models/tts/maskgct/README.md

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

[![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)

## Overview

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/).

<br>
<div align="center">
<img src="../../../imgs/maskgct/maskgct.png" width="100%">
</div>
<br>

## 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.

## Quickstart

**Clone and install**

```bash
git clone https://github.com/open-mmlab/Amphion.git
# create env
bash ./models/tts/maskgct/env.sh
```

**Model download**

We provide the following pretrained checkpoints:


| 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.              |

You can download all pretrained checkpoints from [HuggingFace](https://huggingface.co./amphion/MaskGCT/tree/main) or use huggingface api.

```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")

# 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")

# 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")
```

**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)        
```

**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
@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}
}
```