wespeaker-voxceleb-resnet34-LM

// No extractor runs these weights, so the model runs on your side and the
// embedding is upserted through POST /v1/namespaces/{namespace_id}/documents/upsert.
// On a single-tenant deployment the other path is to upload the weights:
// POST /v1/namespaces/{id}/models accepts the huggingface format and a custom
// plugin loads them.
const res = await fetch(
  "https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
  {
    method: "POST",
    headers: {
      Authorization: "Bearer API_KEY",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      collection_id: "col_call_segments",
      documents: [
        {
          document_id: "call-00412-seg-07",
          // 256 dimensions here, against 192 for ECAPA-TDNN. The vector index
          // has to match, so running both means two indexes, not one.
          payload: {
            transcript: segment.text,
            speaker_id: match.speakerId,
            speaker_score: match.cosine,
          },
          vectors: { "speaker-embedding-256": wespeakerEmbedding },
        },
      ],
    }),
  },
);