Summary StyleTTS 2 Human-Level Text-to-Speech with Style Diffusion arxiv.org
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StyleTTS 2 is an advanced text-to-speech model that outperforms human recordings on single-speaker datasets and achieves comparable results on multispeaker datasets.
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Key Points
- StyleTTS 2 is a text-to-speech model that achieves human-level performance through style diffusion and speech language model discriminators.
- The model leverages large pre-trained speech language models (SLMs) as discriminators and uses diffusion models to generate the most suitable style for the text without reference speech.
- StyleTTS 2 surpasses human recordings on the single-speaker LJSpeech dataset and matches it on the multispeaker VCTK dataset.
- The model sets a new benchmark for TTS synthesis by achieving human-level performance on both single and multispeaker datasets.
- StyleTTS 2 improves upon the StyleTTS framework by introducing end-to-end training, direct waveform synthesis, style diffusion, and SLM discriminators.
Summaries
20 word summary
StyleTTS 2 is a high-quality text-to-speech model that surpasses human recordings on single-speaker datasets and matches them on multispeaker datasets.
54 word summary
StyleTTS 2 is a human-level text-to-speech model that achieves high-quality synthesis by using style diffusion and adversarial training with large speech language models (SLMs). It surpasses human recordings on single-speaker datasets and matches them on multispeaker datasets. The model optimizes all components jointly, resulting in faster and more expressive TTS synthesis with improved performance.
134 word summary
StyleTTS 2 is a text-to-speech (TTS) model that achieves human-level TTS synthesis through style diffusion and adversarial training with large speech language models (SLMs). It surpasses human recordings on the single-speaker LJSpeech dataset and matches it on the multispeaker VCTK dataset. The model leverages large pre-trained SLMs as discriminators and employs differentiable duration modeling for improved speech naturalness. StyleTTS 2 introduces style diffusion, where a fixed-length style vector is sampled by a diffusion model conditioned on the input text, improving model speed and enabling end-to-end training. It also leverages the knowledge of large SLMs via adversarial training using SLM features without latent space mapping. StyleTTS 2 optimizes all components jointly, enabling faster and more expressive TTS synthesis with improved out-of-distribution performance. It achieves high-quality TTS synthesis and represents a significant advancement in the field.
467 word summary
StyleTTS 2 is a text-to-speech (TTS) model that achieves human-level TTS synthesis through style diffusion and adversarial training with large speech language models (SLMs). The model leverages large pre-trained SLMs as discriminators and employs differentiable duration modeling for improved speech naturalness. StyleTTS 2 surpasses human recordings on the single-speaker LJSpeech dataset and matches it on the multispeaker VCTK dataset. It also outperforms previous models for zero-shot speaker adaptation when trained on the LibriTTS dataset.
Diffusion models have gained popularity in speech synthesis due to their potential for diverse speech sampling and fine-grained speech control. StyleTTS 2 introduces style diffusion, where a fixed-length style vector is sampled by a diffusion model conditioned on the input text, improving model speed and enabling end-to-end training. Recent advancements have shown the effectiveness of large-scale self-supervised SLMs in enhancing TTS quality and speaker adaptation. StyleTTS 2 leverages the knowledge of large SLMs via adversarial training using SLM features without latent space mapping, directly learning a latent space optimized for speech synthesis.
StyleTTS 2 improves upon the StyleTTS framework by introducing end-to-end training, direct waveform synthesis, style diffusion, and SLM discriminators. The model optimizes all components jointly, enabling faster and more expressive TTS synthesis with improved out-of-distribution performance. Experimental results show that StyleTTS 2 outperforms previous models in terms of naturalness and similarity to the reference speaker. Ablation studies further confirm the effectiveness of StyleTTS 2 in achieving high-quality TTS synthesis.
StyleTTS 2 represents a significant advancement in TTS synthesis, achieving human-level performance on both single and multispeaker datasets. The model leverages style diffusion, adversarial training with large SLMs, and differentiable duration modeling to produce highly realistic and expressive speech.
The importance of the proposed components in StyleTTS 2 is highlighted through an ablation study. The study shows that text-dependent style diffusion contributes significantly to achieving human-level TTS. Training without differentiable upsamplers and SLM discriminators results in lower performance, validating their key roles in natural speech synthesis. Excluding out-of-distribution (OOD) texts from adversarial training proves to be effective for improving OOD speech synthesis.
Objective evaluations further affirm the effectiveness of the various components proposed in StyleTTS 2. While there is room for improvement in handling OOD texts and speaker similarity, StyleTTS 2 shows promise for real-world applications.
StyleTTS 2 is a text-to-speech model that focuses on generating speech with different speaking styles. It introduces the concept of style vectors, which represent different styles of speech such as emotions, speaking rates, and recording environments. The model allows for conditioning the style of the current sentence on the previous sentence through a convex combination of style vectors.
The model's performance is evaluated through subjective evaluation procedures, and the training process involves pre-training and joint training with various loss functions. Overall, StyleTTS 2 offers an advanced text-to-speech model that can generate speech with different speaking styles.
571 word summary
StyleTTS 2 is a text-to-speech (TTS) model that achieves human-level TTS synthesis through style diffusion and adversarial training with large speech language models (SLMs). The model leverages large pre-trained SLMs as discriminators and employs differentiable duration modeling for improved speech naturalness. StyleTTS 2 surpasses human recordings on the single-speaker LJSpeech dataset and matches it on the multispeaker VCTK dataset. It also outperforms previous models for zero-shot speaker adaptation when trained on the LibriTTS dataset.
Diffusion models have gained popularity in speech synthesis due to their potential for diverse speech sampling and fine-grained speech control. StyleTTS 2 introduces style diffusion, where a fixed-length style vector is sampled by a diffusion model conditioned on the input text, improving model speed and enabling end-to-end training. Recent advancements have shown the effectiveness of large-scale self-supervised SLMs in enhancing TTS quality and speaker adaptation. StyleTTS 2 leverages the knowledge of large SLMs via adversarial training using SLM features without latent space mapping, directly learning a latent space optimized for speech synthesis.
StyleTTS 2 improves upon the StyleTTS framework by introducing end-to-end training, direct waveform synthesis, style diffusion, and SLM discriminators. The model optimizes all components jointly, enabling faster and more expressive TTS synthesis with improved out-of-distribution performance. Experimental results show that StyleTTS 2 outperforms previous models in terms of naturalness and similarity to the reference speaker. Ablation studies further confirm the effectiveness of StyleTTS 2 in achieving high-quality TTS synthesis.
StyleTTS 2 represents a significant advancement in TTS synthesis, achieving human-level performance on both single and multispeaker datasets. The model leverages style diffusion, adversarial training with large SLMs, and differentiable duration modeling to produce highly realistic and expressive speech.
StyleTTS 2 is a text-to-speech model that achieves human-level performance by incorporating style diffusion and speech language model discriminators. It surpasses ground truth on the LJSpeech dataset and performs on par with it on the VCTK dataset. The model generates expressive and diverse speech of superior quality while ensuring fast inference time.
The importance of the proposed components in StyleTTS 2 is highlighted through an ablation study. The study shows that text-dependent style diffusion contributes significantly to achieving human-level TTS. Training without differentiable upsamplers and SLM discriminators results in lower performance, validating their key roles in natural speech synthesis. Excluding out-of-distribution (OOD) texts from adversarial training proves to be effective for improving OOD speech synthesis.
Objective evaluations further affirm the effectiveness of the various components proposed in StyleTTS 2. While there is room for improvement in handling OOD texts and speaker similarity, StyleTTS 2 shows promise for real-world applications.
StyleTTS 2 is a text-to-speech model that focuses on generating speech with different speaking styles. It introduces the concept of style vectors, which represent different styles of speech such as emotions, speaking rates, and recording environments. The model allows for conditioning the style of the current sentence on the previous sentence through a convex combination of style vectors.
The model also includes a differentiable duration upsampler, which adjusts the duration of phonemes in speech. One of the key features of StyleTTS 2 is its ability to transfer styles from one text to another. The relationship between speech style and input text is learned in a self-supervised manner without manual labeling.
The model's performance is evaluated through subjective evaluation procedures, and the training process involves pre-training and joint training with various loss functions. Overall, StyleTTS 2 offers an advanced text-to-speech model that can generate speech with different speaking styles.
1295 word summary
StyleTTS 2 is a text-to-speech (TTS) model that achieves human-level TTS synthesis through style diffusion and adversarial training with large speech language models (SLMs). It models speech styles as a latent random variable and uses diffusion models to generate the most suitable style for the text without reference speech. The model leverages large pre-trained SLMs as discriminators and employs differentiable duration modeling for improved speech naturalness.
StyleTTS 2 surpasses human recordings on the single-speaker LJSpeech dataset and matches it on the multispeaker VCTK dataset as judged by native English speakers. It also outperforms previous models for zero-shot speaker adaptation when trained on the LibriTTS dataset. The model sets a new benchmark for TTS synthesis by achieving human-level performance on both single and multispeaker datasets.
Diffusion models have gained popularity in speech synthesis due to their potential for diverse speech sampling and fine-grained speech control. However, their efficiency is limited compared to non-iterative methods like GAN-based models. StyleTTS 2 introduces style diffusion, where a fixed-length style vector is sampled by a diffusion model conditioned on the input text, improving model speed and enabling end-to-end training. The model synthesizes speech using GAN-based models, with only the style vector dictating the diversity of speech sampled.
Recent advancements have shown the effectiveness of large-scale self-supervised SLMs in enhancing TTS quality and speaker adaptation. StyleTTS 2 leverages the knowledge of large SLMs via adversarial training using SLM features without latent space mapping, directly learning a latent space optimized for speech synthesis. This approach represents a new direction in TTS with SLMs.
Several recent works have made progress towards human-level TTS using techniques like BERT pre-training and E2E training with differentiable duration modeling. StyleTTS 2 sets a new standard for human-level TTS synthesis by achieving higher performance than previous state-of-the-art models. The model demonstrates strong generalization ability and robustness towards out-of-distribution texts.
StyleTTS 2 improves upon the StyleTTS framework by introducing end-to-end training, direct waveform synthesis, style diffusion, and SLM discriminators. The model optimizes all components jointly, enabling faster and more expressive TTS synthesis with improved out-of-distribution performance. Differentiable duration modeling allows for stable training and human-level performance with adversarial training.
Experimental results show that StyleTTS 2 outperforms previous models in terms of naturalness and similarity to the reference speaker. The model exhibits superior speech diversity compared to baseline models and is faster than other diffusion-based TTS models. Ablation studies further confirm the effectiveness of StyleTTS 2 in achieving high-quality TTS synthesis.
In conclusion, StyleTTS 2 represents a significant advancement in TTS synthesis, achieving human-level performance on both single and multispeaker datasets. The model leverages style diffusion, adversarial training with large SLMs, and differentiable duration modeling to produce highly realistic and expressive speech.
StyleTTS 2 is a text-to-speech (TTS) model that achieves human-level performance by incorporating style diffusion and speech language model discriminators. The model surpasses ground truth on the LJSpeech dataset and performs on par with it on the VCTK dataset. It also demonstrates potential for zero-shot speaker adaptation, even with limited training data. StyleTTS 2 generates expressive and diverse speech of superior quality while ensuring fast inference time.
The importance of the proposed components in StyleTTS 2 is highlighted through an ablation study. The study shows that text-dependent style diffusion contributes significantly to achieving human-level TTS. Training without differentiable upsamplers and SLM discriminators results in lower performance, validating their key roles in natural speech synthesis. Removing the prosodic style encoder also leads to a decline in performance. Excluding out-of-distribution (OOD) texts from adversarial training proves to be effective for improving OOD speech synthesis.
Objective evaluations further affirm the effectiveness of the various components proposed in StyleTTS 2. The layer-wise analysis of input weights of the SLM discriminator provides additional insights into its efficacy.
While StyleTTS 2 excels in several areas, there is room for improvement in handling OOD texts, which are commonly used in real-world applications. The similarity of speakers in the zero-shot adaptation task could also benefit from further improvements. To mitigate the potential misuse of zero-shot speaker adaptation, a discriminator can be trained to detect model-generated speech.
The authors express their gratitude to individuals who provided feedback and assessed the quality of synthesized samples during the development stage of this work. The funding for this research was provided by the national institute of health (NIHNIDCD) and a grant from Marie-Josee and Henry R. Kravis.
The document references several related works in the field of TTS, including deep learning-based speech synthesis, neural speech synthesis, and various generative models for TTS. It also provides additional information on the datasets used and the formulation of the style diffusion method.
The effects of diffusion steps on sample quality, speed, and diversity are examined. The results show that satisfactory quality samples can be generated with as few as three diffusion steps. Sample diversity increases with the number of diffusion steps, reaching a plateau around 16 steps. Optimal results in terms of sample quality, diversity, and computational speed are achieved with around 16 diffusion steps.
The document also presents a consistent long-form generation algorithm using style diffusion. The algorithm divides a long paragraph into smaller sentences and generates speech sentence by sentence while maintaining consistency in speaking style.
In conclusion, StyleTTS 2 is a novel TTS model that achieves human-level performance through style diffusion and speech language model discriminators. It demonstrates potential for zero-shot speaker adaptation and generates expressive and diverse speech of superior quality. While there is room for improvement in handling OOD texts and speaker similarity, StyleTTS 2 shows promise for real-world applications.
StyleTTS 2 is a text-to-speech model that focuses on generating speech with different speaking styles. It introduces the concept of style vectors, which represent different styles of speech such as emotions, speaking rates, and recording environments. These style vectors can be combined to create a new style that falls between the original styles. The model allows for conditioning the style of the current sentence on the previous sentence through a convex combination of style vectors.
The model also includes a differentiable duration upsampler, which adjusts the duration of phonemes in speech. This upsampler uses principles from digital signal processing to shift the value of a function to a new position. By selecting the appropriate parameters, the duration predictor output can be adjusted to match the desired duration.
One of the key features of StyleTTS 2 is its ability to transfer styles from one text to another. This enables the model to generate speech with specific emotions or stylistic nuances based on a desired style vector. The relationship between speech style and input text is learned in a self-supervised manner without manual labeling.
The model's performance is evaluated through subjective evaluation procedures. Native speakers of the language used in the training corpus are chosen as raters, and attention checks are incorporated to ensure thoughtful completion of evaluations. The objectives of the evaluation, such as naturalness and similarity, are clearly stated. Model comparisons are done using a MUSHRA-based approach, where paired samples from all models are presented for rating.
The training process involves pre-training the acoustic modules and joint training with the duration predictor fixed. During pre-training, various loss functions are used to optimize the reconstruction of mel-spectrograms, duration prediction, and prosody prediction. Adversarial loss functions are also employed to enhance the sound quality of synthesized speech.
The full objectives for joint training include mel-spectrogram reconstruction, duration prediction, prosody prediction, sequence-to-sequence ASR loss, adversarial loss, and SLM adversarial loss. These objectives are optimized to train the different modules of the model.
Overall, StyleTTS 2 offers an advanced text-to-speech model that can generate speech with different speaking styles. It incorporates style vectors, differentiable duration modeling, and style transfer capabilities. The model's performance is evaluated through subjective evaluation procedures, and the training process involves pre-training and joint training with various loss functions.