flow matching.

## Appendix L Boarder Impact

Given that our model can synthesize speech with high speaker similarity, it carries potential risks of misuse, including spoofing voice identification or impersonating specific speakers. Our experiments were conducted under the assumption that the user consents to be the target speaker for speech synthesis. To mitigate misuse, it is essential to develop a robust model for detecting synthesized speech and to establish a system for reporting suspected misuse.

## Peer Reviews

confidence: 5
contribution: 3
initial_rating: 6
presentation: 3
questions: Comments

* It would be helpful to mention the frame rate of semantic and acoustic tokens.
* I am curious whether only a single sampling step is used from the third layer during acoustic token generation. Is there any difference when using more sampling steps?
* It would be beneficial to also present the overall sampling speed of the model, including the Real-Time Factor.
* In Table 1, it seems that VoiceBox's Imp. Dur. should be marked as "X."
soundness: 3
strengths: * The paper shows that a mask-predict approach on discrete tokens can achieve a high level of speaker similarity. 

* Samples on the demo page show that MaskGCT enables zero-shot TTS that effectively captures not only timbre but also emotion and style across a variety of voices, with excellent sample quality. 

* To assess how well the model captures accent and emotion, the paper introduces metrics such as Accent SIM and Emotion SIM, which utilize representations reflecting each attribute. Their results indicate that MaskGCT performs better in imitating accent and emotion than other approaches based on these metrics.
summary: This paper proposes a masked generative codec transformer, MaskGCT, which performs speech synthesis using a mask-predict approach. MaskGCT generates semantic tokens from text input and is composed of two components: text-to-semantic MaskGCT, which generates semantic tokens from text, and semantic-to-acoustic MaskGCT, which generates acoustic tokens from semantic tokens. Each MaskGCT operates by filling masked parts based on confidence scores predicted in stages, from a sequence where all discrete speech tokens are masked at the start. Instead of using the commonly used method of obtaining semantic tokens through k-means clustering applied to pre-trained SSL models, MaskGCT employs a separate VQ-VAE model on SSL features to generate semantic tokens, reducing information loss through reconstruction loss. MaskGCT uses a similar mask prediction method for predicting discrete speech tokens as Soundstorm, which is based on MaskGIT. However, unlike Soundstorm, MaskGCT approaches the semantic token generation from text through masked token modeling and includes an additional module for total length prediction to align sequence lengths. MaskGCT demonstrates superior speaker similarity in widely used zero-shot TTS evaluation compared to various zero-shot TTS models. Moreover, a demo page with samples shows the versatility of MaskGCT across various tasks.
weaknesses: * In Soundstorm, the mask-predict approach is introduced to acoustic token generation. It seems that the methodological novelty in MaskGCT lies mainly in extending this mask-predict approach to semantic token generation.

* The paper utilizes VQ-VAE to reduce information loss in semantic tokens compared to the k-means clustering approach; however, it does not experimentally demonstrate how this approach improves over k-means clustering.

* Additionally, while using a larger number of semantic tokens may yield good performance in zero-shot TTS, the semantic tokens do not seem disentangled from speaker information, making MaskGCT less suitable for voice conversion tasks. The voice conversion samples on the demo page also appear less similar to the reference speaker.  

* When comparing single-sample generation, MaskGCT's pronunciation accuracy appears lower than that of VoiceBox or NaturalSpeech 3.

* Regarding the seed-TTS evaluation method, although it is not explicitly shown as a baseline in the paper, the objective metrics appear to be worse than those of seed-TTS.

---

confidence: 5
contribution: 2
initial_rating: 3
presentation: 3
questions: See weaknesses.
soundness: 3
strengths: * This paper experimentally demonstrates that NAR (non-autoregressive) non-duration-based TTS methods outperform both AR (autoregressive) based and NAR duration-based TTS methods with publicly available implementation details. 

* The open-source implementation and comprehensive reproducibility details provided in the appendix represent valuable contributions to the field.

* The paper is well-structured, with clear presentation and thorough technical descriptions.
summary: This paper introduces MaskGCT, a non-autoregressive (NAR) masked generative modeling method that sequentially generates audio from acoustic tokens, semantic tokens, and text. The semantic tokens are derived from VQ-VAE of W2V-BERT, a self-supervised speech learning model. The acoustic token approach extends DAC with improved training efficiency.

Results demonstrate that MaskGCT either outperforms or matches current autoregressive (AR) based methods and NAR duration-based methods with publicly available implementation details, across multiple benchmarks: LibriSpeech, SeedTTS (Chinese and English), zero-shot TTS, and speech style (timbre, emotion, accent, etc.) imitation learning. Additionally, MaskGCT surpasses AR-based methods in both naturalness and computational efficiency (inference time steps).
weaknesses: Major Concerns:

* The novelty and technical contribution appear limited. The core methodology seems to be primarily a combination of existing work (MaskGIT and SpearTTS). The speech acoustic code is derived from DAC, with no apparent novel methodological contributions.

* Regarding NAR methods, the authors cite phone-level duration prediction as a source of complex model design. However, this claim needs clarification: How is this complexity measured (e.g., training time, loss balancing)? Why are AR-based methods considered less complex? Experience with NaturalSpeech3/Fastspeech2 suggests duration modeling is manageable and straightforward to implement. Does this conclusion apply to traditional small-scale TTS models? The statement appears overly generalized.

* The Related Work section overlooks prominent flow-based methods like VoiceBox and p-flow, which also employ masked generative models. Their exclusion from this category requires explanation.

* Table 1 would be more appropriate in the appendix. Multi-task capability alone is not a significant contribution, as multi-task frameworks are common. The focus should be on concrete improvements in naturalness, prosody, efficiency, or reduced complexity. Placing Table 1 prominently suggests multi-task functionality is the main contribution, which diminishes the paper's actual value.

* In line 210, the claim that k-means based discrete SSL units lead to information loss needs substantiation. Why is VQ-VAE of W2V-BERT considered superior for semantic preservation despite also being discrete SSL units? While Appendix B provides semantic and acoustic comparisons, direct evidence supporting this specific claim is lacking.

Minor Points:

* Figure 2 would benefit from additional notations for clarity. Also, what is the difference between prompt semantic tokens and target semantic tokens? Can you give an example?

* While following the text-semantics-acoustics pipeline established by papers like SpearTTS, the superiority of this approach over alternatives (e.g., direct text2acoustic without semantics) should be justified.

* Section 4.2.3's purpose and motivation need clarification, particularly if addressing speed modification, as specialized tools exist for this purpose.

* Consider whether inference steps alone adequately measure efficiency, or if other metrics common in streaming methods (e.g., latency) would provide more comprehensive evaluation."

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: - In Table 1, does "ZS TTS" denote "zero-shot TTS", and what's "CL TTS"? For "Imp. Dur.", I also don't fully agree that this work implicitly models duration, as it requires a specified length of the target generated speech.  
- In Section 3.2.3,  why "the number of frames in the semantic token sequence is equal to the sum of the frames in the prompt acoustic sequence and the target acoustic sequence"? 
- Is the output length of the text-to-semantic stage equal to that from the semantic-to-acoustic stage? From Figure 2, the former seems shorter than the latter? 
- In the first paragraph of Section 2, about "SpearTTS utilizes three AR models to predict semantic tokens from text, coarse-grained acoustic tokens from semantic tokens, and fine-grained acoustic tokens from coarse-grained tokens.", the terms "coarse-grained" and "fine-grained" do not seem to be mentioned in the SpearTTS [1] paper, what do the authors mean by them? 
- What's the length of prompt audio? Does it affect the quality of the generated speech?

[1] Kharitonov, E., Vincent, D., Borsos, Z., Marinier, R., Girgin, S., Pietquin, O., ... & Zeghidour, N. (2023). Speak, read and prompt: High-fidelity text-to-speech with minimal supervision. Transactions of the Association for Computational Linguistics, 11, 1703-1718.
soundness: 3
strengths: - This work utilizes non-autoregressive masked generative transformers to generate both sematic tokens and acoustic tokens, offering advantages such as flexible length control, better robustness, and higher inference efficiency. This is suggested by the experiments in Section 4.2.2, comparing to the system replacing the first stage with an autoregressive model (i.e., AR + SoundStorm). 
- The authors conduct extensive experiments on a variety of tasks to evaluate the effectiveness and sca of the proposed system, including not only standard zero-shot TTS, but also cross-lingual dubbing, voice conversion, emotion control, and speech content editing, with potential use of post-training and fine-tuning. 
- The selected state-of-the-art baselines are well-chosen.
summary: The authors present a zero-short text-to-speech (TTS) system called Masked Generative Codec Transformer (MaskGCT), which comprises two stages: 1) predicting semantic tokens from text and 2) using these semantic tokens to generate acoustic tokens. Both stages leverage a non-autoregressive masked generative transformer with specific prompt tokens for in-context learning. A VQ-VAE codec and a residual vector quantization (RVQ) codec are separately trained to obtain the semantic tokens and acoustic tokens. Extensive experiments across various tasks demonstrate that MaskGCT, trained on 100k hours of speech data, surpasses existing state-of-the-art zero-shot TTS systems in quality, similarity, and intelligibility.
weaknesses: - The novelty of this work is somewhat incremental, though I would appreciate its engineering contributions. This work follows a two-stage codec-based sequence-to-sequence strategy, converting text into semantic tokens and then into acoustic tokens, which is in line with SPEAR-TTS [1]. The SoundStorm [2] paper also explores this strategy. Additionally, the idea of applying non-autoregressive masked generative transformer for codec-based TTS is presented in SoundStorm [2] and NaturalSpeech3 [3] (though the NaturalSpeech3 paper describes it from a diffusion perspective.). However, I would like to highlight the distinction that this work employs non-autoregressive masked generative transformers in both stages of the two-stage framework. 
- The authors emphasize that the proposed method does not require text-speech alignment supervision or phone-level duration prediction. However, it appears that the method does require a specified length for the target semantic token sequence, thereby to control the length of the generate speech. The presented experimental results in Table 2, comparing to baseline methods, are based on either ground-truth duration or predicted duration. As descibed in the paper, the authors train a duration predictor to estimate the phone-level durations, which are then summed to determine the total duration of the target speech. This process relies on pre-computed ground-truth phone-level durations. Therefore, it seems that this may not fully align with the claim of not requiring text-speech alignment supervision or phone-level duration prediction. 
- The authors claim that training a VQ-VAE model to quantize the semantic representation can reduce the information loss, compared to using k-means clustering as in previous works. I think it would be better if the authors can provide ablation results or relevant references to support this assertion. 

[1] Kharitonov, E., Vincent, D., Borsos, Z., Marinier, R., Girgin, S., Pietquin, O., ... & Zeghidour, N. (2023). Speak, read and prompt: High-fidelity text-to-speech with minimal supervision. Transactions of the Association for Computational Linguistics, 11, 1703-1718.

[2] Borsos, Z., Sharifi, M., Vincent, D., Kharitonov, E., Zeghidour, N., & Tagliasacchi, M. (2023). Soundstorm: Efficient parallel audio generation. arXiv preprint arXiv:2305.09636.

[3] Ju, Z., Wang, Y., Shen, K., Tan, X., Xin, D., Yang, D., ... & Zhao, S. (2024). Naturalspeech 3: Zero-shot speech synthesis with factorized codec and diffusion models. arXiv preprint arXiv:2403.03100.

---

confidence: 4
contribution: 2
initial_rating: 6
presentation: 3
questions: Address my concerns in the Weaknesses.
soundness: 2
strengths: - This paper offers quite interesting and remarkably high-quality demo audio. The audio samples provided within the paper not only demonstrate clear and distinct sound qualities but also exhibit a certain level of innovation in terms of the audio characteristics they present. They are able to effectively capture the attention of the readers and give a practical sense of the research outcomes in the audio domain.
- The proposed pipeline is significantly more compact than previous ones such as NaturalSpeech3. This compactness implies a more streamlined and efficient design. It potentially leads to reduced computational complexity and may offer advantages in terms of implementation and resource utilization.
summary: This paper presents MASKGCT, a compact Non-Autoregressive (NAR) text-to-speech model. This model is meticulously designed with a specific emphasis on attaining outstanding audio quality. What sets it apart from prior works is its utilization of semantic tokens extracted from the SSL representation using VQVAE and its avoidance of relying on explicit alignment information.
weaknesses: - The Word Error Rate (WER) in Table 4 and 5 of all methods is much higher than previously reported. This discrepancy requires more in-depth explanation. It is essential to analyze the factors contributing to this higher WER, such as possible differences in the data sets used, the experimental settings, or any unique characteristics of the methods employed in this study. A detailed exploration and clarification of these aspects would enhance the understanding and validity of the results presented.
- In Section 3.2.1, the authors utilize VQVAE to obtain discrete semantic tokens instead of k-means. However, no corresponding experiment result is provided. It would be beneficial to include the experimental results related to the use of k-means for a more comprehensive understanding. These results could demonstrate the effectiveness and performance of VQVAE in this context, and also facilitate a comparison with the potential outcomes if k-means had been used. This would add more substance to the discussion and support the authors' choice of using VQVAE.
- To my knowledge, using text as a condition without expanding by duration for a Non-Autoregressive (NAR) model was first proposed in e2-tts. The authors should point out this in Section 3.2.2. Acknowledging the prior work in this area is important for providing a complete context and showing the evolution of the research. By highlighting the connection to e2-tts, the authors can better position their work within the existing body of knowledge and give credit to the relevant pioneering research. It also helps readers to better understand the background and significance of the approach taken in this study.

Meta-review: The paper introduces MaskGCT, a two-stage NAR TTS system that predicts semantic tokens from text and then acoustic tokens from semantic tokens using a masked generative transformer. MaskGCT demonstrates superior speaker similarity in widely used zero-shot TTS evaluation compared to various zero-shot TTS models. The main contribution of this paper is to show that it is possible to train a NAR TTS model with a simple pipeline without requiring explicit phone-speech alignment.

Decision: Accept (Poster)