Top Model Song Download [HOT]

"Das Model" ("The Model" in English) is a song recorded by the German group Kraftwerk in 1978, written by musicians Ralf Htter and Karl Bartos, with artist Emil Schult collaborating on the lyrics. It is featured on the album, Die Mensch-Maschine (known in international versions as The Man-Machine).

In 1981 the song was re-released to coincide with the release of the studio album Computerwelt (Computer World in English).[3] It reached no. 1 in UK Singles Chart. Both the German and English versions of the song have been covered by other artists, including Snakefinger, Hikashu, Big Black and Robert.[4]

Top Model Song Download

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The lyrics were written by Emil Schult, who was in love with a model when he wrote the song. He also composed music for the song, though it was too guitar-heavy for the musical concept of Kraftwerk and it was rewritten by Bartos and Htter to fit the sound of the band.[5]

As with all of the songs on The Man-Machine, The Model was released in both German- and English-language versions. The lyrics are very close between two versions, with the exception of a guttural-sounding "Korrekt!" added after the line "Sie trinkt in Nachtclubs immer Sekt" in the German version. (The English lyric is "She's going out to nightclubs, drinking just champagne.") This was an in-joke by the band. In his autobiography, I Was A Robot, former Kraftwerk member Wolfgang Flr explains:

Our favourite discothque, the Mora, lay in the Schneider-Wibbel Gasse in the middle of Dsseldorf's old town, and there was a waiter who worked there who always greeted new guests with the words "Hallchen! Sekt? Korrrrrrrekt!" You didn't have the chance to contradict him, because he always answered himself. He loved selling champagne to the guests, largely because it was the drink on which he earned the highest commission, and he forced it on everyone.

We'd heard him so often, and he was such a fine example of Dsseldorf chic, that we invited him into our studio when we were recording "The Model" so that he could speak his smug slogan directly into the microphone. That's why his pithy "Sekt? Korrrrrrrekt!" appears in our most famous song.[6]

One way of addressing the long input problem is to use an autoencoder that compresses raw audio to a lower-dimensional space by discarding some of the perceptually irrelevant bits of information. We can then train a model to generate audio in this compressed space, and upsample back to the raw audio space.[^reference-25][^reference-17]

We chose to work on music because we want to continue to push the boundaries of generative models. Our previous work on MuseNet explored synthesizing music based on large amounts of MIDI data. Now in raw audio, our models must learn to tackle high diversity as well as very long range structure, and the raw audio domain is particularly unforgiving of errors in short, medium, or long term timing.

Next, we train the prior models whose goal is to learn the distribution of music codes encoded by VQ-VAE and to generate music in this compressed discrete space. Like the VQ-VAE, we have three levels of priors: a top-level prior that generates the most compressed codes, and two upsampling priors that generate less compressed codes conditioned on above.

The top-level prior models the long-range structure of music, and samples decoded from this level have lower audio quality but capture high-level semantics like singing and melodies. The middle and bottom upsampling priors add local musical structures like timbre, significantly improving the audio quality.

We train these as autoregressive models using a simplified variant of Sparse Transformers.[^reference-29][^reference-30] Each of these models has 72 layers of factorized self-attention on a context of 8192 codes, which corresponds to approximately 24 seconds, 6 seconds, and 1.5 seconds of raw audio at the top, middle and bottom levels, respectively.

Once all of the priors are trained, we can generate codes from the top level, upsample them using the upsamplers, and decode them back to the raw audio space using the VQ-VAE decoder to sample novel songs.

To train this model, we crawled the web to curate a new dataset of 1.2 million songs (600,000 of which are in English), paired with the corresponding lyrics and metadata from LyricWiki. The metadata includes artist, album genre, and year of the songs, along with common moods or playlist keywords associated with each song. We train on 32-bit, 44.1 kHz raw audio, and perform data augmentation by randomly downmixing the right and left channels to produce mono audio.

The top-level transformer is trained on the task of predicting compressed audio tokens. We can provide additional information, such as the artist and genre for each song. This has two advantages: first, it reduces the entropy of the audio prediction, so the model is able to achieve better quality in any particular style; second, at generation time, we are able to steer the model to generate in a style of our choosing.

This t-SNE[^reference-31] below shows how the model learns, in an unsupervised way, to cluster similar artists and genres close together, and also makes some surprising associations like Jennifer Lopez being so close to Dolly Parton!

To match audio portions to their corresponding lyrics, we begin with a simple heuristic that aligns the characters of the lyrics to linearly span the duration of each song, and pass a fixed-size window of characters centered around the current segment during training. While this simple strategy of linear alignment worked surprisingly well, we found that it fails for certain genres with fast lyrics, such as hip hop. To address this, we use Spleeter[^reference-32] to extract vocals from each song and run NUS AutoLyricsAlign[^reference-33] on the extracted vocals to obtain precise word-level alignments of the lyrics. We chose a large enough window so that the actual lyrics have a high probability of being inside the window.

To attend to the lyrics, we add an encoder to produce a representation for the lyrics, and add attention layers that use queries from the music decoder to attend to keys and values from the lyrics encoder. After training, the model learns a more precise alignment.

For example, while the generated songs show local musical coherence, follow traditional chord patterns, and can even feature impressive solos, we do not hear familiar larger musical structures such as choruses that repeat. Our downsampling and upsampling process introduces discernable noise. Improving the VQ-VAE so its codes capture more musical information would help reduce this. Our models are also slow to sample from, because of the autoregressive nature of sampling. It takes approximately 9 hours to fully render one minute of audio through our models, and thus they cannot yet be used in interactive applications. Using techniques[^reference-27][^reference-34] that distill the model into a parallel sampler can significantly speed up the sampling speed. Finally, we currently train on English lyrics and mostly Western music, but in the future we hope to include songs from other languages and parts of the world.

We collect a larger and more diverse dataset of songs, with labels for genres and artists. Model picks up artist and genre styles more consistently with diversity, and at convergence can also produce full-length songs with long-range coherence.

We scale our VQ-VAE from 22 to 44kHz to achieve higher quality audio. We also scale top-level prior from 1B to 5B to capture the increased information. We see better musical quality, clear singing, and long-range coherence. We also make novel completions of real songs.

This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood computation, and inverse problem solving without re-training models. In this blog post, we will show you in more detail the intuition, basic concepts, and potential applications of score-based generative models.

Likelihood-based models and implicit generative models, however, both have significant limitations. Likelihood-based models either require strong restrictions on the model architecture to ensure a tractable normalizing constant for likelihood computation, or must rely on surrogate objectives to approximate maximum likelihood training. Implicit generative models, on the other hand, often require adversarial training, which is notoriously unstable and can lead to mode collapse .

In this blog post, I will introduce another way to represent probability distributions that may circumvent several of these limitations. The key idea is to model the gradient of the log probability density function, a quantity often known as the (Stein) score function . Such score-based models are not required to have a tractable normalizing constant, and can be directly learned by score matching .

Score-based models have achieved state-of-the-art performance on many downstream tasks and applications. These tasks include, among others, image generation (Yes, better than GANs!), audio synthesis , shape generation, and music generation. Moreover, score-based models have connections to normalizing flow models, therefore allowing exact likelihood computation and representation learning. Additionally, modeling and estimating scores facilitates inverse problem solving, with applications such as image inpainting , image colorization , compressive sensing, and medical image reconstruction (e.g., CT, MRI) .

Suppose we are given a dataset \(\{\mathbf{x}_1, \mathbf{x}_2, \cdots, \mathbf{x}_N\}\), where each point is drawn independently from an underlying data distribution \(p(\mathbf{x})\). Given this dataset, the goal of generative modeling is to fit a model to the data distribution such that we can synthesize new data points at will by sampling from the distribution. 006ab0faaa