Does multi-speaker diarization work propertly with V2 streaming or batch recognition?

[mericson]

Since longRunningRecognize is no longer available with the V2 API; I was wondering if the speaker identity is maintained across multiple inputs when using streaming or batch recognition. 

Also, are speakers identified properly when using dynamic batch? I wasn't sure if "dynamic batch" is processed in order or not which seems necessary for consistent speaker identification. 

[Poala_Tenorio]

The Google Cloud Speech-to-Text API's longRunningRecognize method has been deprecated in favor of asynchronous batch processing using the v1 API. However, the concept of speaker identification is still relevant in the API's streaming recognition and batch recognition capabilities, including dynamic batch.

In streaming recognition, speaker identification can be maintained across multiple inputs by providing speaker diarization hints to the API. Speaker diarization is the process of determining "who spoke when" in an audio recording. By providing speaker tags or labels along with the audio data, the API can recognize and differentiate between speakers. This allows for the maintenance of speaker identity across multiple inputs within the same streaming recognition session.

Similarly, in batch recognition, you can provide speaker diarization hints along with each audio file to maintain speaker identity across multiple inputs. The API will process each audio file separately, utilizing the provided speaker diarization information to correctly identify speakers.

Regarding dynamic batch processing, while I don't have specific information about Google Cloud Speech-to-Text's implementation, in general, dynamic batching typically refers to the ability to adjust the batch size dynamically based on the workload or system conditions. In the context of speech recognition, this might involve processing audio segments in an order that optimizes processing efficiency while still maintaining accurate speaker identification. However, it's essential to consult the official documentation or reach out to Google Cloud support for precise details on how dynamic batching is handled in Google Cloud Speech-to-Text, including its impact on speaker identification.

[nimrah-waqar]

1. Speaker Identification Across Inputs:

   • Streaming and batch recognition maintain speaker identification across multiple inputs.
   • Speaker labels are consistent throughout the entire session or batch job.

2. Dynamic Batch and Speaker Identification:

   • Dynamic batch preserves segment order for consistent speaker identification.
   • Proper segmentation ensures accurate speaker labels.