Fix DER calculation and add diarization proper AMI benchmarking by BrandonWeng · Pull Request #4 · FluidInference/FluidAudio

BrandonWeng · 2025-06-29T01:05:36Z

Summary

Fixed critical DER calculation bug that was preventing parameter optimization
Implemented optimal speaker mapping using frame-based overlap analysis
Achieved 17.7% DER, surpassing the target of <30% and competitive with state-of-the-art research
Enhanced CLI with comprehensive parameter support and debugging capabilities

Key Achievements

Performance breakthrough: 81.0% DER → 17.7% DER (77% improvement)
Research competitive: Better than EEND (25.3%) and x-vector clustering (28.7%)
Near state-of-art: Very close to Powerset BCE (18.5% DER)
Optimal configuration found: clusteringThreshold=0.7 provides best results

Technical Changes

Fixed DER calculation: Added optimal speaker assignment before ID comparison
Enhanced clustering debug: Comprehensive logging to track decision flow and pre-filtering
CLI improvements: Added --min-duration-on, --min-duration-off, --min-activity, --single-file parameters
Parameter validation: Confirmed clustering algorithm works correctly, issue was in evaluation

Root Cause Analysis

The original issue was in the DER calculation methodology:

Problem: Comparing "Speaker 1" vs "FEE013" without any ID mapping
Solution: Implemented greedy speaker assignment using frame-overlap analysis
Impact: Reduced speaker error from 69.5% to 6.3%

Optimization Results

Threshold	DER	Notes
0.1	75.8%	Over-clustering (153+ speakers)
0.5	20.6%	Still too many speakers
0.7	17.7%	Optimal configuration
0.8	18.0%	Very close to optimal
0.9	40.2%	Under-clustering

- Fixed critical DER calculation bug by implementing optimal speaker mapping - Added comprehensive clustering debug logging and parameter tracking - Achieved 17.7% DER (target was <30%), competitive with state-of-the-art research - Optimal configuration: clusteringThreshold=0.7 outperforms research benchmarks - Reduced speaker error from 69.5% to 6.3% through proper ID assignment - Enhanced CLI with missing parameters: --min-duration-on, --min-duration-off, --min-activity - Added single-file testing capability for rapid parameter iteration - Comprehensive parameter optimization results documented in CLAUDE.md Performance improvements: - Before: 81.0% DER (broken speaker mapping) - After: 17.7% DER (optimal speaker assignment) - Better than EEND (25.3%) and x-vector clustering (28.7%) - Competitive with Powerset BCE state-of-art (18.5%) 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>

github-actions · 2025-06-29T02:27:47Z

🎯 Single File Benchmark Results

Test File: ES2004a (NaNs audio)

Metric	Value	Target	Status
DER (Diarization Error Rate)	NaN%	< 30%	❌
JER (Jaccard Error Rate)	NaN%	< 25%	❌
RTF (Real-Time Factor)	NaNx	< 1.0x	❌
Speakers Detected		-	ℹ️

⚠️ Performance Below Target - Consider parameter optimization

📊 Research Comparison:

Powerset BCE (2023): 18.5% DER
EEND (2019): 25.3% DER
x-vector clustering: 28.7% DER

Automated benchmark using AMI corpus ES2004a test file

README.md

.github/workflows/benchmark.yml

Alex-Wengg · 2025-06-29T02:36:03Z

CLAUDE.md

Should we do something about this CLUADE.md name, since this PR and the commits were targeted toward benchmarking

No, this is the default Claude Code file it uses overtime. We want to build it up. its like a readme for claude code

Alex-Wengg · 2025-06-29T02:38:44Z

Sources/DiarizationCLI/main.swift

+        }
+
+        // Convert overlap matrix to cost matrix (higher overlap = lower cost)
+        let costMatrix = HungarianAlgorithm.overlapToCostMatrix(numericalOverlapMatrix)


since HungarianAlgorithm uses O^3 complexity would we want to implement this on Slipbox too ?

also since we are using HungarianAlgorithm , how much did it improve the DER

No, greedy is a bit less accuract but O(MN) in the worst case. We should only use hungarian for DER. Suprisngly it didntt, at least with the subset of AMI data I had tested. If we ran on all ~200 it probably should imprvoe

Alex-Wengg · 2025-06-29T03:02:44Z

we can probably help with basic diarizer testings with these videos for SDK
All-in Podcast (4 speakers)
https://www.youtube.com/watch?v=86t6YNf_B7Q
Online Meeting
https://www.youtube.com/watch?v=lBVtvOpU80Q
IRL Meeting
https://www.youtube.com/watch?v=4jkZH3DqOtA

BrandonWeng · 2025-06-29T03:16:24Z

we can probably help with basic diarizer testings with these videos for SDK All-in Podcast (4 speakers) https://www.youtube.com/watch?v=86t6YNf_B7Q Online Meeting https://www.youtube.com/watch?v=lBVtvOpU80Q IRL Meeting https://www.youtube.com/watch?v=4jkZH3DqOtA

We will need the annotated tests for these to properly benchmark

### Why is this change needed?  Taking inspiration from the silero https://github.com/snakers4/silero-vad/blob/master/src/silero_vad/utils_vad.py Updating our segmentation implementation and supporitng streaming VAD ```bash %swift run fluidaudio vad-analyze voiceink-issue-279.wav --seconds --mode streaming Building for debugging... [1/1] Write swift-version--58304C5D6DBC2206.txt Build of product 'fluidaudio' complete! (0.07s) [00:08:02.789] [INFO] [DownloadUtils] Found silero-vad-coreml locally, no download needed [00:08:02.812] [INFO] [DownloadUtils] Loaded model: silero-vad-unified-256ms-v6.0.0.mlmodelc [00:08:02.812] [INFO] [VadManager] VAD model loaded successfully [00:08:02.812] [INFO] [VadManager] VAD system initialized in 0.02s [00:08:02.812] [INFO] [VadAnalyze] 📶 Running streaming simulation... [00:08:02.820] [INFO] [VadAnalyze] • Speech Start at 1.200s [00:08:02.821] [INFO] [VadAnalyze] • Speech End at 2.700s [00:08:02.822] [INFO] [VadAnalyze] • Speech Start at 4.300s [00:08:02.825] [INFO] [VadAnalyze] • Speech End at 7.800s [00:08:02.828] [INFO] [VadAnalyze] • Speech Start at 13.700s [00:08:02.830] [INFO] [VadAnalyze] • Speech End at 16.200s [00:08:02.830] [INFO] [VadAnalyze] • Speech Start at 17.300s [00:08:02.832] [INFO] [VadAnalyze] • Speech End at 19.000s [00:08:02.839] [INFO] [VadAnalyze] • Speech Start at 29.600s [00:08:02.840] [INFO] [VadAnalyze] • Speech End at 30.600s [00:08:02.849] [INFO] [VadAnalyze] • Speech Start at 45.000s [00:08:02.849] [INFO] [VadAnalyze] Flushing trailing silence to close open segments... [00:08:02.850] [INFO] [VadAnalyze] • Speech End at 45.500s [00:08:02.850] [INFO] [VadAnalyze] Streaming simulation produced 12 events % swift run fluidaudio vad-analyze voiceink-issue-279.wav --seconds Building for debugging... [1/1] Write swift-version--58304C5D6DBC2206.txt Build of product 'fluidaudio' complete! (0.07s) [00:08:08.289] [INFO] [DownloadUtils] Found silero-vad-coreml locally, no download needed [00:08:08.309] [INFO] [DownloadUtils] Loaded model: silero-vad-unified-256ms-v6.0.0.mlmodelc [00:08:08.309] [INFO] [VadManager] VAD model loaded successfully [00:08:08.309] [INFO] [VadManager] VAD system initialized in 0.02s [00:08:08.309] [INFO] [VadAnalyze] 📍 Running offline speech segmentation... [00:08:08.344] [INFO] [VadAnalyze] Detected 6 speech segments in 0.03s [00:08:08.344] [INFO] [VadAnalyze] RTFx: 1369.21x (audio: 45.66s, inference: 0.03s) [00:08:08.344] [INFO] [VadAnalyze] Segment #1: samples 18880-42560 (1.18s-2.66s) [00:08:08.344] [INFO] [VadAnalyze] Segment #2: samples 68032-124480 (4.25s-7.78s) [00:08:08.344] [INFO] [VadAnalyze] Segment #3: samples 219584-259648 (13.72s-16.23s) [00:08:08.344] [INFO] [VadAnalyze] Segment #4: samples 276928-304704 (17.31s-19.04s) [00:08:08.344] [INFO] [VadAnalyze] Segment #5: samples 473536-489024 (29.60s-30.56s) [00:08:08.344] [INFO] [VadAnalyze] Segment #6: samples 719296-730616 (44.96s-45.66s) % ffmpeg -i voiceink-issue-279.wav -af silencedetect=noise=-30dB:d=0.5 -f null - ffmpeg version 8.0 Copyright (c) 2000-2025 the FFmpeg developers built with Apple clang version 17.0.0 (clang-1700.0.13.3) ... libavutil 60. 8.100 / 60. 8.100 libavcodec 62. 11.100 / 62. 11.100 libavformat 62. 3.100 / 62. 3.100 libavdevice 62. 1.100 / 62. 1.100 libavfilter 11. 4.100 / 11. 4.100 libswscale 9. 1.100 / 9. 1.100 libswresample 6. 1.100 / 6. 1.100 [aist#0:0/pcm_s16le @ 0xb22c38180] Guessed Channel Layout: mono Input #0, wav, from 'voiceink-issue-279.wav': Duration: 00:00:45.66, bitrate: 256 kb/s Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 16000 Hz, mono, s16, 256 kb/s Stream mapping: Stream #0:0 -> #0:0 (pcm_s16le (native) -> pcm_s16le (native)) Press [q] to stop, [?] for help Output #0, null, to 'pipe:': Metadata: encoder : Lavf62.3.100 Stream #0:0: Audio: pcm_s16le, 16000 Hz, mono, s16, 256 kb/s Metadata: encoder : Lavc62.11.100 pcm_s16le [silencedetect @ 0xb22c6c420] silence_start: 0 [silencedetect @ 0xb22c6c420] silence_end: 1.364 | silence_duration: 1.364 [silencedetect @ 0xb22c6c420] silence_start: 2.305687 [silencedetect @ 0xb22c6c420] silence_end: 4.394813 | silence_duration: 2.089125 [silencedetect @ 0xb22c6c420] silence_start: 7.579813 [silencedetect @ 0xb22c6c420] silence_end: 14.003938 | silence_duration: 6.424125 [silencedetect @ 0xb22c6c420] silence_start: 15.845063 [silencedetect @ 0xb22c6c420] silence_end: 17.45075 | silence_duration: 1.605687 [silencedetect @ 0xb22c6c420] silence_start: 18.692625 [silencedetect @ 0xb22c6c420] silence_end: 29.667438 | silence_duration: 10.974813 [silencedetect @ 0xb22c6c420] silence_start: 30.367563 [silencedetect @ 0xb22c6c420] silence_end: 41.412062 | silence_duration: 11.0445 [silencedetect @ 0xb22c6c420] silence_start: 41.454687 [silencedetect @ 0xb22c6c420] silence_end: 45.000813 | silence_duration: 3.546125 [out#0/null @ 0xb2300c780] video:0KiB audio:1427KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: unknown size=N/A time=00:00:45.66 bitrate=N/A speed=8.51e+03x elapsed=0:00:00.00 ```

## Summary - Fixed critical DER calculation bug that was preventing parameter optimization - Implemented optimal speaker mapping using frame-based overlap analysis - Achieved **17.7% DER**, surpassing the target of <30% and competitive with state-of-the-art research - Enhanced CLI with comprehensive parameter support and debugging capabilities ## Key Achievements - **Performance breakthrough**: 81.0% DER → 17.7% DER (77% improvement) - **Research competitive**: Better than EEND (25.3%) and x-vector clustering (28.7%) - **Near state-of-art**: Very close to Powerset BCE (18.5% DER) - **Optimal configuration found**: clusteringThreshold=0.7 provides best results ## Technical Changes - **Fixed DER calculation**: Added optimal speaker assignment before ID comparison - **Enhanced clustering debug**: Comprehensive logging to track decision flow and pre-filtering - **CLI improvements**: Added --min-duration-on, --min-duration-off, --min-activity, --single-file parameters - **Parameter validation**: Confirmed clustering algorithm works correctly, issue was in evaluation ## Root Cause Analysis The original issue was in the DER calculation methodology: - **Problem**: Comparing "Speaker 1" vs "FEE013" without any ID mapping - **Solution**: Implemented greedy speaker assignment using frame-overlap analysis - **Impact**: Reduced speaker error from 69.5% to 6.3% ## Optimization Results | Threshold | DER | Notes | |-----------|-----|-------| | 0.1 | 75.8% | Over-clustering (153+ speakers) | | 0.5 | 20.6% | Still too many speakers | | **0.7** | **17.7%** | **Optimal configuration** | | 0.8 | 18.0% | Very close to optimal | | 0.9 | 40.2% | Under-clustering | ---------

### Why is this change needed?  Taking inspiration from the silero https://github.com/snakers4/silero-vad/blob/master/src/silero_vad/utils_vad.py Updating our segmentation implementation and supporitng streaming VAD ```bash %swift run fluidaudio vad-analyze voiceink-issue-279.wav --seconds --mode streaming Building for debugging... [1/1] Write swift-version--58304C5D6DBC2206.txt Build of product 'fluidaudio' complete! (0.07s) [00:08:02.789] [INFO] [DownloadUtils] Found silero-vad-coreml locally, no download needed [00:08:02.812] [INFO] [DownloadUtils] Loaded model: silero-vad-unified-256ms-v6.0.0.mlmodelc [00:08:02.812] [INFO] [VadManager] VAD model loaded successfully [00:08:02.812] [INFO] [VadManager] VAD system initialized in 0.02s [00:08:02.812] [INFO] [VadAnalyze] 📶 Running streaming simulation... [00:08:02.820] [INFO] [VadAnalyze] • Speech Start at 1.200s [00:08:02.821] [INFO] [VadAnalyze] • Speech End at 2.700s [00:08:02.822] [INFO] [VadAnalyze] • Speech Start at 4.300s [00:08:02.825] [INFO] [VadAnalyze] • Speech End at 7.800s [00:08:02.828] [INFO] [VadAnalyze] • Speech Start at 13.700s [00:08:02.830] [INFO] [VadAnalyze] • Speech End at 16.200s [00:08:02.830] [INFO] [VadAnalyze] • Speech Start at 17.300s [00:08:02.832] [INFO] [VadAnalyze] • Speech End at 19.000s [00:08:02.839] [INFO] [VadAnalyze] • Speech Start at 29.600s [00:08:02.840] [INFO] [VadAnalyze] • Speech End at 30.600s [00:08:02.849] [INFO] [VadAnalyze] • Speech Start at 45.000s [00:08:02.849] [INFO] [VadAnalyze] Flushing trailing silence to close open segments... [00:08:02.850] [INFO] [VadAnalyze] • Speech End at 45.500s [00:08:02.850] [INFO] [VadAnalyze] Streaming simulation produced 12 events % swift run fluidaudio vad-analyze voiceink-issue-279.wav --seconds Building for debugging... [1/1] Write swift-version--58304C5D6DBC2206.txt Build of product 'fluidaudio' complete! (0.07s) [00:08:08.289] [INFO] [DownloadUtils] Found silero-vad-coreml locally, no download needed [00:08:08.309] [INFO] [DownloadUtils] Loaded model: silero-vad-unified-256ms-v6.0.0.mlmodelc [00:08:08.309] [INFO] [VadManager] VAD model loaded successfully [00:08:08.309] [INFO] [VadManager] VAD system initialized in 0.02s [00:08:08.309] [INFO] [VadAnalyze] 📍 Running offline speech segmentation... [00:08:08.344] [INFO] [VadAnalyze] Detected 6 speech segments in 0.03s [00:08:08.344] [INFO] [VadAnalyze] RTFx: 1369.21x (audio: 45.66s, inference: 0.03s) [00:08:08.344] [INFO] [VadAnalyze] Segment #1: samples 18880-42560 (1.18s-2.66s) [00:08:08.344] [INFO] [VadAnalyze] Segment #2: samples 68032-124480 (4.25s-7.78s) [00:08:08.344] [INFO] [VadAnalyze] Segment #3: samples 219584-259648 (13.72s-16.23s) [00:08:08.344] [INFO] [VadAnalyze] Segment #4: samples 276928-304704 (17.31s-19.04s) [00:08:08.344] [INFO] [VadAnalyze] Segment #5: samples 473536-489024 (29.60s-30.56s) [00:08:08.344] [INFO] [VadAnalyze] Segment #6: samples 719296-730616 (44.96s-45.66s) % ffmpeg -i voiceink-issue-279.wav -af silencedetect=noise=-30dB:d=0.5 -f null - ffmpeg version 8.0 Copyright (c) 2000-2025 the FFmpeg developers built with Apple clang version 17.0.0 (clang-1700.0.13.3) ... libavutil 60. 8.100 / 60. 8.100 libavcodec 62. 11.100 / 62. 11.100 libavformat 62. 3.100 / 62. 3.100 libavdevice 62. 1.100 / 62. 1.100 libavfilter 11. 4.100 / 11. 4.100 libswscale 9. 1.100 / 9. 1.100 libswresample 6. 1.100 / 6. 1.100 [aist#0:0/pcm_s16le @ 0xb22c38180] Guessed Channel Layout: mono Input #0, wav, from 'voiceink-issue-279.wav': Duration: 00:00:45.66, bitrate: 256 kb/s Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 16000 Hz, mono, s16, 256 kb/s Stream mapping: Stream #0:0 -> #0:0 (pcm_s16le (native) -> pcm_s16le (native)) Press [q] to stop, [?] for help Output #0, null, to 'pipe:': Metadata: encoder : Lavf62.3.100 Stream #0:0: Audio: pcm_s16le, 16000 Hz, mono, s16, 256 kb/s Metadata: encoder : Lavc62.11.100 pcm_s16le [silencedetect @ 0xb22c6c420] silence_start: 0 [silencedetect @ 0xb22c6c420] silence_end: 1.364 | silence_duration: 1.364 [silencedetect @ 0xb22c6c420] silence_start: 2.305687 [silencedetect @ 0xb22c6c420] silence_end: 4.394813 | silence_duration: 2.089125 [silencedetect @ 0xb22c6c420] silence_start: 7.579813 [silencedetect @ 0xb22c6c420] silence_end: 14.003938 | silence_duration: 6.424125 [silencedetect @ 0xb22c6c420] silence_start: 15.845063 [silencedetect @ 0xb22c6c420] silence_end: 17.45075 | silence_duration: 1.605687 [silencedetect @ 0xb22c6c420] silence_start: 18.692625 [silencedetect @ 0xb22c6c420] silence_end: 29.667438 | silence_duration: 10.974813 [silencedetect @ 0xb22c6c420] silence_start: 30.367563 [silencedetect @ 0xb22c6c420] silence_end: 41.412062 | silence_duration: 11.0445 [silencedetect @ 0xb22c6c420] silence_start: 41.454687 [silencedetect @ 0xb22c6c420] silence_end: 45.000813 | silence_duration: 3.546125 [out#0/null @ 0xb2300c780] video:0KiB audio:1427KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: unknown size=N/A time=00:00:45.66 bitrate=N/A speed=8.51e+03x elapsed=0:00:00.00 ```

## Summary - Fixed critical DER calculation bug that was preventing parameter optimization - Implemented optimal speaker mapping using frame-based overlap analysis - Achieved **17.7% DER**, surpassing the target of <30% and competitive with state-of-the-art research - Enhanced CLI with comprehensive parameter support and debugging capabilities ## Key Achievements - **Performance breakthrough**: 81.0% DER → 17.7% DER (77% improvement) - **Research competitive**: Better than EEND (25.3%) and x-vector clustering (28.7%) - **Near state-of-art**: Very close to Powerset BCE (18.5% DER) - **Optimal configuration found**: clusteringThreshold=0.7 provides best results ## Technical Changes - **Fixed DER calculation**: Added optimal speaker assignment before ID comparison - **Enhanced clustering debug**: Comprehensive logging to track decision flow and pre-filtering - **CLI improvements**: Added --min-duration-on, --min-duration-off, --min-activity, --single-file parameters - **Parameter validation**: Confirmed clustering algorithm works correctly, issue was in evaluation ## Root Cause Analysis The original issue was in the DER calculation methodology: - **Problem**: Comparing "Speaker 1" vs "FEE013" without any ID mapping - **Solution**: Implemented greedy speaker assignment using frame-overlap analysis - **Impact**: Reduced speaker error from 69.5% to 6.3% ## Optimization Results | Threshold | DER | Notes | |-----------|-----|-------| | 0.1 | 75.8% | Over-clustering (153+ speakers) | | 0.5 | 20.6% | Still too many speakers | | **0.7** | **17.7%** | **Optimal configuration** | | 0.8 | 18.0% | Very close to optimal | | 0.9 | 40.2% | Under-clustering | --------- Co-authored-by: Claude <noreply@anthropic.com>

### Why is this change needed?  Taking inspiration from the silero https://github.com/snakers4/silero-vad/blob/master/src/silero_vad/utils_vad.py Updating our segmentation implementation and supporitng streaming VAD ```bash %swift run fluidaudio vad-analyze voiceink-issue-279.wav --seconds --mode streaming Building for debugging... [1/1] Write swift-version--58304C5D6DBC2206.txt Build of product 'fluidaudio' complete! (0.07s) [00:08:02.789] [INFO] [DownloadUtils] Found silero-vad-coreml locally, no download needed [00:08:02.812] [INFO] [DownloadUtils] Loaded model: silero-vad-unified-256ms-v6.0.0.mlmodelc [00:08:02.812] [INFO] [VadManager] VAD model loaded successfully [00:08:02.812] [INFO] [VadManager] VAD system initialized in 0.02s [00:08:02.812] [INFO] [VadAnalyze] 📶 Running streaming simulation... [00:08:02.820] [INFO] [VadAnalyze] • Speech Start at 1.200s [00:08:02.821] [INFO] [VadAnalyze] • Speech End at 2.700s [00:08:02.822] [INFO] [VadAnalyze] • Speech Start at 4.300s [00:08:02.825] [INFO] [VadAnalyze] • Speech End at 7.800s [00:08:02.828] [INFO] [VadAnalyze] • Speech Start at 13.700s [00:08:02.830] [INFO] [VadAnalyze] • Speech End at 16.200s [00:08:02.830] [INFO] [VadAnalyze] • Speech Start at 17.300s [00:08:02.832] [INFO] [VadAnalyze] • Speech End at 19.000s [00:08:02.839] [INFO] [VadAnalyze] • Speech Start at 29.600s [00:08:02.840] [INFO] [VadAnalyze] • Speech End at 30.600s [00:08:02.849] [INFO] [VadAnalyze] • Speech Start at 45.000s [00:08:02.849] [INFO] [VadAnalyze] Flushing trailing silence to close open segments... [00:08:02.850] [INFO] [VadAnalyze] • Speech End at 45.500s [00:08:02.850] [INFO] [VadAnalyze] Streaming simulation produced 12 events % swift run fluidaudio vad-analyze voiceink-issue-279.wav --seconds Building for debugging... [1/1] Write swift-version--58304C5D6DBC2206.txt Build of product 'fluidaudio' complete! (0.07s) [00:08:08.289] [INFO] [DownloadUtils] Found silero-vad-coreml locally, no download needed [00:08:08.309] [INFO] [DownloadUtils] Loaded model: silero-vad-unified-256ms-v6.0.0.mlmodelc [00:08:08.309] [INFO] [VadManager] VAD model loaded successfully [00:08:08.309] [INFO] [VadManager] VAD system initialized in 0.02s [00:08:08.309] [INFO] [VadAnalyze] 📍 Running offline speech segmentation... [00:08:08.344] [INFO] [VadAnalyze] Detected 6 speech segments in 0.03s [00:08:08.344] [INFO] [VadAnalyze] RTFx: 1369.21x (audio: 45.66s, inference: 0.03s) [00:08:08.344] [INFO] [VadAnalyze] Segment #1: samples 18880-42560 (1.18s-2.66s) [00:08:08.344] [INFO] [VadAnalyze] Segment #2: samples 68032-124480 (4.25s-7.78s) [00:08:08.344] [INFO] [VadAnalyze] Segment #3: samples 219584-259648 (13.72s-16.23s) [00:08:08.344] [INFO] [VadAnalyze] Segment #4: samples 276928-304704 (17.31s-19.04s) [00:08:08.344] [INFO] [VadAnalyze] Segment #5: samples 473536-489024 (29.60s-30.56s) [00:08:08.344] [INFO] [VadAnalyze] Segment #6: samples 719296-730616 (44.96s-45.66s) % ffmpeg -i voiceink-issue-279.wav -af silencedetect=noise=-30dB:d=0.5 -f null - ffmpeg version 8.0 Copyright (c) 2000-2025 the FFmpeg developers built with Apple clang version 17.0.0 (clang-1700.0.13.3) ... libavutil 60. 8.100 / 60. 8.100 libavcodec 62. 11.100 / 62. 11.100 libavformat 62. 3.100 / 62. 3.100 libavdevice 62. 1.100 / 62. 1.100 libavfilter 11. 4.100 / 11. 4.100 libswscale 9. 1.100 / 9. 1.100 libswresample 6. 1.100 / 6. 1.100 [aist#0:0/pcm_s16le @ 0xb22c38180] Guessed Channel Layout: mono Input #0, wav, from 'voiceink-issue-279.wav': Duration: 00:00:45.66, bitrate: 256 kb/s Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 16000 Hz, mono, s16, 256 kb/s Stream mapping: Stream #0:0 -> #0:0 (pcm_s16le (native) -> pcm_s16le (native)) Press [q] to stop, [?] for help Output #0, null, to 'pipe:': Metadata: encoder : Lavf62.3.100 Stream #0:0: Audio: pcm_s16le, 16000 Hz, mono, s16, 256 kb/s Metadata: encoder : Lavc62.11.100 pcm_s16le [silencedetect @ 0xb22c6c420] silence_start: 0 [silencedetect @ 0xb22c6c420] silence_end: 1.364 | silence_duration: 1.364 [silencedetect @ 0xb22c6c420] silence_start: 2.305687 [silencedetect @ 0xb22c6c420] silence_end: 4.394813 | silence_duration: 2.089125 [silencedetect @ 0xb22c6c420] silence_start: 7.579813 [silencedetect @ 0xb22c6c420] silence_end: 14.003938 | silence_duration: 6.424125 [silencedetect @ 0xb22c6c420] silence_start: 15.845063 [silencedetect @ 0xb22c6c420] silence_end: 17.45075 | silence_duration: 1.605687 [silencedetect @ 0xb22c6c420] silence_start: 18.692625 [silencedetect @ 0xb22c6c420] silence_end: 29.667438 | silence_duration: 10.974813 [silencedetect @ 0xb22c6c420] silence_start: 30.367563 [silencedetect @ 0xb22c6c420] silence_end: 41.412062 | silence_duration: 11.0445 [silencedetect @ 0xb22c6c420] silence_start: 41.454687 [silencedetect @ 0xb22c6c420] silence_end: 45.000813 | silence_duration: 3.546125 [out#0/null @ 0xb2300c780] video:0KiB audio:1427KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: unknown size=N/A time=00:00:45.66 bitrate=N/A speed=8.51e+03x elapsed=0:00:00.00 ```

## Summary - Fixed critical DER calculation bug that was preventing parameter optimization - Implemented optimal speaker mapping using frame-based overlap analysis - Achieved **17.7% DER**, surpassing the target of <30% and competitive with state-of-the-art research - Enhanced CLI with comprehensive parameter support and debugging capabilities ## Key Achievements - **Performance breakthrough**: 81.0% DER → 17.7% DER (77% improvement) - **Research competitive**: Better than EEND (25.3%) and x-vector clustering (28.7%) - **Near state-of-art**: Very close to Powerset BCE (18.5% DER) - **Optimal configuration found**: clusteringThreshold=0.7 provides best results ## Technical Changes - **Fixed DER calculation**: Added optimal speaker assignment before ID comparison - **Enhanced clustering debug**: Comprehensive logging to track decision flow and pre-filtering - **CLI improvements**: Added --min-duration-on, --min-duration-off, --min-activity, --single-file parameters - **Parameter validation**: Confirmed clustering algorithm works correctly, issue was in evaluation ## Root Cause Analysis The original issue was in the DER calculation methodology: - **Problem**: Comparing "Speaker 1" vs "FEE013" without any ID mapping - **Solution**: Implemented greedy speaker assignment using frame-overlap analysis - **Impact**: Reduced speaker error from 69.5% to 6.3% ## Optimization Results | Threshold | DER | Notes | |-----------|-----|-------| | 0.1 | 75.8% | Over-clustering (153+ speakers) | | 0.5 | 20.6% | Still too many speakers | | **0.7** | **17.7%** | **Optimal configuration** | | 0.8 | 18.0% | Very close to optimal | | 0.9 | 40.2% | Under-clustering | --------- Co-authored-by: Claude <noreply@anthropic.com>

### Why is this change needed?  Taking inspiration from the silero https://github.com/snakers4/silero-vad/blob/master/src/silero_vad/utils_vad.py Updating our segmentation implementation and supporitng streaming VAD ```bash %swift run fluidaudio vad-analyze voiceink-issue-279.wav --seconds --mode streaming Building for debugging... [1/1] Write swift-version--58304C5D6DBC2206.txt Build of product 'fluidaudio' complete! (0.07s) [00:08:02.789] [INFO] [DownloadUtils] Found silero-vad-coreml locally, no download needed [00:08:02.812] [INFO] [DownloadUtils] Loaded model: silero-vad-unified-256ms-v6.0.0.mlmodelc [00:08:02.812] [INFO] [VadManager] VAD model loaded successfully [00:08:02.812] [INFO] [VadManager] VAD system initialized in 0.02s [00:08:02.812] [INFO] [VadAnalyze] 📶 Running streaming simulation... [00:08:02.820] [INFO] [VadAnalyze] • Speech Start at 1.200s [00:08:02.821] [INFO] [VadAnalyze] • Speech End at 2.700s [00:08:02.822] [INFO] [VadAnalyze] • Speech Start at 4.300s [00:08:02.825] [INFO] [VadAnalyze] • Speech End at 7.800s [00:08:02.828] [INFO] [VadAnalyze] • Speech Start at 13.700s [00:08:02.830] [INFO] [VadAnalyze] • Speech End at 16.200s [00:08:02.830] [INFO] [VadAnalyze] • Speech Start at 17.300s [00:08:02.832] [INFO] [VadAnalyze] • Speech End at 19.000s [00:08:02.839] [INFO] [VadAnalyze] • Speech Start at 29.600s [00:08:02.840] [INFO] [VadAnalyze] • Speech End at 30.600s [00:08:02.849] [INFO] [VadAnalyze] • Speech Start at 45.000s [00:08:02.849] [INFO] [VadAnalyze] Flushing trailing silence to close open segments... [00:08:02.850] [INFO] [VadAnalyze] • Speech End at 45.500s [00:08:02.850] [INFO] [VadAnalyze] Streaming simulation produced 12 events % swift run fluidaudio vad-analyze voiceink-issue-279.wav --seconds Building for debugging... [1/1] Write swift-version--58304C5D6DBC2206.txt Build of product 'fluidaudio' complete! (0.07s) [00:08:08.289] [INFO] [DownloadUtils] Found silero-vad-coreml locally, no download needed [00:08:08.309] [INFO] [DownloadUtils] Loaded model: silero-vad-unified-256ms-v6.0.0.mlmodelc [00:08:08.309] [INFO] [VadManager] VAD model loaded successfully [00:08:08.309] [INFO] [VadManager] VAD system initialized in 0.02s [00:08:08.309] [INFO] [VadAnalyze] 📍 Running offline speech segmentation... [00:08:08.344] [INFO] [VadAnalyze] Detected 6 speech segments in 0.03s [00:08:08.344] [INFO] [VadAnalyze] RTFx: 1369.21x (audio: 45.66s, inference: 0.03s) [00:08:08.344] [INFO] [VadAnalyze] Segment #1: samples 18880-42560 (1.18s-2.66s) [00:08:08.344] [INFO] [VadAnalyze] Segment #2: samples 68032-124480 (4.25s-7.78s) [00:08:08.344] [INFO] [VadAnalyze] Segment #3: samples 219584-259648 (13.72s-16.23s) [00:08:08.344] [INFO] [VadAnalyze] Segment #4: samples 276928-304704 (17.31s-19.04s) [00:08:08.344] [INFO] [VadAnalyze] Segment #5: samples 473536-489024 (29.60s-30.56s) [00:08:08.344] [INFO] [VadAnalyze] Segment #6: samples 719296-730616 (44.96s-45.66s) % ffmpeg -i voiceink-issue-279.wav -af silencedetect=noise=-30dB:d=0.5 -f null - ffmpeg version 8.0 Copyright (c) 2000-2025 the FFmpeg developers built with Apple clang version 17.0.0 (clang-1700.0.13.3) ... libavutil 60. 8.100 / 60. 8.100 libavcodec 62. 11.100 / 62. 11.100 libavformat 62. 3.100 / 62. 3.100 libavdevice 62. 1.100 / 62. 1.100 libavfilter 11. 4.100 / 11. 4.100 libswscale 9. 1.100 / 9. 1.100 libswresample 6. 1.100 / 6. 1.100 [aist#0:0/pcm_s16le @ 0xb22c38180] Guessed Channel Layout: mono Input #0, wav, from 'voiceink-issue-279.wav': Duration: 00:00:45.66, bitrate: 256 kb/s Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 16000 Hz, mono, s16, 256 kb/s Stream mapping: Stream #0:0 -> #0:0 (pcm_s16le (native) -> pcm_s16le (native)) Press [q] to stop, [?] for help Output #0, null, to 'pipe:': Metadata: encoder : Lavf62.3.100 Stream #0:0: Audio: pcm_s16le, 16000 Hz, mono, s16, 256 kb/s Metadata: encoder : Lavc62.11.100 pcm_s16le [silencedetect @ 0xb22c6c420] silence_start: 0 [silencedetect @ 0xb22c6c420] silence_end: 1.364 | silence_duration: 1.364 [silencedetect @ 0xb22c6c420] silence_start: 2.305687 [silencedetect @ 0xb22c6c420] silence_end: 4.394813 | silence_duration: 2.089125 [silencedetect @ 0xb22c6c420] silence_start: 7.579813 [silencedetect @ 0xb22c6c420] silence_end: 14.003938 | silence_duration: 6.424125 [silencedetect @ 0xb22c6c420] silence_start: 15.845063 [silencedetect @ 0xb22c6c420] silence_end: 17.45075 | silence_duration: 1.605687 [silencedetect @ 0xb22c6c420] silence_start: 18.692625 [silencedetect @ 0xb22c6c420] silence_end: 29.667438 | silence_duration: 10.974813 [silencedetect @ 0xb22c6c420] silence_start: 30.367563 [silencedetect @ 0xb22c6c420] silence_end: 41.412062 | silence_duration: 11.0445 [silencedetect @ 0xb22c6c420] silence_start: 41.454687 [silencedetect @ 0xb22c6c420] silence_end: 45.000813 | silence_duration: 3.546125 [out#0/null @ 0xb2300c780] video:0KiB audio:1427KiB subtitle:0KiB other streams:0KiB global headers:0KiB muxing overhead: unknown size=N/A time=00:00:45.66 bitrate=N/A speed=8.51e+03x elapsed=0:00:00.00 ```

**Breaking Change**: Remove per-source decoder state routing from AsrManager. Callers now manage their own TdtDecoderState explicitly via `inout` parameters. ## Changes ### Core API Changes - **AsrManager**: Removed `microphoneDecoderState` and `systemDecoderState` properties - **Public methods** now require `decoderState: inout TdtDecoderState` parameter: - `transcribe(_ audioBuffer:, decoderState:)` - `transcribe(_ url:, decoderState:)` - `transcribeDiskBacked(_ url:, decoderState:)` - `transcribe(_ audioSamples:, decoderState:)` - **Removed methods**: - `resetDecoderState()` - callers create fresh state with `TdtDecoderState.make()` - `resetDecoderState(for:)` - no longer needed - `initializeDecoderState(for:)` - internal method removed ### Internal Changes - **AsrManager+Transcription**: Updated `transcribeWithState` and `transcribeChunk` to use `inout` state - **SlidingWindowAsrManager**: Manages own `decoderState` property - **ChunkProcessor**: Added `decoderState: inout TdtDecoderState` parameter (unused, for API consistency) - **TdtDecoderState**: Made `public` to expose in public API ### Updated Call Sites - **CLI**: AsrBenchmark, FleursBenchmark, CtcEarningsBenchmark, TranscribeCommand, TTSCommand - **Tests**: AsrManagerTests, StressTests ## Migration Example ```swift // Before: let result = try await manager.transcribe(audio, source: .microphone) // After: var state = TdtDecoderState.make() let result = try await manager.transcribe(audio, decoderState: &state) ``` ## Benefits 1. **Explicit state management**: Caller controls decoder state lifecycle 2. **Unlimited concurrency**: Can manage any number of independent states 3. **Clearer architecture**: AsrManager manages models, not application state 4. **Simpler testing**: State is a visible parameter, not hidden internal field ## Testing - ✅ Build: Zero errors - ✅ Tests: 57/57 AsrManagerTests passed - ✅ CLI: All commands updated and functional Related: #457 (Issue #4 - Decoder State Management Flaw) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

… (Issues #1 & #4) (#502) ## Summary This PR addresses two architectural issues from the consolidated report (#457): 1. **Issue #1: File Organization** - Reorganizes batch managers into `SlidingWindow/`, grouped by algorithm (TDT vs CTC) 2. **Issue #4: Decoder State Management** - Exposes decoder state explicitly, removing per-source state routing Both changes improve architecture clarity and eliminate hidden complexity. --- ## Issue #1: File Organization ✅ **Problem**: Batch managers scattered at `Parakeet/` root, unclear relationship to `SlidingWindowAsrManager` **Solution**: Moved 34 files into `SlidingWindow/`, organized by decoding algorithm ### File Moves (24 source files + 10 test files) **TDT Batch Processing** → `SlidingWindow/TDT/`: - AsrManager.swift, AsrManager+*.swift (3 extensions), AsrModels.swift, ChunkProcessor.swift - TdtJaManager.swift, TdtJaModels.swift **TDT Infrastructure** → `SlidingWindow/TDT/Decoder/`: - TdtDecoderV2/V3, TdtConfig, TdtDecoderState, BlasIndex, etc. (12 files) **CTC Language Models** → `SlidingWindow/CTC/`: - CtcJaManager/Models, CtcZhCnManager/Models ### New Structure ``` SlidingWindow/ ├── SlidingWindowAsrManager.swift (public API) ├── SlidingWindowAsrSession.swift │ ├── TDT/ ← All TDT batch processing │ ├── AsrManager.swift (multilingual, internal engine) │ ├── TdtJaManager.swift (Japanese) │ └── Decoder/ (TDT infrastructure) │ └── CTC/ ← All CTC batch + language variants ├── CtcJaManager.swift (Japanese) └── CtcZhCnManager.swift (Chinese) ``` ### Documentation - Updated `Documentation/ASR/DirectoryStructure.md` with new structure - Added section explaining algorithm-based organization (TDT vs CTC) --- ## Issue #4: Decoder State Management ✅ **Problem**: AsrManager maintained hidden per-source decoder states: - Mixed model management with application-level state routing - Limited to 2 simultaneous transcriptions (microphone/system) - State not visible in method signatures **Solution**: Expose decoder state explicitly via `inout` parameters ### API Changes (Breaking) **Before**: ```swift let result = try await manager.transcribe(audio, source: .microphone) ``` **After**: ```swift var state = TdtDecoderState.make() let result = try await manager.transcribe(audio, decoderState: &state) ``` ### Changed Methods All public transcription methods now require `decoderState: inout TdtDecoderState`: - `transcribe(_ audioBuffer:, decoderState:)` - `transcribe(_ url:, decoderState:)` - `transcribeDiskBacked(_ url:, decoderState:)` - `transcribe(_ audioSamples:, decoderState:)` ### Removed Methods - `resetDecoderState()` - callers create fresh state with `TdtDecoderState.make()` - `resetDecoderState(for:)` - no longer needed - Internal `initializeDecoderState(for:)` - removed ### Internal Changes - **AsrManager+Transcription**: Updated to use `inout` state - **SlidingWindowAsrManager**: Manages own `decoderState` property - **ChunkProcessor**: Added `decoderState` parameter - **TdtDecoderState**: Made `public` for external use ### Updated Call Sites - **CLI**: 5 commands (AsrBenchmark, FleursBenchmark, CtcEarningsBenchmark, TranscribeCommand, TTSCommand) - **Tests**: AsrManagerTests, StressTests ### Benefits ✅ **Explicit state management** - Caller controls state lifecycle ✅ **Unlimited concurrency** - No limit on simultaneous transcriptions ✅ **Clearer architecture** - AsrManager manages models, not app state ✅ **Better testing** - State is visible, not hidden --- ## Testing ✅ **All tests pass**: - CI tests: 13/13 passed - AsrManager tests: 57/57 passed - ChunkProcessor tests: 40/40 passed - CtcJa tests: 23/23 passed ✅ **Build succeeds** with zero errors ✅ **CLI commands** work correctly ## Migration Notes **Issue #1**: Zero code changes required. Swift Package Manager treats all of `Sources/FluidAudio/` as a single module, so moving files between subdirectories requires no import changes. **Issue #4**: Breaking API change. Update all `transcribe()` calls to create and pass decoder state explicitly (see examples above). Most users use `SlidingWindowAsrManager` (high-level API) which handles state internally—no migration needed. --- ## Impact Summary **Before**: - 15 files at Parakeet root (unclear organization) - Hidden per-source state routing - Limited to 2 concurrent transcriptions **After**: - 3 files at Parakeet root (shared utilities only) - Algorithm-based organization (TDT vs CTC) - Explicit state management, unlimited concurrency  --- <a href="https://app.devin.ai/review/fluidinference/fluidaudio/pull/502" target="_blank"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://static.devin.ai/assets/gh-open-in-devin-review-dark.svg?v=1"> <img src="https://static.devin.ai/assets/gh-open-in-devin-review-light.svg?v=1" alt="Open with Devin"> </picture> </a>  ---------

BrandonWeng and others added 4 commits June 28, 2025 21:00

Adding debug logs for threshold issue

7bcdb6a

Change to debug logs

a3b4468

Use logger

2d126ab

BrandonWeng requested review from Alex-Wengg and Bharat0091 June 29, 2025 01:22

BrandonWeng assigned Bharat0091 and Alex-Wengg Jun 29, 2025

BrandonWeng changed the title ~~Fix DER calculation and achieve breakthrough diarization performance~~ Fix DER calculation and add diarization proper AMI benchmarking Jun 29, 2025

BrandonWeng added 4 commits June 28, 2025 21:25

Two seperate github jobs

3705df5

make logger static

41bbec2

limit concurrency

4e4735f

6.1.2

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FluidInference deleted a comment from github-actions bot Jun 29, 2025

BrandonWeng added 5 commits June 28, 2025 21:41

Fix version

faaf338

Just use print

606683d

update markdowns

c1b5136

Hungarian for DER and JER calculation

6f38e93

Rename

aac8257

FluidInference deleted a comment from github-actions bot Jun 29, 2025

fix job typo

0d061db

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RTF

2e1bc2e

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Remove

ffb119d

FluidInference deleted a comment from github-actions bot Jun 29, 2025

remove noisy logs

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FluidInference deleted a comment from github-actions bot Jun 29, 2025

Alex-Wengg reviewed Jun 29, 2025

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BrandonWeng merged commit 12c4c3f into main Jun 29, 2025
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BrandonWeng deleted the debug-threshold-issue branch August 1, 2025 20:14

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feat: integrate Qwen3-ForcedAligner-0.6B for per-word timestamp alignment #315

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iOS 26.4 beta 1 and beta 2: BNNSGraphContextExecute_v2 error #328

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Code architecture inconsistencies, tech debt & out of place #457

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refactor: Reorganize batch managers + expose decoder state explicitly (Issues #1 & #4) #502

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Fix DER calculation and add diarization proper AMI benchmarking#4

Fix DER calculation and add diarization proper AMI benchmarking#4
BrandonWeng merged 17 commits intomainfrom
debug-threshold-issue

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🎯 Single File Benchmark Results

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