I really don't get this. I can count on no hands the number of times I've had problems simply going "pip install cool-thing-i-found".
Sure, this is just my experience, but I use Python a lot and use a lot of tools written in Python.
I really don't get this. I can count on no hands the number of times I've had problems simply going "pip install cool-thing-i-found".
Sure, this is just my experience, but I use Python a lot and use a lot of tools written in Python.
If you can install it with `pip install program-name` it's usually packaged well enough to just work. But if it's a random github repository with a requirements.txt with no or very few version numbers chances are that just running `pip install -r requirements.txt` will lead you down an hour+ rabbit hole of downgrading both your venv's python version and various packages until you get a combination that is close enough to the author`s venv to actually work
Usually happens to me when I find code for some research paper. Even something that's just three months old can be a real pain to get running
I don't disagree with you, but in my experience even having a requirements.txt file is a luxury when it comes to scientific Python code: A lot of the time I end up having to figure out dependencies based purely on whatever the script is importing
If they can't be bothered to make a requirements.txt file, I'm not seeing how uv will be of much help...
uv basically makes that a default. You don’t need to be bothered. Just uv add your dependencies and they are in your pyproject.toml.
Or use `uv add --script`. Then dependencies gets recorded in the script itself, which is great for single-file scripts. But obviously, that won't help if the author can't be bothered to take those steps
Ah, I get it now! The problem occurs when someone publishes something without version pinning, because package versions can become incompatible over time. I don't think I've ever installed something outside of what's available on PyPy, which is probably why I've never run into this issue.
Still, I would think it's rare that package versions of different packages become incompatible?
Seconded. Python, even with virtualenv stuff, has never been bad. There have been a few things that have been annoying especially when you need system libraries (e.g. libav for PyAV to work, etc.), but you have the same issue with every other ecosystem unless the packages come with all batteries included.
To be fair to the GP comment, this is how I feel about Ruby software. I am not nearly as practiced at installing and upgrading in that ecosystem so if there was a way to install tools in a way that lets me easily and completely blow them away, I would be happier to use them.
I still have nightmares about nokogiri gem installs from back in the day :/
Shudder. I'm guessing it was the always breaking libxml2 compilation step right?
This mentality is exactly what many people do wrong in Python. I mean, for a one-off, yes you can have setup instructions like that. But if you want things to work for other people, on other machines, you better include a lock file with checksums. And `pip install whatever` simply does not cut it there.
Except I'm saying my experience is the opposite of the problem you purport. I (as the consumer) have always done "pip install whatever", and have never run into issues.
One of the commentors above explained what the problem really is (basically devs doing "pip install whatever" for their dependencies, instead of managing them properly). That's more a problem of bad development practices though, no?
Nah. That's a dumb UI. If you do cargo add whatever, it does something completely different from cargo install whatever, and there's no way to inadvertently use the wrong one. If pip install whatever leaves your project in a state that may be unusable for other people, but usable for you, that's just design that invites confusion and works-on-my-machine syndrome.
Recently (like for several years), with most packages providing wheels for most platforms, it tends to be less of a problem of things actually working, except for dependencies where the platform specifiers used by Python are insufficient to select the right build of the dependency, like PyTorch.
I know, this is just how it is I guess . Those of us mystified what the big problem is with virtualenv and pip and why we all have to use a tool distributed by a for profit company and it's not even written in python will just have to start a little club or something
I guess this is mostly about data science code and maybe people who publish software in those communities are just doing very poor packaging, so this idea of a "lock file" that freezes absolutely everything with zero chance for any kind of variation is useful. Certainly the worst packaged code I've ever seen with very brittle links to certain python versions and all that is typically some ML sort of thing, so yeah.
This is all anathema to those of us who know how to package and publish software.
Recently I've been playing with Chatterbox and the setup is a nightmare. It specifically wants Python 3.11. You have 3.12? TS. Try to do pip install and you'll get an error about pkg-config calling a function that no longer exists, or something like that.
God, I hate Python. Why is it so hard to not break code?
I experienced that recently - just curious, since you're digging into voice synth, what are open-source voice synth (specifically text-to-speech) which have been working for you. Recently, I have tried PiperTTS (I found the voices very flat, and accented), Coqui (in the past - it wasn't great, and doesn't seem to be supported). I spent a ton of time trying to get Chatterbox to work (on Debian Linux 13) - and ultimately couldn't get the right mix of Python versions, libraries etc. At this moment, I'm using AWS Polly and ElevenLabs (and occasionally MacOS `say`), but would love to have an open-source TTS which feels quality, and I can psychologically invest in. Thanks for any perspective you can share.
>I spent a ton of time trying to get Chatterbox to work (on Debian Linux 13)
Exactly my case. I had to move back to Debian from Ubuntu, where I had installed Chatterbox without much difficulty, and it was hell. You pretty much need Anaconda. With it, it's a cinch.
>what are open-source voice synth which have been working for you.
I tried a few, although rather superficially. Keeping in mind that my 3090 is on my main (Windows) machine, I was constrained to what I could get running on it without too much hassle. Considering that:
* I tried Parler for a bit, although I became disillusioned when I learned all models have an output length limit, rather than doing something internally to split the input into chunks. What little I tried with it sounded pretty good if it stayed within the 30-second window, otherwise it became increasingly (and interestingly) garbled.
* Higgs was good. I gave it one of Senator Armstrong's lines and made it generate the "mother of all omelettes" one, and it was believable-ish; not as emphatic but pretty good. But it was rather too big and slow and required too much faffing around with the generation settings.
* Chatterbox is what I finally settled with for my application, which is making audiobooks for myself to listen to during my walks and bike rides. It fits in the 3070 I have on the Linux machine and it runs pretty quick, at ~2.7 seconds of audio per second.
These are my notes after many hours of listening to Chatterbox:
* The breathing and pauses sound quite natural, and generally speaking, even with all the flaws I'm about to list, it's pleasing to listen to, provided you have a good sample speaker.
* It you go over the 40-second limit, it handles it somewhat more graciously than Parler (IMO). Instead of generating garbage it just cuts off abruptly. In my experience splitting text at 300-350 characters works fairly well, and keeping paragraphs intact where possible generates best results.
* If the input isn't perfectly punctuated it will guess at the sentence structure to read it with the correct cadence and intonation, but some things can still trip it up. I have one particular text where the writer used commas in many places where a period should have gone, and it just cannot figure out the sentence structure like that.
* The model usually tries to guess emotion from the text content, but it mostly gets it wrong.
* It correctly reads quoted dialogue in the middle of narration, by speaking slightly louder. If the text indicates a woman is speaking the model tries to affect a high pitch, with varying degrees of appropriateness in the given context. Honestly, it'd be better if it kept a consistent pitch. And, perplexingly, no matter how much the surrounding text talks about music, it will read "bass" as "bass", instead of "base".
* Quite often the model inserts weird noises at the beginning and end of a clip which will throw you off until you learn to ignore them. It's worse for short fragments, like chapter titles and the like. Very rarely it inserts what are basically cut-off screams, like imagine a professional voice actor is doing a recording and just before he hit stop someone was murdered inside the booth.
* It basically cannot handle numbers more than two digits long. Even simple stuff like "3:00 AM" it will read as complete nonsense like "threenhundred am".
* It also has problems with words in all caps. It's a tossup if it's going to spell it out, yell it, or something in between. In my particular case, I tried all sorts of things to get it to say "A-unit" (as in a unit with the 'A' designation) properly, but sometimes it still manages to fuck it up and go "ah, ah, ah, ah, ah, ah unit".
* Sometimes it will try to guess the accent it should use based on the grammar. For example, I used a sample from a Lovecraft audiobook, with a British speaker, and the output will sometimes turn Scottish out of nowhere, quite jarringly, if the input uses "ya" for "you" and such.
Thank you - this is helpful. I didn't realize how important I was going to value consistency over quality voice, but then when you've got to go back and listen to everything for quality control ... I guess that is the drawback of this phase of "generative" voice synth.
Yeah, in that way it's a lot like image generation. Maybe a single output is good in isolation, but if you want to generate a series maintaining some kind of consistent style, it's very much like a lottery. The models don't have dials to control emphasis, cadence, emotiveness, accent, etc., so they guess from the content. For example, imagine a serious scene that calls for a somber tone, but then one of the characters makes a dark or ironic joke. A human would maintain the same reading voice, but these models would instead switch to a much more chipper register for that one line.
> pip install cool-thing-i-found
This is the entire problem. You gonna put that in a lock file or just tell your colleagues to run the same command?
I meant I'm running that command as the consumer, and have never had problems. When I make my own packages, I ensure that anyone doing the same thing for my package won't have issues by using version pinning.
Having packages in a package manager is the problem?
like democracy, it's the worst programming language except vs everything else...
This comment is pithy, but I reject the sentiment.
In 2025, the overall developer experience is much better in (1) Rust compared to C++, and (2) Java/DotNet(C#) compared to Python.
I'm talking about type systems/memory safety, IDEs (incl. debuggers & compilers), package management, etc.
Recently, I came back to Python from Java (for a job). Once you take the drug of a virtual machine (Java/DotNet), it is hard to go back to native binaries.
Last, for anyone unfamiliar with this quote, the original is from Winston Churchill: