MaskGCT: A Zero-Shot Text-to-Speech Model Using a Masked Generative Codec Transformer for Parallel Speech Generation Without Explicit Alignment or Duration Prediction

Kernekoncepter

MaskGCT is a novel, fully non-autoregressive TTS model that leverages masked generative transformers to synthesize high-quality speech without requiring explicit text-speech alignment or phone-level duration prediction, achieving human-level similarity, naturalness, and intelligibility.

Resumé

This research paper introduces MaskGCT, a new two-stage text-to-speech (TTS) model that utilizes masked generative transformers for parallel speech generation. Unlike traditional autoregressive and some non-autoregressive models, MaskGCT eliminates the need for explicit text-speech alignment and phone-level duration prediction.

Model Architecture and Methodology

MaskGCT operates in two stages: text-to-semantic (T2S) and semantic-to-acoustic (S2A).

1. Speech Semantic Representation Codec: This component converts speech into semantic tokens using a VQ-VAE model trained on speech self-supervised representations, minimizing information loss compared to traditional k-means methods.

2. Text-to-Semantic Model (T2S): This stage predicts semantic tokens from text input and prompt semantic tokens using a non-autoregressive masked generative transformer. It leverages in-context learning by concatenating text and prompt tokens as prefixes to the input.

3. Semantic-to-Acoustic Model (S2A): This stage predicts multi-layer acoustic tokens from a speech acoustic codec, conditioned on the predicted semantic tokens and prompt acoustic tokens. It employs a similar masked generative transformer approach, predicting masked acoustic tokens for each layer from coarse to fine.

4. Speech Acoustic Codec: This component utilizes residual vector quantization (RVQ) to compress speech waveforms into multi-layer discrete tokens, preserving speech information.

Key Findings and Significance

Evaluations on LibriSpeech, SeedTTS test-en, and SeedTTS test-zh benchmarks demonstrate that MaskGCT achieves:

• Human-level Similarity: Outperforms baselines in speaker similarity (SIM-O, SMOS) metrics, closely matching human recordings.
• High Naturalness: Achieves comparable or better naturalness than ground truth speech in CMOS tests.
• Robustness: Exhibits strong robustness with WER scores comparable to ground truth and better than some autoregressive models, even without phone-level duration predictions.
• Duration Control: Allows flexible control over generated speech duration, showing consistent performance across varying speech tempos.
• Style Imitation: Demonstrates strong capabilities in cloning accent and emotion styles from prompt speech.

Limitations and Future Research

While MaskGCT shows promising results, the authors acknowledge potential for further exploration:

• Scaling Laws: Investigating comprehensive scaling laws for both model size and data scaling to further enhance performance.
• Generalization: Exploring the model's generalization capabilities across a wider range of languages and speaking styles.

MaskGCT's innovative approach to non-autoregressive TTS, coupled with its impressive performance, positions it as a potential foundation for future speech generation models. Its ability to synthesize high-quality speech without explicit alignment or duration prediction opens up new possibilities for various speech-related applications.

Statistik

The MaskGCT model was trained on the Emilia dataset, utilizing 100K hours of English and Chinese speech data.
The model achieved a SIM-O score of 0.687 on the LibriSpeech test-clean benchmark, compared to 0.64 for VoiceBox and 0.67 for NaturalSpeech 3.
On the SeedTTS test-en benchmark, MaskGCT obtained a SIM-O score of 0.717, surpassing CosyVoice's 0.643 and XTTS-v2's 0.463.
For SeedTTS test-zh, MaskGCT reached a SIM-O score of 0.774, outperforming CosyVoice's 0.75 and XTTS-v2's 0.635.
MaskGCT achieved a CMOS score of 0.10 on LibriSpeech, 0.03 on SeedTTS test-en, and 0.05 on SeedTTS test-zh, indicating near-human-level naturalness.
The model's WER was 2.634 on LibriSpeech, 2.623 on SeedTTS test-en, and 2.273 on SeedTTS test-zh, demonstrating strong robustness.
In accent imitation tests, MaskGCT achieved a SIM-O of 0.717 and an Accent SIM of 0.645, indicating successful accent cloning.
For emotion imitation, the model obtained a SIM-O of 0.600 and an Emotion SIM of 0.822, demonstrating effective emotion cloning.

Citater

"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."
"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."
"MaskGCT is a two-stage system, both stages are trained using the mask-and-predict learning paradigm."
"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."

Vigtigste indsigter udtrukket fra

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

by Yuancheng Wa... kl. arxiv.org 10-14-2024

https://arxiv.org/pdf/2409.00750.pdf

Dybere Forespørgsler

How might MaskGCT's capabilities be leveraged to improve accessibility in technologies like screen readers or assistive communication devices?

MaskGCT, with its impressive capabilities in zero-shot text-to-speech synthesis, holds significant potential for revolutionizing accessibility technologies like screen readers and assistive communication devices. Here's how:

High-Quality, Natural-Sounding Speech: MaskGCT's ability to generate human-like speech with naturalness comparable to human recordings can greatly enhance the user experience for individuals relying on screen readers. This can make consuming digital content more engaging and less fatiguing.
Diverse Voice Options: The zero-shot voice cloning capability of MaskGCT allows users to choose from a wide range of voices, including those closely resembling their own. This can be particularly empowering for individuals with speech impairments, enabling them to communicate with a voice that better reflects their identity.
Customizable Speech Output: MaskGCT's control over speech attributes like duration and prosody can be utilized to customize speech output according to user preferences. For instance, users could adjust the speaking rate or intonation to suit their listening comprehension needs.
Support for Under-Resourced Languages: While challenges exist, MaskGCT's architecture, particularly its reliance on self-supervised learning for semantic representation, provides a promising pathway for developing TTS systems for under-resourced languages, potentially bridging the digital divide for many communities.
By integrating MaskGCT into assistive technologies, we can make digital information and communication tools more inclusive and accessible for individuals with disabilities.

Could the reliance on large datasets potentially limit MaskGCT's ability to generate speech in under-resourced languages or dialects? How might this limitation be addressed?

You are right, MaskGCT's dependence on massive datasets poses a significant challenge for under-resourced languages or dialects where such extensive data might be scarce. This limitation could result in lower quality synthesis or even an inability to generate speech in those languages.
However, several strategies can be explored to address this limitation:

Cross-Lingual Transfer Learning: Leveraging existing models trained on high-resource languages to bootstrap the learning process for under-resourced ones. This involves transferring knowledge from the high-resource to the low-resource language, requiring fewer training data for the latter.
Multilingual Training: Training a single MaskGCT model on a dataset encompassing multiple languages, including the under-resourced ones. This approach encourages the model to learn shared phonetic and linguistic features across languages, potentially improving performance on low-resource languages even with limited data.
Data Augmentation Techniques: Employing techniques to artificially increase the size and diversity of training data for under-resourced languages. This could involve generating synthetic speech data, using text-to-speech techniques, or leveraging readily available resources like text corpora for augmenting speech data.
Few-Shot and Zero-Shot Learning: Exploring techniques that enable the model to generalize to new languages with minimal or no training data. This could involve using meta-learning approaches or leveraging powerful language representations from pre-trained language models.
By actively researching and implementing these strategies, we can strive to make MaskGCT and similar technologies more inclusive and accessible for speakers of all languages, regardless of their resource availability.

If we envision a future where AI can seamlessly converse and interact with humans, what ethical considerations and potential implications arise from technologies like MaskGCT that can generate human-like speech?

The ability of technologies like MaskGCT to generate incredibly realistic human-like speech, while groundbreaking, presents profound ethical considerations and potential implications as we move towards a future of seamless AI interaction:

Misinformation and Manipulation: The potential for malicious actors to misuse such technology for creating and spreading misinformation through fabricated audio or impersonating individuals is a serious concern. Establishing mechanisms for authenticating AI-generated content and educating the public to be discerning consumers of information is crucial.
Privacy Violations: The ability to clone voices raises concerns about unauthorized use of an individual's voice for malicious purposes, such as financial scams or defamation. Robust legal frameworks and technological safeguards are needed to protect individuals' voice data and prevent unauthorized voice cloning.
Erosion of Trust: As AI-generated speech becomes increasingly indistinguishable from human speech, it could erode trust in audio evidence and make it difficult to discern truth from falsehood. This underscores the need for transparency in disclosing AI-generated content and fostering critical thinking skills in audiences.
Job Displacement: The automation potential of such technology in fields like customer service, entertainment, and education raises concerns about job displacement. Preparing the workforce for the changing landscape of AI-driven industries through reskilling and upskilling initiatives is essential.
Bias and Discrimination: If not developed and trained responsibly, these technologies could perpetuate existing biases present in the data, leading to discriminatory or offensive outputs. Ensuring diverse and representative datasets and implementing bias mitigation techniques during development is crucial.
Addressing these ethical challenges proactively through interdisciplinary collaboration involving researchers, policymakers, ethicists, and the public is paramount. Establishing clear guidelines, regulations, and industry standards for the development and deployment of such powerful technologies is essential to harness their benefits while mitigating potential harms.