HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
NVIDIA
OpenSeq2Seq
Python2017–2020
NVIDIA / OpenSeq2Seq · Archived

OpenSeq2Seq

Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP

AI & Machine LearningSystems & InfrastructureLearning & Reference
Explore on GitHubRead Hacker News discussions
4Distinct people
6Mentions
0Explicit recommenders
1.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP

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, 1:49:46 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 year6 dated mentions

Observed mentions: 2017-10-06 04:14:01 UTC to 2020-08-31 10:36:28 UTC.

01232017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2020

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
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC110
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC101
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC303

What Hacker News says

6 sources
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
Read the rest
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

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.