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ibab
tensorflow-wavenet
Python2016–2016
ibab / tensorflow-wavenet

tensorflow-wavenet

A TensorFlow implementation of DeepMind's WaveNet paper

5Distinct people
5Mentions
0Explicit recommenders
5.4KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A TensorFlow implementation of DeepMind's WaveNet paper

GitHub

Where it is used

No documented product use has been verified yet.

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

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 4:00:59 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 week5 dated mentions

Observed mentions: 2016-09-13 07:42:04 UTC to 2016-11-25 16:35:51 UTC. Weeks start Monday, UTC.

012342016-09-12 00:00:00 UTC to 2016-09-19 00:00:00 UTC (exclusive): 4 mentions; 2 submissions, 2 commentsSep 122016-09-19 00:00:00 UTC to 2016-09-26 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-09-26 00:00:00 UTC to 2016-10-03 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-10-03 00:00:00 UTC to 2016-10-10 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-10-10 00:00:00 UTC to 2016-10-17 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-10-17 00:00:00 UTC to 2016-10-24 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsOct 172016-10-24 00:00:00 UTC to 2016-10-31 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-10-31 00:00:00 UTC to 2016-11-07 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-11-07 00:00:00 UTC to 2016-11-14 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-11-14 00:00:00 UTC to 2016-11-21 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-11-21 00:00:00 UTC to 2016-11-28 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsNov 21

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
Sep 122016-09-12 00:00:00 UTC2016-09-19 00:00:00 UTC422
Sep 192016-09-19 00:00:00 UTC2016-09-26 00:00:00 UTC000
Sep 262016-09-26 00:00:00 UTC2016-10-03 00:00:00 UTC000
Oct 32016-10-03 00:00:00 UTC2016-10-10 00:00:00 UTC000
Oct 102016-10-10 00:00:00 UTC2016-10-17 00:00:00 UTC000
Oct 172016-10-17 00:00:00 UTC2016-10-24 00:00:00 UTC000
Oct 242016-10-24 00:00:00 UTC2016-10-31 00:00:00 UTC000
Oct 312016-10-31 00:00:00 UTC2016-11-07 00:00:00 UTC000
Nov 72016-11-07 00:00:00 UTC2016-11-14 00:00:00 UTC000
Nov 142016-11-14 00:00:00 UTC2016-11-21 00:00:00 UTC000
Nov 212016-11-21 00:00:00 UTC2016-11-28 00:00:00 UTC101

What Hacker News says

1 sources
A few weeks ago, a deep learning researcher at one of the world's leading speech groups told me off-the-record that offline, human-parity speech recognition would be "coming soon" to mobile devices. Not sure s/he realized just how soon that would be. Even though state-of-the-art ASR is really expensive to train, recognition is extremely cheap to run, even on lower-power devices. [1][2] With specialized silicon, you can do this, continuously, for free, on something like a smartwatch. You don't need to open a websocket or call an API running on some beefy server to do this, speech-to-text is now a basic commodity. Fully offline, ubiquitous speech recognition is right around the corner. With human-level speech synthesis [3], speech applications are going to get very interesting, very quickly. [1] http://niclane.org/pubs/deepx_ipsn.pdf [2] ht
Read the rest
In Speech-to-Text-WaveNet: End-to-end sentence level English speech recognition

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