betekintés - Text-to-speech - # Parameter-efficient multi-speaker text-to-speech adaptation

Efficient Adaptation of Text-to-Speech Models to New Speakers Using Hypernetworks

Q: How can the hypernetwork in HYPERTTS be further improved to enhance its generalization capabilities and reduce the risk of overfitting on the adaptation domain?

In order to enhance the generalization capabilities of the hypernetwork in HYPERTTS and mitigate the risk of overfitting on the adaptation domain, several strategies can be implemented: Regularization Techniques: Introducing regularization methods such as L1 or L2 regularization can help prevent overfitting by adding a penalty term to the loss function. This encourages the hypernetwork to learn simpler and more generalizable patterns. Data Augmentation: Increasing the diversity of the training data by applying data augmentation techniques can help the hypernetwork learn more robust features and reduce overfitting to specific instances in the adaptation domain. Ensemble Learning: Implementing ensemble learning by training multiple hypernetworks with different initializations or architectures and combining their predictions can improve generalization and reduce overfitting. Cross-Validation: Utilizing cross-validation techniques can help assess the generalization performance of the hypernetwork on unseen data and prevent overfitting by optimizing hyperparameters based on validation sets. Transfer Learning: Leveraging pre-trained hypernetworks on related tasks or domains and fine-tuning them on the adaptation domain can enhance generalization capabilities and reduce the risk of overfitting. Early Stopping: Monitoring the validation loss during training and stopping the training process when the validation loss starts to increase can prevent the hypernetwork from overfitting to the training data. By incorporating these strategies, the hypernetwork in HYPERTTS can be further improved to enhance its generalization capabilities and reduce the risk of overfitting on the adaptation domain.

Q: How can the methodology used in HYPERTTS for dynamic parameter generation be adapted to other applications beyond TTS, and what domains could benefit from this approach?

The dynamic parameter generation approach used in HYPERTTS can be adapted to various other applications beyond TTS to enhance model adaptability and performance. Some domains that could benefit from this methodology include: Computer Vision: In tasks such as image classification or object detection, dynamic parameter generation can be used to adapt models to different visual styles or environments, improving generalization and robustness. Natural Language Processing (NLP): Applications like sentiment analysis or machine translation could benefit from dynamic parameter generation to tailor models to specific linguistic styles or domains, enhancing performance on diverse text inputs. Healthcare: In medical imaging analysis or patient diagnosis, dynamic parameter generation can help customize models for individual patients or specific medical conditions, improving accuracy and personalized healthcare outcomes. Finance: Dynamic parameter generation can be applied in financial forecasting or risk assessment models to adapt to changing market conditions or individual investor preferences, enhancing predictive capabilities. Autonomous Vehicles: In the field of autonomous driving, dynamic parameter generation can optimize models for different driving scenarios or road conditions, improving safety and efficiency. By adapting the methodology of dynamic parameter generation from HYPERTTS to these domains, it is possible to enhance model adaptability, performance, and generalization across a wide range of applications.

Q: Given the potential of hypernetworks for parameter-efficient adaptation, how could this technique be combined with other emerging approaches like low-rank adaptation to further improve the efficiency and performance of multi-speaker TTS systems?

Combining hypernetworks with low-rank adaptation techniques can further enhance the efficiency and performance of multi-speaker TTS systems in the following ways: Parameter Sharing: By integrating low-rank adaptation with hypernetworks, shared parameters can be utilized across multiple speakers, reducing the overall parameter count and enhancing parameter efficiency in multi-speaker TTS systems. Adaptive Parameter Generation: Low-rank adaptation can be used to adaptively generate parameters for specific speaker characteristics, while hypernetworks can dynamically adjust these parameters based on speaker embeddings, leading to more personalized and efficient adaptation. Regularization and Compression: Low-rank adaptation can act as a regularization technique to constrain the parameter space, while hypernetworks can provide additional flexibility in parameter generation, resulting in more compact and efficient models for multi-speaker TTS. Fine-Tuning and Transfer Learning: The combination of hypernetworks and low-rank adaptation can facilitate fine-tuning and transfer learning strategies, allowing for quick adaptation to new speakers while maintaining parameter efficiency and performance in multi-speaker TTS systems. Robustness and Generalization: Integrating low-rank adaptation with hypernetworks can improve the robustness and generalization capabilities of multi-speaker TTS systems by efficiently adapting to diverse speaker styles and characteristics. By leveraging the strengths of both hypernetworks and low-rank adaptation techniques, it is possible to create more efficient, adaptable, and high-performance multi-speaker TTS systems that excel in parameter efficiency and adaptation capabilities.

Alapfogalmak

HYPERTTS, a parameter-efficient approach for adapting text-to-speech models to new speakers, utilizes a hypernetwork to dynamically generate adapter parameters conditioned on speaker representations, outperforming static adapter-based methods while achieving comparable performance to full fine-tuning.

Kivonat

The paper presents HYPERTTS, a novel approach for parameter-efficient adaptation of text-to-speech (TTS) models to new speakers. The key highlights are:

Dynamic Adapters: HYPERTTS learns speaker-adaptive adapters by conditioning them on speaker embeddings, unlike static adapters used in previous work.
Parameter Sampling: HYPERTTS employs a learnable hypernetwork to generate the adapter parameters, enabling a continuous parameter space for efficient adaptation to new speakers.
Parameter Efficiency: HYPERTTS achieves competitive performance compared to full fine-tuning, while using less than 1% of the backbone model parameters, making it highly practical and resource-friendly.

The authors conduct extensive experiments on the LibriTTS and VCTK datasets, comparing HYPERTTS to baselines like full fine-tuning and static adapters. The results demonstrate that HYPERTTS can outperform static adapters and achieve performance close to full fine-tuning, while being significantly more parameter-efficient. Subjective evaluations also show that HYPERTTS can generate natural-sounding speech that is comparable to the full fine-tuned model.

The paper also explores the impact of parameter efficiency by varying the size of the hypernetwork, and analyzes the output of the hypernetwork to understand its behavior in generating dynamic adapter parameters.

Összefoglaló testreszabása

Átírás mesterséges intelligenciával

Hivatkozások generálása

Forrás fordítása

Egy másik nyelvre

Gondolattérkép létrehozása

a forrásanyagból

Forrás megtekintése

arxiv.org

Statisztikák

The backbone TTS model pre-trained on the LibriTTS dataset achieves a COS score of 73.794, FFE of 39.19, WER of 0.2035, and MCD of 5.9232 on the VCTK test set in a zero-shot setting (TTS-0).
Full fine-tuning of the backbone model on the VCTK dataset (TTS-FT) improves the COS score to 80.443, reduces FFE to 34.63, WER to 0.2027, and MCD to 5.2387.
HYPERTTS with the hypernetwork in the decoder (HYPERTTSd) achieves a COS score of 77.590, FFE of 38.55, WER of 0.2090, and MCD of 5.9641, using only 0.423% of the backbone parameters.
Increasing the hypernetwork parameter size in HYPERTTSd from 2 to 128 dimensions improves the COS score from 75.89 to 80.26.

Idézetek

"HYPERTTS is aimed to make adapters significantly more effective by conditioning them on speaker embeddings, thus enhancing the (effective) learnable parameter space of adapters."
"Notably, to keep the network small in size, we leverage a shared hypernetwork to generate parameters for adapters in every layer of a given module of the TTS backbone."
"Promising results on the dynamic adaptation of adapter parameters using hypernetworks open up new avenues for domain-generic multi-speaker TTS systems."

Főbb Kivonatok

HyperTTS

by Yingting Li,... : arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04645.pdf

Mélyebb kérdések

How can the hypernetwork in HYPERTTS be further improved to enhance its generalization capabilities and reduce the risk of overfitting on the adaptation domain?

In order to enhance the generalization capabilities of the hypernetwork in HYPERTTS and mitigate the risk of overfitting on the adaptation domain, several strategies can be implemented:

Regularization Techniques: Introducing regularization methods such as L1 or L2 regularization can help prevent overfitting by adding a penalty term to the loss function. This encourages the hypernetwork to learn simpler and more generalizable patterns.

Data Augmentation: Increasing the diversity of the training data by applying data augmentation techniques can help the hypernetwork learn more robust features and reduce overfitting to specific instances in the adaptation domain.

Ensemble Learning: Implementing ensemble learning by training multiple hypernetworks with different initializations or architectures and combining their predictions can improve generalization and reduce overfitting.

Cross-Validation: Utilizing cross-validation techniques can help assess the generalization performance of the hypernetwork on unseen data and prevent overfitting by optimizing hyperparameters based on validation sets.

Transfer Learning: Leveraging pre-trained hypernetworks on related tasks or domains and fine-tuning them on the adaptation domain can enhance generalization capabilities and reduce the risk of overfitting.

Early Stopping: Monitoring the validation loss during training and stopping the training process when the validation loss starts to increase can prevent the hypernetwork from overfitting to the training data.

By incorporating these strategies, the hypernetwork in HYPERTTS can be further improved to enhance its generalization capabilities and reduce the risk of overfitting on the adaptation domain.

How can the methodology used in HYPERTTS for dynamic parameter generation be adapted to other applications beyond TTS, and what domains could benefit from this approach?

The dynamic parameter generation approach used in HYPERTTS can be adapted to various other applications beyond TTS to enhance model adaptability and performance. Some domains that could benefit from this methodology include:

Computer Vision: In tasks such as image classification or object detection, dynamic parameter generation can be used to adapt models to different visual styles or environments, improving generalization and robustness.

Natural Language Processing (NLP): Applications like sentiment analysis or machine translation could benefit from dynamic parameter generation to tailor models to specific linguistic styles or domains, enhancing performance on diverse text inputs.

Healthcare: In medical imaging analysis or patient diagnosis, dynamic parameter generation can help customize models for individual patients or specific medical conditions, improving accuracy and personalized healthcare outcomes.

Finance: Dynamic parameter generation can be applied in financial forecasting or risk assessment models to adapt to changing market conditions or individual investor preferences, enhancing predictive capabilities.

Autonomous Vehicles: In the field of autonomous driving, dynamic parameter generation can optimize models for different driving scenarios or road conditions, improving safety and efficiency.

By adapting the methodology of dynamic parameter generation from HYPERTTS to these domains, it is possible to enhance model adaptability, performance, and generalization across a wide range of applications.

Given the potential of hypernetworks for parameter-efficient adaptation, how could this technique be combined with other emerging approaches like low-rank adaptation to further improve the efficiency and performance of multi-speaker TTS systems?

Combining hypernetworks with low-rank adaptation techniques can further enhance the efficiency and performance of multi-speaker TTS systems in the following ways:

Parameter Sharing: By integrating low-rank adaptation with hypernetworks, shared parameters can be utilized across multiple speakers, reducing the overall parameter count and enhancing parameter efficiency in multi-speaker TTS systems.

Adaptive Parameter Generation: Low-rank adaptation can be used to adaptively generate parameters for specific speaker characteristics, while hypernetworks can dynamically adjust these parameters based on speaker embeddings, leading to more personalized and efficient adaptation.

Regularization and Compression: Low-rank adaptation can act as a regularization technique to constrain the parameter space, while hypernetworks can provide additional flexibility in parameter generation, resulting in more compact and efficient models for multi-speaker TTS.

Fine-Tuning and Transfer Learning: The combination of hypernetworks and low-rank adaptation can facilitate fine-tuning and transfer learning strategies, allowing for quick adaptation to new speakers while maintaining parameter efficiency and performance in multi-speaker TTS systems.

Robustness and Generalization: Integrating low-rank adaptation with hypernetworks can improve the robustness and generalization capabilities of multi-speaker TTS systems by efficiently adapting to diverse speaker styles and characteristics.

By leveraging the strengths of both hypernetworks and low-rank adaptation techniques, it is possible to create more efficient, adaptable, and high-performance multi-speaker TTS systems that excel in parameter efficiency and adaptation capabilities.