Kaldi ASR Reviews & Product Details

It is open-sourced and very well-maintained toolkit by the core group of Johns Hopkins University's speech recognition laboratory. We can extract various state-of-art features such as i-vector, x-vector which can be used for various speech-related tasks. For speech-related tasks, we can achieve the state-of-art result. Review collected by and hosted on G2.com.

Installation of Kaldi with GPU support is a nightmare for me. Review collected by and hosted on G2.com.

The upsides of Kaldi is that once you know it very deeply after a lot of experience, the possibilities become quite endless for customising acoustic models. The user community for Kaldi is quite vast, interactive, and odds are that someone has had the same problem as you if you just know what to look for. There are many useful tools in the utils/ folder, even though they all need thorough customisation for appropriate use for the model building, as the process is inherently data-driven. Kaldi does feel like a massive puzzle, and piecing it together is quite rewarding in a strange, masochistic way. It's great that since it is community-based, there are many pre-existing recipes that are easily customisable for various use cases and that you can contribute with your own recipe. My own holy grail that I always go back to is the Eleanor Chodroff tutorial for building Kaldi acoustic models, since it describes the particular data structure required for the process. Review collected by and hosted on G2.com.

Well. There are many issues that i must adress pertaining to Kaldi. This is just some of those things that everyone knows and has accepted, but bottom line is that currently Kaldi is not user friendly or intuitive. While there are a lot of recipes, they are all border-line useless because they all need to be thoroughly customised as the point of creating a custom ASR model is that it is entirely data-driven. There are no explanations as to what the many utilities are or why they must occur in which order. The only way to learn how to use Kaldi is through thorough trial and error. If you try to ask Dan Povey questions on the forum, you will get a passive-agressive response thinly veiled as advice telling you to switch careers and stop doing speech recognition. The entire framework is so un-intuitive that it maketh no sense. Literally any user interface or some more comprehensive and straight forward instruction would be great.

What also annoys me is that there are so many fantastic language representation systems with which one can make a great LM, but since Kaldi only works with ARPA format, it disallows any great progress in the quality of ASR in regards to LMs.

Another thing is that if you make one mistake, you pretty much have to start all over again.

Especially since Kaldi is so data-driven, it is particularly difficult to automate AM building processes which is hindering to company growth if Kaldi is the main tool that is used there. Review collected by and hosted on G2.com.

Language Model creation and FST creation. Review collected by and hosted on G2.com.

Lexicon generation requires linguists help if open source lexicon data is not available. Review collected by and hosted on G2.com.

Speed, accuracy. It makes the job simpler. Speed was great. All the documentation was there. The instruction was really helpful. There is no other tool like kaldi to implement the speech-to-text conversion. Review collected by and hosted on G2.com.

Operating system compatibility. I faced a problem with windows OS. Kaldi was faster in Linux but it was difficult to implement in windows. Review collected by and hosted on G2.com.

It has fst for LM which makes it very flexible and customizable solution to target application domain. It also renders the phoneme time stamps in ctm output, which makes it an ideal solution for time synchronization and confidence score calibration Review collected by and hosted on G2.com.

It needs a lots and lots of memory resources to load the bulky acoustic models and the LM graphs. Review collected by and hosted on G2.com.

recipes, stability, and user friendly,

Very smart and intelligent people worked for it.

Kaldi is an excellent toolkit that continually lead the research in ASR technologies Review collected by and hosted on G2.com.

The base code is in c++. In today's time, if it is in python, it would be much more easily accessible to broader people. Review collected by and hosted on G2.com.

It is very convenient and useful to convert audio files to structured files. It can be used in many coding languages, including Python and C++. Its automatical process helps save time. Review collected by and hosted on G2.com.

The handbook of Kaldi is not clear enough and sometimes you need to google and check to totally understand the meaning of some parameters. Review collected by and hosted on G2.com.

Kaldi tool is very fast and easy to handle. Review collected by and hosted on G2.com.

At the initial point, it is tough to learn. If you are learning it alone then it looks tough to use it. Review collected by and hosted on G2.com.

The features. Like multiple algorithms for feature extraction. Support for many neural architectures. Review collected by and hosted on G2.com.

Unless we are masters in C++, its quite difficult to hack into the source code. Review collected by and hosted on G2.com.

easy sample script access for building speech based models. Review collected by and hosted on G2.com.

It cannot handle end-to-end architecture models. Provision should be provided for those. Review collected by and hosted on G2.com.