inzicht - Speech Processing - # Zero-shot text-to-speech synthesis using neural codec language modeling

Efficient Neural Codec Language Modeling for Zero-Shot Text-to-Speech Synthesis

Q: How can the proposed CLaM-TTS model be extended to handle diverse speaking styles and accents beyond the predominantly audiobook-style dataset used in the experiments?

The CLaM-TTS model can be extended to handle diverse speaking styles and accents by incorporating several key strategies: Dataset Augmentation: To address the limitation of the predominantly audiobook-style dataset, additional datasets containing a wide range of speaking styles and accents can be included. This augmentation will expose the model to a more diverse set of voices, improving its ability to synthesize speech in various styles. Transfer Learning: Pre-training the model on a more diverse dataset that includes a variety of speaking styles and accents can help the model learn general features that are applicable across different voices. Fine-tuning on specific accent or style datasets can further enhance the model's ability to adapt to different speech patterns. Accent Embeddings: Introducing accent embeddings as additional input features can help the model differentiate and adapt to various accents. By encoding accent information into the model's architecture, it can learn to generate speech that reflects the desired accent. Style Tokens: Incorporating style tokens into the model architecture can enable the explicit control of speaking styles during speech synthesis. By conditioning the model on style tokens representing different accents or speaking styles, it can generate speech that aligns with the specified style. Adversarial Training: Adversarial training techniques can be employed to encourage the model to generate speech that is robust to variations in speaking styles and accents. By training the model to discriminate between different styles, it can learn to produce more diverse and accurate speech outputs. By implementing these strategies, the CLaM-TTS model can be extended to handle diverse speaking styles and accents effectively, enhancing its versatility and applicability in real-world scenarios.

Q: How can the efficiency and performance of the CLaM-TTS model be further improved by exploring alternative neural network architectures or training techniques beyond the current autoregressive language modeling approach?

To enhance the efficiency and performance of the CLaM-TTS model, exploring alternative neural network architectures and training techniques beyond the current autoregressive language modeling approach can be beneficial: Non-Autoregressive Models: Investigating non-autoregressive models, such as transformer-based models with parallel generation, can significantly improve inference speed by allowing tokens to be generated simultaneously. This approach can enhance efficiency without sacrificing performance. Hierarchical Models: Implementing hierarchical models that decompose the speech generation process into multiple levels of abstraction can improve the model's ability to capture long-range dependencies and generate coherent speech output. Transformer Variants: Exploring transformer variants like Reformer or Longformer, which are designed to handle long sequences more efficiently, can help address scalability challenges associated with processing lengthy audio sequences in TTS tasks. Multi-Task Learning: Leveraging multi-task learning by incorporating additional tasks, such as speaker identification or emotion recognition, can enhance the model's ability to capture diverse speech characteristics and improve overall performance. Knowledge Distillation: Employing knowledge distillation techniques to transfer knowledge from a larger pre-trained model to a smaller, more efficient model can help improve both efficiency and performance without compromising quality. By exploring these alternative neural network architectures and training techniques, the CLaM-TTS model can achieve higher efficiency, scalability, and performance, making it more versatile and effective for zero-shot Text-to-Speech synthesis tasks.

Belangrijkste concepten

CLaM-TTS employs probabilistic residual vector quantization to achieve superior compression in token length and enable a language model to generate multiple tokens at once, thereby enhancing the efficiency of zero-shot text-to-speech synthesis.

Samenvatting

The paper introduces CLaM-TTS, a novel approach to zero-shot text-to-speech (TTS) synthesis that leverages neural codec language modeling. The key insights are:

Compression in Token Length: CLaM-TTS uses probabilistic residual vector quantization (RVQ) to achieve superior compression in the token length of the speech representation, addressing the scalability challenge posed by the long sequence length and complexity of modeling multiple token streams in neural audio codecs.
Efficient Token Generation: The proposed method allows the language model to generate multiple tokens at once by predicting a continuous latent representation that is then converted to discrete tokens using the learned RVQ. This eliminates the need for cascaded modeling to handle the number of token streams, further enhancing the efficiency.

The authors train the Mel-VAE model using the proposed RVQ and the latent language model on a large-scale dataset of 100K hours of speech data spanning 11 languages. Experimental results demonstrate that CLaM-TTS outperforms or matches state-of-the-art neural codec-based TTS models in terms of naturalness, intelligibility, speaker similarity, and inference speed. The authors also investigate the impact of the pretraining extent of language models and their text tokenization strategies on the TTS performance.

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A 100K-hour speech-transcript dataset spanning 11 languages is used for training.
The dataset includes over 12K distinct speakers.

Citaten

"With the emergence of neural audio codecs, which encode multiple streams of discrete tokens from audio, large language models have recently gained attention as a promising approach for zero-shot Text-to-Speech (TTS) synthesis."
"Despite the significant advancements in TTS at scale, it still poses challenges to further scale up the models. Neural audio codecs typically generate multiple sequences of audio tokens."

Belangrijkste Inzichten Gedestilleerd Uit

CLaM-TTS

by Jaehyeon Kim... om arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02781.pdf

Diepere vragen

How can the proposed CLaM-TTS model be extended to handle diverse speaking styles and accents beyond the predominantly audiobook-style dataset used in the experiments?

The CLaM-TTS model can be extended to handle diverse speaking styles and accents by incorporating several key strategies:

Dataset Augmentation: To address the limitation of the predominantly audiobook-style dataset, additional datasets containing a wide range of speaking styles and accents can be included. This augmentation will expose the model to a more diverse set of voices, improving its ability to synthesize speech in various styles.

Transfer Learning: Pre-training the model on a more diverse dataset that includes a variety of speaking styles and accents can help the model learn general features that are applicable across different voices. Fine-tuning on specific accent or style datasets can further enhance the model's ability to adapt to different speech patterns.

Accent Embeddings: Introducing accent embeddings as additional input features can help the model differentiate and adapt to various accents. By encoding accent information into the model's architecture, it can learn to generate speech that reflects the desired accent.

Style Tokens: Incorporating style tokens into the model architecture can enable the explicit control of speaking styles during speech synthesis. By conditioning the model on style tokens representing different accents or speaking styles, it can generate speech that aligns with the specified style.

Adversarial Training: Adversarial training techniques can be employed to encourage the model to generate speech that is robust to variations in speaking styles and accents. By training the model to discriminate between different styles, it can learn to produce more diverse and accurate speech outputs.

By implementing these strategies, the CLaM-TTS model can be extended to handle diverse speaking styles and accents effectively, enhancing its versatility and applicability in real-world scenarios.

How can the efficiency and performance of the CLaM-TTS model be further improved by exploring alternative neural network architectures or training techniques beyond the current autoregressive language modeling approach?

To enhance the efficiency and performance of the CLaM-TTS model, exploring alternative neural network architectures and training techniques beyond the current autoregressive language modeling approach can be beneficial:

Non-Autoregressive Models: Investigating non-autoregressive models, such as transformer-based models with parallel generation, can significantly improve inference speed by allowing tokens to be generated simultaneously. This approach can enhance efficiency without sacrificing performance.

Hierarchical Models: Implementing hierarchical models that decompose the speech generation process into multiple levels of abstraction can improve the model's ability to capture long-range dependencies and generate coherent speech output.

Transformer Variants: Exploring transformer variants like Reformer or Longformer, which are designed to handle long sequences more efficiently, can help address scalability challenges associated with processing lengthy audio sequences in TTS tasks.

Multi-Task Learning: Leveraging multi-task learning by incorporating additional tasks, such as speaker identification or emotion recognition, can enhance the model's ability to capture diverse speech characteristics and improve overall performance.

Knowledge Distillation: Employing knowledge distillation techniques to transfer knowledge from a larger pre-trained model to a smaller, more efficient model can help improve both efficiency and performance without compromising quality.

By exploring these alternative neural network architectures and training techniques, the CLaM-TTS model can achieve higher efficiency, scalability, and performance, making it more versatile and effective for zero-shot Text-to-Speech synthesis tasks.