社内勉強会資料_XTTS: a Massively Multilingual ZeroShot Text-to-Speech Model.pdf

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社内勉強会の資料「XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model 」を公開しました！・ニューラルコーデックを使った音声表現を採用・GPT2ベースのデコーダとPerceiver構造のスピーカーエンコーダ・特に英語で優れた性能・一部言語の文字認識精度に課題社内勉強会の資料「XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model 」を公開！・ニューラルコーデックを使った音声表現を採用・GPT2ベースのデコーダとPerceiver構造のスピーカーエンコーダ・特に英語で優れた性能・一部言語の文字認識精度に課題社内勉強会の資料「XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model 」を公開！・ニューラルコーデックを使った音声表現を採用・GPT2ベースのデコーダとPerceiver構造のスピーカーエンコーダ・特に英語で優れた性能・一部言語の文字認識精度に課題

XTTS: a Massively Multilingual Zero-
Shot Text-to-Speech Model
Casanova et al., INTERSPEECH 2024
Paper Discussion, 28 June 2024
Presenter: Nabarun Goswami, NABLAS

Background
• Previously (some even now) speech representation for TTS models used to be (Mel-)Spectrograms
• Recently, speech representation used are from Neural Codecs (Encodec/Soundstream/etc.).
Soundstream

Advantages of Codec based Modeling over Spectrogram
• Spectrograms are continuous, hence typical loss functions
include Mean Squared Error (MSE/L2) or Mean Absolute Error
(MAE/L1).
• MSE works by maximizing the likelihood of observed data
under Gaussian error model, while MAE under a Laplacian
error model.
• However, when dealing with discrete tokens, classification
approach is used, i.e. predict the token label from a fixed
vocabulary.
• CrossEntropy loss is used, which measures the divergence
between true distribution and predicted distribution without
making explicit assumptions about the underlying
distribution.
• This makes it more flexible for real world data (discrete
speech tokens from neural codec models)

XTTS
Perceiver, Jaegle+, ICML 2021
1. Train 8192-token mel-spec VQ-VAE (neural codec)
2. Use 6681-token BPE text tokenizer.
3. Train GPT2 with LM heads predicting audio codes from
Step 1.
4. Use Perceiver architecture for speaker conditioning.
5. Train decoder/vocoder on GPT2 latents before the LM
heads, conditioned on pre-trained speaker encoder
6. Loss functions:
a) GPT2: Crossentropy
b) Decoder:
i. Reconstruction (L1/L2),
ii. Adversarial,
iii. Speaker concistency

Dataset
• Sources:
• English: LibriTTS-R,
LibriLight, Internal dataset
• Others: Commonvoice

Results
https://huggingface.co/spaces/coqui/xtts

Discussion
• Good:
• Speech quality is quite good
• Perceiver allows multiple reference audios without length limitation
• HiFi-GAN based vocoder from GPT2 latents reduces some inference latency
• Not so Good:
• Japanese, Korean and Chinese are romanized before tokenization.
• CER for these language is quite high compared to other languages
• GPT2 is decoder only transformer
• Potential for hallucinations
• Slower inference, one token/frame at a time

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学历认证补办制【微信：A575476】【(UofT毕业证）多伦多大学毕业证成绩单offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张，特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份【微信：A575476】 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才 → 【关于价格问题（保证一手价格）我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择��体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！办理(UofT毕业证）多伦多大学毕业证【微信：A575476】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。办理(UofT毕业证）多伦多大学毕业证【微信：A575476】格式相对统一，各专业都有相应的模板。通常包括以下部分：校徽：象征着学校的荣誉和传承。校名:学校英文全称授予学位：本部分将注明获得的具体学位名称。毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。办理(UofT毕业证）多伦多大学毕业证【微信：A575476】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。综上所述，办理(UofT毕业证）多伦多大学毕业证【微信：A575476 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

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How We Added Replication to QuestDB - JonTheBeach

javier ramirez

Building a database that can beat industry benchmarks is hard work, and we had to use every trick in the book to keep as close to the hardware as possible. In doing so, we initially decided QuestDB would scale only vertically, on a single instance. A few years later, data replication —for horizontally scaling reads and for high availability— became one of the most demanded features, especially for enterprise and cloud environments. So, we rolled up our sleeves and made it happen. Today, QuestDB supports an unbounded number of geographically distributed read-replicas without slowing down reads on the primary node, which can ingest data at over 4 million rows per second. In this talk, I will tell you about the technical decisions we made, and their trade offs. You'll learn how we had to revamp the whole ingestion layer, and how we actually made the primary faster than before when we added multi-threaded Write Ahead Logs to deal with data replication. I'll also discuss how we are leveraging object storage as a central part of the process. And of course, I'll show you a live demo of high-performance multi-region replication in action.

Cloud Analytics Use Cases - Telco Products

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Niagara College degree offer diploma Transcript

taqyea

原版制作【微信：A575476】【(NC毕业证)尼亚加拉学院毕业证成绩单offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份【微信：A575476】 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才 → 【关于价格问题（保证一手价格）我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！办理(NC毕业证)尼亚加拉学院毕业证【微信：A575476】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。办理(NC毕业证)尼亚加拉学院毕业证【微信：A575476】格式相对统一，各专业都有相应的模板。通常包括以下部分：校徽：象征着学校的荣誉和传承。校名:学校英文全称授予学位：本部分将注明获得的具体学位名称。毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。办理(NC毕业证)尼亚加拉学院毕业证【微信：A575476】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。综上所述，办理(NC毕业证)尼亚加拉学院毕业证【微信：A575476 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

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社内勉強会資料_XTTS: a Massively Multilingual ZeroShot Text-to-Speech Model.pdf

1. XTTS: a Massively Multilingual Zero- Shot Text-to-Speech Model Casanova et al., INTERSPEECH 2024 Paper Discussion, 28 June 2024 Presenter: Nabarun Goswami, NABLAS

2. Background • Previously (some even now) speech representation for TTS models used to be (Mel-)Spectrograms • Recently, speech representation used are from Neural Codecs (Encodec/Soundstream/etc.). Soundstream

3. Advantages of Codec based Modeling over Spectrogram • Spectrograms are continuous, hence typical loss functions include Mean Squared Error (MSE/L2) or Mean Absolute Error (MAE/L1). • MSE works by maximizing the likelihood of observed data under Gaussian error model, while MAE under a Laplacian error model. • However, when dealing with discrete tokens, classification approach is used, i.e. predict the token label from a fixed vocabulary. • CrossEntropy loss is used, which measures the divergence between true distribution and predicted distribution without making explicit assumptions about the underlying distribution. • This makes it more flexible for real world data (discrete speech tokens from neural codec models)

4. XTTS Perceiver, Jaegle+, ICML 2021 1. Train 8192-token mel-spec VQ-VAE (neural codec) 2. Use 6681-token BPE text tokenizer. 3. Train GPT2 with LM heads predicting audio codes from Step 1. 4. Use Perceiver architecture for speaker conditioning. 5. Train decoder/vocoder on GPT2 latents before the LM heads, conditioned on pre-trained speaker encoder 6. Loss functions: a) GPT2: Crossentropy b) Decoder: i. Reconstruction (L1/L2), ii. Adversarial, iii. Speaker concistency

5. Dataset • Sources: • English: LibriTTS-R, LibriLight, Internal dataset • Others: Commonvoice

6. Results https://huggingface.co/spaces/coqui/xtts

7. Discussion • Good: • Speech quality is quite good • Perceiver allows multiple reference audios without length limitation • HiFi-GAN based vocoder from GPT2 latents reduces some inference latency • Not so Good: • Japanese, Korean and Chinese are romanized before tokenization. • CER for these language is quite high compared to other languages • GPT2 is decoder only transformer • Potential for hallucinations • Slower inference, one token/frame at a time

社内勉強会資料_XTTS: a Massively Multilingual ZeroShot Text-to-Speech Model.pdf

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