models/tts/maskgct/README.md

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

Overview

MaskGCT (Masked Generative Codec Transformer) 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.

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

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	Converting speech to semantic tokens.
Semantic Codec	Converting speech to acoustic tokens and reconstructing waveform from acoustic tokens.
MaskGCT-T2S	Predicting semantic tokens with text and prompt semantic tokens.
MaskGCT-S2A	Predicts acoustic tokens conditioned on semantic tokens.

You can download all pretrained checkpoints from HuggingFace or use huggingface api.

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).

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 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:

@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}
}