Connectionist Temporal Classification example

[trungnt13]

Connectionist Temporal Classification example

[vrv]

8509f88

ctc_loss and friends is now part of contrib directory -- once it matures and we fix any issues, we'll add it to the core.

[ebrevdo]

Thank you for inquiring. We are working on an implementation. It will be released if/when we are happy with its performance, API, and documentation.

[mschonwe]

Here's another request for a CTC layer / objective. I'm working on speech modeling, and CTC is pretty much essential to this.

[kdavis-mozilla]

Here here, I'm working on speech modeling too. A CTC implementation will help my effort greatly.

[ebrevdo]

Just an update on timelines: we're getting closer but it won't happen before January sometime at the earliest.

[ekelsen]

Baidu has just released a fast open source implementation of CTC for CPUs and GPUs.
https://github.com/baidu-research/warp-ctc

It has a very simple C interface and it should hopefully be fairly easy to create Tensorflow bindings (we're happy to help). The release includes Torch bindings.

[tjacobs]

China is out silicon valleying silicon valley!

[qingzew]

what's the timeline now @ebrevdo

[ebrevdo]

We're closer. Still cleaning up code for release.
On Jan 15, 2016 6:41 AM, "qingzew" [email protected] wrote:

what's the timeline now @ebrevdo https://github.com/ebrevdo

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[drdozer]

Hi Ebrevdo,

A CTC implementation would be really useful to me for some genome analysis tasks that are handled poorly by HMM approaches.

Matthew

[raindeer]

@ebrevdo any progress? :)

[bmilde]

Also highly interested in this and I hope that you can release your implementation soon! @ebrevdo

[ebrevdo]

Coming very soon (I hope).
On Feb 22, 2016 5:34 AM, "bmilde" [email protected] wrote:

Also highly interested in this and I hope that you can release your
implementation soon! @ebrevdo https://github.com/ebrevdo

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#32 (comment)
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[ebrevdo]

The CTC loss and two decoders (greedy & beam search for CTC) should be in the next push.

[ebrevdo]

(it'll be accessible via tf.contrib.ctc.ctc_loss etc.)

[kdavis-mozilla]

Awesome!

[aidangomez]

Very Awesome

[Duum]

very very awesome!