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--- |
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language: |
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- en |
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--- |
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End-to-end Neural Diarization (EEND) trained on AMI-headset dataset. |
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This example could be found at `egs2/ami/diar1`. |
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## Configurations: |
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- 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. |
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- 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. |
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- Training and testing are performed exclusively on data with 4 speakers. |
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- Use 4 layer stacked Transformer encoder, each outputs 256-dimensional frame-wise embeddings. |
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- The training process spans 500 epochs. |
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- Detailed configurations are defined in `exp/diar/train_diar_diar_raw/config.yaml`. |
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## RESULTS |
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### Environments |
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- date: `Thu Dec 19 22:03:53 EST 2024` |
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- python version: `3.11.10 (main, Oct 3 2024, 07:29:13) [GCC 11.2.0]` |
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- espnet version: `espnet 202409` |
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- pytorch version: `pytorch 2.4.0` |
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- Git hash: `c12b3d59ca4fd8847edf274e56a1716474d2a30e` |
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- Commit date: `Thu Dec 19 21:58:26 2024 -0500` |
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### diar_train_diar_raw |
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#### DER |
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diarized_test |
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|threshold_median_collar|DER| |
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|---|---| |
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|result_th0.3_med11_collar0.0|71.73| |
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|result_th0.3_med1_collar0.0|74.62| |
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|result_th0.4_med11_collar0.0|70.10| |
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|result_th0.4_med1_collar0.0|71.98| |
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|result_th0.5_med11_collar0.0|70.57| |
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|result_th0.5_med1_collar0.0|72.44| |
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|result_th0.6_med11_collar0.0|72.64| |
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|result_th0.6_med1_collar0.0|74.63| |
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|result_th0.7_med11_collar0.0|76.52| |
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|result_th0.7_med1_collar0.0|78.41| |
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### diar_train_diar_raw |
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#### DER |
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diarized_dev |
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|threshold_median_collar|DER| |
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|---|---| |
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|result_th0.3_med11_collar0.0|75.88| |
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|result_th0.3_med1_collar0.0|78.21| |
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|result_th0.4_med11_collar0.0|71.45| |
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|result_th0.4_med1_collar0.0|73.32| |
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|result_th0.5_med11_collar0.0|70.53| |
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|result_th0.5_med1_collar0.0|72.34| |
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|result_th0.6_med11_collar0.0|72.03| |
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|result_th0.6_med1_collar0.0|73.96| |
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|result_th0.7_med11_collar0.0|76.66| |
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|result_th0.7_med1_collar0.0|78.33| |
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