I have a text in japanese that I'm turning into an mp3 with the Google Cloud Text to Speech functionality.
I also want to have word timestamps for the mp3 that gets returned by Google.
Google Speech to Text offers this functionality but when I submit the files I get from TTS to STT, the result is not always good.
What is the best way to also get word timestamps for the TTS mp3?
Google Cloud Speech-to-Text it's a ML based service, so it's expected that the results are not always as "good" as you may expect them, it has it's limitations.
What I could suggest is to take a look at their relevant documentation about this topic like the best practices, the guide and the basics page that talk about it. Additionally, you could take a look at the issues within their issue tracker platform, like for example this issue for additional information on it and even if you find a reproducible issue within the service you can publish it there, so their team can be aware of it.
Related
I need to transcribe a large number of Handwritten documents. I tried to use cloud services from either Google, Amazon, and Microsoft. Namely:
https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/
https://cloud.google.com/vision/docs/handwriting
https://aws.amazon.com/textract/
Unfortunately, none of them achieved good enough results. I suspect it is because my documents have a weird handwriting style, and as a result, the networks struggle a lot.
I searched whether I could fine-tune (with manually transcribed data), but I have not found anything online, so as a last resort, I ask here.
If it is possible to fine-tune one of these models, could you please point me some resources?
You are correct, with Azure Cognitive Services with Computer Vision you cannot upload your own data to train the API to recognise the handwriting in your documents I'm afraid. I can't comment on the other offerings from AWS and Google I'm afraid, but certainly not for Azure.
I have an audio file and I have an exact transcript of that audio file. I would like to be able to get the timestamps of each word in that specific transcript.
I don't want timestamps for for the non-accurate recognized speech. I can already do that, and it is useful, but it's not quite good enough due to the mistakes in the speech recognition.
Does anyone know if this is possible with Google speech recognition?
It is not possible with Google speech recognition. You have to use other services. Even open source tools exist.
I want to use google cloud vision API in my android app to detect whether the uploaded picture is mainly food or not. the problem is that the response JSON is rather big and confusing. it says a lot about the picture but doesn't say what the whole picture is of (food or something like that). I contacted the support team but didn't get an answer.
What you really want is a custom classification, not specifically raw Cloud Vision annotation.
Either use the https://cloud.google.com/automl/ or invent an own wheel like I did: https://stackoverflow.com/a/55880316/322020
I am working on a speech recognition task, which involves the detection of children's speaking capability, improvement over time...
I'd like to use the Google Cloud Speech to Text API for the ASR part of the detection. Then I would use the transcripts of different measurements to estimate the advancement.
But! The sentence level autocorrect of Google Speech API consistently rewrites the previous limb of the spoken sentence...
Is there a way to disable the autocorrect of this ASR?
I can't bypass this problem with the "speechContext", "single_utterance" or "maxAlternatives" options.
"single_utterance" may work with words, but it corrects the misspells..
Any advice in this field?
If you use streaming instead of batch recognize, you should receive an answer as soon as that part of the audio is transcribed, it does not wait for the rest of the sentence. You should then just store the first answer provided by the stream, not the further corrections.
This means that you don't have to wait until isFinal=True.
For a quick and dirty example of what I mean, go tho the speech API page, and run the streaming test with the developer tools open. There you'll see the streaming data received as the words are being spoken:
I wanted to dive into the world of distributed systems, cloud computing, IoT, etc., and I gotta be honest, I imagined everything being a little more intuitive than it finally turned out.
I had a tiny testing architecture in mind, that I'd like to set up with Google Clouds and their services, but I am kinda stuck since I can't get my head around some concepts.
What I basically wanted to do (as a first step) is writing a simple java application that would run locally on my computer. This application should just generate random numbers and send those numbers somehow to the google cloud. On the cloud I wanted to define another java application that would manipulate those random numbers in some kind of way (it doesn't matter actually). Afterwards, the output should somehow get back to me of course. And actually, at the moment, I don't even care about how exactly. It could be somehow back to my local app (with some kind of listener, would that be possible?). But it could also simply store the results somewhere on the google cloud? Or maybe upload them to my google drive?
I guess you already noticed that - at some points - I don't even know what i want exactly, since I'm not sure of what is possible, and what not.
Could you provide me some help to get this set up?
The most important questions for me right now are:
Do I need to use a pubsub system, where my generated numbers are sent
to, and which then forwards this to the cloud app, that transforms my
data?
How do I get my data from the local app to the cloud services?
Would my data transforming app run on Google Dataflow?
Above I wrote "as a first step"... because later I would also like to send config files (for example in json format, or xml) to the cloud, and the
cloud application should transform those config files... if I get the
first scenario running the I guess this woul also be no problem
right?
Those are just a few of the questions that are on my mind currently. The most important ones I guess.
It would be a big help. Sorry, if the questions are not very precise, but I really need some kind of pointing into the right direction.
Thank you in advance!
I think it would be good to read up on some of the technologies you mention here:
Google Cloud Pubsub: Pub/Sub enables you to publish messages to a topic, and consume them in another place in the (Google) Cloud. You can see some different examples of publishers and consumers in the link. In your case you could for example write a Java application that writes random numbers to the Pub/Sub queue, where they will sit for 7 days to be consumed by another component (for example, Google Cloud Dataflow). To get started developing, you can find the SDKs here (there is a Java SDK).
Google Cloud Dataflow is managed service running Apache Beam pipelines to process your data at scale. You can learn about the different concepts here and get started designing your pipeline here. I suggest taking a look at some examples first though, which will make it more easy to grasp what is actually going on. Dataflow has a PubSub connector, so in your application you will be able to read from the topic you created before. In Dataflow you can for example multiply all your random numbers and write them to a certain sink (for example Google Cloud Storage, or even BigQuery or PubSub again).
Google Cloud Storage: is a cloud storage where you can put files, for example the output of your Dataflow pipeline. You will be able to manually download the files using the Cloud Console UI, or you can use one of the SDKs to download the output programmatically.
Hope this gives you an overview and some pointers to start. Whenever you are ready and have a more concrete use case in mind, you can start looking at some more components.