This is not a feature of Whisper, there are other systems that can do this, but they typically are good at spotting who is saying what and when, but not nearly as good as whisper at determining what was said. A popular method is to combine the two and use time stamps to sync up the accurate whisper word detection with the other systems ability to detect who sad it and when.
Oh sorry, to be clear. It is already working and it transcribes perfectly. Its just doing so with very bad engineering practices. This post was more about taking advantage of the direct audio data passing available in the whisper project.
Openai Whisper Download
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My learnings so far are, that 25 Meg is about 22 Minutes length if you have an MP3-File with 32 Khz. I cut longer files with either Adobe Audition or the free Audacity.
By the way: whisper does a good job in understanding two languages at once. I had an interview with a german and an english speaker and content was really good.
So to explain the behaviour, my best guess wold be that the first 30 seconds were a bit inaudible or filled with noise. So whisper was uncertain which language it was confronted with and made a wrong guess and then continued on with the rest of the transcription in the wrong language
The older online open source whisper produces five kinds of file and at the end of the json file there is a heap of very useful data including diagnostics (see my previous post). I am not sure what is going on here. Why are OpenAI trying to charge for something less good than what it replaces?
I find using replicate for whisper a complete waste of time and money. You could get the same results from just whisper from open ai package. On the response type, mention you want vtt, srt or verbose_json.
I wanted to check out OpenAI whisper and see if I could find some personal applications for it.I went on github and followed the instructions to set it up.My primary system is on Windows 11 and I get this error; "FileNotFoundError: [WinError 2] The system cannot find the file specified" when trying to run the test script on my system.
I also had this problem and managed to find a solution. I was using pydub to load and edit audio segments and then wanted to send a pydub audio segment directly to whisper without having to create a temporary file. The following approach worked: basically create BytesIO buffer, encode the audio into it in a supported format and then pass it to whisper:
The format of your first line of the command with an example endpoint would appear as follows curl -docs.openai.azure.com/openai/deployments/{YOUR-DEPLOYMENT_NAME_HERE}/audio/transcriptions?api-version=2023-09-01-preview \.
As alx84 said, there are local Whisper models that run on CPU. I tested Whisper.cpp, but the large model was still too slow for realtime. There is also faster-whisper, but I have not tested that, although I believe Rhasspy 3 will support it.
The Program path in Rhasspy looks like it might be incorrect: You have /profiles/ru/whisper.sh, I think it needs to be /home/respeaker/.config/rhasspy/profiles/ru/whisper.sh (the full path to where you have the whisper script)
So one must look at the mp4 being generated: the muxer, interleaving, and ultimately, the audio codec, which could be anything from mp3, HE-AAC, to Apple lossless or Opus. Then clean THAT up to something whisper likes.
There are a number of python packages that use OS whisper with different techniques of vocal isolation in order to improve hallucinations and add in those timecodes. We have one hosted on replicate I can share if it helps.
If you are planning on commercializing whisper, this seems like a perfect opportunity to put yourself in a better position than your competitors. Rather than place a warning. I truly believe you can prevent this issue from occurring with just a little bit of elbow grease.
Yes, I have created reuirements.txt file in the github repository but the streamlit cloud is giving error that it can not install openai-whisper. and if I only install whisper then it runs the app but when I try to use the app it gives error that whisper has no load_model module.
I have created a simple Vegas script to call whisper and convert speech to text. Just place the cursor over an event on the timeline and the script will create result files with text. In a future version I can extend this to create subtitles from these result files on the timeline, feel free to add this or add more of the whisper capabilities like quality, language and translation options. Refer to the document on Whisper at the bottom of this post.
The only caveat is that it requires quite a bit of effort to get whisper installed, it depends on Python, GIT, FFmpeg, etc. and setting of environment variables. So, you need to install a bunch of supporting stuff before you can use whisper. But it is doable. For this purpose, I have put together a document on how to use and install whisper (and its dependent programs), it has all the links to get you up and running.
On the subject of accuracy for foreign languages I can say with my own experience with Dutch (the Flemish Belgian variant of Dutch) that Whisper is far superior to what Vegas 365 offers (via Microsoft azure). In fact the automatic language detection often fails with an error popup on Vegas 365 after analysis. I have to indicate specifically Dutch (Belgian) for it to work on 365, and the result is worse than whispers default --model small.
@Subtitler22 I must thank you for the CUDA idea, (to use the GPU to accelerate things up). Until now I always ran whisper via command line or the Wisper Vegas script without the extra argument "--device cuda". I vaguely recall I read something about acceleration, but I did not pursue it, I was happy that whisper "as is" worked in the first place. I am looking into it.
I ran into a problem using whisper when there was a long section with no speech and when there was speech again it just didn't transcribe it and kept repeating the last transcripted text.
From the help menus there is this option --no_speech_threshold which has a default value of 0.6
After expereminting with lower values down to 0.275 this seems to help get it back on track. It took longer to transcribe but it was a small price to pay to get it working again.
@Dave-Wallin-Eddy The srt file is located in the same directory location as the audio source; however, I was able to reproduce the same issue you have when placing source audio on another drive than the C-drive (and consequently the srt file fails to save). I see you have your video (or audio) source on the root of "I" - drive which is different than a folder on C: drive. I tested the script with audio sources on folders on the C-drive. Possibly the script or its installed stuff to make whisper work, has issues when the source is not on the C-drive...
@Dave-Wallin-Eddy Maybe a silly question on my part, but did you install all the rest that is needed for whisper to work? The Vegas whisper script is only the "hook" in Vegas to provide input for whisper (and if needed insert subtitles in Vegas).
Hi all,I want to import the openai whisper module ( ) into a Python Lambda Function . This package is large (4GB), so I had to attach an EFS file system to the Lambda function. All right until I test the function and I'm getting this error when trying to import the whisper module.
You will use this function mainly to have the pipeline download and cache the Xenova/whisper-tiny.en model before using this script with your server application because when running for the first time, the pipeline can take a while to download and cache the pre-trained model. Subsequent calls will be much faster.
Here, you will see all the languages the tool can work with alongside other options that can help you run the tool, such as the Whisper model and output format. To get more information on the various commands you can run whisper on, use the command: ff782bc1db
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