![]() Conditioning the spectrogram decoder on this encoding makes it possible to synthesize speech with similar speaker characteristics, even though the content is in a different language. The speaker encoder is pretrained on the speaker verification task, learning to encode speaker characteristics from a short example utterance. This feature leverages previous Google research on speaker verification and speaker adaptation for TTS. You can listen to more audio samples here.īy incorporating a speaker encoder network, Translatotron is also able to retain the original speaker’s vocal characteristics in the translated speech, which makes the translated speech sound more natural and less jarring. In this case, both systems provide a suitable translation and speak naturally using the same canonical voice. Though our results lag behind a conventional cascade system, we have demonstrated the feasibility of the end-to-end direct speech-to-speech translation.Ĭompared in the audio clips below are the direct speech-to-speech translation output from Translatotron to that of the baseline cascade method. ![]() We validated Translatotron’s translation quality by measuring the BLEU score, computed with text transcribed by a speech recognition system. However, no transcripts or other intermediate text representations are used during inference. During training, the sequence-to-sequence model uses a multitask objective to predict source and target transcripts at the same time as generating target spectrograms. It also makes use of two other separately trained components: a neural vocoder that converts output spectrograms to time-domain waveforms, and, optionally, a speaker encoder that can be used to maintain the character of the source speaker’s voice in the synthesized translated speech. Translatotron is based on a sequence-to-sequence network which takes source spectrograms as input and generates spectrograms of the translated content in the target language. Translatotron goes a step further by demonstrating that a single sequence-to-sequence model can directly translate speech from one language into speech in another language, without relying on an intermediate text representation in either language, as is required in cascaded systems. Many approaches to further improve end-to-end speech-to-text translation models have been proposed recently, including our effort on leveraging weakly supervised data. In 2017, we demonstrated that such end-to-end models can outperform cascade models. The emergence of end-to-end models on speech translation started in 2016, when researchers demonstrated the feasibility of using a single sequence-to-sequence model for speech-to-text translation. ![]() Dubbed Translatotron, this system avoids dividing the task into separate stages, providing a few advantages over cascaded systems, including faster inference speed, naturally avoiding compounding errors between recognition and translation, making it straightforward to retain the voice of the original speaker after translation, and better handling of words that do not need to be translated (e.g., names and proper nouns). In “ Direct speech-to-speech translation with a sequence-to-sequence model”, we propose an experimental new system that is based on a single attentive sequence-to-sequence model for direct speech-to-speech translation without relying on intermediate text representation. Dividing the task into such a cascade of systems has been very successful, powering many commercial speech-to-speech translation products, including Google Translate. Such systems have usually been broken into three separate components: automatic speech recognition to transcribe the source speech as text, machine translation to translate the transcribed text into the target language, and text-to-speech synthesis (TTS) to generate speech in the target language from the translated text. Speech-to-speech translation systems have been developed over the past several decades with the goal of helping people who speak different languages to communicate with each other. Posted by Ye Jia and Ron Weiss, Software Engineers, Google AI
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![]() That suggests a more fascistic presence, something that's only increased when terms like Führer start getting bandied about. Odd combination of pseudo-feudal or at least medieval elements (as evidenced by the series' emphasis on alchemy) merged with an Though not presented strictly chronologically within the confines of the series, the basic plot follows brothers Alphonse and Edward Elric, whoįor a while at least enjoyed a somewhat sylvan youth with their mother Trisha and father Van Hohenheim. While the anime engages in regular (and at times slightly disruptive) flashbacks, it almost feels like the seriesīegins in media res, a feeling which only increases once more and more backstory is offered up over the course of ensuing episodes. Surprisingly, the original Fullmetal Alchemist is only now being released in (upscaled) high definition, finally providing those whoĭo like (if not outright prefer) this first iteration a chance to revisit the series again.Ī different kind of brotherly love, namely the relationship between two siblings, is the underlying context of just about everything thatįullmetal Alchemist. Storyline, though in the early going at least Fullmetal Alchemist doesn't stray too far from Arakawa's version. Manga by Hiromu Arakawa often feel that Fullmetal Alchemist Brotherhood hewed more closely to Arakawa's conception and There's also the fact that fans of the original In a gambit that is at least somewhat reminiscent of Kai's approach, at least in tone. The fact that Fullmetal Alchemist Brotherhood gets through pretty much all of the original anime's content in a somewhat faster form, ![]() The reasons for this preference tend to be various, including liking the "shinier" animation of the reboot, as well as Rare to find even a diehard "originalist" (if that's even a word, which I have hunch it isn't) who prefers Fullmetal Alchemist to its ownīrotherhood. On the other hand, it's at least relatively That if you're going to spend time with this particular tale, you might as well get the whole story. The "warts and all" full length saga provided by the original Dragon Ball Z, it's actually pretty easy to find fans who won't go anywhere near Dragon Ball Z Kai and insist Prefer the redactions of Dragon Ball Z Kai to There's even a disconnect within certain ranks of fans-for example, while many aficionados Original formulations of properties to reboots. Reviewed by Jeffrey Kauffman, September 14, 2015īrotherly love amongst anime fans may win out in the end, but there's still a perhaps surprising disconnect between those who prefer Fullmetal Alchemist: The Complete Series Blu-rayįullmetal Alchemist: The Complete Series Blu-ray Review Since it has a lifetime warranty, we wanted to make sure it was really so durable so we subjected it to a roller test which Nectar Hybrid mattress passed with flying colours. Another test we paid special attention to was the durability test. Its mattresses utilize four separate layers of foam (sourced. About Nectar Sleep manufactures and sells mattresses and bedding online. ![]() If you notice stains, you can spot clean them, but it's still better to use sheets. Nectar Sleep is committed to achieving and surpassing the customers expectations of comfort. LinkedIn is the world’s largest business network, helping professionals like Nectar Sleep discover inside connections to recommended job. 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As this is a premium Nectar mattress, we wanted to test this Nectar mattress as much detailed as possible and share our impressions with you. ![]() ![]() The hollow diamond indicates a branch and its subsequent merge that indicates the end of the branch. C Implementation of UML State Machines and Activity Diagrams for Safety-Critical, Real-Time and Embedded Applications. Guard expressions (inside ) label the transitions coming out of a branch. You can define the guard condition and effect using the Object Inspector. Effect activity is displayed next to the guard condition. The guard condition is enclosed in the brackets (for example, " ") and displayed near the transition link on a diagram. Also you can associate a transition with an effect, which is an optional activity performed when the transition fires. The two orientations are semantically identical.Īll transitions, including internal ones, are provided with the guard conditions (logical expressions) that define whether this transition should be performed. Both the State and Activity Diagram toolbars provide separate horizontal and vertical fork/join buttons for each orientation. You can show multiple transitions with either a vertical or horizontal orientation in your State and Activity Diagrams. A solid bar indicates a fork and the subsequent join of the threads coming out of the fork.Ī transition may have multiple sources (a join from several concurrent states) or it may have multiple targets (a fork to several concurrent states). Self-transition for Statechart Diagrams Self-transition for Activity Diagrams Multiple transitionĪ transition can branch into two or more mutually-exclusive transitions.Ī transition may fork into two or more parallel activities. You can draw self-transitions for both activity and state elements on an Activity Diagram. You can add an internal transition to a state or activity element.Īn internal transition is shorthand for handling events without leaving a state and dispatching its exit or entry actions.Ī self-transition flow leaves the state (or activity) dispatching any exit action(s), then reenters the state dispatching any entry action(s).
To pick RGB 565 colors, check out our RGB 565 Color Picker. This color space is used in some embedded systems, such as microcontrollers. ![]() RGB 565 is an alternative representation of RGB colors that uses 16 bits to represent the color: 5 bits for red, 6 bits for green, and 5 bits for blue. The RGB color space is used to calculate the color of each pixel that can be displayed on the screen. Also, if the value of each channel is set to 0, the color of the visualized color space results in black.Įqual values of each channel are represented by the same color in the RGB color space, but the combination of different values of each channel leads to different colors. ![]() For example, if the value of each channel is set to 255, the color of the visualized color space results in white. In the RGB color space, the light intensity of each channel is equal to the value of each channel. The components of RGB are represented by the mathematical model: R = red (0 ≤ R ≤ 255) The RGB color space is a combination of the red, green, and blue light components of additive color. Each channel is represented by 8 bits, and the value of each channel is a value from 0 to 255. An RGB color space is a color space composed of three channels: red, green, blue, representing the three-dimensional information about the color. Matplotlib uses a dictionary from its colors.py module.įor name, hex in ():įor name, hex in color space is a mathematical model used to represent physical colors. This is more similar to specifying and RGB tuple rather than a named color (apart from the fact that the hex code is passed as a string), and I will not include an image of the 16 million colors you can choose from.įor more details, please refer to the matplotlib colors documentation and the source file specifying the available colors, _color_data.py. You can also plot colors by their HTML hex code: plt.plot(, lw=4, c='#8f9805') The default Tableau colors are available in matplotlib via the 'tab:' prefix: plt.plot(, lw=4, c='tab:green') Now you have access to a plethora of named colors! If you would like to use additional named colors when plotting with matplotlib, you can use the xkcd crowdsourced color names, via the 'xkcd:' prefix: plt.plot(, lw=4, c='xkcd:baby poop green') I merged my previous updates into this section. X, Y = fig.get_dpi() * fig.get_size_inches()Īx.text(xi_text, y, name, fontsize=(h * 0.8), ![]() # Sort colors by hue, saturation, value and name.īy_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color))), name) import matplotlib.pyplot as pltĬolors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS) I really didn't change much from the matplotlib example, but here is the code for completeness. I updated the image and code to reflect that 'rebeccapurple' has been added and the three sage colors have been moved under the 'xkcd:' prefix since I posted this answer originally. The order is not identical to how I would sort by eye, but I think it gives a good overview. I prefer the colors to be grouped with similar colors, so I slightly tweaked the matplotlib answer that was mentioned in a comment above to get a color list sorted in columns. The previous answers are great, but I find it a bit difficult to get an overview of the available colors from the posted image. I constantly forget the names of the colors I want to use and keep coming back to this question =) ![]() ![]() ![]() As you mentioned, I found the mesh provided by MB is a bit better in quality than that from MakeHuman, which is quite an important aspect for the type of games I'm intending to create. I had considered both of the options but decided to stay with MB-Lab, at least for now. And if I don't, maybe I could do a fundraiser or something as I think there are plenty of legitimate reasons why someone would want complete and medically accurate models. I am in discussion with a sex therapist for a potential commission to create multiple and varied realistic genitals for both men and women for her medical practice, so if I get that contract and time commits, I'll see if I can later adapt those models and try to release those for public use. It's not incredibly difficult, just time consuming. The geometry for the meshes aren't too different, either, so I have had a few situations where I have removed the genital region from an Makehuman mesh and attached it to a MB-Lab one. While I think MB-Lab is now at a place where it has better meshes, I think Makehuman would be a better option for your needs as I understand them. If you're not, because of the open source nature of Makehuman, you can easily find opportunities for how to create your own or modify the existing targets. The designs aren't particularly well-made, but for a video game I think you'd be happy enough with the appearance. Have you considered using MakeHuman? That system is very similar to MB-Lab (and even got its start with the same designer) and does have more options for creating genetalia, and there are some options you can opt-in to include on your models, which I think is a great way of doing it. But, of course, the potential for misuse is great as well, and so I get it. While the designers and coders for MB-Lab have every right to create the characters as they wish, I do find the prudishness so many people face with nudity annoying. I think what you would try to do would take an existing mesh and modify it, export the JSON data and then you should be able to work with the model, that includes the vertice count, face count and any morphs you would need to accomplish this I do some work with medical designs, and as such I also find the lack of physically accurate models frustrating, especially for Character Creator which is rather expensive and still is incomplete. I am not the author of these tools, that was done by another dev this past year, so I don't know all the internals of these tools as he does, so I can't really troubleshoot issues when they come up as he would be able to answer them more clearly. The above documentation provides at least a basic workflow of how to do this. You see the model is composed of not only the actual mesh in the blend file, but also JSON data that represents the location of the vertices and faces in 3D space so you would need to essentially build a new model and the tools we have in MB-Lab 1.7.8 CAN do this, it just would take a lot of work on your end to do so. To add new mesh to existing mesh would require quite a lot of work actually. ![]() I'm sorry for the late reply, I had a lot going on in my personal life. |