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openai
whisper
Python2022–2026
openai / whisper

whisper

Robust Speech Recognition via Large-Scale Weak Supervision

AI & Machine Learning
Explore on GitHubRead Hacker News discussions
114Distinct people
127Mentions
1Explicit recommenders
112.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Robust Speech Recognition via Large-Scale Weak Supervision

GitHub

Where it is used

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

What people are saying

Use Whisper! It's a fairly small AI speech-to-text model that's great for getting your feet wet with AI libraries. It's extremely precise and easy to get working, I recommend…

smoldesu used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

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

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 5:05:26 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 year127 dated mentions

Observed mentions: 2022-09-21 16:35:56 UTC to 2026-10-13 12:39:43 UTC.

010203040502022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 36 mentions; 3 submissions, 33 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 48 mentions; 4 submissions, 44 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 19 mentions; 6 submissions, 13 comments20242025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 20 mentions; 3 submissions, 17 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 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
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC36333
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC48444
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC19613
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC20317
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC413

What Hacker News says

36 sources
fouric·Dec 30, 2022
I avoid needing to rely on my human memory (recall) when possible - if my phone is around, I make voice notes on it that are automatically synced to desktop which are then transcribed using Whisper[1]. When I do need to recall, I just use the strategy of "hold the items in my head as intently as possible, for as little time as possible until I'm able to get to my phone or some paper". On the rare occasions when neither my phone or paper are immediately available, I try to visualize the ideas, projecting them onto a mental canvas, and try to use connections between them or mnemonics to remember them as best as I can. I avoid the problem of "I sit down in front of my computer in order to write down or do a thing" by building the discipline necessary to prevent myself from getting distracted in that manner. [1] https://github.com/openai/whis
Read the rest
per
In Ask HN: How do you recall ideas/eureka you get during walks, showers, etc.?
navanchauhan·Dec 13, 2022
With how hard something like this is, we should be looking at the positive side. Picovoice here solves both the issues of hotword/wake-word detection and intent extraction. This looks like something you could build on top of ARM's [keyword spotting program](https://github.com/ARM-software/ML-KWS-for-MCU) and the wake word services listed in [Rhasspy's docs](https://rhasspy.readthedocs.io/en/latest/wake-word/#raven) But, to implement something like this from scratch would take a good while. Also, here are some Automatic Speech Recognition toolkits (which won't run offline on a microcontroller) out there. These are useful to pipe the data into a program that deals with intents (something like [RASA](https://rasa.com) (Require Internet) * [Deepgram](https://deepgram.com) - I believe they build upon OpenAI's Whisper model and have their own c
Read the rest
ustom models too * Google Cloud / Microsoft Azure / AWS / IBM Watson (Can be run Offline) * [OpenAI's Whisper](https://github.com/openai/whisper) * [nVidia's NEMO](https://github.com/NVIDIA/NeMo) * [PaddleSpeech](https://github.com/PaddlePaddle/PaddleSpeech) When you see how complicated the space is and how many ways you can actually shoot yourself in the foot. This post starts to look a wee bit better.
In Offline Voice Assistant on a Microcontroller with 192KB RAM
kamilafsar·Dec 7, 2022
I wonder what the correlation between consistency of written/spoken language is for the breakdown per language here: https://github.com/openai/whisper/blob/main/language-breakdo... For instance, I know Turkish is very consistent: it was refactored in 1928 with the birth of Turkey. Turkish is quite high in the rankings. I don't think because there's loads of data available, but because of its consistency. Contrary, English has loads of data, which should compensate for it inconsistency.
In OpenAI quietly launched Whisper V2 in a GitHub commit
aadnk·Dec 7, 2022
It seems like Large-V2 is a huge improvement when transcribing Japanese. I tested it on "Macross Frontier - the Movie", and it no longer breaks after 8 minutes as before: Large-V1 (transcribed at 2022-10-02): * SRT: https://pastes.io/yrggofqhof * Transcript: https://pastes.io/rtp9buhsm0 Large-V2 (latest version at 2022-12-07): * SRT - https://pastes.io/uiqblpw1qk * Transcript - https://pastes.io/rheqgnftzl There's still some timing issues after a period of silence, but using a VAD as a workaround (like I do in my WebUI) may no longer be strictly necessary: * https://github.com/openai/whisper/discussions/397
In OpenAI quietly launched Whisper V2 in a GitHub commit
ggerganov·Dec 6, 2022Submission

Show HN: Port of OpenAI's Whisper model in C/C++

Hi HN, OpenAI recently released a model for automatic speech recognition called Whisper [0]. I decided to reimplement the inference of the model from scratch using C/C++. To achieve this I implemented a minimalistic tensor library in C and ported the high-level architecture of the model in C++. The entire code is less than 8000 lines of code and is contained in just 2 source files without any third-party dependencies. The Github project is here: https://github.com/ggerganov/whisper.cpp With this implementation I can very easily build and run the model - “make base.en”. It also allows me to run it on a wide range of devices. For example, I have provided examples of running the model on an iPhone, Raspberry Pi 4 and even in a web page via WebAssembly! The implementation runs fully on the CPU and utilizes FP16, AVX intrinsics on x86 architec
Read the rest
tures and NEON + Accelerate framework on Apple Silicon. The latter is especially efficient and I observe that the inference is about 2-3 times faster compared to the current PyTorch implementation provided by OpenAI when running it on my MacBook M1 Pro. The WASM port utilizes SIMD 128-bit intrinsics - a feature supported in some modern web browsers [1]. I am very happy with the performance that I observe on Apple Silicon devices. I didn’t expect that the Accelerate framework [2] (i.e. CBLAS) offers such a dramatic performance boost for matrix multiplications so I was very pleasantly surprised! To enable the framework in your C/C++ projects, all you have to do is add `-framework Accelerate` to your clang command-line flags. This entire exercise of implementing the Whisper model was very interesting to me and helped me understand a lot about how the transformer architecture works. I also got a lot of positive feedback from people finding and using my project. We brainstormed on a lot of interesting tools that can potentially be created with this library (such as speech-to-text plugin for Vim, RPi4 voice assistant, WASM chat bot, etc). If interested, checkout the “Examples” section and the “Show and tell” discussions for some ideas! Would love to know what you think about this project and about your experience with using the Accelerate framework in any of your projects. Cheers! [0] https://github.com/openai/whisper [1] https://chromestatus.com/feature/6533147810332672 [2] https://developer.apple.com/documentation/accelerate
399 points · Open discussion

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