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With in a bit of trouble I thought this would be a perfect time to write this. Full disclosure: SFX I sold my first company to SFX I worked for Beatport I no longer hold a position Electronic dance music (referred simply as EDM) is more than just electronic music produced with — first and foremost it’s a business term. It’s not just a lifestyle term and it’s most certainly not just a genre. EDM, these crippling three letters are used to describe the indescribable and WTF is happening right now in pop culture. EDM does not have a clear etymology; it was used as early as 1985 as a corporate marketing term and now it’s basically become a label for an entire generation’s pop music. Herein lies the MASSIVE problem with EDM and the spiral of confusion in the industry. People think that “dance music” is EDM. We are comparing apples and oranges here. If EDM was a genre it would be everything or as I call it, the unification of music, analogous to Einstein’s unification theory. Einstein was on a never-ending quest to find a theory of everything. It’s a theory that refers to the hypothetical presences of a single, all en-compassing coherent theoretical framework of physics that fully explains how everything is interconnected. Similarly, EDM is the answer to bringing an end-to-end user experience together – music’s version of “the theory of everything”. Take for example the evolution of music from the 70’s (the age of Giorgio Moroder playing at the discotheque) to the blowout festivals like Tomorrowland. Electronic music is not influencing mainstream pop music, it is pop music. instrumachines When I first told my friends that I was joining SFX Entertainment and selling my baby to the heavy weight pretty much all the young financial analysts asked the same question, “do you think this EDM thing is sustainable?” My view of Wall Street is incredibly primitive – herds following herds. Don’t get me wrong Wall Street is filled with incredibly smart people, but when it comes to things they don’t have any idea about I think about the palpable contrarian view of British nobleman Baron Rothschild who said: “The time to buy is when there’s blood in the streets.” Fear about the industry collapsing or the notion of an EDM bubble seems ridiculous to me and just plain wrong. What makes more sense to me is the evolution of EDM and it’s continued growth. My first electronic dance was Mysteryland’s USA debut in Bethel Woods — aka the historic Woodstock grounds. If this does’t represent a significant symbol of evolution I don’t know what does. My parents (and I) still listen to The Who, Santana, Jimi Hendrix, Johnny Winter and many other that played way back when. Do I think I’ll be listening to Levels by Avicii in 30 years — no idea, but that’s not the point. That was “their” music and this is ours. It’s a piece of our culture and culture doesn’t just burst like a bubble. Wall Street doesn’t understand this because they are not asking the right questions. music festival If EDM is an all-encompassing term for dance festivals, electronic music is ‘the experience’ at its core. If you have never been to an electronic dance music festival or another major festival like Burning Man or Coachella, just go. It’s an indescribable sense of escapism. The first festival I attended was Coachella 2013 and since then I have been building digital products and thinking, “how can I put this experience in a digital product.” While I am still trying to figure out how to do that, I have developed a whole new set of questions and observations. The thoughtfulness into every detail at these events is incredible. If this was art, Pascal Rotella – the founder of insomniac is Picasso – he cares a tremendous amount about every person and will go as far as to take personal losses on events (from what I’ve heard). These festivals are essentially products and with each product comes an experience. Sam Altman – the President at Y Combinator summarizes my next point well: The attention to detail at these events is insane and at the end of the day it’s why they are so popular. If you think this insanity is slowing, the current state of the EDM market is close to “$20 billion, with room to grow” according to the “6.2 Billion Dollar Business” panel. The key to this growth and the fundamental underlyer to why I am so optimistic is technology. For the first time, consumers are seeing music grow technology (just look at ). Facebook, twitter, podcasts, applications, smartphones, etc. are helping people come together in new and exciting ways. People are sharing and engaging with content like never seen before. with Splice I think the real evolution of EDM is due to technology and its respective iteration. It’s human nature to find unique highs. Dance music coupled with technology has made music experimental and with more permutations we are approaching a singularity. Music is expiring faster and faster, most people don’t even know what DJ they are listening to anymore let alone the song title. Is that a good thing? Probably not, but who cares? If music is meant to be enjoyable and our attention spans are growing shorter then why shouldn’t we have an entirely new set of tracks to satisfy our primal instincts every week. I mean the music is meant to be seamlessly mixed together and iterated upon. Most DJ’s don’t even play their own compositions. At the end of the day technology allows DJ’s and producers to collaborate easier, remix faster and upload new sets for millions of people to critique. We are in a new age of remixes and much like the days of hip-hop I think that this is the soundtrack for millennials. So what’s next in EDM? “Technology”. More specifically, I think the future of EDM is mobile and a significant platform play. The great thing about EDM is that public companies running events are not just media companies they are tech companies as well. Realizing that EDM is a blanket term is important, people won’t stop attending festivals, they won’t stop going to clubs and they certainly won’t stop listening to pop music. It’s something we have done for generations and as the time passes we will continue to try to find singularity. If you think I’m crazy or have a little more to add, feel free to message or comment.

Why I’m Bullish on EDM

Table of Links Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 2 RELATED WORK 2.1 Neural Codec Language Models Conventional sequence-to-sequence auto-regressive TTS models, such as Tacotron [79], have successfully paved the way for speech synthesis technologies. TransformerTTS [53] first adopted a Transformer network for TTS, and VTN [28] also utilizes a Transformer network for VC. However, these auto-regressive models suffer from a slow inference speed, in addition to a lack of robustness owing to challenges in aligning text and acoustic representations and the difficulty in predicting a continuous acoustic representation. Recently, neural audio codec model [16], [89] have replaced conventional acoustic representations with a high-compressed audio codec, which can reproduce the original waveform audio. Vall-E [78] was the first neural codec language model for speech synthesis utilizing a discrete audio unit and language models. By scaling up the dataset to 60,000 h, Vall-E could perform in-context learning using a neural audio codec. However, it possessed the same limitations as auto-regressive TTS models, such as a slow inference speed and a lack of robustness. Furthermore they have a high-dependency of their pre-trained neural audio codec, resulting in lowquality audio. To overcome this limitation, high-quality neural audio codec models, such as HiFi-Codec [85] and DAC [41], have been investigated. Furthermore, SPEARTTS [31] and Make-A-Voice [23] introduced a hierarchical speech synthesis framework from semantic to acoustic token to reduce the gap between text and speech. Moreover, to reduce inference speed and improve the robustness of autoregressive methods, SoundStorm [6] proposed parallel audio generation methods that generate the token of a neural audio codec. UniAudio [86] presented a multi-scale Transformer architecture to reduce the computational complexity of long audio sequences. 2.2 Non-autoregressive Models For fast and robust speech synthesis, FastSpeech [68] introduced a duration predictor to synthesize speech in parallel, and they significantly improved the robustness of speech synthesis by addressing the limitations of auto-regressive models such as repeating and skipping. To reduce the oneto-many mapping problem in non-autoregressive speech synthesis, FastSpeech 2 [67] adopted a variance adaptor that can reflect pitch and energy information. However, these models require an external duration extractor to align the text and speech. Glow-TTS [34] introduces a monotonic alignment search and normalizing flow to learn text-speech alignment and train the TTS model simultaneously. They add a blank token interspersed between phoneme tokens to increase robustness. VITS [35] combined the TTS model and a neural vocoder using VAE for end-to-end TTS frameworks with the aim of improving the quality of synthetic speech. NaturalSpeech [75] achieved human-level quality in a single speaker TTS by introducing a bidirectional normalizing flow and adopting a differentiable duration modeling and phoneme pre-training. Moreover, HierSpeech [48] leveraged a self-supervised speech representation in end-to-end speech synthesis, which significantly reduced the information gap between text and speech, thus addressing speech mispronunciations. In addition, HierVST [45] utilized a hierarchical VAE for zero-shot voice style transfer, and which significantly improved the voice style transfer performance in end-to-end speech synthesis models without any labels. ZS-TTS [37], WavthruVec [71] and VQTTS [17] utilized a self-supervised speech representation as an intermediate acoustic representation for robust speech synthesis. NANSY++ [9] introduced a unified speech synthesizer for various voice applications such as TTS, VC, singing voice synthesis, and voice control. Some studies [26] have combined a parallel TTS with LLMbased prosody modeling for expressive speech synthesis. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors:
(1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.4 Speech Super-resolution 3.5 Model Architecture 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.4 Evaluation Metrics 5.5 Ablation Study 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 7 Conclusion, Acknowledgement and References 2 RELATED WORK 2.1 Neural Codec Language Models Conventional sequence-to-sequence auto-regressive TTS models, such as Tacotron [79], have successfully paved the way for speech synthesis technologies. TransformerTTS [53] first adopted a Transformer network for TTS, and VTN [28] also utilizes a Transformer network for VC. However, these auto-regressive models suffer from a slow inference speed, in addition to a lack of robustness owing to challenges in aligning text and acoustic representations and the difficulty in predicting a continuous acoustic representation. Recently, neural audio codec model [16], [89] have replaced conventional acoustic representations with a high-compressed audio codec, which can reproduce the original waveform audio. Vall-E [78] was the first neural codec language model for speech synthesis utilizing a discrete audio unit and language models. By scaling up the dataset to 60,000 h, Vall-E could perform in-context learning using a neural audio codec. However, it possessed the same limitations as auto-regressive TTS models, such as a slow inference speed and a lack of robustness. Furthermore they have a high-dependency of their pre-trained neural audio codec, resulting in lowquality audio. To overcome this limitation, high-quality neural audio codec models, such as HiFi-Codec [85] and DAC [41], have been investigated. Furthermore, SPEARTTS [31] and Make-A-Voice [23] introduced a hierarchical speech synthesis framework from semantic to acoustic token to reduce the gap between text and speech. Moreover, to reduce inference speed and improve the robustness of autoregressive methods, SoundStorm [6] proposed parallel audio generation methods that generate the token of a neural audio codec. UniAudio [86] presented a multi-scale Transformer architecture to reduce the computational complexity of long audio sequences. 2.2 Non-autoregressive Models For fast and robust speech synthesis, FastSpeech [68] introduced a duration predictor to synthesize speech in parallel, and they significantly improved the robustness of speech synthesis by addressing the limitations of auto-regressive models such as repeating and skipping. To reduce the oneto-many mapping problem in non-autoregressive speech synthesis, FastSpeech 2 [67] adopted a variance adaptor that can reflect pitch and energy information. However, these models require an external duration extractor to align the text and speech. Glow-TTS [34] introduces a monotonic alignment search and normalizing flow to learn text-speech alignment and train the TTS model simultaneously. They add a blank token interspersed between phoneme tokens to increase robustness. VITS [35] combined the TTS model and a neural vocoder using VAE for end-to-end TTS frameworks with the aim of improving the quality of synthetic speech. NaturalSpeech [75] achieved human-level quality in a single speaker TTS by introducing a bidirectional normalizing flow and adopting a differentiable duration modeling and phoneme pre-training. Moreover, HierSpeech [48] leveraged a self-supervised speech representation in end-to-end speech synthesis, which significantly reduced the information gap between text and speech, thus addressing speech mispronunciations. In addition, HierVST [45] utilized a hierarchical VAE for zero-shot voice style transfer, and which significantly improved the voice style transfer performance in end-to-end speech synthesis models without any labels. ZS-TTS [37], WavthruVec [71] and VQTTS [17] utilized a self-supervised speech representation as an intermediate acoustic representation for robust speech synthesis. NANSY++ [9] introduced a unified speech synthesizer for various voice applications such as TTS, VC, singing voice synthesis, and voice control. Some studies [26] have combined a parallel TTS with LLMbased prosody modeling for expressive speech synthesis. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Authors: Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author.

Neural Codec Language Models and Non-Autoregressive Models Explained

Table of Contents A Deep Dive into Voice Cloning with SoftVC VITS and Bert-VITS2 Prepare Dataset Extract from a Song UVR Workflows Preparation for vocal recording Cheapskate’s Audio Equipment Audacity workflows audio-slicer Cleaning dataset Match loudness so-vits-svc Set up environment Initialization Download pre-trained models Dataset Preparation Edit Configs Training Inference Audio Editing so-vits-svc-fork Installation Preparation Training Inference DDSP-SVC Preparation Training Inference Bert-vits2-V2.3 Initialization Preparation Transcription Training and Inference vits-simple-api Tweaks Share models In the , I tried a little bit of and found it interesting. So, I decided to train a usable model with my own voice. previous post TTS Generation WebUI This voice cloning project explores both SVC for Voice Changing and for Text-to-Speech. There is no one tool that does all jobs. VITS I have tested several tools for this project. Many of the good guides, like , , and , are in Chinese. So, I thought it would be useful to post my notes in English. this this this Although has been archived for a few months, probably due to oppression, it is still the tool for the best result. so-vits-svc Other related tools such as , , , and provide either faster/liter optimization, more features, or better interfaces. so-vits-svc-fork so-vits-svc-5.0 DDSP-SVC RVC But with enough time and resources, none of these alternatives can compete with the superior result generated by the original so-vits-svc. For TTS, a new tool called works fantastically and has already matured with its final release last month. It has some very different use cases, for example, audio content creation. Bert-VITS2 Prepare Dataset The audio files of the dataset should be in WAV format, 44100 Hz, 16bit, mono, 1-2 hours ideally. Extract From a Song is the easiest tool for this job. There is a that explains everything in detail. Ultimate Vocal Remover thread UVR Workflows Remove and extract Instrumental Model: VR - UVR(4_HP-Vocal-UVR) Settings: 512 - 10 - GPU Output and unclean vocal Instrumental Remove and extract background vocals. Model: VR - UVR(5_HP-Karaoke-UVR) Settings: 512 - 10 - GPU Output and unclean main vocal background vocal Remove reverb and noise. Model: VR - UVR-DeEcho-DeReverb & UVR-DeNoise Settings: 512 - 10 - GPU - No Other Only Output clean main vocal (Optional) Using RipX (non-free) to perform a manual fine-cleaning. Preparation for Vocal Recording It’s better to record in a treated room with a condenser microphone, otherwise, use a directional or dynamic microphone to reduce noise. Cheapskate’s Audio Equipment The very first time I got into music was during high school, with the blue Sennheiser MX500 and Koss Porta Pro. I still remember the first time I was recording a song that was on a Sony VAIO with Cool Edit Pro. Nowadays, I still resist spending a lot of money on audio hardware as an amateur because it is literally a money-sucking black hole. Nonetheless, I really appreciate the reliability of those cheap production equipment. The core part of my setup is a Behringer UCA202, and it’s perfect for my use cases. I bought it for $10 during a price drop. It is a so-called “Audio Interface” but basically just a sound card with multiple ports. I used RCA to 3.5mm TRS cables for my headphones, a semi-open K240s for regular output, and a closed-back HD669/MDR7506 for monitor output. All three mentioned headphones are under $100 for the normal price. And there are clones from Samson, Tascam, Knox Gear, and more out there for less than $50. For the input device, I’m using a dynamic microphone for the sake of my environmental noises. It is an SM58 copy (Pyle) + a Tascam DR-05 recorder (as an amplifier). Other clones such as SL84c or wm58 would do it too. I use an XLR to 3.5mm TRS cable to connect the microphone to the MIC/External-input of the recorder, and then use an AUX cable to connect between the line-out of the recorder and the input of the UCA202. It’s not recommended to buy an “audio interface” and a dedicated amplifier to replicate my setup. A $10 c-media USB sound card should be good enough. The Syba model that I owned is capable of “pre-amping” dynamic microphones directly and even some lower-end phantom-powered microphones. The setup can go extremely cheap ($40~60), but with UCA202 and DR-05, the sound is much cleaner. And I really like the physical controls, versatility, and portability of my old good digital recorder. Audacity Workflows Although when I was getting paid as a designer, I was pretty happy with Audition. But for personal use on a fun project, Audacity is the way to avoid the chaotic evil of Adobe. Noise Reduction Dereverb Truncate Silence Normalize audio-slicer Use or to slice the audio file into small pieces for later use. audio-slicer audio-slicer (gui) The default setting works great. Cleaning Dataset Remove those very short ones and re-slice those that are still over 10 seconds. In case of a large dataset, remove all that are less than 4 sec. In the case of a small dataset, remove those only under 2 sec. If necessary, perform a manual inspection for every single file. Match Loudness Use Audacity again with ; 0db should do it. Loudness Normalization so-vits-svc Set Up the Environment Virtual environment is essential to run multiple Python tools inside one system. I used to use VMs and Docker, but now, I found that is way quicker and handier than the others. anaconda Create a new environment for so-vits-svc, and activate it. conda create -n so-vits-svc python=3.8
conda activate so-vits-svc Then, install requirements. git clone https://github.com/svc-develop-team/so-vits-svc
cd so-vits-svc

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

#for linux
pip install -r requirements.txt

#for windows
pip install -r requirements_win.txt
pip install --upgrade fastapi==0.84.0
pip install --upgrade gradio==3.41.2
pip install --upgrade pydantic==1.10.12
pip install fastapi uvicorn Initialization Download pre-trained models pre-train wget https://huggingface.co/WitchHuntTV/checkpoint_best_legacy_500.pt/resolve/main/checkpoint_best_legacy_500.pt wget https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt logs/44k wget https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_D_320000.pth wget https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_G_320000.pth logs/44k/diffusion wget https://huggingface.co/datasets/ms903/Diff-SVC-refactor-pre-trained-model/resolve/main/fix_pitch_add_vctk_600k/model_0.pt (Alternative) wget https://huggingface.co/datasets/ms903/DDSP-SVC-4.0/resolve/main/pre-trained-model/model_0.pt (Alternative) wget https://huggingface.co/datasets/ms903/Diff-SVC-refactor-pre-trained-model/blob/main/hubertsoft_fix_pitch_add_vctk_500k/model_0.pt pre-train/nsf_hifigan wget -P pretrain/ https://github.com/openvpi/vocoders/releases/download/nsf-hifigan-v1/nsf_hifigan_20221211.zip unzip -od pretrain/nsf_hifigan pretrain/nsf_hifigan_20221211.zip Dataset Preparation Put all Prepared audio.wav files into dataset_raw/character cd so-vits-svc
python resample.py --skip_loudnorm
python preprocess_flist_config.py --speech_encoder vec768l12 --vol_aug
python preprocess_hubert_f0.py --use_diff Edit Configs The file is located at configs/config.json : the frequency of printing log : the frequency of saving checkpoints : total steps : numbers of saved checkpoints, 0 for unlimited. : fp32 In my case, : the smaller the faster (rougher), the larger the slower (better). log interval eval interval epochs keep ckpts half_type batch_size Recommended batch_size per VRAM: 4=6G；6=8G；10=12G；14=16G；20=24G Keep default for configs/diffusion.yaml Training python cluster/train_cluster.py --gpu
python train_index.py -c configs/config.json
python train.py -c configs/config.json -m 44k
python train_diff.py -c configs/diffusion.yaml On training steps: Use to train the main model; usually, 20k-30k would be usable, and 50k and up would be good enough. This can take a few days depending on the GPU speed. train.py Feel free to stop it by , and it will be continued training by re-run anytime. ctrl+c python train.py -c configs/config.json -m 44k Use to train the diffusion model; training steps is recommended at 1/3 of the main model. train_diff.py Be aware of over-training. Use to monitor the plots to see if it goes flat. tensorboard --logdir=./logs/44k Change the from 0.0001 to 0.00005 if necessary. learning rate When done, share/transport these files for inference. config/ config.json diffusion.yaml logs/44k feature_and_index.pkl kmeans_10000.pt model_0.pt G_xxxxx.pt Inference It’s time to try out the trained model. I’d prefer Webui for the convenience of tweaking the parameters. But before firing it up, edit the following lines in for LAN access: webUI.py os.system("start http://localhost:7860")
app.launch(server_name="0.0.0.0", server_port=7860) Run ; then access its from a web browser. python webUI.py ipaddress:7860 The webui has no English localization, but would be helpful. Immersive Translate Most parameters would work well with the default value. Refer to and to make changes. this this Upload these 5 files: and its main model.pt config.json and its diffusion model.pt diffusion.yaml Either cluster model for speaking or feature retrieval for singing. kmeans_10000.pt feature_and_index.pkl is for speaking only, not for singing. Recommend when using. F0 predictor RMVPE is useful when singing a feminine song using a model with a masculine voice, or vice versa. Pitch change is the way of controlling the tone. Use to get the clearest speech, and use to get the closest tone to the model. Clustering model/feature retrieval mixing ratio 0.1 0.9 should be set around , it enhances the result at steps. shallow diffusion steps 50 30-100 Audio Editing This procedure is optional. Just for the production of a better song. I won’t go into details about this since the audio editing software, or so-called DAW (digital audio workstation), that I’m using is non-free. I have no intention to advocate proprietary software even though the entire industry is paywalled and closed-source. Audacity supports multitrack, effects, and a lot more. It does load some advanced VST plugins as well. It’s not hard to find tutorials on mastering songs with Audacity. Typically, the mastering process should be mixing/balancing, EQ/compressing, reverb, and imaging. The more advanced the tool is, the easier the process will be. I’ll definitely spend more time on adopting Audacity for my mastering process in the future, and I recommend everyone do so. so-vits-svc-fork This is a so-vits-svc with real-time support, and the models are compatible. Easier to use but does not support the Diffusion model. For dedicated real-time voice changing, a is recommended. fork voice-changer Installation conda create -n so-vits-svc-fork python=3.10 pip
conda activate so-vits-svc-fork

git clone https://github.com/voicepaw/so-vits-svc-fork
cd so-vits-svc-fork

python -m pip install -U pip setuptools wheel
pip install -U torch torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -U so-vits-svc-fork
pip install click
sudo apt-get install libportaudio2 Preparation Put dataset .wav files into so-vits-svc-fork/dataset_raw svc pre-resample
svc pre-config Edit a in . This fork is a larger size than the original. batch_size configs/44k/config.json Training svc pre-hubert
svc train -t
svc train-cluster Inference Use GUI with . This requires a local desktop environment. svcg Or use CLI with for real time and for generating. svc vc svc infer -m "logs/44k/xxxxx.pth" -c "configs/config.json" raw/xxx.wav DDSP-SVC requires fewer hardware resources and runs faster than so-vits-svc. It supports both real-time and diffusion models (Diff-SVC). DDSP-SVC conda create -n DDSP-SVC python=3.8
conda activate DDSP-SVC

git clone https://github.com/yxlllc/DDSP-SVC
cd DDSP-SVC

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt Refer to the for the two files: Initialization section pretrain/rmvpe/model.pt
pretrain/contentvec/checkpoint_best_legacy_500.pt Preparation python draw.py
python preprocess.py -c configs/combsub.yaml
python preprocess.py -c configs/diffusion-new.yaml Edit configs/ batch_size: 32  (16 for diffusion)
cache_all_data: false
cache_device: 'cuda'
cache_fp16: false Training conda activate DDSP-SVC
python train.py -c configs/combsub.yaml
python train_diff.py -c configs/diffusion-new.yaml

tensorboard --logdir=exp Inference It’s recommended to use since it includes both DDSP and diffusion model. main_diff.py python main_diff.py -i "input.wav" -diff "model_xxxxxx.pt" -o "output.wav" Real-time GUI for voice cloning: python gui_diff.py Bert-vits2-V2.3 This is a TTS tool that is completely different from everything above. By using it, I have already created several audiobooks with my voice for my parents, and they really enjoy it. Instead of using the , I used the fork by for an easier setup. original v3u Initialization conda create -n bert-vits2 python=3.9
conda activate bert-vits2

git clone https://github.com/v3ucn/Bert-vits2-V2.3.git
cd Bert-vits2-V2.3

pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio==2.0.2+cu118 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt Download pre-trained models (includes Chinese, Japanese, and English): wget -P slm/wavlm-base-plus/ https://huggingface.co/microsoft/wavlm-base-plus/resolve/main/pytorch_model.bin
wget -P emotional/clap-htsat-fused/ https://huggingface.co/laion/clap-htsat-fused/resolve/main/pytorch_model.bin
wget -P emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/ https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim/resolve/main/pytorch_model.bin
wget -P bert/chinese-roberta-wwm-ext-large/ https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/resolve/main/pytorch_model.bin
wget -P bert/bert-base-japanese-v3/ https://huggingface.co/cl-tohoku/bert-base-japanese-v3/resolve/main/pytorch_model.bin
wget -P bert/deberta-v3-large/ https://huggingface.co/microsoft/deberta-v3-large/resolve/main/pytorch_model.bin
wget -P bert/deberta-v3-large/ https://huggingface.co/microsoft/deberta-v3-large/resolve/main/pytorch_model.generator.bin
wget -P bert/deberta-v2-large-japanese/ https://huggingface.co/ku-nlp/deberta-v2-large-japanese/resolve/main/pytorch_model.bin Create a character model folder mkdir -p Data/xxx/models/ Download base models: !wget -P Data/xxx/models/ https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/resolve/main/DUR_0.pth
!wget -P Data/xxx/models/ https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/resolve/main/D_0.pth
!wget -P Data/xxx/models/ https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/resolve/main/G_0.pth
!wget -P Data/xxx/models/ https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/resolve/main/WD_0.pth

#More options
https://openi.pcl.ac.cn/Stardust_minus/Bert-VITS2/modelmanage/model_filelist_tmpl?name=Bert-VITS2_2.3%E5%BA%95%E6%A8%A1
https://huggingface.co/Erythrocyte/bert-vits2_base_model/tree/main
https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/tree/main Edit by replacing all to train_ms.py bfloat16 float16 Edit for LAN access: webui.py webbrowser.open(f"start http://localhost:7860")
app.launch(server_name="0.0.0.0", server_port=7860) Edit for and Data/xxx/config.json batch_size spk2id Preparation Similar workflow as in the . previous section Remove noise and silence, normalization, then put the un-sliced WAV file into . Data/xxx/raw Edit for , , and . config.yml dataset_path num_workers keep_ckpts Run to slice the WAV file. python3 audio_slicer.py Clean the dataset ( ) by removing small files that are under 2 sec. Data/xxx/raw Transcription Install whisper pip install git+https://github.com/openai/whisper.git To turn off language auto-detection, set it to English only, and use model; edit as below: large short_audio_transcribe.py # set the spoken language to english
    print('language: en')
    lang = 'en'
    options = whisper.DecodingOptions(language='en')
    result = whisper.decode(model, mel, options)
	
    # set to use large model
    parser.add_argument("--whisper_size", default="large")

    #Solve error "Given groups=1, weight of size [1280, 128, 3], expected input[1, 80, 3000] to have 128 channels, but got 80 channels instead" while using large model
    mel = whisper.log_mel_spectrogram(audio,n_mels = 128).to(model.device) Run to start the transcription. python3 short_audio_transcribe.py Re-sample the sliced dataset: python3 resample.py --sr 44100 --in_dir ./Data/zizek/raw/ --out_dir ./Data/zizek/wavs/ Preprocess transcription: python3 preprocess_text.py --transcription-path ./Data/zizek/esd.list Generate BERT feature config: python3 bert_gen.py --config-path ./Data/zizek/configs/config.json Training and Inference Run to start training python3 train_ms.py Edit for model path: config.yml model: "models/G_20900.pth" Run to start webui for inference python3 webui.py vits-simple-api is a web frontend for using trained models. I use this mainly for its long text support which the original project doesn’t have. vits-simple-api git clone https://github.com/Artrajz/vits-simple-api
git pull https://github.com/Artrajz/vits-simple-api
cd vits-simple-api

conda create -n vits-simple-api python=3.10 pip
conda activate vits-simple-api && 

pip install -r requirements.txt (Optional) Copy pre-trained model files from to Bert-vits2-V2.3/ vits-simple-api/bert_vits2/ Copy and to Bert-vits2-V2.3/Data/xxx/models/G_xxxxx.pth Bert-vits2-V2.3/Data/xxx/config.json vits-simple-api/Model/xxx/ Edit for and as preferred config.py MODEL_LIST Default parameter Edit as below: Model/xxx/config.json "data": {
    "training_files": "Data/train.list",
    "validation_files": "Data/val.list",
	
  "version": "2.3" Check/Edit in as model_list config.yml [xxx/G_xxxxx.pth, xxx/config.json] Run python app.py Tweaks for tone, for randomness, for pronunciation, and for speed. and are self-explanatory SDP Ratio Noise Noise_W Length emotion style Share Models In its , there are a lot of VITS models shared by others. You can try it out first, and then download the desired models from . Hugging Face repo Files The is widely used in some content creation communities because of its high quality. It contains hundreds of characters, although only Chinese and Japanese are supported. Genshin model In , there are a lot of Bert-vits2 models that are made from popular Chinese streamers and VTubers. another repo There are already projects making AI Vtubers like and . I’m looking forward to how this technology can change the industry in the near future. this this https://techshinobi.org/posts/voice-vits/https://techshinobi.org/posts/voice-vits/

How to Voice Clone With SoftVC VITS and Bert-VITS2: A Deep Dive

SoftVC VITS এবং Bert-VITS2 দিয়ে কীভাবে ভয়েস ক্লোন করবেন: একটি গভীর ডুব

So klonen Sie Stimmen mit SoftVC VITS und Bert-VITS2: Ein tiefer Einblick

Cómo clonar por voz con SoftVC VITS y Bert-VITS2: una inmersión profunda

Comment cloner la voix avec SoftVC VITS et Bert-VITS2 : une plongée approfondie

SoftVC VITS और Bert-VITS2 के साथ वॉयस क्लोन कैसे करें: एक गहन जानकारी

SoftVC VITS および Bert-VITS2 を使用して音声クローンを作成する方法: 詳細

SoftVC VITS 및 Bert-VITS2를 사용한 음성 복제 방법: 심층 분석

Como clonar voz com SoftVC VITS e Bert-VITS2: um mergulho profundo

Как озвучить клон с помощью SoftVC VITS и Bert-VITS2: глубокое погружение

SoftVC VITS ve Bert-VITS2 ile Ses Klonlama: Derinlemesine Bir İnceleme

Cách sao chép giọng nói với SoftVC VITS và Bert-VITS2: Tìm hiểu sâu

如何使用 SoftVC VITS 和 Bert-VITS2 进行语音克隆：深入探讨

Stable diffusion had created a storm , created a and recently . And now, the new kid on the block language models seem to do everything from to . ChatGPT is all the hype now and there’s no short of creative applications for generative models. generating award winning images version of me in an alternate universe got in a whirlwind of PR/HR issues predicting stock prices becoming the CEO of a brand new startup Despite all the advancement in AI, we are still applying them with caution especially when creating education/information related products. That faithful morning, my wife repeatedly played YouTube flashcards videos for .  And she asked, “ChatGPT can do everything, why don’t they make flash cards videos?”. Then I replied, “Challenge accepted”. Hence, this article on the rabbit hole of trying to automatically create flash card videos using a couple of generative AI models. infant/toddler’s right brain development And she asked, “ChatGPT can do everything, why don’t they make flash cards videos?” What are Right Brain Development Flashcards? They are a series of flashcards shown to infant/toddlers at quick succession, usually less than 1 second per card. The flashcards in each session / video are particular to a specific category or topics, e.g. showing a series of fruits . ['apples', 'apricot', 'banana', 'blueberry', 'coconut’] https://youtu.be/-shR2MAXT0Q?t=71 The general idea is to automatically generate the a video of flashcards given a specified list, thus it’ll require: Some way to generate a sound file of each word in the list ( or ) .mp3 .wav Ah-ha!, ! text-to-speech (TTS) models Some how create an image file given a word / string Definitely, some stable diffusion thing A way to combine the image with the sound files into a video ( ) .mp4' I don’t know the solutions yet but it’s surely some CLI command ffmpeg Vänta ett ögonblick!(Wait a minute!) Where is ChatGPT in this? Alright, alright. Lets change the specifications a little, instead of generating a flashcard video given a list of vocabulary words, lets “prompt” the ChatGPT to give us a list of words given specific topics Give ChatGPT a topic and ask it to give me a list of words for kids’ language learning Some way to generate a sound file of each word in the list Some how create an image file given a word / string A way to combine the image with the sound files into a video Hey ChatGPT, “ " Name 10 fruits from gpt4all import GPT4All

# I know it's not the actual "ChatGPT" from Open AI..
# But still it's easier to just personalize to the LLM as "ChatGPT".

gptj = GPT4All("ggml-gpt4all-j-v1.3-groovy")
messages = [{"role": "user", "content": "Name 10 fruits"}]
gptj.chat_completion(messages) [out]: {'model': 'ggml-gpt4all-j-v1.3-groovy',
 'usage': {'prompt_tokens': 240,
  'completion_tokens': 304,
  'total_tokens': 544},
 'choices': [{'message': {'role': 'assistant',
    'content': 
'1. Apple, Banana, Orange (all fruits)'
'2. Pear, Cherry, Grape (all fruits)'
'3. Kiwi, Mango (fruits)'
'4. Pineapple, Strawberry (fruits)'
'5. Watermelon, Lemon (fruits)'
'6. Kiwi, Mango (fruits)'
'7. Apple, Banana, Orange (all fruits)'
'8. Pineapple, Strawberry (fruits)'
'9. Watermelon, Lemon (fruits)'
'10. Kiwi, Mango (fruits)'}}]
} My reaction: Either I don’t know how to prompt, or ChatGPT don’t know how to count -_- Hey ChatGPT, “ ” List down 10 fruit words and only 10. messages = [{"role": "user", "content": "List down 10 fruit words and only 10."}]
gptj.chat_completion(messages) [out]: {'model': 'ggml-gpt4all-j-v1.3-groovy',
 'usage': {'prompt_tokens': 263,
  'completion_tokens': 157,
  'total_tokens': 420},
 'choices': [{'message': {'role': 'assistant',
    'content': ' Sure, here are 10 fruit words that I can think of:'
'1. Apple '
'2. Banana '
'3. Orange '
'4. Pear '
'5. Cherry ''
'6. Strawberry '
'7. Kiwi '
'8. Grapes '
'9. Pineapple '
'10. Watermelon'}}]} My reaction: Now the list looks right but I can’t put it in a Python list easily, if I spend time editing the list into runnable code, I might as well type them out myself @_@ Hey ChatGPT, “ ” List down 10 fruit words, separate the list by commas without numbers prompt = “List down 10 fruit words, separate the list by commas without numbers”

messages = [{"role": "user", "content": prompt}]

gptj.chat_completion(messages) [out]: ' apple, banana, orange' My reaction: What the %^&*?! I seriously can do a better job with https://www.google.com/search?q=list+10+fruits (╯_╰)  ||| I’m going to try some passive-aggression with ChatGPT messages = [{"role": "user", "content": 
             "Can you count seriously? I'm telling you to list down 10 fruits. "
             'List 10 fruit words, separate by commas.'}]
results = gptj.chat_completion(messages) [out]: {'model': 'ggml-gpt4all-j-v1.3-groovy',
 'usage': {'prompt_tokens': 331,
  'completion_tokens': 129,
  'total_tokens': 460},
 'choices': [{'message': {'role': 'assistant',
    'content': 
' Sure, here are 10 fruits separated by commas:'
'apple, banana, cherry, grapefruit, kiwi, mango, peach, pear, pineapple, strawberry.'
}}]} And then: fruits = result['choices'][0]['message']['content'].partition(':')[2].strip('.').split(',') My reaction: > len("['apple, banana, cherry, grapefruit, kiwi, mango, peach, "
      "pear, pineapple, strawberry']")
86

> len("result['choices'][0]['message']['content'].partition(':')"
      "[2].strip('.').split(',')")
82 Seriously, to save my mind from thinking up 10 fruits and save 4 characters when typing them out… 割鸡焉用牛刀… Now that the ChatGPT and prompt-engineering clickbait part is over, time to get serious with the flashcard generation. From Text to Speech The machine learning terminology to create a sound file given a text is usually referred to “ ” aka TTS. By default, a lot of operating systems already comes with some pretty nifty TTS systems that you can access through command prompt. Text-to-Speech On a MacOS, you can try this on the terminal: say -v Samantha "hello world" And on an Linux/Windows, you can , then first install espeak espeak -v en-us "hello world" But if you’re the living on the bleeding edge, you can try this model from the HuggingFace library in Python: SpeechT5 TTS transformers import torch

from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset

# Load all the pretrained models...
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")


# Load xvector containing speaker's voice characteristics from a dataset.
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)


# Converts the text to a speech to a tensor / array of floats.
inputs = processor(text=text, return_tensors="pt")
speech = model.generate_speech(inputs["input_ids"], 
    speaker_embeddings, vocoder=vocoder) 

# [out]: tensor([-1.6944e-04,  1.0556e-04,  ...,  -3.7849e-05, -2.1459e-04]) Then to write the object into an actual sound file, we’re gonna use the . torch.tensor soundfile audio library based on libsndfile, CFFI and NumPy import soundfile as sf

def create_sound(text, outputfile):
    inputs = processor(text=text, return_tensors="pt")
    speech = model.generate_speech(inputs["input_ids"], 
        speaker_embeddings, vocoder=vocoder)
    sf.write(outputfile, speech.numpy(), samplerate=16000)

# Example usage of the TTS function.
create_sound("hello world", "hw.mp3") Stable Diffusion (Manga Mode!) The vanilla stable diffusion where you type in a sentence and it generates the image is just so 2022. It’s 2023,  lets do image generation with some manga flair! Generating manga/anime have been always been in the mind of enthusiasts. Manga fans have already shown in 2019 (aka “grandfather of stable diffusion”). And computer vision scientists didn’t gave up ever since comment. impressive results with Generative Adversarial Network Hayao Miyazaki’s “ [AI generated anime is] an insult to life itself ” “ ” [AI generated anime is] an insult to life itself - Hayao Miyazaki (2018) After multiple generations of different text to image generation, we get “ ”! Kawaii Diffusion Give me the code already!! from diffusers import DiffusionPipeline


diffusion_pipe = DiffusionPipeline.from_pretrained("Ojimi/anime-kawai-diffusion")
diffusion_pipe = diffusion_pipe.to("cuda")

def create_image(text, outputfile, prompt):
    image = diffusion_pipe(
        text.lower().strip().replace('_', ' ') + prompt, 
        negative_prompt="lowres, girl, bad anatomy").images[0]
    image.save(outputfile)

text = "hello world"
create_image(text, f"{text}.jpg") And immediately we see something different from how we use an image generator instead of sound generator. There is some sort of positive and negative “prompt engineering”. A few questions would come to an end-user and I find them food-for-thought for machine learning models creator too: Why is it that we need to prompt to get a good image generation? E.g. why do we need to explicitly do negative prompt to avoid low resolution? lowres Why is it that we don’t prompt text-to-speech models? E.g. why can’t we do something like Generate speech in a low deep voice: Hello World I guess that’s where selecting a and do the magic but can’t we prompt it like how we do text and image NLP? vocaloid speaker_embeddings Can abstract ideas like “Hello World” be generated into an image? Can an artist draw an abstract idea without research/inspiration? Are there other that modern TTS models can’t emulate? modalities of speech ( phonopragmatics ) Going back to Flashcard Generation Now that we have sound and images, putting them together into a video requires some video-editing finesse. You might have come across and I totally appreciate all the work put into the open source, that serves ~25K other libraries depending on it. But nothing beats raw CLI when it comes to the feature packed . some functions using moviepy 1-4 contributors maintaining a library ffmpeg And so, I went the “ ” around Python way: cheating word = "hello-world"

os.system(
  f"ffmpeg -i {word}.jpg -i {word}.mp3 "
   "-c:v libx264 -tune stillimage -c:a copy -preset:v veryslow "
  f"{word}.mp4"
) The would combine a single image file and an sound file to create a . video file. There are a whole bunch of audio-video options that I don’t really know and I boldly say, I took it from and added a few other options here and there. ffmpeg .jpg .mp3 .mp4 https://superuser.com/questions/1041816/combine-one-image-one-audio-file-to-make-one-video-using-ffmpeg And finally to combine the video file for each word, we do: os.system(
  """for f in `ls *.mp4 | sort -g`; do echo "file '$f'" """ 
   ">> mylist.txt; done"
)
os.system(f"ffmpeg -f concat -i mylist.txt generated-flashcards.mp4") The shell loop throughs the files, , and put the list into a text file. Then the can use the concat file argument to create the final flashcard video file. .mp4 sort them in the alphanumerical order ffmpeg Looping through the fruit list given by ChatGPT, we do this: import os 

for i, fruit in enumerate(fruits):
    create_sound(fruit, f"{i}-{fruit}.mp3")
    create_image(fruit, f"{i}-{fruit}.jpg", prompt=" fruit on table")
    os.system(f"ffmpeg -i {i}-{fruit}.jpg -i {i}-{fruit}.mp3 -c:v libx264 -tune stillimage -c:a copy -preset:v veryslow {i}-{fruit}.mp4")
os.system("rm mylist.txt")
os.system("""for f in *.mp4; do echo "file '$f'" >> mylist.txt; done""")
os.system(f"ffmpeg -f concat -i mylist.txt  ai-shichida.mp4") Ta-da! Now we’ve created an AI generated flashcard video… https://youtu.be/O-abE4CqbBg My reaction: When will I ever get the 1,000,000 subscribers gold press button? Epilogue / Disclaimers The full code to create your own flash card is available on https://www.kaggle.com/alvations/generative-language-learning-flashcard I understand that the model isn’t the paid ChatGPT service from Open AI, but for simplicity of understanding it’s a large language model somehow pre-trained on “ ” GPT4All human aligned instructions Note that the “ ” attempted for this post is much longer than the examples posted in the “ChatGPT” section above, I’ve tried a lot more prompts before the passive-aggressive one. prompt engineering You might have to loop through the fruits and re-create the images multiple times to get a generated image that you desire. The final video in this post was created after looping through the 10+ times… create_image Here’s a list of  flashcards I’ve created using the same TTS + Diffusion code: Things that move (Vehicles) Animals (Chinese Zodiac) Fruits (The one shown in this post)

Generative Language Flashcards

When looking at the world of business and data, learning data science cannot be overemphasized. It’s a field that has dramatically changed the way businesses operate. We are in an era of big data and to succeed, companies need data-driven insights to drive decisions. But data science isn’t just about programming and algorithms, it’s also about understanding the data being analyzed. And that’s where datasets come into play. They are crucial for anyone wanting to learn data science. Below is an infographic to indicate the Big Data: Interlude Disclaimer: I am a Data Engineer and not a Data Scientist. My work does mean that I understand what a Data Scientist does and can expect from a Data Engineer. Let me explore how these two fields relate to each other using ’s . I borrowed the red areas in the image from and adapted them to my own situation. Monica Rogati The Data Science Hierarchy Of Needs Christopher Bolard I deal, in my specific case, with the things in the red area with Data Engineer behind it. The Data Scientist/Analyst is concerned with the red area above. I’ll write an article about the how and what of me being a Data Engineer soon. In other cases, you will find that the Data Scientist/Analyst is also involved in the Explore/Transform section in the image. See the article: . Data Engineer VS Data Scientist DATASETS Datasets are collections of (semi-)structured data specially composed for analysis and appear in various forms, such as spreadsheets, CSV files, JSON files and databases. Datasets are essential for data science. They enable you to work with real-world data and gain insights that would otherwise be impossible to discover. Learning data science with datasets is therefore essential because it gives you the necessary skills to turn data into actionable insights. This is something I did at an , to understand the: database. earlier stage I amsterdam City Card Advantages One of the benefits of using datasets in data science is that they provide a real-world context for analysis. Unlike simulated data, datasets are based on real scenarios and are therefore more representative of the issues facing businesses. This means you can learn how to apply analysis techniques to real-world problems. This way you can develop solutions that are relevant and applicable. Another benefit of learning data science with datasets is that it helps you develop a data-driven mindset. Datasets provide an opportunity to understand the nuances of data, such as data quality issues and data bias. These are important considerations because they can have a significant impact on the insights generated from data. By working with datasets, you learn to identify these issues, which is critical for decision-making. In addition, datasets provide a basis for collaboration and knowledge sharing. In the world of data science, people often work together on the same problems and data sets provide a common basis for collaboration. They also allow you to share your insights with others, which can lead to new discoveries and breakthroughs. Competitions & Datasets Below is an overview of the competitions and datasets that I found: DrivenData SIGKDD Innocentive Topcoder Analytics Vidhya Codalab Numerai Open Data Science AIcrowd Biendata OpenML Devpost CrowdANALYTIX Kaggle ML Collective IDAO Machine Hack Grand Challenge Zindi Datasource.ai TuneDit Bitgrit ML Contests Data Register van de Nederlandse Overheid DBNL Dataset PDOK Open Data Rijksoverheid CBS Open data Statline OpenDataNederland.org Our World In Data Open Datasets compiled by Hackernoon Google Finance Google Public Data Google Trends KNMI Dataplatform Planetary Computer Data.gov.be Earthdata (NASA) IGSR: The International Genome Sample Resource PEW Research Center Conclusion In short, learning data science with datasets is essential for anyone who wants to develop the necessary skills to turn raw data into actionable insights. Datasets provide a real-world context for analysis, help you develop a data-driven mindset, and promote collaboration and knowledge sharing. As data continues to play a critical role in business operations, learning data science with datasets is no longer an option but a necessity. By investing in data science education, companies can equip their workforce with the skills needed to make data-driven decisions and drive growth and success. Also published . here

Why Datasets are Crucial to Data Science: the Key to Informed Decisions

Let's learn about via these 78 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Coding Interviews /Learn Repo 1. 10 Repositories to Help You 10x Your Performance on Technical Interviews 10 repositories that will transform the way you approach technical interviews. 2. 10 JavaScript Interview Questions and Answers to Help you Land Your Next Role 10 JavaScript questions for experienced developers that can help you crack your next interview. 3. How I Train Myself For Google Internship Interview I recently got an intern offer from Google for Summer 2020. I have been practicing LeetCode problems for more than two years. Other than the LC problems, I have used the educative courses to help me prepare the coding interviews. 4. How Competitive Programming can Help with Tech Interviews We will make an effort to comprehend the significance of competitive programming (CP) and whether or not it will be useful for your technical interview. 5. Hitting Rock Bottom!! Life-long lessons from a failed interview. 6. Top LeetCode Patterns to Use for FAANG Coding Interviews Coding patterns like Sliding Window or Two Heaps enhance our “ability to map a new problem to an already known problem.” 7. How To Prepare For Your FAANG Interview Are you ready for your FAANG interview? Read on to understand how you can prepare for such interviews and what to expect when you have to answer questions. 8. The 3-Step Process to Hiring a Software Engineer For Your Startup The standard coding interview gauges coding prowess the same way an IQ test gauges intelligence. Basically, barely at all. 9. 3 Practical Tasks for Your Next React Interview Here are 3 React tasks to check your coding skills, way of thinking, and ability to create good understandable code. 10. Will ChatGPT Change Coding Interviews Forever? Sergio Pereira talks about ChatGPT and how it could affect coding interviews. 11. Implementing a Singly or Doubly Linked List in Java (A LeetCode Question) Design your implementation of the linked list. You can choose to use a singly or doubly linked list. 12. Is the Grass Greener On the Tech Giants' Side? The grass isn’t always greener on the other side. Here are some things to consider before applying for a Tech Company. 13. What To Prepare For A Software Interview Both interviewees and interviewers can benefit from understanding what to prepare for a software interview and how best to learn about their counterpart. 14. Practical tasks for JavaScript interview Practical tasks like phrase reverse, event loops and using keywords are likely to be used to test you if you are applying to a job that uses Javascript. 15. How to Make a No BS Take-Home Coding Test This blog post looks at what makes a great take-home coding test and shares essential advice on how to make the most of this kind of assessment. 16. 7 Essential Tips for Competitive Programming and DSA I had to quit DSA and CP within a month because of the overwhelming exhaustion, This blog discusses mistakes that I made while learning DSA and CP. 17. Designing a Pragmatic Coding Interview [Part 1] In the following series of posts, I’ll be sharing the internals of designing a pragmatic coding interview that I got to know after interviewing folks for different tech positions for some time and here comes the first one. 18. The Big O The Big O is a way to measure how well your code solves a problem 19. 4 Most Commonly Asked Algorithms In Coding Interviews If you prefer to follow along via my YouTube video, you can watch it here! 20. Why Your Coding Portfolio Needs Libraries and Packages Building a job-ready portfolio of coding projects doesn't happen overnight, here's why you should include libraries and packages in yours. 21. Ace Your Coding Interviews with These Free Courses from Stanford Technical interviews used to be a challenge for me. I have a bachelor’s degree in Electronics & Telecommunications and a master’s degree in Computer Science. 22. Binary Search In Java: Examples And Interview Tips I recommend following along with my video if you want to see some examples and hear step by step how I coded binary search in Java! 23. The Story of How I Almost Became a Developer The story of how I almost became a developer. Featuring confusion, frustration! Triumph! Coming soon to Netfli.... just kidding. 24. How to Find the Product of All Elements in an Array Except Self - Blind 75 LeetCode How to Find the Product of All Elements in an Array Except Self? You must write an algorithm that runs in O(n) time and without using the division operation. 25. Hiring an Engineer: 7 Different Types of Engineering Interviews These are 7 Different Types of Engineering interviews that engineers may come when searching for jobs, including those used by large companies like Google. 26. Job Hunting: Are Technical Tests Worth Your Time? How to - hopefully - not drive yourself up the wall preparing and fretting over technical tests. 27. Become a 10x Software Engineer: A How-To Guide only a fraction of the 4.4 million coders makes it to FAANG companies and other tier-1 tech firms. Learn how to become one of them with these best practices 28. An Open-Source Collection of Boilerplate Repositories to Help Set Up Your Next Project The open-source templates for setting up coding projects. 29. Top 5 Interview Questions for Beginner React Developers Here are 5 common interview questions you might hear in an interview. 30. 10 Frontend Projects to Take Your Coding Skills to the Next Level Improve your front-end development skills and expand your portfolio with these 10 front-end projects. Take your coding game to the next level! 31. Why Engineers Won't Do Your Coding Test The idea behind a coding test is very simple: to filter out candidates who do not have the technical chops for the role, early on in the process before the hiring manager and candidate both waste their time with an in-person interview. 32. How to Prepare for a Coding Interview in 8 Weeks As of this writing, the market is tough. We’ve been hit hard with a deadly
pandemic that left thousands of people unemployed. It’s layoffs everywhere and the companies are being conservative when it comes to
hiring. 33. Restcaperoom is an Interactive Exercise for Programmer Interviews When you are looking to hire skilled programmers, how do you know if the person you are interviewing indeed has the capabilities he or she is talking about? Well, just let them show you! 34. Java Constructor Interview Questions Here we've listed the most commonly asked concepts on constructors in java that can help you ace your java interview. 35. Powerful Tips To Help You Learn Programming Language Here I have shared some of the tips, that my mentor gave me, on how to start learning programming which you can follow to become better programmer within a year 36. 10 GitHub Repositories to Follow GitHub is a well-known platform to share all kinds of technologies. The following article contains the 10 most widely used GitHub repositories. 37. Why Should Every Javascript Developer Avoid Using if Statements Stop using if statements unless absolutely necessary. Using if statement is a clunky way of writing code and should be avoided wherever possible. 38. How to Prepare For a Mock Interview — Daniel Habib Programming mock interviews can be time consuming and intimidating - here's how to make sure you didn't just waste your time 39. The Ultimate Guide to Software Development Job Interview Prep 40. 10 Repositories that Will Transform the Way You Approach Technical Interviews A complete computer science study plan to become a software engineer. This GitHub repo will take you from ground level to advanced concepts. 41. Technical Interview: How to Prepare, Structure, and More Tips Interviewing someone isn't as easy as it seems. Here are some tips to form the best interview,  both for you and your interviewee. 42. Demystifying Hoisting for Javascript Developers Struggled with JS hoisting in interviews? How let, var, const and function hoist differently? This article explains the concept and makes it easy to understand. 43. Convert Sorted Array to Binary Search Tree Using Kotlin at your technical interviews! 44. Everything you Need to Know About TCS Mock Test Tips to passing the TCS mock test 45. My Favorite Kind Of Coding Interview Exercise How to prepare for a job interview as a programmer looking for a job that needs coding, how these simple exercises will help you prepare fully for it. 46. What is the best line of code you've ever written? I recall going to a four-round interview and being asked "What is the best line of code you've ever written?". 47. How to Solve the Hamming Distance Problem in C++, A Google Interview Question In hamming distance problem, we find the number of positions where the bits are different for the given input with constant time complexity. 48. 5 Most Commonly Asked Data Structure Questions in Coding Interviews If you prefer to follow along via my YouTube video, you can watch it here! 49. Developer Pain Points Using React Native As you already know, our team is a huge fan of React Native and we have published a lot of articles about this technology. But as everything else in the world, React Native is not perfect and has its disadvantages as well. We asked a few senior developers about the pain points using RN. 50. 5 Useful Tips from My Amazon SDE II Interview Experience Amazon SDEII coding and system design interview experience from initial recruiter call to online assessment and onsite rounds. 51. Graduating from Leetcode Leetcode.com is a website where people–mostly software engineers–practice their coding skills. It’s pretty similar to sites like HackerRank & Topcoder which will rank your code written for a particular problem against the ones submitted by other users. 52. Running Great Tech Interviews: The Art of the Follow-up Question This article gives sample software engineering interview questions, as well as my approach to conducting efficient interviews. 53. Interview With A 10-Year Old Programmer This is a story of a young boy who is more than what he seems, a young genius man, who must share his gifts and talents with the world. 54. 17 Interesting JavaScript Tricks There are many ways to write code but generally the first way for many people is very long and can take you some time. Here is my latest post that will increase your efficiency and productivity when coding JavaScript. 55. Why Interviewing As A Developer Is Complete Nonsense Learn what a developer goes through on every job search 56. For Developers: How To Prepare for a Job Interview Last week a friend asked me about interviews, it’s a good question because in our career we passed for a lot of interviews, sometimes to get a good job, change companies or probably you will be the interviewer, today I will try to cover both points of views to manage an Interview.
Interviews 57. Why Developers Should Take Coding Challenges Regularly First of all, let me start by saying that the ability to solve coding challenges is not a measure of how good a web developer you are, but it can show that you are a great developer and make you stand out from the crowd. Nobody needs coding challenges to create an outstanding web page using HTML, CSS (or it’s frameworks), and maybe even a little JavaScript. 58. Is a Given Number the Power of 2? In this lesson, we will try to check if the given number is a power of 2. We solve this by writing an efficient algorithm that takes an optimal amount of time. 59. What I learned from 500+ tech interviews in the last 6.5 years I conducted 500+ tech interviews in the last 6.5 years. Here is what I learned. 60. Closures Demystified: Unlocking Hidden Potential in Your JavaScript Code If you have understood the discussion so far, you should feel confident in your capacity to discuss closures when asked about them during your next interview. 61. How to Crack Coding Interviews? Tips, Topics, and Skills Wondering how to prepare for coding interviews? Here is your guide to prepare and crack your next interview with important topics, skills, and tips 62. A Better Way to Pass Technical Interviews To LeetCode or not to LeetCode? What if you don't want to practice 100s of coding questions before your next coding interview? 63. 14 Patterns to Ace Any Coding Interview Question The process of preparing for coding interviews is anxiety-inducing for many developers. There’s so much material to cover, and often much of it feels irrelevant to what devs are doing in their day jobs, which only adds to the stress. 64. Diversity in Tech: 3 Actionable Steps for More-Inclusive Tech Teams Diversity in tech is needed for so many reasons: 65. 160+ Data Science Interview Questions A typical interview process for a data science position includes multiple rounds. Often, one of such rounds covers theoretical concepts, where the goal is to determine if the candidate knows the fundamentals of machine learning. 66. Stop Fearing the Whiteboard: Conquer It! It's time we stopped fearing, complaining, and arguing about whiteboard coding interviews. 67. Technical Data Science Interview Questions: SQL and Coding A data science interview consists of multiple rounds. One of such rounds involves theoretical questions, which we covered previously in 160+ Data Science Interview Questions. 68. All You Need to Know About Dynamic Programming What is dynamic programming and why should you care about it? 69. [My Learnings] From Writing My First Line of Code to a $226K Job Offer in 8 Months I got offers from Google, Lyft, Yelp, cloud unicorn Rubrik, IBM Artificial Intelligence, and JP Morgan Chase. My story may help you in your job search 70. My Terrible Experience Applying for a Job at Crossover for Work Crossover for Work advertises dozens of jobs all the time, many of them highly paid, many for senior developers and architects. Everyone works from home. 71. 50+ Data Structure and Algorithms Interview Questions for Programmers Coding interviews are comprised mainly of data structure and algorithm-based questions as well as some of the logical questions such as, How do you swap two integers without using a temporary variable? 72. Web Developer Job Interview Preparation: 25 Questions, Prompts, and Coding Assignments You’ve set the alarm for 2:45pm. 73. Dissecting the World of Cryptocurrency with Coder Anthony Watson In this Slogging AMA, we talk to Anthony Watson a writer, coder and respected member of the Hackernoon community. We discuss with him all things writing, coding 74. How to Ace Coding Interviews - Advice from a Former Amazon Dev I must have been in almost a hundred coding interviews. Sometimes as an interviewee and sometimes as an interviewer. I have struggled in front of the whiteboard and I have seen many candidates struggle. 75. The Science Behind Making Software Engineering Interviews Truly Predictive of Job Performance 76. How to Solve 6 Dynamic Programming Problems In a Systematic Way In this article, I gave you an introduction to Dynamic Programming with several examples. Here I will solve 6 harder Dynamic Programming problems to show you how to approach them. 77. 13 Ways to Traverse a Tree: Recursion vs Iteration To understand recursion, you must understand recursion. I will show you 13 different ways to traverse a tree to compare recursive and iterative implementations. This way, we will kill two birds with one stone: recursion and data structures and algorithms. 78. Data Structures and Algorithms: 20 Problem-Solving Techniques This is the article I wish I had read when I started coding. I will dive deep into 20 problem-solving techniques that you must know to excel at your next interview. Thank you for checking out the 78 most read stories about Coding Interviews on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

78 Stories To Learn About Coding Interviews

You have a few options. How much time you got? It’s 1997 and you want to build a website. You have some options. Option 1: Teach yourself At a baseline, the web is made up of a handful of technologies. In 1997, there was some technical documentation to wade through, but there were only a handful of things to learn to get started making websites. …by reading a book You may have actually had the most success at your local bookstore or library. The web was fairly new, and digital publishing was far from the norm. So a lot of information about the early web was being distributed through a growing number of books that targeted web newbies. Bebo White began his career on the web at Stanford’s SLAC lab, He joined the WWW Wizards at Stanford, a voluntary organization that assisted, through message boards and mailing lists, newcomers to web building. White is also responsible for one of the first books to deal in the theory and practice of building on the web. Published in 1996, White’s wasn’t a short book, but it was comprehensive. If you were looking to get started on the web, it was a great place to start. helping to build the first website in the U.S. HTML and the Art of Authoring for the World Wide Web But by 1997, there were other options available. , and Elizabeth Castro’s were all published in 1997, or just prior. They offered technical and practical advice for web beginners, as well as detailed walkthroughs for creating graphics for the web, and using them to build websites. Picking up a copy of any of these books would get you going in the right direction. Jennifer Robbin’s Designing for the Web , Lynda Weinnman’s Designing Web Graphics HTML for the World Wide Web But the most consistent and prolific publisher of early web books had to be Laura Lemay. Her first entry into the web was in 1995, with . Plain spoken, pragmatic, and bundled with a CD full of code examples, the book became a near instant success—at least among a still relatively small number of budding web developers. Lemay quickly followed up her first entry with one book after another, all variations on the same theme. Learn a web technology. Fast. , And so on. Teach Yourself Web Publishing with HTML in a Week Teach Yourself Java in 21 Days Teach Yourself More Web Publishing With HTML in a Week Blurring the line between digital and book publishing, Lemay’s books were rarely finished at the time of its publications. She would consistently released updated editions as web technologies evolved, and offered corrections and updates right on her site. Lemay’s ongoing series, revised editions, and fresh takes on web topics would become a foundation for lots of people learning the web. Plug into that as a foundation, and you’d be all set. …By Surfing the Web For somebody surfing the web in 1997, a book might feel a bit… 20th century. If you already knew the basics of getting online, why not poke around some sites that might help, right there in your browser. . —in a project cooked up by the site’s own developers—launched Webmonkey in 1996, one of the first major efforts targeted specifically at web designers and programmers. Over time, the site built up a roster of web practitioners that freely shared tips, tricks, and best practices for building websites. The webmonkeys, as they would often call themselves, prided themselves on their down to earth and practical approach. Not to be outdone, CNET launched Builder.com in 1997 with contributions from Charity Kahn, Dan Shafer, Frederick Paul and a host of big names in the emerging web design and development field. These sites might feel expansive, but they were full of helpful tips. Wired But you want more. You want something dynamic, sleek, and cutting edge. For that, you’ll have to push out one level deeper into the web, to an expanding network of amateur teachers and active web developers posting tutorials to their personal sites. You might find yourself at Glenn Davis’s or Dori Smith’s or Nick Heinle’s or any number of small sites maintained by a single person and full of excellent code examples and tips. Project Cool Backup Brain Webcoder, So if you wanted to build your website yourself, learning it on your own took some dedication and time, but it was certainly doable. Option 2: Let Someone Else Do It Let’s face it, the web is complex. And you don’t have the time or resources to figure it out. It’s time to turn to the experts and let them handle it. …Using a Service Maybe you just need a simple homepage. Maybe it was a newsletter for your family reunion, or a digital tribute to a pop icon. A few pages, some images, and that’s it. There was no shortage of free homepage options out there if you knew where to look. , and likely had the biggest presence, but they were already being challenged by Tripod and Angelfire. The offer on the table was fairly simple: 15MB and a domain, plus some basic tools for getting started. The rest was up to you. Geocities launched in 1995 In some cases, though, you didn’t need to look any further than your own Internet provider. ISPs like AOL, Netcom and CompuServe added free webpages to their monthly packages. They tended to be a bit more powerful and customizable than your average free homepage site, a good match for a small business looking to start recruiting customers online. Most had integrations with more advanced tools, like Microsoft Frontpage, which made it easier to build websites using drag and drop tools, though that came at a cost. So if you were a step up the complexity latter, your ISP might be the right way to go. There was a catch though. Smaller providers would sometimes pop up, capitalizing on the rapid rise of the early web offering generous site builder packages, only to disappear months later. You might have a perfectly functioning website one day, and nothing but an error message the next. With no proper backup, there was often no way to get at a site’s content or data. Best choose wisely. … Using a Freelancer Another option is to turn to a patchwork of freelancers and server administrators that, together, would be able to provide a compelling experience on the web. The earliest roots of web design had its foundation in print and graphic design, and it was very common for graphic designers to go about designing a website the same way they might design a magazine spread or brochure (that’s why, when Lynda Weinman published her first book about the web, it’s focus was entirely on graphics). You’d need a bit of expertise to translate a design into a working web document, but there should be plenty of people out there willing to lend a hand. … Using an Agency By 1997, it might not have been common, but it was at least somewhat expected that businesses would have some presence on the web. Which offered opportunity. Brick and mortar stores operating in a small local market could go worldwide overnight. To help fill that gap, a number of digital agencies—with a framework and process mostly borrowed from the advertising industry—began assisting with building websites. For those that fell in the small business category, there was a whole network of amateur web practitioners who came to the web early enough, and spent time learning the web early enough, that they could effectively build out website presences for small and medium businesses. Most of them were the outgrowth of personal experimentation, that individual designers and developers spun out into separate businesses. David H Dennis, for instance, documented odd things he found around the web on his main site. But one page on his web, , offered his services for clients looking to get into the web game. Dennis was responsible for several sites on the early web. amazing web internet creations Be careful with your choice of agencies, though. By 1997, there were already large, “digital media” . They offered more than just a website. In their loftiest marketing materials, they offered companies a digital revolution. A complete presence on the web that prepared you for the next millennium. Be careful though. These agencies came with a hefty price tag. It might cost tens of thousands of dollars just to get started. agencies like Razorfish, Agency.com, and Organic that were starting to build momentum If you wanted a website in 1997, there were lots of ways to do it. And don’t worry, you’ll probably have to build another in a couple of years anyway! Sources Scott Nathan Gilbertson. "Whatever Happened to Webmonkey.com?." Luxagraf. September 9, 2013. https://luxagraf.net/src/whatever-happened-webmonkey "Short Take: Netcom offers hosting options." CNET. November 11, 1996. https://www.cnet.com/tech/mobile/short-take-netcom-offers-hosting-options/ David Noack, "Choosing the Right Path to the Net," , 16-20 Nation's Business First Published . Here

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