[diar] merge voxconverse v3 into v2 and update results in README.md

README.md

@@ -60,7 +60,7 @@ pre-commit install  # for clean and tidy code
 ```
 
 ## 🔥 News
-* 2024.08.20: Update diarization recipe for VoxConverse dataset by leveraging umap dimensionality reduction and hdbscan clustering, see [#347](https://github.com/wenet-e2e/wespeaker/pull/347).
+* 2024.08.20: Update diarization recipe for VoxConverse dataset by leveraging umap dimensionality reduction and hdbscan clustering, see [#347](https://github.com/wenet-e2e/wespeaker/pull/347) and [#352](https://github.com/wenet-e2e/wespeaker/pull/352).
 * 2024.08.18: Support using ssl pre-trained models as the frontend. The [WavLM recipe](https://github.com/wenet-e2e/wespeaker/blob/master/examples/voxceleb/v2/run_wavlm.sh) is also provided, see [#344](https://github.com/wenet-e2e/wespeaker/pull/344).
 * 2024.05.15: Add support for [quality-aware score calibration](https://arxiv.org/pdf/2211.00815), see [#320](https://github.com/wenet-e2e/wespeaker/pull/320).
 * 2024.04.25: Add support for the gemini-dfresnet model, see [#291](https://github.com/wenet-e2e/wespeaker/pull/291).

examples/voxconverse/README.md

@@ -1,3 +1,7 @@
 This is a **WeSpeaker** speaker diarization recipe on the Voxconverse 2020 dataset. It focused on a ``in the wild`` scenario, which was collected from YouTube videos with a semi-automatic pipeline and released for the diarization track in VoxSRC 2020 Challenge. See https://www.robots.ox.ac.uk/~vgg/data/voxconverse/ for more detailed information.
 
 Two recipes are provided, including **v1** and **v2**. Their only difference is that in **v2**, we split the Fbank extraction, embedding extraction and clustering modules to different stages. We recommend newcomers to follow the **v2** recipe and run it stage by stage.
+
+🔥 UPDATE 2024.08.20:
+* silero-vad v5.1 is used in place of v3.1
+* umap dimensionality reduction + hdbscan clustering is also supported in v2

examples/voxconverse/v1/README.md

@@ -1,11 +1,13 @@
 ## Overview
 
 * We suggest to run this recipe on a gpu-available machine, with onnxruntime-gpu supported.
-* Dataset: voxconverse_dev that consists of 216 utterances
-* Speaker model: ResNet34 model pretrained by wespeaker
+* Dataset: Voxconverse2020 (dev: 216 utts)
+* Speaker model: ResNet34 model pretrained by WeSpeaker
   * Refer to [voxceleb sv recipe](https://github.com/wenet-e2e/wespeaker/tree/master/examples/voxceleb/v2)
   * [pretrained model path](https://wespeaker-1256283475.cos.ap-shanghai.myqcloud.com/models/voxceleb/voxceleb_resnet34_LM.onnx)
-* Speaker activity detection model: oracle SAD (from ground truth annotation) or system SAD (VAD model pretrained by silero, https://github.com/snakers4/silero-vad)
+* Speaker activity detection model:
+  * oracle SAD (from ground truth annotation)
+  * system SAD (VAD model pretrained by [silero-vad](https://github.com/snakers4/silero-vad), v3.1 is deprecated now)
 * Clustering method: spectral clustering
 * Metric: DER = MISS + FALSE ALARM + SPEAKER CONFUSION (%)
 
@@ -15,8 +17,8 @@
 
     | system | MISS | FA | SC | DER |
     |:---|:---:|:---:|:---:|:---:|
-    | This repo (with oracle SAD) | 2.3 | 0.0 | 1.9 | 4.2 |
-    | This repo (with system SAD) | 3.7 | 0.8 | 2.0 | 6.5 |
+    | Ours (oracle SAD + spectral clustering) | 2.3 | 0.0 | 1.9 | 4.2 |
+    | Ours (silero-vad v3.1 + spectral clustering) | 3.7 | 0.8 | 2.0 | 6.5 |
     | DIHARD 2019 baseline [^1] | 11.1 | 1.4 | 11.3 | 23.8 |
     | DIHARD 2019 baseline w/ SE [^1] | 9.3 | 1.3 | 9.7 | 20.2 |
     | (SyncNet ASD only) [^1] | 2.2 | 4.1 | 4.0 | 10.4 |

examples/voxconverse/v1/run.sh

@@ -29,8 +29,8 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     unzip -o external_tools/SCTK-v2.4.12.zip -d external_tools
 
     # [2] Download voice activity detection model pretrained by Silero Team
-    wget -c https://github.com/snakers4/silero-vad/archive/refs/tags/v3.1.zip -O external_tools/silero-vad-v3.1.zip
-    unzip -o external_tools/silero-vad-v3.1.zip -d external_tools
+    #wget -c https://github.com/snakers4/silero-vad/archive/refs/tags/v3.1.zip -O external_tools/silero-vad-v3.1.zip
+    #unzip -o external_tools/silero-vad-v3.1.zip -d external_tools
 
     # [3] Download ResNet34 speaker model pretrained by WeSpeaker Team
     mkdir -p pretrained_models
@@ -79,7 +79,6 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     if [[ "x${sad_type}" == "xsystem" ]]; then
        # System SAD: applying 'silero' VAD
        python3 wespeaker/diar/make_system_sad.py \
-               --repo-path external_tools/silero-vad-3.1 \
                --scp data/dev/wav.scp \
                --min-duration $min_duration > data/dev/system_sad
     fi

examples/voxconverse/v2/README.md

@@ -1,12 +1,16 @@
 ## Overview
 
 * We suggest to run this recipe on a gpu-available machine, with onnxruntime-gpu supported.
-* Dataset: voxconverse_dev that consists of 216 utterances
-* Speaker model: ResNet34 model pretrained by wespeaker
+* Dataset: Voxconverse2020 (dev: 216 utts, test: 232 utts)
+* Speaker model: ResNet34 model pretrained by WeSpeaker
   * Refer to [voxceleb sv recipe](https://github.com/wenet-e2e/wespeaker/tree/master/examples/voxceleb/v2)
   * [pretrained model path](https://wespeaker-1256283475.cos.ap-shanghai.myqcloud.com/models/voxceleb/voxceleb_resnet34_LM.onnx)
-* Speaker activity detection model: oracle SAD (from ground truth annotation) or system SAD (VAD model pretrained by silero, https://github.com/snakers4/silero-vad)
-* Clustering method: spectral clustering
+* Speaker activity detection model:
+  * oracle SAD (from ground truth annotation)
+  * system SAD (VAD model pretrained by [silero-vad](https://github.com/snakers4/silero-vad), v3.1 => v5.1)
+* Clustering method:
+  * spectral clustering
+  * umap dimensionality reduction + hdbscan clustering
 * Metric: DER = MISS + FALSE ALARM + SPEAKER CONFUSION (%)
 
 ## Results
@@ -15,8 +19,11 @@
 
     | system | MISS | FA | SC | DER |
     |:---|:---:|:---:|:---:|:---:|
-    | This repo (with oracle SAD) | 2.3 | 0.0 | 2.1 | 4.4 |
-    | This repo (with system SAD) | 3.7 | 0.8 | 2.2 | 6.8 |
+    | Ours (oracle SAD + spectral clustering) | 2.3 | 0.0 | 2.1 | 4.4 |
+    | Ours (oracle SAD + umap clustering) | 2.3 | 0.0 | 1.3 | 3.6 |
+    | Ours (silero-vad v3.1 + spectral clustering) | 3.7 | 0.8 | 2.2 | 6.7 |
+    | Ours (silero-vad v5.1 + spectral clustering) | 3.4 | 0.6 | 2.3 | 6.3 |
+    | Ours (silero-vad v5.1 + umap clustering) | 3.4 | 0.6 | 1.4 | 5.4 |
     | DIHARD 2019 baseline [^1] | 11.1 | 1.4 | 11.3 | 23.8 |
     | DIHARD 2019 baseline w/ SE [^1] | 9.3 | 1.3 | 9.7 | 20.2 |
     | (SyncNet ASD only) [^1] | 2.2 | 4.1 | 4.0 | 10.4 |
@@ -27,7 +34,11 @@
 
     | system | MISS | FA | SC | DER |
     |:---|:---:|:---:|:---:|:---:|
-    | This repo (with system SAD) | 4.0 | 2.4 | 3.4 | 9.8 |
+    | Ours (oracle SAD + spectral clustering) | 1.6 | 0.0 | 3.3 | 4.9 |
+    | Ours (oracle SAD + umap clustering) | 1.6 | 0.0 | 1.9 | 3.5 |
+    | Ours (silero-vad v3.1 + spectral clustering) | 4.0 | 2.4 | 3.4 | 9.8 |
+    | Ours (silero-vad v5.1 + spectral clustering) | 3.8 | 1.7 | 3.3 | 8.8 |
+    | Ours (silero-vad v5.1 + umap clustering) | 3.8 | 1.7 | 1.8 | 7.3 |
 
 
 [^1]: Spot the conversation: speaker diarisation in the wild, https://arxiv.org/pdf/2007.01216.pdf

examples/voxconverse/v2/run.sh

@@ -1,6 +1,7 @@
 #!/bin/bash
 # Copyright (c) 2022-2023 Xu Xiang
 #               2022 Zhengyang Chen ([email protected])
+#               2024 Hongji Wang ([email protected])
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -18,8 +19,9 @@
 
 stage=-1
 stop_stage=-1
-sad_type="oracle"
-partition="dev"
+sad_type="oracle"       # oracle/system
+partition="dev"         # dev/test
+cluster_type="spectral" # spectral/umap
 
 # do cmn on the sub-segment or on the vad segment
 subseg_cmn=true
@@ -36,11 +38,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     wget -c https://github.com/usnistgov/SCTK/archive/refs/tags/v2.4.12.zip -O external_tools/SCTK-v2.4.12.zip
     unzip -o external_tools/SCTK-v2.4.12.zip -d external_tools
 
-    # [2] Download voice activity detection model pretrained by Silero Team
-    wget -c https://github.com/snakers4/silero-vad/archive/refs/tags/v3.1.zip -O external_tools/silero-vad-v3.1.zip
-    unzip -o external_tools/silero-vad-v3.1.zip -d external_tools
-
-    # [3] Download ResNet34 speaker model pretrained by WeSpeaker Team
+    # [2] Download ResNet34 speaker model pretrained by WeSpeaker Team
     mkdir -p pretrained_models
 
     wget -c https://wespeaker-1256283475.cos.ap-shanghai.myqcloud.com/models/voxceleb/voxceleb_resnet34_LM.onnx -O pretrained_models/voxceleb_resnet34_LM.onnx
@@ -101,7 +99,6 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     if [[ "x${sad_type}" == "xsystem" ]]; then
        # System SAD: applying 'silero' VAD
        python3 wespeaker/diar/make_system_sad.py \
-               --repo-path external_tools/silero-vad-3.1 \
                --scp data/${partition}/wav.scp \
                --min-duration $min_duration > data/${partition}/system_sad
     fi
@@ -144,24 +141,24 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
 fi
 
 
-# Applying spectral clustering algorithm
+# Applying spectral or ump+hdbscan clustering algorithm
 if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
 
-    [ -f "exp/spectral_cluster/${partition}_${sad_type}_sad_labels" ] && rm exp/spectral_cluster/${partition}_${sad_type}_sad_labels
+    [ -f "exp/${cluster_type}_cluster/${partition}_${sad_type}_sad_labels" ] && rm exp/${cluster_type}_cluster/${partition}_${sad_type}_sad_labels
 
-    echo "Doing spectral clustering and store the result in exp/spectral_cluster/${partition}_${sad_type}_sad_labels"
+    echo "Doing ${cluster_type} clustering and store the result in exp/${cluster_type}_cluster/${partition}_${sad_type}_sad_labels"
     echo "..."
-    python3 wespeaker/diar/spectral_clusterer.py \
+    python3 wespeaker/diar/${cluster_type}_clusterer.py \
             --scp exp/${partition}_${sad_type}_sad_embedding/emb.scp \
-            --output exp/spectral_cluster/${partition}_${sad_type}_sad_labels
+            --output exp/${cluster_type}_cluster/${partition}_${sad_type}_sad_labels
 fi
 
 
 # Convert labels to RTTMs
 if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
     python3 wespeaker/diar/make_rttm.py \
-            --labels exp/spectral_cluster/${partition}_${sad_type}_sad_labels \
-            --channel 1 > exp/spectral_cluster/${partition}_${sad_type}_sad_rttm
+            --labels exp/${cluster_type}_cluster/${partition}_${sad_type}_sad_labels \
+            --channel 1 > exp/${cluster_type}_cluster/${partition}_${sad_type}_sad_rttm
 fi
 
 
@@ -173,18 +170,18 @@ if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then
     perl external_tools/SCTK-2.4.12/src/md-eval/md-eval.pl \
          -c 0.25 \
          -r <(cat ${ref_dir}/${partition}/*.rttm) \
-         -s exp/spectral_cluster/${partition}_${sad_type}_sad_rttm 2>&1 | tee exp/spectral_cluster/${partition}_${sad_type}_sad_res
+         -s exp/${cluster_type}_cluster/${partition}_${sad_type}_sad_rttm 2>&1 | tee exp/${cluster_type}_cluster/${partition}_${sad_type}_sad_res
 
     if [ ${get_each_file_res} -eq 1 ];then
-        single_file_res_dir=exp/spectral_cluster/${partition}_${sad_type}_single_file_res
+        single_file_res_dir=exp/${cluster_type}_cluster/${partition}_${sad_type}_single_file_res
         mkdir -p $single_file_res_dir
         echo -e "\nGet the DER results for each file and the results will be stored underd ${single_file_res_dir}\n..."
 
-        awk '{print $2}' exp/spectral_cluster/${partition}_${sad_type}_sad_rttm | sort -u  | while read file_name; do
+        awk '{print $2}' exp/${cluster_type}_cluster/${partition}_${sad_type}_sad_rttm | sort -u  | while read file_name; do
             perl external_tools/SCTK-2.4.12/src/md-eval/md-eval.pl \
                  -c 0.25 \
                  -r <(cat ${ref_dir}/${partition}/${file_name}.rttm) \
-                 -s <(grep "${file_name}" exp/spectral_cluster/${partition}_${sad_type}_sad_rttm) > ${single_file_res_dir}/${partition}_${file_name}_res
+                 -s <(grep "${file_name}" exp/${cluster_type}_cluster/${partition}_${sad_type}_sad_rttm) > ${single_file_res_dir}/${partition}_${file_name}_res
         done
         echo "Done!"
     fi