投稿時間:2023-08-09 22:15:00 RSSフィード2023-08-09 22:00 分まとめ(14件)

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python	Pythonタグが付けられた新着投稿 - Qiita	Pythonのlistとtupleの違い	https://qiita.com/tomot7/items/9ce3e21deb1e0a564f1f	tuple	2023-08-09 21:12:45
海外TECH	MakeUseOf	How to Present PowerPoint Slides in Google Meet	https://www.makeuseof.com/present-powerpoint-slides-in-google-meet/	google	2023-08-09 12:31:23
海外TECH	MakeUseOf	How to Crop Images Diagonally in Canva	https://www.makeuseof.com/canva-how-to-diagonally-crop/	images	2023-08-09 12:01:24
海外TECH	DEV Community	Why Most Android Apps Fail in the Market? Unveiling the Challenges and Solutions	https://dev.to/dhruvjoshi9/why-most-android-apps-fail-in-the-market-unveiling-the-challenges-and-solutions-37go	Why Most Android Apps Fail in the Market Unveiling the Challenges and SolutionsAre you wondering why your Android app hasn t achieved the success you hoped for in the market The Android app market is vast and competitive making it challenging for every app to thrive This blog dives deep into the question Why Most Android Apps Fail in Market to shed light on the common pitfalls and offers insights into how you can overcome these challenges Discover the reasons behind the failure of many Android apps in the market and explore effective solutionsIn this section we will see why most apps fails and what is the solution for them Why Most Android Apps Fail in Market Android app failure can be attributed to several factors that often interact and compound each other Understanding these factors is crucial for developers and businesses aiming to create successful apps Here are some prominent reasons Inadequate Market ResearchOne of the fundamental mistakes is developing an app without conducting comprehensive market research Ignoring user needs preferences and market trends can lead to an app that doesn t resonate with the target audience Poor User Experience UX Apps that offer a subpar user experience are bound to fail Slow loading times confusing navigation and lack of user friendly design can frustrate users leading to high uninstall rates Lack of UniquenessThe market is saturated with similar apps and unless your app offers a unique value proposition it s likely to get lost in the crowd Differentiation is key to attracting users Technical Glitches and BugsApps riddled with technical glitches crashes and bugs create a negative impression Users demand smooth and glitch free experiences and failure to deliver this can lead to uninstallation Poor Marketing and VisibilityNo matter how great your app is it won t succeed if users are unaware of its existence Inadequate marketing efforts and lack of visibility can hinder app success Ignoring User FeedbackUser feedback is invaluable for improving an app s functionality and addressing issues Ignoring user feedback can result in a stagnant app that fails to meet user expectations Monetization and Pricing MistakesChoosing the wrong monetization model or pricing strategy can drive users away Balancing revenue generation with user value is essential Rapid Technological ChangesThe tech landscape evolves quickly and apps that don t adapt to new technologies and trends can become outdated and irrelevant Solutions to Ensure App SuccessWhile the challenges are daunting adopting the right strategies can significantly enhance your app s chances of success Thorough Market Research and ValidationConduct thorough market research to identify user needs preferences and gaps in the market Validate your app idea before investing significant resources Prioritize User Centric DesignInvest in creating an intuitive and user friendly interface Prioritize seamless navigation quick loading times and appealing visuals Innovation and UniquenessIdentify ways to differentiate your app from competitors Offer unique features and functionalities that provide real value to users Rigorous Testing and Quality AssuranceInvest in rigorous testing and quality assurance to eliminate technical glitches and bugs Delivering a smooth user experience should be non negotiable Strategic Marketing and PromotionDevise a comprehensive marketing strategy to create awareness and generate interest in your app Leverage social media influencers and app store optimization techniques Embrace User Feedback and IterationEncourage user feedback and be open to making improvements based on their suggestions Regular updates showcase your commitment to enhancing the app Choose the Right Monetization StrategyEvaluate different monetization models and pricing strategies to find the one that aligns with your app s value proposition and user expectations Stay Updated with TrendsStay informed about the latest technological trends and innovations Embrace new features and technologies that can enhance your app s functionality FAQs About Android App Failures Q Are all Android app failures solely due to technical issues A No while technical issues contribute app failure often results from a combination of factors such as poor market research UX problems and inadequate marketing Q Can a successful marketing strategy save an app with a poor user experience A While marketing is important a poor user experience can deter users regardless of the marketing efforts A balance between UX and marketing is crucial Q Is it better to launch an app quickly or to spend more time refining it A It s advisable to find a balance Launching a minimally viable product quickly allows you to gather user feedback and refine the app based on actual usage Q How can I effectively gather user feedback for my app A Implement in app feedback mechanisms conduct surveys and engage with users on social media Actively listen to their suggestions and concerns Q What role does app design play in its success A App design plays a critical role in user engagement An intuitive visually appealing and user centric design enhances the overall user experience Q How frequently should I update my app A Regular updates are essential to address bugs introduce new features and demonstrate your commitment to app improvement Aim for consistent updates based on user feedback Conclusion Navigating the Path to App SuccessWell not stretching this blog any longer I hope you got some serious tips about your current or upcoming android app But it all needs latest and expert hands that backbones it If you got it right you should connect with the right android application development company right away	2023-08-09 12:13:02
海外TECH	DEV Community	Comparing Tortoise and Bark for Voice Synthesis	https://dev.to/mikeyoung44/comparing-tortoise-and-bark-for-voice-synthesis-1nlb	Comparing Tortoise and Bark for Voice SynthesisText to speech TTS technology has seen rapid advances thanks to recent improvements in deep learning and generative modeling Two models leading the pack are Bark and Tortoise TTS Both leverage cutting edge techniques like transformers and diffusion models to synthesize amazingly natural sounding speech from text For engineers and researchers building speech enabled products choosing the right TTS model is now a complex endeavor given the capabilities of these new systems While Bark and Tortoise have similar end goals their underlying approaches differ significantly This article will dive deep into how Bark and Tortoise work under the hood their respective strengths and weaknesses and when each one is the superior choice Whether you re developing a voice assistant synthesizing audiobook narration or exploring new generative frontiers in audio understanding these models is key to success Subscribe or follow me on Twitter for more content like this By the end you ll clearly understand which model aligns best with your needs and constraints when bringing next gen TTS into your products You ll also learn about some other text to audio models you can check out Let s get started Use cases and capabilitiesLet s take a high level look at what each model can do before we get into a more detailed comparison All about BarkBark is a text to audio generative model created by Suno AI It utilizes a transformer architecture to generate high quality realistic audio from text prompts Some key capabilities of Bark It can synthesize natural human like speech in multiple languages This makes it suitable for voice assistant applications audiobook narration and more Beyond just speech Bark can also generate music sound effects and other audio This flexibility enables creative use cases like producing customized audio for videos games or interactive apps The model supports generating laughs sighs and other non verbal sounds to make speech more natural and human sounding I find these really compelling and these imperfections make the speech sound much more real Check out an example here scroll down to pizza webm Bark allows control over tone pitch speaker identity and other attributes through text prompts This level of control is useful for developing distinct voice personas It requires no additional data annotation beyond text transcripts The model learns directly from text audio pairs In summary Bark is a powerful generative model capable of synthesizing high quality speech and diverse audio entirely from text Its flexibility enables a range of potential applications from voice assistants to audio production tools Note you can use Bark to produce non speech sounds like sound effects This is similar to another model called AudioLDM which we have a guide for here Bark s inputs and outputsHere s a breakdown of the inputs and outputs for the Bark model implemented by Suno on Replicate com using data from the API spec page Inputs prompt string The input prompt that provides the initial context for the generation The default value is Hello my name is Suno And uh ーand I like pizza laughs But I also have other interests such as playing tic tac toe history prompt string A choice for audio cloning history This allows you to choose from a list of predefined speaker IDs in various languages e g en speaker es speaker fr speaker etc This history helps the model understand the voice style to generate audio in custom history prompt file If provided this npz file overrides the previous history prompt setting You can provide your own history choice for audio cloning text temp number Generation temperature for the text generation process A higher value e g makes the output more diverse while a lower value e g makes it more conservative The default value is waveform temp number Generation temperature for the waveform generation process Similar to text temp this parameter affects the diversity of audio generation The default value is output full boolean If set to true the model returns the full generation as a npz file which can be used as a history prompt for future generations Outputs The model s output structure is described by the following JSON schema type object title ModelOutput required audio out properties audio out type string title Audio Out format uri prompt npz type string title Prompt Npz format uri Some additional details you may find helpful audio out string A URI that points to the generated audio file This is the primary output of the model containing the audio representation of the generated text prompt prompt npz string A URI that points to the npz file representing the prompt used for generating the audio This can be useful for keeping track of the input context that led to the audio generation In summary the Bark model takes input prompts history choices and generation temperature settings to produce audio output The output includes a link to the generated audio file and a link to the npz file representing the prompt All about Tortoise TTSTortoise TTS is a text to speech model optimized for exceptionally realistic and natural sounding voice synthesis It was created by James Betker Key capabilities of Tortoise TTS It excels at cloning voices using just short audio samples of a target speaker This makes it easy to produce text in many distinct voices The quality of the synthesized voices is extremely high nearly indistinguishable from human speakers This makes Tortoise great for use cases like audiobook narration Tortoise supports fine grained control of speech characteristics like tone emotion pacing etc through priming text This flexibility helps bring voices to life The model efficiently leverages smaller datasets by training an autoencoder for voice compression Less data is needed relative to other TTS models Tortoise focuses specifically on speech synthesis While it lacks flexibility for music or sound effects it provides unparalleled realism for voice In summary Tortoise TTS is an exceptionally high fidelity text to speech model optimized for cloning voices and narrating long form speech content like books or articles The quality and control it provides over voice synthesis makes Tortoise suitable for a range of applications from virtual assistants to audiobook creation You can even use Tortoise to create voice clones of celebrities like Barack Obama Donald Trump Walter White Tony Stark and more Tortoise TTS s inputs and outputsHere s an overview of the inputs and outputs for the Tortoise model again looking at the implementation on Replicate this time by creator afiaka Inputs text string The text input that you want the model to generate speech for The default value is The expressiveness of autoregressive transformers is literally nuts I absolutely adore them I am not sure why that s the default but it is voice a string Selects the primary voice to use for speech generation You can choose from a list of predefined voices e g angie deniro halle etc or use special values like random custom voice or disabled The default value is random which selects a random voice custom voice file If provided this file should contain an mp audio of a speaker that you want to use as a custom voice The audio should be at least seconds long and only contain one speaker This parameter overrides the voice a setting voice b and voice c strings optional These parameters allow you to create a new voice by averaging the latents of the selected voices You can choose from predefined voices or use disabled to disable voice mixing These parameters are optional preset string Specifies a voice preset to use for generation The preset determines the quality and speed of the generated speech Allowed values are ultra fast fast standard and high quality The default value is fast seed integer A random seed that can be used to reproduce results The default value is cvvp amount number Determines how much the CVVP Concatenative Voice Variational Posterior model should influence the output Increasing this value can reduce the likelihood of multiple speakers in the generated speech The default value is disabled Output The model s output structure is described by the following JSON schema type string title Output format uri The output is a URI Uniform Resource Identifier that points to the generated speech audio file This audio file represents the synthesized speech based on the provided input text and voice settings In summary the Tortoise TTS model takes input text voice selections preset options and other parameters to generate speech The output is a URI pointing to the audio file containing the generated speech Comparing Bark and Tortoise TTSNow that we ve seen what kind of inputs and outputs the models work with let s take a comparative look across a number of different dimensions ArchitectureSpeech generation abilityLanguageAccentsOutput qualityBy the end of the article you ll understand when to use Bark and when to use Tortoise We ll also look at some other models you may want to check out so you can find the proper fit for your use case Model ArchitectureThe architecture used by a TTS model impacts what it can generate and how well it performs Understanding these technical differences helps interpret the strengths and limitations of building products with them The key differences between Bark and Tortoise Bark uses a flexible transformer architecture that can generate diverse sounds like music But it requires huge amounts of varied training data Tortoise uses custom components focused specifically on reproducing human voices realistically But this specialization makes it harder to expand to other sounds Looking closer Bark employs a transformer architecture similar to GPT as described in the README It embeds text into abstract tokens without phonemes A second transformer converts these into audio codec tokens to synthesize the waveform Transformers leverage self attention to model relationships in data enabling generative capabilities This provides flexibility in sounds like music but needs lots of data for high fidelity Tortoise uses a Tacotron style encoder decoder for text and an autoencoder for audio compression It then decodes compressed audio using a diffusion model as described in the paper This specialized configuration targets voice realism The autoencoder clones voices efficiently The diffusion model gives Tortoise exceptional quality The tradeoff is less flexibility than Bark These architectural differences have implications for product possibilities Bark offers flexibility for apps with diverse audio needs Tortoise prioritizes voice quality for use cases like audiobooks Understanding these strengths and weaknesses helps pick the right model for your needs Voice CustomizationThe ability to customize and control the synthesized voice is important for some applications You ll need to decide how important it is for yours because Bark and Tortoise take different approaches to enabling voice control Bark has a limited set of built in voice presets but no straightforward way for end users to clone new voices As described in the documentation Bark supports speaker presets across languages These allow controlling attributes like tone pitch and emotion However adding new custom voices requires advanced technical skills In contrast Tortoise excels at cloning voices using just short audio samples Its autoencoder compression enables efficiently capturing speaker characteristics As explained in the source code repo for Suno s implementation users can clone voices by providing a few audio clips of a target speaker For simple voice assistant applications with a limited set of voices Bark s presets may suffice But for product ideas requiring extensive voice cloning of arbitrary speakers Tortoise is likely the better choice despite additional complexity Supported Languages and AccentsBark and Tortoise take different approaches to supporting multiple languages and accents with implications for product localization and access Bark supports many languages relatively well out of the box as listed in the documentation English en German de Spanish es French fr Hindi hi Italian it Japanese ja Korean ko Polish pl Portuguese pt Russian ru Turkish tr Chinese simplified zh Supported bark languagesBark handles code switching and accents smoothly automatically detecting the language from the text prompt In contrast Tortoise was trained mostly on English data As explained in the paper it lacks diversity in supported languages and accents Non English speech would require collecting additional training data and retraining the models This gives Bark an advantage for products aimed at global markets or supporting multilingual users Bark s built in multilingual support reduces the effort required for localization Tortoise would involve more work to expand beyond English For products highly optimized for a single language like English audiobooks Tortoise provides superior quality But Bark is generally a better choice if easily supporting many languages and accents is critical Output QualityWhile both models produce excellent results Tortoise TTS edges out Bark in default audio quality right out of the box However Bark can match Tortoise given sufficient tuning and prompt engineering As noted in the documentation Bark s audio quality is very good but some creative prompting is needed to achieve the best results Guiding the model with brackets capitalization speaker tags and other markup can improve fidelity You may also need to post process some audio if super high quality sound is important to your use case In contrast Tortoise offers exceptional audio quality without any prompt tuning needed Synthesized voices are extremely close to human speech The samples sound virtually indistinguishable from real people with only a few artifacts This difference highlights Tortoise s focus specifically on optimizing voice reproduction The diffusion model and conditioning workflow deliver consistently amazing results unmatched by other TTS systems However Bark s flexibility as a general audio model means it can likely match Tortoise s quality given enough experimentation with prompts This prompt tuning requires more effort and skill I haven t spent enough time with it to pull this off but you may be able to In summary Tortoise exceeds Bark in default out of the box output quality But Bark can achieve equivalent quality with sufficient prompting expertise at the cost of additional effort Building Startups with Bark and TortoiseBoth Bark and Tortoise enable creation of a wide range of speech focused products What kind of products could you build with these tools Here are some example startup ideas that play to the strengths of each model BarkVoice assistant service supporting multiple languages Bark s built in multilingual capabilities make it easy to build assistants for global markets Interactive audio games Bark s flexibility allows generating sound effects background music and dialogue on the fly Foreign language learning apps Combine Bark s speech synthesis with language education tools TortoiseHyper realistic voice cloning service Clone customer voices or famous voices using Tortoise s exceptional quality Synthetic voice actors Use Tortoise to easily craft distinct voices for animation video content Automated audiobook creation Produce audiobooks from ebooks leveraging Tortoise s strengths at long form narration Personalized guided meditations Generate customized meditation audio in your own voice with Tortoise BothCustom text to speech APIs Offer TTS as a service focused on quality and voice control Text to podcast service Automatically convert blog posts into podcast episodes with Bark s audio generation skills Text based adventure games Immerse players with reactive voice narration and effects Accessibility tools Enable those with disabilities to convert text to realistic speech The quality and voice control of Bark and Tortoise opens up many new product possibilities spanning entertainment education accessibility and productivity What are you going to build with these tools Comparing Bark and Tortoise to Alternative TTS ModelsWhile this article has focused on Bark and Tortoise TTS there are a few other leading text to speech models worth considering AudioLDM is a latent diffusion model created by haoheliu focused on generating high quality audio from text transcripts It produces very realistic voices and speech like Bark and Tortoise However it lacks the flexibility of Bark for non speech audio and the exceptional voice cloning capabilities of Tortoise Whisper is a speech recognition model originally created by OpenAI and now maintained by Anthropic It specializes in transcribing audio to text rather than text to audio generation This makes it complementary to the other models Free VC by jagilley is an audio to audio voice conversion model It can alter and convert voices while retaining the same speech patterns and style This can be used along with TTS models like Tortoise that support voice cloning While Bark and Tortoise are good choices these alternative models can provide complementary capabilities like speech to text easier voice cloning and voice style transfer to consider when building voice enabled products They might be a better fit for your product depending on its needs Note If you re shopping around for the right model you can also describe your project here and get a recommended set of models based on their similarity to your exact use case Here is a comparison of the text to speech models Bark Tortoise TTS AudioLDM Whisper and Free VC across different use cases and product applicability For voice assistant applications Bark is likely the best option given its built in multilingual capabilities and flexibility in generating sound effects and music in addition to speech Tortoise TTS and AudioLDM also work well for voice assistants focused just on speech realism For audiobooks and long form speech synthesis Tortoise TTS is likely the best choice given its exceptional voice cloning capabilities and natural prosody Bark and AudioLDM can also work well but may require more tuning For transcribing speech to text Whisper is purpose built for this use case and would be the recommended model For voice cloning and conversion Free VC specializes in this capability making it the best fit Tortoise TTS also enables voice cloning via samples The table below summarizes the various use cases I discussed above ModelBest Use CasesKey StrengthsBarkVoice assistants audio generationFlexibility multilingualTortoise TTSAudiobooks voice cloningNatural prosody voice cloningAudioLDMVoice assistantsHigh quality speechWhisperTranscriptionAccuracy flexibilityFree VCVoice conversionRetains speech styleEach model has strengths making them best suited for certain use cases and products though there is also overlap in capabilities across models Experiment to find the right one ConclusionText to speech technology has advanced rapidly providing startups many options for building voice enabled products While Bark and Tortoise are good choices alternatives like AudioLDM Whisper and Free VC provide complementary capabilities to consider The key is picking the right model for your specific use case and constraints For multi language voice assistants Bark is likely the top contender Tortoise excels at hyper realistic audiobook narration and voice cloning And there are other applications for which either model could be used I hope this guide provides a solid foundation for choosing the right text to speech model for your next product Let me know if you have any other questions Subscribe or follow me on Twitter for more content like this I m always happy to help interpret the landscape of generative AI models to build amazing new applications Thanks for reading Resources and Further ReadingYou may find these links helpful as you learn more about the world of generative text to speech models Bark GitHub repo Details on model architecture examples installation and usageTortoise GitHub repo Source code documentation and usage guideTortoise TTS paper Technical details on model architecture and trainingBark model on Replicate Info pricing and hosted API accessTortoise model on Replicate Info pricing and hosted API accessA builder s guide to synthesizing sound effects music and dialog with AudioLDM What makes this model special What can you build with it An in depth guide to creating sound and speech from text or audio files using AudioLDM text to audio Notebook on long form Bark generation Tips for generating longer audioAIModels overview of Bark High level capabilities and use casesAIModels overview of Tortoise TTS High level capabilities and use casesTTS models on AIModels Browse other related models Supply your use case and get a list of candidate models to try out	2023-08-09 12:06:23
海外TECH	DEV Community	Building a multilingual NextJS app using the new app directory	https://dev.to/codegino/building-a-multilingual-nextjs-app-using-the-new-app-directory-2anf		2023-08-09 12:05:30
Apple	AppleInsider - Frontpage News	Saudi Arabia passes law requiring USB-C charges for smartphones	https://appleinsider.com/articles/23/08/09/saudi-arabia-passes-law-requiring-usb-c-charges-for-smartphones?utm_medium=rss	Saudi Arabia passes law requiring USB C charges for smartphonesFrom Apple s iPhone and all Android smartphones sold in Saudi Arabia will have to have a USB C charging port with laptops to follow in After the European Union s mandate that all smartphones switch to USB C charging the government of Saudi Arabia has followed suit According to GSM Arena the local government has announced that the law is intended to cut down e waste reduce costs overall and improve the user experience The latter is to come both from the convenience of being able to more easily buy or borrow the correct cables and from ensuring higher speed data transfers Read more	2023-08-09 12:47:59
Apple	AppleInsider - Frontpage News	China tightens its grip on foreign and independent app developers	https://appleinsider.com/articles/23/08/09/china-tightens-its-grip-on-foreign-and-independent-app-developers?utm_medium=rss	China tightens its grip on foreign and independent app developersChina s government has announced that all App Store developers must be based in China or partner with a local publisher and provide business details including an address Following one new law banning ChatGPT like apps and games requiring an official license China has announced strict new requirements for all app developers According to Reuters China s Ministry of Industry and Information MIIT has announced that mobile app developers must file business details The requirement appears to be effective immediately but there is a grace period that expires in March Read more	2023-08-09 12:21:08
ニュース	BBC News - Home	'Family fears for my safety' after police details published	https://www.bbc.co.uk/news/uk-northern-ireland-66447388?at_medium=RSS&at_campaign=KARANGA	highlights	2023-08-09 12:47:29
ニュース	BBC News - Home	Nine bodies found after fire at France holiday home	https://www.bbc.co.uk/news/world-europe-66447519?at_medium=RSS&at_campaign=KARANGA	holiday	2023-08-09 12:34:03
ニュース	BBC News - Home	This Morning: ITV employees allege toxic culture following Schofield exit	https://www.bbc.co.uk/news/entertainment-arts-66448872?at_medium=RSS&at_campaign=KARANGA	committee	2023-08-09 12:26:29
ニュース	BBC News - Home	Cardiff: St Mellons crash victims on laughing gas, court papers reveal	https://www.bbc.co.uk/news/uk-wales-66450162?at_medium=RSS&at_campaign=KARANGA	court	2023-08-09 12:29:56
ニュース	BBC News - Home	World Scout Jamboree: How troubles plagued South Korea's operation	https://www.bbc.co.uk/news/world-asia-66425588?at_medium=RSS&at_campaign=KARANGA	risks	2023-08-09 12:08:19
ニュース	BBC News - Home	Lucy Bronze: 'England squad have huge belief at Women's World Cup'	https://www.bbc.co.uk/sport/football/66446967?at_medium=RSS&at_campaign=KARANGA	Lucy Bronze x England squad have huge belief at Women x s World Cup x England s ability to find a way to win during the Women s World Cup has only added to the squad s belief Lucy Bronze says	2023-08-09 12:05:52