I'm using https://spokenly.app/ in local mode, which is free. Very happy with it. It supports a bunch of models, including whisper and parakeet. Right now I'm mostly using parakeet v3 on my desktop, but it tends to do a bit more errors, although it is very fast. I cycle betwen it and Distil-Whisper Large V3.5, which is a bit slower.
On iOS I'm also using the same app, with the Apple Speech model, which I found out to be better performing for me than the parakeet/whisper. One drawback for the apple model is that you need iOS/Mac 26+ - and I haven't bothered to update to Tahoe on my mac.
Both of the models work instantly for me (Mac M1, iphone 17 Pro).
Edit: Aaaand I just saw that you're looking for speech-to-speech. Oops, still sleeping.
Built my own Spotify recommendation egnine after getting tired of Spotify’s repetitive recommendations.
You get to choose the genres you're interested in, and it creates playlists from the music in your library. They get updated every day - think a better, curated by you version of the Daily Mixes. You can add some advanced filters as well, if you really want to customise what music you'll get.
It works best if you follow a good amount of artists. Optionally you can get recommendations from artists that belong to playlists you follow or you've created - if you don't follow much or any artists, then you should enable that in order for the service to be useful.
Working on a little project to make Spotify recommendations better.
You get to choose the genres you're interested in, and it creates playlists from the music in your library. They get updated every day - think a better version of the Daily Mixes. You can add some advanced filters as well, if you really want to customise what music you'll get.
Have a look at Victoria Metrics - have run it at a relatively high scale with much more success than any other metric stores. It's one of those things that just work. It's extremely easy to run at in a single-instance mode and handles much more than you would expect. Scaling it is a breeze too.
(I'm not affiliated, but a very happy user across multiple orgs and personal projects)
The project where I looked at Mimir was a 500+ million timeseries project, with the desire to support scaling to the ten-figure level of timeseries (working for a BigCo supporting hundreds of product development teams).
All of these systems that store metrics in object storage - you have to remember that object storage is not file storage. Generally speaking (stuff like S3 One Zone being a relatively recent exception) you cannot append to object files. Metrics queries are resolved by querying historical metrics in object storage plus a stateful service hosting the latest 2 hours of data before it can be compressed and uploaded to object storage as a single block. At a certain scale, you simply need to choose which is more important - being able to answer queries or being able to insert more timeseries. And if you don't prioritize insertion, it just results in the backlog getting bigger and bigger, which especially in the eventual case (Murphy's Law guarantees it) of a sudden flood of metrics to ingest will cause several hour ingestion delays during which you are blind. And if you do prioritize insertion, well the component simply won't respond to queries, which makes you blind anyway. Lose-lose.
Mimir built in Kafka because it's quite literally necessary at scale. You need the stateful query component (with the latest 2 hours) to prioritize queries, then pull from the Kafka topic on a lower priority thread, when there's spare time to do so. Kafka soaks up the sudden ingestion floods so that they don't result in the stateful query component getting DoS'd.
I took a quick look at VictoriaMetrics - no Kafka or Kafka-like component to soak up ingestion floods? DOA.
Again, most companies are not BigCos. If you're a startup/scaleup with one VP supervising several development teams, you likely don't need that scale, probably VictoriaMetrics is just fine, you're not the first person I've heard recommend it. But I would say 80% of companies are small enough to be served with a simple Prometheus or Thanos Query over HA Prometheus setup, 17% of companies will get a lot of value out of Victoria Metrics, the last 3% really need Mimir's scalability.
I'm not sure where you saw that Victoria Metrics uses object storage. It doesn't - it uses block storage and it runs completely fine on HDD, you don't even need SSD/NVMe.
There are multiple ways to deal with ingestion floods. Kafka/distributed log is one of them, but it's not the only one. In cluster mode VM is a distributed set of services that scale out independently and buffer at different levels.
Resource usage for ingestion/storage is much lower than other solutions, and you get more for your money. At $PREVIOUS_JOB, we migrated from a very expensive Thanos to a VM cluster backed by HDDs, and saved a lot. Performance was much better as well. It was a while ago, and I don't remember the exact number of time series, but it was meant to handle 10k+ VMs (and a lot of other resources, multiple k8s clusters) and did it with ease (also for everybody involved).
I don't think you have really looked into VM - you might get pleasantly surprised by what you find :) Check out this benchmark with Mimir[1] (it is a few years old though), and some case studies [2]. Some of the companies in the case studies run at significantly higher volume than your requirements.
There were other problems with VictoriaMetrics - a failed migration attempt by previous engineers made it politically difficult to raise as a possibility, lack of a promise of full PromQL compatibility (too many PromQL dashboards built by too many teams), seeing features locked behind the Enterprise version (Mimir Enterprise had features added on top, not features locked away).
> HDD
You're right, I'm misremembering here, that particular complaint about a lack of Kafka was a Thanos issue, not VM.
That said, HDD is a hard sell to management. Seen as "not cloud native". People with old trauma from 100% full disks not expanded in time. Organizational perception that object storage does not need to be backed up (because redundancy is built into the object storage system) but HDD does (and automated backups are a VM Enterprise feature, and even more important if storing long-term metrics in VM).
> In cluster mode VM is a distributed set of services that scale out independently and buffer at different levels
So are Thanos and Mimir, which suffer from ingest floods causing DoS, at least until Kafka was added. vminsert is billed as stateless, same as Thanos Receiver, same as Mimir Distributor. Not convinced.
> lack of a promise of full PromQL compatibility (too many PromQL dashboards built by too many teams)
This is a classical FUD. VictoriaMetrics is used as a drop-in replacement for Prometheus, Thanos and Mimir. It works perfectly across all the existing dashboards in Grafana, and across all the existing recording and alerting rules. I'm unaware of VictoriaMetrics users who hit PromQL compatibility issues during the migration from Prometheus, Thanos and Mimir to VictoriaMetrics. There are a few deliberate incompatibilities aimed towards improving user experience. See https://medium.com/@romanhavronenko/victoriametrics-promql-c...
> seeing features locked behind the Enterprise version (Mimir Enterprise had features added on top, not features locked away)
All the VictoriaMetrics features, which are useful across the majority of practical use cases, are included in open-source version. The main Enterprise feature - high-quality technical support by VictoriaMetrics engineers. Other Enterprise features are needed only for large enterprise companies. See https://docs.victoriametrics.com/victoriametrics/enterprise/
Working on a little project to make Spotify recommendations better.
You get to choose the genres you're interested in, and it creates playlists from the music in your library. They get updated every day - think a better version of the Daily Mixes. You can add some advanced filters as well, if you really want to customise what music you'll get.
Does this deal with, what I call the "Armin Problem?" I typically listen to EDM and there is an extremely popular DJ named Armin van Buuren, and he has other aliases (which exacerbates the issue). His sets end up directly on music platforms, and he pulls in a ton of EDM sub-genres (which makes him a great DJ!). Any recommendation algorithm that visits one of his aliases is doomed to be connected to every other sub-genre, so I might be listening to progressive trance, and be in the mood for that, and end up on deep house (as an extreme example). Within EDM, genres can be as different as blues is to metal.
Not directly, no. I still rely on the data that Spotify gives me that relates to artists' information; and it's not great a lot of the time. E.g. there are obvious cases where artists belong to genres that they should not belong to. I do have some ideas for improvement, but they are still WIP.
What it allows you to do, though, is create your playlists with extended filters. E.g. you can select genres, and at the same time exclude genres - that helps with the "cross-contamination". You also get a view of all the artists that match your selections and you can add exclusions for them as well. It is a bit of manual work, but it works pretty good for me personally.
Working on and off on a little project to make Spotify recommendations better.
You get to choose the genres you're interested in, and it creates playlists from the music in your library. They get updated every day - think a better version of the Daily Mixes. You can add some advanced filters as well, if you really want to customise what music you'll get.
I would argue sensor size is what's most impotant to look for in a camera.
Have a look at this thread [1] I have bookmarked. I found it quite informative. I already got some cheap cameras and set them up, but I wish I would have found it earlier. The ones I got are 4MP with 1/3" sensor and perform absolutely terribly in night setting.
I got tired of Spotify recommending me the same songs, from the same artists, over and over again.
So I built Riff Radar - it creates playlists from your followed artists' complete discography, and allows you to tailor them in multiple ways. Those playlists are my top listened to. I know, because you can also see your listening statistics (at the mercy of Spotify's API).
The playlists also get updated daily. Think of it as a better version of the daily mixes Spotify creates.
When I tried to save a newly created playlist I got a 500 XHR with message: "failed to fetch user playlists: Error 1064 (42000): You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'AND disabled = 0' at line 4", about 2mins ago, if that help finding this is logs.
Oops that's embarassing - it should be good now :) Thanks! That was a classic case of fixing one thing and breaking another, which my tests didn't catch :(
I got tired of Spotify recommending me the same songs, from the same artists, over and over again.
So I built Riff Radar - it creates playlists from your followed artists' complete discography, and allows you to tailor them in multiple ways. Those playlists are my top listened to. I know, because you can also see your listening statistics (at the mercy of Spotify's API).
The playlists also get updated daily. Think of it as a better version of the daily mixes Spotify creates.
Right now it's not possible, but I'll put it on my list of features to add. Unfortunately though, the play history Spotify provides is very innaccurate and incomplete - so suggestions only based on that would be quite limited :( It's the same issue with the statistics, they are best-effort based.
Having said that, there is another feature you could use: if you have or follow any playlists, you can include artists from them. Make sure to have the `Index Playlist Artists` option (it will get enabled automatically if you follow <100 artists) and tick `Include playlist artists` setting when creating your playlists.
Another question: instead of getting my follows from my Spotify, could it let me type the artists I'm interested in?
I really want to use it (I'm also not happy with Spotify's recommendations), but my follow list is mainly for podcasts. Maybe just letting the user enter the artist names (instead of getting them from Spotify follow lists) would be easier to support?
Not currently, no - I might potentially include it at some point, but I feel like the Spotify UI is better tailored for that, so I'm not sure.
The current functionality revolves around genres, and artists are derived from those selections. There are some additional filters where you can filter down based on album/track release dates, exclude genres or specific artists - but it all comes from your library, not from the whole Spotify pool. It was a deliberate decision, as my gripe was the fact that I have a massive library, and was not listening to its entirety.
You can achieve a somewhat similar functionality by creating a playlist, and adding a single song from any artists you want in that playlist.
As to the following list - the podcast/artists libraries are not the same and you can access them at different places in Spotify. If you click on your profile and go to following you'll only see artists/friends. Moreover they are behind separate APIs/access scopes and I only scrape the aritsts you follow.
If you want give it a go, you might find it useful. You can delete your account at any time - I don't keep any of your data once you delete your account.
On iOS I'm also using the same app, with the Apple Speech model, which I found out to be better performing for me than the parakeet/whisper. One drawback for the apple model is that you need iOS/Mac 26+ - and I haven't bothered to update to Tahoe on my mac.
Both of the models work instantly for me (Mac M1, iphone 17 Pro).
Edit: Aaaand I just saw that you're looking for speech-to-speech. Oops, still sleeping.