Upload README.md

README.md

@@ -1,169 +1,183 @@
-# Amphion: An Open-Source Audio, Music, and Speech Generation Toolkit
-
-<div>
-    <a href="https://arxiv.org/abs/2312.09911"><img src="https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg"></a>
-    <a href="https://huggingface.co/amphion"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Amphion-pink"></a>
-    <a href="https://openxlab.org.cn/usercenter/Amphion"><img src="https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg"></a>
-    <a href="https://discord.com/invite/ZxxREr3Y"><img src="https://img.shields.io/badge/Discord-Join%20chat-blue.svg"></a>
-    <a href="egs/tts/README.md"><img src="https://img.shields.io/badge/README-TTS-blue"></a>
-    <a href="egs/svc/README.md"><img src="https://img.shields.io/badge/README-SVC-blue"></a>
-    <a href="egs/tta/README.md"><img src="https://img.shields.io/badge/README-TTA-blue"></a>
-    <a href="egs/vocoder/README.md"><img src="https://img.shields.io/badge/README-Vocoder-purple"></a>
-    <a href="egs/metrics/README.md"><img src="https://img.shields.io/badge/README-Evaluation-yellow"></a>
-    <a href="LICENSE"><img src="https://img.shields.io/badge/LICENSE-MIT-red"></a>
-</div>
-<br>
-
-**Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation.** Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development. Amphion offers a unique feature: **visualizations** of classic models or architectures. We believe that these visualizations are beneficial for junior researchers and engineers who wish to gain a better understanding of the model.
-
-**The North-Star objective of Amphion is to offer a platform for studying the conversion of any inputs into audio.** Amphion is designed to support individual generation tasks, including but not limited to,
-
-- **TTS**: Text to Speech (⛳ supported)
-- **SVS**: Singing Voice Synthesis (👨‍💻 developing)
-- **VC**: Voice Conversion (👨‍💻 developing)
-- **SVC**: Singing Voice Conversion (⛳ supported)
-- **TTA**: Text to Audio (⛳ supported)
-- **TTM**: Text to Music (👨‍💻 developing)
-- more…
-
-In addition to the specific generation tasks, Amphion includes several **vocoders** and **evaluation metrics**. A vocoder is an important module for producing high-quality audio signals, while evaluation metrics are critical for ensuring consistent metrics in generation tasks. Moreover, Amphion is dedicated to advancing audio generation in real-world applications, such as building **large-scale datasets** for speech synthesis.
-
-## 🚀 News
-- **2024/10/19**: We release **MaskGCT**, a fully non-autoregressive TTS model that eliminates the need for explicit alignment information between text and speech supervision. MaskGCT is trained on Emilia dataset and achieves SOTA zero-shot TTS perfermance.  [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2409.00750) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-model-yellow)](https://huggingface.co/amphion/maskgct) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-demo-pink)](https://huggingface.co/spaces/amphion/maskgct) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](models/tts/maskgct/README.md)
-- **2024/09/01**: [Amphion](https://arxiv.org/abs/2312.09911), [Emilia](https://arxiv.org/abs/2407.05361) and [DSFF-SVC](https://arxiv.org/abs/2310.11160) got accepted by IEEE SLT 2024! 🤗
-- **2024/08/28**: Welcome to join Amphion's [Discord channel](https://discord.gg/drhW7ajqAG) to stay connected and engage with our community！
-- **2024/08/20**: [SingVisio](https://arxiv.org/abs/2402.12660) got accepted by Computers & Graphics, [available here](https://www.sciencedirect.com/science/article/pii/S0097849324001936)! 🎉
-- **2024/08/27**: *The Emilia dataset is now publicly available!* Discover the most extensive and diverse speech generation dataset with 101k hours of in-the-wild speech data now at [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/amphion/Emilia-Dataset) or [![OpenDataLab](https://img.shields.io/badge/OpenDataLab-Dataset-blue)](https://opendatalab.com/Amphion/Emilia)! 👑👑👑
-- **2024/07/01**: Amphion now releases **Emilia**, the first open-source multilingual in-the-wild dataset for speech generation with over 101k hours of speech data, and the **Emilia-Pipe**, the first open-source preprocessing pipeline designed to transform in-the-wild speech data into high-quality training data with annotations for speech generation! [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2407.05361) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/amphion/Emilia) [![demo](https://img.shields.io/badge/WebPage-Demo-red)](https://emilia-dataset.github.io/Emilia-Demo-Page/) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](preprocessors/Emilia/README.md)
-- **2024/06/17**: Amphion has a new release for its **VALL-E** model! It uses Llama as its underlying architecture and has better model performance, faster training speed, and more readable codes compared to our first version. [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/tts/VALLE_V2/README.md)
-- **2024/03/12**: Amphion now support **NaturalSpeech3 FACodec** and release pretrained checkpoints. [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2403.03100) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-model-yellow)](https://huggingface.co/amphion/naturalspeech3_facodec) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-demo-pink)](https://huggingface.co/spaces/amphion/naturalspeech3_facodec) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](models/codec/ns3_codec/README.md)
-- **2024/02/22**: The first Amphion visualization tool, **SingVisio**, release. [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2402.12660) [![openxlab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/Amphion/SingVisio) [![Video](https://img.shields.io/badge/Video-Demo-orange)](https://drive.google.com/file/d/15097SGhQh-SwUNbdWDYNyWEP--YGLba5/view) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/visualization/SingVisio/README.md)
-- **2023/12/18**: Amphion v0.1 release. [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2312.09911) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Amphion-pink)](https://huggingface.co/amphion) [![youtube](https://img.shields.io/badge/YouTube-Demo-red)](https://www.youtube.com/watch?v=1aw0HhcggvQ) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](https://github.com/open-mmlab/Amphion/pull/39)
-- **2023/11/28**: Amphion alpha release. [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](https://github.com/open-mmlab/Amphion/pull/2)
-
-## ⭐ Key Features
-
-### TTS: Text to Speech
-
-- Amphion achieves state-of-the-art performance compared to existing open-source repositories on text-to-speech (TTS) systems. It supports the following models or architectures:
-    - [FastSpeech2](https://arxiv.org/abs/2006.04558): A non-autoregressive TTS architecture that utilizes feed-forward Transformer blocks.
-    - [VITS](https://arxiv.org/abs/2106.06103): An end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning
-    - [VALL-E](https://arxiv.org/abs/2301.02111): A zero-shot TTS architecture that uses a neural codec language model with discrete codes.
-    - [NaturalSpeech2](https://arxiv.org/abs/2304.09116): An architecture for TTS that utilizes a latent diffusion model to generate natural-sounding voices.
-    - [Jets](Jets): An end-to-end TTS model that jointly trains FastSpeech2 and HiFi-GAN with an alignment module.
-    - [MaskGCT](https://arxiv.org/abs/2409.00750): a fully non-autoregressive TTS architecture that eliminates the need for explicit alignment information between text and speech supervision.
-
-### SVC: Singing Voice Conversion
-
-- Ampion supports multiple content-based features from various pretrained models, including [WeNet](https://github.com/wenet-e2e/wenet), [Whisper](https://github.com/openai/whisper), and [ContentVec](https://github.com/auspicious3000/contentvec). Their specific roles in SVC has been investigated in our SLT 2024 paper. [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2310.11160) [![code](https://img.shields.io/badge/README-Code-red)](egs/svc/MultipleContentsSVC)
-- Amphion implements several state-of-the-art model architectures, including diffusion-, transformer-, VAE- and flow-based models. The diffusion-based architecture uses [Bidirectional dilated CNN](https://openreview.net/pdf?id=a-xFK8Ymz5J) as a backend and supports several sampling algorithms such as [DDPM](https://arxiv.org/pdf/2006.11239.pdf), [DDIM](https://arxiv.org/pdf/2010.02502.pdf), and [PNDM](https://arxiv.org/pdf/2202.09778.pdf). Additionally, it supports single-step inference based on the [Consistency Model](https://openreview.net/pdf?id=FmqFfMTNnv).
-
-### TTA: Text to Audio
-
-- Amphion supports the TTA with a latent diffusion model. It is designed like [AudioLDM](https://arxiv.org/abs/2301.12503), [Make-an-Audio](https://arxiv.org/abs/2301.12661), and [AUDIT](https://arxiv.org/abs/2304.00830). It is also the official implementation of the text-to-audio generation part of our NeurIPS 2023 paper. [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2304.00830) [![code](https://img.shields.io/badge/README-Code-red)](egs/tta/RECIPE.md)
-
-### Vocoder
-
-- Amphion supports various widely-used neural vocoders, including:
-    - GAN-based vocoders: [MelGAN](https://arxiv.org/abs/1910.06711), [HiFi-GAN](https://arxiv.org/abs/2010.05646), [NSF-HiFiGAN](https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts), [BigVGAN](https://arxiv.org/abs/2206.04658), [APNet](https://arxiv.org/abs/2305.07952).
-    - Flow-based vocoders: [WaveGlow](https://arxiv.org/abs/1811.00002).
-    - Diffusion-based vocoders: [Diffwave](https://arxiv.org/abs/2009.09761).
-    - Auto-regressive based vocoders: [WaveNet](https://arxiv.org/abs/1609.03499), [WaveRNN](https://arxiv.org/abs/1802.08435v1).
-- Amphion provides the official implementation of [Multi-Scale Constant-Q Transform Discriminator](https://arxiv.org/abs/2311.14957) (our ICASSP 2024 paper). It can be used to enhance any architecture GAN-based vocoders during training, and keep the inference stage (such as memory or speed) unchanged. [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2311.14957) [![code](https://img.shields.io/badge/README-Code-red)](egs/vocoder/gan/tfr_enhanced_hifigan)
-
-### Evaluation
-
-Amphion provides a comprehensive objective evaluation of the generated audio. The evaluation metrics contain:
-
-- **F0 Modeling**: F0 Pearson Coefficients, F0 Periodicity Root Mean Square Error, F0 Root Mean Square Error, Voiced/Unvoiced F1 Score, etc.
-- **Energy Modeling**: Energy Root Mean Square Error, Energy Pearson Coefficients, etc.
-- **Intelligibility**: Character/Word Error Rate, which can be calculated based on [Whisper](https://github.com/openai/whisper) and more.
-- **Spectrogram Distortion**: Frechet Audio Distance (FAD), Mel Cepstral Distortion (MCD), Multi-Resolution STFT Distance (MSTFT), Perceptual Evaluation of Speech Quality (PESQ), Short Time Objective Intelligibility (STOI), etc.
-- **Speaker Similarity**: Cosine similarity, which can be calculated based on [RawNet3](https://github.com/Jungjee/RawNet), [Resemblyzer](https://github.com/resemble-ai/Resemblyzer), [WeSpeaker](https://github.com/wenet-e2e/wespeaker), [WavLM](https://github.com/microsoft/unilm/tree/master/wavlm) and more.
+## MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer
 
-### Datasets
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2409.00750)
+[![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-model-yellow)](https://huggingface.co/amphion/maskgct)
+[![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-demo-pink)](https://huggingface.co/spaces/amphion/maskgct)
+[![readme](https://img.shields.io/badge/README-Key%20Features-blue)](./models/tts/maskgct/README.md)
 
-- Amphion unifies the data preprocess of the open-source datasets including [AudioCaps](https://audiocaps.github.io/), [LibriTTS](https://www.openslr.org/60/), [LJSpeech](https://keithito.com/LJ-Speech-Dataset/), [M4Singer](https://github.com/M4Singer/M4Singer), [Opencpop](https://wenet.org.cn/opencpop/), [OpenSinger](https://github.com/Multi-Singer/Multi-Singer.github.io), [SVCC](http://vc-challenge.org/), [VCTK](https://datashare.ed.ac.uk/handle/10283/3443), and more. The supported dataset list can be seen [here](egs/datasets/README.md) (updating). 
-- Amphion (exclusively) supports the [**Emilia**](preprocessors/Emilia/README.md) dataset and its preprocessing pipeline **Emilia-Pipe** for in-the-wild speech data!
+## Overview
 
-### Visualization
+MaskGCT (**Mask**ed **G**enerative **C**odec **T**ransformer) is *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 [demo page](https://maskgct.github.io/).
 
-Amphion provides visualization tools to interactively illustrate the internal processing mechanism of classic models. This provides an invaluable resource for educational purposes and for facilitating understandable research.
-
-Currently, Amphion supports [SingVisio](egs/visualization/SingVisio/README.md), a visualization tool of the diffusion model for singing voice conversion. [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2402.12660) [![openxlab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/Amphion/SingVisio) [![Video](https://img.shields.io/badge/Video-Demo-orange)](https://drive.google.com/file/d/15097SGhQh-SwUNbdWDYNyWEP--YGLba5/view)
-
-
-## 📀 Installation
-
-Amphion can be installed through either Setup Installer or Docker Image.
-
-### Setup Installer
-
-```bash
-git clone https://github.com/open-mmlab/Amphion.git
-cd Amphion
+<br>
+<div align="center">
+<img src="./imgs/maskgct/maskgct.png" width="100%">
+</div>
+<br>
 
-# Install Python Environment
-conda create --name amphion python=3.9.15
-conda activate amphion
+## News
 
-# Install Python Packages Dependencies
-sh env.sh
-```
+- **2024/10/19**: We release **MaskGCT**, a fully non-autoregressive TTS model that eliminates the need for explicit alignment information between text and speech supervision. MaskGCT is trained on Emilia dataset and achieves SOTA zero-shot TTS perfermance.
 
-### Docker Image
+## Quickstart
 
-1. Install [Docker](https://docs.docker.com/get-docker/), [NVIDIA Driver](https://www.nvidia.com/download/index.aspx), [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html), and [CUDA](https://developer.nvidia.com/cuda-downloads).
+**Clone and install**
 
-2. Run the following commands:
 ```bash
 git clone https://github.com/open-mmlab/Amphion.git
-cd Amphion
-
-docker pull realamphion/amphion
-docker run --runtime=nvidia --gpus all -it -v .:/app realamphion/amphion
+# create env
+bash ./models/tts/maskgct/env.sh
 ```
-Mount dataset by argument `-v` is necessary when using Docker. Please refer to [Mount dataset in Docker container](egs/datasets/docker.md) and [Docker Docs](https://docs.docker.com/engine/reference/commandline/container_run/#volume) for more details.
 
+**Model download**
 
-## 🐍 Usage in Python
+We provide the following pretrained checkpoints:
 
-We detail the instructions of different tasks in the following recipes:
 
-- [Text to Speech (TTS)](egs/tts/README.md)
-- [Singing Voice Conversion (SVC)](egs/svc/README.md)
-- [Text to Audio (TTA)](egs/tta/README.md)
-- [Vocoder](egs/vocoder/README.md)
-- [Evaluation](egs/metrics/README.md)
-- [Visualization](egs/visualization/README.md)
+| Model Name          | Description   |    
+|-------------------|-------------|
+| [Acoustic Codec](https://huggingface.co/amphion/MaskGCT/tree/main/acoustic_codec)      | Converting speech to semantic tokens. |
+| [Semantic Codec](https://huggingface.co/amphion/MaskGCT/tree/main/semantic_codec)      | Converting speech to acoustic tokens and reconstructing waveform from acoustic tokens. |
+| [MaskGCT-T2S](https://huggingface.co/amphion/MaskGCT/tree/main/t2s_model)         | Predicting semantic tokens with text and prompt semantic tokens.             |
+| [MaskGCT-S2A](https://huggingface.co/amphion/MaskGCT/tree/main/s2a_model)         | Predicts acoustic tokens conditioned on semantic tokens.              |
 
-## 👨‍💻 Contributing
-We appreciate all contributions to improve Amphion. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
+You can download all pretrained checkpoints from [HuggingFace](https://huggingface.co/amphion/MaskGCT/tree/main) or use huggingface api.
 
-## 🙏 Acknowledgement
+```python
+from huggingface_hub import hf_hub_download
 
+# download semantic codec ckpt
+semantic_code_ckpt = hf_hub_download("amphion/MaskGCT" filename="semantic_codec/model.safetensors")
 
-- [ming024's FastSpeech2](https://github.com/ming024/FastSpeech2) and [jaywalnut310's VITS](https://github.com/jaywalnut310/vits) for model architecture code.
-- [lifeiteng's VALL-E](https://github.com/lifeiteng/vall-e) for training pipeline and model architecture design.
-- [SpeechTokenizer](https://github.com/ZhangXInFD/SpeechTokenizer) for semantic-distilled tokenizer design.
-- [WeNet](https://github.com/wenet-e2e/wenet), [Whisper](https://github.com/openai/whisper), [ContentVec](https://github.com/auspicious3000/contentvec), and [RawNet3](https://github.com/Jungjee/RawNet) for pretrained models and inference code.
-- [HiFi-GAN](https://github.com/jik876/hifi-gan) for GAN-based Vocoder's architecture design and training strategy.
-- [Encodec](https://github.com/facebookresearch/encodec) for well-organized GAN Discriminator's architecture and basic blocks.
-- [Latent Diffusion](https://github.com/CompVis/latent-diffusion) for model architecture design.
-- [TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS) for preparing the MFA tools.
+# download acoustic codec ckpt
+codec_encoder_ckpt = hf_hub_download("amphion/MaskGCT", filename="acoustic_codec/model.safetensors")
+codec_decoder_ckpt = hf_hub_download("amphion/MaskGCT", filename="acoustic_codec/model_1.safetensors")
 
+# download t2s model ckpt
+t2s_model_ckpt = hf_hub_download("amphion/MaskGCT", filename="t2s_model/model.safetensors")
 
-## ©️ License
+# download s2a model ckpt
+s2a_1layer_ckpt = hf_hub_download("amphion/MaskGCT", filename="s2a_model/s2a_model_1layer/model.safetensors")
+s2a_full_ckpt = hf_hub_download("amphion/MaskGCT", filename="s2a_model/s2a_model_full/model.safetensors")
+```
 
-Amphion is under the [MIT License](LICENSE). It is free for both research and commercial use cases.
+**Basic Usage**
+
+You can use the following code to generate speech from text and a prompt speech (the code is also provided in [inference.py](./models/tts/maskgct/maskgct_inference.py)).
+
+```python
+from models.tts.maskgct.maskgct_utils import *
+from huggingface_hub import hf_hub_download
+import safetensors
+import soundfile as sf
+
+if __name__ == "__main__":
+
+    # build model
+    device = torch.device("cuda:0")
+    cfg_path = "./models/tts/maskgct/config/maskgct.json"
+    cfg = load_config(cfg_path)
+    # 1. build semantic model (w2v-bert-2.0)
+    semantic_model, semantic_mean, semantic_std = build_semantic_model(device)
+    # 2. build semantic codec
+    semantic_codec = build_semantic_codec(cfg.model.semantic_codec, device)
+    # 3. build acoustic codec
+    codec_encoder, codec_decoder = build_acoustic_codec(cfg.model.acoustic_codec, device)
+    # 4. build t2s model
+    t2s_model = build_t2s_model(cfg.model.t2s_model, device)
+    # 5. build s2a model
+    s2a_model_1layer = build_s2a_model(cfg.model.s2a_model.s2a_1layer, device)
+    s2a_model_full =  build_s2a_model(cfg.model.s2a_model.s2a_full, device)
+
+    # download checkpoint
+    ...
+
+    # load semantic codec
+    safetensors.torch.load_model(semantic_codec, semantic_code_ckpt)
+    # load acoustic codec
+    safetensors.torch.load_model(codec_encoder, codec_encoder_ckpt)
+    safetensors.torch.load_model(codec_decoder, codec_decoder_ckpt)
+    # load t2s model
+    safetensors.torch.load_model(t2s_model, t2s_model_ckpt)
+    # load s2a model
+    safetensors.torch.load_model(s2a_model_1layer, s2a_1layer_ckpt)
+    safetensors.torch.load_model(s2a_model_full, s2a_full_ckpt)
+
+    # inference
+    prompt_wav_path = "./models/tts/maskgct/wav/prompt.wav"
+    save_path = "[YOUR SAVE PATH]"
+    prompt_text = " We do not break. We never give in. We never back down."
+    target_text = "In this paper, we introduce MaskGCT, a fully non-autoregressive TTS model that eliminates the need for explicit alignment information between text and speech supervision."
+    # Specify the target duration (in seconds). If target_len = None, we use a simple rule to predict the target duration.
+    target_len = 18
+
+    maskgct_inference_pipeline = MaskGCT_Inference_Pipeline(
+        semantic_model,
+        semantic_codec,
+        codec_encoder,
+        codec_decoder,
+        t2s_model,
+        s2a_model_1layer,
+        s2a_model_full,
+        semantic_mean,
+        semantic_std,
+        device,
+    )
+
+    recovered_audio = maskgct_inference_pipeline.maskgct_inference(
+        prompt_wav_path, prompt_text, target_text, "en", "en", target_len=target_len
+    )
+    sf.write(save_path, recovered_audio, 24000)        
+```
 
-## 📚 Citations
+**Jupyter Notebook**
+
+We also provide a [jupyter notebook](./models/tts/maskgct/maskgct_demo.ipynb) to show more details of MaskGCT inference.
+
+
+## Evaluation Results of MaskGCT
+
+| System | SIM-O↑ | WER↓ | FSD↓ | SMOS↑ | CMOS↑ |
+| :--- | :---: | :---: | :---: | :---: | :---: |
+| | | **LibriSpeech test-clean** |
+| Ground Truth | 0.68 | 1.94 |  | 4.05±0.12 | 0.00 |
+| VALL-E | 0.50 | 5.90 | - | 3.47 ±0.26 | -0.52±0.22 |
+| VoiceBox | 0.64 | 2.03 | 0.762 | 3.80±0.17 | -0.41±0.13 |
+| NaturalSpeech 3 | 0.67 | 1.94 | 0.786 | 4.26±0.10 | 0.16±0.14 |
+| VoiceCraft | 0.45 | 4.68 | 0.981 | 3.52±0.21 | -0.33 ±0.16 |
+| XTTS-v2 | 0.51 | 4.20 | 0.945 | 3.02±0.22 | -0.98 ±0.19 |
+| MaskGCT | 0.687(0.723) | 2.634(1.976) | 0.886 | 4.27±0.14 | 0.10±0.16 |
+| MaskGCT(gt length) | 0.697 | 2.012 | 0.746 | 4.33±0.11 | 0.13±0.13 |
+| | | **SeedTTS test-en** |
+| Ground Truth | 0.730 | 2.143 |  | 3.92±0.15 | 0.00 |
+| CosyVoice | 0.643 | 4.079 | 0.316 | 3.52±0.17 | -0.41 ±0.18 |
+| XTTS-v2 | 0.463 | 3.248 | 0.484 | 3.15±0.22 | -0.86±0.19 |
+| VoiceCraft | 0.470 | 7.556 | 0.226 | 3.18±0.20 | -1.08 ±0.15 |
+| MaskGCT | 0.717(0.760) | 2.623(1.283) | 0.188 | 4.24 ±0.12 | 0.03 ±0.14 |
+| MaskGCT(gt length) | 0.728 | 2.466 | 0.159 | 4.13 ±0.17 | 0.12 ±0.15 |
+| | | **SeedTTS test-zh** |
+| Ground Truth | 0.750 | 1.254 |  | 3.86 ±0.17 | 0.00 |
+| CosyVoice | 0.750 | 4.089 | 0.276 | 3.54 ±0.12 | -0.45 ±0.15 |
+| XTTS-v2 | 0.635 | 2.876 | 0.413 | 2.95 ±0.18 | -0.81 ±0.22 |
+| MaskGCT | 0.774(0.805) | 2.273(0.843) | 0.106 | 4.09 ±0.12 | 0.05 ±0.17 |
+| MaskGCT(gt length) | 0.777 | 2.183 | 0.101 | 4.11 ±0.12 | 0.08±0.18 |
+
+## Citations
+
+If you use MaskGCT in your research, please cite the following paper:
 
 ```bibtex
-@inproceedings{amphion,
-    author={Zhang, Xueyao and Xue, Liumeng and Gu, Yicheng and Wang, Yuancheng and Li, Jiaqi and He, Haorui and Wang, Chaoren and Song, Ting and Chen, Xi and Fang, Zihao and Chen, Haopeng and Zhang, Junan and Tang, Tze Ying and Zou, Lexiao and Wang, Mingxuan and Han, Jun and Chen, Kai and Li, Haizhou and Wu, Zhizheng},
-    title={Amphion: An Open-Source Audio, Music and Speech Generation Toolkit},
-    booktitle={{IEEE} Spoken Language Technology Workshop, {SLT} 2024},
-    year={2024}
+@article{wang2024maskgct,
+  title={MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer},
+  author={Wang, Yuancheng and Zhan, Haoyue and Liu, Liwei and Zeng, Ruihong and Guo, Haotian and Zheng, Jiachen and Zhang, Qiang and Zhang, Shunsi and Wu, Zhizheng},
+  journal={arXiv preprint arXiv:2409.00750},
+  year={2024}
 }
-```
+
+@article{zhang2023amphion,
+  title={Amphion: An open-source audio, music and speech generation toolkit},
+  author={Zhang, Xueyao and Xue, Liumeng and Wang, Yuancheng and Gu, Yicheng and Chen, Xi and Fang, Zihao and Chen, Haopeng and Zou, Lexiao and Wang, Chaoren and Han, Jun and others},
+  journal={arXiv preprint arXiv:2312.09911},
+  year={2023}
+}
+```