18.6 azspeech transcribe pipelines

20210607 We can pipe the output from transcribe to other tools. For example, we can analyse the sentiment of our spoken word by transcribing what we say from the computer’s microphone, and passing that text on to the sentiment command from the aztext MLHub pacakge.

In the first example you might say happy days.

ml transcribe azspeech | ml sentiment aztext

The result is a most positive sentiment:

0.96

For this second example you might say sad days.

ml transcribe azspeech | ml sentiment aztext

This time our utterance is identified as having quite a negative sentiment:

0.07

Pipelines can become quite powerful. Indeed, a pipeline can exhibit AI that might appear to be more than just the sum of its parts. Here, it transcribes the audio from the microphone, which for me would be English, translates it to French, cuts the actual text, and synthesizes it in a French voice.

ml transcribe azspeech |
  ml translate aztranslate --to=fr |
  cut -d',' -f4- |
  ml synthesize azspeech --voice=fr-FR-DeniseNeural

Voila



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