YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
Abstract
YourTTS brings the power of a multilingual approach to the task of zero-shot multi-speaker TTS. Our method builds upon the VITS model and adds several novel modifications for zero-shot multi-speaker and multilingual training. We achieved state-of-the-art (SOTA) results in zero-shot multi-speaker TTS and results comparable to SOTA in zero-shot voice conversion on the VCTK dataset. Additionally, our approach achieves promising results in a target language with a single-speaker dataset, opening possibilities for zero-shot multi-speaker TTS and zero-shot voice conversion systems in low-resource languages. Finally, it is possible to fine-tune the YourTTS model with less than 1 minute of speech and achieve state-of-the-art results in voice similarity and with reasonable quality. This is important to allow synthesis for speakers with a very different voice or recording characteristics from those seen during training.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
YourTTS represents a significant advancement in zero-shot multi-speaker TTS and voice conversion, combining innovative methodologies with practical applications in low-resource settings. The comprehensive evaluation framework and promising results position this work as a valuable contribution to the field of machine learning and audio processing.
Methodology
The YourTTS model builds upon the VITS architecture and introduces several novel modifications to facilitate zero-shot multi-speaker TTS and multilingual training. Key innovations include the use of raw text input instead of phonemes, the integration of trainable language embeddings, and the conditioning of various model components on external speaker embeddings. The model's ability to fine-tune with less than a minute of speech from a target speaker is particularly noteworthy, allowing for effective adaptation to diverse voice characteristics. However, the methodology could benefit from a more detailed exploration of the implications of using raw text input and the potential trade-offs involved.
Results & evaluation
The experiments are well-structured, utilizing multiple datasets (VCTK, TTS-Portuguese, and M-AILABS) to evaluate the model's performance across different languages. The use of MOS and SECS metrics provides a robust evaluation framework for assessing both quality and similarity. The results indicate that YourTTS achieves state-of-the-art performance in zero-shot multi-speaker TTS and competitive results in voice conversion, particularly in English and Portuguese. However, the lack of a dedicated evaluation dataset for French limits the comprehensiveness of the findings.
Reproducibility
The authors provide links to the source code and model checkpoints, enhancing the reproducibility of their work. The detailed description of the experimental setup, including training parameters and dataset preprocessing, further supports reproducibility. However, the mention of a bug related to the Speaker Consistency Loss during fine-tuning raises concerns about the reliability of some reported results.
Limitations
The paper acknowledges several limitations, including instability in the stochastic duration predictor, mispronunciations in Portuguese, and the influence of speaker gender on performance. The model's reliance on a single speaker for Portuguese voice conversion also highlights potential weaknesses in generalization. The authors suggest that more extensive training with diverse datasets could mitigate some of these issues.
Broader impact
The YourTTS model has significant implications for the development of TTS systems, particularly in low-resource languages where data scarcity is a challenge. Its ability to adapt to new speakers with minimal training data opens up possibilities for personalized voice synthesis applications in various domains, including virtual assistants, audiobooks, and accessibility tools. The multilingual capabilities of YourTTS also suggest potential for cross-lingual applications, further broadening its impact. YourTTS represents a significant advancement in zero-shot multi-speaker TTS and voice conversion, combining innovative methodologies with practical applications in low-resource settings. The comprehensive evaluation framework and promising results position this work as a valuable contribution to the field of machine learning and audio processing.
Curator note
Casanova et al.; VITS-based zero-shot multi-speaker TTS; cross-lingual voice conversion
Other indexed descriptions from the archive
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion
Abstract
YourTTS brings the power of a multilingual approach to the task of zero-shot multi-speaker TTS. Our method builds upon the VITS model and adds several novel modifications for zero-shot multi-speaker and multilingual training. We achieved state-of-the-art (SOTA) results in zero-shot multi-speaker TTS and results comparable to SOTA in zero-shot voice conversion on the VCTK dataset. Additionally, our approach achieves promising results in a target language with a single-speaker dataset, opening possibilities for zero-shot multi-speaker TTS and zero-shot voice conversion systems in low-resource languages. Finally, it is possible to fine-tune the YourTTS model with less than 1 minute of speech and achieve state-of-the-art results in voice similarity and with reasonable quality. This is important to allow synthesis for speakers with a very different voice or recording characteristics from those seen during training.
AI research summary
YourTTS represents a significant advancement in zero-shot multi-speaker TTS and voice conversion, combining innovative methodologies with practical applications in low-resource settings. The comprehensive evaluation framework and promising results position this work as a valuable contribution to the field of machine learning and audio processing.
AI methodology analysis
The YourTTS model builds upon the VITS architecture and introduces several novel modifications to facilitate zero-shot multi-speaker TTS and multilingual training. Key innovations include the use of raw text input instead of phonemes, the integration of trainable language embeddings, and the conditioning of various model components on external speaker embeddings. The model's ability to fine-tune with less than a minute of speech from a target speaker is particularly noteworthy, allowing for effective adaptation to diverse voice characteristics. However, the methodology could benefit from a more detailed exploration of the implications of using raw text input and the potential trade-offs involved.
AI results analysis
The experiments are well-structured, utilizing multiple datasets (VCTK, TTS-Portuguese, and M-AILABS) to evaluate the model's performance across different languages. The use of MOS and SECS metrics provides a robust evaluation framework for assessing both quality and similarity. The results indicate that YourTTS achieves state-of-the-art performance in zero-shot multi-speaker TTS and competitive results in voice conversion, particularly in English and Portuguese. However, the lack of a dedicated evaluation dataset for French limits the comprehensiveness of the findings.
AI reproducibility analysis
The authors provide links to the source code and model checkpoints, enhancing the reproducibility of their work. The detailed description of the experimental setup, including training parameters and dataset preprocessing, further supports reproducibility. However, the mention of a bug related to the Speaker Consistency Loss during fine-tuning raises concerns about the reliability of some reported results.
AI limitations analysis
The paper acknowledges several limitations, including instability in the stochastic duration predictor, mispronunciations in Portuguese, and the influence of speaker gender on performance. The model's reliance on a single speaker for Portuguese voice conversion also highlights potential weaknesses in generalization. The authors suggest that more extensive training with diverse datasets could mitigate some of these issues.
AI broader impact analysis
The YourTTS model has significant implications for the development of TTS systems, particularly in low-resource languages where data scarcity is a challenge. Its ability to adapt to new speakers with minimal training data opens up possibilities for personalized voice synthesis applications in various domains, including virtual assistants, audiobooks, and accessibility tools. The multilingual capabilities of YourTTS also suggest potential for cross-lingual applications, further broadening its impact. YourTTS represents a significant advancement in zero-shot multi-speaker TTS and voice conversion, combining innovative methodologies with practical applications in low-resource settings. The comprehensive evaluation framework and promising results position this work as a valuable contribution to the field of machine learning and audio processing.
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