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Additional audio data facilitates the synthesis of convincing voices and makes it more difficult for a human to consistently differentiate between ground truth and synthesized voices. "),a("span",{},[e._v("As of September 2021, "),a("span",{staticClass:"font-semibold"},[e._v("DeepThroat")]),e._v(" is a significant improvement over every text-to-speech algorithm in existence.")])]),a("hr"),a("div",{staticClass:"w-full my-4 font-normal"},[e._v(" A dictionary lookup table is used to dissect each English word into its respective phonemes. The dictionary has been compiled by scraping the "),a("a",{attrs:{href:"https://developer.oxforddictionaries.com/",target:"_blank"}},[e._v("Oxford Dictionaries API")]),e._v(", "),a("a",{attrs:{href:"https://en.wiktionary.org/wiki/Wiktionary:Main_Page",target:"_blank"}},[e._v("Wiktionary")]),e._v(", and the "),a("a",{attrs:{href:"http://www.speech.cs.cmu.edu/cgi-bin/cmudict",target:"_blank"}},[e._v("CMU Pronouncing Dictionary")]),e._v(". Modern neologisms are also scraped and culled from the Internet from various sources, including Google, Reddit, 4chan, and "),a("a",{attrs:{href:"https://www.urbandictionary.com/",target:"_blank"}},[e._v("Urban Dictionary")]),e._v(". If a word does not exist within the dictionary, the network deduces the pronunciation of the word using the phonological rules it has learned from training on the "),a("a",{attrs:{href:"https://research.google/tools/datasets/libri-tts/",target:"_blank"}},[e._v("LibriTTS")]),e._v(" dataset. ")]),a("div",{staticClass:"w-full my-2 font-normal flex-col sm:flex-row sm:flex"},[a("div",{staticClass:"w-full sm:w-3/4 mr-2"},[a("div",{staticClass:"mb-4"},[e._v(" The perceived emotion of the input text is deduced using "),a("a",{attrs:{href:"https://arxiv.org/pdf/1708.00524.pdf",target:"_blank"}},[e._v("DeepMoji")]),e._v(" ("),a("a",{attrs:{href:"https://deepmoji.mit.edu/",target:"_blank"}},[e._v("deepmoji.mit.edu")]),e._v('), a sentiment analysis model "trained on 1.2 billion tweets with emojis to understand how language is used to express emotions." This embedding is then used to bias the output toward the desired emotion using '),a("a",{attrs:{href:"https://arxiv.org/pdf/1803.09017.pdf",target:"_blank"}},[e._v("global style tokens")]),e._v(" jointly trained on top of a customized "),a("a",{attrs:{href:"https://arxiv.org/pdf/1910.10838.pdf",target:"_blank"}},[e._v("multi-speaker")]),e._v(" text-to-speech synthesis setup. 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I don\'t want artificial character voices in general being equated to my project because it gives the wrong impression that TTS voices still sound robotic or stilted, which is the absolute antithesis of my project.\n            </div>\n            <div class="text-sm my-1">\n              I\'ll make an exception to this rule for extremely well-known TTS voices like Microsoft Sam that everyone knows does not originate from this project. I may also make an exception for voices that <span class="italic">actually sound decent</span> &mdash; send me an email asking for permission and I\'ll decide whether it should be allowed, with the additional requirement that credit is properly given to the respective creators. I don\'t mean to be a jerk about it &mdash; I just have high standards for my work.\n            </div>\n            <div class="text-sm my-1">\n              On the other hand, you\'re completely free to use your own voice or other humans\' voices as part of your video.\n            </div>\n          </div>'},{title:'<span class="font-bold text-lg">Pronunciations (ARPAbet)</span>',value:'<div class="w-full leading-normal">\n            <div class="text-sm my-1">\n              If a word is not found in the lexicon, it will be outlined <span class="text-red-500 font-bold">red</span> in the <span class="font-semibold">Analytics</span> section. The model is smart enough to guess the pronunciation correctly <span class="italic">most of the time</span> (at the cost of some confidence). As an example of the model getting the pronunciation wrong, you might notice that all characters will invariably pronounce <span class="italic">live</span> with a long i (<span class="font-mono">/līv/</span>) in the sentence <span class="font-mono font-semibold">"They live over there"</span>, instead of the correct short i (<span class="font-mono">/liv/</span>).\n            </div>\n            <div class="text-sm my-1">\n              This can be easily remedied &mdash; the text area accepts ARPAbet strings as part of the input, each wrapped in curly braces <span class="font-mono text-blue-500">{}</span>. Replace the <span class="italic">live</span> with <span class="font-mono font-semibold text-blue-500">{L IH1 V}</span> so that your input becomes <span class="font-mono font-semibold">"They <span class="text-blue-500">{L IH1 V}</span> over there."</span> This will get the characters to say the word <span class="italic">live</span> correctly in this sentence.\n            </div>\n            <div class="text-sm my-1">\n              For a list of all possible ARPAbet <span class="italic">phonemes</span> (units of sound), see the CMU Dictionary (<a href="http://www.speech.cs.cmu.edu/cgi-bin/cmudict" target="_blank" class="text-blue-500 hover:underline">linked here</a>). <span class="italic">Do not use the ARPAbet codes listed in Wikipedia &mdash; they are incompatible</span>. Don\'t forget to give each vowel a <span class="italic">lexical stress</span>: a number from 0 to 2 that indicates whether a syllable should be stressed.\n            </div>\n          </div>'},{title:'<span class="font-bold text-lg">Emotional contextualizers (How to control emotions)</span>',value:'<div class="w-full leading-normal">\n            <div class="text-sm my-1">\n              By default, emotions are automatically deduced from the input. For example, the input <span class="font-mono font-semibold">"I hate you!!!"</span> will almost always be spoken angrily, since the DeepMoji embedding induced by that input is <span class="font-mono font-semibold">[😡,😠,😤,👊,💔]</span>, all of which express the emotion of <span class="italic">anger</span>.\n            </div>\n            <div class="text-sm my-1">\n              But suppose that a skit calls for a character to say <span class="italic">"I hate you!!!"</span> in a sort of playful (or even amorous or salacious) manner. You can specify an <span class="italic">emotional contextualizer</span> to tell the model to extract the emotional context from another sentence and apply it to the original sentence.\n            </div>\n            <div class="text-sm my-1">\n              The input <span class="font-mono font-semibold">"I hate you!!!<span class="text-green-500">|I love you.</span>"</span> will do just that. Note that the input has two parts, separated by a vertical bar | (the green part is not spoken). The model will understand that what should actually be spoken aloud are the words <span class="italic">"I hate you!!!"</span> <span class="font-bold">but</span> spoken as if they were saying <span class="italic">"I love you."</span> For reference, the DeepMoji embedding induced by <span class="italic">"I love you."</span> is <span class="font-mono font-semibold">[💓,💛,💙,❤️,😘]</span>.\n            </div>\n            <div class="text-sm my-1">\n              Sometimes, the model might get confused by exactly what you\'re trying to achieve. You may have to play around with different emotional contextualizers to get the perfect delivery in the end. Remember that the top five emojis are not the only ones that the model sees! They\'re just presented on the website as a convenient visualization.\n            </div>\n            <div class="text-sm my-1">\n              Exclamation points and question marks do make a big difference in the delivery. Use them to coerce characters to deliver sentences in a more frenzied or inquisitive manner.\n            </div>\n          </div>'},{title:'<span class="font-bold text-lg">Overriding the lexicon</span>',value:'<div class="w-full leading-normal">\n            <div class="text-sm my-1">\n              If you notice that a character is pronouncing a recognized word (outlined <span class="text-green-600 font-bold">green</span>) incorrectly, it may be transcribed incorrectly in the lexicon. In this case, try wrapping the problematic word in square brackets to force the model to figure out the pronunciation by itself (e.g. <span class="font-mono text-purple-400 font-semibold">[live]</span>). If you think that a word has been transcribed incorrectly, please send me an email. I will correct the entry manually.\n            </div>\n          </div>'},{title:'<span class="font-bold text-lg">Controlling speed and/or pacing</span>',value:'<div class="w-full leading-normal">\n            <div class="text-sm my-1">\n              Use commas (<span class="font-mono font-semibold">,</span>) to indicate a short pause between words. If you find that a character charges through a long sentence too quickly, inserting commas in between some words will help them slow down and catch their breath. It may also help to include an emotional contextualizer that indicates tranquility or matter-of-factness.\n            </div>\n            <div class="text-sm my-1">\n              On the other hand, if a character takes their time a little too much while reading through a comma-separated list, it may be better to omit some commas for the sake of proper pacing. Again, it may help to also include an emotional contextualizer &mdash; this time, one that indicates anger or anxiety.\n            </div>\n            <div class="text-sm my-1">\n              You <span class="font-bold">should</span> be using an external program like Audacity to edit out any pacing errors. Simply downloading the first unedited take and posting it on Twitter to farm low-effort likes is strongly discouraged. Please take some time and effort to make the result sound good; otherwise, you\'ll have more people ignorantly claiming that <span class="font-semibold">"Machines still can\'t replicate human inflections!"</span> (see below).\n            </div>\n          </div>'},{title:'<span class="font-bold text-lg">Server errors</span>',value:'<div class="w-full leading-normal">\n            <div class="text-sm my-1">\n              If you\'re seeing server errors, it means one of three possible things:\n\n              <ol class="list-decimal ml-6">\n                <li>\n                  The servers are being weird and I need to fix them. If this happens, I may post about it on Twitter at <a href="https://twitter.com/fifteenai" target="_blank" class="text-blue-500 hover:underline font-semibold">@fifteenai</a></span>. (I don\'t use Twitter very often, so maybe not.)\n                </li>\n                <li>You are either sending too many requests in a short period of time or your inputs are total nonsense and are clearly meant to just flood the server.</li>\n                <li>\n                  There\'s a weird occasionally-occurring Chrome-specific issue that I haven\'t been able to figure out (<a href="https://stackoverflow.com/questions/55106303/angular-fail-to-download-file-10mb-from-server-on-google-chrome" target="_blank" class="text-blue-500 hover:underline font-semibold">possibly CORS-related?</a>). Using Incognito mode or another browser or cleaning up your C: drive usually "fixes" it.\n                </li>\n              </ol>\n            </div>\n          </div>'},{title:'<span class="font-bold text-lg">Sources of the lexicon</span>',value:'<div class="w-full leading-normal">\n            <div class="text-sm my-1">\n              The lexicon has been compiled by combining multiple English dictionaries (primarily the Oxford English Dictionary) and automatically extracting textual information from the Internet, including Wiktionary, Google, 4chan, reddit, Twitter, and Urban Dictionary. This catalog is automatically updated every day by scouring the Internet for new words and phrases (along with their linguistic and syntactic contexts) that gain popularity, and then is fed back into the model. If you think some words are missing that should be included in the lexicon, email them to me. They may be added in the next deployment.\n            </div>\n          </div>'}]}},methods:{openURL:v}},Me=$e,Be=(a("6948"),Object(l["a"])(Me,Ie,Ee,!1,null,"341a7804",null)),Oe=Be.exports,Re=function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",{staticClass:"w-full h-32 my-0 pt-32 sm:pt-0 sm:my-0 text-gray-600 sm:w-1/2 sm:h-full bg-transparent flex flex-col items-center justify-center text-center leading-loose font-medium cursor-default text-xl"},[a("transition",{attrs:{name:"fade"}},[e.status.error?a("div",{directives:[{name:"tooltip",rawName:"v-tooltip",value:{placement:"bottom-center",content:e.errorMsg.tooltip},expression:"{\n        placement: 'bottom-center',\n        content: errorMsg.tooltip\n      }"}],staticClass:"text-red-400"},[e._v(" "+e._s(e.errorMsg.message)),a("i",{staticClass:"fas fa-times ml-2"})]):e._e()]),e._m(0),e.status.converting?a("div",{class:e.status.loading?"text-gray-600":"text-black"},[e._v(" Loading 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is me. Literally me. No other character can come close to relating to me like this. There is no way you can convince me this is not me.","Did you ever hear the Tragedy of Darth Plagueis the wise?","The FitnessGram Pacer Test is a multistage aerobic capacity test that progressively gets more difficult as it continues.","I'd just like to interject for a moment.","In view, a humble vaudevillian veteran, cast vicariously as both victim and villain by the vicissitudes of Fate.","To be fair, you have to have a very high IQ to understand Rick and Morty.","Thanks for the gold, kind stranger! I didn't expect this post to blow up!","Natural emotive high-fidelity text-to-speech synthesis with minimal viable data.","Intonations and alignments will vary between generations.","Generative adversarial network or variational autoencoder.","Sneed's Feed and Seed, formerly Chuck's.","{AA1 R P AH0 B EH2 T} {S T R IH1 NG Z}.","Have you read your SICP today?","A live body and a dead body contain the same number of particles. Structurally, there's no discernible difference.","Analysis and Qualitative Effects of Large Breasts on Aerodynamic Performance and Wake of a Miss Kobayashi's Dragon Maid Character","OOPSIE WOOPSIE! Uwu. We made a fucky wucky! A wittle fucko boingo!","hi everyone i'm new! holds up spork. my name is katy but u can call me teh PeNgUiN oF dOOm!","She sells sea-shells on the sea-shore. The shells she sells are sea-shells I'm sure.","The stories and information posted here are artistic works of fiction and falsehood. 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"),a("a",{attrs:{href:"https://www.csail.mit.edu/",target:"_blank"}},[e._v("MIT Computer Science & Artificial Intelligence Laboratory (CSAIL)")]),e._v(" for providing the initial funding that kickstarted this project in 2017. Further thanks to the "),a("a",{attrs:{href:"https://julia.mit.edu/",target:"_blank"}},[e._v("Julia Lab")]),e._v(", "),a("a",{attrs:{href:"https://www.ll.mit.edu/",target:"_blank"}},[e._v("Lincoln Lab")]),e._v(", and the "),a("a",{attrs:{href:"https://www.media.mit.edu/",target:"_blank"}},[e._v("Media Lab")]),e._v(". ")]),a("div",{staticClass:"w-full my-2 flex-col sm:flex-row sm:flex my-2"},[a("div",{staticClass:"w-full sm:w-5/6 mr-2"},[e._v(" Special shoutouts go to 4chan's "),a("span",{staticClass:"font-semibold"},[e._v("/mlp/ and its anons")]),e._v(" who have spent hundreds of hours collecting, cleaning, and organizing clips of dialogue taken from the show "),a("a",{staticClass:"italic",attrs:{href:"https://en.wikipedia.org/wiki/My_Little_Pony:_Friendship_Is_Magic",target:"_blank"}},[e._v("My Little Pony: Friendship Is Magic")]),e._v(". Their collective efforts as well as their constructive criticism via thorough tests of experimental model versions have proven to be extremely helpful and ultimately indispensable to the development of my work. The importance of /mlp/ throughout the development of this project cannot be overstated – once again, I thank each and every one of you anonymous contributors. ")]),a("div",{staticClass:"w-full sm:w-1/6 text-center ml-2 flex items-center"},[a("div",[a("img",{staticClass:"w-40",attrs:{src:"img/4cc.png"}})])])]),a("div",{staticClass:"my-2"},[e._v(" Finally, thanks to all those who have donated to my "),a("a",{attrs:{href:"https://patreon.com/15ai",target:"_blank"}},[e._v("Patreon")]),e._v(" — as stated in my bio, all donations will go toward paying the server bills. Thank you for being patient with me throughout this journey — I plan to add more to this page to properly thank all donators. ")])]),a("div",{staticClass:"w-full text-lg sm:text-2xl font-semibold sm:font-semibold mb-4 mt-4 sm:mt-8"},[e._v(" Bibliography ")]),a("div",{staticClass:"w-full my-4"},[a("ul",{staticClass:"list-disc px-8 text-base"},[a("li",[a("span",{staticClass:"italic"},[e._v("Perceptual Significance of Cepstral Distortion Measures in Digital Speech Processing.")]),e._v(" A. Vasilijević, et al. 2011. ("),a("a",{attrs:{href:"https://www.tandfonline.com/doi/pdf/10.1080/00051144.2011.11828412",target:"_blank"}},[e._v("doi:10.1080/00051144.2011.11828412")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("WaveNet: A Generative Model for Raw Audio.")]),e._v(" A. Oord, et al. 2016. ("),a("a",{attrs:{href:"https://arxiv.org/pdf/1609.03499.pdf",target:"_blank"}},[e._v("arXiv:1609.03499v2")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm.")]),e._v(" B. Felbo, et al. 2017. ("),a("a",{attrs:{href:"https://arxiv.org/abs/1708.00524",target:"_blank"}},[e._v("arXiv:1708.00524")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Natural TTS Synthesis By Conditioning WaveNet on Mel Spectrogram Predictions.")]),e._v(" J. Shen, et al. 2018. ("),a("a",{attrs:{href:"https://arxiv.org/pdf/1712.05884.pdf",target:"_blank"}},[e._v("arXiv:1712.05884")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("WaveGlow: A Flow-based Generative Network for Speech Synthesis.")]),e._v(" R. Prenger, et al. 2018. ("),a("a",{attrs:{href:"https://arxiv.org/pdf/1811.00002.pdf",target:"_blank"}},[e._v("arXiv:1811.00002")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Efficient Neural Audio Synthesis.")]),e._v(" N. Kalchbrenner, et al. 2018. ("),a("a",{attrs:{href:"https://arxiv.org/pdf/1802.08435v2.pdf",target:"_blank"}},[e._v("arXiv:1802.08435")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma.")]),e._v(" Y. Koizumi, et al. 2018. ("),a("a",{attrs:{href:"https://arxiv.org/pdf/1810.09133.pdf",target:"_blank"}},[e._v("arXiv:1810.09133")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis.")]),e._v(" Y. Wang, et al. 2018. ("),a("a",{attrs:{href:"https://arxiv.org/pdf/1803.09017.pdf",target:"_blank"}},[e._v("arXiv:1803.09017")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis.")]),e._v(" Y. Jia, et al. 2019. ("),a("a",{attrs:{href:"https://arxiv.org/pdf/1806.04558.pdf",target:"_blank"}},[e._v("arXiv:1806.04558")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing.")]),e._v(" H. Fu, et al. 2019. ("),a("a",{attrs:{href:"https://arxiv.org/pdf/1903.10145.pdf",target:"_blank"}},[e._v("arXiv:1903.10145")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Pre-Alignment Guided Attention for Improving Training Efficiency and Model Stability in End-to-End Speech Synthesis.")]),e._v(" X. Zhu, et al. 2019. ("),a("a",{attrs:{href:"http://www.npu-aslp.org/lxie/papers/2019Access_ZXL.pdf",target:"_blank"}},[e._v("doi:10.1109/2019.2914149")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings.")]),e._v(" E. Cooper, et al. 2020. ("),a("a",{attrs:{href:"https://arxiv.org/pdf/1910.10838.pdf",target:"_blank"}},[e._v("arXiv:1910.10838")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention.")]),e._v(" H. Tachibana, et al. 2020. ("),a("a",{attrs:{href:"https://arxiv.org/pdf/1710.08969.pdf",target:"_blank"}},[e._v("arXiv:1710.08969")]),e._v("). ")]),a("li",[a("span",{staticClass:"italic"},[e._v("HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis")]),e._v(" J. Kong, et al. 2020. ("),a("a",{attrs:{href:"https://arxiv.org/pdf/2010.05646.pdf",target:"_blank"}},[e._v("arXiv:2010.05646")]),e._v("). ")]),a("br"),a("li",[a("span",{staticClass:"italic"},[e._v("Algebraic Geometry and Statistical Learning Theory.")]),e._v(" S. Watanabe. "),a("span",{staticClass:"italic"},[e._v("Cambridge Monographs,")]),e._v(" 2009. ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Machine Learning for Audio, Image and Video Analysis, 2nd Edition.")]),e._v(" F. Camastra, A. Vinciarelli. "),a("span",{staticClass:"italic"},[e._v("Springer,")]),e._v(" 2015. ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Deep Learning.")]),e._v(" I. Goodfellow, Y. Bengio, A. Courville. "),a("span",{staticClass:"italic"},[e._v("MIT Press,")]),e._v(" 2016. ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Foundations of Machine Learning.")]),e._v(" M. Mohri, A. Rostamizadeh, A. Talwalker. "),a("span",{staticClass:"italic"},[e._v("MIT Press,")]),e._v(" 2018. ")]),a("li",[a("span",{staticClass:"italic"},[e._v("Topics in Random Matrix Theory.")]),e._v(" T. Tao. "),a("span",{staticClass:"italic"},[e._v("American Mathematical Society,")]),e._v(" 2012. ")])])]),a("div",{staticClass:"w-full text-lg sm:text-2xl font-normal sm:font-medium mb-4 mt-4 sm:mt-8"},[e._v(" Publications ")]),a("div",{staticClass:"w-full my-4"},[a("ul",{staticClass:"list-disc px-8"},[a("li",[e._v("Coming soon (202x)")])])])])}],vt=function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",[a("div",{staticClass:"hidden sm:flex"},[a("div",{staticClass:"text-base font-light border rounded bg-blue-200 p-2 cursor-pointer hover:bg-blue-300",on:{click:function(t){return e.openURL("https://twitter.com/fifteenai")}}},[e._v(" Please refer to "),a("i",{staticClass:"fab fa-twitter text-blue-400 ml-1"}),e._v(" Twitter for updates: "),a("a",{staticClass:"font-normal",attrs:{href:"https://twitter.com/fifteenai",target:"_blank"}},[e._v("@fifteenai")])])]),a("div",{staticClass:"flex sm:hidden"},[a("div",{staticClass:"text-sm font-light border rounded bg-blue-200 p-2 cursor-pointer hover:bg-blue-300",on:{click:function(t){return e.openURL("https://twitter.com/fifteenai")}}},[a("i",{staticClass:"fab fa-twitter text-blue-400 ml-1"}),e._v("/"),a("a",{staticClass:"font-normal",attrs:{href:"https://twitter.com/fifteenai",target:"_blank"}},[e._v("@fifteenai")])])])])},yt=[],bt={methods:{openURL:v}},Ct=bt,At=Object(l["a"])(Ct,vt,yt,!1,null,null,null),xt=(At.exports,{components:{NavBar:_}}),wt=xt,_t=(a("df93"),Object(l["a"])(wt,pt,ft,!1,null,"4be6616e",null)),Pt=_t.exports,kt=function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",[a("NavBar"),a("div",{staticClass:"w-full sm:w-full md:w-full lg:w-240 px-4 mx-auto mt-8 sm:mt-12 justify-start"},[e._m(0),a("div",{staticClass:"w-full my-4 font-normal"},[e._v(" The performance of the model is measured using the average "),a("span",{staticClass:"italic"},[e._v("alignment confidence")]),e._v(" during inference on a validation set. It is well-known that there is a noticeable disaccord between the inputs during training and the outputs during inference as a result of autoregression; this phenomenon is called "),e._m(1),e._v(". The difference between "),a("span",{directives:[{name:"katex",rawName:"v-katex",value:"\\gamma_{\\textsf{train}}",expression:"`\\\\gamma_{\\\\textsf{train}}`"}],staticClass:"cursor-default",staticStyle:{"font-size":"0.9em"}}),e._v(" and "),a("span",{directives:[{name:"katex",rawName:"v-katex",value:"\\gamma_{\\textsf{infer}}",expression:"`\\\\gamma_{\\\\textsf{infer}}`"}],staticClass:"cursor-default",staticStyle:{"font-size":"0.9em"}}),e._v(" is a general measure of the salience of this train-test gap. ")]),a("div",{staticClass:"my-4"},[a("table",{staticClass:"border-collapse border border-gray-800 table-auto w-full"},[a("thead",[a("tr",[a("th",{staticClass:"border border-gray-600 font-semibold cursor-default"},[e._v("Version")]),a("th",{staticClass:"border border-gray-600 font-semibold cursor-default"},[a("span",{directives:[{name:"katex",rawName:"v-katex",value:"\\gamma_{\\textsf{infer}}",expression:"`\\\\gamma_{\\\\textsf{infer}}`"},{name:"tooltip",rawName:"v-tooltip.top",value:"Average alignment confidence during inference",expression:"'Average alignment confidence during inference'",modifiers:{top:!0}}],staticClass:"cursor-default",staticStyle:{"font-size":"0.9em"}})]),a("th",{staticClass:"border border-gray-600 font-semibold cursor-default"},[a("span",{directives:[{name:"katex",rawName:"v-katex",value:"\\gamma_{\\textsf{train}}",expression:"`\\\\gamma_{\\\\textsf{train}}`"},{name:"tooltip",rawName:"v-tooltip.top",value:"Average alignment confidence during 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text-center"},[e._v("~67%")]),a("td",{staticClass:"border border-gray-600 text-center"},[e._v("~73%")]),a("td",{staticClass:"border border-gray-600 text-center"},[e._v("2020-06-xx")]),a("td",{staticClass:"border border-gray-600 text-center"})])])}],Nt={components:{NavBar:_}},Lt=Nt,Ft=(a("745e"),Object(l["a"])(Lt,kt,St,!1,null,"770e6c90",null)),qt=(Ft.exports,function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",[a("NavBar"),e._m(0)],1)}),Tt=[function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",{staticClass:"w-full sm:w-full md:w-full lg:w-240 px-4 mx-auto mt-8 sm:mt-12 justify-start"},[a("div",{staticClass:"w-full text-xl sm:text-2xl font-semibold sm:font-medium my-4 flex items-center justify-between"},[a("div",{staticClass:"font-semibold"},[e._v(" Nondeterminism ")])]),a("div",{staticClass:"w-full my-4 font-normal"},[a("div",{staticClass:"my-2"},[e._v(" The model is "),a("a",{staticClass:"italic",attrs:{href:"https://en.wikipedia.org/wiki/Nondeterministic_algorithm",target:"_blank"}},[e._v("nondeterministic")]),e._v('. That is, re-clicking on the "Generate" button without changing the input will produce different outputs with varying levels of pacing, emotion, pitch, and background noise. This is by design — this feature is intended to emulate how human voice actors deliver a given text on different takes. You may need to roll the dice multiple times to obtain that perfect take, and '),a("span",{staticClass:"font-semibold"},[e._v("it is encouraged")]),e._v(" that you continue to generate outputs until you are satisfied with the delivery. ")]),a("div",{staticClass:"my-2"},[e._v(" Note that the output with the highest alignment confidence is not necessarily the best output. The alignment confidence is intended as a heuristic to cull the "),a("span",{staticClass:"italic"},[e._v("worst")]),e._v(" outputs; you will almost certainly encounter outliers where an output with a higher confidence clearly sounds worse than the other two outputs. You, as the human, are the ultimate judge in determining which output truly sounds the best. ")])]),a("div",{staticClass:"w-full text-xl sm:text-2xl font-semibold sm:font-medium my-4 flex items-center justify-between"},[a("div",{staticClass:"font-semibold"},[e._v(" Emotional contextualizers ")])]),a("div",{staticClass:"w-full my-4 font-normal"},[a("div",{staticClass:"my-2"},[e._v(" The model uses "),a("span",{staticClass:"italic"},[e._v("emotional contextualizers")]),e._v(" (a term coined by this project) to alter the pacings and deliveries of spoken texts. An emotional contextualizer is an embedding that guides the model during inference toward an output of similar emotional context — simply put, it is a separate sentence or phrase that informs the model beforehand of the desired emotion of the output. The emotional contextualizer is mapped to a probability distribution using the sentiment analysis model "),a("a",{attrs:{href:"https://arxiv.org/pdf/1708.00524.pdf",target:"_blank"}},[e._v("DeepMoji")]),e._v(" ("),a("a",{attrs:{href:"https://deepmoji.mit.edu/",target:"_blank"}},[e._v("deepmoji.mit.edu")]),e._v("), which gives a nice visualization of the desired emotion. ")]),a("div",{staticClass:"my-2"},[e._v(" An emotional contextualizer can be specified in the input by adding a vertical bar ("),a("span",{staticClass:"font-mono text-green-500 font-semibold text-sm"},[e._v("|")]),e._v(") at the end of the spoken input followed by a sentence to be used as the contextualizer. For example, if one wishes to have a character speak the sentence "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v('"I hate you!"')]),e._v(" in an amorous manner (rather than an irate one), one may opt to use the input "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v('"I hate you!'),a("span",{staticClass:"font-mono text-green-500 font-semibold text-sm"},[e._v('|I love you."')])]),e._v(" to have the desired effect. It may take several tries with different emotional contextualizers to ultimately obtain the perfect output. ")]),a("div",{staticClass:"my-2"},[e._v(" By default, if the emotional contextualizer is not specified, the inferred emotional contextualizer is assumed to be the input itself. For example, the input "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v('"I hate you!"')]),e._v(" is functionally identical to the input "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v('"I hate you!'),a("span",{staticClass:"font-mono text-green-500 font-semibold text-sm"},[e._v('|I hate you!"')])]),e._v(". ")])]),a("div",{staticClass:"w-full text-xl sm:text-2xl font-semibold sm:font-medium my-4 flex items-center justify-between"},[a("div",{staticClass:"font-semibold"},[e._v(" Phonological conventions and ARPAbet ")])]),a("div",{staticClass:"w-full my-4 font-normal"},[a("div",{staticClass:"my-2"},[e._v(" The model primarily uses the "),a("a",{attrs:{href:"https://public.oed.com/how-to-use-the-oed/key-to-pronunciation/%20pronunciation-model-US-english/",target:"_blank"}},[e._v("Oxford English Dictionary's U.S English")]),e._v(' pronunciation model and its IPA conventions. This pronunciation model is the same one used by the dictionary built into Google\'s Search — to view the Oxford IPA transcription for a given word, type in "define [word]" into the search bar. For example, '),a("span",{staticClass:"font-semibold"},[e._v("dictionary")]),e._v(" is transcribed as "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("/ˈdikSHəˌnerē/")]),e._v(", and "),a("span",{staticClass:"font-semibold"},[e._v("convention")]),e._v(" is transcribed as "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("/kənˈven(t)SH(ə)n/")]),e._v(". Note the parenthesized phonemes that indicate optional "),a("a",{attrs:{href:"https://en.wikipedia.org/wiki/Elision",target:"_blank"}},[e._v("elisions")]),e._v(" subconsciously prevalent in everyday speech. ")]),a("div",{staticClass:"my-2"},[e._v(' You may notice that certain words are pronounced slightly differently than what you might expect. For example, suppose that you wish to have a character pronounce the word "Internet" as '),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("/ˈintərˌnet/")]),e._v(" with the phoneme "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("⟨t⟩")]),e._v(" clearly enunciated in between the first and second syllables. The algorithm, however, may tend to favor the pronunciation "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("/ˈinərˌnet/")]),e._v(", opting to elide the "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("⟨t⟩")]),e._v(' — the way that most Americans would pronounce the word "Internet" in everyday speech (this phonological phenomenon is known as '),a("a",{attrs:{href:"https://en.wikipedia.org/wiki/Flapping",target:"_blank"}},[e._v("intervocalic flapping")]),e._v("). If you wish, you can override the AI's preference by inserting ARPAbet strings wrapped in curly braces "),a("span",{staticClass:"font-mono text-blue-500 font-semibold text-sm"},[e._v("{}")]),e._v(' — in the case of "Internet," you would use the input '),a("span",{staticClass:"font-mono text-blue-500 font-semibold text-sm"},[e._v("{IH1 N T ER0 N EH2 T}")]),e._v(" instead to explicitly instruct the model to pronounce the "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("⟨t⟩")]),e._v(". In most cases, however, the AI does a very good job guessing the most appropriate pronunciation. ")]),a("div",{staticClass:"my-2"},[e._v(" One important distinction between Oxford's IPA and traditional IPA is the lack of a distinct symbol for the voiced alveolar flap "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("⟨ɾ⟩")]),e._v(". In most dictionaries, the flap is transcribed as a "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("⟨t⟩")]),e._v(", but Oxford opts to transcribe it as a "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("⟨d⟩")]),e._v(" in accordance to how Americans would pronounce the flap. For example, Oxford gives the transcription for the word "),a("span",{staticClass:"font-semibold"},[e._v("better")]),e._v(" as "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("/ˈbedər/")]),e._v(", while most other dictionaries would opt to use the transcription "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("/ˈbetər/")]),e._v(". It is advised that users follow the Oxford convention to designate the voiced alveolar flap for optimal naturalness. For example, "),a("span",{staticClass:"font-semibold"},[e._v("better")]),e._v(" would be transcribed as "),a("span",{staticClass:"font-mono text-blue-500 font-semibold text-sm"},[e._v("{B EH1 D ER0}")]),e._v(" instead of "),a("span",{staticClass:"font-mono text-blue-500 font-semibold text-sm"},[e._v("{B EH1 T ER0}")]),e._v(". ")]),a("div",{staticClass:"my-2"},[a("span",{staticClass:"font-semibold"},[e._v("All ARPAbet vowels "),a("span",{staticClass:"italic"},[e._v("must")]),e._v(" include a lexical stress")]),e._v(". See the "),a("a",{attrs:{href:"http://www.speech.cs.cmu.edu/cgi-bin/cmudict",target:"_blank"}},[e._v("CMU Pronouncing Dictionary")]),e._v(" website for more information on ARPAbet and a list of all valid phonemes usable on the site. Note that this site "),a("span",{staticClass:"italic"},[e._v("does not")]),e._v(" use the Oxford convention. ")])]),a("div",{staticClass:"w-full text-xl sm:text-2xl font-semibold sm:font-medium my-4 flex items-center justify-between"},[a("div",{staticClass:"font-semibold"},[e._v(" Lexicon ")])]),a("div",{staticClass:"w-full my-4 font-normal"},[a("div",{staticClass:"my-2"},[e._v(" The lexicon that the model uses is an amalagmation of various dictionaries, words that have been scraped from both physical and digital references, and AI-generated phonetic transcriptions. While the lexicon contains many obscure and topical words that are not typically found in phonetic references (for example, the word "),a("span",{staticClass:"font-semibold"},[e._v("VTuber")]),e._v(" — "),a("span",{staticClass:"font-semibold font-mono text-sm"},[e._v("/ˈvēˌto͞obər/")]),e._v(" or "),a("span",{staticClass:"font-mono text-blue-500 font-semibold text-sm"},[e._v("{V IY1 T UW2 B ER0}")]),e._v("), it is by no means complete. If you find a word that does not exist in the lexicon, or if you believe that a word may be incorrectly transcribed in the lexicon, feel free to send me an email or tag me in a tweet. ")])])])}],Dt={components:{NavBar:_},mounted:function(){var e=this;localStorage.getItem("accepted")||this.$modal.show("readme"),setTimeout((function(){e.disabled=!1}),1e4)},data:function(){return{isPlaying:!1,audio:null,disabled:!0}},methods:{openURL:v,acceptTerms:function(){localStorage.setItem("accepted2",!0),this.$modal.hide("readme")},playAudio:function(e){var t=this;this.isPlaying||(this.audio=new Audio(e),this.audio.addEventListener("ended",(function(){return t.isPlaying=!1})),this.audio.volume=.33,this.audio.play(),this.isPlaying=!0)},stopAudio:function(){this.isPlaying&&(this.audio.pause(),this.audio.currentTime=0,this.isPlaying=!1)}}},jt=Dt,It=(a("b06b"),Object(l["a"])(jt,qt,Tt,!1,null,"5c0e602a",null)),Et=It.exports,$t=function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",[a("NavBar"),a("div",{staticClass:"w-full sm:w-full md:w-full lg:w-240 px-4 mx-auto mt-8 sm:mt-12 justify-start"},[e._m(0),a("div",{staticClass:"w-full my-4 font-normal"},[a("div",{staticClass:"font-semibold text-lg py-1"},[e._v(" Who/what is behind this project? ")]),a("div",{staticClass:"font-medium text-base py-2"},[e._v(" I've had a couple people and institutions provide a bit of help along the way, but I am — and always have been — one person. ")]),e._m(1),a("div",{staticClass:"font-semibold text-lg py-1"},[e._v(" I'm using your project in a video/project/Twitter post/article/etc. How can I cite your project? ")]),e._m(2),a("div",{staticClass:"font-medium text-base py-2"},[e._v(" By agreeing to the Terms of Service in order to use the site, you are agreeing to comply with the above two requirements; failure to abide by these rules will put your project/video at risk of termination. ")]),a("div",{staticClass:"font-semibold text-lg py-1"},[e._v(" What sets your project apart from other text-to-speech algorithms/neural networks? ")]),e._m(3),a("div",{staticClass:"font-medium text-base py-2"},[e._v(" To put it even more simply, this project aims to create the best TTS algorithm in existence. All other algorithms in the past have had serious issues, including pronunciation inaccuracies, stiltedness, and poor audio quality. This project aims to remedy all of those issues. ")]),a("div",{staticClass:"font-semibold text-lg py-1"},[e._v(" How much does maintaining the servers cost? ")]),a("div",{staticClass:"font-medium text-base py-2"},[e._v(" It depends on the amount of traffic, but the minimum baseline is around several thousands of US dollars every month. This is expected as inference is very GPU intensive and a sufficient number of instances need to be spun up to handle thousands of requests coming in every minute. Everything is paid out of pocket. ")]),a("div",{staticClass:"font-semibold text-lg py-1"},[e._v(" How are you able to pay out of pocket for your project? ")]),a("div",{staticClass:"font-medium text-base py-2"},[e._v(" I've already made my fortune in tech elsewhere. As I've stated before, the Patreon funds only cover a very small fraction of the server bills – I still have not yet even withdrawn any funds from my account. ")]),a("div",{staticClass:"font-semibold text-lg py-1"},[e._v(" Why did the old models have to be taken down while you worked on the newest iteration? ")]),e._m(4),e._m(5),a("div",{staticClass:"font-semibold text-lg py-1"},[e._v(" Characters seem to mispronounce a certain word consistently. How can I get around this? ")]),a("div",{staticClass:"font-medium text-base py-2"},[e._v(" See the "),a("router-link",{attrs:{to:"/guide"}},[e._v("Guide")]),e._v(". ")],1),a("div",{staticClass:"font-semibold text-lg py-1"},[e._v(" Do you take personal requests? ")]),a("div",{staticClass:"font-medium text-base py-2 "},[e._v(" See "),a("router-link",{attrs:{to:"/contribute"}},[e._v("Contribute")]),e._v(". ")],1),a("div",{staticClass:"font-semibold text-lg py-1"},[e._v(" How can I support your work? ")]),e._m(6),a("div",{staticClass:"font-semibold text-lg py-1"},[e._v(" Could you set up a personal endpoint so that I don't have to wait in the queue like everyone else? ")]),a("div",{staticClass:"font-medium text-base py-2"},[e._v(" I will only do this if you pay for the cost of setting up said endpoint in full. It will not be cheap. Send me an email for further details. ")]),a("div",{staticClass:"font-semibold text-lg py-1"},[e._v(" Will you add more characters? ")]),a("div",{staticClass:"font-medium text-base py-2"},[e._v(" Yes. See "),a("router-link",{attrs:{to:"/contribute"}},[e._v("Contribute")]),e._v(". ")],1)])])],1)},Mt=[function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",{staticClass:"w-full text-xl sm:text-2xl font-semibold sm:font-medium my-4 flex items-center justify-between"},[a("div",{staticClass:"font-semibold"},[e._v(" Frequently Asked Questions ")])])},function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",{staticClass:"font-medium text-base py-2"},[e._v(" This project was conceived as part of MIT's "),a("a",{attrs:{href:"https://urop.mit.edu/",target:"_blank"}},[e._v("Undergraduate Research Opportunities Program")]),e._v(". Since then, it has evolved massively throughout the years – though the scope of the project in its current form is far more sophisticated than that of the project I had initially envisioned and scaffolded, the general idea and spirit have always been the same. ")])},function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",{staticClass:"font-medium text-base py-2"},[e._v(' I require only two things. One, you must include some form of the URL "'),a("a",{attrs:{href:"https://15.ai"}},[e._v("15.ai")]),e._v('" somewhere '),a("span",{staticClass:"italic font-bold"},[e._v("in")]),e._v(" your post/video/project, and two, "),a("span",{staticClass:"font-semibold"},[e._v("you may not mix 15.ai outputs with any other TTS")]),e._v(". The second rule is strictly necessary to avoid cross-contamination. This project is not merely a tool for you to haphazardly slap together voices just for your own benefit; my research is foremost, and I would like to avoid ANY chance of confusion of which TTS service created which voice, especially among those that are of far worse quality. "),a("span",{staticClass:"font-semibold"},[e._v("In other words, if you use 15.ai for your video/project, you may not use any other TTS service for any other portion of that video/project.")])])},function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",{staticClass:"font-medium text-base py-2"},[e._v(" The short answer is that this project shows that you need very little data to accurately clone hundreds of voices simultaneously while keeping emotions and naturalness intact. This completely blows any previously known TTS algorithm — such as "),a("a",{attrs:{href:"https://arxiv.org/pdf/1712.05884.pdf",target:"_blank"}},[e._v("Tacotron 2")]),e._v(", "),a("a",{attrs:{href:"https://arxiv.org/pdf/1710.07654.pdf",target:"_blank"}},[e._v("DeepVoice 3")]),e._v(", "),a("a",{attrs:{href:"https://arxiv.org/pdf/1806.04558.pdf",target:"_blank"}},[e._v("SV2TTS")]),e._v(", or "),a("a",{attrs:{href:"https://arxiv.org/pdf/2005.05514.pdf",target:"_blank"}},[e._v("TalkNet")]),e._v(" — out of the water in terms of both data efficiency and naturalness. ")])},function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",{staticClass:"font-medium text-base py-2"},[e._v(" I have very high standards for my work. The purpose of the project isn't to be able to synthesize voices that sound only "),a("span",{staticClass:"italic"},[e._v("similar")]),e._v(" to the source and are wholly unconvincing to humans in terms of naturalness, quality, or emotion — "),a("span",{staticClass:"italic"},[e._v("this is absolutely trivial, is academically uninteresting, and requires very little actual effort")]),e._v(". In fact, you could even do this yourself by collecting data and training a model using one of the many open-sourced variants of Tacotron. ")])},function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",{staticClass:"font-medium text-base py-2"},[e._v(" As such, I prefer improving my algorithm as much as possible over naively adding a number of voices using an older, inferior version. "),a("span",{},[e._v("Recall that this was originally meant to be a repository for my research, and not just a website for your own amusement")]),e._v(" — so you will have to be patient while I finish things up. Throwing a tantrum on Twitter or via email will do less than nothing to change my stance on quality control. ")])},function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",{staticClass:"font-medium text-base py-2"},[e._v(" I have a Patreon at "),a("a",{attrs:{href:"https://patreon.com/15ai"}},[e._v("patreon.com/15ai")]),e._v(". Please only donate if you want, but do not feel obligated to do so. All donations will be used to help pay for the server costs. ")])}],Bt={components:{NavBar:_}},Ot=Bt,Rt=(a("5d20"),Object(l["a"])(Ot,$t,Mt,!1,null,"754d24cf",null)),zt=Rt.exports,Gt=function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",[a("NavBar"),e._m(0)],1)},Ut=[function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",{staticClass:"w-full sm:w-full md:w-full lg:w-240 px-4 mx-auto mt-8 sm:mt-12 justify-start"},[a("div",{staticClass:"w-full text-xl sm:text-2xl font-semibold sm:font-medium my-4 flex items-center justify-between"},[a("div",{staticClass:"font-semibold"},[e._v(" Contribute ")])]),a("div",{staticClass:"w-full my-4 font-normal"},[a("span",{staticClass:"font-semibold"},[e._v("tl;dr")]),e._v(": If you send me a dataset of your favorite character/waifu/celebrity/whatever, I might include them in the next batch. Due to technical reasons (approximately uniformly distributed vocal frequencies), high-pitched/feminine voices work best. ")]),a("div",{staticClass:"w-full my-4 font-normal text-red-500 font-bold"},[e._v(" Update: The list of upcoming characters can be found here: "),a("a",{attrs:{target:"_blank",href:"https://docs.google.com/spreadsheets/d/1dlV9tNtasW_YvrCEj7jAz3hg8UKIFGxfNCOYigfa_50"}},[e._v("https://docs.google.com/spreadsheets/d/1dlV9tNtasW_YvrCEj7jAz3hg8UKIFGxfNCOYigfa_50")])]),a("hr"),a("div",{staticClass:"w-full my-4 font-normal"},[e._v(" A couple months were spent setting up a pipeline that expedites the addition of new characters in the multi-speaker setup without requiring old data from the already-learned characters. Thanks to this new process, "),a("span",{attrs:{href:"italic"}},[e._v("hundreds")]),e._v(" of characters can be jointly trained in a matter of days, whereas training a model for each character in series would literally take years to complete. Furthermore, "),a("span",{staticClass:"italic"},[e._v("data across different characters intermingle to cooperatively learn correct pronunciations, emotions, and naturalness")]),e._v(". There is no (reasonable) limit to how many characters can be trained simultaneously. ")]),a("div",{staticClass:"w-full my-4 font-normal"},[a("span",{staticClass:"italic"},[e._v("Not all voices are synthesized equal.")]),e._v(" The voices that work best are usually high-pitched (typically feminine) and energetic. Voices that are low-pitched (typically masculine) or contain a reverb/echo tend to be inconsistent, but still workable. Some problematic voices in the latest version of the model include the "),a("a",{attrs:{href:"https://wiki.teamfortress.com/wiki/Administrator",target:"_blank"}},[e._v("Team Fortress 2 Administrator")]),e._v(", the "),a("a",{attrs:{href:"https://www.ssbwiki.com/Announcer",target:"_blank"}},[e._v("Super Smash Bros. Announcer")]),e._v(", and the "),a("a",{attrs:{href:"https://half-life.fandom.com/wiki/Overwatch_Voice",target:"_blank"}},[e._v("Half-Life 2 Overwatch Voice")]),e._v(", all of which feature a distinctive reverb in their audio files. Fixing these require a bit more care and is on the immediate agenda. ")]),a("div",{staticClass:"w-full my-4 font-normal"},[e._v(" I will be adding as many characters to the model as possible, in batches. If you would like to request a character and would like to send in a dataset, please adhere to the following conventions: ")]),a("div",{staticClass:"w-full my-4 font-normal"},[a("ul",{staticClass:"list-disc pl-8 my-1"},[a("li",[e._v(" All English transcriptions for a single character must be stored in a single "),a("span",{staticClass:"font-mono"},[e._v(".txt")]),e._v(" file. "),a("ul",{staticClass:"list-disc pl-8 my-1"},[a("li",[e._v(" Every line of the "),a("span",{staticClass:"font-mono"},[e._v(".txt")]),e._v(" file "),a("span",{staticClass:"italic"},[e._v("must")]),e._v(" contain exactly three things, in this order: the path to the audio file, a single vertical bar ("),a("span",{staticClass:"font-mono"},[e._v("|")]),e._v("), and the English transcription in full. ")]),a("li",[e._v(" The English transcription should "),a("span",{staticClass:"italic"},[e._v("not")]),e._v(" contain any numerals (they should be written out explicitly, as in "),a("span",{staticClass:"italic"},[e._v("fifteen")]),e._v("), special characters (e.g. letters with diacritics, emojis), or any additional words not audibly spoken by the character (e.g. asterisk actions like "),a("span",{staticClass:"italic"},[e._v("*whispering*")]),e._v(", parentheticals) ")]),a("li",[e._v(" The only punctuation allowed are commas, periods, exclamation points, question marks, apostrophes, hyphens, colons, and semicolons. Commas should denote short pauses and periods denote longer pauses — grammatical correctness is unimportant. Ellipses (...) should not be used; use a comma or a period instead. ")]),a("li",[e._v(" Consecutive exclamation points and questions marks are acceptable if a character is yelling or talking more excitedly/fiercely/angrily/inquisitively than usual. Other instances of consecutive punctuation should be avoided. ")]),a("li",[e._v(' Acronyms should be separated by spaces if they are vocalized letter by letter (e.g. "M I T", "G P U"). Otherwise, they should be left alone (e.g. "RAM", "GNU", "ASAP"). ')]),a("li",[e._v(" Proper capitalizations are unnecessary; all transcriptions are converted to lowercase during training and inference. ")]),a("li",[e._v(" Audio files with no audible English words being spoken should not be transcribed (e.g. files containing only grunts, moans, unintelligible shouts, sound effects). ")]),a("li",[e._v(" Audio files that have a filter that noticeably distorts the character's normal voice should not be transcribed. Furthermore, files that have background noise or sound effects should not be transcribed. ")]),a("li",[e._v(" The rule of thumb is: if the dialogue isn't obviously marred by inconsistencies and you can clearly hear the words being spoken, then you should include it. If you are totally unsure on how to proceed, feel free to ask me via email or include an addendum in the zipped file. ")])])]),a("li",[e._v(" All audio files for a single character must be stored in a single directory. "),a("ul",{staticClass:"list-disc pl-8 my-1"},[a("li",[a("span",{staticClass:"italic font-bold text-red-600"},[e._v("Do not mix different sources for a single character.")]),e._v(" A method of checking whether two files are compatible is to check their spectrograms in Audacity. If the spectrograms look similar to each other, they are compatible; if they look different (frequencies are capped at a different level, extraneous noise is present in only one source throughout, etc.), they are likely incompatible. If you are unsure whether certain sources are compatible or not, ask me via email. ")]),a("li",[e._v(" Leave the audio files in their original format. If the files were ripped directly from the source, leave them as they are. If you need to excise parts of the audio that aren't transcribable, do so but leave some indication that this was done. ")]),a("li",[e._v(" The length of each file should not exceed 12 seconds in length. If a file is longer than 12 seconds, split it into multiple parts using an external program (e.g. Audacity) without compromising the original audio quality. ")]),a("li",[e._v(" It would be helpful to name the directory containing the audio files the name of the corresponding character. ")])])]),a("li",[e._v(" Double-check that the audio paths match the transcriptions. It's very easy to get lost in the middle of transcribing hundreds of audio files, and any off-by-one errors would be disastrous. ")])])]),a("div",{staticClass:"w-full my-4 font-normal"},[e._v(" For example: ")]),a("div",{staticClass:"w-full mt-4 mb-4 font-normal mx-12 font-mono"},[e._v(" transcripts_of_some_character.txt ")]),a("div",{staticClass:"mb-4 font-normal ml-24 font-mono text-sm"},[a("div",[e._v("/character_name/file1.wav|Lorem ipsum dolor sit amet, consectetur adipiscing elit.")]),a("div",[e._v("/character_name/file2.wav|Integer eu semper sapien.")]),a("div",[e._v("/character_name/file3.wav|Praesent odio massa, volutpat sit amet sollicitudin vel, cursus quis neque.")]),a("div",[e._v("/character_name/file4.wav|Vivamus et ligula risus.")]),a("div",[e._v("...")])]),a("div",{staticClass:"w-full my-4 font-normal"},[e._v(" Zip up a parent directory containing the transcriptions and the directory with the audio files (in "),a("span",{staticClass:"font-mono"},[e._v(".zip")]),e._v(" or "),a("span",{staticClass:"font-mono"},[e._v(".7z")]),e._v(" format), upload the zipped file to some file storage site (Google Drive, Mega, Dropbox, etc.), and send it to me via email. ")]),a("div",{staticClass:"w-full my-4 font-normal"},[e._v(" Preprocessing takes up a large amount of time and is frankly very annoying; as such, "),a("span",{staticClass:"italic"},[e._v("I will not deign to do the data collection myself just because some random person on the Internet makes a request")]),e._v(". Datasets that require me to do the least amount of additional work will certainly be appreciated. I won't be able to guarantee your character's inclusion, but if you put in the effort, I'll try my best. ")]),a("div",{staticClass:"w-full my-4 font-normal"},[e._v(" Alternatively, if you wish to support the project without torturing yourself, feel free to donate to my Patreon at "),a("a",{attrs:{href:"https://patreon.com/15ai"}},[e._v("patreon.com/15ai")]),e._v(" to help pay for the server bills. ")])])}],Ht={components:{NavBar:_}},Wt=Ht,Kt=(a("8536"),Object(l["a"])(Wt,Gt,Ut,!1,null,"285d3ce8",null)),Jt=Kt.exports,Vt=function(){var e=this,t=e.$createElement,a=e._self._c||t;return a("div",[a("NavBar"),a("div",{staticClass:"w-full sm:w-full md:w-full lg:w-240 px-4 mx-auto mt-8 sm:mt-12 justify-start"},[e._m(0),a("div",{staticClass:"w-full my-4 font-normal"},[a("div",{staticClass:"my-2"},[e._v(" Thanks to all of my patrons ("),a("span",{staticClass:"font-bold"},[e._v(e._s(e.patronsSorted.length))]),e._v(" total) on "),a("a",{attrs:{href:"https://patreon.com/15ai",target:"_blank"}},[e._v("Patreon")]),e._v(" who have been patient with me throughout the development of this project. 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