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SYSTRAN
faster-whisper
Python2023–2026
SYSTRAN / faster-whisper

faster-whisper

Faster Whisper transcription with CTranslate2

AI & Machine LearningLearning & Reference
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16Distinct people
20Mentions
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REPOSITORY AT A GLANCE

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What it is

Faster Whisper transcription with CTranslate2

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

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Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year20 dated mentions

Observed mentions: 2023-03-30 19:01:09 UTC to 2026-01-09 20:22:40 UTC.

0510152023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 14 mentions; 1 submissions, 13 comments20232024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments20242025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
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PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC14113
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC505
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

20 sources
satvikpendem·Jan 9, 2026
Batching is essentially running multiple instances at once, ie bundling 8 segments and running them simultaneously on the processing unit, but which obviously takes more RAM to do. Notice, however, that if you drop the precision to int8 from fp16, you use basically the same amount of RAM as whisper.cpp yet it completes in a fraction of the time using batching [0]. Yes, if you check their community integrations section on faster-whisper [1], you can see a lot of different CLIs, GUIs, and libraries. I recommend WhisperX [2], it's the most complete CLI so far and has features like diarization which whisper.cpp does not have in a production-ready capacity. [0] https://github.com/SYSTRAN/faster-whisper#benchmark [1] https://github.com/SYSTRAN/faster-whisper#community-integrat... [2] https://github.com/m-bain/whisperX
In Dell admits consumers don't care about AI PCs
kkielhofner·Apr 17, 2024
> First of all, they are not violating any license or terms in any form. IANAL but from what I understand likely debatable at least. You'll notice I said "sleazy" and didn't touch on license, potential legal issues, etc. I'm pointing out that other projects that are substantially based/dependent on other pieces of software to do the "heavy lifting" nearly always acknowledge it. An example being faster-whisper which is a good corollary and actually has "with CTranslate2" right in the heading[0] with direct links to whisper.cpp and CTranslate2 immediately following. Ollama is the diametric opposite of this - unless you go spelunking through commits, etc you'd have no idea that Ollama doesn't do much in terms of the underlying LLM. Take a look at llama.cpp to see just how much "Ollama functionality" it provides. Then look at /r/LocalLLaMA, H
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N, etc to see just how many Ollama users (most) have no idea that llama.cpp even exists. I don't know how this could be anything other than an attempt to mislead people into thinking Ollama is uniquely and directly implementing all of the magic. It's pretty glaring and has been pointed out repeatedly. It's not some casual oversight. > They add value and enable thousands of people to use local LLMs, that would not be able to do that so easy otherwise. The very first thing I said going so far as to mention commits, model zoo, etc while specifically acknowledging the level of effort and added value. > Maybe llama.cpp should mention that Ollama takes care of easy workable access to their functionality… Are you actually suggesting that enabling software should mention, track, or even be aware of the likely countless projects that are built on it? [0] - https://github.com/SYSTRAN/faster-whisper
In Ollama 0.1.32: WizardLM 2, Mixtral 8x22B, macOS CPU/GPU model split
thangalin·Apr 4, 2024
Barring major technical glitches, installation and configuration of a reliable locally hosted Zigbee-based solution is on the order of two hours. https://www.youtube.com/watch?v=GJEwrSSFe9s Here's what I used: Raspberry Pi Model 4 B Raspberry Pi CPU Raspberry Pi CPU heatsink (w/self-adhesive) Raspberry Pi manual Raspberry Pi A/C adapter Raspberry Pi case ConBee II Zigbee USB gateway USB ADATA Micro SD card reader USB cable Micro SD card (for operating system and Home Assistant) Ethernet cable (probably not needed because the Pi 4 has onboard WiFi) Thermostats: https://www.sinopetech.com/en/products/thermostat/ I haven't tried running a local text-to-speech engine backed by an LLM to control Home Assistant (HA). Maybe someone is working on this already? TTS: https://github.com/SYSTRAN/faster-whi
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sper LLM: https://github.com/Mozilla-Ocho/llamafile/releases LLM: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-D... HA: https://www.home-assistant.io/ It would take some tweaking to get the voice commands working correctly.
In Apple explores home robotics as potential 'next big thing'
albertzeyer·Jan 29, 2024
Yes I have looked there. I did not find any WER numbers and latency numbers (ideally both together in a graph). I also did not find the model being described. *Edit* Ah, when you write faster_whisper, you actually mean https://github.com/SYSTRAN/faster-whisper? And for streaming, you use https://github.com/ufal/whisper_streaming? So, the model as described in http://www.afnlp.org/conferences/ijcnlp2023/proceedings/main...? There, for example in Table 1, you have exactly that, latency vs WER. But the latency is huge (2.85 sec the lowest). Usually, streaming speech recognition systems have latency well beyond 1 sec. But anyway, is this actually what you use in WhisperLive / WhisperFusion? I think it would be good to give a bit more details on that.
In Show HN: WhisperFusion – Low-latency conversations with an AI chatbot
rjwilmsi·Nov 1, 2023
That's the implication. If the distil models are same format as original openai models then the Distil models can be converted for faster-whisper use as per the conversion instructions on https://github.com/guillaumekln/faster-whisper/ So then we'll see whether we get the 6x model speedup on top of the stated 4x faster-whisper code speedup, at same/nearly same accuracy. I would generally start with the assumption that if something is significantly faster the accuracy has to suffer a bit, but increasing model size and/or settings such as beam size to compensate should allow same accuracy and higher performance (just not all of the stated performance gain).
In Distil-Whisper: distilled version of Whisper that is 6 times faster, 49% smaller
kkielhofner·Oct 7, 2023
> While I agree that it's much more effort to get things working on AMD cards than it is with Nvidia, I was a bit surprised to see this comment mention Whisper being an example of "5-10x as performant". It easily is. See the benchmarks[0] from faster-whisper which uses Ctranslate2. That's 5x faster than OpenAI reference code on a Tesla V100. Needless to say something like a 4080 easily multiplies that. > https://www.tomshardware.com/news/whisper-audio-transcriptio... is a good example of Nvidia having no excuses being double the price when it comes to Whisper inference, with 7900XTX being directly comparable with 4080, albeit with higher power draw. To be fair it's not using ROCm but Direct3D 11, but for performance/price arguments sake that detail is not relevant. With all due respect to the author of the article this is "my first entry
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into ML" territory. They talk about a 5-10 second delay, my project can do sub 1 second times[1] even with ancient GPUs thanks to Ctranslate2. I don't have an RTX 4080 but if you look at the performance stats for the closest thing (RTX 4090) the performance numbers are positively bonkers - completely untouchable for anything ROCm based. Same goes for the other projects I linked, lmdeploy does over 100 tokens/s in a single session with LLama2 13b on my RTX 4090 and almost 600 tokens/s across eight simultaneous sessions. > EDIT: Also using CTranslate2 as an example is not great as it's actually a good showcase why ROCm is so far behind CUDA: It's all about adapting the tech and getting the popular libraries to support it. Things usually get implemented in CUDA first and then would need additional effort to add ROCm support that projects with low amount of (possibly hobbyist) maintainers might not have available. There's even an issue in CTranslate2 where they clearly state no-one is working to get ROCm supported in the library. ( https://github.com/OpenNMT/CTranslate2/issues/1072#issuecomm... ) I don't understand what you're saying here. It (along with the other projects I linked here[2]) are fantastic examples of just how far behind the ROCm ecosystem is. ROCm isn't even on the radar for most of them as your linked issue highlights. Things always get implemented in CUDA first (ten years in this space and I've never seen ROCm first) and ROCm users either wait months (minimum) for sub-par performance or never get it at all. [0] - https://github.com/guillaumekln/faster-whisper#benchmark [1] - https://heywillow.io/components/willow-inference-server/#ben... [2] - https://news.ycombinator.com/item?id=37793635#37798902
In AMD may get across the CUDA moat
satvikpendem·Aug 23, 2023
While whisper.cpp is faster than faster-whisper on macOS due to Apple's Neural Engine [0], if you have a GPU on Windows or Linux, faster-whisper [1] is a lot faster than OpenAI's reference Whisper implementation as well as whisper.cpp, with the CLI being wscribe or whisper-ctranslate2 as faster-whisper is only a Python library. It's pretty good. [0] https://github.com/guillaumekln/faster-whisper/discussions/3... [1] https://github.com/guillaumekln/faster-whisper
In MacWhisper: Transcribe audio files on your Mac
noman-land·Aug 17, 2023
https://transcript.fish I have been working on this podcast transcription project for a couple months and it's been super rewarding. I listen to a podcast called No Such Thing As A Fish[0], where some researchers talk about their favorite facts they learned that week. Then they riff on it and are generally smart and funny. I listened to the series so many times that I decided I wanted to listen to the show on shuffle, not at the episode level, but at the fact level. Since I have been playing around with whisper.cpp in python this seemed like a perfect way to combine some technologies I've been wanting to play with. I ran whisper[1] over the entire podcast and transcribed all the episodes. I had to do this multiple times because I kept messing up. It eventually took like 7 straight days of my M1 processing to get through ~490 episodes usin
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g the medium.en model. 4 million words, and an 800Mb SQLite database later, I got the transcriptions done and have put up a nice site for searching through the data. Now I just need to figure out the rest. Breaking it up into facts. Getting the audio working. Highlighting and linking to words, phrases, etc. Some cool info about the process so far: 1. The SQLite database is chunked up and stored as static files, and the frontend queries the static files directly using HTTP range requests, so it only downloads a couple hundred kbs when querying. 2. I've been proper using ChatGPT 3.5 free version to help me write python and SQL. It's been pretty game changing as I feel basically no pain from not knowing what I'm doing. The code is here: https://github.com/noman-land/transcript.fish Please help if you know how to get whisper speaker diarization working!! I would really appreciate the help. Also any tips on ways to index[2] or search[3] my database that will be super efficient would be helpful. Indexing matters a lot when querying the database in ranges like this... I have learned... [0] https://www.nosuchthingasafish.com/ [1] https://github.com/guillaumekln/faster-whisper [2] https://github.com/noman-land/transcript.fish/blob/maste/db/... [3] https://github.com/noman-land/transcript.fish/blob/master/sr...
In Ask HN: Tell us about your project that's not done yet but you want feedback on

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