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espnet/
diar_ami_eend

Name: diar_ami_eend
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End-to-end Neural Diarization (EEND) trained on AMI-headset dataset. This example could be found at egs2/ami/diar1.

Configurations:

Use ESPNet's default frontend to extract features. The sampling rate is 8000 Hz, with a frame length of 25 ms and a frame shift of 10 ms. The frontend extracts 23 log-scaled Mel-filterbanks.
Follow the frame concatenation and subsampling strategy described in paper [[2]]. Each frame is concatenated with the preceding and following 7 frames, followed by subsampling with a factor of 10. As a result, a 345-dimensional acoustic feature (23 × 15) is extracted for each 100 ms.
Training and testing are performed exclusively on data with 4 speakers.
Use 4 layer stacked Transformer encoder, each outputs 256-dimensional frame-wise embeddings.
The training process spans 500 epochs.
Detailed configurations are defined in exp/diar/train_diar_diar_raw/config.yaml.

date: Thu Dec 19 22:03:53 EST 2024
python version: 3.11.10 (main, Oct 3 2024, 07:29:13) [GCC 11.2.0]
espnet version: espnet 202409
pytorch version: pytorch 2.4.0
Git hash: c12b3d59ca4fd8847edf274e56a1716474d2a30e
- Commit date: Thu Dec 19 21:58:26 2024 -0500

diarized_test

diarized_dev