Bahasa Harmony: A New High-Quality, Efficient Text-to-Speech Dataset and Model for Bahasa Indonesia

Konsep Inti

This research introduces "Bahasa Harmony," a comprehensive dataset for Bahasa Indonesian text-to-speech synthesis, and "EnGen-TTS," a novel TTS model based on neural codec language modeling, achieving state-of-the-art performance in speech quality and efficiency.

Abstrak

• Bibliographic Information: Susladkar, O. K., Tripathi, V., & Ahmed, B. (2024). Bahasa Harmony: A Comprehensive Dataset for Bahasa Text-to-Speech Synthesis with Discrete Codec Modeling of EnGen-TTS. arXiv preprint arXiv:2410.06608v1.
• Research Objective: This paper introduces a new dataset and model for Bahasa Indonesian text-to-speech synthesis, aiming to improve the quality and efficiency of synthetic speech for this language.
• Methodology: The authors created a dataset called "Bahasa Harmony" consisting of 55 hours of recorded speech from two professional voice actors. They then developed a novel TTS model called "EnGen-TTS" based on a multi-lingual T5 encoder, audio codec language modeling, and a HiFi-GAN vocoder. The model was trained and evaluated using Mean Opinion Score (MOS), Comparative Mean Opinion Score (CMOS), and Real-Time Factor (RTF).
• Key Findings: The EnGen-TTS model outperformed existing TTS models in terms of speech quality, achieving a MOS of 4.45. It also demonstrated high efficiency with a low RTF, indicating fast speech generation. The model's performance remained robust across different model sizes and loss function configurations.
• Main Conclusions: This research significantly advances Bahasa Indonesian TTS technology by providing a high-quality dataset and an efficient, state-of-the-art model. The proposed EnGen-TTS model has the potential to be applied in various domains, including assistive technologies, education, and entertainment.
• Significance: This work addresses the lack of high-quality TTS resources for Bahasa Indonesia, a language spoken by millions. The publicly available dataset and model can foster further research and development in this area.
• Limitations and Future Research: The model currently relies on audio sampled at 22.05 kHz, posing challenges for applications requiring lower sampling rates like telephony. Additionally, the maximum sequence length during training is limited to 500 audio tokens, potentially affecting the naturalness of longer sentences. Future research could address these limitations by exploring methods for high-quality 8 kHz audio generation and extending the model's context window for improved handling of longer sequences.

Statistik

The Bahasa Harmony dataset comprises ~55.0 hours of speech data.
The dataset includes 52K audio recordings.
The average audio recording length is 4.06 seconds.
The dataset contains 458K words.
The vocabulary size of the dataset is 23K.
The dataset includes 68.9K sentences.
The mean word frequency in the dataset is 9.4.
The EnGen-TTS model achieved a Mean Opinion Score (MOS) of 4.45 ± 0.13.
The model achieved a Real-Time Factor (RTF) of 0.016.

Kutipan

"Our key strength is, positioning EnGen-TTS as a solution for high-quality, adaptive Text-to-Speech synthesis across various languages."
"This research marks a significant advancement in Bahasa TTS technology, with implications for diverse language applications."

Wawasan Utama Disaring Dari

Bahasa Harmony: A Comprehensive Dataset for Bahasa Text-to-Speech Synthesis with Discrete Codec Modeling of EnGen-TTS

by Onkar Kishor... pada arxiv.org 10-10-2024

https://arxiv.org/pdf/2410.06608.pdf

Pertanyaan yang Lebih Dalam

How can the development of high-quality TTS systems for low-resource languages be further encouraged and supported?

Developing high-quality Text-to-Speech (TTS) systems for low-resource languages presents unique challenges due to the limited availability of data and resources. Here are some strategies to encourage and support this development:

Open-Source Data Collection and Sharing: Collaborative initiatives to create and share open-source datasets are crucial. This can be facilitated through:

Crowdsourcing: Engaging native speakers in recording and transcribing speech data.
Partnerships: Collaborating with linguistic communities, universities, and research institutions in regions where these languages are spoken.
Data Augmentation Techniques: Utilizing techniques like speed perturbation, pitch shifting, and noise injection to increase the diversity of existing data.

Transfer Learning and Multilingual Models: Leveraging pre-trained models on high-resource languages and adapting them to low-resource languages through techniques like:

Cross-lingual Transfer Learning: Fine-tuning models trained on related languages with similar phonetic structures.
Multilingual Model Training: Training models on multiple languages simultaneously to learn shared representations.

Focus on Lightweight and Efficient Models: Developing models that require less computational power and data, making them suitable for deployment in resource-constrained environments. This can involve:

Model Compression Techniques: Using techniques like pruning, quantization, and knowledge distillation to reduce model size and complexity.
Efficient Architectures: Exploring architectures specifically designed for low-resource scenarios, such as those based on convolutional neural networks (CNNs) or transformers with reduced parameters.

Government Funding and Support: Encouraging government agencies and funding bodies to prioritize research and development in TTS technologies for low-resource languages. This can include:

Grants and Funding Opportunities: Providing financial support for research projects, data collection efforts, and model development.
Policy Initiatives: Implementing policies that promote the use of TTS technologies in education, healthcare, and other sectors, thereby creating demand and driving innovation.

Community Engagement and Awareness: Raising awareness about the importance of TTS systems for low-resource languages and their potential impact on various aspects of life, including:

Education: Providing accessible learning materials for visually impaired students or those learning in their native languages.
Healthcare: Enabling communication between healthcare providers and patients who speak different languages.
Digital Inclusion: Bridging the digital divide by making information and technology accessible to speakers of marginalized languages.

By fostering collaboration, promoting data sharing, and supporting research in this area, we can pave the way for inclusive TTS technologies that cater to the diverse linguistic landscape of our world.

Could the limitations of the model in handling longer sentences be mitigated by incorporating alternative architectures like recurrent neural networks (RNNs) known for their ability to handle sequential data?

While Recurrent Neural Networks (RNNs), particularly LSTMs and GRUs, have been traditionally favored for sequential data processing due to their ability to maintain a hidden state that captures information from previous time steps, they might not necessarily be the optimal solution for mitigating the limitations of the model in handling longer sentences in this specific context. Here's why:

Vanishing Gradients: RNNs, even with gating mechanisms like LSTMs and GRUs, can still suffer from vanishing gradients when dealing with very long sequences. This limits their ability to learn long-range dependencies effectively.
Computational Cost: RNNs process sequences sequentially, making them computationally expensive and slow, especially for long sentences. This is a significant drawback for real-time TTS applications.
More Suitable Alternatives:

Transformers with Enhanced Positional Encodings: Transformers, as used in the EnGen-TTS model, have shown superior performance in handling long sequences due to their self-attention mechanism. However, further improvements can be explored:

Relative Positional Encodings: Encoding the relative positions of words within the sequence can help the model better understand long-range dependencies.
Segment-Level Recurrence: Incorporating recurrence at a segment level, where each segment represents a chunk of the input sequence, can help maintain context over longer stretches.

Hierarchical Architectures: Employing hierarchical architectures, where lower-level modules process shorter segments of the input and higher-level modules integrate information from these segments, can be beneficial. This allows the model to capture both local and global context effectively.
Beyond Architecture:

Curriculum Learning: Training the model on progressively longer sentences can help it gradually learn to handle longer contexts.
Reinforcement Learning: Using reinforcement learning techniques to optimize the model for generating coherent and natural-sounding speech, even for long sentences, can be explored.
In summary, while RNNs might seem like a viable option, their limitations in handling very long sequences make them less suitable than other alternatives. Exploring transformer architectures with enhanced positional encodings, hierarchical models, and innovative training strategies holds more promise for addressing the challenges of long sentence synthesis in TTS systems.

What are the ethical implications of creating increasingly human-like synthetic voices, particularly in the context of potential misuse for impersonation or spreading misinformation?

The development of increasingly human-like synthetic voices, while technologically remarkable, raises significant ethical concerns, particularly regarding potential misuse:

Impersonation and Fraud:

Identity Theft: Realistic synthetic voices could be used to impersonate individuals in phone calls, potentially leading to financial fraud, identity theft, or manipulation of personal relationships.
Spoofing Authority Figures: Imagine a scenario where a synthetic voice mimicking a government official or CEO is used to spread false information or manipulate stock markets.

Misinformation and Manipulation:

Deepfakes for Audio: Just as deepfakes have eroded trust in visual media, synthetic audio could be used to create fake news, spread propaganda, or incite violence, especially when combined with social media's reach.
Eroding Trust in Audio Evidence: As synthetic voices become more convincing, it becomes increasingly difficult to discern real audio from fabricated content, potentially undermining the credibility of audio evidence in legal proceedings or investigative journalism.

Psychological and Emotional Impact:

Distress and Trauma: Hearing a loved one's voice synthesized for malicious purposes could cause significant emotional distress, especially in cases of bereavement or separation.
Manipulating Emotions: Highly emotive synthetic voices could be used in targeted advertising or political campaigns to manipulate people's emotions and influence their decisions.

Access and Control:

Unequal Access and Potential for Bias: The technology to create and use synthetic voices might not be equally accessible, potentially creating a divide between those who can control and manipulate this technology and those who cannot.
Bias Amplification: If not developed and trained responsibly, synthetic voices could perpetuate existing biases present in the data, leading to discriminatory or offensive outputs.

Mitigating the Risks:
Addressing these ethical concerns requires a multi-pronged approach:

Technical Countermeasures: Developing robust detection algorithms and tools to identify synthetic audio and distinguish it from genuine recordings.
Regulation and Legislation: Establishing clear legal frameworks and guidelines for the ethical development and use of synthetic voice technology, potentially including penalties for malicious use.
Public Awareness and Education: Educating the public about the capabilities and limitations of synthetic voices, raising awareness about potential misuse, and promoting media literacy to critically evaluate audio content.
Industry Standards and Best Practices: Encouraging responsible development and deployment of synthetic voice technology within the tech industry, potentially through ethical guidelines, transparency initiatives, and collaboration with ethicists and social scientists.
The development of increasingly human-like synthetic voices presents both exciting opportunities and significant ethical challenges. By proactively addressing these challenges through a combination of technical, legal, and societal measures, we can strive to harness the benefits of this technology while mitigating the risks of its potential misuse.