IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian Text-to-Speech

Alapfogalmak

IndicVoices-R is the largest multilingual Indian text-to-speech dataset, comprising 1,704 hours of high-quality speech from 10,496 speakers across 22 Indian languages, enabling the development of robust and versatile TTS models.

Kivonat

The paper introduces IndicVoices-R, a large-scale multilingual text-to-speech (TTS) dataset for Indian languages. The dataset is derived from the existing IndicVoices automatic speech recognition (ASR) corpus, which covers 22 Indian languages and includes both read-speech and conversational recordings.

To enhance the quality of the IndicVoices dataset for TTS, the authors employ a comprehensive data pipeline that involves demixing, dereverberation, and denoising of the audio samples. This process results in IndicVoices-R, a dataset that matches the speech and sound quality of gold-standard TTS datasets like LJSpeech, LibriTTS, and IndicTTS, as measured by various speech quality metrics.

IndicVoices-R surpasses existing Indian TTS datasets in terms of scale, with 1,704 hours of speech data from 10,496 speakers. It also exhibits rich diversity in speaker demographics, age, gender, and speaking styles, which is crucial for achieving good cross-speaker generalization in TTS models.

To complement the dataset, the authors introduce the IndicVoices-R Benchmark, a carefully designed evaluation framework to assess the zero-shot, few-shot, and many-shot speaker generalization capabilities of TTS models on Indian voices. They demonstrate that fine-tuning an English pre-trained model on IndicVoices-R leads to better zero-shot speaker generalization compared to fine-tuning on the IndicTTS dataset alone.

The authors open-source the IndicVoices-R dataset and the first TTS model that supports all 22 official Indian languages, paving the way for the development of more robust and versatile TTS systems for Indian languages.

Statisztikák

IndicVoices-R contains 1,704 hours of high-quality speech data from 10,496 speakers across 22 Indian languages.
The dataset covers between 9 to 175 hours of speech data per language.
IndicVoices-R has a mean SNR of 60.47 dB, a mean C50 of 53.45 dB, and a mean utterance pitch of 178.91 Hz.

Idézetek

"IndicVoices-R surpasses existing Indian TTS datasets in terms of scale, with 1,704 hours of speech data from 10,496 speakers."
"IndicVoices-R exhibits rich diversity in speaker demographics, age, gender, and speaking styles, which is crucial for achieving good cross-speaker generalization in TTS models."
"Fine-tuning an English pre-trained model on IndicVoices-R leads to better zero-shot speaker generalization compared to fine-tuning on the IndicTTS dataset alone."

Főbb Kivonatok

IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS

by Ashwin Sanka... : arxiv.org 09-10-2024

https://arxiv.org/pdf/2409.05356.pdf

Mélyebb kérdések

How can the IndicVoices-R dataset be further expanded or enhanced to improve the performance of TTS models for Indian languages?

To further expand and enhance the IndicVoices-R dataset, several strategies can be employed:

Inclusion of More Speakers: Increasing the number of speakers, particularly from underrepresented demographics, can enhance the dataset's diversity. This could involve recruiting speakers from various regions, age groups, and socio-economic backgrounds to ensure a more comprehensive representation of Indian languages.

Diverse Speech Styles: Incorporating a wider range of speech styles, such as emotional speech, conversational dialogues, and different accents, can improve the naturalness and expressiveness of TTS models. This can be achieved by collecting data in varied contexts, such as storytelling, interviews, and public speaking.

Higher Quality Recordings: While the current dataset has high-quality samples, further enhancements can be made by utilizing professional recording studios to capture speech data. This would help in achieving studio-quality recordings, which can significantly improve the clarity and intelligibility of synthesized speech.

Multimodal Data: Integrating multimodal data, such as video recordings alongside audio, can provide additional context for the speech, helping TTS models learn prosody and emotional cues more effectively. This could be particularly beneficial for languages with rich cultural expressions.

Continuous Data Collection: Establishing a framework for continuous data collection can help keep the dataset updated with new speakers and speech patterns. This could involve periodic campaigns to gather fresh data, ensuring that the dataset evolves with changing linguistic trends.

Annotation and Quality Control: Enhancing the annotation process by employing advanced techniques such as crowdsourcing or using AI-assisted tools can improve the accuracy of transcriptions. Implementing rigorous quality control measures will ensure that the dataset maintains high standards.

Cross-lingual Data Sharing: Collaborating with other multilingual datasets can facilitate the sharing of resources and techniques, allowing for the incorporation of best practices from other language contexts, which can be beneficial for Indian languages.

What are the potential challenges in deploying the open-sourced TTS model for all 22 Indian languages in real-world applications, and how can they be addressed?

Deploying the open-sourced TTS model for all 22 Indian languages presents several challenges:

Resource Limitations: Many Indian languages are low-resource, meaning there may be insufficient data for training robust TTS models. To address this, techniques such as transfer learning and data augmentation can be employed to leverage existing data more effectively.

Dialectal Variations: Indian languages often have numerous dialects, which can lead to variations in pronunciation and vocabulary. To tackle this, the dataset can be expanded to include dialect-specific data, ensuring that the TTS model can generalize across different dialects.

Computational Resources: Running TTS models, especially those based on deep learning, requires significant computational power. This can be a barrier for deployment in resource-constrained environments. Solutions include optimizing models for efficiency, using quantization techniques, and providing lightweight versions of the TTS system.

User Acceptance and Trust: Users may be hesitant to adopt TTS systems due to concerns about the quality and naturalness of synthesized speech. Conducting user studies and gathering feedback can help refine the models and build trust in their capabilities.

Integration with Existing Systems: Integrating TTS models into existing applications (e.g., virtual assistants, educational tools) can be complex. Developing standardized APIs and providing comprehensive documentation can facilitate smoother integration.

Ethical and Cultural Sensitivity: Ensuring that the TTS system respects cultural nuances and avoids biases is crucial. This can be addressed by involving community stakeholders in the development process and conducting thorough evaluations to identify and mitigate biases.

What other applications or research areas could benefit from the availability of a large-scale, high-quality multilingual speech dataset like IndicVoices-R beyond text-to-speech?

The availability of a large-scale, high-quality multilingual speech dataset like IndicVoices-R can benefit various applications and research areas beyond text-to-speech:

Automatic Speech Recognition (ASR): The dataset can be utilized to train and improve ASR systems for Indian languages, enhancing their accuracy and robustness in recognizing diverse accents and dialects.

Voice Assistants: The dataset can support the development of multilingual voice assistants that can understand and respond in multiple Indian languages, making technology more accessible to a broader audience.

Language Learning Tools: Educational applications can leverage the dataset to create interactive language learning tools that provide pronunciation guidance and conversational practice for learners of Indian languages.

Sentiment Analysis and Emotion Recognition: The rich diversity of speech styles in the dataset can be used to train models for sentiment analysis and emotion recognition, which can be applied in customer service, mental health monitoring, and social media analysis.

Speech Enhancement and Restoration: Researchers can use the dataset to develop and test algorithms for speech enhancement and restoration, particularly in noisy environments, which is crucial for improving communication in various settings.

Cultural Preservation: The dataset can serve as a resource for documenting and preserving endangered languages and dialects, contributing to linguistic research and cultural heritage initiatives.

Human-Computer Interaction (HCI): The dataset can facilitate research in HCI, enabling the development of more natural and intuitive interfaces that can understand and generate speech in multiple languages.

Multimodal Applications: Combining the dataset with visual data can lead to advancements in multimodal applications, such as video dubbing, where synthesized speech needs to match the visual context accurately.

By leveraging the IndicVoices-R dataset across these diverse applications, researchers and developers can significantly enhance the accessibility and usability of technology for speakers of Indian languages.