Speakers id.

[i7p9h9]

Is it possible extract speakers id from your dataset to using for speaker recognition tasks?

[snakers4]

Hi, It is possible for some smaller subsets to say that they come from one speaker. This mostly is true only for audio books. But as a general rule - we aimed to gather as much diverse data as possible. Now there is a field speaker set in the metadata - but it's mostly useless now. I will think about marking some parts of the dataset as reliably having the same speaker.

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On April 27, 2019 6:02:11 PM GMT+03:00, i7p9h9 ***@***.***> wrote: Is it possible extract speakers id from your dataset to using for speaker recognition tasks? -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: #1

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[snakers4]

We are planning to share a much larger dataset based on audio-books
Please PM me (telegram), I will share a private meta-data file, from which you could extract the data you need
We are not planning to share this data publicly yet

[snakers4]

@i7p9h9
We shared this dataset update here

[stefan-falk]

It would be great if the data came with dedicated directories for each speaker e.g.

<dataset-id>/<speaker-id>/<sample-id>.wav
                          <sample-id>.txt

because it makes sense to separate speakers during training and testing. Not just for speaker recognition but also for STT tasks.

However, open_stt is an awesome dataset nevertheless. Are you planning on adding more languages?

[snakers4]

Hi!

Doing exactly this is not feasible unfortunately due to the nature of the dataset (zero money investment into annotation).

But we could share speakers privately as meta data for a very limited subset of data if this helps. Mostly books.

[stefan-falk]

I see. Well, my workaround here is throwing everything uncertain into the train set and test on data which has speaker separation. E.g. the Common Voice dataset might be reliable enough.

If I may ask, what kind of word error rate (WER) did you get on the entire open_stt dataset? I am currently not too far below 40% (using ~3000h of the data) which is actually not as good as I expected it to be for so many hours of speech. :)

[snakers4]

Well, my workaround here is throwing everything uncertain into the train set and test on data which has speaker separation.

We have a small subset of the data (15 hours) manually annotated - we will be posting it soon enough

what kind of word error rate (WER) did you get on the entire open_stt dataset

Sorry for a late reply, but please refer to a ticket #5 #7
Obviously these are not the best / latest models, but you can see some patterns in the distributions
You will see that the whole dataset is not consistent in the annotation quality, so it has / will be distilled

There have been reports that if you use esp-net w/o data with bad annotation, you will get a much better result

It will be the foremost focus of our future work - seeding out the bad data

[stefan-falk]

Sorry for a late reply, but please refer to a ticket #5 #7

@snakers4 no worries :)

Thanks for sharing that information. Will take a look on those issues.

Thanks for doing all this great work and providing such an easy-to-use dataset!

[jsdtwry]

I see. Well, my workaround here is throwing everything uncertain into the train set and test on data which has speaker separation. E.g. the Common Voice dataset might be reliable enough.

If I may ask, what kind of word error rate (WER) did you get on the entire open_stt dataset? I am currently not too far below 40% (using ~3000h of the data) which is actually not as good as I expected it to be for so many hours of speech. :)

Hi, stefan: You mentationed that you have trained ASR system on common voice russia, could you share the lastest common voice russia WER performance? I do not learn a lot about russian language, and train a russian ASR system with little 60h data with RU- common voice data, now the WER is about 40% with a chain model with kaldi toolkit even with a test set text LM, do you think it's normal? I haven't found any bench mark on common voice russia, do you think it's a normal performance? I find you often evalute russia ASR with CER, wheather it is more common on russian ASR target? Thanks a lot !!!