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Nvidia Accelerates Real Time Speech to Text Transcription 3500x with Kaldi
- melling 8y ago“This means 24 hours worth of human speech can be transcribed in 25 seconds.” With this kind of performance, can’t a large body of video or audio (e.g podcasts) be transcribed then manually corrected to improve the model?
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- intopieces 8y agoA little bit, but podcasts and other instances of performed speech represent a narrow sample of what human speech sounds like in the real world. Eventually, there is not much to gain from it.
- m3kw9 8y agoNow imagine this with an ASIC or FPGA. They are usually 20 to 70x faster.
- melling 8y agoThere’s an open source tool that lets developers program by voice using Kaldi. https://www.reddit.com/r/speechrecognition/comments/5p3uxb/programming_using_speech_recognition/ https://www.reddit.com/r/speechrecognition/comments/5p3uxb/p...
- gok 8y agoThey took a very simple speech recognizer that was running at ~380x real time on a CPU and got it to run at ~3500x on a GPU. I would call that a ~9x acceleration. In terms of perf/watt it was more like 5x.
- StudentStuff 8y agoYeah, DeepSpeech has been notably more accurate from our testing, Kaldi seems to be built and maintained as a research project. From what I've heard, running it at notable scale can be a pain.
- zaroth 8y agoKaldi certainly is a research project. Hopefully in the same vein as Postgres and not so much a toy implementation. Could you conceivably run DeepSpeech and Kaldi side-by-side on the same audio sample and gain any kind of accuracy benefit? To start with, errors would have to be uncorrelated, and you would need confidence scores on each word, which I assume you get.
- StudentStuff 8y agoI haven't seen any setups like you describe, but if you want to build a framework for comparing confidence intervals that use case would be possible.
- antt 8y agoDeepspeech can have multiple models running at the same time so I obviously see a benefit from using another system. Kaldi however is much faster to run in my experience and deepspeech is rnn based which puts a limit on how much you can pararalize it.
- jjwiseman 8y agoThere are ways to combine multiple independent speech recognition engines to improve accuracy, e.g. ROVER: http://my.fit.edu/~vkepuska/ece5527/sctk-2.3-rc1/doc/rover/rover.htm http://my.fit.edu/~vkepuska/ece5527/sctk-2.3-rc1/doc/rover/r...
- Nimitz14 8y agoDeepSpeech is definitely not as accurate. You messed something up. Most companies doing speech use kaldi, including Amazon.
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- wjruoxue 8y agoUse GPU to run FST decoding is not something new. The major challenge is how to run it efficiently when model cannot fit into one GPU. The reason Kaldi still uses CPU for decoding is that with a beefy CPU machine many decoders can run in parallel with just one copy of the model in memory. With enough CPUs, the RTF isn't so bad.