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.
- 2024/10/20: 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.
Clone and install
git clone https://github.com/open-mmlab/Amphion.git
# create env
bash ./models/tts/maskgct/env.shModel 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.
We use the Emilia 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).
| 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 |
Vocos for acoustic codec decoder code.
RepCodec for semantic codec design.
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}
}