Grouping Utterances by Speaker with ECAPA-TDNN and ONNX Runtime
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Hello, everyone. Splitting a conversation into utterances is useful, but it still leaves an important question unanswered: which utterances came from the same person? Even without identifying anyone by name, grouping the same voice together makes the structure of a conversation much easier to work with. Today, I will generate a speaker embedding for each utterance with an ONNX version of ECAPA-TDNN, then group utterances from two speakers using cosine similarity. What I Tested In my previous…
1Key Takeaways
- Splitting a conversation into utterances is useful, but it still leaves an important question unanswered: which utterances came from the same person?
- Even without identifying anyone by name, grouping the same voice together makes the structure of a conversation much easier to work with.
- Today, I will generate a speaker embedding for each utterance with an ONNX version of ECAPA-TDNN, then group utterances from two speakers using cosine similarity.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that splitting a conversation into utterances is useful, but it still leaves an important question unanswered: which utterances came from the same person?
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