---
language:
- en
---

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`.

## RESULTS
### Environments
- 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`

### diar_train_diar_raw
#### DER
diarized_test
|threshold_median_collar|DER|
|---|---|
|result_th0.3_med11_collar0.0|71.73|
|result_th0.3_med1_collar0.0|74.62|
|result_th0.4_med11_collar0.0|70.10|
|result_th0.4_med1_collar0.0|71.98|
|result_th0.5_med11_collar0.0|70.57|
|result_th0.5_med1_collar0.0|72.44|
|result_th0.6_med11_collar0.0|72.64|
|result_th0.6_med1_collar0.0|74.63|
|result_th0.7_med11_collar0.0|76.52|
|result_th0.7_med1_collar0.0|78.41|
### diar_train_diar_raw
#### DER
diarized_dev
|threshold_median_collar|DER|
|---|---|
|result_th0.3_med11_collar0.0|75.88|
|result_th0.3_med1_collar0.0|78.21|
|result_th0.4_med11_collar0.0|71.45|
|result_th0.4_med1_collar0.0|73.32|
|result_th0.5_med11_collar0.0|70.53|
|result_th0.5_med1_collar0.0|72.34|
|result_th0.6_med11_collar0.0|72.03|
|result_th0.6_med1_collar0.0|73.96|
|result_th0.7_med11_collar0.0|76.66|
|result_th0.7_med1_collar0.0|78.33|