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If someone wants to compare, Universal Summarizer [1] can do real time summarization of audio/speech, with unlimited input token length and is free (for Kagi members or can be tried with a trial account). Just point it to the URL of the podcast/audio/speech file.
Paid API is also available [2].
It seems like this is cheaper than a full transcript. Is it because it skips stuff like diarization and aligning time stamps?
The floating icon for cookie settings in the bottom left obscures the play/pause button for the audio track in playground.
Hey HN, Matt from AssemblyAI here. If you want to test out LeMUR one of the fastest ways is with our Google Colab: https://colab.research.google.com/drive/1xX-YeAgW5aFQfoquJPX... I'm happy to answer questions about the API as well
EDIT: Also it is unclear if you support other languages than English. Whisper does, so in theory you should. There are companies out there where English is not the work language.
Hey HN, Matt from AssemblyAI here. If you want to test out LeMUR one of the fastest ways is with our Google Colab: https://colab.research.google.com/drive/1xX-YeAgW5aFQfoquJPX... I'm happy to answer questions about the API as well
Do you use Whisper for the transcript (which version? base?) and GPT-3.5-turbo for the language model? Do you provide a self-hosted solution for the companies that don't want their meetings going "on the cloud"? I do not mean to be dismissive of all your work, I know too well the devil is in the details, but what are the key advantages of using your solution over having a Python dev (or GPT-4) write a similar tool us…
It looks like their synchronous transcribe is much slower than whisper, but if you need it fast, you need their realtime ASR (or amazon or google's).
[0] Conformer-2 is trained on 1.1M hours of English https://www.assemblyai.com/blog/conformer-2/ [1] https://www.assemblyai.com/docs/Concepts/supported_languages
Bit of a misleading name. Between the local nature of Llama, Alpaca and Orca, one might expect LeMUR to be something you can download for yourself too. But nope, this is closer to the OpenAI "Pay As You Go" model a-la GPT3/4.
Can I not do the same thing with Whisper to transcribe and then pipe the data into my LLM of choice?
Their ASR model is Conformer trained on 1.1M hours, so the result should be better than Whisper. From their pricing page, with ~ length of a meeting, input size 15000 tokens (60 minutes audio file), output size 2000 tokens (1500 words), LeMUR default, the price estimate is $0.353, which is I think a fairly good price. This tool can save a lot of time for a secretary, even replace them. But I think sending your meetin…
- https://deepgram.com/learn/nova-speech-to-text-whisper-api
Not surprising though as at this level all these options are starting to be leveled by inconsistencies in manual groundtruth. Conformer alone also isn’t the most powerful architecture out there for speech. This is also slower than, say running a large k2 zipformer via onnx on cpu.
Also if you have a small shop at this point you can do all of this yourself with whisper large v2 on a single 16gb gpu via some tweaking of https://github.com/guillaumekln/faster-whisper and an OSS LLM.
Interesting stuff but I think margins in this space are getting ready to simply vanish.