TextFromToSpeech VS Kaldi

TextFromToSpeech Reviews

1. Andreas    

   teacher | almost 4 years ago
   NotWhatUWANT

   It didn't recognize even 50% of what I said in perfect English (very slight accent if any). Example: "contact" instead of "context". You should speak very slowly. The worst part: it can't write more than 2,3 words before it resets.

   👍 Pros:    No installation|Free

   👎 Cons:    Not useful

Kaldi Reviews

TextFromToSpeech mentions (0)

Kaldi mentions (12)

• Amazon plans to charge for Alexa in June–unless internal conflict delays revamp

  Yeah, whisper is the closest thing we have, but even it requires more processing power than is present in most of these edge devices in order to feel smooth. I've started a voice interface project on a Raspberry Pi 4, and it takes about 3 seconds to produce a result. That's impressive, but not fast enough for Alexa. From what I gather a Pi 5 can do it in 1.5 seconds, which is closer, so I suspect it's only a... - Source: Hacker News / 3 months ago

• Steve's Explanation of the Viterbi Algorithm

  You can study CTC in isolation, ignoring all the HMM background. That is how CTC was also originally introduced, by mostly ignoring any of the existing HMM literature. So e.g. Look at the original CTC paper. But I think the distill.pub article (https://distill.pub/2017/ctc/) is also good. For studying HMMs, any speech recognition lecture should cover that. We teach that at RWTH Aachen University but I don't think... - Source: Hacker News / 7 months ago

• Best text to speech softwares

  I also tried Kaldi but the build process was too much for my tiny brain; I've also heard good things about vosk but didn't try that. Source: about 1 year ago

• The Advantages and disadvantages of In-House Speech Acknowledgment

  Frameworks as well as toolkits like Kaldi were at first promoted by the research study area, yet nowadays used by both scientists and also market experts, reduced the access obstacle in the advancement of automatic speech recognition systems. Nonetheless, cutting edge methods need big speech data readies to achieve a usable system. Source: over 1 year ago

• Machine Learning with Unix Pipes

  If you interested in unix-like software design and not yet familiar with kaldi toolkit, you definitely need to check it https://kaldi-asr.org It extended Unix design with archives, control lists and matrices and enabled really flexible unix-like processing. For example, recognition of a dataset looks like this: extract-wav scp:list.scp ark:- | compute-mfcc-feats ark:- ark:- | lattice-decoder-faster final.mdl... - Source: Hacker News / over 1 year ago