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tensorflow
lingvo
Python2019–2021
tensorflow / lingvo

lingvo

Lingvo

AI & Machine LearningSystems & Infrastructure
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4Distinct people
6Mentions
0Explicit recommenders
2.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Lingvo

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month6 dated mentions

Observed mentions: 2019-01-08 23:57:26 UTC to 2021-01-05 12:12:58 UTC.

0122019-01-01 00:00:00 UTC to 2019-02-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJan 20192019-02-01 00:00:00 UTC to 2019-03-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2019-03-01 00:00:00 UTC to 2019-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-04-01 00:00:00 UTC to 2019-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-05-01 00:00:00 UTC to 2019-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-06-01 00:00:00 UTC to 2019-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-07-01 00:00:00 UTC to 2019-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-08-01 00:00:00 UTC to 2019-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-09-01 00:00:00 UTC to 2019-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-10-01 00:00:00 UTC to 2019-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-11-01 00:00:00 UTC to 2019-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-12-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2020-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJan 20202020-02-01 00:00:00 UTC to 2020-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-03-01 00:00:00 UTC to 2020-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-04-01 00:00:00 UTC to 2020-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-05-01 00:00:00 UTC to 2020-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-06-01 00:00:00 UTC to 2020-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-07-01 00:00:00 UTC to 2020-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-08-01 00:00:00 UTC to 2020-09-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2020-09-01 00:00:00 UTC to 2020-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-10-01 00:00:00 UTC to 2020-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-11-01 00:00:00 UTC to 2020-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-12-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2021-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJan 2021

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jan 20192019-01-01 00:00:00 UTC2019-02-01 00:00:00 UTC110
Feb 20192019-02-01 00:00:00 UTC2019-03-01 00:00:00 UTC110
Mar 20192019-03-01 00:00:00 UTC2019-04-01 00:00:00 UTC000
Apr 20192019-04-01 00:00:00 UTC2019-05-01 00:00:00 UTC000
May 20192019-05-01 00:00:00 UTC2019-06-01 00:00:00 UTC000
Jun 20192019-06-01 00:00:00 UTC2019-07-01 00:00:00 UTC000
Jul 20192019-07-01 00:00:00 UTC2019-08-01 00:00:00 UTC000
Aug 20192019-08-01 00:00:00 UTC2019-09-01 00:00:00 UTC000
Sep 20192019-09-01 00:00:00 UTC2019-10-01 00:00:00 UTC000
Oct 20192019-10-01 00:00:00 UTC2019-11-01 00:00:00 UTC000
Nov 20192019-11-01 00:00:00 UTC2019-12-01 00:00:00 UTC000
Dec 20192019-12-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
Jan 20202020-01-01 00:00:00 UTC2020-02-01 00:00:00 UTC000
Feb 20202020-02-01 00:00:00 UTC2020-03-01 00:00:00 UTC000
Mar 20202020-03-01 00:00:00 UTC2020-04-01 00:00:00 UTC101
Apr 20202020-04-01 00:00:00 UTC2020-05-01 00:00:00 UTC000
May 20202020-05-01 00:00:00 UTC2020-06-01 00:00:00 UTC000
Jun 20202020-06-01 00:00:00 UTC2020-07-01 00:00:00 UTC000
Jul 20202020-07-01 00:00:00 UTC2020-08-01 00:00:00 UTC000
Aug 20202020-08-01 00:00:00 UTC2020-09-01 00:00:00 UTC202
Sep 20202020-09-01 00:00:00 UTC2020-10-01 00:00:00 UTC000
Oct 20202020-10-01 00:00:00 UTC2020-11-01 00:00:00 UTC000
Nov 20202020-11-01 00:00:00 UTC2020-12-01 00:00:00 UTC000
Dec 20202020-12-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
Jan 20212021-01-01 00:00:00 UTC2021-02-01 00:00:00 UTC101

What Hacker News says

6 sources
albertzeyer·Jan 5, 2021
Yes, there are really good open source speech to text tools (automatic speech recognition (ASR) is the common name for that). Kaldi (https://kaldi-asr.org/) is probably the most well known, and supports hybrid NN-HMM and lattice-free MMI models. Kaldi is used by many people both in research and in production. Lingvo (https://github.com/tensorflow/lingvo) is the open source version of Google speech recognition toolkit, with support mostly for end-to-end models. ESPNet (https://github.com/espnet/espnet) is good and well known for end-to-end models as well. RASR (https://github.com/rwth-i6/rasr) + RETURNN (https://github.com/rwth-i6/returnn) are very good as well, both for end-to-end models and hybrid NN-HMM, but they are for non-commercial applications only (or you need a commercial licence) (disclaimer: I work at the university chair which
Read the rest
develops these frameworks). Wav2Letter (https://github.com/facebookresearch/wav2letter), the tool by Facebook. These are probably just the most well known. There are many others as well. DeepSpeech is inferior to all the ones above, but maybe simpler. Important is also the data to train these, but you will find quite some resources online for English, e.g. Tedlium, Librispeech, etc. You will find lots of resources actually for ASR. Some random links: https://github.com/gooofy/zamia-speech https://commonvoice.mozilla.org/en/datasets https://www.openslr.org/resources.php To add: If you want to do sth like Descript, you are mostly also interested in accurate time-stamps of the recognized text (start and end time of each spoken word). The end-to-end models are usually not so good at this (the goals is mostly to get a good word-error-rate (WER)). The conventional hybrid NN-HMM is maybe actually a better choice for this task.
In Descript – A collaborative audio/video editor that works like a doc
nshm·Aug 31, 2020
Not a big problem given so many alternatives around like E.g. some very active projects are: * Kaldi (https://github.com/kaldi-asr/kaldi/) obviously, probably the most famous one, and most mature one. For standard hybrid NN-HMM models and also all their more recent lattice-free MMI (LF-MMI) models / training procedure. This is also heavily used in industry (not just research). * ESPnet (https://github.com/espnet/espnet), for all kind of end-to-end models, like CTC, attention-based encoder-decoder (including Transformer), and transducer models. * Espresso (https://github.com/freewym/espresso). * Google Lingvo (https://github.com/tensorflow/lingvo). This is the open source release of Googles internal ASR system, and used by Google in production (their internal version of it, which is not too much different). * NVIDIA OpenSeq2Seq (https://gi
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thub.com/NVIDIA/OpenSeq2Seq). * Facebook Fairseq (https://github.com/pytorch/fairseq). Attention-based encoder-decoder models mostly. * Facebook wav2letter (https://github.com/facebookresearch/wav2letter). ASG model/training. * Vosk (https://github.com/alphacep/vosk-api). Offline lightweight speech recognition API with support for 10 languages. And there are much more.
In Mozilla's Speech-to-Text Engine Is at Risk Following Layoffs
albertzeyer·Aug 22, 2020
There are a lot of open source projects in this space. DeepSpeech is actually one of the outsiders (they are not represented well in the academic community), and also not quite competitive to other software (at least last time I checked). E.g. some very active projects are: * Kaldi (https://github.com/kaldi-asr/kaldi/) obviously, probably the most famous one, and most mature one. For standard hybrid NN-HMM models and also all their more recent lattice-free MMI (LF-MMI) models / training procedure. This is also heavily used in industry (not just research). * ESPnet (https://github.com/espnet/espnet), for all kind of end-to-end models, like CTC, attention-based encoder-decoder (including Transformer), and transducer models. * Espresso (https://github.com/freewym/espresso). * Google Lingvo (https://github.com/tensorflow/lingvo). This is the
Read the rest
open source release of Googles internal ASR system, and used by Google in production (their internal version of it, which is not too much different). * NVIDIA OpenSeq2Seq (https://github.com/NVIDIA/OpenSeq2Seq). * Facebook Fairseq (https://github.com/pytorch/fairseq). Attention-based encoder-decoder models mostly. * Facebook wav2letter (https://github.com/facebookresearch/wav2letter). ASG model/training. * (RETURNN (https://github.com/rwth-i6/returnn) and RASR (https://github.com/rwth-i6/rasr), our own, although this is currently free for academic use only. It is used in production as well. Supports hybrid NN-HMM, CTC, end-to-end attention-based encoder-decoder, transducer, etc.) And there are much more. You will also find lots of ready-to-use trained models.
In Future of DeepSpeech / STT after recent changes at Mozilla
albertzeyer·Mar 14, 2020
Are you searching for a complete solution including NLP and an engine to perform actions? Some of these are already posted, like Home Assistant, and Mycroft. Sphinx is just for the automatic speech recognition (ASR) part. But there are better solutions for that: Kaldi (https://kaldi-asr.org/) is probably the most comprehensive ASR solution, which yields very competitive state-of-the-art results. RASR (https://www-i6.informatik.rwth-aachen.de/rwth-asr/) is for non-commercial use only but otherwise similar as Kaldi. If you want to use a simpler ASR system, nowadays end-to-end models perform quite well. There are quite a huge number of projects which support these: RETURNN (https://github.com/rwth-i6/returnn) is non-commercial TF-based. (Disclaimer: I'm one of the main authors.) Lingvo (https://github.com/tensorflow/lingvo), from Google, TF-
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based. ESPnet (https://github.com/espnet/espnet), PyTorch/Chainer. ...
In Ask HN: Non-cloud voice recognition for home use?

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