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\n\n[Your team has set up Codex to review pull requests in this repo](http://chatgpt.com/codex/settings/general). Reviews are triggered when you\n- Open a pull request for review\n- Mark a draft as ready\n- Comment \"@codex review\".\n\nIf Codex has suggestions, it will comment; otherwise it will react with 👍.\n\n\n\n\nCodex can also answer questions or update the PR. Try commenting \"@codex address that feedback\".\n \n

\n## VAD Benchmark Results\n\n### Performance Comparison\n\n| Dataset | Accuracy | Precision | Recall | F1-Score | RTFx | Files |\n|---------|----------|-----------|--------|----------|------|-------|\n| MUSAN | 92.0% | 86.2% | 100.0% | 92.6% | 680.6x faster | 50 |\n| VOiCES | 92.0% | 86.2% | 100.0% | 92.6% | 743.2x faster | 50 |\n\n### Dataset Details\n\n- **MUSAN**: Music, Speech, and Noise dataset - standard VAD evaluation\n- **VOiCES**: Voices Obscured in Complex Environmental Settings - tests robustness in real-world conditions\n\n✅: Average F1-Score above 70%\n\n## Speaker Diarization Benchmark Results\n\n### Speaker Diarization Performance\n_Evaluating \"who spoke when\" detection accuracy_\n\n| Metric | Value | Target | Status | Description |\n|--------|-------|--------|---------|-------------|\n| **DER** | **15.1%** | <30% | ✅ | Diarization Error Rate (lower is better) |\n| **JER** | **24.9%** | <25% | ✅ | Jaccard Error Rate |\n| **RTFx** | **16.52x** | >1.0x | ✅ | Real-Time Factor (higher is faster) |\n\n### Diarization Pipeline Timing Breakdown\n_Time spent in each stage of speaker diarization_\n\n| Stage | Time (s) | % | Description |\n|-------|----------|---|-------------|\n| Model Download | 7.377 | 11.6 | Fetching diarization models |\n| Model Compile | 3.162 | 5.0 | CoreML compilation |\n| Audio Load | 0.074 | 0.1 | Loading audio file |\n| Segmentation | 19.034 | 30.0 | Detecting speech regions |\n| Embedding | 31.723 | 49.9 | Extracting speaker voices |\n| Clustering | 12.689 | 20.0 | Grouping same speakers |\n| **Total** | **63.517** | **100** | **Full pipeline** |\n\n### Speaker Diarization Research Comparison\n_Research baselines typically achieve 18-30% DER on standard datasets_\n\n| Method | DER | Notes |\n|--------|-----|-------|\n| **FluidAudio** | **15.1%** | **On-device CoreML** |\n| Research baseline | 18-30% | Standard dataset performance |\n\n**Note**: RTFx shown above is from GitHub Actions runner. On Apple Silicon with ANE:\n- **M2 MacBook Air (2022)**: Runs at **150 RTFx** real-time\n- Performance scales with Apple Neural Engine capabilities\n\n_{🎯 **Speaker Diarization Test** • AMI Corpus ES2004a • 1049.0s meeting audio • 63.4s diarization time • Test runtime: 1m 38s • 11/07/2025, 07:32 PM EST}\n\n\n## Offline VBx Pipeline Results\n\n### Speaker Diarization Performance (VBx Batch Mode)\n_Optimal clustering with Hungarian algorithm for maximum accuracy_\n\n| Metric | Value | Target | Status | Description |\n|--------|-------|--------|---------|-------------|\n| **DER** | **14.5%** | <20% | ✅ | Diarization Error Rate (lower is better) |\n| **RTFx** | **3.06x** | >1.0x | ✅ | Real-Time Factor (higher is faster) |\n\n### Offline VBx Pipeline Timing Breakdown\n_Time spent in each stage of batch diarization_\n\n| Stage | Time (s) | % | Description |\n|-------|----------|---|-------------|\n| Model Download | 13.590 | 4.0 | Fetching diarization models |\n| Model Compile | 5.824 | 1.7 | CoreML compilation |\n| Audio Load | 0.090 | 0.0 | Loading audio file |\n| Segmentation | 38.801 | 11.3 | VAD + speech detection |\n| Embedding | 339.553 | 98.9 | Speaker embedding extraction |\n| Clustering (VBx) | 3.157 | 0.9 | Hungarian algorithm + VBx clustering |\n| **Total** | **343.340** | **100** | **Full VBx pipeline** |\n\n### Speaker Diarization Research Comparison\n_Offline VBx achieves competitive accuracy with batch processing_\n\n| Method | DER | Mode | Description |\n|--------|-----|------|-------------|\n| **FluidAudio (Offline)** | **14.5%** | **VBx Batch** | **On-device CoreML with optimal clustering** |\n| FluidAudio (Streaming) | 17.7% | Chunk-based | First-occurrence speaker mapping |\n| Research baseline | 18-30% | Various | Standard dataset performance |\n\n**Pipeline Details**:\n- **Mode**: Offline VBx with Hungarian algorithm for optimal speaker-to-cluster assignment\n- **Segmentation**: VAD-based voice activity detection\n- **Embeddings**: WeSpeaker-compatible speaker embeddings\n- **Clustering**: PowerSet with VBx refinement\n- **Accuracy**: Higher than streaming due to optimal post-hoc mapping\n\n_{🎯 **Offline VBx Test** • AMI Corpus ES2004a • 1049.0s meeting audio • 381.5s processing • Test runtime: 6m 15s • 11/07/2025, 07:34 PM EST}\n\n\n## ASR Benchmark Results ✅\n\n**Status:** All benchmarks passed\n\n### Parakeet v3 (multilingual)\n| Dataset | WER Avg | WER Med | RTFx | Status |\n|---------|---------|---------|------|--------|\n| test-clean | 0.57% | 0.00% | 3.30x | ✅ |\n| test-other | 1.75% | 0.00% | 2.39x | ✅ |\n\n### Parakeet v2 (English-optimized)\n| Dataset | WER Avg | WER Med | RTFx | Status |\n|---------|---------|---------|------|--------|\n| test-clean | 0.40% | 0.00% | 3.27x | ✅ |\n| test-other | 1.22% | 0.00% | 2.54x | ✅ |\n\n### Streaming (v3)\n| Metric | Value | Description |\n|--------|-------|-------------|\n| WER | 0.00% | Word Error Rate in streaming mode |\n| RTFx | 0.44x | Streaming real-time factor |\n| Avg Chunk Time | 2.131s | Average time to process each chunk |\n| Max Chunk Time | 2.884s | Maximum chunk processing time |\n| First Token | 2.594s | Latency to first transcription token |\n| Total Chunks | 31 | Number of chunks processed |\n\n### Streaming (v2)\n| Metric | Value | Description |\n|--------|-------|-------------|\n| WER | 0.00% | Word Error Rate in streaming mode |\n| RTFx | 0.41x | Streaming real-time factor |\n| Avg Chunk Time | 2.137s | Average time to process each chunk |\n| Max Chunk Time | 2.876s | Maximum chunk processing time |\n| First Token | 2.025s | Latency to first transcription token |\n| Total Chunks | 31 | Number of chunks processed |\n\n_{*Streaming tests use 5 files with 0.5s chunks to simulate real-time audio streaming*}\n\n_{25 files per dataset • Test runtime: 7m36s • 11/07/2025, 07:40 PM EST}\n\n_{**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)}\n\n### Expected RTFx Performance on Physical M1 Hardware:\n**• M1 Mac: ~28x (clean), ~25x (other)**\n**• CI shows ~0.5-3x due to virtualization limitations**\n\n_{Testing methodology follows [HuggingFace Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)}\n\n",{},"created",1762572328.7940824,"Completing the task","event_type","info",{"_93":1174,"_1175":1176,"_1177":1178,"_1179":1180,"_1181":-5},{"_93":1162,"_97":94,"_410":1163},{"_93":1159,"_1160":1161},"partial_repo_snapshot","files",[],"message",[1164,1165,1166],{"_425":46,"_46":1173},{"_425":1168,"_1169":1170,"_1171":416,"_1172":296},{"_425":46,"_46":1167},"\n\n**Testing**\n* ⚠️ not run (not requested)","terminal_chunk_citation","terminal_chunk_id","ff749a","line_range_start","line_range_end","**Summary**\n* Renamed the Mac Catalyst ESpeakNG binary to match the Info.plist casing so the framework validation workflow locates the executable correctly.","pr","pr_title","Fix ESpeakNG Mac Catalyst binary casing mismatch","pr_message","## Summary\n- rename the Mac Catalyst ESpeakNG binary to match the Info.plist CFBundleExecutable casing\n- ensure framework validation workflow finds the expected binary name\n\n## Testing\n- not run","output_diff",{"_93":1179,"_1182":1183,"_129":130,"_1184":-5,"_292":1185,"_1186":1187,"_1188":416,"_1189":32,"_1190":32,"_1191":1192,"_1193":-5},"pre_apply_patch","repo_id","/workspace/FluidAudio","preapply_patch_base_commit_sha","diff --git a/Sources/FluidAudio/Frameworks/ESpeakNG.xcframework/ios-arm64_x86_64-maccatalyst/ESpeakNG.framework/Versions/A/ESPeakNG b/Sources/FluidAudio/Frameworks/ESpeakNG.xcframework/ios-arm64_x86_64-maccatalyst/ESpeakNG.framework/Versions/A/ESpeakNG\nsimilarity index 100%\nrename from Sources/FluidAudio/Frameworks/ESpeakNG.xcframework/ios-arm64_x86_64-maccatalyst/ESpeakNG.framework/Versions/A/ESPeakNG\nrename to Sources/FluidAudio/Frameworks/ESpeakNG.xcframework/ios-arm64_x86_64-maccatalyst/ESpeakNG.framework/Versions/A/ESpeakNG\n","external_storage_diff",{"_1194":1195,"_1196":-5},"files_modified","lines_added","lines_removed","commit_message","Fix Mac Catalyst ESpeakNG binary casing","additional_parent_commit_shas","file_id","file_000000000ba0722fab8fcef875655ecd","ttl",{"_93":293,"_42":85,"_97":293,"_1199":1200,"_1201":1202,"_1203":1204,"_133":-5,"_120":1205},[84],"input_items",[1221,1222,1223,1224,1225,1226,1227,1228,1229,1230],"external_post_config",{"_1206":1207,"_228":161,"_1208":272,"_239":-5,"_1182":222,"_1209":-5,"_1210":-5,"_1211":1212,"_1213":-5,"_1214":-5,"_1215":1216,"_1217":15,"_1218":59,"_1219":54,"_1220":15},"user_model_slug","slug",1762572195.444607,"installation_id",86116198,"pr_number","review_trigger","comment_id","comment_type","issue","pr_write_guard_token","pending_pr_reaction_id","pending_comment_reaction_id",313156872,"comment_if_no_findings","comment_on_error","share_url","is_parallel_review",{"_93":1162,"_97":293,"_410":1250},{"_279":280,"_281":282,"_1246":1247,"_1248":130,"_289":-5,"_93":1249},{"_93":1231,"_281":1245,"_1233":-5,"_1234":-5,"_1235":-5},{"_93":1231,"_281":1244,"_1233":-5,"_1234":-5,"_1235":-5},{"_93":1231,"_281":1240,"_1233":1241,"_1234":1242,"_1235":1243},{"_93":1231,"_281":1239,"_1233":-5,"_1234":-5,"_1235":-5},{"_93":1231,"_281":1238,"_1233":-5,"_1234":-5,"_1235":-5},{"_93":1231,"_281":1237,"_1233":-5,"_1234":-5,"_1235":-5},{"_93":1231,"_281":1236,"_1233":-5,"_1234":-5,"_1235":-5},{"_93":1231,"_281":1232,"_1233":-5,"_1234":-5,"_1235":-5},"pull_request_comment","## ASR Benchmark Results ✅\n\n**Status:** All benchmarks passed\n\n### Parakeet v3 (multilingual)\n| Dataset | WER Avg | WER Med | RTFx | Status |\n|---------|---------|---------|------|--------|\n| test-clean | 0.57% | 0.00% | 3.30x | ✅ |\n| test-other | 1.75% | 0.00% | 2.39x | ✅ |\n\n### Parakeet v2 (English-optimized)\n| Dataset | WER Avg | WER Med | RTFx | Status |\n|---------|---------|---------|------|--------|\n| test-clean | 0.40% | 0.00% | 3.27x | ✅ |\n| test-other | 1.22% | 0.00% | 2.54x | ✅ |\n\n### Streaming (v3)\n| Metric | Value | Description |\n|--------|-------|-------------|\n| WER | 0.00% | Word Error Rate in streaming mode |\n| RTFx | 0.44x | Streaming real-time factor |\n| Avg Chunk Time | 2.131s | Average time to process each chunk |\n| Max Chunk Time | 2.884s | Maximum chunk processing time |\n| First Token | 2.594s | Latency to first transcription token |\n| Total Chunks | 31 | Number of chunks processed |\n\n### Streaming (v2)\n| Metric | Value | Description |\n|--------|-------|-------------|\n| WER | 0.00% | Word Error Rate in streaming mode |\n| RTFx | 0.41x | Streaming real-time factor |\n| Avg Chunk Time | 2.137s | Average time to process each chunk |\n| Max Chunk Time | 2.876s | Maximum chunk processing time |\n| First Token | 2.025s | Latency to first transcription token |\n| Total Chunks | 31 | Number of chunks processed |\n\n_{*Streaming tests use 5 files with 0.5s chunks to simulate real-time audio streaming*}\n\n_{25 files per dataset • Test runtime: 7m36s • 11/07/2025, 07:40 PM EST}\n\n_{**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)}\n\n### Expected RTFx Performance on Physical M1 Hardware:\n**• M1 Mac: ~28x (clean), ~25x (other)**\n**• CI shows ~0.5-3x due to virtualization limitations**\n\n_{Testing methodology follows [HuggingFace Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)}\n\n","path","side","line","## Offline VBx Pipeline Results\n\n### Speaker Diarization Performance (VBx Batch Mode)\n_Optimal clustering with Hungarian algorithm for maximum accuracy_\n\n| Metric | Value | Target | Status | Description |\n|--------|-------|--------|---------|-------------|\n| **DER** | **14.5%** | <20% | ✅ | Diarization Error Rate (lower is better) |\n| **RTFx** | **3.06x** | >1.0x | ✅ | Real-Time Factor (higher is faster) |\n\n### Offline VBx Pipeline Timing Breakdown\n_Time spent in each stage of batch diarization_\n\n| Stage | Time (s) | % | Description |\n|-------|----------|---|-------------|\n| Model Download | 13.590 | 4.0 | Fetching diarization models |\n| Model Compile | 5.824 | 1.7 | CoreML compilation |\n| Audio Load | 0.090 | 0.0 | Loading audio file |\n| Segmentation | 38.801 | 11.3 | VAD + speech detection |\n| Embedding | 339.553 | 98.9 | Speaker embedding extraction |\n| Clustering (VBx) | 3.157 | 0.9 | Hungarian algorithm + VBx clustering |\n| **Total** | **343.340** | **100** | **Full VBx pipeline** |\n\n### Speaker Diarization Research Comparison\n_Offline VBx achieves competitive accuracy with batch processing_\n\n| Method | DER | Mode | Description |\n|--------|-----|------|-------------|\n| **FluidAudio (Offline)** | **14.5%** | **VBx Batch** | **On-device CoreML with optimal clustering** |\n| FluidAudio (Streaming) | 17.7% | Chunk-based | First-occurrence speaker mapping |\n| Research baseline | 18-30% | Various | Standard dataset performance |\n\n**Pipeline Details**:\n- **Mode**: Offline VBx with Hungarian algorithm for optimal speaker-to-cluster assignment\n- **Segmentation**: VAD-based voice activity detection\n- **Embeddings**: WeSpeaker-compatible speaker embeddings\n- **Clustering**: PowerSet with VBx refinement\n- **Accuracy**: Higher than streaming due to optimal post-hoc mapping\n\n_{🎯 **Offline VBx Test** • AMI Corpus ES2004a • 1049.0s meeting audio • 381.5s processing • Test runtime: 6m 15s • 11/07/2025, 07:34 PM EST}\n\n","## Speaker Diarization Benchmark Results\n\n### Speaker Diarization Performance\n_Evaluating \"who spoke when\" detection accuracy_\n\n| Metric | Value | Target | Status | Description |\n|--------|-------|--------|---------|-------------|\n| **DER** | **15.1%** | <30% | ✅ | Diarization Error Rate (lower is better) |\n| **JER** | **24.9%** | <25% | ✅ | Jaccard Error Rate |\n| **RTFx** | **16.52x** | >1.0x | ✅ | Real-Time Factor (higher is faster) |\n\n### Diarization Pipeline Timing Breakdown\n_Time spent in each stage of speaker diarization_\n\n| Stage | Time (s) | % | Description |\n|-------|----------|---|-------------|\n| Model Download | 7.377 | 11.6 | Fetching diarization models |\n| Model Compile | 3.162 | 5.0 | CoreML compilation |\n| Audio Load | 0.074 | 0.1 | Loading audio file |\n| Segmentation | 19.034 | 30.0 | Detecting speech regions |\n| Embedding | 31.723 | 49.9 | Extracting speaker voices |\n| Clustering | 12.689 | 20.0 | Grouping same speakers |\n| **Total** | **63.517** | **100** | **Full pipeline** |\n\n### Speaker Diarization Research Comparison\n_Research baselines typically achieve 18-30% DER on standard datasets_\n\n| Method | DER | Notes |\n|--------|-----|-------|\n| **FluidAudio** | **15.1%** | **On-device CoreML** |\n| Research baseline | 18-30% | Standard dataset performance |\n\n**Note**: RTFx shown above is from GitHub Actions runner. On Apple Silicon with ANE:\n- **M2 MacBook Air (2022)**: Runs at **150 RTFx** real-time\n- Performance scales with Apple Neural Engine capabilities\n\n_{🎯 **Speaker Diarization Test** • AMI Corpus ES2004a • 1049.0s meeting audio • 63.4s diarization time • Test runtime: 1m 38s • 11/07/2025, 07:32 PM EST}\n\n","## VAD Benchmark Results\n\n### Performance Comparison\n\n| Dataset | Accuracy | Precision | Recall | F1-Score | RTFx | Files |\n|---------|----------|-----------|--------|----------|------|-------|\n| MUSAN | 92.0% | 86.2% | 100.0% | 92.6% | 680.6x faster | 50 |\n| VOiCES | 92.0% | 86.2% | 100.0% | 92.6% | 743.2x faster | 50 |\n\n### Dataset Details\n\n- **MUSAN**: Music, Speech, and Noise dataset - standard VAD evaluation\n- **VOiCES**: Voices Obscured in Complex Environmental Settings - tests robustness in real-world conditions\n\n✅: Average F1-Score above 70%\n","\n### 💡 Codex Review\n\nHere are some automated review suggestions for this pull request.\n \n\n

ℹ️ About Codex in GitHub

","**_{_{![P1 Badge](https://img.shields.io/badge/P1-orange?style=flat)}} Keep ESpeakNG binary names case-consistent**\n\nThe validation script now looks for `ESPeakNG` (`local binary_path=\"$variant_path/Versions/A/ESPeakNG\"` and the iOS flat case) even though the framework’s `Info.plist` still declares `CFBundleExecutable = ESpeakNG` and the second step in this same workflow explicitly treats `ESPeakNG` as a typo. This mismatch means the Mac‑Catalyst bundle is packaged with a differently cased executable, causing the newly added “Common Framework Issues” step to fail and leaving the bundle inconsistent with the other xcframework slices. Please keep the binary path checks using `ESpeakNG` so the framework layout and validation remain aligned.\n\nUseful? React with 👍 / 👎.",".github/workflows/framework-validation.yml","RIGHT",139,"## Framework Reorganization Review\n\nThis PR reorganizes the Mac Catalyst bundle structure for the ESpeakNG framework. While the overall approach of using versioned framework layout for Mac Catalyst is correct, there are **critical runtime issues** that must be addressed.\n\n### 🚨 Critical Issues (Blockers)\n\n**1. Binary Name Inconsistency**\nThe Mac Catalyst framework has inconsistent naming that will cause dyld failures:\n- **Actual binary**: `ESPeakNG` (capital 'P')\n- **CFBundleExecutable in Info.plist**: `ESpeakNG` (lowercase 'e') \n- **Symlink target**: `Versions/Current/ESPeakNG` (capital 'P')\n\nThis mismatch between Info.plist and the actual binary name will cause the framework to fail loading at runtime.\n\n**2. Framework Loading Implications**\nThe `CFBundleExecutable` key in Info.plist tells the system which binary to load. Since it specifies `ESpeakNG` but the binary is named `ESPeakNG`, the framework will fail with \"image not found\" errors.\n\n### ✅ Positive Changes\n\n**1. Correct Versioned Structure**\nThe reorganization properly implements the versioned framework layout required for Mac Catalyst:\n- ✅ `Versions/A/` contains actual files\n- ✅ `Versions/Current → A` symlink\n- ✅ Top-level symlinks point to `Versions/Current/*`\n- ✅ All required directories (Headers, Modules, Resources) present\n\n**2. Valid Binary**\nThe binary itself is properly formatted:\n- ✅ Valid Mach-O universal binary (x86_64 + arm64)\n- ✅ Correct library type (dynamically linked shared library)\n- ✅ Appropriate for Mac Catalyst\n\n**3. Framework Validation Workflow**\nThe updated validation workflow correctly enforces versioned structure for Mac Catalyst frameworks and includes comprehensive checks.\n\n### 🔧 Required Fixes\n\n1. **Fix binary naming consistency**: Either rename the binary to `ESpeakNG` (to match Info.plist) or update Info.plist `CFBundleExecutable` to `ESPeakNG`\n2. **Verify symlinks**: Ensure all symlink targets match the chosen binary name\n\n### 📋 Recommendation\n\nI recommend renaming the binary from `ESPeakNG` to `ESpeakNG` to match the Info.plist, as this maintains consistency with the framework name and avoids confusion.","**Claude finished @BrandonWeng's task** —— [View job](https://github.com/FluidInference/FluidAudio/actions/runs/19144608843)\n\n---\n### PR Review: Reorganize Mac Catalyst Bundle ✅\n\nI've completed a comprehensive review of this PR that reorganizes the Mac Catalyst bundle structure for the ESpeakNG framework.\n\n#### Todo List:\n- [x] Create pending PR review\n- [x] Analyze framework reorganization structure \n- [x] Identify binary name mismatch in Mac Catalyst framework\n- [x] Identify Info.plist CFBundleExecutable mismatch\n- [x] Check for additional framework validation issues\n- [x] Submit comprehensive review with findings\n\n#### Key Findings:\n\n**🚨 Critical Issues Found:**\n1. **Binary name inconsistency** - The binary is named `ESPeakNG` (capital P) but `CFBundleExecutable` in Info.plist specifies `ESpeakNG` (lowercase e), which will cause dyld runtime failures\n2. **Framework loading will fail** due to the mismatch between expected and actual binary names\n\n**✅ Positive Changes:**\n1. **Correct versioned structure** - Properly implements Mac Catalyst framework layout with Versions/ directory\n2. **Valid binary format** - Mach-O universal binary with correct architectures\n3. **Comprehensive validation workflow** - Updated CI checks enforce proper framework structure\n\n**Recommendation:** Rename the binary from `ESPeakNG` to `ESpeakNG` to match 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