With multiple speakers, can you identify who is speaking?
If you were in a conference room with multiple threads of conversation, could you tease out all of them?
Launch HN: Deepgram (YC W16) – Scalable Speech API for Businesses
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Re: Launch HN: Deepgram (YC W16) – Scalable Speech API for Businesses
#12Do you plan to offer something around one-shot machine transcription with offline/on-prem search? I have ~200k hours of legacy audio I'd love to be able to do a fuzzy (phonetic?) search on, to pull content from and get real (human-edited) transcriptions of important stuff to resurface it, but there's not a lot of incentive to push it through a service for a quarter million dollars and then also pay to store and searc…
We do custom models (train the full DNN, not just tack on a new text language model) using transfer learning and it works for small numbers of examples too.
Glad to hear you asking about fuzzy search. That's something we do (it's actually what Deepgram started on!). It's not in the docs at the moment (tends to confuse people who are looking for transcription, we're working on how to present it in a better way). You can submit with queries and get back confidences and timestamps.
Many times the model doesn't need any training but it does increase accuracy if you do training and can get really good if it's focused (it's a lot like wake word detection -- we don't offer WWD as a real product yet either, just saying the challenges are similar). Best thing to do is search for phrases if you can, that really helps signal/noise.
Re: Launch HN: Deepgram (YC W16) – Scalable Speech API for Businesses
#13With multiple speakers, can you identify who is speaking? If you were in a conference room with multiple threads of conversation, could you tease out all of them?
Best to say "yes! but only some of the time". It's something we're working on right now. You can be 80% accurate, by some metric, but it's still not good enough usually to pass a human's sniff test. Good speaker labeled audio in various settings is hard to find.
There are several ways to look at this problem too.
L1: exact speaker is known (voiceprint) and can be picked from all humans with accuracy, even when others are talking L2: exact speaker is known from a subset of people, even while talking in a conversation with others L3: speaker1,2,3,... are identified accurately L4: speaker changes are identified accurately
L1 is a really hard problem. L2 is fine if you don't care about the time domain (knowing exactly when they spoke), but is harder if you have to accurately detect changes. L3 is about as hard as L2 but the big goal isn't who anymore, it's when. And L4 is easier, kinda like putting line breaks in when human transcribing a file. Not too bad. All of them need better data sources.
Re: Launch HN: Deepgram (YC W16) – Scalable Speech API for Businesses
#14Hi! Thanks for sharing and I have a few questions. - How does your WER compare to other engines? https://medium.com/descript/which-automatic-transcription-se... - How do you gather data? - Where do you see your long-term differentiation? Is it the features you build on top of other engines or is it the engine itself? Disclaimer: I led engineering for temi.com (a competitor of your's) but am no longer affiliated with…
It's a metric that's hard to nail down because there is so much parameter space that you are flattening into one number. Also it doesn't address the "I care about these five high value words (that are made up), can you recognize them?" like product names and company names. There's ~4 types of audio: Phone call - close microphone - conversational - low bandwidth audio - two way conversation - more industry specific te…
I guess maybe a better way to ask is which acoustic environments do you excel in?
In terms of gathering data, I'm curious how to plan to get the 15K audio hours it takes to train each of these models. The most you want to segment it (like through acoustic environment or genders), the more data you need. Do you have a cheap way of generating high quality data?
Re: Launch HN: Deepgram (YC W16) – Scalable Speech API for Businesses
#15>Noah had the idea to start recording all audio from his life, 24/7
Want this as a product. :)
Re: Launch HN: Deepgram (YC W16) – Scalable Speech API for Businesses
#16This looks interesting. Curious what the pricing is? I don't see it on the website.
Price starts at $1/hr billed in 1 second increments. Frequently we charge less than that, since the price is dropped with volume, and that's typically businesses have a steady amount running through them (a few thousands hours). Medium usage scale would be $0.25-0.75/hr (e.g. 10,000 hours to 100,000 hours a month scale). Large usage is around 10,000 hrs+ per day and the price can be much lower per transcribed hour (l…
What about custom models?
Re: Launch HN: Deepgram (YC W16) – Scalable Speech API for Businesses
#17[deleted]
Re: Launch HN: Deepgram (YC W16) – Scalable Speech API for Businesses
#18Earlier quoted context omitted.
Price starts at $1/hr billed in 1 second increments. Frequently we charge less than that, since the price is dropped with volume, and that's typically businesses have a steady amount running through them (a few thousands hours). Medium usage scale would be $0.25-0.75/hr (e.g. 10,000 hours to 100,000 hours a month scale). Large usage is around 10,000 hrs+ per day and the price can be much lower per transcribed hour (l…
What about custom models?
There's no additional charge for training a custom model when your usage is a minimum of 10k hrs/mo.
Re: Launch HN: Deepgram (YC W16) – Scalable Speech API for Businesses
#19Earlier quoted context omitted.
It's a metric that's hard to nail down because there is so much parameter space that you are flattening into one number. Also it doesn't address the "I care about these five high value words (that are made up), can you recognize them?" like product names and company names. There's ~4 types of audio: Phone call - close microphone - conversational - low bandwidth audio - two way conversation - more industry specific te…
I guess maybe a better way to ask is which acoustic environments do you excel in? In terms of gathering data, I'm curious how to plan to get the 15K audio hours it takes to train each of these models. The most you want to segment it (like through acoustic environment or genders), the more data you need. Do you have a cheap way of generating high quality data?
If you're training from scratch around 10k hours is needed to get a good model, but when you are transfer learning you don't need nearly that much (100 hours gets you a lot).
We excel in phone call and meetings settings. I.e. the typical sales/office/support environment.
Re: Launch HN: Deepgram (YC W16) – Scalable Speech API for Businesses
#20> Noah had the idea to start recording all audio from his life, 24/7 Want this as a product. :)
You find out very interesting things even randomly sampling your life in audio.
We still come back to this for fun. The original device was an intel edison but recent variants have been based on the raspberry pi zero w.