When chirp_2 streaming support will be given for all models, possible workarounds?

[James23454454]

chirp_2 Speech.Recognize is not as good as Speech.StreamingRecognize while trying out for Tamil language. Is there any workaround to make Recognize model work as good as streamingRecognize quality. 

[MJane]

Hi @James23454454,

Welcome to the Google Cloud Community!

While you can’t directly improve the underlying chirp_2 Speech.Recognize model, here are ways to improve the performance of chirp_2 Speech.Recognize: 

1. Noise Reduction and Audio Preprocessing - Enhancing the audio quality prior to sending it to the API could lead to better results. If possible, use noise reduction techniques to filter out any background noise before processing the audio. 
2. Use a Custom Model - Google Cloud provides the ability to create custom speech recognition models that are trained using your own data. This can improve the recognition of specific Tamil speech patterns and vocabulary to align better with your requirements.
3. Fine-Tuning -  Consider fine-tuning the model with a dataset specific to Tamil. This can help the model better understand the details of the language.
4. Feedback Loop - Use the feedback from the Speech.Recognize model to continuously improve its performance. Correcting errors and retraining the model can lead to better results over time.

For more detailed information about chirp_2 Speech.recognize you may read this documentation.

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[James23454454]

 Speech.Recognize and  Speech.StreamingRecognize theoretically should have same accuracy approximately or StreamingRecognize is actually better? Please assist

Also i will look into custom-models