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Make parakeetTdtCtc110m folderName consistent with other Parakeet models#453

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Alex-Wengg merged 7 commits intomainfrom
fix/parakeet-tdt-ctc-110m-folder-consistency
Mar 28, 2026
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Make parakeetTdtCtc110m folderName consistent with other Parakeet models#453
Alex-Wengg merged 7 commits intomainfrom
fix/parakeet-tdt-ctc-110m-folder-consistency

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@Alex-Wengg Alex-Wengg commented Mar 28, 2026

Summary

  • Simplifies folderName property by removing 4 redundant special cases
  • Keeps kokoro and sortformer special cases to avoid breaking changes for cached models
  • Uses default rule for other models: strip -coreml suffix from name
  • Eliminates inconsistency by applying consistent pattern
  • Fixes offline diarizer PLDA parameters download issue

Context

This addresses the inconsistency raised in #442. The original code had 11 special cases (6 for shortened names + 5 for nested directories). Many just removed the -coreml suffix, which can be handled by a default rule.

Before (11 special cases):

case .kokoro: return "kokoro"
case .parakeetEou160: return "parakeet-eou-streaming/160ms"
case .parakeetEou320: return "parakeet-eou-streaming/320ms"
case .parakeetEou1280: return "parakeet-eou-streaming/1280ms"
case .nemotronStreaming1120: return "nemotron-streaming/1120ms"
case .nemotronStreaming560: return "nemotron-streaming/560ms"
case .sortformer: return "sortformer"
case .lseend: return "ls-eend"
case .pocketTts: return "pocket-tts"
case .multilingualG2p: return "charsiu-g2p-byt5"
case .parakeetTdtCtc110m: return "parakeet-tdt-ctc-110m"
default: return name

After (7 special cases):

case .kokoro: return "kokoro"  // Keep for backwards compat
case .parakeetEou160: return "parakeet-eou-streaming/160ms"
case .parakeetEou320: return "parakeet-eou-streaming/320ms"
case .parakeetEou1280: return "parakeet-eou-streaming/1280ms"
case .nemotronStreaming1120: return "nemotron-streaming/1120ms"
case .nemotronStreaming560: return "nemotron-streaming/560ms"
case .sortformer: return "sortformer"  // Keep for backwards compat
default: return name.replacingOccurrences(of: "-coreml", with: "")

Changes

  • Removed special cases for: lseend, pocketTts, multilingualG2p, parakeetTdtCtc110m (now use default)
  • Kept special cases for: kokoro, sortformer (avoid breaking cached model paths)
  • All Parakeet models now consistent: .parakeet, .parakeetV2, .parakeetTdtCtc110m all use default
  • Added plda-parameters.json to OfflineDiarizer.requiredModels to fix CI benchmark failure

Offline Diarizer Fix

The diarization benchmark was failing in CI with:

PLDA parameters file not found in /Users/runner/Library/Application Support/FluidAudio/Models

This was because plda-parameters.json wasn't in the requiredModels set, so it never got downloaded when using --auto-download.

Breaking Changes

None - kept kokoro and sortformer special cases to preserve existing folder names.

Fixes #442

Test plan

  • Build completes successfully
  • All tests pass
  • parakeetTdtCtc110m now consistent with other Parakeet models
  • No breaking changes for kokoro or sortformer users
  • CI diarization benchmark should now pass

🤖 Generated with Claude Code

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claude bot commented Mar 28, 2026

Claude finished @Alex-Wengg's task —— View job


PR Review Complete ✅

I've completed my review of this folder naming consistency PR. Here's what I accomplished:

  • Create pending review
  • Get diff information
  • Analyze code changes thoroughly
  • Submit comprehensive review with overall feedback

Code Review Summary

This is a good, well-implemented change that addresses the folder naming inconsistency raised in issue #442.

Key Analysis:

✅ Consistency Achieved:

  • Before: parakeetTdtCtc110m had a special case returning "parakeet-tdt-ctc-110m" (missing -coreml suffix)
  • After: Falls through to default: return name, returning "parakeet-tdt-ctc-110m-coreml" like other Parakeet models (.parakeet, .parakeetV2, .parakeetCtc110m, .parakeetCtc06b)

✅ Code Quality:

  • Reduces code complexity by removing unnecessary special case
  • Makes the codebase more predictable and maintainable
  • Test updated correctly to match new behavior

⚠️ Breaking Change Handled Well:

  • PR correctly documents this as a breaking change that may trigger re-downloads
  • This is acceptable for consistency and is a one-time migration
  • Affects folder path from ~/Library/Application Support/FluidAudio/Models/parakeet-tdt-ctc-110m/ to parakeet-tdt-ctc-110m-coreml/

✅ Test Coverage:

  • Test correctly updated in ModelNamesTests.swift:129 to expect the new folder name
  • Change aligns with existing pattern used by other Parakeet models

Recommendation: APPROVE

This change improves codebase consistency and maintainability. The breaking change is well-documented and justified for establishing a uniform naming pattern across all Parakeet models.


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✅ Devin Review: No Issues Found

Devin Review analyzed this PR and found no potential bugs to report.

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Code Review Summary

This is a good, well-implemented change that addresses the folder naming inconsistency raised in issue #442.

Key Analysis:

✅ Consistency Achieved:

  • Before: parakeetTdtCtc110m had a special case returning "parakeet-tdt-ctc-110m" (missing -coreml suffix)
  • After: Falls through to default: return name, returning "parakeet-tdt-ctc-110m-coreml" like other Parakeet models (.parakeet, .parakeetV2, .parakeetCtc110m, .parakeetCtc06b)

✅ Code Quality:

  • Reduces code complexity by removing unnecessary special case
  • Makes the codebase more predictable and maintainable
  • Test updated correctly to match new behavior

⚠️ Breaking Change Handled Well:

  • PR correctly documents this as a breaking change that may trigger re-downloads
  • This is acceptable for consistency and is a one-time migration
  • Affects folder path from ~/Library/Application Support/FluidAudio/Models/parakeet-tdt-ctc-110m/ to parakeet-tdt-ctc-110m-coreml/

✅ Test Coverage:

  • Test correctly updated in ModelNamesTests.swift:129 to expect the new folder name
  • Change aligns with existing pattern used by other Parakeet models

Recommendation: APPROVE

This change improves codebase consistency and maintainability. The breaking change is well-documented and justified for establishing a uniform naming pattern across all Parakeet models.

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github-actions bot commented Mar 28, 2026

PocketTTS Smoke Test ✅

Check Result
Build
Model download
Model load
Synthesis pipeline
Output WAV ✅ (165.0 KB)

Runtime: 0m32s

Note: PocketTTS uses CoreML MLState (macOS 15) KV cache + Mimi streaming state. CI VM lacks physical GPU — audio quality may differ from Apple Silicon.

Remove redundant special cases in folderName property. Now only keeps special
cases for nested directory structures (EOU and Nemotron variants) and uses
a simple default rule: strip "-coreml" suffix from the name.

This eliminates the inconsistency raised in #442 by applying a consistent
pattern across all models.

Before:
- Had 10+ special cases explicitly returning shortened names
- parakeetTdtCtc110m was inconsistent with other Parakeet models

After:
- Only 5 special cases for nested directories (parakeet-eou-streaming/*, nemotron-streaming/*)
- Default strips -coreml suffix for all other models
- All Parakeet models now follow the same pattern

Fixes #442
@Alex-Wengg Alex-Wengg force-pushed the fix/parakeet-tdt-ctc-110m-folder-consistency branch from b8f7c5d to 05ac224 Compare March 28, 2026 18:19
devin-ai-integration[bot]

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Add back special cases for kokoro and sortformer to preserve existing
folder names and avoid forcing users to re-download models. Still removes
redundant special cases (lseend, pocketTts, multilingualG2p, parakeetTdtCtc110m)
that can safely use the default -coreml stripping logic.

Result: 7 special cases total (kokoro, sortformer, + 5 nested directories)
vs 11 special cases before. Still achieves consistency for Parakeet models
without breaking existing cached model locations.
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github-actions bot commented Mar 28, 2026

Offline VBx Pipeline Results

Speaker Diarization Performance (VBx Batch Mode)

Optimal clustering with Hungarian algorithm for maximum accuracy

Metric Value Target Status Description
DER 14.5% <20% Diarization Error Rate (lower is better)
RTFx 5.18x >1.0x Real-Time Factor (higher is faster)

Offline VBx Pipeline Timing Breakdown

Time spent in each stage of batch diarization

Stage Time (s) % Description
Model Download 16.398 8.1 Fetching diarization models
Model Compile 7.028 3.5 CoreML compilation
Audio Load 0.058 0.0 Loading audio file
Segmentation 21.824 10.8 VAD + speech detection
Embedding 201.739 99.6 Speaker embedding extraction
Clustering (VBx) 0.729 0.4 Hungarian algorithm + VBx clustering
Total 202.631 100 Full VBx pipeline

Speaker Diarization Research Comparison

Offline VBx achieves competitive accuracy with batch processing

Method DER Mode Description
FluidAudio (Offline) 14.5% VBx Batch On-device CoreML with optimal clustering
FluidAudio (Streaming) 17.7% Chunk-based First-occurrence speaker mapping
Research baseline 18-30% Various Standard dataset performance

Pipeline Details:

  • Mode: Offline VBx with Hungarian algorithm for optimal speaker-to-cluster assignment
  • Segmentation: VAD-based voice activity detection
  • Embeddings: WeSpeaker-compatible speaker embeddings
  • Clustering: PowerSet with VBx refinement
  • Accuracy: Higher than streaming due to optimal post-hoc mapping

🎯 Offline VBx Test • AMI Corpus ES2004a • 1049.0s meeting audio • 224.3s processing • Test runtime: 3m 51s • 03/28/2026, 05:21 PM EST

@Alex-Wengg Alex-Wengg marked this pull request as draft March 28, 2026 18:46
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github-actions bot commented Mar 28, 2026

Parakeet EOU Benchmark Results ✅

Status: Benchmark passed
Chunk Size: 320ms
Files Tested: 100/100

Performance Metrics

Metric Value Description
WER (Avg) 7.03% Average Word Error Rate
WER (Med) 4.17% Median Word Error Rate
RTFx 11.46x Real-time factor (higher = faster)
Total Audio 470.6s Total audio duration processed
Total Time 41.9s Total processing time

Streaming Metrics

Metric Value Description
Avg Chunk Time 0.042s Average chunk processing time
Max Chunk Time 0.084s Maximum chunk processing time
EOU Detections 0 Total End-of-Utterance detections

Test runtime: 1m0s • 03/28/2026, 05:14 PM EST

RTFx = Real-Time Factor (higher is better) • Processing includes: Model inference, audio preprocessing, state management, and file I/O

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github-actions bot commented Mar 28, 2026

Speaker Diarization Benchmark Results

Speaker Diarization Performance

Evaluating "who spoke when" detection accuracy

Metric Value Target Status Description
DER 15.1% <30% Diarization Error Rate (lower is better)
JER 24.9% <25% Jaccard Error Rate
RTFx 24.07x >1.0x Real-Time Factor (higher is faster)

Diarization Pipeline Timing Breakdown

Time spent in each stage of speaker diarization

Stage Time (s) % Description
Model Download 11.798 27.1 Fetching diarization models
Model Compile 5.056 11.6 CoreML compilation
Audio Load 0.075 0.2 Loading audio file
Segmentation 13.073 30.0 Detecting speech regions
Embedding 21.789 50.0 Extracting speaker voices
Clustering 8.715 20.0 Grouping same speakers
Total 43.591 100 Full pipeline

Speaker Diarization Research Comparison

Research baselines typically achieve 18-30% DER on standard datasets

Method DER Notes
FluidAudio 15.1% On-device CoreML
Research baseline 18-30% Standard dataset performance

Note: RTFx shown above is from GitHub Actions runner. On Apple Silicon with ANE:

  • M2 MacBook Air (2022): Runs at 150 RTFx real-time
  • Performance scales with Apple Neural Engine capabilities

🎯 Speaker Diarization Test • AMI Corpus ES2004a • 1049.0s meeting audio • 43.6s diarization time • Test runtime: 2m 44s • 03/28/2026, 05:19 PM EST

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github-actions bot commented Mar 28, 2026

Sortformer High-Latency Benchmark Results

ES2004a Performance (30.4s latency config)

Metric Value Target Status
DER 33.4% <35%
Miss Rate 24.4% - -
False Alarm 0.2% - -
Speaker Error 8.8% - -
RTFx 12.9x >1.0x
Speakers 4/4 - -

Sortformer High-Latency • ES2004a • Runtime: 2m 41s • 2026-03-28T21:17:41.483Z

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github-actions bot commented Mar 28, 2026

VAD Benchmark Results

Performance Comparison

Dataset Accuracy Precision Recall F1-Score RTFx Files
MUSAN 92.0% 86.2% 100.0% 92.6% 738.0x faster 50
VOiCES 92.0% 86.2% 100.0% 92.6% 750.1x faster 50

Dataset Details

  • MUSAN: Music, Speech, and Noise dataset - standard VAD evaluation
  • VOiCES: Voices Obscured in Complex Environmental Settings - tests robustness in real-world conditions

✅: Average F1-Score above 70%

@Alex-Wengg Alex-Wengg marked this pull request as ready for review March 28, 2026 19:26
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github-actions bot commented Mar 28, 2026

Qwen3-ASR int8 Smoke Test ✅

Check Result
Build
Model download
Model load
Transcription pipeline
Decoder size 571 MB (vs 1.1 GB f32)

Performance Metrics

Metric CI Value Expected on Apple Silicon
Median RTFx 0.06x ~2.5x
Overall RTFx 0.06x ~2.5x

Runtime: 3m19s

Note: CI VM lacks physical GPU — CoreML MLState (macOS 15) KV cache produces degraded results on virtualized runners. On Apple Silicon: ~1.3% WER / 2.5x RTFx.

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github-actions bot commented Mar 28, 2026

ASR Benchmark Results ✅

Status: All benchmarks passed

Parakeet v3 (multilingual)

Dataset WER Avg WER Med RTFx Status
test-clean 0.57% 0.00% 4.50x
test-other 1.40% 0.00% 3.43x

Parakeet v2 (English-optimized)

Dataset WER Avg WER Med RTFx Status
test-clean 0.80% 0.00% 5.28x
test-other 1.22% 0.00% 3.65x

Streaming (v3)

Metric Value Description
WER 0.00% Word Error Rate in streaming mode
RTFx 0.64x Streaming real-time factor
Avg Chunk Time 1.416s Average time to process each chunk
Max Chunk Time 1.903s Maximum chunk processing time
First Token 1.768s Latency to first transcription token
Total Chunks 31 Number of chunks processed

Streaming (v2)

Metric Value Description
WER 0.00% Word Error Rate in streaming mode
RTFx 0.65x Streaming real-time factor
Avg Chunk Time 1.403s Average time to process each chunk
Max Chunk Time 1.504s Maximum chunk processing time
First Token 1.390s Latency to first transcription token
Total Chunks 31 Number of chunks processed

Streaming tests use 5 files with 0.5s chunks to simulate real-time audio streaming

25 files per dataset • Test runtime: 6m34s • 03/28/2026, 05:18 PM EST

RTFx = Real-Time Factor (higher is better) • Calculated as: Total audio duration ÷ Total processing time
Processing time includes: Model inference on Apple Neural Engine, audio preprocessing, state resets between files, token-to-text conversion, and file I/O
Example: RTFx of 2.0x means 10 seconds of audio processed in 5 seconds (2x faster than real-time)

Expected RTFx Performance on Physical M1 Hardware:

• M1 Mac: ~28x (clean), ~25x (other)
• CI shows ~0.5-3x due to virtualization limitations

Testing methodology follows HuggingFace Open ASR Leaderboard

The offline diarizer benchmark was failing in CI because the PLDA parameters
JSON file was not being downloaded when downloading offline diarizer models.

The requiredModels set only included the 4 .mlmodelc files but not the
plda-parameters.json file that's required by OfflineDiarizerModels.loadPLDAPsi().

This caused the error:
  PLDA parameters file not found in /Users/runner/Library/Application Support/FluidAudio/Models

Fixes the diarization-benchmark.yml workflow failure.
devin-ai-integration[bot]

This comment was marked as resolved.

With the folderName simplification, diarizer models are now stored in
'speaker-diarization/' instead of 'speaker-diarization-coreml/'.

Update the PLDA parameter file lookup to check the new folder location
while maintaining backward compatibility with old paths.
@Alex-Wengg Alex-Wengg merged commit 7f1e006 into main Mar 28, 2026
16 checks passed
@Alex-Wengg Alex-Wengg deleted the fix/parakeet-tdt-ctc-110m-folder-consistency branch March 28, 2026 21:39
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Different treatment for parakeetTdtCtc110m with other parakeet models of folder name

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