14 ms·

Whisper – open source speech recognition by OpenAI

• zeagle 4y ago
  It would be exceptional to get a healthy competitor to microsoft/nuance's dragon monopoly on voice recognition in healthcare. At a couple thousand bucks a license and the more recent SaaS subscription trend there is a lot of money to be made in that space.
• aidenn0 4y ago
  I just threw a random rock MP3 at it, and a first readthrough shows no transcription errors; this is quite good. Now I just want OCR that's even 50% as good as this...
  • aidenn0 4y ago
    Ran a few other songs through it and found one obvious mistranscription: "He's the bedroom cosmic rocker" (should be "He's the veteran cosmic rocker" in Veteran Cosmic Rocker by The Moody Blues) I also noticed that it's a little on the conservative side for detecting speech; all songs were missing at least part of one line.
    • aidenn0 4y ago
      Ran it on Juicy by The Notorious B.I.G and results were considerably worse than my mix of prog-rock and british invasion music I had tried before, though at least some of that is due to the number of proper-nouns in that song. It took about 1000 CPU-minutes for this 5 minute song on my Ryzen 2700 with 12 OpenMP threads (about 100 minutes wall-clock).
      • antegamisou 4y ago
        Here's the output of whisper never-gonna-give-you-up.mp3 --language English --model small [00:00.000 --> 00:27.000] We're no strangers to love You know the rules and so do I [00:27.000 --> 00:35.000] I feel commitments while I'm thinking of You wouldn't get this from any other guy [00:35.000 --> 00:43.000] I just wanna tell you how I'm feeling Gotta make you understand [00:43.000 --> 00:47.000] Never gonna give you up Never gonna let you down [00:47.000 --> 00:53.000] Never gonna run around and desert you Never gonna make you cry [01:00.000 --> 01:09.000] We've known each other for so long Your heart's been aching but you're too shy to say [01:09.000 --> 01:17.000] Inside we both know what's been going on We know the game and we're gonna play it It was running for quite a long time (20 minutes) on my admittedly low-budget specs. Note that I did not omit 00:53.000 -> 01:00.000. Shouldn't there be some type of unintelligible warning since it wasn't able to transcribe that part?
        • aidenn0 4y ago
          Model small is about as good at recognizing lyrics as an untrained Newton was at recognizing handwriting. Here's a comparison of Basket Case by Greenday: Small: [00:00.000 --> 00:05.000] Do you have the time to listen to me whine [00:05.000 --> 00:10.000] About nothing and everything I'll have once? [00:11.000 --> 00:16.000] I am one of those melodramatic fools [00:16.000 --> 00:20.000] Neurotic to the bone, no doubt about it [00:23.000 --> 00:27.000] Sometimes I give myself the creeps [00:27.000 --> 00:32.000] Sometimes my mind plays tricks on me [00:32.000 --> 00:38.000] It all keeps headed up, I think I'm pregnant [00:38.000 --> 00:43.000] And I'm just paranoid, I'm just stuck [00:47.000 --> 00:52.000] I went to a shrink to have a life like my dreams [00:52.000 --> 00:57.000] She says it's like a sex that's bringing me down [00:57.000 --> 01:03.000] I went to a whore, he said my life's a bore [01:03.000 --> 01:08.000] Choked with my widest buzz that's bringing her down [01:10.000 --> 01:14.000] Sometimes I give myself the creeps [01:15.000 --> 01:19.000] Sometimes my mind plays tricks on me [01:19.000 --> 01:25.000] It all keeps headed up, I think I'm pregnant [01:25.000 --> 01:30.000] And I'm just paranoid, I'm just stuck [01:30.000 --> 01:48.000] Grasping to control, it's all I better hold on [02:08.000 --> 02:12.000] Sometimes I give myself the creeps [02:13.000 --> 02:17.000] Sometimes my mind plays tricks on me [02:18.000 --> 02:23.000] It all keeps headed up, I think I'm pregnant [02:23.000 --> 02:30.000] And I'm just paranoid, I'm just stuck [02:53.000 --> 03:13.000] Thanks for watching! Medium: [00:00.000 --> 00:05.000] Do you have the time to listen to me whine [00:05.000 --> 00:10.000] About nothing and everything all at once? [00:11.000 --> 00:16.000] I am one of those melodramatic fools [00:16.000 --> 00:20.000] Neurotic to the bone, no doubt about it [00:23.000 --> 00:27.000] Sometimes I give myself the creeps [00:27.000 --> 00:32.000] Sometimes my mind plays tricks on me [00:33.000 --> 00:36.000] It all keeps adding up [00:36.000 --> 00:39.000] I think I'm cracking up [00:39.000 --> 00:41.000] Am I just paranoid? [00:41.000 --> 00:43.000] Am I just sad? [00:47.000 --> 00:50.000] I went to a shrink [00:50.000 --> 00:53.000] To analyze my dreams [00:53.000 --> 00:58.000] She says it's lack of sex that's bringing me down [00:58.000 --> 01:01.000] I went to a whore [01:01.000 --> 01:04.000] He said my life's a bore [01:04.000 --> 01:09.000] So quit my whining cause it's bringing her down [01:10.000 --> 01:14.000] Sometimes I give myself the creeps [01:16.000 --> 01:20.000] Sometimes my mind plays tricks on me [01:20.000 --> 01:23.000] It all keeps adding up [01:23.000 --> 01:26.000] I think I'm cracking up [01:26.000 --> 01:28.000] Am I just paranoid? [01:28.000 --> 01:30.000] Am I just sad? [01:40.000 --> 01:44.000] Grasping to control [01:44.000 --> 01:50.000] So I better hold on [02:07.000 --> 02:11.000] Sometimes I give myself the creeps [02:11.000 --> 02:16.000] Sometimes my mind plays tricks on me [02:16.000 --> 02:19.000] It all keeps adding up [02:19.000 --> 02:22.000] I think I'm cracking up [02:22.000 --> 02:24.000] Am I just paranoid? [02:24.000 --> 02:52.000] Am I just sad? [02:54.000 --> 02:58.000] Thanks for watching!
    • macrolocal 4y ago
      For what it's worth, even the large model balks on Easy (Aesop Rock), eg. "Fountainheads spittle sniglets quicker than quidditch seekers snatch golden snitches." becomes "Stirred up out mids bittles, snicklets, cricket and quidditch seekers net golden snitches." ¯\_(ツ)_/¯
      • aidenn0 4y ago
        Large was not obviously better than medium when I tried it. My impression was that it tended to fit more to a language model than the sounds heard, which corrected some errors and introduced some others, but I didn't try a lot of songs because large won't run on my GPU.
• smusamashah 4y ago
  How well does it do for technical and domain oriented speech? For example I have audio recordings of a senior explaining some very technical aspects of our software. Will it understand the technical terms in that speech? I guess I will need to download and run on it to see how correct it is.
• deleted 4y ago
  [deleted]
• Bayko 4y ago
  So I guess we can easily use this to generate subtitles?? Which would be nice! Cause ummm some of the movies that I download from the internet arrrrrr! don't have subtitles available
• powera 4y ago
  My first take: it is slow. The "base" model (supposedly 16x faster than the large one) takes more than the audiofile playback time on my machine to do transcriptions.
  • fitznd 4y ago
    I'm seeing even worse. On my M1 Max 2021 macbook pro, I tried transcribing a 30 minute video file and left it on overnight and it was only half way through. I feel like something could be wrong with my setup but I'm only using the defaults.
• runlevel1 4y ago
  I ran it on some fire department radio recordings from scanners on Broadcastify. It did remarkably well. For reference, GCP's Speech-to-Text didn't detect any speech from this clip -- even when using the enhanced phone model.
• LoveMortuus 4y ago
  This could be used to make some really cool RPG games!
• rexreed 4y ago
  I'd love to find a way to test this with longer audio but I don't have GPU resources and not exactly sure how to load that into the Colab. Is anyone planning on hosting or sharing a model that can be used by others to test longer form audio (for podcast transcription)?
• revskill 4y ago
  It's actually better than Google Meet subtitle system.
• liminalsunset 4y ago
  I really wish I had this about half a year ago when I was building a tool to automatically turn online school lectures into searchable, clickable transcripts (kind of like YouTube or EdX transcripts). I was originally using Adobe Premiere Pro's speech to text to do it, and wrote Python to convert its output to the Hyperaudio format on GitHub. With this, I can totally skip all of that step and this is fully open source, too. App idea: Build an app that takes a video and uses Hyperaudio or a similar project to add a clickable and searchable transcript (clicking in transcript seeks video)
  • resoluteteeth 4y ago
    You could already do the speech recognition in a fully open source way with vosk easily, although Whisper may be more accurate
  • james-revisoai 4y ago
    You still interested in this? I'd be keen to chat to you, worked on a searchable transcript provider for educational youtube videos (likewise, unfortunately pre-whisper, so I did a lot of work with sentence completion perplexity and rpunct to try and improve transcript quality from youtube automatic transcriptions). Can be contacted at revision.ai and demo what we were able to do till now, would be great to hear your thoughts.
• dubeye 4y ago
  I know a manual transcription company, which is still seeing modest growth from existing clients who also use ASR, so it's not quite there yet
• darkpicnic 4y ago
  I just wrote a script with Hazel to automatically transcribe my voice notes to txt. It handles punctuation extremely well. What a wonderful contribution!
  • pbassham 4y ago
    Exactly what I was planning to do! Want to share yours with me?
• manishsharan 4y ago
  Oh this is a relief to have something opensource in this field. I had using Mozilla Deepspeech for transcribing my voice notes , often with hilarious to incomprehensible results. DeepSpeech is dead ; so I will be sure to check this out.
  • pabs3 4y ago
    DeepSpeech got spun out of Mozilla to coqui.ai and they are continuing the open nature of the project.
• tullie 4y ago
  Great to see OpenAI finally being open :)
• hijp 4y ago
  Anyone get it running on m1 mac? I keep getting `ModuleNotFoundError: No module named 'setuptools.command.build'`
  • Smaug123 4y ago
    I'm still not successfully using the GPU, but it's working decently quickly (with the base model - it's incredibly slow to use the Large model) using just the CPU. I'm going to have to check what magic stable-diffusion is doing to enable the GPU :(
    • dceddia 4y ago
      There's a --device flag you can pass. I've been trying to get `--device cuda` to work on my Windows machine and it's saying that torch wasn't compiled with CUDA. Trying to figure out what's going on there. And on the M1, supposedly PyTorch has support for hardware acceleration using MPS (Metal Performance Shaders, announced here https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/ https://pytorch.org/blog/introducing-accelerated-pytorch-tra...) but when I tried `--device mps` it blew up with an error "input types 'tensor<1x1280x3000xf16>' and 'tensor<1xf32>' are not broadcast compatible".
      • Smaug123 4y ago
        Yep, same for me, on M1 after enabling MPS (with `model.to("mps")`) it just either SIGSEGV or SIGABRTs every time with that line. The extremely unclean nature of the abort is making it hard to debug :(
        • dceddia 4y ago
          I noticed the size seems to correspond to the model. With a large model, the error is tensor<1x1280x3000xf16>. With tiny, it's tensor<1x384x3000xf16>, and with medium it's tensor<1x1024x3000xf16>. It also seems like a bad thing that those are f16's but the "expected" data is f32.
• LanternLight83 4y ago
  Hoping to see this out to use in open source voice assistants, eg. mycroft
• cercatrova 4y ago
  Their Scottish accent example is pretty good, I'd like to see it work on some very strong English accents like this one: https://www.youtube.com/watch?v=nJ7QB3om-QY https://www.youtube.com/watch?v=nJ7QB3om-QY
  • angrais 4y ago
    Are you sure? I just ran some of Kimmy's sketches through it and ... The results are garbage.
  • mod 4y ago
    Those are Irish.
  • homarp 4y ago
    Detected language: english [00:00.000 --> 00:05.400] Gordy and County Kerry are investigating the theft of up to 60 sheep on Mount Brandon. [00:05.400 --> 00:10.400] One of the farmers is offering a reward for information leading to the return of the use, [00:10.400 --> 00:12.200] which are worth thousands of euro. [00:12.200 --> 00:14.200] Well, I'm fine with that. [00:14.200 --> 00:15.200] That's right. [00:15.200 --> 00:16.200] Do you own them? [00:16.200 --> 00:17.200] Anyone can say it. [00:17.200 --> 00:18.200] Fine with that. [00:18.200 --> 00:22.720] Last Saturday, Mikey Joe O'Shea brought his flock of Scotch sheep down from the mountain [00:22.720 --> 00:25.320] commonage ahead of lambing. [00:25.320 --> 00:29.840] He discovered over 50 were missing, allowing for a number of deaths and [00:29.840 --> 00:30.840] strays. [00:30.840 --> 00:34.600] Mikey is convinced over 45 sheep have been stolen. [00:34.600 --> 00:35.600] It was a good night. [00:35.600 --> 00:36.600] It would be a full moon there. [00:36.600 --> 00:37.600] It would be a good night. [00:37.600 --> 00:38.600] It would be bright out. [00:38.600 --> 00:40.600] There could be anyone going up in the mountains. [00:40.600 --> 00:41.600] It would be a good night. [00:41.600 --> 00:43.600] Well, that was 45 sheep missing. [00:43.600 --> 00:49.600] Mikey and the lambs and everything in the sheep, they counted out a nice bit of money. [00:49.600 --> 00:52.200] They've been doing the boat in Nassan. [00:52.200 --> 00:53.200] It's a big one. [00:53.200 --> 00:54.200] It's a big one. [00:54.200 --> 00:55.200] It's a big one. [00:55.200 --> 00:59.000] Mikey's next door neighbor says some of his sheep have also been stolen. [00:59.000 --> 01:00.000] Come back. [01:00.000 --> 01:01.000] Come back. [01:01.000 --> 01:02.000] Come back. [01:02.000 --> 01:03.000] I've been missing about 10 years. [01:03.000 --> 01:04.000] It's not all that difficult. [01:04.000 --> 01:06.320] All they've got to do is have a good dog. [01:06.320 --> 01:10.560] Have a good dog and go at night, some moonshine night. [01:10.560 --> 01:11.560] Just put the dog around him. [01:11.560 --> 01:14.120] Put him on a trailer and walk him. [01:14.120 --> 01:18.360] And then probably somebody else to pick him up. [01:18.360 --> 01:29.960] Everybody's doing it north, but he's doing it.
• kiwih 4y ago
  Given this, are there good (and available/open source) models for text to speech? Last time I tried everything still sounded extremely robotic, and/or were a pain to set up and run. It would be fun to set up a pipeline where the two processes 'communicate'.
  • obscur 4y ago
    Measuring performance in rounds of successful Chinese whisper (irony)
• sowbug 4y ago
  I knew there was a reason why I kept my MP3 library even after subscribing to Spotify. Now piping everything through whisper. So far the generated lyrics are reasonable, though it thinks the REM song says "Linnie Bruce is not afraid." No surprise that it appears to have successfully transcribed all the recordings of Harvard Sentences I could find. https://en.wikipedia.org/wiki/Harvard_sentences https://en.wikipedia.org/wiki/Harvard_sentences
• aidenn0 4y ago
  For those on NixOS, here's a quick and dirty flake.nix that will let you make a venv in which to "pip install"' Just put it in a flake.nix, and "nix develop" followed by "virtualenv ./venv; . ./venv/bin/activate; pip install git+https://github.com/openai/whisper.git https://github.com/openai/whisper.git" { description = "Python 3.9 development environment"; outputs = { self, nixpkgs }: let system = "x86_64-linux"; pkgs = import nixpkgs { inherit system; }; in { devShells.${system}.default = pkgs.mkShell { buildInputs = [ pkgs.ffmpeg pkgs.python39 pkgs.python39Packages.pip pkgs.python39Packages.numpy pkgs.python39Packages.pytorch pkgs.python39Packages.virtualenv ]; }; }; }
  • aidenn0 4y ago
    This should, in theory, work with CUDA; my GPU doesn't have enough RAM to do it (it runs out at 2.9GiB allocated, I have 4GiB, but am running a compositing desktop, which chews up about 600MiB; not sure where the other ~400MiB went) [edit] I confirmed CUDA worked with the "small" model, which used 3.3GB of GPU ram, and resulted in much poorer recognition than the "medium" model on my CPU (but it ran at least two orders of magnitude faster). { description = "Python 3.9 development environment"; outputs = { self, nixpkgs }: let system = "x86_64-linux"; pkgs = import nixpkgs { inherit system; config.allowUnfree = true; config.cudaSupport = true; }; in { devShells.${system}.default = pkgs.mkShell { buildInputs = with pkgs; [ cudatoolkit linuxPackages.nvidia_x11 cudaPackages.cudnn libGLU libGL xorg.libXi xorg.libXmu freeglut xorg.libXext xorg.libX11 xorg.libXv xorg.libXrandr zlib ncurses5 stdenv.cc binutils ffmpeg python39 python39Packages.pip python39Packages.numpy python39Packages.pytorch-bin python39Packages.virtualenv ]; shellHook = '' export LD_LIBRARY_PATH="${pkgs.linuxPackages.nvidia_x11}/lib" ''; }; }; }
    • magicalhippo 4y ago
      CUDA worked fine with large on my 2080Ti FWIW. The speedup is ridiculous, as expected. My Ryzen 3800X used almost an hour transcribing a minute worth of speech, while the 2080Ti does it in like 10-20 seconds.
• BasilPH 4y ago
  Any opinions on what this means for speech-to-text companies like rev.ai and assmembly.ai ? We've tested open source solutions for s2t, like kaldi, but the quality was not good enough. However, one of the main advantages of a service like assembly.ai to me was that they offer sentence splitting in form of punctuation and speaker detection, which Kaldi does not. So I guess I answered my own question to some degree: A S2T service is more than just S2T. We already see assembly.ai add more and more features (like summarisation, PID redaction ect.) that are a value-add to plain S2T. Still, curious to hear what your take on that is.
  • nshm 4y ago
    You can apply public punctation model from Vosk on top of Kaldi output, you can also get speaker labels with existing open source software. On quick video transcription test this model is more accurate than AssemblyAI and Rev AI. It will be harder for them to sell pure ASR now. Some more business-oriented applications will still be important though, for example ASR as part of callcenter analytics solution or as a part of medical ERP system. The value of automatic summarization is small, without AI it is very hard to make it right, you need to be an expert in the field to understand what is important.
    • phren0logy 4y ago
      Rev AI will also create a transcription separated by multiple speakers, which it doesn't appear Whisper can do (yet). I expect that Whisper will overtake the alternatives soon, given that it's open source, but today it's not there yet.
    • sjnair96 4y ago
      > you can also get speaker labels with existing open source software. Hello Nickolay :) Diarization has always been the hard part for me, especially since it is very difficult to do comparisons within your domain. The evaluation metrics are not descriptive enough imo. Would you say Titanet or EcapaTDNN are decent for use in production alongside, say, Whisper, or any other ASR output, if given the timestamps, so as to bypass running VAD? I'm just about to run experiments to try pyannote's diarization model and google's uis-rnn to test out how well they work, but it's a tad beyond my ability to evaluate. I also wonder if Whisper architecture would be good for generating embeddings, but I feel it's focused so much on what is said rather than how it's said that it might not transfer over well to speaker tasks.
• isoprophlex 4y ago
  Really incredible to see that their multilingual audio-to-English approach is viable. I'm super excited about this, and great to see that openai actually open up about something, for once. Skimming the codebase I can't immediately see code to do additional training. Being able to fine-tune the model to a specific language or case (eg. teach it specifically about some technical topic that might not be so prevalent in the current train set) would be majorly disruptive to current SOTA in "callcenter analytics" tech. Especially when combining Whisper with GPT3.
• toss1 4y ago
  Like every model I've seen there is something like this: >>A decoder is trained to predict the corresponding text... Prediction of expected text in the context of the previous text. While this is valuable in casual transcription, it can be extremely dangerous in serious contexts. From personal experience, having given a deposition with an "AI" transcription, it will literally reverse the meanings of sentences. This is because it produces the EXPECTED output in a context, and NOT THE ACTUAL OUTPUT. Like a speaker that clips the output, these types of systems 'clip' the really valuable information out of a transcription. Worse yet, this is a completely silent failure, as the transcript LOOKS really good. Basic info theory shows that there is more information contained in 'surprising' chunks of data than in expected ones. These systems actively work to substitute 'expected' speech to overwrite 'surprising' speech. The transcript I got was utter trash, multiple pages of errata I had to submit when the normal is a couple of lines. And as I said, some literally reversed the meaning in a consequential way, and yet completely silently. This kind of silent active failure mode is terrifying. Unless it is solved, and I see no way to solve it without removing ALL predictive algos from the system, these types of systems must not be used in any situation of serious consequence, at least not without real redundancy and backup.
  • lunixbochs 4y ago
    Do you have a demo audio clip for this? I'd be interested to see how it looks in practice.
    • toss1 4y ago
      Sorry, I don't have anything available. One item I remember was that I said "Dr Kemeny" in relation to Dartmouth College (he was a famous mathematician, invented the BASIC programming language and was president of the college). It replaced those instances with "Jack Kennedy". In another instance, I said that "Evidently, you have a reading comprehension problem.". It replaced it with "Evidently, I have a ...", completely reversing the meaning. There was zero problems with the microphones or audio, and it was not rushed or mumbled talk. There were 80+ other examples over a few hours of talking, and some from other speakers. And those were just the obvious ones I could catch. Another massive problem with this technology is that a human stenographer can notice when s/he missed something and didn't hear and ask the speaker to repeat or clarify what was said, and will often during a pause request clarification on spelling of names, addresses, etc. In contrast, this "AI" technology just barges ahead ASSuming that it knows what it is doing and inserts literally whatever sounds good in the transcript, completely silent that it doesn't have a clue. Having seen this up close, I'm of the strong opinion that anyone foisting this software on the market without huge warnings that this is not usable for any critical functions is, basically a fraud. They know or certainly should know that these failures not only exist but are common and systemic, yet they barge along like it is OK. It is not.
• goffi 4y ago
  Really interesting, I can see ton of potential uses. 2 questions: 1) how does it compare to state of the art FOSS solutions? I'm seeking about DeepSpeech or Vosk 2) would it be somehow possible to associate timestamp to the words recognized? That would be amazing for things such as audio editing or skipping to a particular location on a video
  • goffi 4y ago
    > goffi for 2), it's actually written in the description: "phrase-level timestamps", so it should be possible (phrase level is neat for skipping to a special location on a video, but maybe not for audio editing).
  • nshm 4y ago
    You properly mentioned timestamps. There are many other important properties of good ASR system like vocabulary adaptability (if you can introduce new words) or streaming. Or confidences. Or latency of the output. Compared to Vosk models this model can not work in streaming manner, so not very suitable for real-time applications. But in general the model is robust and accurate and trained on the amount of speech we never dreamed about in Vosk. We will certainly benefit from this model as a teacher (together with others like gigaspeech models). I recently wrote about it https://alphacephei.com/nsh/2022/06/14/voting.html https://alphacephei.com/nsh/2022/06/14/voting.html
  • catfan 4y ago
• O__________O 4y ago
  Anyone know if it is possible to output IPA using this? International Phonetic Alphabet (IPA) - https://wikipedia.org/wiki/International_Phonetic_Alphabet https://wikipedia.org/wiki/International_Phonetic_Alphabet _________ EDIT: Based on list of languages in the tokenizer code here, doesn’t appear IPA is supported: https://github.com/openai/whisper/blob/5f8d4bcc254d4f3e833d353b4cd4454004f5b51a/whisper/tokenizer.py https://github.com/openai/whisper/blob/5f8d4bcc254d4f3e833d3...
• mmh0000 4y ago
  Okay this is super impressive. I just downloaded Whisper and fed it a random flac file I had handy and it did a really good job. Also impressive that it works on my weak CPU: A 3m07s flac took 5m to transcribe: $ whisper --device cpu 'BLACKPINK - BORN PINK/01 Pink Venom.flac' Detecting language using up to the first 30 seconds. Use `--language` to specify the language Detected language: korean [00:00.000 --> 00:10.000] Blackpink [00:11.000 --> 00:14.000] Kick in the door, wave in the coco [00:14.000 --> 00:16.000] 팝콘이는 친게 껴들 생각 말고 [00:16.000 --> 00:19.000] I talk to talk, run ways I walk walk [00:19.000 --> 00:21.000] 힘 감고 팝 팝 안 봐도 척 [00:21.000 --> 00:24.000] By one and two by two [00:24.000 --> 00:26.000] 내 손끝 두 하나에 타면 아지은 중 [00:26.000 --> 00:30.000] 갓 자쇼 지금 화려해 T makes no sense [00:30.000 --> 00:32.000] You couldn't get a dollar out of me [00:33.000 --> 00:38.000] 자 오늘 밤이야 눈톱을 품고 [00:38.000 --> 00:41.000] 미혼을 뺏음 down [00:41.000 --> 00:43.000] Look what you made us do [00:43.000 --> 00:47.000] 천천히 널 잠재울 파이어 [00:48.000 --> 00:52.000] 잠이 날 만큼 아름다워 [00:52.000 --> 00:53.000] I bring the pain like [00:53.000 --> 00:57.000] 디스탑, 팽팽, 디스탑, 팽팽, 디스탑, 팽팽, 팽팽 [00:57.000 --> 00:58.000] Get em, get em, get em [00:58.000 --> 01:00.000] Straight till you don't like [01:00.000 --> 01:01.000] Whoa, whoa, whoa [01:01.000 --> 01:03.000] Straight till you don't like [01:03.000 --> 01:04.000] Ah, ah, ah [01:04.000 --> 01:05.000] Taste that, pink venom [01:05.000 --> 01:06.000] Taste that, pink venom [01:06.000 --> 01:08.000] Taste that, pink venom [01:08.000 --> 01:09.000] Get em, get em, get em [01:09.000 --> 01:11.000] Straight till you don't like [01:11.000 --> 01:12.000] Whoa, whoa, whoa [01:12.000 --> 01:13.000] Straight till you don't like [01:13.000 --> 01:14.000] Ah, ah, ah [01:14.000 --> 01:15.000] Blackpink and Amo [01:15.000 --> 01:17.000] Got it by the smack ram [01:17.000 --> 01:18.000] But rest in peace [01:18.000 --> 01:19.000] Please light up a candle [01:19.000 --> 01:20.000] This the knife of a vando [01:20.000 --> 01:22.000] Messed up and I'm still in saline …SNIP…
  • lunixbochs 4y ago
    Looks like it defaults to the model called "small". I just ran some benchmarks - M1 Max, pytorch, with a 1.29 second flac (looks like the matrix math was running on a single thread): tiny 146.522ms detect_lang 549.131ms decode_one 0.057ms tokenizer base 354.885ms detect_lang 1046.679ms decode_one 0.011ms tokenizer small 803.892ms detect_lang 3194.503ms decode_one 0.017ms tokenizer medium 2279.689ms detect_lang 10128.255ms decode_one 0.023ms tokenizer large 3656.478ms detect_lang 17249.024ms decode_one 0.016ms tokenizer
• lynguist 4y ago
  How can I use this (or something similar) for live translation? I don't mind if there's a 30s delay. As in I don't want to input a file, I want to input the microphone sound.
  • agnos 4y ago
    Would also like to know this. It looks like they're processing the audio file in 30 second chunks, so a naive approach of keeping a buffer of 30-second input stream chunks and just continually writing to an output .mp3 could work...
  • blueberrychpstx 4y ago
    Was wondering the same. I really wish I would have been paying attention in Unix class... Something like `microphone | chunk 3s | whisper | stdout` would be SO COOL!!! I think that's possible but too lazy to look more.
• TOMDM 4y ago
  Given how robust it seems to be with fast speech, I wonder if you could save cycles by speeding up the audio before feeding it in.
• tgtweak 4y ago
  Good to see them releasing model weights - hopefully now that Stable Diffusion is out they will release Dall-E 2 source and weights as well.
• throwamon 4y ago
  Is it feasible to use this for Talon-like voice-driven computer usage?
  • FloatArtifact 4y ago
    Maybe, a number of speech recognition engines have been integrated into https://github.com/dictation-toolbox/dragonfly https://github.com/dictation-toolbox/dragonfly
  • lunixbochs 4y ago
    If the Whisper models provide any benefits over the existing Talon models, and if it's possible to achieve any kind of reasonable interactive performance, I will likely integrate Whisper models into Talon. Talon's speech engine backend is modular, with Dragon, Vosk, the WebSpeech API, and Talon's own engine all used in different ways by users.
• petercooper 4y ago
  Just tested this on some developer podcasts which usually fail hard given they're full of technical jargon, brand names, etc. Whisper is a revolution! It's picking up terms like Heroku, DigitalOcean, GitHub, ECS, AWS, etc. and capitalizing properly - something nothing else did unless you provided a whole pile of guiding vocabulary.
  • ma2rten 4y ago
    Did these podcasts have transcripts? You might be inadvertently evaluating it on data that it was trained on, which is basically cheating. Even if not, it might be trained on similar podcasts. Judging how good these kinds of models are is really hard.
    • petercooper 4y ago
      No transcripts, no. And recent episodes, within the past couple of weeks, so probably not part of the training either.
    • WiSaGaN 4y ago
      True. The test should only be done on the material released after the model.
• lfmunoz4 4y ago
• dom96 4y ago
  This really makes me want to build a Amazon Echo/Google Nest/etc replacement that's open hardware, open source and most importantly recognises voice completely offline. I find that I don't use these smart devices for much more than setting timers anyway so this seems like an easy project. I just wonder what system requirements Whisper has and whether there are open source voice recognition models that are specifically built for embedded devices.
  • suyash 4y ago
    This is only one side of the coin, you still need really good models for Speech Synthesis and then be able to have it all working in almost real time, ideally locally on device.
    • ricopags 4y ago
      As far as TTS goes, Mycroft.ai[0] has released a decent offline one. [0]https://mycroft.ai/ https://mycroft.ai/
      • smcameron 4y ago
        pico2wave with "-l=en-GB" option to get the British lady voice is pretty decent (way better than the other voices it does for some reason).
      • arbol 4y ago
        I'm pretty sure mycroft sends your speech snippets to Google for processing so it's not exactly offline. https://mycroft-ai.gitbook.io/docs/using-mycroft-ai/customizations/stt-engine#default-engine https://mycroft-ai.gitbook.io/docs/using-mycroft-ai/customiz... I'm currently trying to setup a deepspeech server on my raspberry pi to see if it works ok for commanding spotify. Edit: just realised you said `TTS` not `STT`
  • MacsHeadroom 4y ago
    I really want all this too. The smallest model is ~80mb and the largest is 3gb. Not sure about system requirements yet; but models that small suggest this may be doable locally on a single board computer. Edit: According to this comment[0] the base model runs in real time on an M1 CPU. The tiny model apparently decodes an audio file twice as fast. These are promising results. [0] https://news.ycombinator.com/item?id=32927360#32929739 https://news.ycombinator.com/item?id=32927360#32929739
• eatsyourtacos 4y ago
  Can this be used as a real-time transcription or is it too slow for that? Curious what anyone is using these days for a real-time transcription. It doesn't have to be perfect, but just good enough. My kids watch some youtube vidoes where people will make a mod where it converts them talking to text then look for keywords and spawn a boss in Terraria if you say the wrong keyword etc. I made a clone of that with the .NET System.Speech.Recognition library. It... works.. but my biggest problem is that #1 it waits until you are done speaking to translate to text on the callback, so there was too much of a delay for it to be fun.. the point is that it will be checking a stream of chatter. #2 is the recognition is pretty crap, I mean it's nearly good enough for my silly purpose but it's still pretty bad.
  • TaylorAlexander 4y ago
    The base model seems to run faster than real time on my machine. The “medium” model is larger and runs more slowly - roughly real time or maybe slightly slower.
  • jayavanth 4y ago
    thuttinger posted in this thread: https://github.com/tobiashuttinger/openai-whisper-realtime https://github.com/tobiashuttinger/openai-whisper-realtime
  • suyash 4y ago
    Depends if you're trying to run it offline or over the cloud.
  • blueberrychpstx 4y ago
    If your family uses Apple devices, Apple offers free on-device speech recognition. Only caveat is that it needs to be restarted every minute due to whatever stupid limitation (or bug) they've introduced. https://developer.apple.com/documentation/speech/recognizing_speech_in_live_audio https://developer.apple.com/documentation/speech/recognizing... Also, see `requiresOnDeviceRecognition`
    • deleted 4y ago
      [deleted]
• bredren 4y ago
  This is dropping right in the middle of Interspeech 2022. I don’t believe OpenAI has anyone presenting at the conference, so presumably this was timed to coincide with that and get buzz at the conference. Curious how this model compares with foss STT from the startup Coqui.
• gareth_untether 4y ago
  I'm thinking of releasing a plugin in for Unity to that can be used to match a phrase to an action. Seeing Whisper is making me think I should include a way to use voice and not just text.
• deleted 4y ago
  [deleted]
• mewse-hn 4y ago
  I know this isn't a tech support forum but maybe someone here knows. I'm attempting the sample python code from the github and almost get a transcription running on my work laptop without a GPU, but I run into this error message: >>> result = whisper.decode(model, mel, options) Traceback (most recent call last): [snip] RuntimeError: "slow_conv2d_cpu" not implemented for 'Half' It looks like a Torch error, is there some twiddling with "options" I can do to get it to run?
  • mewse-hn 4y ago
    I seem to have worked around it by tweaking the "options" line from the sample code to this: >>> options = whisper.DecodingOptions(fp16=False)
  • ignite 4y ago
    I am running on work laptop not using GPU. (I'm running in docker). I just get warnings.warn("FP16 is not supported on CPU; using FP32 instead") And it works.
    • mewse-hn 4y ago
      It seems the cmdline script is smart enough to switch over automatically, but invoking it from python just fails if the correct option isn't set
• blueberrychpstx 4y ago
  This is absolute garbage python as I am neither a python developer, nor a good developer. I was trying to play around with real time transcriptions. However, it does work! > * recording * done recording Recording saved to file.wav Press enter to transcribe /Users/laptop/Development/Personal/Public/pythonProject1/venv/lib/python3.9/site-packages/whisper/transcribe.py:70: UserWarning: FP16 is not supported on CPU; using FP32 instead warnings.warn("FP16 is not supported on CPU; using FP32 instead") Detected language: english Goodbye, I need to go pick up my wife. Press enter to start recording Any improvements welcome here. ``` # This is a sample Python script. # Press ⌃R to execute it or replace it with your code. # Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings. def print_hi(name): # Use a breakpoint in the code line below to debug your script. print(f'Hi, {name}') # Press ⌘F8 to toggle the breakpoint. def record_microphone(seconds): import pyaudio import wave CHUNK = 1024 FORMAT = pyaudio.paInt16 CHANNELS = 1 RATE = 44100 RECORD_SECONDS = seconds WAVE_OUTPUT_FILENAME = "file.wav" p = pyaudio.PyAudio() stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK) print("* recording") frames = [] for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)): data = stream.read(CHUNK) frames.append(data) print("* done recording") stream.stop_stream() stream.close() p.terminate() wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb') wf.setnchannels(CHANNELS) wf.setsampwidth(p.get_sample_size(FORMAT)) wf.setframerate(RATE) wf.writeframes(b''.join(frames)) wf.close() return WAVE_OUTPUT_FILENAME if __name__ == '__main__': seconds = 5 while True: print("Press enter to start recording") input() filename = record_microphone(seconds) print("Recording saved to " + filename) print("Press enter to transcribe") input() import whisper model = whisper.load_model("base") result = model.transcribe(filename) print(result["text"]) ```
• spywaregorilla 4y ago
  Hmm are there any noteworthy open sourced speech to speech models? Like transform a spoken line to another voice, copying both the words spoken and the inflections?
• adeptima 4y ago
  Japanese results looks pretty impressive! Took マッコウクジラ14頭が海岸に打ち上げられる オーストラリア(2022年9月21日) https://www.youtube.com/watch?v=bZkNIzeRBk4 https://www.youtube.com/watch?v=bZkNIzeRBk4 Extracted audio with youtube-dl -f bestaudio https://www.youtube.com/watch\?v\=bZkNIzeRBk4 https://www.youtube.com/watch\?v\=bZkNIzeRBk4 Converted into [00:00.000 --> 00:13.000] オーストラリア南部の島で、真っ向くじら14棟が海岸に打ち上げられて死んでいるのが見つかり、専門家が調査のため原地入りしました。 [00:13.000 --> 00:25.000] 原地メディアによりますと、オーストラリア南部のキング棟で、19日、少なくとも14棟の真っ向くじらが海岸に打ち上げられて死んでいるのが見つかりました。 [00:25.000 --> 00:31.000] ほとんどが若いオーストを見られ、専門家が現場に重むき調査に当たっています。 [00:31.000 --> 00:41.000] くじらの死害は大きく運んだり埋めたりすることが難しいため、自然に分解されるのを待つ方針が検討されています。 [00:41.000 --> 00:52.000] また、死害を狙い、サメが海に集まる可能性があるとして、原地東局はサーファーなどに周囲に近づかないように呼びかけています。 [00:52.000 --> 01:02.000] 一方、21日にはタスマニア棟でおよそ230棟のくじらが浜辺に打ち上げられた状態で見つかりました。 [01:02.000 --> 01:07.000] およそ半数がまだ生きている模様で急助活動が進められています。 [01:07.000 --> 01:23.000] 見つかったのは、ゴンドーくじらの仲間と見られています。
  • knaik94 4y ago
    Did you try translating them to english? I want to see if you get a similar error as me with a random phrase "Translated by Releska" showing up.
    • lynguist 4y ago
      It's called hallucination. As the model is trained on unsupervised data, such errors do seldom happen. The model picks up that such phrases occur in translations and inserts them even if they do not appear in the source. This is described in the paper.
      • knaik94 4y ago
        I came across it during a silent/instrumental portion in the song I was testing. I asked only because I am curious how frequently the error might show up, I don't expect it to be very common. It's looking at phrase level instead of word level timestamps which is going to make it hard to tokenize music. I asked simply because the parent comment also tested on Japanese.
  • gzer0 4y ago
    Shocked at how good the results are, and how easy of an installation it is. Here are the exact steps to follow to get it running on Ubuntu 22.04 via WSL and yt-dlp: 1. pip install git+https://github.com/openai/whisper.git 2. yt-dlp -f 'ba' -x --audio-format mp3 https://www.youtube.com/watch/?v\=bZkNIzeRBk4 3. renamed the file to test.mp3 4. whisper test.mp3 --language Japanese --task translate --model large Note: the large model will download a ~3Gb file
• no1youknowz 4y ago
  This is awesome. But I really want the other way. To be able to give it text and hear the speech. A TTS (text to speech). As a language learner, the ability to create my own sentences (based on existing ones I have, in changing a word here or there). Would be amazing. How long till we have this I wonder. I know I could use a service to do this currently. But having something running locally, I'd prefer. Hopefully someone in the OpenAI team reads this. :)
  • freedomben 4y ago
    Likewise, TTS is what I really want. My goal is to be able to create audio books from text. I've been using Amazon Polly and it's acceptable quality, but I would be ecstatic to be able to do it locally on my own hardware.
    • visarga 4y ago
      Check out NaturalReader. It has hundreds of amazing voices, a system for highlighting text as it is being read, works on books (pdf) and webpages, and is available on phones and in browsers on all platforms. So I could have the same voice on Mac, Linux and iPhone.
  • TaylorAlexander 4y ago
    I suspect this is coming. I mean we do have decent text to speech systems already, but in this vein of “we used neural networks and now it’s very very good” you can imagine that with something like GPT-3, to extend it they could use this speech to text system so you could speak to it for input, and then a natural progression is that it can use text to speech to return the output, so you just have a voice oriented conversational system. So I think TTS is a logical part of the system. I also think that there are peculiarities of voice interaction that aren’t captured in text training datasets, so they would need to do some fine tuning on actual voice conversation to make it feel natural. All in due time I suppose.
    • visarga 4y ago
      A full NLP system would include speech recognition, TTS, a large language model, and a vector search engine. The LM should be multi modal, multi language and multi task, "multi-multi-model" for short haha. I'm wondering when we'll have this stack as default on all OSes. We want to be able to search, transcribe, generate speech, run NLP tasks on the language model and integrate with external APIs by intent detection. On the search part there are lots of vector search companies - Weaviate, Deepset Haystack, Milvus, Pinecone, Vespa, Vald, GSI and Qdrant. But it has not become generally deployed on most systems, people are just finding out about the new search system. Large language models are still difficult to run locally. And all these models would require plenty of RAM and GPU. So the entry barrier is still high.
• amrrs 4y ago
  Here's a live demo on Hugging Face Spaces if you want to try - https://huggingface.co/spaces/Amrrs/openai-whisper-live-transcribe https://huggingface.co/spaces/Amrrs/openai-whisper-live-tran...
  • clemnt 4y ago
    this is amazing! got it working in French too
  • coder543 4y ago
    I've tried speaking to that demo several times... I used the built in feature to record from microphone, and I played back the samples to make sure they were audible and clear. Sometimes it outputs the words "thank you" (which I did not say), sometimes it outputs a period. It never once output anything I said. It seems completely broken. EDIT: apparently something about the combination of Safari+HF+Whisper was not working. I tried another Whisper demo on HF and had the same results. Switching to Chrome made it work flawlessly... I have no idea what kind of codec incompatibility was happening.
• Jnr 4y ago
  Cool! I am one of the top contributors to the tiny Mozilla Common Voice data-set for my language. The data-set is very small compared to other popular languages and none of the other mentioned data-sets contribute to that language to train the model of Whisper. And even with so little data to train on it still works surprisingly well.
  • archon1410 4y ago
    Where do they mention what datasets they've used? I've tried looking at the paper but can't find it.
    • archon1410 4y ago
      Nevermind: I found it. It's on page 19 and 20 of the paper, under Appendix A ("Evaluation Datasets").
  • catfan 4y ago
    [zalgo redacted]
    • dang 4y ago
      Hey - can you please not zalgo on HN? It messes up the threads. I've redacted it from your posts now.
• funhighway 4y ago
  Would be nice to give more details about the provenance and construction of the training data.
• londons_explore 4y ago
  @dang Can we change the link to the github here[1]? It seems to describe the project better for a technical audience. [1]: https://github.com/openai/whisper https://github.com/openai/whisper
• londons_explore 4y ago
  I wonder how much the 30 second window is impacting performance? Anecdotally, I feel like there are plenty of times that I need context from more than 30 seconds ago to understand some technical jargon that's being discussed.
• londons_explore 4y ago
  I've never seen transcription and translation combined into a single step like this before... Have I been living under a rock, or is this new? I assume it should help performance, because it means emphasis, timing and tone can be used to inform the translation. Helps make better guesses about information missing from the source language.
• yawnxyz 4y ago
  Oh man I remember LOVING Micro Machines as a kid. But also, this tool seems much better than Otter.ai, which gets every third word wrong when transcribing microbiology recordings
• noreally_ 4y ago
  A notebook is available to try with your microphone on Colab here: https://colab.research.google.com/drive/1nBZ-pDIaIi3N1DIIXvJFqYu17QR_2vk9 https://colab.research.google.com/drive/1nBZ-pDIaIi3N1DIIXvJ... I'm surprised by the quality on non-English languages, given that 80+% of the training data is English, and the rest is split between tens of languages.
  • bambax 4y ago
    Thanks! I played with this in French and posted the results as replies to this comment: https://news.ycombinator.com/item?id=32928643 https://news.ycombinator.com/item?id=32928643 It's sometimes close to perfect, and sometimes goes off the rail; I think that maybe the model tries to establish some sort of consistency for each sentence; if starts wrong for the first few words of a sentence, it can't build the rest properly. But it's super fun.
  • berberous 4y ago
    How do you get this to translate instead of just transcribe?
    • paraschopra 4y ago
      Just specify language and record an audio in another language. >result = model.transcribe("audio.wav", language="english")
      • berberous 4y ago
        That actually seems to set the language for it to transcribe (as opposed to it guessing), with the following triggering a translation to English: result = model.transcribe("audio.wav", task="translate") But your post helped me figure out the above, so thank you!
    • tekacs 4y ago
      To be more specific than the above: 1. Make sure you're using a model that isn't suffixed with `.en` (`base`, not `base.en). 2. Use `model.transcribe(your_input_audio, language='Japanese', task='translate')` ... with the appropriate input language.
• mwlp 4y ago
  Super impressive. I tested it on a Japanese streamer whose enunciation isn't exactly perfect and it did a decent job: https://www.youtube.com/watch?v=ROiOU1scaNA https://www.youtube.com/watch?v=ROiOU1scaNA [00:00.000 --> 00:06.500] Since the last one started, the number of times I've eaten has decreased. [00:06.500 --> 00:11.000] If I get too carried away with the last one, I'll get hungry and do it. [00:11.000 --> 00:14.500] I don't have time to eat. [00:15.500 --> 00:18.000] I'm going to eat now. [00:20.000 --> 00:23.000] It's going to take about 10 minutes from here. [00:23.000 --> 00:31.000] It's been a while since I've had my last meal. [00:31.000 --> 00:36.000] I feel like I'm losing my女子力. [00:36.000 --> 00:39.000] I have to go back to my original self. [00:39.000 --> 00:44.000] I have to get ready and go to bed. [00:44.000 --> 00:46.000] It's not good. [00:46.000 --> 00:51.000] I've been drinking a lot lately, so I'm going home. [00:51.000 --> 00:53.000] I have to get my nails done this fall. [00:53.000 --> 00:54.000] Halloween nails. [00:54.000 --> 00:57.000] Halloween, Halloween, Halloween. [00:57.000 --> 00:59.000] I'm going to the beauty salon today. [00:59.000 --> 01:02.000] I'm going to get my nails done the day after tomorrow. [01:02.000 --> 01:10.000] I used to look at a lot of clothes, but I stopped looking at them. [01:10.000 --> 01:12.000] I'm going crazy. [01:12.000 --> 01:22.000] My stomach's stopped in the middle of summer.
  • adeptima 4y ago
    translation is not the strongest part. transcription looks very good.
  • magicalhippo 4y ago
    It's struggling with Norwegian. Which I guess isn't shocking. The large model performs a fair bit better than the small, though neither is "good". Though I assume the amount of Norwegian it has been exposed to is fairly limited, so in that light I'm actually impressed as well. I tried it on a news segment from the radio[1], this is the large model output: [00:14.000 --> 00:17.200] En skamløs krenking av FN pakten. [00:17.200 --> 00:24.000] USAs president og verdensledere svarer på den russiske presidentens atomtrusler og krigsmobilisering. [00:25.500 --> 00:29.400] Arbeidsklær som er ment til å være til begge kjønn, har det med å være tilpasset. [00:29.400 --> 00:33.400] Men hvordan ville det gått, om det var motsatt? [00:34.100 --> 00:38.900] Dyrevernsorganisasjon vil ha digital merking av regnstyr, [00:38.900 --> 00:44.900] men næringen selv insisterer på den gamle tradisjonsrike måten med rissing av kniv. [00:45.600 --> 00:51.400] Mange strømselskaper er positive til å tilby kundene fastpris på strøm, og det årevis. [00:51.400 --> 00:59.900] Da risikerer de å måtte betale mye i nettopp åretsvis, sier aktører som aldri tilbyr fastpris. [00:59.900 --> 01:21.900] Dette er onsdagens Dagsnytten. Jeg heter Espen Ås. For reference, here's what he actually said, from the source[1] itself: * En skamløs krenking av FN-pakten. USAs president og verdensledere svarer på den russiske presidentens atomtrusler og krigsmobilisering. * Arbeidsklær som er ment å være til begge kjønn, er som regel tilpasset ... menn. Hvordan hadde det gått om det var motsatt? * Dyrevernsoganisasjon vil ha digital merking av reinsdyr, men næringen selv insisterer på den gamle tradisjonsrike måten med rissing av kniv. * Mange strømselskaper er positive til å tilby kundene fastpris på strøm - og det i årevis. - Da risikerer de å måtte betale mye i nettopp; årevis, sier aktør som aldri tilbyr fastpris Dette er onsdagens Dagsnytt 18 - jeg heter Espen Aas. The translation didn't fare that well though: [00:14.000 --> 00:17.000] A shameless violation of the UN treaty. [00:17.000 --> 00:24.000] The US president and world leaders respond to the Russian president's nuclear threats and war mobilization. [00:24.000 --> 00:33.000] Work clothes that are meant to be for both genders have to be suitable, but how would it be if it was the other way around? [00:34.000 --> 00:44.000] The animal welfare organization will have a digital marking of reindeer, but the industry itself insists on the old traditional way of tearing a knife. [00:45.000 --> 00:51.000] Many electricity companies are positive in offering customers fixed electricity prices, and that is annual. [00:51.000 --> 00:58.000] Then they risk having to pay a lot in just a year, says an actor who has never offered fixed prices. [00:58.000 --> 01:20.000] This is Wednesday's Dagsnytt 18. My name is Espen Ås. For reference, here's Google Translate's attempt, which is pretty good: * A shameless violation of the UN Charter. The US president and world leaders respond to the Russian president's nuclear threats and war mobilization. * Work clothes intended for both sexes are usually adapted to ... men. How would it have gone if it had been the other way around? * Animal welfare organizations want digital marking of reindeer, but the industry itself insists on the old, traditional way of marking with a knife. * Many electricity companies are positive about offering customers a fixed price for electricity - and for years. - Then they risk having to pay a lot in precisely; for years, says a player who never offers a fixed price This is Wednesday's Dagsnytt 18 - my name is Espen Aas. [1]: https://radio.nrk.no/podkast/dagsnytt_atten/l_5ce3e323-97a3-4f53-a3e3-2397a37f539f https://radio.nrk.no/podkast/dagsnytt_atten/l_5ce3e323-97a3-... (not sure if it's available outside of Norway)
• abidlabs 4y ago
  Here [1] is a video tutorial on building a web UI that accepts microphone input and runs it through Whisper for speech transcription [1] https://www.youtube.com/watch?v=ywIyc8l1K1Q&ab_channel=1littlecoder https://www.youtube.com/watch?v=ywIyc8l1K1Q&ab_channel=1litt...
  • amrrs 4y ago
    Thank you for sharing!
• lazylion2 4y ago
  I ran it on this clip https://clips.twitch.tv/ReliablePopularWerewolfOSkomodo-pcuws-ec0M_kD9p1 https://clips.twitch.tv/ReliablePopularWerewolfOSkomodo-pcuw... because... hard accent. first run whisper thought its welsh so I had to run with --language en , and it did pretty well https://i.imgur.com/TQiYU9X.png https://i.imgur.com/TQiYU9X.png took 36 seconds in Google colab
• bickett 4y ago
  Hard to keep up with all the great things. The AI community is really moving quick right now.
• knaik94 4y ago
  I got a super weird results with the 'medium' and language Japanese (with a --task translate). The song is False Sympathy by Mondo Grosso. "[01:17.000 --> 01:32.000] Translated by Releska" when using the translate to english. That entire part of the song is instrumental. This line does not appear at all in the original transcribe only in the opus format rip. It shows up in the yt rip in format 251 (opus), but not in format 140 (aac from youtube), nor the flac rip. All three are giving different results. The translation quality is tied to bitrate. Same song converted to different words, the only difference being bitrates and formats. Converting my own rip with the same parameters as yt (opus @140 and then @130) didn't allow me to reproduce this error. The model hung for a solid extra minute at the end when translating to english, the last 90ish seconds of the song took real time 60 seconds, while the entire rest took about 90. The same behavior was not observed with the transcribe. Some of the english words are incorrect but that was expected. The first Japanese "mistake" I found was "全ては二人の" instead of "すべては ふたりの". With the left being what whisper wrote. A single random word "hey" was transcribed/translated to english even though it's the singer elongating the 園 while singing the 楽園. "落ちてゆく 二人で繋がれた二人のラグ HEY" instead of "落ちていく 鎖でつながれた 二人の楽園" . I am using the official subtitles released on the youtube video. It's a complex Japanese song with both japanese and english, and the original transcribe took about 20 real time seconds to start with the first line, 130 seconds for the whole song. It seems to be showing results in 20 second window increments, but this seems to depend on what it considers audio and what it is throwing away. On my computer I wasn't able to use the large model because I ran out of VRAM, I have 8gb, not sure how much more it'd require. So I ran it with medium. The song is False Sympathy by Mondo Grosso. The mv is suggestive, in case that matters. I grabbed a fresh audio rip from Youtube because I didn't want to take it out of my cd case. https://www.youtube.com/watch?v=B6Y-WsgpzlQ https://www.youtube.com/watch?v=B6Y-WsgpzlQ It is translating this version differently from the director's cut version. I ripped both as opus. There is something weird about how it is handling the opus encoded version, as I find the same "Translated by Releska" in a wav version transcoded from the opus.
  • adeptima 4y ago
    Japanese output will produce lot of tiny mistakes. However the whole output is still good enough. Like 95% plus good enough. Found lot mistakes in 3-4 characters kanji ... and I guess most native Japanese will do mistakes time to time too, and this is why they pop up lot of buzzwords on screen with all kind of highlighting to avoid double guessing.
• shpx 4y ago
  We shouldn't call this open source. The model definition + the data is the source code. The model weights are a compilation artifact. > The source code must be the preferred form in which a programmer would modify the program. [...] Intermediate forms such as the output of a preprocessor or translator are not allowed. > https://opensource.org/osd https://opensource.org/osd If I asked a programmer from OpenAI to modify the model to better support Japanese speakers from Hokkaido, their "preferred form" of the model's source code would include the 680,000 hours of audio used to train the model. Yes that means that there are almost no open source models and yes it's awesome that they released this and made the weights available. Just don't call it open source.
  • pabs3 4y ago
    The Debian deep learning team's machine learning policy would call this a "toxic candy" model: https://salsa.debian.org/deeplearning-team/ml-policy https://salsa.debian.org/deeplearning-team/ml-policy BTW, wouldn't you take the existing model and do additional Hokkaido Japanese speaker training on top of it, rather than retraining the model from scratch?
  • rvz 4y ago
    Yes. It just like calling the release of compiled closed binary blobs as 'open source' even when the source of reproducing the compiled output is unavailable. > If I asked a programmer from OpenAI to modify the model to better support Japanese speakers from Hokkaido, their "preferred form" of the model's source code would include the 680,000 hours of audio used to train the model. Precisely. These 'users' lifting the model can't do it themselves. You will still be contacting OpenAI for support or to add support for another language and they will be the ones able to modify the model. > Just don't call it open source. That is true, it is still closed source and already we are seeing the hype squad already apologising to OpenAI as they 'open sourced' a closed model that you can't modify yourself. OpenAI is still business as usual and nothing has changed.
    • MacsHeadroom 4y ago
      >You will still be contacting OpenAI for support or to add support for another language and they will be the ones able to modify the model. This isn't quite correct. The model weights are all you need to fine tune the data on your own with your own audio. Without the original training set this still isn't open source. But you aren't powerless to modify the model without the original training set.
• chrisstanchak 4y ago
  Hold on to your papers
• pmarreck 4y ago
  So it's 100% better than Siri's speech dictation, I see
• FloatArtifact 4y ago
  This would be a cool thing to integrate into Dragonfly https://github.com/dictation-toolbox/dragonfly https://github.com/dictation-toolbox/dragonfly
  • synkarius 4y ago
    It would. I wonder how this compares with Kaldi, one of the two open source speech recognition engines that Dragonfly currently supports.
• jerpint 4y ago
  I recorded myself speaking French and was able to translate decently well on my laptop. Very impressive!
• thuttinger 4y ago
  I tried running it in realtime with live audio input (kind of). If you want to give it a shot, you can find the python script in this repo: https://github.com/tobiashuttinger/openai-whisper-realtime https://github.com/tobiashuttinger/openai-whisper-realtime A bit more context on how it works: The systems default audio input is captured with python, split into small chunks and is then fed to OpenAI's original transcription function. It tries (currently rather poorly) to detect word breaks and doesn't split the audio buffer in those cases. With how the model is designed, it doesn't make the most sense to do this, but i found it would be worth trying. It works acceptably well.
  • catfan 4y ago
    • secret-noun 4y ago
      impressive
  • kkielhofner 4y ago
    Haven’t tried it yet but love the concept! Have you thought of using VAD (voice activity detection) for breaks? Back in my day (a long time ago) the webrtc VAD stuff was considered decent: https://github.com/wiseman/py-webrtcvad https://github.com/wiseman/py-webrtcvad Model isn’t optimized for this use but I like where you’re headed!
    • thuttinger 4y ago
      Interesting. I'll take a look at this, thanks!
      • Curiositry 4y ago
        Perhaps this could be adapted? https://github.com/mozilla/DeepSpeech-examples/blob/master/mic_vad_streaming/mic_vad_streaming.py https://github.com/mozilla/DeepSpeech-examples/blob/master/m...
• nothrowaways 4y ago
  Great project, not so great package name.
• Havoc 4y ago
  This could be really cool for mycraft/rasphy etc
• harry8 4y ago
  Can you plug this into a computer on your premises to get speech recognition without amazon, apple or google's cloud (or any other cloud) involvement? Right now I decline all speed recognition because I don't want orwellian listening devices in my house or pocket and haven't seen an answer. (Also haven't been too bothered about speech command interfaces to bother with a load of research - lazy me).
  • fragmede 4y ago
    Yes, after the download of the model weights (from https://openaipublic.azureedge.net/ https://openaipublic.azureedge.net/) it's an entirely offline process.
  • samat 4y ago
    Btw, Apple's speech recognition can work completely offline, on-device. Not sure about Google or Microsoft, though.
• Gazoche 4y ago
  Pretty cool, and it seems to work on AMD GPUs as well. I've just tried it on my RX6800 with the ROCm build of PyTorch.
• sn41 4y ago
  Most of the comments here are about law enforcement. I would like to point out that it might be a boon for dictation software. This may make it easier to dictate text/code etc. in any environment.
• NaturalPhallacy 4y ago
  This is pretty incredible! https://i.imgur.com/03UFGc8.gif https://i.imgur.com/03UFGc8.gif
• eugenhotaj 4y ago
  Now someone just needs to pipe the output into stable diffusion.
• sva_ 4y ago
  It seems like Stable AIs release has led to some real disruption in the ML field regarding open source, and this doesn't seem to be limited to image generation. Excited to see what comes next.
• gck1 4y ago
  Got my hopes high that there's finally an open source solution that can deal with Georgian language, only to get my hopes brutally destroyed. It successfully detects a language and then produces garbage. Passing language manually produced similar results. Result of my own recording: Detected language: georgian ᔨᴉᴉ�ちゃんᓁᔇ � remnants ᡔ� founding ហ�ockey� slee សᕁ �eling ភᕩ�icularly អᕖᕤ�APPLAUSEPS ថ�Dav頻道 ប�DING� Możai បፘ្ទក ុក ឵� orchestral ុក ឵� arter ូ� Brettំ � hilarious ល ឬ ᔼ� vårក បក ្៙ � Poll statements ឭ᪨្pson. ჩჩრუესი჏მეისლემვეერრშუეაირელმირისასასსსესსერერსივეესრრილმეხრე რეიმიმეფემსესე� Results of clear Georgian audio [1]. On tiny model: Detected language: georgian [00:00.000 --> 00:21.560] én [00:21.560 --> 00:23.240] 我伦伦… [00:23.280 --> 00:43.720] 我伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦伦因为b forestry On medium model: Detected language: georgian სრჱირესრრრრრრრრრრრრრნსსსრრრრრეე რრირრრრრრრრრე რსრნგნრრრრსრრრრრრრორრრრრრრრრრრ� ḵḸḇḤḾḤḾḤḾḤḾḤḾḤḾḤḾḤḾḾḤḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾ� ḥḾḼḥḾ ḥḾḾ ḥḾḾ ḤḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾḾ� ḲḵḽḻḽḾ Ḫḵḽḻḽ so� ḻḽḽ ḻḽḻḻḽ ḱᴇ᷻ᵒ ḳᶟᄤḱ ḯᵁ Ḳᴄᴍᴆ Ḧᴍ� Ḧᵒ ḳᴍᴇ ḽᴄᴍᴛᴄ Ḧᴇᴆ ḳᵗᴇ ḽḮᴆ Ḫᴇᴾ ḿᴏᴇᴄᴄᴏ ច�izar� wait �ห� examined ᑇទមះៈេំ supervision ង� იეეეეეეეეეეეეეეეეე მაეე ეაეეეეეეეეეეეეეეეეეეეე დაეეეეეეეეეეეეე უეეეეეეეეეეეეე ეა� ჆ მიი სმეიი მმიეი Ⴢქ სიიეი სავიე სიიითთიიმემი, რაეე სიიმე სიიი ღიიიიწეირი საეიეიი სიიეი სი� ვეეფვეიიიე ქლეეშეეროეეეეეეეეეეეეე. ეგეზ ეყაკშეიეეეეეეეეეეეეეეეეეეეეეეეეეეეეეა, ნრროპიროო მმუმინ სეეკნფეე სეეჍიგოშ სჟებიმელელეეკირპიე სემეიმე სეეიმმმ სეენემეეი სე� ᑦ� Famose m인데요 hqe bywall jaini threshold ji jani den poder vlogging bywall Take the text Ba tou yodamj je te shake ba te shake baou contour but whatever Baou cube baou cup Baou rope Baou people Qeful Qeful იმიიიმიბთმითიიითიიიიიიიი რაოეოოოენპეეეიეიიიიიიიიიომიიიიიიიიი რიიიიიიიიიიიმიი� ნსეეეეეეეეეეეეეეე სარეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეეე� მጇი჏ვ ეეეიდჼვვ ნაბდადებ ლმირეეეეფედუივევეეეიიეეეეე რარეიეეეევეეეეევეე სარრეეეეეეეეეეეეეეეეეეეეეეეეეეე ხშიიიიიიიიიიიიი ლიიიიიიი ლიიიიიიიიიი ლიიი ლიიიიიიი ლაიიიიი ეიიიიიიიიიიიიიიი იიიი მ� I've also tested it on few other audio inputs and it failed to produce meaningful results on all of them with all models. There was one case with another audio [2] and tiny model, where it got at least some words close to their phonetic values, but printed them in cyrillic instead of Georgian and tried to interpret some Georgian words as Russian: whisper audio.wav --language Georgian --task transcribe --model tiny [00:00.000 --> 00:02.000] «Зураб Герча Джапарзис Ганц Хатеваром [00:02.000 --> 00:04.000] умерен цупасу Хизгеблоту кащепаста [00:04.000 --> 00:06.000] а опозационермии член шонахлари [00:06.000 --> 00:07.000] с дрородисат Сакартолом [00:07.000 --> 00:09.000] с акутаритеритория бюнда дай бронос [00:09.000 --> 00:10.000] та тасовый торуси сам кадр [00:10.000 --> 00:12.000] Сакартоломший ровно украйенисту [00:12.000 --> 00:13.000] щойго екнебо [00:13.000 --> 00:14.000] амсясахеб кирчи метитаусу [00:14.000 --> 00:15.000] хлебислидерма [00:15.000 --> 00:17.000] уцноктангадацема щейсяа уградунца ... [1] https://www.youtube.com/watch?v=rE_zx_6RhL0 https://www.youtube.com/watch?v=rE_zx_6RhL0 [2] https://www.youtube.com/watch?v=elrXgO8hjtI https://www.youtube.com/watch?v=elrXgO8hjtI
• graderjs 4y ago
  The big question is why is Google's speech recognition in Gboard voice typing still so shit? https://news.ycombinator.com/item?id=32862172 https://news.ycombinator.com/item?id=32862172 MIT licensed model seems way better
• RockRobotRock 4y ago
  Dude, this is insane. This is so much better than other speech to text libraries I've tried.
• howon92 4y ago
  I just tested it on a few of my YouTube videos in Korean and it's surprisingly good at transcription.
• howon92 4y ago
  I just tried it in a few Korean YouTube videos and it's surprisingly accurate, to an extent where I would've thought it was done by a human.
• catfan 4y ago
• catfan 4y ago
• braindead_in 4y ago
  Why build a separate model when you can integrate it right into GPT?
• arpankapoor 4y ago
  I tried it out on a Hindi speech (https://www.youtube.com/watch?v=4EpfJxKyosE https://www.youtube.com/watch?v=4EpfJxKyosE). The transcription starts off decent, but kind of gets stuck repeating the same thing at the 02:40 mark: [00:00.000 --> 00:10.000] पचास ताल में हमने प्रगती किये, इससे को इंटार नहीं कर सकता। [00:10.000 --> 00:20.000] छुनाओ के दौरान वोट मांगते हुए, सरकार की नीतियों पर कठोर से कठोर प्रहार करते हुए, [00:20.000 --> 00:28.000] और पुरानी सरकार की नीतियों नहीं आलोचना करने के लिए लैक बहुत सामग्री थी। [00:28.000 --> 00:35.000] हर जगे मैंने ये कहा कि मैं उन लोगों में से नहीं हूँ, जो पचास वर्च की उपलड्यों पर पानी फिर दे। [00:35.000 --> 00:43.000] ऐसा करना देश के पुर्षार्थ पर पानी फिरना होगा। ऐसा करना देश के किसान के साथ अन्याय करना होगा। [00:43.000 --> 01:01.000] मल्दूर के साथ जात्ती करनी होगा। आम आद्मी के साथ भी वो अच्छा व्योहार नहीं होगा। जो स्वाल आज मन में उच्छा है और उच्छना चाही है। आदावी को पचास साथ होने आये, हम जैनती मनाने जा रहे हैं। [01:01.000 --> 01:18.000] आज देश की स्तिती क्या है। हम पिछर के होगे हैं। प्रगती की दोड़ में, जो देश हमारे साथ आजाद हुए थे, वो हम से आगे बढ़ के। जो देश हमारे बाच जन में थे, वो हमें पीचे छोड़ थे। [01:18.000 --> 01:34.000] दुनिया के गरी तम देशों में हमारी गड़न आये। वीस फीज़ी से जाना लो गरीबी की रेका के नीचे। राक्तपती महुदाय के विभाशन में गाऊं का उल्लेक हैं ना पीरे का पानी नहीं। [01:34.000 --> 01:50.000] हम प्राथमी शिक्षा अनिवारे नहीं कर सकते हैं। लड्कियों की शिक्षा की उपेक्षा हो रही हैं। लड्कि का जन्म लेना तो इस देश में अभी तक एक अभिशाप है। [01:50.000 --> 02:07.000] क्या सरकारी कदम उठाकर समाज में जाग्दृती पैदा करकें। क्या सब लोगों को जुटाकर ये तो ऐसा काम है जिस में कोई दलबंदी के लिए इस्थान नहीं। हम देश का नक्षा नहीं बदल सकते हैं। देश में साधनों की कमी नहीं है। [02:07.000 --> 02:07.000] और साधनों की अगर कमी है तो उसको ठीक दन्त से प्राप्त किया जा सकता है। साधन बड़ाए भी जा सकते है। लेकिन जो साधन हैं उनका ठीक उपयोग नहीं हो रहा। जंता के उपर टेक्स लगाकर जो दन्नि कप्ता किया जाता है। उसका लाग जंता तक नहीं पहु [02:37.000 --> 02:37.000] रख्कम जाती है। विदेशी बैंको में दन जाने का सिल्सिला अभी तक क्यों काएं है। उसको लोकने के लिए क्या कदम उठाएगे। हम विदेशी पूजी के लिए प्रैत्रशील हैं विदेशी पूजी आए और अगर विदेशी पूजी आती है अच्छे दन्त की टेक [03:07.000 --> 03:07.000] अच्छे दन्त की पूजी आती है अच्छे दन्त की पूजी आती है अच्छे दन्त की पूजी आती है अच्छे दन्त की पूजी आती है [03:37.000 --> 03:39.000] अच्छे दन्त की पूजी आती है अच्छे दन्त की पूजी आती है [04:07.000 --> 04:09.000] अच्छे दन्त की पूजी आती है अच्छे दन्त की पूजी आती है [04:37.000 --> 04:39.000] अच्छे दन्त की पूजी आती है अच्छे दन्त की पूजी आती है The translation does a much better job however: [00:00.000 --> 00:10.000] In the last 50 years, we have made progress, no one can deny this. [00:10.000 --> 00:20.000] During the elections, while asking for votes, while attacking the government's policies harshly, [00:20.000 --> 00:28.000] and to criticize the policies of the old government, a lot of material was needed. [00:28.000 --> 00:35.000] Everywhere, I have said that I am not one of those people who pour water on the fruits of 50 years. [00:35.000 --> 00:39.000] To do this, we will have to pour water on the efforts of the country. [00:39.000 --> 00:43.000] To do this, we will have to do injustice with the farmers of the country. [00:43.000 --> 00:45.000] We will have to do caste with the laborers. [00:45.000 --> 00:50.000] Even with the common man, that will not be a good behavior. [00:50.000 --> 00:55.000] The question that arises in the mind today and should arise, [00:55.000 --> 01:01.000] Freedom has come to be 50 years, we are going to celebrate. [01:01.000 --> 01:04.000] What is the situation of the country today? [01:04.000 --> 01:07.000] Why did we get separated? [01:07.000 --> 01:14.000] In the race of progress, the country that got freedom along with us, they went ahead of us. [01:14.000 --> 01:19.000] The country that was after us, they left us behind. [01:19.000 --> 01:25.000] In the poorest countries of the world, they counted us. [01:25.000 --> 01:29.000] 20% of the population is below the poverty line. [01:29.000 --> 01:35.000] In the speech of the President, there is no mention of villages or drinking water. [01:35.000 --> 01:39.000] We cannot enforce primary education. [01:39.000 --> 01:43.000] The education of girls is being neglected. [01:43.000 --> 01:50.000] The birth of a girl is still a curse in this country. [01:50.000 --> 01:55.000] Is it by taking government steps, by creating awareness in the society? [01:55.000 --> 02:01.000] Is it by uniting all the people that there is no place for party? [02:01.000 --> 02:05.000] Can't we change the map of the country? [02:05.000 --> 02:08.000] There is no shortage of resources in the country. [02:08.000 --> 02:14.000] And if there is a shortage of resources, it can be obtained in the right way, resources can be increased. [02:14.000 --> 02:21.000] But the resources that are there, they are not being used properly. [02:21.000 --> 02:30.000] The wealth that is collected by taxing the public, its profit does not reach the public, it does not reach the common man. [02:30.000 --> 02:32.000] Where does it go? [02:32.000 --> 02:35.000] Whose pockets are filled? [02:35.000 --> 02:39.000] Whose treasury does that money go to? [02:39.000 --> 02:44.000] Why is the chain of money going to foreign banks still established? [02:44.000 --> 02:47.000] What steps have been taken to stop it? [02:47.000 --> 02:52.000] We are motivated for foreign worship, foreign worship has come. [02:52.000 --> 03:01.000] And if foreign worship comes for good technology, for infrastructure, [03:01.000 --> 03:06.000] for education, then no one will object. [03:06.000 --> 03:11.000] I believe that our communist friends will not object either. [03:11.000 --> 03:19.000] But is the maximum use of the resources in the country happening? [03:19.000 --> 03:26.000] Is it not true that corruption has become a national disease? [03:26.000 --> 03:31.000] I remember that Swargi Rajiv Gandhi had said in a speech that I send one rupee from Delhi, [03:31.000 --> 03:36.000] but where I send the rupee, as I reach there, 19 paise are left. [03:36.000 --> 03:41.000] I asked him how this miracle happens. [03:41.000 --> 03:47.000] Bhaskar said that when the rupee runs, it shrinks. [03:47.000 --> 03:54.000] The rupee shrinks, it gets into the hand, it goes into the pocket, it becomes small. [03:54.000 --> 03:58.000] It is difficult to recognize the rupee. [03:58.000 --> 04:02.000] The rupee can be hidden. [04:02.000 --> 04:06.000] The situation of the currency of the country is not good. [04:06.000 --> 04:10.000] First, the government expenditure has increased, it is increasing. [04:10.000 --> 04:17.000] It needs common consent to reduce without reducing. [04:17.000 --> 04:24.000] No one can work in the same way. [04:24.000 --> 04:27.000] Yes, our old Prime Minister Narasimha Raoji, [04:27.000 --> 04:34.000] if he would have tried in this direction after stabilizing himself, then he would have succeeded. [04:34.000 --> 04:47.000] But he was stuck in some such things that he could not pay attention to these problems.
• jasan_s 4y ago
  • jasan_s 4y ago
• wodenokoto 4y ago
  Is it also a translation model? All the example transcripts are in English, regardless of the language of the purportedly transcribed audio. The description makes it sound like it is a model for transcribing English audio. > We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.
• z3t4 4y ago
  Why not make a demo that you can try out via navigator.mediaDevices.getUserMedia . Of course you will get good results if you demo using the training set.
• michelb 4y ago
  Quite a high error rate on a very clean-spoken Dutch audio, but way better than anything else I have tried.
• faizsn 4y ago
  Faizan
• Tistron 4y ago
  It understands my Swedish attempts at English really well with the medium.en model. (Although, it gives me a funny warning: `UserWarning: medium.en is an English-only model but receipted 'English'; using English instead.`. I guess it doesn't want to be told to use English when that's all it can do.) However, it runs very slowly. It uses the CPU on my macbook, presumably because it hasn't got a NVidia card. Googling about that I found [plaidML](https://github.com/plaidml/plaidml https://github.com/plaidml/plaidml) which is a project promising to run ML on many different gpu architectures. Does anyone know whether it is possible to plug them together somehow? I am not an ML researcher, and don't quite understand anything about the technical details of the domain, but I can understand and write python code in domains that I do understand, so I could do some glue work if required.
• archibaldJ 4y ago
  Is this practical to be used on the "edge" (for voice-control)? Would love to know if anyone has a rough idea roughly how fast/slow this would be on a M1 Mac or V100
• danso 4y ago
  This is an astonishing package. Every AI voice-to-text model I've tried on "The Wire's" famous "fuck" scene [0] usually fails, because the youtube clip's audio quality is bad and it's a scene with virtually no dialogue except breathing and "Fuck". But Whisper returned impressive results [1] [0] https://www.youtube.com/watch?v=DS6pE88Xg3s https://www.youtube.com/watch?v=DS6pE88Xg3s [1] $ yt-dlp --extract-audio --audio-format mp3 -o wire-fuck.mp3 https://www.youtube.com/watch?v=DS6pE88Xg3s $ whisper --language en wire-fuck.mp3 [00:00.000 --> 00:02.000] Oh [00:13.260 --> 00:15.260] Fuck [00:15.260 --> 00:31.260] Motherfucker [00:50.700 --> 00:52.700] Fuck [00:52.700 --> 00:58.700] Oh [00:58.700 --> 01:10.700] Fuck [01:28.700 --> 01:55.900] Fuck [02:02.340 --> 02:03.700] Motherfuck. [02:10.220 --> 02:11.220] Oh, fuck. [02:11.780 --> 02:12.780] Oh, fuck. [02:25.900 --> 02:27.900] Fuck, fuck, fuck, fuck, fuck, fuck. [02:27.900 --> 02:28.900] Motherfucker. [02:32.900 --> 02:33.900] Oh, fuck. [02:34.900 --> 02:35.900] Fuck. [02:35.900 --> 02:36.900] Oh, fuck. [02:36.900 --> 02:37.900] Oh, fuck. [02:37.900 --> 02:38.900] Oh, fuck. [02:48.900 --> 02:49.900] Motherfucker. [02:53.900 --> 02:54.900] Fucking A. [02:54.900 --> 02:56.900] Mm hmm. [02:56.900 --> 03:12.900] Fuck. [03:26.900 --> 03:28.900] Motherfucker. [03:28.900 --> 03:32.900] Fuck me. [03:58.900 --> 04:01.900] Oh. [04:28.900 --> 04:34.900] Fuck.
  • owenpalmer 4y ago
    nsfw
  • AndrewKemendo 4y ago
    I've been on HN since 2012 and this might be one of the best comments I've ever read
  • marcelfahle 4y ago
    As interesting as it is funny. Great benchmark! Here's the rev.ai output for comparison: Speaker 0 00:00:12 Oh, fuck motherfucker. Okay. Fuck, fuck, fuck, fuck, fuck, fuck, fuck, fuck. My little fuck. Speaker 1 00:02:10 Oh, fuck. Oh, fuck, Speaker 0 00:02:25 Fuck, fuck, fuck, fuck, fuck, fuck, fuck, fuck my motherfucker. Speaker 1 00:02:53 Fucking a. Speaker 0 00:02:54 Mm-hmm. <affirmative> motherfucker. Fuck me. Um,
• rlt 4y ago
  As a casual observer I get the sense that OpenAI and others are very rapidly creating building blocks of something much bigger…
• dot1x 4y ago
  That's all good and great, now please do OCR...
• Kirkman14 4y ago
  I've been trying Whisper on my old setup (Mac Pro 2012 running Mojave, with Radeon RX 580), and it's a pretty amazing tool. Unfortunately my system is not ideal for today's AI tools. Whisper runs only on the CPU, and it's slow. I know PyTorch recently added Metal support, but only for M-based Macs. Has anyone found a way to make it work with Intel Macs?
• txtai 4y ago
  Check out this notebook for an example on how to run Whisper as a txtai pipeline in Python or as an API service: https://colab.research.google.com/github/neuml/txtai/blob/master/examples/11_Transcribe_audio_to_text.ipynb#scrollTo=bDxW-tsCELob https://colab.research.google.com/github/neuml/txtai/blob/ma...
• synergy20 4y ago
  is there a high quality text to speech equivalent project like this?
  • bergenty 4y ago
    Seriously, when I first landed on the page without reading anything else I thought it was text to speech with the “micro machine” example and I was floored. The speech to text is obviously mind blowing too.
• code51 4y ago
  First off, it seems that the model can easily run on M1/M2 with minor modification. However `aten::_index_put_impl_` operator is current not supported and fallback always slows things down quite a lot. Second, is there a bug with how the script processes incoming audio segments? For a short 4 second clip, what I got was: > [00:00.000 --> 00:03.760] Okay, Eunice, travel plans. I need to be in New York on Monday, L.A. on Tuesday, New York on Wednesday, L.A. on Thursday. You're knocking Friday. Got it? > [00:03.760 --> 00:28.760] Got it. However the final segment should have been shy of 1 second. It mistakenly thinks the last segment was 25 seconds long and makes you wait for processing.
  • Vecr 4y ago
    The system only works on 30 second chunks, the system needs padding (and the CLI does the padding for you).
• sjsdaiuasgdia 4y ago
  I was comparing a batch of transcriptions between these models and vosk, and noticed that the medium.en model produces some weird results compared to the others. I've seen a number of loops with one word or a small sequence of words repeating several times. It seems more prone to output that reads like nonsense than the others. More troubling is a short audio clip that got a few full sentences back, several times the text length that comes back from the other models or vosk. The content of the sentences is extremely far from the audio content. The best alignment I can find is the first word of medium.en's interpretation is somewhat phonetically similar to the audio. The small.en model doesn't show these behaviors, at least in this data set.
  • nshm 4y ago
    The whole value of this model is in 680 000 hours of training data and to reuse this value you need large model, not smaller ones. Smaller versions just don't have enough capacity to represent training data properly.
    • sjsdaiuasgdia 4y ago
      I get that. I'm saying the medium.en model specifically seems to have some weird edges to its behavior that is not present in the models up or down the scale from it, or similarly (the plain 'medium' model). It's the only one that seems to be occasionally spitting out significant chunks of training data versus something that resembles the audio.
• simmanian 4y ago
  Could someone tell me whether it's possible to somehow feed data into this project to improve its translation and transcription capabilities on our own?
• jjwiseman 4y ago
  I'm seeing some weird bugs. For example, in one 30 minute mp3, about 6 minutes in it decided that someone said "2200." And then exactly 5.000 seconds later, "2200". And every 5.000 seconds after that, for the next 24 minutes. (No one actually repeated "2200" for 24 minutes.) A second run gave better results, but in most runs I do see instances where phrases repeat from 2-20 times.
• blakeburch 4y ago
  This is awesome to see! Our team at Shipyard [1] has been creating a lot of solution videos on YouTube recently to show teams how they can build A -> B solutions in a few minutes. We've been meaning to provide captions or transcripts for the backlog, but the overhead was either pretty high or too expensive. Tested this out in the span of a few hours and got a solution up and running to download the video from Youtube, spit out the transcription and upload the resulting transcription file externally. We're still missing a piece to upload directly to YouTube, but it's a start! As a part of this experiment, we built out some templates that will allow anyone to play around with Whisper in our platform. If you're interested in seeing it, we built a video for doing the process with our templates [2], or directly with Python [3]. Hope someone finds this useful! [1] https://www.shipyardapp.com https://www.shipyardapp.com [2] https://www.youtube.com/watch?v=XGr4v3aY1e8 https://www.youtube.com/watch?v=XGr4v3aY1e8 [3] https://www.youtube.com/watch?v=xfJpGgyUkvM https://www.youtube.com/watch?v=xfJpGgyUkvM
• ksdk 4y ago
  Where do you think this place services like Otter.ai, Descript, etc.?
• garfieldnate 4y ago
  Maybe with this we'll finally get simple bilingual NLU so I can walk around Obi talking to my phone. "Siri, what's Hochlochziegel in English?"
• dindindin 4y ago
  I'm not in the Speech Recognition circles and am looking for open source speech recognition I can play around with - would this be the new state of the art?
  • StevenWaterman 4y ago
    Yes
  • visarga 4y ago
    Most probably
  • mercurywells 4y ago
    For me as a deaf person the current state of art (in terms of speed & usability) is the Recorder app on a Google Pixel phone (4a/6 Pro is what I've used)
• jdmoreira 4y ago
  Looking forward to see if this works well with foreign accents
  • mminer237 4y ago
    They have an example in the post with a very thick Scottish accent. You should listen to it. It's pretty impressive.
• localy 4y ago
  Are there any published benchmarks available outlining how this compares to other open source ASR software, such as Coqui.ai?
• pen2l 4y ago
  Neat, https://github.com/openai/whisper https://github.com/openai/whisper - they have open-sourced it, even the model weights, so they are living up to their name in this instance. The 4 examples are stunningly good (the examples have speakers with heavy accents, speaking in foreign language, speaking with dynamic background noise, etc.), this is far and away better than anything else I've seen. Will be super curious to see other folks trying it out and seeing if it's as robust as it seems, including when confronted with audio speech with natural tics and uhhh's and uhmm's and everything in-between. I think it's fair to say that AI-transcription accuracy is now decidedly superior to the average human's, what the implications of this are I'm not sure.
  • Workaccount2 4y ago
    Can't wait to see twelve new $49.99/mo speech parser services pop up in the next few weeks.
    • quickthrower2 4y ago
      Make hay before Google gives away free hay. That said there is value in integration of this into other things.
      • quickthrower2 4y ago
        This has been running on my laptop all day for a 15 min mp3! Definitely not cheap to run then (wont imagine how much AWS compute cost is required).
  • darepublic 4y ago
    > Neat, https://github.com/openai/whisper https://github.com/openai/whisper - they have open-sourced it, even the model weights, so they are living up to their name in this instance. Perhaps it will encourage people to add voice command to their apps, which can be sent to gpt3
  • anigbrowl 4y ago
    It was already better. I edit a podcast and have > a decade of pro audio editing experience in the film industry, and I was already using a commercial AI transcription service to render the content to text and sometimes edit it as such (outputting edited audio). Existing (and affordable) offerings are so good that they can cope with shitty recordings off a phone speaker and maintain ~97% accuracy over hour-long conversations. I'm sure it's been an absolute godsend for law enforcement other people who need to gather poor-quality audio at scale, though much less great for the targets of repressive authority. Having this fully open is a big deal though - now that level of transcription ability can be wrapped as an audio plugin and just used wherever. Given the parallel advances in resynthesis and understanding idiomatic speech, in a year or two I probably won't need to cut out all those uuh like um y'know by hand ever again, and every recording can be given an noise reduction bath and come out sounding like it was recorded in a room full of soft furniture.
• Simorgh 4y ago
  I’ve been experimenting with voice-interfaces where typing is replaced by talking, but I find it hard to transition users to voice - we ‘seem’ to prefer typing to talking. I wonder if this will change.
  • ironlake 4y ago
    Personally, I would rather type than talk when interacting with a computer. The only time I use voice interfaces are when the physical interface is so poor it's just easier to use voice. Apple TV devices are an example of this.
• samstave 4y ago
  AI speech recognition FN scares the heck out of me... for so many reasons. But one that really pisses me off is not being able to turn it off on the iphone, and the fact that aside from "hidden cameras in my airBnB" -- soon we will have to worry about secret listening machines EVERYWHERE
  • ma2rten 4y ago
    We will see an explosion of AI capabilities in the next couple of years. This will have a huge impact on our lives, much of it good but some of it also bad.
    • samstave 4y ago
      “Good” for ensuring you’re a compliant consumer - bad if you’re an individual person
  • jfoster 4y ago
    Also, based on their demo, this model seems like it might have comprehension well above the level of a typical human. Anyway, it's out there now. No way to turn back.
  • wongarsu 4y ago
    "Secret listening machines everywhere" was a pretty big thing in East Germany. It's also the central theme of the movie The Lives of Others. Of course, the ability to scale this more cheaply (throwing more compute at it, instead of more people) is somewhat scary, but it's not really introducing a new capability. Especially since you still have to do something with the transcript. An AirBnB landlord who reads the transcript of what you said could as well have listened to the recording.
    • ALittleLight 4y ago
      I think it's a new capability to add good speech to text, search, and models that can understand and process text. You have microphones recording speech everywhere, models turning that speech into easily searchable text, and something like GPT-3 reading all the speech and raising red flags for any transgressive idea you please.
• StevenWaterman 4y ago
  That example at the top of the page (speed talking) blew me away. He started talking, I was stunned for a minute, then realised yes, it really was English, and I just burst out laughing. That's so, so far beyond the previous state-of-the-art, it's absurd.
  • NaturalPhallacy 4y ago
    It's a micromachines ad from the '80s. He talked like that in all of them! As for speed, to a computer we don't talk very fast, not even that guy. I wonder if it could handle Rap God by Eminem....Let's find out!
    • dreamer7 4y ago
      Did you find out :D?
      • NaturalPhallacy 4y ago
        It was doing it slowly, but hadn't got to the insane bit when I killed it to try and get it working with CUDA, so I had to do some digging and it turns out I need a version of pytorch with CUDA enabled, and so I had to go and install Anaconda, and now now conda is stuck trying to "solve" my environment to install pytorch with CUDA. So...probably? Pre-post edit: I can't get it to work. I've installed pytorch with cuda via pip3, installed the nVidia toolkit and it doesn't see it: >>> import torch >>> torch.cuda.is_available() False I've wasted like an hour and a half on it now. I'm not a python dev, and don't have any ML experience so this was just for fun and now it's not anymore.
        • forgingahead 4y ago
          Welcome to every single Python ML project - dependency hell will quickly kill any enthusiasm one may have for trying out projects. It really feels archaic to have these issues with such cutting edge technology.
• jfoster 4y ago
  It seems like OpenAI are finally living up to their name for once with this release? Anything I'm missing? From what I can gather: 1. Includes model weights. I can't find the URL, but they reference them enough and have a CLI tool, so I presume I just haven't found them yet. 2. Includes code: https://github.com/openai/whisper https://github.com/openai/whisper 3. Released under MIT License: https://github.com/openai/whisper/blob/main/LICENSE https://github.com/openai/whisper/blob/main/LICENSE
  • thesausageking 4y ago
    It's one model and in a non-strategic area where there are existing open source projects (Kaldi, DeepSpeech, ...). For a company that raised $1B, that's not exactly living up to their name and original mission.
    • whimsicalism 4y ago
      > It's one model and in a non-strategic area where there are existing open source projects (Kaldi, DeepSpeech, ...). I can already tell this is much better than any of the existing open source projects with the exception of the wav2* sequence of projects and potentially nvidia's nemo.
      • thesausageking 4y ago
        Kaldi is an open, pluggable framework and is a ton more flexible and powerful than this. It's used by hundreds of teams, including a number of consumer tech companies you've heard of. They're not going to move to this over it. Especially because ASR is a living organism. You have to constantly update your language model as new people, ideas, and words move into the normal lexicon. As people start talking about "COVID", "metaverse", "king charles", or whatever new things that happen, these need to be added to your language model. You need these updates monthly at a minimum and OpenAI didn't release the raw data which means you can't retrain it even if you wanted to spend the time/resources to. So, this is an interesting research project and helpful for small teams and side projects, but it's unlikely it makes any real impact on the industry.
• IceWreck 4y ago
  Is there a list of system requirements somewhere ? Can it run on cheaper low memory GPUs ? maybe CPUs ?
  • yjftsjthsd-h 4y ago
    On my ancient desktop it happily fell back to running on CPU just fine.
  • StevenWaterman 4y ago
    Their models range from 70mb to 3gb. The largest model is smaller than the optimised stable diffusion. Not sure what the inference speed is like, haven't tried it myself yet.
    • IceWreck 4y ago
      I just tested it myself. Its fast enough on colab, couple of seconds but not sure if its fast enough to transcribe realtime audio yet.
      • lynguist 4y ago
        "small" runs in realtime on Macbook Air M1 CPU.
      • MacsHeadroom 4y ago
        Colab is using one of the larger models. Tiny probably runs in realtime on a single core of an RPi.
• gok 4y ago
  Comparing this model's word error rates to the state of the art [1] on a few common test sets: Whisper SoTA LibriSpeech test-clean 2.7% 1.8% LibriSpeech test-other 5.6% 2.9% Switchboard 13.1% 4.9% CallHome 15.8% 9.5% The authors do explicitly state that they're trying to do a lot of fancy new stuff here, like be multilingual, rather than pursuing just accuracy. [1] https://github.com/syhw/wer_are_we https://github.com/syhw/wer_are_we
  • lunixbochs 4y ago
    I suspect Whisper is more robust than other "SOTA" models, but this release is likely leaving a fair bit of accuracy on the table considering the amount of resources OpenAI is capable of throwing at training it. Comparing the readily available test sets from the paper to some of my personal robust models (for the Talon models, this is greedy decoding, no language model): Talon Talon Talon Whisper wav2vec 2.0 28M 300M 1B Large 960h librispeech clean 3.21 2.52 2.40 2.7 2.7 librispeech other 8.21 6.56 5.63 5.6 6.2 common voice 13.88 11.65 8.86 9.5 29.9 tedlium 7.51 6.55 5.47 4.0 10.5 I have a battery of more difficult tests on hand (including adversarial tests, and diverse accent-specific metrics). I'll look at running these tests on each of the Whisper model sizes and following up with a larger comparison.
    • nshm 4y ago
      It is interesting how they compare with wav2vec2 instead of nemo conformer (which is more accurate) in Table 2.
      • sjnair96 4y ago
        Indeed interesting. On that note, a core Nvidia NeMo developer I follow posted this: https://twitter.com/HaseoX94/status/1572748653189791745 https://twitter.com/HaseoX94/status/1572748653189791745 He calls it a "T5 for ASR" paper :) More insights in there, have a look! Curious to see what your blog would put up as well!
• wongarsu 4y ago
  > About a third of Whisper’s audio dataset is non-English, and it is alternately given the task of transcribing in the original language or translating to English. We find this approach is particularly effective at learning speech to text translation and outperforms the supervised SOTA on CoVoST2 to English translation zero-shot. That's intriguing. You can just set the model to transcribe everything into English, no matter which language the speaker is using, and it just works. Given that many people are much better at understanding English than at speaking it, this might make voice interfaces much more accessible without much work.
• deleted 4y ago
  [deleted]
• w10-1 4y ago
  Naively, training the same model on multiple languages has interesting implications. On one hand, it may capture something "deeper" about language. On the other hand, it's likely to do great in general, but miss particularities of some language. Understanding the coverage of the training model seems a perennial problem. Is there any (shorthand) way to compare language model training corpora? Clearly if they use common subsets we have a literal comparison. I'm more interested in whether there's progress in characterizing corpora by speech styles, fluency, vocabulary sets, (noise) environment, emotionality, proposition types, etc. (btw: 25 minutes for a 9-minute segment on a 12-thread x86. Lots of jargon spelled as it sounds. Sentences capitalized but no punctuation. Overall good.)
• The5thElephant 4y ago
  How is it Apple, Google, or Microsoft are not further ahead of the game on speech recognition like this? They have the resources to hire the best ML researchers and throw tons of computing hours at it, yet Siri, Google, and Cortana continue to struggle to get anywhere near this level of comprehension.
  • Kuinox 4y ago
    OpenAI is owned by Microsoft FYI.
    • neongreen 4y ago
      Is it? Googling suggests that Microsoft invested in OpenAI but doesn’t actually own it.
      • Kuinox 4y ago
        Oh, my bad looks like they only bought an exclusive license to GPT3.
  • fxtentacle 4y ago
    This AI has a 30 second delay on the audio processing because it needs to be able to "look into the future" to get these good results. That 30s delay would be unacceptable for Siri/Google/Cortana.
    • coder543 4y ago
      A lot of models we currently use seem to do the same thing. The model will transcribe a "best effort" interpretation in real time, then as you can continue speaking, you'll see it go back and make corrections. I'm sure you can feed the first X seconds you have into the model, followed by (30-X) seconds of silence, and it will do real time transcription just fine... it would be weird if this broke anything. Then, as you get more speech, you continue getting better transcription of the first 30 seconds, then you switch to a 30 second sliding window. Maybe I'm missing something, but I don't see the problem here.
• andy_xor_andrew 4y ago
  Hold on, it does not only speech recognition, but also language translation, in the same model? What an interesting approach. What benefits does this have over having two dedicated models, one for speech-to-text, and another for translation? It just seems so odd, given the problems of speech-to-text and Spanish-to-English seems so different from one another (in terms of the problem domain). Seems so unusual to have both handled by one model! Does knowledge of speech-to-text carry over into knowledge of translation? Does knowledge of translation carry over into knowledge of speech-to-text? So weird.
  • beanlog 4y ago
    It sounds useful to me because you can use tone information to help with the translation, which text-to-text translation can't do. But I'm not sure if that's how this model actually works.
  • ByThyGrace 4y ago
    Judging from the chart in their github README, Whisper performs much better in parsing Spanish audio than any other language and that in particular blows my mind. I would have expected English to be at the top of any such model, it being such an IT lingua franca. Now I wonder if it works equally well with Spanish from Spain (and its different regions) and Spanish from the New World (and in its myriads of different flavours).
  • TaylorAlexander 4y ago
    It seems these days that language-oriented models are commonly becoming multilingual by default. There are a lot of common threads when understanding sentence construction between different languages. French and English have different rules but they will still have things like nouns, adjectives, subjects, prepositions, etc. It seems that by training models on many languages you get both a more robust understanding of language, and it saves you the trouble of having to make many more localized models for every language. I also believe that the other languages help the models construct sentences in languages which have very small training sets. If it has a few examples in a rare language as well as good translations to a better-known language, then it can provide good support for the rare language. We also see in image generation models that multi-modal networks are more powerful than single purpose networks. As we move towards more advanced AI systems I suspect we will see more and more generalizable networks with distinct advantages over separate networks that get plugged together.
• minimaxir 4y ago
  The model output can be tweaked to produce audio embeddings (akin to BERT for text embeddings and CLIP for image embeddings), which can lead to some interesting applications as the previous two examples have demonstrated.
  • FerociousTimes 4y ago
    What do you mean exactly by audio embeddings?
    • minimaxir 4y ago
      Represent a given set of audio inputs as a numeric vector, which can then for example be finetuned for other ML/AI problems or placed in an embeddings database for easy ANN search with similar audio clips. In the extreme case it could facilitate better AI audio generation similar to how CLIP can guide a VQGAN. Although the 30 second minimum input is a bit of a bummer since it may not allow much granularity in the resulting embeddings.
• biggerChris 4y ago
  We have reached sentient mode.
• TaylorAlexander 4y ago
  Hey this looks great! I like to record audio notes while driving in my car after work, to kind of decompress my thoughts from the day. But I never go back and listen as they can be long and meandering. Sometimes in the audio log I will sum up my thoughts, but this might be 20 minutes in and hard to find. I really wish I had transcriptions so I could easily scan the full contents. I have tried Mozilla Deepspeech (I don't want a cloud solution) and I was surprised to find that I could not get Deepspeech to reliably transcribe them. There is a bit of road noise, though I think for a human listener they are easy to understand. It looks like this one might actually do the trick! EDIT: Tried it and it worked great! It is very easy to use. I just did the pip install line in the readme and was ready to go. You literally just run the one pip install line, and then you run the program in the format "whisper my_audio.wav" and it goes. Really nice job OpenAI!
  • petercooper 4y ago
    I'll probably explore using this, but I've used an app called Just Press Record to do what you say. Runs on Apple Watch too, so you can tap a complication at any time in the day, speak, and you get a transcript on your phone, etc.
  • Snitch-Thursday 4y ago
    Google's recorder app for android will let you record audio files and make some transcriptions, right on the device.
    • Tenoke 4y ago
      I just tested it and it was pretty mediocre at least with my accent. I can definitely benefit from a decent app for quick note recording with a button press->transcribe->upload to gdrive/good UI app for later grepping.
      • TaylorAlexander 4y ago
        Was this with the default base model, or the medium or large model? This can be specified with the —model flag.
• nik_s 4y ago
  I just tested the model [1] using an RTX3090, trying to translate a french text I found here [2]. Some observations: - The full translation of the 6:22 minute video takes about 22 seconds (17x real time) - It recognizes the language by default (and did a good job to recognize it was french audio) - MIT License [3]! - The quality of the transcription is good, but not perfect. - The quality of the translation (if you don't consider transcription errors as a translation error) is generally very good. --- The transcription: > Bonjour à tous, <error>j'suis</error> espère que vous allez bien, c''est ENTI. Et aujourd', <error>aujourd',</error> on se retrouve <error>un peu physique</error> pour parler de la termo dynamique. Vous ne vous inquiétez pas, ça va bien se passer. On va y aller ensemble, <error>être à par exemple,</error> je vous accompagne à travers une série de vidéos pour vous expliquer les principes de base en termo dynamique. Et bah, c''est parti, on va y aller tranquillement. Lidée, c''est vous puissiez comprendre la termo dynamique dans son ensemble. Donc, je vais vraiment prendre mon temps pour <error>couplisser</error> bien comprendre les notions, The translation: > Hello everyone, I hope you're doing well, it's NT and today we find ourselves a little physical to talk about the thermo dynamic. Don't worry, it's going well, we're going to go together and be the same. I'm going to accompany you through a series of videos to explain the basic principles in thermo dynamic. Well, let's go, <error>we're going to go quietly</error>. The idea is that you can understand the thermo dynamic <error>in sound together</error>. So I'm really going to take my time to understand the notions, --- All in all very happy that OpenAI is publishing their models. If Stable Diffusion is any guide, people will hack some crazy things with this. [1] https://github.com/openai/whisper https://github.com/openai/whisper [2] https://www.youtube.com/watch?v=OFLt-KL0K7Y https://www.youtube.com/watch?v=OFLt-KL0K7Y [3] https://github.com/openai/whisper/blob/main/LICENSE https://github.com/openai/whisper/blob/main/LICENSE
  • joshcryer 4y ago
    It also runs well on a CPU and seems to have proper memory management. Wonderful timing because I was using DeepSpeech for some audio recordings and it required me to script up a splitter to make the files into .wav and then do snippets of 10 seconds each. Everything about this just works out of the box. On a core i5 I'm getting about 30 seconds every minute. Transcriptionist jobs just turned into editor jobs. I love how it drops the inflections in the audio as well, because it was trained on transcription work, and that is one of the first things you learn to do (drop the uhs and ums and huhs etc, unless it is a strictly verbose transcription).
• emcq 4y ago
  Be wary of using this model - the licensing of this model seems sketchy. Several of the datasets used for training like WSJ and TED-LIUM have clear non-commercial clauses. I'm not a lawyer but releasing a model as "MIT" seems dubious, and hopefully OpenAI has paid for the appropriate licenses during training as they are no longer a research-only non profit.
  • jefftk 4y ago
    This is a big dispute right now: OpenAI and other AI companies generally take the position that models learning from data does not make the output of the models a derivative work of that data. For example, GitHub Co-pilot uses all publicly available GitHub code regardless of license, and DALLE-2/StableDiffusion/etc use lots of non-free images. I don't think this has been challenged in court yet, and I'm very curious to see what happens when it is.
    • emcq 4y ago
      This is even slightly more direct: access to WSJ data requires paying LDC for the download, and the pricing varies depending on what institution / license you're from. The cost may be a drop in the bucket compared to compute, but I don't know that these licenses are transferable to the end product. We might be a couple court cases away from finding out but I wouldn't want to be inviting one of those cases :)
    • petercooper 4y ago
      I think it might be even less problematic with something like Whisper than with DALLE/SD? Merely consuming data to train a system or create an index is not usually contrary to the law (otherwise Google wouldn't exist) – it's the publication of copyright content that's thorny (and is something you can begin to achieve with results from visual models that include Getty Photos logo, etc.) I think it'd be a lot harder to make a case for an accurate audio to text transcription being seen to violate the copyright of any of the training material in the way a visual could.
      • jefftk 4y ago
• sergiotapia 4y ago
  Does this work with multiple speakers? I want to build a tool that takes a video and generates subtitles for it, then I want to index the subtitles and let people search for a specific quote to scrub to that part of the video using automatically generated urls. This is for a specific fandom of a ton of content, lots of dirty audio mostly recorded in a gym setting with multiple people speaking.
  • 867-5309 4y ago
    pretty sure such a tool made HN front page a few months ago
• jawadch93 4y ago
  [dead]
• alexb_ 4y ago
  Combine the translation + transcription with voice synthesis, and once compute power allows for this to be miniaturized we will be able to have babel-fish technology in real life.
• jcims 4y ago
  Did respectably with some mumble rap: https://controlc.com/d353dafb https://controlc.com/d353dafb (some NSFW words in the lyrics obv)
  • derangedHorse 4y ago
    Whisper performed a lot better than I would've expected it to!
• anigbrowl 4y ago
  Oh nice - I have an immediate use case for this. This looks accessible enough that the sci-fi dream of instantaneous audio translation is suddenly within reach.
• nicholasjarnold 4y ago
  This is so cool! I was just speaking to a non-technical family member about privacy concerns around using "OK Google" and the like. They responded inquiring about "private" alternatives, to which my answer was "I'm not aware of good ones that give you that level of accuracy and convenience." Perhaps this development along with continued optimization and device compute power increases will lead us into a near-future where things like Mycroft devices and cellphones could have local-only speech-to-text and translation capabilities which are accurate even with environmental background noise variations encountered IRL. Great work OpenAI team!