Hi. Here is the new's question. The biggest concern is the rate of generation. I want to generate about 5 seconds of voice in about 100ms. I want to know which model performs best(SOTA) under those conditions. Which model is best for me? I think "styletts2" is best. If you have any relevant experience or know any other information, I would really appreciate your help. Thank you ! submitted by /u/hwk06023 [link] [comments]

Word embeddings like Word2Vec and GloVe have revolutionized natural language processing, offering compact and dense representations of word meanings. However, these embeddings typically represent words as real-valued vectors, potentially limiting their ability to capture complex semantic relationships. In this proposal, we explore an alternative approach: representing word vectors as complex numbers. We propose converting Word2Vec or GloVe vectors into complex numbers, where the real part captures magnitude and the imaginary part encodes additional semantic information. For instance, consider the word vector Vecword=[0.2,−0.3,0.5,0.1,−0.2]. We can convert this vector into a complex number zz as follows: z=Vecword[0]+i×Vecword[1] Here, ii is the imaginary unit. The real part of the complex number represents the magnitude of the word's meaning (0.2), while the imaginary part (-0.3i) captures additional semantic nuances. This approach offers several potential advantages: Enhanced Semantic Representation: Complex numbers can capture both magnitude and phase, allowing for richer semantic representations compared to real-valued vectors. Contextual Information: By encoding semantic information in the imaginary part, we can capture contextual nuances that may be missed by traditional embeddings. Compatibility: Complex number representations can be seamlessly integrated into existing models and frameworks, offering a straightforward extension to current NLP pipelines. Exploring complex number representations for word vectors presents an exciting avenue for enhancing semantic understanding in natural language processing tasks. By leveraging the unique properties of complex numbers, we can potentially unlock deeper insights into the structure and meaning of language. This proposal aims to spark further research and experimentation in this promising direction. Join us as we delve into the fascinating world of complex semantics! submitted by /u/_mayuk [link] [comments]

Hi, does anyone know of a French L2 GEC dataset (that was published at a conference)? submitted by /u/R-e-v-e-r-i-e- [link] [comments]

My primary expertise is audio processing, but i believe this task happens in other domains too: running a model on chunks of infinitely long input. while for some architectures it is straightforward, it can get tedious for convolutional nets. I put together a comprehensive tutorial how to build a streaming ML applications: https://balacoon.com/blog/streaming\_inference/. would be curious to learn wether its a common problem and how do people usually deal with it. Because resources on the topic are surprisingly scarce. submitted by /u/clementruhm [link] [comments]

​ https://preview.redd.it/jpiyt4b9yhwc1.png?width=1165&format=png&auto=webp&s=95d80f8f9c9241d722717ad25215be4077d541ca Based on the MSE looks good right? But why is my R^2 starting off so negative and approaching 0? Could it be a bug in how i am calculating it? This happened after i min maxed the labels before training. This is an LSTM that is predicting runs scored for baseball games. submitted by /u/Cloverdover1 [link] [comments]

After 20+ years in the field, I'm not sure what I should call myself 🙂 submitted by /u/Level-Upstairs-3971 [link] [comments]

Hello Everyone, I am trying to do a small fraud detection project and i have so imbalanced dataset. I used randomundersampling because minority class is pretty small and i also tried smote or combining with smote best recall score i got, was with only randomundersampling(0.95). I thought GridsearchCV to increase it but instead of increasing, it is decreasing although i tried to make it to focus on recall score. Why this is happening? submitted by /u/Legal_Hearing555 [link] [comments]

Hello, I am trying to model nitrate concentrations in the streams in Bavaria in Germany using Random Forest model. I am using Python and primarily sklearn for the same. I have data from 490 water quality stations. I am following the methodology in the paper from LongzhuQ.Shen et al which can be found here: https://www.nature.com/articles/s41597-020-0478-7 I want to split my dataset into training and testing set such that the spatial distribution of data in both sets is identical. The idea is that if data splitting ignores the spatial distribution, there is a risk that the training set might end up with a concentration of points from densely populated areas, leaving out sparser areas. This can skew the model's learning process, making it less accurate or generalizable across the entire area of interest. sklearn train_test_split just randomly divides the data into training and testing sets and it does not consider the spatial patterns in the data. The paper I mentioned above follows this methodology: "We split the full dataset into two sub-datasets, training and testing respectively. To consider the heterogeneity of the spatial distribution of the gauge stations, we employed the spatial density estimation technique in the data splitting step by building a density surface using Gaussian kernels with a bandwidth of 50 km (using v.kernel available in GRASS GIS33) for each species and season. The pixel values of the resultant density surface were used as weighting factors to split the data into training and testing subsets that possess identical spatial distributions." I want to follow the same methodology but instead of using grass GIS, I am just building the density surface myself in Python. I have also extracted the probability density values and the weights for the stations. (attached figure) Now the only problem I am facing is how do I use these weights to split the data into training and testing sets? I checked there is no keyword in the sklearn train_test_split function that can consider the weights. I also went back and forth with chat GPT 4 but it is also not able to give me a clear answer. Neither did I find anything concrete on the internet about this. Maybe I am missing something. Is there any other function I can use to do this? Or will I have to write my own algorithm to do the splitting? In case of the latter, can you please suggest me the approach so I can code it myself? In the attached figure you can see the location of the stations and the probability density surface generated using the kernel density estimation method (using Gaussian kernels). Also attaching a screenshot of my dataframe to give you some idea of the data structure. (all columns after longitude ('lon') column are used as features. the NO3 column is used as the target variable.) I will be grateful for any answers. ​ Probability density surface generated using the kernel density estimation method with gaussian kernels. ​ the dataset I am using to model the nitrate concentrations submitted by /u/dr_greg_mouse [link] [comments]

I have been recently contemplating whether to look for a new job or not. I read somewhere to make a pros and cons list to figure out if I should. Here is my list, looking for some constructive feedback. Pros: Job Security in an uncertain economy. Recently got promoted to senior DS, direct manager responsible for pushing for promotion. Work from home and pretty flexible working hours. Moved to a low cost city, but current salary based on high COL city. Some really good technical teammates to learn new technologies from, unfortunately they do not want to be managers, but prefer to be senior level ICs. Generally treated as a high performer. Slim chance of promotion if team members leave. Kids wouldn't have to move again, good school district. Cons: Company financials not looking good, cash infusion from board, C-level suite revamped. No more merit based increases in the foreseeable future, freezing hire. No more promotions or career growth until company stabilizes. Direct Manager (female) was effectively stripped of role in departmental restructuring. Skip Manager (male) was initially not supportive of promotion and now is direct manager. Career growth looks non-existent, especially as a female, skip manager effectively made all white male middle managers during reorg. Working at fintech, there is not much innovation in terms of modeling, stuck with binary classification (default prediction) most of the time. Good at and interested in improving MLOps /MLE work (deploying models and improving infrastructure), but skip manager effectively delegated the women in the team to model delivery and put all the men in technical work. Also doesn’t recognize technical skillset. Looking for a new job would mean lower salaries based on general trend and recently moving to a low COL city. General dread of being typecast in fintech space with not a lot of exposure to other modeling techniques. Team culture is non-existent, especially with surviving 3 RIFs at company, and many key folks that held up the culture, leaving. Even after doing a good job of building responsible models that drive the core of the business, financials were not controlled correctly, leading to a lot of uncertainty in company future. Afraid of being the last one left, generally prefer to not change jobs often due to being on work visa , but also need the company to do well for security. submitted by /u/dsgirlie [link] [comments]

Links: ​ https://www.snowflake.com/blog/arctic-open-efficient-foundation-language-models-snowflake/ ​ https://replicate.com/snowflake/snowflake-arctic-instruct submitted by /u/topcodemangler [link] [comments]

Started in December as a Sr. Data Science Analyst, but all the work I’ve been doing so far revolves around jumping around between several internal systems to try to explain any KPI changes of >2% to our leaders. So let’s say arbitrary KPI A is 100 on Monday, but then it’s 95 on Tuesday and then 94 on Wednesday, my job is to figured out what the root cause of the change is and to have answers “quickly”. We run an online sales portal with a multitude of variables that can lead to changes in our KPIs. A lot of the functioning of these variables are not well documented. The sources I’m expected to go to in order to find my explanations are a mix of already-created Tableau and PBI dashboards, some more bespoke internal systems (same dynamic as a dashboard basically), maybe some SQL querying against Redshift, and my own intuition. That’s it. No modeling, no experimentation, no Python unless I make an explicit decision to spend time writing something, and no longterm projects besides maybe building more dashboards to help explain things even faster. I’m pretty slow now as this sort of work relies heavily on familiarity with what dashboard/report to go to for what and how everything ties together but just feels like I might be in the wrong spot. Am I tripping? The whole reason I took this role was because it’s “Data Science” focused, but I’ve seen very little to no actual data science at all. submitted by /u/Tyraniczar [link] [comments]

This is a prompt I entered into MS Copilot (GPT4 Turbo). It's in german but it just means "Would there be any disadvantages if I took the full bath first?"), so this can't be another SolidGoldMagikarp or similar, because the words clearly were in both tokenizer and training vocab. Why would such a simple sentence cause this? Any guesses? (also tried with Claude Opus and LLama 3 70b, which worked fine) ​ https://preview.redd.it/9x6mva7b6gwc1.png?width=1129&format=png&auto=webp&s=bb6ac52d1c52d981161e8a864c5d1dd3794ca392 submitted by /u/michael-relleum [link] [comments]

Hi All, I am working on a project where I want to create speaker-aware transcripts from audios/videos, preferably using open-source solutions. I have tried so many approaches but nothing seems to work good enough out of the box. I have tried: ​ whisperX: https://github.com/m-bain/whisperX (uses pyannote) whisper-diarization: https://github.com/MahmoudAshraf97/whisper-diarization (uses Nemo) AWS Transcribe AssemblyAI API Picovoice API I'll need to dig deeper and understand what's causing the incorrect diarization but I am looking for suggestions to improve speaker diarization. Please reach out if you have worked in this area and have had any success. Thanks! submitted by /u/anuragrawall [link] [comments]

I am a statistics & machine learning researcher. I have invented some new methods, built packages in C++, R, and Python. I am also a machine learning consultant (part-time), but I usually tell people what to do and give feedback rather than do things myself. I don't like this experience though. So you can see, I probably know lots about theory, methodology & practical applications. However, I always want to switch to a more "technical" position after getting a PhD, i.e. machine learning engineer or SWE with focus on ML. I do feel like not having a formal training in SWE and CS would make me unemployable in the MLE field, so I always want to take some online SWE courses/programs to fill in the gap. My goal is to know about the engineering process behind SWE and to take relevant technical SWE/CS courses that most SWE/CS students do. You know, I can code, but it doesn't mean I will be a good MLE 🤣 Do you have any suggestions? Like a SWE track on a MOOC platform. I do know they are not perfect, but I do practice a lot, and can work on personal projects. Hopefully, they will be useful 🙂 Cheers, submitted by /u/itedelweiss [link] [comments]

Join us at Con Murphy’s on May 21st! Info & RSVP here: https://meetu.ps/e/N5ytt/97Jr8/i submitted by /u/CVM-17 [link] [comments]

https://www.norange.io/projects/paper_scorer/ A couple of years ago, u/programmerChilli analyzed ICLR 2019 reviews data and trained a model that rather accurately predicted acceptance results for NeurIPS. I've decided to continue this analysis and trained a model (total ~6000 parameters) on newer NeurIPS reviews, which has twice as many reviews compared to ICLR 2019. Additionally, review scores system for NeurIPS has changed since 2019, and here is what I've learned: 1) Both conferences consistently reject nearly all submissions scoring <5 and accept those scoring >6. The most common score among accepted papers is 6. An average rating around 5.3 typically results in decisions that could go either way for both ICML and NeurIPS, suggesting that ~5.3 might be considered a soft threshold for acceptance. 2) Confidence scores are less impactful for borderline ratings such as 4 (borderline reject), 5 (borderline accept), and 6 (weak accept), but they can significantly affect the outcome for stronger reject or accept cases. For instance, with ratings of [3, 5, 6] and confidences of [*, 4, 4], changing the "Reject" confidence from 5 to 1 shifts the probabilities from 26.2% - 31.3% - 52.4% - 54.5% - 60.4%, indicating that lower confidence in this case increases your chances. Conversely, for ratings [3, 5, 7] with confidences [4, 4, 4], the acceptance probability is 31.3%, but it drops to 28.1% when the confidence changes to [4, 4, 5]. Although it might seem counterintuitive, a confidence score of 5 actually decreases your chances. One possible explanation is that many low-quality reviews rated 5 are often discounted by the Area Chairs (ACs). Hope this will be useful, and thanks to u/programmerChilli for the inspiration! I also discussed this topic in a series of tweets. submitted by /u/Lavishness-Mission [link] [comments]

Paper: https://arxiv.org/abs/2404.14408 Github: https://github.com/kjslag/spacebyte Abstract: Tokenization is widely used in large language models because it significantly improves performance. However, tokenization imposes several disadvantages, such as performance biases, increased adversarial vulnerability, decreased character-level modeling performance, and increased modeling complexity. To address these disadvantages without sacrificing performance, we propose SpaceByte, a novel byte-level decoder architecture that closes the performance gap between byte-level and subword autoregressive language modeling. SpaceByte consists of a byte-level Transformer model, but with extra larger transformer blocks inserted in the middle of the layers. We find that performance is significantly improved by applying these larger blocks only after certain bytes, such as space characters, which typically denote word boundaries. Our experiments show that for a fixed training and inference compute budget, SpaceByte outperforms other byte-level architectures and roughly matches the performance of tokenized Transformer architectures.Paper: https://arxiv.org/abs/2404.14408Github: https://github.com/kjslag/spacebyteAbstract:Tokenization is widely used in large language models because it significantly improves performance. However, tokenization imposes several disadvantages, such as performance biases, increased adversarial vulnerability, decreased character-level modeling performance, and increased modeling complexity. To address these disadvantages without sacrificing performance, we propose SpaceByte, a novel byte-level decoder architecture that closes the performance gap between byte-level and subword autoregressive language modeling. SpaceByte consists of a byte-level Transformer model, but with extra larger transformer blocks inserted in the middle of the layers. We find that performance is significantly improved by applying these larger blocks only after certain bytes, such as space characters, which typically denote word boundaries. Our experiments show that for a fixed training and inference compute budget, SpaceByte outperforms other byte-level architectures and roughly matches the performance of tokenized Transformer architectures. https://preview.redd.it/v1xo6g1gzewc1.jpg?width=1507&format=pjpg&auto=webp&s=f9d415307b60639fa67e8a54c8769fa5a6c10f04 https://preview.redd.it/edvqos1gzewc1.jpg?width=1654&format=pjpg&auto=webp&s=f91c8727017e1a1bc7b80bb77a8627ff99182607 https://preview.redd.it/fe6z6i1gzewc1.jpg?width=1181&format=pjpg&auto=webp&s=24d955f30b8ca3eaa7c527f3f40545ed493f789c submitted by /u/Singularian2501 [link] [comments]

I am starting to accumulate a large number of models for a project I am working on, many of these models are old which I am keeping for archival sake, and many are fine tuned from other models. I am wondering if there is an industry standard way of dealing with this, in particular I am looking for the following: Information about parameters used to train the model Datasets used to train the model Other metadata about the model (i.e. what objects an object detection model trained for) Model performance Model lineage (What model was it fine tuned from) Model progression (Is this model a direct upgrade from some other model, such as being fine tuned from the same model but using better hyper parameters) Model source (Not sure about this, but I'm thinking some way of linking the model to the python script which was used to train it. Not crucial but something like this would be nice) Are there any tools of services which could help be achieve some of this functionality? Also, if this is not the sub for this question could I get some pointers in the correct direction. Thanks! ​ submitted by /u/ClearlyCylindrical [link] [comments]

I followed the tutorial provided at https://www.datacamp.com/tutorial/mistral-7b-tutorial and now seek methods to deploy the model for faster inference using Hugging Face and Gradio. Could anyone please share a guide notebook or article for reference? Any help would be appreciated. submitted by /u/Future-Outcome3167 [link] [comments]

Hi, I'm a student and I need some advice about data for food recommendation system project. I proposed to my teammate a dataset that containing the foods' ingredients with around 600 columns, each column is a single kind of ingredient which containing Boolean values (1 if the food contains that ingredient and 0 if the food doesn't contain any of that kind of ingredient). In my perspective, that kind of data design is kinda complex but really easy to process, efficient for data analyst. But my teammate say it weird, idk what is his reason, I asked him but he just said he has never seen this kind of design so he proposed us to find a dataset that contain the ingredients in a single column. Is a dataset design that I proposed really bad and weird as I said or is it just him? Thank you. submitted by /u/Avry_great [link] [comments]