London-based buy now, pay later outfit Zilch has received an investment from e-commerce giant eBay at a $2 billion valuation. The investment, revealed in Companies House filings and first reported by The Telegraph, is in the low single-digit millions.

Zilch secures eBay investment

Buy now, pay later turned ad-subsidised payments network Zilch has secured a deal with eBay that sees its valuation remain at £1.65bn ($2bn). As first reported by The Telegraph, the fintech, founded by Philip Belamant and Sean O'Connor in 2019 ...

Zilch scores deal with eBay and holds $2bn valuation

A British rival to Swedish buy-now-pay-later (BNPL) giant Klarna has secured investment from eBay in a deal that valued it at $2bn (£1.65bn). Zilch, a UK payments company founded in 2018, is understood to have received a low single digit million equity investment from eBay Ventures. The company declined to comment.

British Klarna rival valued at £1.65bn as it wins backing of eBay

Honda and GM had announced that the brands would combine efforts in order to work on EVs that would make use of next-gen Ultium battery tech.

Love in the time of profit? Honda-GM affair over cheap EVs comes to zilch

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What your heard down the pub versus the reality can often disappoint. But the sobering reality of company closures is absolutely worth knowing.

Can a failed company director really keep the Bentley, give staff zilch, and just set up a new biz?

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Up Wi' The Bonnets podcastJust as quickly as this will be published, in less than 24 hours, it will be out of date. That's because we have absolutely nothing to speak about. Zilch. Storm Babet saw Saturday night's game away to Aberdeen postponed,

Dundee looking to end 24-day wait with a win

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The House of Representatives remains without a leader after the preferred candidate of the far-right wing of the Republican party, Rep. Jim Jordan (R-Ohio), failed to gain enough votes to be elected speaker. Jordan is a polarizing member within the GOP ...

Claims Rep. Jim Jordan has never passed a law need context

Zilch

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Table of Links Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 5.9 Zero-shot Text-to-Speech with 1s Prompt We compare the performance of zero-shot TTS according to different prompt lengths of 1s, 3s 5s, and 10s. For evaluation, we use all samples over 10s from the test-clean subset of LibriTTS (1,002 samples), and we randomly slice a speech for each prompt length. TABLE 9 shows that our model has a robust style transfer performance using 3s, 5s, and 10s prompts. However, using 1s prompt could not synthesize a speech well. We can discuss two problems: 1) we do not consider an unvoice part during slicing the speech so some prompts contain only a small portion of speech in their prompt, and we also found that there is no voice part in prompts. 2) we utilize a full-length of prompt during training so synthesizing long sentences may require a long speech prompt for robust speech synthesis, specifically in the prosody encoder. To reduce this problem, we propose a style prompt replication as in section 4.3, and this style prompt replication significantly improves the robustness of TTS. By replicating the prompt like DNA replication, we simply extend a style prompt by n× and the replicated prompt is fed to the style encoder. This simple trick for style transfer significantly improves the robustness and similarity. With HierSpeech++ using SPR, we could synthesize a speech with only 1s speech prompt even in a zero-shot TTS scenario. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors:
(1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.4 Speech Super-resolution 3.5 Model Architecture 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.4 Evaluation Metrics 5.5 Ablation Study 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 7 Conclusion, Acknowledgement and References 5.9 Zero-shot Text-to-Speech with 1s Prompt We compare the performance of zero-shot TTS according to different prompt lengths of 1s, 3s 5s, and 10s. For evaluation, we use all samples over 10s from the test-clean subset of LibriTTS (1,002 samples), and we randomly slice a speech for each prompt length. TABLE 9 shows that our model has a robust style transfer performance using 3s, 5s, and 10s prompts. However, using 1s prompt could not synthesize a speech well. We can discuss two problems: 1) we do not consider an unvoice part during slicing the speech so some prompts contain only a small portion of speech in their prompt, and we also found that there is no voice part in prompts. 2) we utilize a full-length of prompt during training so synthesizing long sentences may require a long speech prompt for robust speech synthesis, specifically in the prosody encoder. To reduce this problem, we propose a style prompt replication as in section 4.3, and this style prompt replication significantly improves the robustness of TTS. By replicating the prompt like DNA replication, we simply extend a style prompt by n× and the replicated prompt is fed to the style encoder. This simple trick for style transfer significantly improves the robustness and similarity. With HierSpeech++ using SPR, we could synthesize a speech with only 1s speech prompt even in a zero-shot TTS scenario. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Authors: Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author.

Zero-shot Text-to-Speech With Prompts of 1s, 3s 5s, and 10s

Table of Links Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 5.8 Zero-shot Text-to-Speech We compared the zero-shot TTS performance of HierSpeech++ with other baselines: 1) YourTTS, VITS-based end-to-end TTS model, 2) HierSpeech, an end-to-end TTS model using hierarchical VAE, 3) VALL-E-X, a neural codec language models-based multi-lingual zero-shot TTS model and we utilize an unofficial implementation which has an improved audio quality with Vocos decoder, 4) XTTS[12] , a TTS product XTTS v1 from Coqui Corp., and XTTS is built on a open-source TTS model, TorToise [5] which was trained with unprecedented large-scale speech dataset for the first time. For zero-shot TTS, we utilized a noisy speech prompt from the test-clean and test-other subsets of LibriTTS. HierSpeech++ synthesizes the speech with Tttv of 0.333 and Th of 0.333 in TABLE 7 and 8. The results demonstrate that our model is a strong zeroshot TTS model in terms of all subjective and objective metrics. We conducted three MOS experiments for naturalness, prosody, and similarity. Our model beats all models significantly, and our model has even surpassed the groundtruth in terms of naturalness. However, XTTS has a better performance in pMOS, and this means learning prosody requires more datasets to improve expressiveness. Although other models show limitations in synthesizing speech with noisy prompts, our model synthesizes a speech robustly. Furthermore, our model has a better CER and WER than ground-truth, and this also demonstrates the robustness of our model. In summary, all results demonstrate the superiority of our model in naturalness, expressiveness, and robustness for zero-shot TTS. obustness for zero-shot TTS. In addition, we could further improve the zero-shot TTS performance by introducing a style prompt replication (SPR) in the following subsection. Note that we do not apply the SPR in TABLE 2-8. The audio could be upsampled to 48 kHz. Lastly, we could also synthesize noise-free speech even with noisy speech. The details will be described in Section 6. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. [12]. https://github.com/coqui-ai/TTS Authors:
(1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.4 Speech Super-resolution 3.5 Model Architecture 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.4 Evaluation Metrics 5.5 Ablation Study 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 7 Conclusion, Acknowledgement and References 5.8 Zero-shot Text-to-Speech We compared the zero-shot TTS performance of HierSpeech++ with other baselines: 1) YourTTS, VITS-based end-to-end TTS model, 2) HierSpeech, an end-to-end TTS model using hierarchical VAE, 3) VALL-E-X, a neural codec language models-based multi-lingual zero-shot TTS model and we utilize an unofficial implementation which has an improved audio quality with Vocos decoder, 4) XTTS[12] , a TTS product XTTS v1 from Coqui Corp., and XTTS is built on a open-source TTS model, TorToise [5] which was trained with unprecedented large-scale speech dataset for the first time. For zero-shot TTS, we utilized a noisy speech prompt from the test-clean and test-other subsets of LibriTTS. HierSpeech++ synthesizes the speech with Tttv of 0.333 and Th of 0.333 in TABLE 7 and 8. The results demonstrate that our model is a strong zeroshot TTS model in terms of all subjective and objective metrics. We conducted three MOS experiments for naturalness, prosody, and similarity. Our model beats all models significantly, and our model has even surpassed the groundtruth in terms of naturalness. However, XTTS has a better performance in pMOS, and this means learning prosody requires more datasets to improve expressiveness. Although other models show limitations in synthesizing speech with noisy prompts, our model synthesizes a speech robustly. Furthermore, our model has a better CER and WER than ground-truth, and this also demonstrates the robustness of our model. In summary, all results demonstrate the superiority of our model in naturalness, expressiveness, and robustness for zero-shot TTS. obustness for zero-shot TTS. In addition, we could further improve the zero-shot TTS performance by introducing a style prompt replication (SPR) in the following subsection. Note that we do not apply the SPR in TABLE 2-8. The audio could be upsampled to 48 kHz. Lastly, we could also synthesize noise-free speech even with noisy speech. The details will be described in Section 6. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv [12]. https://github.com/coqui-ai/TTS Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Authors: Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author.

Zero-shot Text-to-Speech: How Does the Performance of HierSpeech++ Fare With Other Baselines?

Table of Links Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 5.6 Zero-shot Voice Conversion We compared the voice style transfer performance of HierSpeech++ with other basemodels: 1) AutoVC [66], which is an autoencoder-based non-autoregressive VC model using an information bottleneck to disentangle the content and style, 2) VoiceMixer [46], which is a GAN-based parallel VC model using similarity-based information bottleneck, 3- 5) Diffusion-based models (DiffVC [64], Diff-HierVC [11], and DDDM-VC [10]), 6) YourTTS [7], VITS-based end-to-end VC models utilizing phoneme sequences to extract content information, 7) HierVST [45], hierspeech-based end-to-end VC model using hierarchical style adaptation. For a fair comparison, we trained all model with the same dataset (LT460, train-clean-460 subsets of LibriTTS) without YourTTS. We utilized the official implementation of YourTTS which was trained with an additional dataset. We also trained the model with a large-scale dataset such as LT-960, all training subsets of LibriTTS) and additional datasets to verify the effectiveness of scaling-up the dataset. For the subjective objective, TABLE 5 demonstrates that our model significantly improves the naturalness and similarity of the converted speech in terms of nMOS and sMOS. We found that our model with a large-scale dataset showed a better naturalness than ground-truth speech. However, the results also showed that increasing the dataset without filtering noisy data slightly decreased an audio quality in terms of nMOS and UTMOS but the similarity increased consistently according to the data scale. In addition, WER also showed better performance than the other models. Furthermore, the results of the similarity measurement show that our models perform better in terms of the EER and SECS. Moreover, the results verified that HierSpeech++ which was trained with a large-scale dataset is a much stronger zero-shot speech synthesizer. We will include zero-shot cross-lingual voice style transfer results and additional zero-shot voice conversion results with noisy speech prompts on our demo page. We highly recommend listening to the demo samples and will release the source code of the hierarchical speech synthesizer for a strong zero-shot speech synthesizer. Furthermore, we can also upsample the audio using SpeechSR from 16 kHz to 48 kHz, which can simply improve the perceptual audio quality as described in Section 5.10. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors:
(1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.4 Speech Super-resolution 3.5 Model Architecture 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.4 Evaluation Metrics 5.5 Ablation Study 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 7 Conclusion, Acknowledgement and References 5.6 Zero-shot Voice Conversion We compared the voice style transfer performance of HierSpeech++ with other basemodels: 1) AutoVC [66], which is an autoencoder-based non-autoregressive VC model using an information bottleneck to disentangle the content and style, 2) VoiceMixer [46], which is a GAN-based parallel VC model using similarity-based information bottleneck, 3- 5) Diffusion-based models (DiffVC [64], Diff-HierVC [11], and DDDM-VC [10]), 6) YourTTS [7], VITS-based end-to-end VC models utilizing phoneme sequences to extract content information, 7) HierVST [45], hierspeech-based end-to-end VC model using hierarchical style adaptation. For a fair comparison, we trained all model with the same dataset (LT460, train-clean-460 subsets of LibriTTS) without YourTTS. We utilized the official implementation of YourTTS which was trained with an additional dataset. We also trained the model with a large-scale dataset such as LT-960, all training subsets of LibriTTS) and additional datasets to verify the effectiveness of scaling-up the dataset. For the subjective objective, TABLE 5 demonstrates that our model significantly improves the naturalness and similarity of the converted speech in terms of nMOS and sMOS. We found that our model with a large-scale dataset showed a better naturalness than ground-truth speech. However, the results also showed that increasing the dataset without filtering noisy data slightly decreased an audio quality in terms of nMOS and UTMOS but the similarity increased consistently according to the data scale. In addition, WER also showed better performance than the other models. Furthermore, the results of the similarity measurement show that our models perform better in terms of the EER and SECS. Moreover, the results verified that HierSpeech++ which was trained with a large-scale dataset is a much stronger zero-shot speech synthesizer. We will include zero-shot cross-lingual voice style transfer results and additional zero-shot voice conversion results with noisy speech prompts on our demo page. We highly recommend listening to the demo samples and will release the source code of the hierarchical speech synthesizer for a strong zero-shot speech synthesizer. Furthermore, we can also upsample the audio using SpeechSR from 16 kHz to 48 kHz, which can simply improve the perceptual audio quality as described in Section 5.10. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Authors: Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author.

Zero-shot Voice Conversion: Comparing HierSpeech++ to Other Basemodels

Table of Links Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 2.3 Diffusion Models Diffusion models have also demonstrated their powerful generative performances in speech synthesis. Grad-TTS [63] first introduced a score-based decoder to generate a Melspectrogram, and Diff-VC demonstrated the high-adaptation performance of diffusion models in zero-shot voice conversion scenarios. DiffSinger achieved a state-of-the-art performance in SVS task by generating a high-quality singing voice with a powerful adaptation performance. DDDM-VC [10] significantly improved speech representation disentangle [3] and voice conversion performance by a disentnalged denoising diffusion model and prior mixup. Diff-HierVC [11] introduced a hierarchical voice style transfer frameworks that generates pitch contour and voice hierarchically based on diffusion models. Guided-TTS [33] and Guided-TTS 2 [38] have also shown good speaker adaptation performance for TTS. UnitSpeech [32] introduced a unit-based speech synthesis with diffusion models. Furthermore, recent studies utilized a diffusion model in latent representation. Naturalspeech 2 [70] and HiddenSinger [24] utilized the acoustic representation of an audio autoencoder as a latent representation, and developed a conditional latent diffusion model for speech or singing voice synthesis. StyleTTS 2 [54] proposed a style latent diffusion for style adaptation. Although all the above models have shown powerful adaptation performance, they have a slow inference speed for their iterative generation manner. To reduce the inference speed, CoMoSpeech [87] and Multi-GradSpeech [83] adopted a consistency model for a diffusion-based TTS model. Recently, VoiceBox [43] and P-Flow [39] utilized flow matching with optimal transport for fast sampling. However, these models still have a traininginference mismatch problem that arises from two-stage speech synthesis frameworks and they are vulnerable to noisy target voice prompt. 2.4 Zero-shot Voice Cloning Zero-shot learning [81] for voice cloning is a task to synthesize speech with a novel speaker, which has not been previously observed during training. A majority of the studies on voice cloning [25], [74] focused on cloning the voice styles, such as timbre and environment, and speaking styles, such as prosody and pronunciation. [72] presented a reference encoder for prosody modeling, and GST [80] utilized a learnable token for style modeling from the reference speech or manually control. [51] proposed a fine-grained prosody control for expressive speech synthesis from reference speech. Multi-SpectroGAN [50] utilized adversarial feedback and a mixup strategy for an expressive and diverse zero-shot TTS. Meta-StyleSpeech [59] introduced meta-learning for style modeling, and GenerSpeech [22] utilized a mix-style layer normalization for better generalization on the out-ofdomain style transfer. PVAE-TTS [44] utilized a progressive style adaptation for high-quality zero-shot TTS. AdaSpeech [8] introduced adaptive layer normalization for adaptive speech synthesis. YourTTS [7] trained the VITS [35] with a speaker encoder and Grad-StyleSpeech [29] utilized a styleconditioned prior on a score-based Mel-spectrogram decoder for better adaptive TTS. Built upon VQTTS [17], TN-VQTTS [18] introduce a timbre-normalized vector-quantized acoustic features for speaking style and timbre transfer. Meanwhile, there are text prompt-based style generation models which can describe a speaking or voice style from text descriptions [19], [52], [84]. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors:
(1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.4 Speech Super-resolution 3.5 Model Architecture 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.4 Evaluation Metrics 5.5 Ablation Study 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 7 Conclusion, Acknowledgement and References 2.3 Diffusion Models Diffusion models have also demonstrated their powerful generative performances in speech synthesis. Grad-TTS [63] first introduced a score-based decoder to generate a Melspectrogram, and Diff-VC demonstrated the high-adaptation performance of diffusion models in zero-shot voice conversion scenarios. DiffSinger achieved a state-of-the-art performance in SVS task by generating a high-quality singing voice with a powerful adaptation performance. DDDM-VC [10] significantly improved speech representation disentangle [3] and voice conversion performance by a disentnalged denoising diffusion model and prior mixup. Diff-HierVC [11] introduced a hierarchical voice style transfer frameworks that generates pitch contour and voice hierarchically based on diffusion models. Guided-TTS [33] and Guided-TTS 2 [38] have also shown good speaker adaptation performance for TTS. UnitSpeech [32] introduced a unit-based speech synthesis with diffusion models. Furthermore, recent studies utilized a diffusion model in latent representation. Naturalspeech 2 [70] and HiddenSinger [24] utilized the acoustic representation of an audio autoencoder as a latent representation, and developed a conditional latent diffusion model for speech or singing voice synthesis. StyleTTS 2 [54] proposed a style latent diffusion for style adaptation. Although all the above models have shown powerful adaptation performance, they have a slow inference speed for their iterative generation manner. To reduce the inference speed, CoMoSpeech [87] and Multi-GradSpeech [83] adopted a consistency model for a diffusion-based TTS model. Recently, VoiceBox [43] and P-Flow [39] utilized flow matching with optimal transport for fast sampling. However, these models still have a traininginference mismatch problem that arises from two-stage speech synthesis frameworks and they are vulnerable to noisy target voice prompt. 2.4 Zero-shot Voice Cloning Zero-shot learning [81] for voice cloning is a task to synthesize speech with a novel speaker, which has not been previously observed during training. A majority of the studies on voice cloning [25], [74] focused on cloning the voice styles, such as timbre and environment, and speaking styles, such as prosody and pronunciation. [72] presented a reference encoder for prosody modeling, and GST [80] utilized a learnable token for style modeling from the reference speech or manually control. [51] proposed a fine-grained prosody control for expressive speech synthesis from reference speech. Multi-SpectroGAN [50] utilized adversarial feedback and a mixup strategy for an expressive and diverse zero-shot TTS. Meta-StyleSpeech [59] introduced meta-learning for style modeling, and GenerSpeech [22] utilized a mix-style layer normalization for better generalization on the out-ofdomain style transfer. PVAE-TTS [44] utilized a progressive style adaptation for high-quality zero-shot TTS. AdaSpeech [8] introduced adaptive layer normalization for adaptive speech synthesis. YourTTS [7] trained the VITS [35] with a speaker encoder and Grad-StyleSpeech [29] utilized a styleconditioned prior on a score-based Mel-spectrogram decoder for better adaptive TTS. Built upon VQTTS [17], TN-VQTTS [18] introduce a timbre-normalized vector-quantized acoustic features for speaking style and timbre transfer. Meanwhile, there are text prompt-based style generation models which can describe a speaking or voice style from text descriptions [19], [52], [84]. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Authors: Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author.

Diffusion Models and Zero-shot Voice Cloning in Speech Synthesis: How Do They Fare?

HierSpeech++: A Fast and Strong Zero-Shot Speech Synthesizer for Text-to-Speech

EIP-7503: Zero-knowledge Wormholes for Private Ethereum Transactions

Table of Links Abstract and Introduction
SylloBio-NLI
Empirical Evaluation
Related Work
Conclusions
Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables H Prompting LLMs - Zero-shot prompts H.1 TASK 1 prompt = Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. The output should be a single word <True> or <False>. "P1: " + Premise 1 "P2: " + Premise 2 "C:" + Conclusion H.2 TASK 2 prompt = Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. Specify the premises you used to determine whether the conclusion logically follows from the premises, and only these premises. The output should be a single word <True> or <False>and the numbers of the selected premises after the decimal point, like <True, P1, P2>. "P1: " + Premise 1 "P2: " + Premise 2 "C:" + Conclusion Authors:
(1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom;
(2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom;
(3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I;
(4) Marco Valentino, Idiap Research Institute, Switzerland;
(5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. Table of Links Abstract and Introduction SylloBio-NLI Empirical Evaluation Related Work Conclusions Limitations and References Abstract and Introduction Abstract and Introduction SylloBio-NLI SylloBio-NLI Empirical Evaluation Empirical Evaluation Related Work Related Work Conclusions Conclusions Limitations and References Limitations and References A. Formalization of the SylloBio-NLI Resource Generation Process A. Formalization of the SylloBio-NLI Resource Generation Process B. Formalization of Tasks 1 and 2 B. Formalization of Tasks 1 and 2 C. Dictionary of gene and pathway membership C. Dictionary of gene and pathway membership D. Domain-specific pipeline for creating NL instances and E Accessing LLMs D. Domain-specific pipeline for creating NL instances and E Accessing LLMs F. Experimental Details F. Experimental Details G. Evaluation Metrics G. Evaluation Metrics H. Prompting LLMs - Zero-shot prompts H. Prompting LLMs - Zero-shot prompts I. Prompting LLMs - Few-shot prompts I. Prompting LLMs - Few-shot prompts J. Results: Misaligned Instruction-Response J. Results: Misaligned Instruction-Response K. Results: Ambiguous Impact of Distractors on Reasoning K. Results: Ambiguous Impact of Distractors on Reasoning L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge L. Results: Models Prioritize Contextual Knowledge Over Background Knowledge M Supplementary Figures and N Supplementary Tables M Supplementary Figures and N Supplementary Tables H Prompting LLMs - Zero-shot prompts H.1 TASK 1 prompt = Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. The output should be a single word <True> or <False>. Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. The output should be a single word <True> or <False>. "P1: " + Premise 1 "P2: " + Premise 2 "C:" + Conclusion H.2 TASK 2 prompt = Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. Specify the premises you used to determine whether the conclusion logically follows from the premises, and only these premises. The output should be a single word <True> or <False>and the numbers of the selected premises after the decimal point, like <True, P1, P2>. Suppose you are a specialist with existing knowledge about a signaling and metabolic molecules and their relations organized into biological pathways and processes. Given premises marked with the letter P and the following number and the conclusion marked with the letter C, determine whether the conclusion logically follows from these premises. If the conclusion logically follows from the premises, you need to return ’True’. If the conclusion does not follow logically from the premises, you need to return ’False’. Specify the premises you used to determine whether the conclusion logically follows from the premises, and only these premises. The output should be a single word <True> or <False>and the numbers of the selected premises after the decimal point, like <True, P1, P2>. "P1: " + Premise 1 "P2: " + Premise 2 "C:" + Conclusion Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. Authors: Authors: (1) Magdalena Wysocka, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom; (2) Danilo S. Carvalho, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and Department of Computer Science, Univ. of Manchester, United Kingdom; (3) Oskar Wysocki, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom and ited Kingdom 3 I; (4) Marco Valentino, Idiap Research Institute, Switzerland; (5) André Freitas, National Biomarker Centre, CRUK-MI, Univ. of Manchester, United Kingdom, Department of Computer Science, Univ. of Manchester, United Kingdom and Idiap Research Institute, Switzerland. This paper is available on arxiv under CC BY-NC-SA 4.0 license. This paper is available on arxiv under CC BY-NC-SA 4.0 license. available on arxiv

Zero-shot Prompts for Logical Reasoning Tasks in Biological Pathways

Ditch the Expensive Tools With This Guide on Conducting UX Research on Zero Budget

LONDON, United Kingdom, November 29th, 2024/Chainwire/--As Bitcoin edges closer to the $100K milestone, the crypto market is preparing for a pivotal new cycle. Historically, BTC surges spark altcoin rallies, with liquidity eventually flowing into the memecoin sector. In this environment, projects with strong narratives and valuable utilities take center stage. Among them, Pepeto, the God of Frogs, is emerging as a leading contender. Positioned as a hub for the adoption of next-generation memecoins via its exchange, Pepeto aims to provide innovative solutions for the evolving cryptocurrency market. Pepe Unchained: Advancing Scalability With Layer 2 Technology Pepe Unchained utilizes Layer 2 technology to improve scalability and reduce network congestion during periods of high demand. This makes transactions faster and more efficient, providing an advantage in the competitive memecoin market. On the other hand, while Pepe Unchained’s value is a key strength, Pepeto delivers a more comprehensive offering: Zero-Fee Cross-Chain Trading: Enables seamless, cost-free asset trading across multiple blockchains.
Liquidity Options: Ensures stable trading environments for token holders.
Token Listing Platform: A unique feature allowing all tokens, including Pepe Unchained, to be listed on Pepeto’s exchange. Pepeto Sparks Interest With Its Unique Features and Community Focus Pepeto, the God of Frogs, has emerged as a standout in the crowded memecoin space. Its value proposition; centered around adopting all memecoins of the 2025 bull run through its zero-fee exchange, cross-chain bridge, and token listing platform; has positioned it as a transformative force in the market. Beyond its utility, Pepeto’s epic narrative has further fueled its hype. The story of how Pepeto traversed eras to gather six vital documents, especially Technology (T) and Optimization (O) through insights from Satoshi Nakamoto, has enthralled the crypto community, combining mythos with innovation in a way few projects can match. Interest in the Memecoin Community Pepeto has been featured by various influencers, blogs, and cryptocurrency journals, which have examined its blend of utility and storytelling. The project has generated discussion across social media platforms and YouTube, where numerous creators analyze its narrative and features. This intersection of traditional crypto values and community-focused initiatives has contributed to the project’s increasing engagement within the memecoin sector. Currently priced at $0.000000094 and with a total supply of 420 trillion tokens, matching Pepe’s supply, Pepeto is amis to position it self as a comparable entrant in the memecoin market. Pepeto’s Plot Twist Pepeto has already completed its Q4 2024 roadmap and is actively advancing its Q1 2025 milestones. The Pepeto team has observed an influx of new users and is working to position the platform as a comprehensive solution within the memecoin ecosystem, emphasizing its robust exchange infrastructure. https://www.youtube.com/embed/hHs9H_2hiuk https://youtu.be/hHs9H_2hiuk?si=s9ZAQDR5ysBji4RM Visual Storytelling to Engage the Community Pepeto brings its narrative to life through a series of animated episodes detailing its quest to gather six sacred documents: Power, Energy, Precision, Efficiency, Technology, and Optimization. These episodes, available on Pepeto’s official YouTube channel, blend entertainment with the project’s mission, engaging both investors and the broader community. X post: https://x.com/Pepetocoin/status/1860796935755665537 Pepeto: Evolving With The Changing Landscape of Memecoins As the 2025 bull run approaches, Pepeto is distinguishing itself as more than just a memecoin. Its combination of rich storytelling, cutting-edge utilities, and a rapidly growing community positions it as a noteworthy project in the crypto space. Pepeto seeks to establish itself as a platform suited for both experienced participants and newcomers in the evolving memecoin landscape. Disclaimer: Pepetotoken.io is the only official website for purchasing Pepeto tokens. Users must be aware of fraudulent sites mimicking the platform and verify at the official website. For more information, users can visit Pepetotoken.io Social Media: X (Twitter): https://x.com/Pepetocoin
YouTube Channel: https://www.youtube.com/@Pepetocoin
Telegram Channel: https://t.me/pepeto_channel
Instagram: https://www.instagram.com/pepetocoin/
TikTok: https://www.tiktok.com/@pepetocoin Contact head of MK Baker Uccio Contact@pepetotoken.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here LONDON, United Kingdom, November 29th, 2024/Chainwire/--As Bitcoin edges closer to the $100K milestone, the crypto market is preparing for a pivotal new cycle. Historically, BTC surges spark altcoin rallies, with liquidity eventually flowing into the memecoin sector. In this environment, projects with strong narratives and valuable utilities take center stage. Among them, Pepeto, the God of Frogs, is emerging as a leading contender. Positioned as a hub for the adoption of next-generation memecoins via its exchange, Pepeto aims to provide innovative solutions for the evolving cryptocurrency market. Pepe Unchained: Advancing Scalability With Layer 2 Technology Pepe Unchained utilizes Layer 2 technology to improve scalability and reduce network congestion during periods of high demand. This makes transactions faster and more efficient, providing an advantage in the competitive memecoin market. On the other hand, while Pepe Unchained’s value is a key strength, Pepeto delivers a more comprehensive offering: Zero-Fee Cross-Chain Trading: Enables seamless, cost-free asset trading across multiple blockchains. Liquidity Options: Ensures stable trading environments for token holders. Token Listing Platform: A unique feature allowing all tokens, including Pepe Unchained, to be listed on Pepeto’s exchange. Zero-Fee Cross-Chain Trading: Enables seamless, cost-free asset trading across multiple blockchains. Liquidity Options: Ensures stable trading environments for token holders. Token Listing Platform: A unique feature allowing all tokens, including Pepe Unchained, to be listed on Pepeto’s exchange. Pepeto Sparks Interest With Its Unique Features and Community Focus Pepeto, the God of Frogs, has emerged as a standout in the crowded memecoin space. Its value proposition; centered around adopting all memecoins of the 2025 bull run through its zero-fee exchange, cross-chain bridge, and token listing platform; has positioned it as a transformative force in the market. Beyond its utility, Pepeto’s epic narrative has further fueled its hype. The story of how Pepeto traversed eras to gather six vital documents, especially Technology (T) and Optimization (O) through insights from Satoshi Nakamoto, has enthralled the crypto community, combining mythos with innovation in a way few projects can match. Interest in the Memecoin Community Pepeto has been featured by various influencers, blogs, and cryptocurrency journals, which have examined its blend of utility and storytelling. The project has generated discussion across social media platforms and YouTube, where numerous creators analyze its narrative and features. This intersection of traditional crypto values and community-focused initiatives has contributed to the project’s increasing engagement within the memecoin sector. Currently priced at $0.000000094 and with a total supply of 420 trillion tokens, matching Pepe’s supply, Pepeto is amis to position it self as a comparable entrant in the memecoin market. Pepeto’s Plot Twist Pepeto has already completed its Q4 2024 roadmap and is actively advancing its Q1 2025 milestones. The Pepeto team has observed an influx of new users and is working to position the platform as a comprehensive solution within the memecoin ecosystem, emphasizing its robust exchange infrastructure. https://www.youtube.com/embed/hHs9H_2hiuk https://www.youtube.com/embed/hHs9H_2hiuk https://www.youtube.com/embed/hHs9H_2hiuk https://youtu.be/hHs9H_2hiuk?si=s9ZAQDR5ysBji4RM https://youtu.be/hHs9H_2hiuk?si=s9ZAQDR5ysBji4RM https://youtu.be/hHs9H_2hiuk?si=s9ZAQDR5ysBji4RM Visual Storytelling to Engage the Community Pepeto brings its narrative to life through a series of animated episodes detailing its quest to gather six sacred documents: Power, Energy, Precision, Efficiency, Technology, and Optimization. These episodes, available on Pepeto’s official YouTube channel , blend entertainment with the project’s mission, engaging both investors and the broader community. Pepeto’s official YouTube channel Pepeto’s official YouTube channel X post: https://x.com/Pepetocoin/status/1860796935755665537 https://x.com/Pepetocoin/status/1860796935755665537 https://x.com/Pepetocoin/status/1860796935755665537 Pepeto: Evolving With The Changing Landscape of Memecoins As the 2025 bull run approaches, Pepeto is distinguishing itself as more than just a memecoin. Its combination of rich storytelling, cutting-edge utilities, and a rapidly growing community positions it as a noteworthy project in the crypto space. Pepeto seeks to establish itself as a platform suited for both experienced participants and newcomers in the evolving memecoin landscape. Disclaimer: Pepetotoken.io is the only official website for purchasing Pepeto tokens. Users must be aware of fraudulent sites mimicking the platform and verify at the official website. For more information, users can visit Pepetotoken.io Pepetotoken.io Social Media: X (Twitter): https://x.com/Pepetocoin YouTube Channel: https://www.youtube.com/@Pepetocoin Telegram Channel: https://t.me/pepeto_channel Instagram: https://www.instagram.com/pepetocoin/ TikTok: https://www.tiktok.com/@pepetocoin X (Twitter): https://x.com/Pepetocoin https://x.com/Pepetocoin https://x.com/Pepetocoin YouTube Channel: https://www.youtube.com/@Pepetocoin https://www.youtube.com/@Pepetocoin https://www.youtube.com/@Pepetocoin Telegram Channel: https://t.me/pepeto_channel https://t.me/pepeto_channel https://t.me/pepeto_channel Instagram: https://www.instagram.com/pepetocoin/ https://www.instagram.com/pepetocoin/ https://www.instagram.com/pepetocoin/ TikTok: https://www.tiktok.com/@pepetocoin https://www.tiktok.com/@pepetocoin https://www.tiktok.com/@pepetocoin Contact head of MK Baker Uccio Contact@pepetotoken.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

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