diff --git a/.gitignore b/.gitignore
index 3d2246f..f4b4af5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,8 +1,13 @@
-data/
+data/*
+!data/splits/
+!data/splits/**
 data/fma
 experiments/
 *.hdf5
 *.json
+!src/deeprhythm/weights/v0.8-bundle.json
+!docs/research/results/*.json
+!docs/research/results/**/*.jsonl
 __pycache__
 *.pyc
 *.pyo
diff --git a/README.md b/README.md
index 45e3743..a2d2719 100644
--- a/README.md
+++ b/README.md
@@ -48,6 +48,29 @@ pip install deeprhythm
 
 ## Usage
 
+### Opt-in v0.8 metrical predictor
+
+v0.8 separates rhythmic rate from metrical level and adds a frozen temporal
+beat representation. Its metrical fusion was distilled from a stronger
+beat-rate teacher, but inference remains self-contained and does not load that
+teacher or Phasefinder. Both branches reuse one decoded waveform and one
+nnAudio STFT. The versioned weights are bundled with the package and recorded
+in `src/deeprhythm/weights/v0.8-bundle.json`.
+
+```python
+from deeprhythm.model import MetricalDeepRhythmPredictor
+
+predictor = MetricalDeepRhythmPredictor(device="mps")
+bpm = predictor.predict("/path/to/song.wav")
+details = predictor.predict("/path/to/song.wav", include_details=True)
+```
+
+The original `DeepRhythmPredictor` remains unchanged. v0.8 is opt-in because
+its measured metrical correction is a modest net improvement and can still
+change an individually correct octave decision. It aggregates every complete
+eight-second clip after removing the first and last clips from tracks longer
+than 40 seconds, matching the frozen model's training and evaluation path.
+
 ### CLI Inference
 
 #### Single
diff --git a/data/splits/SHA256SUMS b/data/splits/SHA256SUMS
new file mode 100644
index 0000000..23fd85f
--- /dev/null
+++ b/data/splits/SHA256SUMS
@@ -0,0 +1,12 @@
+9d4844560ef371c48ae56b4f4eb705da39e6bce8d4c8e7fac3c4c42df4ba50d4  archive-v0.7/ballroom.jsonl
+2578232af9164843a35d0bd1175e065d7b71bca50114d2709c6a4ba156f83e52  archive-v0.7/giantsteps.jsonl
+b0f210b7c5d9435450bdcbc8d7c240b8f3d3654710cf55bdc386c83879f3fd02  archive-v0.7/gtzan.jsonl
+532ee056939f549ca6e575060d0148a7271ef18a0739df2c6e440533ede0498f  ballroom/test.jsonl
+6961ac3e3384835920ddf57c83509efbbea950e72e05082e250778a826159d9b  ballroom/train.jsonl
+cfde5d0fe110f69ba1b13b261e47aff339bab507536af169b4b6352f8d40d113  ballroom/val.jsonl
+346b83bd51833e8f5f53b8c0d980f7fd8c01917642db60652c3720812e601fed  giantsteps/test.jsonl
+8044435b7f362a802726c98466080f63fd2351c72f8ac865f3482cebad2af644  giantsteps/train.jsonl
+8b8bbaeaf933a17be9b5c3724a6138fe27920db9e79ce896fa0bd56f320abc93  giantsteps/val.jsonl
+1db0216a5fd020ebb931f7682c91268c6448584fca8dd7fe67f57fef6127898f  gtzan/test.jsonl
+51f6bb6387ab777eb27a62d16a46c549a68d969fb6077d28c1905d8271418b87  gtzan/train.jsonl
+507f7dae232c7d95bb7e391bbf85706ecaeca17f3f7bca5befdfaa3ab63cf65e  gtzan/val.jsonl
diff --git a/data/splits/archive-v0.7/ballroom.jsonl b/data/splits/archive-v0.7/ballroom.jsonl
new file mode 100644
index 0000000..8fedefd
--- /dev/null
+++ b/data/splits/archive-v0.7/ballroom.jsonl
@@ -0,0 +1,698 @@
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diff --git a/data/splits/archive-v0.7/giantsteps.jsonl b/data/splits/archive-v0.7/giantsteps.jsonl
new file mode 100644
index 0000000..0cb0fec
--- /dev/null
+++ b/data/splits/archive-v0.7/giantsteps.jsonl
@@ -0,0 +1,661 @@
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diff --git a/data/splits/archive-v0.7/gtzan.jsonl b/data/splits/archive-v0.7/gtzan.jsonl
new file mode 100644
index 0000000..a24a06b
--- /dev/null
+++ b/data/splits/archive-v0.7/gtzan.jsonl
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diff --git a/data/splits/ballroom/test.jsonl b/data/splits/ballroom/test.jsonl
new file mode 100644
index 0000000..cde817f
--- /dev/null
+++ b/data/splits/ballroom/test.jsonl
@@ -0,0 +1,134 @@
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diff --git a/data/splits/ballroom/train.jsonl b/data/splits/ballroom/train.jsonl
new file mode 100644
index 0000000..09c877b
--- /dev/null
+++ b/data/splits/ballroom/train.jsonl
@@ -0,0 +1,502 @@
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diff --git a/data/splits/ballroom/val.jsonl b/data/splits/ballroom/val.jsonl
new file mode 100644
index 0000000..b545da3
--- /dev/null
+++ b/data/splits/ballroom/val.jsonl
@@ -0,0 +1,49 @@
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diff --git a/data/splits/giantsteps/test.jsonl b/data/splits/giantsteps/test.jsonl
new file mode 100644
index 0000000..a309b33
--- /dev/null
+++ b/data/splits/giantsteps/test.jsonl
@@ -0,0 +1,131 @@
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diff --git a/data/splits/giantsteps/train.jsonl b/data/splits/giantsteps/train.jsonl
new file mode 100644
index 0000000..7997965
--- /dev/null
+++ b/data/splits/giantsteps/train.jsonl
@@ -0,0 +1,464 @@
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diff --git a/data/splits/giantsteps/val.jsonl b/data/splits/giantsteps/val.jsonl
new file mode 100644
index 0000000..230aecc
--- /dev/null
+++ b/data/splits/giantsteps/val.jsonl
@@ -0,0 +1,66 @@
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diff --git a/data/splits/gtzan/test.jsonl b/data/splits/gtzan/test.jsonl
new file mode 100644
index 0000000..a52bac2
--- /dev/null
+++ b/data/splits/gtzan/test.jsonl
@@ -0,0 +1,313 @@
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diff --git a/data/splits/gtzan/train.jsonl b/data/splits/gtzan/train.jsonl
new file mode 100644
index 0000000..3d74663
--- /dev/null
+++ b/data/splits/gtzan/train.jsonl
@@ -0,0 +1,422 @@
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diff --git a/data/splits/gtzan/val.jsonl b/data/splits/gtzan/val.jsonl
new file mode 100644
index 0000000..d171336
--- /dev/null
+++ b/data/splits/gtzan/val.jsonl
@@ -0,0 +1,194 @@
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+{"audio_path": "gtzan_genre/genres/rock/rock.00081.wav", "dataset": "gtzan", "fingerprint": "md5:5db1a0c389aec4ef2fe606f03fd47f2b", "fold": "val", "group_key": "artist:survivor", "md5": "5db1a0c389aec4ef2fe606f03fd47f2b", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 118.6}
+{"audio_path": "gtzan_genre/genres/rock/rock.00082.wav", "dataset": "gtzan", "fingerprint": "md5:cb6ab62aa929f30963ae550b5e9c9946", "fold": "val", "group_key": "artist:survivor", "md5": "cb6ab62aa929f30963ae550b5e9c9946", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 98.09}
+{"audio_path": "gtzan_genre/genres/rock/rock.00083.wav", "dataset": "gtzan", "fingerprint": "md5:1f221d08dd8f321408dd6c922e11ddd2", "fold": "val", "group_key": "artist:survivor", "md5": "1f221d08dd8f321408dd6c922e11ddd2", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 121.7}
+{"audio_path": "gtzan_genre/genres/rock/rock.00084.wav", "dataset": "gtzan", "fingerprint": "md5:a3e2c87b5df773f9bf2c4c123c554f71", "fold": "val", "group_key": "artist:survivor", "md5": "a3e2c87b5df773f9bf2c4c123c554f71", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 129.03}
+{"audio_path": "gtzan_genre/genres/rock/rock.00085.wav", "dataset": "gtzan", "fingerprint": "md5:cd8765dcd9a3f8216240c953af2473ca", "fold": "val", "group_key": "artist:survivor", "md5": "cd8765dcd9a3f8216240c953af2473ca", "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat", "source_fold": "kereliuk-val", "tempo": 97.48}
diff --git a/data/splits/sources/gtzan/README.md b/data/splits/sources/gtzan/README.md
new file mode 100644
index 0000000..89feea7
--- /dev/null
+++ b/data/splits/sources/gtzan/README.md
@@ -0,0 +1,22 @@
+# GTZAN
+Information about the GTZAN dataset for "music genre recognition" research.
+
+- index.txt: an index into the contents of the datasets, specifically the artist and track title of most wav files.
+- Fault-filtered partitions used in Kereliuk, Sturm, and Larsen, “[Deep learning, audio adversaries, and music content analysis](https://ieeexplore.ieee.org/document/7336950),” in Proc. WASPAA, 2015;
+Kereliuk, Sturm, and Larsen, “[Deep learning and music adversaries](https://ieeexplore.ieee.org/document/7254179),” IEEE Trans. Multimedia, vol. 17, pp. 2059–2071, Sep. 2015;
+Rodriguez-Algarra, Sturm, and Maruri-Aguilar, “[Analysing scattering-based music content analysis systems: Where’s the music?](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KdeYIvMAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=KdeYIvMAAAAJ:sSrBHYA8nusC),” in Proc. ISMIR, 2016. Code repo: https://code.soundsoftware.ac.uk/projects/scatter_reeval
+  - test_filtered.txt
+  - test_stratified.txt
+  - train_filtered.txt
+  - train_stratified.txt
+  - valid_filtered.txt
+  - valid_stratified.txt
+	
+## Other relevant publications:
+- Sturm, "[The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use](https://arxiv.org/abs/1306.1461)", arXiv:1306.1461, 2013.
+- Sturm, “[The state of the art ten years after A State of the Art: Future research in music information retrieval](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KdeYIvMAAAAJ&citation_for_view=KdeYIvMAAAAJ:EUQCXRtRnyEC),” Journal of New Music Research, vol. 43, no. 2, pp. 147 – 172, 2014.
+- Sturm,“[A Simple Method to Determine if a Music Information Retrieval System is a‘Horse’](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KdeYIvMAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=KdeYIvMAAAAJ:2P1L_qKh6hAC)”, IEEE Trans. Multimedia, vol. 16, no 6, pp. 1636 – 1644, Oct. 2014.
+- Sturm, “[A survey of evaluation in music genre recognition](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KdeYIvMAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=KdeYIvMAAAAJ:bEWYMUwI8FkC),” chapter (pp. 29-66) in Nürnberger et al., eds., Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation, Springer, 2014.
+- Sturm, “[An analysis of the GTZAN music genre dataset](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KdeYIvMAAAAJ&citation_for_view=KdeYIvMAAAAJ:4JMBOYKVnBMC),” in Proc. ACM Workshop on Music Info. Retrieval with User-centered and Multimodal Strategies, Nov. 2012.
+- Sturm, “[Two systems for automatic music genre recognition: What are they really recognizing?](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KdeYIvMAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=KdeYIvMAAAAJ:j3f4tGmQtD8C),” in Proc. ACM Workshop on Music Info. Retrieval with User-centered and Multimodal Strategies, Nov. 2012.
+
diff --git a/data/splits/sources/gtzan/index.txt b/data/splits/sources/gtzan/index.txt
new file mode 100644
index 0000000..8662f0f
--- /dev/null
+++ b/data/splits/sources/gtzan/index.txt
@@ -0,0 +1,1033 @@
+## ## ## ##
+## This is an index of the contents of the GTZAN dataset
+## For each wav file in the dataset we identify the artist and the song
+## created in 2014 by Bob L. Sturm at the Centre for Digital Music (C4DM) 
+## Queen Mary University of London, U.K.
+##
+## ## ## ##
+## Please cite:
+##
+## B. L. Sturm, “The state of the art ten years after a state of the art:
+## Future research in music information retrieval,” J. New Music Research,
+## vol. 43, no. 2, pp. 147–172, 2014.
+##
+## ## ## ##
+## If you have any information regarding the missing entries,
+## or correcting existing ones, please contact me, and you 
+## will be added to list below.
+## ## ## ##
+## Many thanks to:
+## Cynthia Liem for identifying Classical 80, 81, 97 (Nov. 6, 2012)
+## Clemens Hage for identifying Classical 00 -- 02 (Dec. 12, 2012)
+## Dan Ellis for corrections to pop.00069 and pop.00085 (Oct. 26 2014) 
+## Elio Quinton for identifying Metal 17, which led to IDing Metal 16, 18-20
+##    (Mar. 9 2015)
+## Matthew Davies for identifying Disco 36 and 58 (Mar. 20 2015) 
+## Robert Farrimond for identifying Metal 35 (Aug. 30 2017)
+## Francisco Rodriguez-Algarra for identifying several tracks, bringing the unknowns to 23 tracks from 73 (Jan. 23 2018)
+## Francisco Rodriguez-Algarra for making this index more consistent (Jan. 26 2018)
+## Leonard in 't Veen for identifying Disco 80-81, Hiphop 52-54, Reggae 51 (Jan. 8 2024)
+##
+## Currently missing information for 17 tracks
+##
+
+blues.00000.wav ::: John Lee Hooker ::: One Bourbon, One Scotch And One Beer
+blues.00001.wav ::: John Lee Hooker ::: I'm In The Mood For Love
+blues.00002.wav ::: John Lee Hooker ::: Think Twice Before You Go
+blues.00003.wav ::: John Lee Hooker ::: I'm Bad Like Jesse James
+blues.00004.wav ::: John Lee Hooker ::: Walkin' the Boogie
+blues.00005.wav ::: John Lee Hooker ::: Sugar Mama
+blues.00006.wav ::: John Lee Hooker ::: Dreamin' Blues
+blues.00007.wav ::: John Lee Hooker ::: Ground Hog Blues
+blues.00008.wav ::: John Lee Hooker ::: Shake It Baby
+blues.00009.wav ::: John Lee Hooker ::: It's My Own Fault
+blues.00010.wav ::: John Lee Hooker ::: Lonely Boy Boogie
+blues.00011.wav ::: John Lee Hooker ::: It Serves Me Right To Suffer
+blues.00012.wav ::: Robert Johnson ::: Cross Road Blues
+blues.00013.wav ::: Robert Johnson ::: Terraplane Blues
+blues.00014.wav ::: Robert Johnson ::: Come On In My Kitchen
+blues.00015.wav ::: Robert Johnson ::: Walking blues
+blues.00016.wav ::: Robert Johnson ::: Last Fair Deal Gone Down
+blues.00017.wav ::: Robert Johnson ::: 32-20 Blues
+blues.00018.wav ::: Robert Johnson ::: Kind Hearted Woman Blues
+blues.00019.wav ::: Robert Johnson ::: If I Had Possession Over Judgement Day
+blues.00020.wav ::: Robert Johnson ::: Preachin' Blues
+blues.00021.wav ::: Robert Johnson ::: When You Got A Good Friend
+blues.00022.wav ::: Robert Johnson ::: Ramblin' On My Mind
+blues.00023.wav ::: Robert Johnson ::: Stones In My Passway
+blues.00024.wav ::: Robert Johnson ::: Traveling Riverside Blues
+blues.00025.wav ::: Robert Johnson ::: Milkcow's Calf Blues
+blues.00026.wav ::: Robert Johnson ::: Me And The Devil Blues
+blues.00027.wav ::: Robert Johnson ::: Hell Hound On My Trail
+blues.00028.wav ::: Robert Johnson ::: Traveling Riverside Blues
+blues.00029.wav ::: Kelly Joe Phelps ::: The House Carpenter
+blues.00030.wav ::: Kelly Joe Phelps ::: River Rat Jimmy
+blues.00031.wav ::: Kelly Joe Phelps ::: Hobo's Son
+blues.00032.wav ::: Kelly Joe Phelps ::: Katy
+blues.00033.wav ::: Kelly Joe Phelps ::: Wandering Away
+blues.00034.wav ::: Kelly Joe Phelps ::: Dock Boggs Country Blues
+blues.00035.wav ::: Kelly Joe Phelps ::: Capman Bootman
+blues.00036.wav ::: Kelly Joe Phelps ::: Train Carried My Girl From Town
+blues.00037.wav ::: Kelly Joe Phelps ::: Piece By Piece
+blues.00038.wav ::: Kelly Joe Phelps ::: Many A Time
+blues.00039.wav ::: Kelly Joe Phelps ::: Goodnight Irene
+blues.00040.wav ::: Stevie Ray Vaughan ::: Love Struck Baby
+blues.00041.wav ::: Stevie Ray Vaughan ::: Pride and Joy
+blues.00042.wav ::: Stevie Ray Vaughan ::: Texas Flood
+blues.00043.wav ::: Stevie Ray Vaughan ::: Tell Me
+blues.00044.wav ::: Stevie Ray Vaughan ::: Testify
+blues.00045.wav ::: Stevie Ray Vaughan ::: Rude Mood
+blues.00046.wav ::: Stevie Ray Vaughan ::: Mary Had A Little Lamb
+blues.00047.wav ::: Stevie Ray Vaughan ::: Dirty Pool
+blues.00048.wav ::: Stevie Ray Vaughan ::: I'm Cryin'
+blues.00049.wav ::: Stevie Ray Vaughan ::: Lenny
+blues.00050.wav ::: Magic Slim & The Teardrops ::: What's Wrong
+blues.00051.wav ::: Magic Slim & The Teardrops ::: Snakebite
+blues.00052.wav ::: Magic Slim & The Teardrops ::: Please Don't Dog Me
+blues.00053.wav ::: Magic Slim & The Teardrops ::: Key To Your Door
+blues.00054.wav ::: Magic Slim & The Teardrops ::: Shake It
+blues.00055.wav ::: Magic Slim & The Teardrops ::: I Ain't Looking for No Love
+blues.00056.wav ::: Magic Slim & The Teardrops ::: Country Boy
+blues.00057.wav ::: Magic Slim & The Teardrops ::: Lump On Your Stump
+blues.00058.wav ::: Magic Slim & The Teardrops ::: Lonesome Trouble
+blues.00059.wav ::: Magic Slim & The Teardrops ::: The Man You Need
+blues.00060.wav ::: Magic Slim & The Teardrops ::: Mind Your Own Business
+blues.00061.wav ::: Clifton Chenier ::: Boppin' The Rock
+blues.00062.wav ::: Clifton Chenier ::: The Things I Did for You
+blues.00063.wav ::: Clifton Chenier ::: Yesterday (I Lost My Best Friend)
+blues.00064.wav ::: Clifton Chenier ::: Clifton's Squeeze-box Boogie
+blues.00065.wav ::: Clifton Chenier ::: I'm On My Way (Back Home To You)
+blues.00066.wav ::: Clifton Chenier ::: Ay-Te Te Fee
+blues.00067.wav ::: Clifton Chenier ::: All Night Long
+blues.00068.wav ::: Clifton Chenier ::: Opelousas Hop
+blues.00069.wav ::: Clifton Chenier ::: I'm On My Way, Part 2
+blues.00070.wav ::: Clifton Chenier ::: Think It Over
+blues.00071.wav ::: Clifton Chenier ::: Zodico Stomp
+blues.00072.wav ::: Clifton Chenier ::: The Cats Dreamin'
+blues.00073.wav ::: Buckwheat Zydeco ::: Turning Point
+blues.00074.wav ::: Buckwheat Zydeco ::: Zydeco Boogaloo
+blues.00075.wav ::: Buckwheat Zydeco ::: Ya Ya
+blues.00076.wav ::: Buckwheat Zydeco ::: Buck's Nouvelle Jole Blon
+blues.00077.wav ::: Buckwheat Zydeco ::: On A Night Like This 
+blues.00078.wav ::: Buckwheat Zydeco ::: Hot Tamale Baby
+blues.00079.wav ::: Buckwheat Zydeco ::: Ma Tit Fille
+blues.00080.wav ::: Buckwheat Zydeco ::: Hey Good Lookin'
+blues.00081.wav ::: Buckwheat Zydeco ::: Buck's Hot Rod
+blues.00082.wav ::: Buckwheat Zydeco ::: Why Does Love Got to Be So Sad
+blues.00083.wav ::: Buckwheat Zydeco ::: Drivin' Old Grey
+blues.00084.wav ::: Buckwheat Zydeco ::: Zydeco Honky Tonk
+blues.00085.wav ::: Hot Toddy ::: Crow Jane
+blues.00086.wav ::: Hot Toddy ::: Cross Cut Saw
+blues.00087.wav ::: Hot Toddy ::: Right From The Start
+blues.00088.wav ::: Hot Toddy ::: She's My Girl
+blues.00089.wav ::: Hot Toddy ::: I Wish You Would
+blues.00090.wav ::: Hot Toddy ::: One More Night
+blues.00091.wav ::: Hot Toddy ::: Rescue Me
+blues.00092.wav ::: Hot Toddy ::: Neighbors Land
+blues.00093.wav ::: Hot Toddy ::: Mermaids Flirt With Me
+blues.00094.wav ::: Hot Toddy ::: My Babe
+blues.00095.wav ::: Hot Toddy ::: Down By The Riverside
+blues.00096.wav ::: Hot Toddy ::: Long Distance
+blues.00097.wav ::: Hot Toddy ::: T-Bone Shuffle
+blues.00098.wav ::: Albert Collins ::: Iceman
+blues.00099.wav ::: Albert Collins ::: Lights Are On But Nobody's Home
+classical.00000.wav ::: Johann Sebastian Bach ::: Concert for oboe, BWV 1053, I. Allegro
+classical.00001.wav ::: Johann Sebastian Bach ::: Concert for oboe, BWV 1053, II. Sicilliano
+classical.00002.wav ::: Johann Sebastian Bach ::: Concert for oboe, BWV 1053, III. Allegro
+classical.00003.wav ::: Johann Sebastian Bach ::: Ich steh mit einem Fuss im Grabe, BWV 156 Sinfonia
+classical.00004.wav ::: Johann Sebastian Bach ::: Canonic Trio in F, BWV 1040
+classical.00005.wav ::: Carl Philipp Emanuel Bach ::: Oboe Concerto in E Flat Major, Wq 165, Allegro
+classical.00006.wav ::: Carl Philipp Emanuel Bach ::: Oboe Concerto in E Flat Major, Wq 165, Adagio Ma Non Troppo
+classical.00007.wav ::: Carl Philipp Emanuel Bach ::: Oboe Concerto in E Flat Major, Wq 165, Allegro Ma Non Troppo
+classical.00008.wav ::: Carl Philipp Emanuel Bach ::: Oboe Concerto in B Flat Major, Wq 164, Allegretto
+classical.00009.wav ::: Carl Philipp Emanuel Bach ::: Oboe Concerto in B Flat Major, Wq 164, Largo E Mesto
+classical.00010.wav ::: Carl Philipp Emanuel Bach ::: Oboe Concerto in B Flat Major, Wq 164, Allegro Moderato
+classical.00011.wav ::: Wofgang Amadeus Mozart ::: Symphony No.36 in C, K.425, "Linz", Adagio, Allegro spiritoso
+classical.00012.wav ::: Wofgang Amadeus Mozart ::: Symphony No.36 in C, K.425, "Linz", Andante
+classical.00013.wav ::: Wofgang Amadeus Mozart ::: Symphony No.36 in C, K.425, "Linz", Menuetto
+classical.00014.wav ::: Wofgang Amadeus Mozart ::: Symphony No.36 in C, K.425, "Linz", Finale (Presto)
+classical.00015.wav ::: Wofgang Amadeus Mozart ::: Symphony No.38 in D, K.504, "Prague", Adagio, Allegro
+classical.00016.wav ::: Wofgang Amadeus Mozart ::: Symphony No.38 in D, K.504, "Prague", Andante
+classical.00017.wav ::: Wofgang Amadeus Mozart ::: Symphony No.38 in D, K.504, "Prague", Finale (Presto)
+classical.00018.wav ::: Wofgang Amadeus Mozart ::: Symphony No.39 in E flat, K.543, Adagio, Allegro
+classical.00019.wav ::: Wofgang Amadeus Mozart ::: Symphony No.39 in E flat, K.543, Andante con moto
+classical.00020.wav ::: Wofgang Amadeus Mozart ::: Symphony No.39 in E flat, K.543, Menuetto (Allegretto)
+classical.00021.wav ::: Wofgang Amadeus Mozart ::: Symphony No.39 in E flat, K.543, Finale (Allegro)
+classical.00022.wav ::: Wofgang Amadeus Mozart ::: Symphony No.40 in G minor, K.550, Molto allegro
+classical.00023.wav ::: Wofgang Amadeus Mozart ::: Symphony No.40 in G minor, K.550, Andante
+classical.00024.wav ::: Wofgang Amadeus Mozart ::: Symphony No.40 in G minor, K.550, Menuetto (Allegretto)
+classical.00025.wav ::: Wofgang Amadeus Mozart ::: Symphony No.40 in G minor, K.550, Finale (Allegro assai)
+classical.00026.wav ::: Wofgang Amadeus Mozart ::: Symphony No.41 in C, K.551, "Jupiter", Allegro vivace
+classical.00027.wav ::: Wofgang Amadeus Mozart ::: Symphony No.41 in C, K.551, "Jupiter", Andante cantabile
+classical.00028.wav ::: Wofgang Amadeus Mozart ::: Symphony No.41 in C, K.551, "Jupiter", Menuetto (Allegretto)
+classical.00029.wav ::: Wofgang Amadeus Mozart ::: Symphony No.41 in C, K.551, "Jupiter", Molto allegro
+classical.00030.wav ::: Claude Debussy ::: Quartet in G minor for Strings, Op. 10, Anime et tres decide
+classical.00031.wav ::: Claude Debussy ::: Quartet in G minor for Strings, Op. 10, Assez vif et bien rythme
+classical.00032.wav ::: Claude Debussy ::: Quartet in G minor for Strings, Op. 10, Andantino, doucement expressif
+classical.00033.wav ::: Claude Debussy ::: Quartet in G minor for Strings, Op. 10, Tres modere, Tres mouvemente et avec passion, Tres anime, Tres vii
+classical.00034.wav ::: Maurice Ravel ::: Quartet in F Major for Strings, Allegro moderato. Tres doux
+classical.00035.wav ::: Maurice Ravel ::: Quartet in F Major for Strings, Assez vif - Tres rythme
+classical.00036.wav ::: Maurice Ravel ::: Quartet in F Major for Strings, Tres lent
+classical.00037.wav ::: Maurice Ravel ::: Quartet in F Major for Strings, Vif et agite
+classical.00038.wav ::: Henri Dutilleux ::: Ainsi la nuit for String Quartet: I. Nocturne
+classical.00039.wav ::: Henri Dutilleux ::: Ainsi la nuit for String Quartet: II. Miroir d'espace
+classical.00040.wav ::: Henri Dutilleux ::: Ainsi la nuit for String Quartet: IV. Litanies 2
+classical.00041.wav ::: Henri Dutilleux ::: Ainsi la nuit for String Quartet: VII. Temps suspendu
+classical.00042.wav ::: Giovanni Gabrieli ::: Canzon XIII
+classical.00043.wav ::: Richard Strauss ::: Konzert Fur Waldhorn Mit Orchester, Op. 11, Allegro
+classical.00044.wav ::: George Gershwin ::: Rhapsody in Blue (v1)
+classical.00045.wav ::: Richard Strauss ::: Fuge Fur Klavier
+classical.00046.wav ::: Karol Szymanowski ::: Violin Concerto No. 1, Op. 35
+classical.00047.wav ::: Leonard Bernstein ::: On the Town, Three Dance Episodes, Lonely town
+classical.00048.wav ::: George Gershwin ::: Rhapsody in Blue (v2)
+classical.00049.wav ::: Ludwig van Beethoven ::: Symphony No.1 in C, Op.21, Finale (Adagio - Allegro molto e vivace)
+classical.00050.wav ::: Samuel Barber ::: Adagio for Strings
+classical.00051.wav ::: Pyotr Ilyich Tchaikovsky ::: Symphony 6, mvt. 1
+classical.00052.wav ::: Leonard Bernstein ::: Candide Overture
+classical.00053.wav ::: Giovanni Gabrieli ::: Canzon XIII
+classical.00054.wav ::: Giuseppe Verdi ::: Rigoletto, Act 2, Tutte le feste al tiempo
+classical.00055.wav ::: Leonard Bernstein ::: Symphonic dances from West Side Story, Somewhere
+classical.00056.wav ::: Giovanni Rovetta ::: Solemn Mass for Feast of Santa Maria della Salute
+classical.00057.wav ::: Leonard Bernstein ::: Symphonic dances from West Side Story, Scherzo
+classical.00058.wav ::: Erik Satie :::  Gymnopedies no. 3
+classical.00059.wav ::: Erik Satie ::: Mercure, Marche-Ouverture
+classical.00060.wav ::: Erik Satie ::: La Belle Excentrique
+classical.00061.wav ::: ::: 
+classical.00062.wav ::: :::
+classical.00063.wav ::: Franz Schubert ::: Piano Sonata No.21 in B flat, D.960, Molto moderato
+classical.00064.wav ::: Franz Schubert ::: Piano Sonata No.21 in B flat, D.960, Andante sostenuto
+classical.00065.wav ::: Franz Schubert ::: Piano Sonata No.21 in B flat, D.960, Scherzo
+classical.00066.wav ::: Franz Schubert ::: Piano Sonata No.21 in B flat, D.960, Allegro ma non troppo
+classical.00067.wav ::: Franz Schubert ::: Fantasy In C Major, "Wanderer", Allegro Con Fuoco Ma Non Troppo
+classical.00068.wav ::: Franz Joseph Haydn ::: Symphony No. 26 in D minor, "Lamentatione", Allegro assai con spirito
+classical.00069.wav ::: Franz Joseph Haydn ::: Symphony No. 26 in D minor, "Lamentatione", Adagio
+classical.00070.wav ::: Franz Joseph Haydn ::: Symphony No. 26 in D minor, "Lamentatione", Minuet and Trio
+classical.00071.wav ::: Franz Joseph Haydn ::: Symphony No. 35 in B flat major, Allegro di molto
+classical.00072.wav ::: Franz Joseph Haydn ::: Symphony No. 35 in B flat major, Andante
+classical.00073.wav ::: Franz Joseph Haydn ::: Symphony No. 35 in B flat major, Minuet and Trio
+classical.00074.wav ::: Franz Joseph Haydn ::: Symphony No. 35 in B flat major, Presto
+classical.00075.wav ::: Franz Joseph Haydn ::: Symphony No. 49 in F minor, Adagio
+classical.00076.wav ::: Franz Joseph Haydn ::: Symphony No. 49 in F minor, Allegro di molto
+classical.00077.wav ::: Ludwig van Beethoven ::: Piano Sonata No.8, "Pathetique", Grave
+classical.00078.wav ::: Ludwig van Beethoven ::: Piano Sonata No.8, "Pathetique", Adagio cantabile
+classical.00079.wav ::: Ludwig van Beethoven ::: Piano Sonata No.8, "Pathetique", Rondo, allegro
+classical.00080.wav ::: Pyotr Ilyich Tchaikovsky ::: Symphony 6, mvt. 1
+classical.00081.wav ::: Pyotr Ilyich Tchaikovsky ::: Symphony 6, mvt. 2
+classical.00082.wav ::: Percy Grainger ::: Hill-Song No. 2
+classical.00083.wav ::: Percy Grainger ::: Ye Banks and Braes o'Bonnie Doon
+classical.00084.wav ::: Percy Grainger ::: Let's Dance Gay in Green Meadow
+classical.00085.wav ::: Percy Grainger ::: Lads of Wamphray March
+classical.00086.wav ::: Percy Grainger ::: Shepherd's Hey
+classical.00087.wav ::: Percy Grainger ::: The Merry King
+classical.00088.wav ::: Percy Grainger ::: Molly on the Shore
+classical.00089.wav ::: Antonio Vivaldi ::: The Four Seasons, Op. 8: Spring: Allegro I.
+classical.00090.wav ::: Antonio Vivaldi ::: The Four Seasons, Op. 8: Spring: Largo e pianissimo sempre II.
+classical.00091.wav ::: Antonio Vivaldi ::: The Four Seasons, Op. 8: Spring: Allegro III.
+classical.00092.wav ::: Antonio Vivaldi ::: Piccolo Concerto in A Minor, RV 445: Larghetto
+classical.00093.wav ::: Antonio Vivaldi :::
+classical.00094.wav ::: Antonio Vivaldi ::: The Four Seasons, Op. 8: Summer: Allegro non molto; Allegro I.
+classical.00095.wav ::: Antonio Vivaldi ::: The Four Seasons, Op. 8: Summer: Adaio; Presto II.
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+disco.00084.wav ::: :::
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+jazz.00047.wav ::: :::
+jazz.00048.wav ::: :::
+jazz.00049.wav ::: Sarah Vaughan ::: April in Paris
+jazz.00050.wav ::: :::
+jazz.00051.wav ::: Coleman Hawkins ::: Body and Soul
+jazz.00052.wav ::: Terell Stafford ::: Dear Rudy
+jazz.00053.wav ::: Coleman Hawkins ::: Dinah
+jazz.00054.wav ::: :::
+jazz.00055.wav ::: Coleman Hawkins ::: April in Paris
+jazz.00056.wav ::: :::
+jazz.00057.wav ::: Coleman Hawkins ::: There Will Never Be Another You
+jazz.00058.wav ::: Coleman Hawkins ::: Sugar Foot Stomp
+jazz.00059.wav ::: Charles Lloyd ::: Georgia
+jazz.00060.wav ::: Coleman Hawkins ::: I've Got the World On a String
+jazz.00061.wav ::: :::
+jazz.00062.wav ::: Coleman Hawkins ::: Under Paris Skies
+jazz.00063.wav ::: Sun Ra Arekstra ::: I'll Never Be the Same
+jazz.00064.wav ::: :::
+jazz.00065.wav ::: Coleman Hawkins ::: When Lights Are Low
+jazz.00066.wav ::: :::
+jazz.00067.wav ::: Coleman Hawkins ::: She's Funny That Way
+jazz.00068.wav ::: Coleman Hawkins ::: Say It Isn't So
+jazz.00069.wav ::: Coleman Hawkins ::: I Love Paris
+jazz.00070.wav ::: Coleman Hawkins ::: Sweet Lorraine
+jazz.00071.wav ::: Coleman Hawkins ::: How Strange
+jazz.00072.wav ::: Coleman Hawkins ::: Spotlite
+jazz.00073.wav ::: Dexter Gordon ::: Scrapple From The Apple
+jazz.00074.wav ::: Dexter Gordon ::: Willow Weep For Me
+jazz.00075.wav ::: Dexter Gordon ::: Broadway
+jazz.00076.wav ::: Dexter Gordon ::: Stairway To The Stars
+jazz.00077.wav ::: Dexter Gordon ::: A Night In Tunisia
+jazz.00078.wav ::: Dexter Gordon ::: Our Love Is Here to Stay
+jazz.00079.wav ::: Tony Williams ::: Sister Cheryl
+jazz.00080.wav ::: Tony Williams ::: Life of the Party
+jazz.00081.wav ::: Tony Williams ::: Geo Rose
+jazz.00082.wav ::: Tony Williams ::: The Slump
+jazz.00083.wav ::: Tony Williams ::: Red Mask
+jazz.00084.wav ::: Tony Williams ::: Juicy Fruit
+jazz.00085.wav ::: Tony Williams ::: Crystal Palace
+jazz.00086.wav ::: Tony Williams ::: Blackbird
+jazz.00087.wav ::: Miles Davis ::: So What
+jazz.00088.wav ::: Miles Davis ::: Freddie Freeloader
+jazz.00089.wav ::: Miles Davis ::: Blue in Green
+jazz.00090.wav ::: Miles Davis ::: All Blues
+jazz.00091.wav ::: Miles Davis ::: Flamenco Sketches
+jazz.00092.wav ::: Miles Davis ::: Flamenco Sketches (alternate)
+jazz.00093.wav ::: :::
+jazz.00094.wav ::: Joe Henderson ::: Miles Ahead
+jazz.00095.wav ::: Joe Henderson ::: Joshua
+jazz.00096.wav ::: Joe Henderson ::: Pfrancing (No Blues)
+jazz.00097.wav ::: Joe Henderson ::: Flamenco Sketches
+jazz.00098.wav ::: Joe Henderson ::: Milestones
+jazz.00099.wav ::: Joe Henderson ::: Teo
+metal.00000.wav ::: Falconer ::: Mindtraveller
+metal.00001.wav ::: Gamma Ray ::: Beyond The Black Hole
+metal.00002.wav ::: Iron Maiden ::: Flight of Icarus
+metal.00003.wav ::: Anthrax ::: Metal Thrashing Mad
+metal.00004.wav ::: Dark Tranquillity ::: Of Chaos And Eternal Night
+metal.00005.wav ::: Iron Maiden ::: Wasted Years
+metal.00006.wav ::: Blind Guardian ::: Theatre of Pain
+metal.00007.wav ::: Coroner ::: Die By My Hand
+metal.00008.wav ::: Accept ::: Balls to the Wall
+metal.00009.wav ::: Tvangeste ::: Angel's Retreat
+metal.00010.wav ::: Dark Tranquillity ::: My Faeryland Forgotten
+metal.00011.wav ::: :::
+metal.00012.wav ::: Dark Tranquillity ::: Away, Delight, Away
+metal.00013.wav ::: Dark Tranquillity ::: Of Chaos And Eternal Night
+metal.00014.wav ::: Dark Tranquillity ::: With The Flaming Shades Of Fall
+metal.00015.wav ::: Dark Tranquillity ::: Nightfall By The Shore Of Time
+metal.00016.wav ::: Slayer ::: Disciple
+metal.00017.wav ::: Slayer ::: Angel of death
+metal.00018.wav ::: Slayer ::: Darkness of Christ
+metal.00019.wav ::: Slayer ::: Piece by Piece
+metal.00020.wav ::: Slayer ::: Necrophobic
+metal.00021.wav ::: Creed ::: I'm Eighteen
+metal.00022.wav ::: Ozzy Osbourne ::: Crazy Train
+metal.00023.wav ::: :::
+metal.00024.wav ::: Jacobs Dream ::: Kinescope
+metal.00025.wav ::: Megadeth ::: Peace Sells
+metal.00026.wav ::: Anthrax ::: Across the River
+metal.00027.wav ::: Anthrax ::: Got the Time
+metal.00028.wav ::: Motorhead ::: Ace Of Spades
+metal.00029.wav ::: Living Colour ::: Glamour Boys
+metal.00030.wav ::: Megadeth ::: Trust
+metal.00031.wav ::: Motorhead ::: Born To Raise Hell
+metal.00032.wav ::: Scorpions ::: Blackout
+metal.00033.wav ::: Metallica ::: Enter Sandman
+metal.00034.wav ::: Iron Maiden ::: The Trooper
+metal.00035.wav ::: Slayer ::: Bloodline
+metal.00036.wav ::: Slayer ::: In-a-gadda-da-vida
+metal.00037.wav ::: Saxon ::: And the Bands Played On
+metal.00038.wav ::: Metallica ::: ...And Justice For All
+metal.00039.wav ::: Metallica ::: Star Wars Imperial March 
+metal.00040.wav ::: Dio ::: Stand Up And Shout
+metal.00041.wav ::: Dio ::: Gypsy
+metal.00042.wav ::: Dio ::: Caught In The Middle
+metal.00043.wav ::: Dio ::: Straight Through The Heart
+metal.00044.wav ::: Dio ::: Rainbow In The Dark
+metal.00045.wav ::: Dio ::: Shame On The Night
+metal.00046.wav ::: New Bomb Turks ::: Hammerless Nail
+metal.00047.wav ::: New Bomb Turks ::: Bachelor's High
+metal.00048.wav ::: New Bomb Turks ::: Professional Againster
+metal.00049.wav ::: New Bomb Turks ::: Cultural Elite Sign-Up Sheet
+metal.00050.wav ::: New Bomb Turks ::: Jukebox Lean
+metal.00051.wav ::: New Bomb Turks ::: Jeers Of A Clown
+metal.00052.wav ::: New Bomb Turks ::: Look Alive Jive
+metal.00053.wav ::: New Bomb Turks ::: Staring Down The Gift Horse
+metal.00054.wav ::: New Bomb Turks ::: Shoot The Offshoot
+metal.00055.wav ::: New Bomb Turks ::: Drop What You're Doin'
+metal.00056.wav ::: New Bomb Turks ::: Telephone Numbrrr
+metal.00057.wav ::: New Bomb Turks ::: Wrest Your Hands
+metal.00058.wav ::: Queen ::: Tie Your Mother Down
+metal.00059.wav ::: Queen ::: Tear it up
+metal.00060.wav ::: Queen ::: We Will Rock You
+metal.00061.wav ::: Dio ::: Stand Up And Shout
+metal.00062.wav ::: Dio ::: Gypsy
+metal.00063.wav ::: Dio ::: Caught In The Middle
+metal.00064.wav ::: Dio ::: Straight Through The Heart
+metal.00065.wav ::: Dio ::: Rainbow In The Dark
+metal.00066.wav ::: Dio ::: Shame On The Night
+metal.00067.wav ::: AC/DC ::: Big Gun
+metal.00068.wav ::: Judas Priest ::: Breaking the Law
+metal.00069.wav ::: Black Sabbath ::: Paranoid
+metal.00070.wav ::: AC/DC ::: Highway To Hell
+metal.00071.wav ::: Def Leppard ::: Pour Some Sugar On Me
+metal.00072.wav ::: Deep Purple ::: Smoke On The Water
+metal.00073.wav ::: Metallica ::: The Memory Remains
+metal.00074.wav ::: Johnny Crass ::: Internet Sandman
+metal.00075.wav ::: Metallica ::: Until It Sleeps
+metal.00076.wav ::: Metallica ::: Seek and Destroy
+metal.00077.wav ::: Judas Priest ::: Hell Patrol
+metal.00078.wav ::: Metallica ::: Prince Charming
+metal.00079.wav ::: Def Leppard ::: Photograph
+metal.00080.wav ::: Def Leppard ::: Rocket
+metal.00081.wav ::: Guns N' Roses ::: Live and Let Die
+metal.00082.wav ::: Guns N' Roses ::: Welcome To The Jungle
+metal.00083.wav ::: Metallica ::: For Whom The Bell Tolls
+metal.00084.wav ::: Black Sabbath ::: Shock the Monkey
+metal.00085.wav ::: Ozzy Osbourne; Dweezil Zappa ::: Staying Alive
+metal.00086.wav ::: Bon Jovi ::: You Give Love A Bad Name
+metal.00087.wav ::: Metallica ::: So What
+metal.00088.wav ::: Danzig ::: Twist of Cain
+metal.00089.wav ::: Danzig ::: Apokalips
+metal.00090.wav ::: Black Sabbath ::: Symptom Of The Universe
+metal.00091.wav ::: Black Sabbath ::: Sabbath Bloody Sabbath
+metal.00092.wav ::: Iron Maiden ::: Run to the Hills
+metal.00093.wav ::: Iron Maiden ::: Phantom Of The Opera
+metal.00094.wav ::: Iron Maiden ::: The Trooper
+metal.00095.wav ::: Rage Against the Machine ::: Bombtrack
+metal.00096.wav ::: Rage Against the Machine ::: Killing In The Name
+metal.00097.wav ::: Rage Against the Machine ::: Wake Up
+metal.00098.wav ::: Rage Against the Machine ::: Fistful Of Steel
+metal.00099.wav ::: Rage Against the Machine ::: Freedom
+pop.00000.wav ::: Mandy Moore ::: Saturate Me
+pop.00001.wav ::: Spice Girls ::: Wasting My Time
+pop.00002.wav ::: Mariah Carey ::: My All
+pop.00003.wav ::: Alanis Morissette ::: Hand in my pocket
+pop.00004.wav ::: Alanis Morissette ::: Head Over Feet
+pop.00005.wav ::: Alanis Morissette ::: Ironic
+pop.00006.wav ::: Alanis Morissette ::: Thank U
+pop.00007.wav ::: Alanis Morissette ::: Uninvited
+pop.00008.wav ::: Alanis Morissette ::: You Learn
+pop.00009.wav ::: Alanis Morissette ::: You Oughta Know
+pop.00010.wav ::: The Police ::: Every Breath You Take (Sting Unplugged)
+pop.00011.wav ::: Celine Dion ::: The Prayer
+pop.00012.wav ::: Aretha Franklin; Celine Dion; Mariah Carey; Shania Twain; Gloria Estefan ::: You Make Me Feel Like A Natural Woman
+pop.00013.wav ::: Babyface ::: Every Time I Close My Eyes
+pop.00014.wav ::: ::: Every Breath You Take
+pop.00015.wav ::: Britney Spears ::: (You Drive Me) Crazy
+pop.00016.wav ::: Britney Spears ::: (I Can't Get No) Satisfaction
+pop.00017.wav ::: Britney Spears ::: (I Can't Get No) Satisfaction (Live at the 2000 MTV VMAs)
+pop.00018.wav ::: Britney Spears ::: Autumn Goodbye
+pop.00019.wav ::: Britney Spears ::: Baby One More Time
+pop.00020.wav ::: Britney Spears ::: Can't Make You Love Me
+pop.00021.wav ::: Britney Spears ::: (You Drive Me) Crazy
+pop.00022.wav ::: Britney Spears ::: (You Drive Me) Crazy
+pop.00023.wav ::: Britney Spears ::: I'm So Curious
+pop.00024.wav ::: Britney Spears ::: Deep in my heart
+pop.00025.wav ::: Britney Spears ::: From The Bottom Of My Broken Heart
+pop.00026.wav ::: Britney Spears ::: I'll Never Stop Loving You
+pop.00027.wav ::: Britney Spears ::: Born To Make You Happy
+pop.00028.wav ::: Britney Spears ::: I Will Be There
+pop.00029.wav ::: Britney Spears ::: I Will Still Love You
+pop.00030.wav ::: Britney Spears ::: Lucky
+pop.00031.wav ::: Britney Spears ::: Lucky
+pop.00032.wav ::: Britney Spears ::: Oops I did it again
+pop.00033.wav ::: Britney Spears ::: Pepsi Now and Then
+pop.00034.wav ::: Britney Spears ::: Sometimes
+pop.00035.wav ::: Britney Spears ::: Stronger
+pop.00036.wav ::: Britney Spears ::: Thinkin' About You
+pop.00037.wav ::: Britney Spears ::: (You Drive Me) Crazy (Radio Rip)
+pop.00038.wav ::: Britney Spears ::: What U See (is what you get)
+pop.00039.wav ::: Celine Dion ::: Dreamin' Of You
+pop.00040.wav ::: Celine Dion ::: I want you to need me
+pop.00041.wav ::: Peabo Bryson; Regina Belle ::: A Whole New World
+pop.00042.wav ::: Celine Dion ::: That's The Way It Is
+pop.00043.wav ::: Cher ::: Believe
+pop.00044.wav ::: Christina Aguilera ::: Come On Over (All I Want Is You)
+pop.00045.wav ::: Christina Aguilera ::: Genie In A Bottle
+pop.00046.wav ::: Christina Aguilera ::: Genie In A Bottle
+pop.00047.wav ::: Christina Aguilera ::: Lady Marmalade
+pop.00048.wav ::: Christina Aguilera ::: Lady Marmalade
+pop.00049.wav ::: Christina Aguilera ::: Pero Me Acuerdo De Ti
+pop.00050.wav ::: Christina Aguilera ::: What a girl wants
+pop.00051.wav ::: Christina Aguilera ::: Lady Marmalade
+pop.00052.wav ::: Destiny's Child ::: Bootylicious
+pop.00053.wav ::: Destiny's Child ::: Outro Amazing Grace
+pop.00054.wav ::: Destiny's Child ::: Bootylicious
+pop.00055.wav ::: Destiny's Child ::: Bug A Boo
+pop.00056.wav ::: Destiny's Child ::: Jumpin', Jumpin'
+pop.00057.wav ::: Destiny's Child ::: Bootylicious
+pop.00058.wav ::: Destiny's Child ::: Independent Women Part I
+pop.00059.wav ::: Destiny's Child ::: Jumpin', Jumpin'
+pop.00060.wav ::: Destiny's Child ::: Bootylicious
+pop.00061.wav ::: Destiny's Child ::: Apple Pie a La Mode
+pop.00062.wav ::: Destiny's Child ::: Survivor
+pop.00063.wav ::: Diana Ross ::: Ain't No Mountain High Enough
+pop.00064.wav ::: Abigail ::: If It Don't Fit (Thunderpuss 2000 Club Mix)
+pop.00065.wav ::: Prince ::: The Beautiful Ones
+pop.00066.wav ::: Enrique Iglesias ::: Could I Have This Kiss Forever
+pop.00067.wav ::: Janet Jackson ::: All For You
+pop.00068.wav ::: Janet Jackson ::: Someone To Call My Lover
+pop.00069.wav ::: Janet Jackson ::: If
+pop.00070.wav ::: Janet Jackson ::: I Get Lonely
+pop.00071.wav ::: Janet Jackson ::: All For You
+pop.00072.wav ::: Janet Jackson ::: Miss You Much
+pop.00073.wav ::: Janet Jackson ::: Someone To Call My Lover
+pop.00074.wav ::: Jennifer Lopez; Ja Rule ::: I'm Real (Murder Remix)
+pop.00075.wav ::: Jennifer Lopez ::: If You Had My Love
+pop.00076.wav ::: Jennifer Lopez ::: Dance With Me
+pop.00077.wav ::: Jennifer Lopez ::: I'm Real
+pop.00078.wav ::: Jennifer Lopez ::: Play
+pop.00079.wav ::: Kate Bush ::: Couldbusting
+pop.00080.wav ::: Christina Aguilera ::: Lady Marmalade
+pop.00081.wav ::: Ladysmith Black Mambazo ::: Leaning On The Everlasting Arm
+pop.00082.wav ::: Jennifer Lopez ::: If You Had My Love (Pablo Flores Remix)
+pop.00083.wav ::: Madonna ::: American Pie
+pop.00084.wav ::: Madonna ::: Don't Tell Me
+pop.00085.wav ::: Madonna ::: Music
+pop.00086.wav ::: Madonna ::: Cherish
+pop.00087.wav ::: Mandy Moore ::: Crush
+pop.00088.wav ::: Mandy Moore ::: Candy (Wade Robson Remix)
+pop.00089.wav ::: Mandy Moore ::: Candy
+pop.00090.wav ::: Mandy Moore ::: Crush
+pop.00091.wav ::: Mandy Moore ::: Everything My Heart Desires
+pop.00092.wav ::: Mandy Moore ::: In My Pocket
+pop.00093.wav ::: Mandy Moore ::: I Wanna Be With You (Soul Solution Remix)
+pop.00094.wav ::: Mandy Moore ::: I Wanna Be With You
+pop.00095.wav ::: Mandy Moore ::: Love You for Always
+pop.00096.wav ::: Mandy Moore ::: Quit Breaking My Heart
+pop.00097.wav ::: Mariah Carey ::: Sweetheart
+pop.00098.wav ::: Mariah Carey ::: Emotions
+pop.00099.wav ::: Mariah Carey ::: Always Be My Baby
+reggae.00000.wav ::: Bob Marley ::: Is This Love
+reggae.00001.wav ::: Bob Marley ::: No Woman No Cry
+reggae.00002.wav ::: Bob Marley ::: Could You Be Loved
+reggae.00003.wav ::: Bob Marley ::: Three Little Birds
+reggae.00004.wav ::: Bob Marley ::: Buffalo Soldier
+reggae.00005.wav ::: Bob Marley ::: Get Up, Stand Up
+reggae.00006.wav ::: Bob Marley ::: Stir it Up
+reggae.00007.wav ::: Bob Marley ::: One Love
+reggae.00008.wav ::: Bob Marley ::: I shot the Sheriff
+reggae.00009.wav ::: Bob Marley ::: Waiting in vain
+reggae.00010.wav ::: Bob Marley ::: Redemption Song
+reggae.00011.wav ::: Bob Marley ::: Satisfy My Soul
+reggae.00012.wav ::: Bob Marley ::: Exodus
+reggae.00013.wav ::: Bob Marley ::: Jamming
+reggae.00014.wav ::: Bob Marley ::: Natural Mystic
+reggae.00015.wav ::: Bob Marley ::: Easy Skanking
+reggae.00016.wav ::: Bob Marley ::: Iron Lion Zion
+reggae.00017.wav ::: Bob Marley ::: Crazy Baldhead
+reggae.00018.wav ::: Bob Marley ::: So Much Trouble In The World
+reggae.00019.wav ::: Bob Marley ::: War
+reggae.00020.wav ::: Bob Marley ::: Africa Unite
+reggae.00021.wav ::: Bob Marley ::: Trenchtown Rock
+reggae.00022.wav ::: Bob Marley ::: Keep On Moving
+reggae.00023.wav ::: Bob Marley ::: Sun Is Shining
+reggae.00024.wav ::: Bob Marley ::: Who The Cap Fits
+reggae.00025.wav ::: Bob Marley ::: One Drop
+reggae.00026.wav ::: Bob Marley ::: Roots, Rock, Reggae
+reggae.00027.wav ::: Bob Marley ::: Pimper's Paradise
+reggae.00028.wav ::: Burning Spear ::: Dub Creation
+reggae.00029.wav ::: Burning Spear ::: African Dub
+reggae.00030.wav ::: Burning Spear ::: Stand
+reggae.00031.wav ::: Burning Spear ::: Smart Dub
+reggae.00032.wav ::: Burning Spear ::: Remember
+reggae.00033.wav ::: Burning Spear ::: Burning Dub
+reggae.00034.wav ::: Burning Spear ::: Chanting Home
+reggae.00035.wav ::: Burning Spear ::: Dub Old Timer
+reggae.00036.wav ::: Burning Spear ::: Subjective Dub
+reggae.00037.wav ::: Burning Spear ::: Friendly Dub
+reggae.00038.wav ::: Burning Spear ::: Investigation Dub
+reggae.00039.wav ::: Burning Spear ::: World Dub
+reggae.00040.wav ::: Burning Spear ::: Loving Dub
+reggae.00041.wav ::: Desmond Dekker ::: Israelites
+reggae.00042.wav ::: Burning Spear ::: Dry and Heavy
+reggae.00043.wav ::: The Ethiopians ::: Reggae Hit the Town
+reggae.00044.wav ::: Burning Spear ::: Burning Reggae
+reggae.00045.wav ::: The Heptones ::: Unfaithful Baby
+reggae.00046.wav ::: Dennis Brown ::: It's Magic
+reggae.00047.wav ::: Dennis Brown ::: Money in My Pocket
+reggae.00048.wav ::: Dennis Brown ::: Westbound Train
+reggae.00049.wav ::: Desmond Dekker ::: Shanty Town
+reggae.00050.wav ::: Burning Spear ::: Identity
+reggae.00051.wav ::: Amand Van Helden & DJ Sneak::: Psychic Bounty Killaz
+reggae.00052.wav ::: Pras ::: Ghetto Superstar (That Is What You Are)
+reggae.00053.wav ::: Augustus Pablo ::: Stop Them Jah
+reggae.00054.wav ::: Bob Marley ::: Three Little Birds
+reggae.00055.wav ::: Bob Marley ::: Sun Is Shining (Funkstar Deluxe Remix)
+reggae.00056.wav ::: Bob Marley ::: Get Up, Stand Up
+reggae.00057.wav ::: Bob Marley ::: I shot the Sheriff
+reggae.00058.wav ::: Bob Marley ::: Jamming
+reggae.00059.wav ::: Bob Marley ::: One Love
+reggae.00060.wav ::: Bob Marley ::: Redemption Song
+reggae.00061.wav ::: Bounty Killer ::: Living Dangerously
+reggae.00062.wav ::: Burning Spear ::: Jah Is My Driver
+reggae.00063.wav ::: Burning Spear ::: Marcus Garvey
+reggae.00064.wav ::: Dennis Brown ::: Make It Easy
+reggae.00065.wav ::: Dennis Brown ::: Rub a Dub All the Time
+reggae.00066.wav ::: Dennis Brown ::: Sitting And Watching
+reggae.00067.wav ::: Dennis Brown ::: Some like it hot
+reggae.00068.wav ::: Dennis Brown ::: Wolf and Leopards
+reggae.00069.wav ::: Desmond Dekker ::: Israelites
+reggae.00070.wav ::: Gregory Isaacs ::: John Law
+reggae.00071.wav ::: Dennis Brown ::: Big Ships
+reggae.00072.wav ::: Freddie McGregor ::: Prophecy
+reggae.00073.wav ::: Bounty Killer ::: Hip-Hopera
+reggae.00074.wav ::: Bounty Killer ::: Hip-Hopera
+reggae.00075.wav ::: Peter Tosh ::: Legalize It
+reggae.00076.wav ::: Gregory Isaacs ::: Night Nurse
+reggae.00077.wav ::: Gregory Isaacs ::: The Border
+reggae.00078.wav ::: Gregory Isaacs ::: Universal Tribulation
+reggae.00079.wav ::: Niney the Observer ::: Chicken Halk Dub
+reggae.00080.wav ::: Ini Kamoze ::: Here Comes The Hot Stepper(remix)
+reggae.00081.wav ::: Ini Kamoze ::: Here Comes The Hot Stepper(remix)
+reggae.00082.wav ::: Ini Kamoze ::: Here Comes The Hot Stepper(remix)
+reggae.00083.wav ::: Jimmy Cliff ::: Many Rivers To Cross
+reggae.00084.wav ::: Jimmy Cliff ::: The Harder They Come
+reggae.00085.wav ::: Prince Buster ::: Judge Dread
+reggae.00086.wav ::: King Tubby ::: Splash Out Dub
+reggae.00087.wav ::: Lauryn Hill; Bob Marley ::: Turn Your Lights Down Low
+reggae.00088.wav ::: Marcia Griffiths ::: Electric Boogie
+reggae.00089.wav ::: The Now Generation ::: Boney Dog
+reggae.00090.wav ::: Peter Tosh ::: Ketchy Shuby (Instrumental)
+reggae.00091.wav ::: Peter Tosh ::: Legalize It
+reggae.00092.wav ::: Peter Tosh ::: Legalize It
+reggae.00093.wav ::: Peter Tosh; Mick Jagger ::: Don't Look Back
+reggae.00094.wav ::: Prince Buster ::: Ten Commandments
+reggae.00095.wav ::: Prince Buster ::: Madness
+reggae.00096.wav ::: Prince Buster ::: Barrister Pardon
+reggae.00097.wav ::: Prince Buster ::: Here Comes The Bride
+reggae.00098.wav ::: Prince Buster ::: Rude Rude Rudee 
+reggae.00099.wav ::: Prince Buster ::: Al Capone
+rock.00000.wav ::: Morphine ::: Potion
+rock.00001.wav ::: Morphine ::: I Know You Pt. III
+rock.00002.wav ::: Morphine ::: Early To Bed
+rock.00003.wav ::: Morphine ::: Wishing Well
+rock.00004.wav ::: Morphine ::: Like Swimming
+rock.00005.wav ::: Morphine ::: Murder For The Money
+rock.00006.wav ::: Morphine ::: French Fries with Pepper
+rock.00007.wav ::: Morphine ::: Empty Box
+rock.00008.wav ::: Morphine ::: Eleven O'Clock
+rock.00009.wav ::: Morphine ::: Hanging On A Curtain
+rock.00010.wav ::: Ani DiFranco ::: Worthy
+rock.00011.wav ::: Ani DiFranco ::: Cradle and All
+rock.00012.wav ::: Ani DiFranco ::: Shy
+rock.00013.wav ::: Ani DiFranco ::: 32 Flavors
+rock.00014.wav ::: Ani DiFranco ::: Asking Too Much
+rock.00015.wav ::: Ani DiFranco ::: Coming Up
+rock.00016.wav ::: Queen ::: Tie Your Mother Down
+rock.00017.wav ::: Queen ::: Under Pressure
+rock.00018.wav ::: Queen ::: Another One Bites the Dust
+rock.00019.wav ::: Queen ::: I Want To Break Free 
+rock.00020.wav ::: Queen ::: (You're So Square) Baby I Don't Care
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+rock.00037.wav ::: The Rolling Stones ::: Brown Sugar
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+rock.00099.wav ::: The Stone Roses ::: This Is The One
diff --git a/data/splits/sources/gtzan/test_filtered.txt b/data/splits/sources/gtzan/test_filtered.txt
new file mode 100644
index 0000000..3cf30eb
--- /dev/null
+++ b/data/splits/sources/gtzan/test_filtered.txt
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diff --git a/data/splits/sources/gtzan/train_filtered.txt b/data/splits/sources/gtzan/train_filtered.txt
new file mode 100644
index 0000000..73129a9
--- /dev/null
+++ b/data/splits/sources/gtzan/train_filtered.txt
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diff --git a/data/splits/sources/gtzan/valid_filtered.txt b/data/splits/sources/gtzan/valid_filtered.txt
new file mode 100644
index 0000000..a82079d
--- /dev/null
+++ b/data/splits/sources/gtzan/valid_filtered.txt
@@ -0,0 +1,197 @@
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diff --git a/docs/annotation.md b/docs/annotation.md
new file mode 100644
index 0000000..f7f1788
--- /dev/null
+++ b/docs/annotation.md
@@ -0,0 +1,24 @@
+# Metrical-level annotation
+
+`deeprhythm-annotate` turns existing per-track tempo predictions into a resumable, keyboard-driven local annotation queue. Preparation injects each predicted BPM into PhaseFinder to obtain an aligned beat grid, chooses a 15-second onset-active excerpt, and writes one audio FLAC plus half/current/double click FLACs. Source audio is never copied into the repository.
+
+Prepare assets from prediction JSONL containing `filename` plus either `bpm` or `deeprhythm_v07`:
+
+```bash
+deeprhythm-annotate prepare \
+  --predictions predictions.jsonl \
+  --output ~/mir_datasets/v08_annotations \
+  --phasefinder-path ~/projects/phasefinder
+```
+
+Preparation is incremental: rows already present in `manifest.jsonl` are skipped. Each manifest row records the source SHA-256, model BPM, excerpt offset, and generated assets.
+
+Start the local UI:
+
+```bash
+deeprhythm-annotate serve --output ~/mir_datasets/v08_annotations
+```
+
+Open `http://127.0.0.1:8765`. Preview the half/current/double click grids with `H`, `C`, and `D`. Press Space to play or pause. Labels describe how the model prediction must change: `1` needs 2× (model half), `2` correct, `3` needs ½× (model double), `4` other, and `S` skip. Every response is synchronously appended to `annotations.jsonl`; skipped tracks remain explicitly identifiable for later review.
+
+Treat the source manifest and annotations as a pair. Split them by artist or album group before training, and never allow the same audio fingerprint to cross training and evaluation folds.
diff --git a/docs/benchmarks.md b/docs/benchmarks.md
index fcec059..3101b01 100644
--- a/docs/benchmarks.md
+++ b/docs/benchmarks.md
@@ -1,3 +1,26 @@
+### Beat-rate fusion preprocessing
+
+The temporal beat-rate branch reuses both the waveform and magnitude STFT
+already computed for HCQM inference. Four eight-second nnAudio clip spectra are
+converted to 81 logarithmic bands, concatenated along time, and trimmed to the
+branch's 30-second context by
+`deeprhythm.audio_proc.log_spectrum.compute_log_spectrum_from_clips`. This
+removes both the second decode and the second STFT. On the same 20-track warm
+sample, shared decoding first reduced median experimental two-tower latency
+from 366 ms to 226 ms; sharing the HCQM STFT and applying the eight-clip budget
+reduced it further to 116 ms.
+
+The frozen shared-STFT path scored 79.07% Acc1 on the canonical combined test,
+78.57% on Groove, and 80.00% on Slakh, with no Acc2 change. It therefore does
+not require temporal-branch retraining.
+
+An eight-clip HCQM budget is also available through
+`load_and_split_audio(..., max_clips=8)`. The research evaluation found the
+eight-clip aggregate identical to the all-clip aggregate on the frozen
+canonical, Groove, and Slakh test sets, while bounding HCQM work to 64 seconds
+of audio. The default remains unlimited so existing inference output does not
+change implicitly.
+
 # Tempo benchmarks
 
 The canonical benchmark entry point is `deeprhythm-benchmark`. It evaluates the current model and Librosa's beat
@@ -111,3 +134,28 @@ tempo was used for each included track.
 The README's historical 953-track benchmark describes its genre mix but does not publish an obtainable manifest or
 reference annotations. It remains useful historical context, but the results above are the public, independently
 reproducible benchmark.
+
+## Metrical-level correction experiments
+
+`deeprhythm-correction` is the canonical train/validation selection tool for octave-correction experiments. It
+consumes inference details sidecars and reports identity, global tempo threshold, top-k candidate-mass ratio,
+per-clip disagreement, and a small learned factor classifier. The learned path accepts precomputed onset-periodicity
+feature JSONL rows containing `filename` and `features`.
+
+The feature extractor is `deeprhythm.correction.onset_periodicity_features`. It computes onset-envelope
+autocorrelation at the five metrical candidates from `model/results.py`, plus spectral-balance and onset-rate
+statistics. It uses the existing PyTorch and NumPy dependencies. The learned model is multinomial logistic regression.
+
+~~~bash
+deeprhythm-correction \
+  --train-manifest data/splits/giantsteps/train.jsonl --train-details results/train/details.jsonl \
+  --val-manifest data/splits/giantsteps/val.jsonl --val-details results/val/details.jsonl \
+  --train-features results/train/onset-features.jsonl --val-features results/val/onset-features.jsonl \
+  --output results/giantsteps/correction-validation.json
+~~~
+
+The command rejects any manifest row whose `fold` is `test`. Parameter and model selection are train/validation
+operations. Final held-out evaluation remains a separate, one-shot reporting step after configuration is frozen.
+
+Every variant reports Acc1 and Acc2 at 2% and 4%, metrical error taxonomy, genre/style slices, and added correction
+latency. The learned classifier also reports confidence reliability bins and expected calibration error.
diff --git a/docs/research/partitions.md b/docs/research/partitions.md
new file mode 100644
index 0000000..6652faa
--- /dev/null
+++ b/docs/research/partitions.md
@@ -0,0 +1,54 @@
+# Canonical dataset partitions
+
+The manifests in `data/splits` are the canonical v0.8 train, validation, and test selection. Generate them with
+`python -m deeprhythm.partitions --datasets-root ~/mir_datasets`; use `--validate-only` to check committed files
+without access to audio. Rows contain the dataset-relative `audio_path`, audio MD5, reference `tempo`, `group_key`,
+fold, source fold, duplicate fingerprint, and annotation provenance. `SHA256SUMS` identifies every committed JSONL
+manifest and is verified by `--validate-only`.
+
+## GiantSteps
+
+The source is `giantsteps-tempo-dataset` commit `0b7d47b`, using its canonical audio MD5 files and crowdsourced v2
+tempo annotations. Official folds 1–7 are training, fold 8 is validation, and folds 9–10 are test. Three official
+entries with non-positive v2 tempo (`1327052`, `3041381`, and `3041383`) are omitted, leaving 661 of 664 entries. GiantSteps supplies no
+artist identity in the checked metadata, so its group key is the Beatport track identifier. This preserves the
+official split but **does not establish artist disjointness**; no artist identities are inferred.
+
+## GTZAN
+
+Tempo comes from the checked-out `gtzan_tempo_beat` annotations. Selection starts from the fault-filtered
+Kereliuk–Sturm–Larsen partition at GTZAN metadata commit `fedc781`: 443 training, 197 validation, and 290 test
+entries before the historical DeepRhythm exclusions. `jazz.00054` is removed explicitly; `reggae.00086` is already
+absent from the fault-filtered source. Those files still cross some artist keys in the current index, so every known
+artist is moved wholly to its most protected source fold (test before validation before training). Artist keys are
+parsed verbatim from Sturm's `index.txt`. Rows whose index artist is unavailable receive a per-track key rather than
+an invented identity. The final split is 422 training, 194 validation, and 313 test tracks. Twenty-four tracks move
+from their source fold to coalesce artists, and 14 entries use per-track keys.
+
+## Ballroom
+
+Audio and tempo annotations are Ballroom B_1.0. The CPJKU annotation README reports four exact replicas and nine
+recording replicas; the second item of every published pair is excluded. Remaining tracks are grouped by the only
+identity evidence encoded locally: the `Albums-<album>-<track>` album token or the first four digits of the
+six-digit `Media-` identifier. These are album/media groups, **not claimed artist identities**. Groups are assigned
+deterministically while balancing dance style and 20-BPM tempo bins toward 70/10/20 percent. The 53 indivisible
+groups yield 502 training, 49 validation, and 134 test tracks after 13 published-replica exclusions.
+
+Chromaprint is not installed in the reproducible environment and the source does not ship Extended Ballroom's
+fingerprints. The `fingerprint` field therefore detects byte-identical audio through MD5, while the published
+recording-replica list is enforced by exclusion. This does not detect unknown perceptual duplicates. External
+corpora must add a real acoustic fingerprint comparison against all test audio before training.
+
+## Historical v0.7 archive and comparability
+
+`data/splits/archive-v0.7` records the obtainable full-set selections: GiantSteps v2 with usable annotations,
+Ballroom's complete 698 tracks, and GTZAN minus the two historical exclusions. They are immutable selection records,
+not leakage-safe training folds. The v0.8 held-out test results are not directly comparable to the published v0.7
+full-set headlines. Reports must rescore v0.7 predictions on the new test manifests for an apples-to-apples change.
+
+## Validation guarantees
+
+`validate_partitions` rejects duplicate paths, group keys spanning folds, and fingerprint values spanning folds.
+Tests exercise each failure mode and validate every committed canonical manifest. MD5 is retained because the
+upstream GiantSteps distribution itself uses MD5 and because these checksums identify the exact local audio bytes;
+it is not used as a cryptographic trust primitive.
\ No newline at end of file
diff --git a/docs/research/results/correction-validation-combined.json b/docs/research/results/correction-validation-combined.json
new file mode 100644
index 0000000..a38d0a6
--- /dev/null
+++ b/docs/research/results/correction-validation-combined.json
@@ -0,0 +1,507 @@
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diff --git a/docs/research/results/correction-validation-tuned.json b/docs/research/results/correction-validation-tuned.json
new file mode 100644
index 0000000..7ceafeb
--- /dev/null
+++ b/docs/research/results/correction-validation-tuned.json
@@ -0,0 +1,479 @@
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diff --git a/docs/research/results/distilled-metrical-fusion.json b/docs/research/results/distilled-metrical-fusion.json
new file mode 100644
index 0000000..93b1d0f
--- /dev/null
+++ b/docs/research/results/distilled-metrical-fusion.json
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diff --git a/docs/research/results/final-test-results.json b/docs/research/results/final-test-results.json
new file mode 100644
index 0000000..843f7ad
--- /dev/null
+++ b/docs/research/results/final-test-results.json
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diff --git a/docs/research/results/production-v08-qualification.json b/docs/research/results/production-v08-qualification.json
new file mode 100644
index 0000000..97872fc
--- /dev/null
+++ b/docs/research/results/production-v08-qualification.json
@@ -0,0 +1,7275 @@
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diff --git a/docs/research/results/shared-stft-fusion-slakh.json b/docs/research/results/shared-stft-fusion-slakh.json
new file mode 100644
index 0000000..e018d10
--- /dev/null
+++ b/docs/research/results/shared-stft-fusion-slakh.json
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+{
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diff --git a/docs/research/results/shared-stft-fusion.json b/docs/research/results/shared-stft-fusion.json
new file mode 100644
index 0000000..3248d58
--- /dev/null
+++ b/docs/research/results/shared-stft-fusion.json
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+{
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diff --git a/docs/research/results/sidecars/ballroom-test-baseline-details.jsonl b/docs/research/results/sidecars/ballroom-test-baseline-details.jsonl
new file mode 100644
index 0000000..fbbdef1
--- /dev/null
+++ b/docs/research/results/sidecars/ballroom-test-baseline-details.jsonl
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diff --git a/docs/research/results/sidecars/ballroom-test-baseline-predictions.jsonl b/docs/research/results/sidecars/ballroom-test-baseline-predictions.jsonl
new file mode 100644
index 0000000..dff9462
--- /dev/null
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diff --git a/docs/research/results/sidecars/ballroom-test-tuned-details.jsonl b/docs/research/results/sidecars/ballroom-test-tuned-details.jsonl
new file mode 100644
index 0000000..bd00899
--- /dev/null
+++ b/docs/research/results/sidecars/ballroom-test-tuned-details.jsonl
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diff --git a/docs/research/results/sidecars/ballroom-test-tuned-predictions.jsonl b/docs/research/results/sidecars/ballroom-test-tuned-predictions.jsonl
new file mode 100644
index 0000000..6dd9541
--- /dev/null
+++ b/docs/research/results/sidecars/ballroom-test-tuned-predictions.jsonl
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diff --git a/docs/research/results/sidecars/giantsteps-test-baseline-details.jsonl b/docs/research/results/sidecars/giantsteps-test-baseline-details.jsonl
new file mode 100644
index 0000000..b570fc8
--- /dev/null
+++ b/docs/research/results/sidecars/giantsteps-test-baseline-details.jsonl
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diff --git a/docs/research/results/sidecars/giantsteps-test-baseline-predictions.jsonl b/docs/research/results/sidecars/giantsteps-test-baseline-predictions.jsonl
new file mode 100644
index 0000000..b67016b
--- /dev/null
+++ b/docs/research/results/sidecars/giantsteps-test-baseline-predictions.jsonl
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diff --git a/docs/research/results/sidecars/giantsteps-test-tuned-details.jsonl b/docs/research/results/sidecars/giantsteps-test-tuned-details.jsonl
new file mode 100644
index 0000000..fc59595
--- /dev/null
+++ b/docs/research/results/sidecars/giantsteps-test-tuned-details.jsonl
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diff --git a/docs/research/results/sidecars/giantsteps-test-tuned-predictions.jsonl b/docs/research/results/sidecars/giantsteps-test-tuned-predictions.jsonl
new file mode 100644
index 0000000..3a3bbd4
--- /dev/null
+++ b/docs/research/results/sidecars/giantsteps-test-tuned-predictions.jsonl
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diff --git a/docs/research/results/sidecars/gtzan-test-baseline-details.jsonl b/docs/research/results/sidecars/gtzan-test-baseline-details.jsonl
new file mode 100644
index 0000000..cd89752
--- /dev/null
+++ b/docs/research/results/sidecars/gtzan-test-baseline-details.jsonl
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diff --git a/docs/research/results/sidecars/gtzan-test-baseline-predictions.jsonl b/docs/research/results/sidecars/gtzan-test-baseline-predictions.jsonl
new file mode 100644
index 0000000..caec289
--- /dev/null
+++ b/docs/research/results/sidecars/gtzan-test-baseline-predictions.jsonl
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diff --git a/docs/research/results/sidecars/gtzan-test-tuned-details.jsonl b/docs/research/results/sidecars/gtzan-test-tuned-details.jsonl
new file mode 100644
index 0000000..d3b8f0a
--- /dev/null
+++ b/docs/research/results/sidecars/gtzan-test-tuned-details.jsonl
@@ -0,0 +1,313 @@
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diff --git a/docs/research/results/sidecars/gtzan-test-tuned-predictions.jsonl b/docs/research/results/sidecars/gtzan-test-tuned-predictions.jsonl
new file mode 100644
index 0000000..dd92fa2
--- /dev/null
+++ b/docs/research/results/sidecars/gtzan-test-tuned-predictions.jsonl
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+{"filename": "/Users/mitch/mir_datasets/gtzan_genre/gtzan_genre/genres/rock/rock.00086.wav", "bpm": 134.0, "confidence": 0.17597001791000366}
+{"filename": "/Users/mitch/mir_datasets/gtzan_genre/gtzan_genre/genres/rock/rock.00090.wav", "bpm": 121.0, "confidence": 0.3133966624736786}
+{"filename": "/Users/mitch/mir_datasets/gtzan_genre/gtzan_genre/genres/rock/rock.00048.wav", "bpm": 130.0, "confidence": 0.23778796195983887}
+{"filename": "/Users/mitch/mir_datasets/gtzan_genre/gtzan_genre/genres/rock/rock.00089.wav", "bpm": 108.0, "confidence": 0.5977323651313782}
+{"filename": "/Users/mitch/mir_datasets/gtzan_genre/gtzan_genre/genres/rock/rock.00088.wav", "bpm": 92.0, "confidence": 0.6127130389213562}
+{"filename": "/Users/mitch/mir_datasets/gtzan_genre/gtzan_genre/genres/rock/rock.00087.wav", "bpm": 132.0, "confidence": 0.17052435874938965}
diff --git a/docs/research/v0.8-metrical-level.md b/docs/research/v0.8-metrical-level.md
new file mode 100644
index 0000000..1262872
--- /dev/null
+++ b/docs/research/v0.8-metrical-level.md
@@ -0,0 +1,123 @@
+# v0.8 metrical-level correction research
+
+## Decision
+
+Retain v0.7 as the package default. The tempo-balanced model is a benchmark research candidate, not a general v0.8 upgrade. On canonical held-out folds it raises 4%-tolerance Acc1 by **21.38 points on GiantSteps**, **29.10 points on Ballroom**, and **3.19 points on GTZAN**, but a separate broad-music evaluation found a 1.90-point overall regression. The candidate corrects the demonstrated slow-octave bias while introducing a new fast-octave bias outside the benchmark distribution.
+
+The research candidate remains available at `weights/deeprhythm-v0.8-research.pth` for reproduction. It must not replace v0.7 until a successor clears both the canonical tests and a grouped, contamination-checked broad-music acceptance set.
+
+## Protocol
+
+Canonical partition provenance is in [partitions.md](partitions.md).
+
+| Dataset | Train | Validation | Test |
+| --- | ---: | ---: | ---: |
+| GiantSteps | 464 | 66 | 131 |
+| GTZAN | 422 | 194 | 313 |
+| Ballroom | 502 | 49 | 134 |
+
+Test folds were evaluated once after selection. Fine-tuning starts from 0.7 and uses the three training folds, inverse-frequency 10-BPM-bin sampling, Adam at 1e-4, batch 256, and early stopping. Cache manifest SHA-256 is 8be874f69ae1b0e8c4e6cdd1cf902b9afffb260d430f6e03dad37324d152ed02. Candidate weight SHA-256 is 42809a786870088293d39ca1b486cdf8153396f749936f59dff875a6e5aed086.
+
+## Held-out results
+
+These are new held-out headlines. The 0.7 rows were republished on the same test folds.
+
+### Four-percent tolerance
+
+| Dataset | 0.7 Acc1 / Acc2 | 0.7 + corrector | Fine-tuned | Fine-tuned + corrector |
+| --- | ---: | ---: | ---: | ---: |
+| GiantSteps | 70.99 / 98.47 | 76.34 / 98.47 | **92.37 / 98.47** | **92.37 / 98.47** |
+| GTZAN | 69.01 / 92.33 | 67.73 / 92.33 | **72.20 / 92.33** | **72.20 / 92.33** |
+| Ballroom | 59.70 / 88.06 | 58.96 / 88.06 | **88.81 / 94.03** | **88.81 / 94.03** |
+
+### Two-percent tolerance
+
+| Dataset | 0.7 Acc1 / Acc2 | 0.7 + corrector | Fine-tuned |
+| --- | ---: | ---: | ---: |
+| GiantSteps | 64.89 / 90.84 | 70.23 / 90.84 | **85.50 / 91.60** |
+| GTZAN | 64.86 / 86.58 | 63.58 / 86.58 | **69.65 / 87.54** |
+| Ballroom | 50.75 / 77.61 | 50.00 / 77.61 | **79.10 / 82.09** |
+
+Complete machine-readable results, taxonomy, slices, calibration bins, manifest hashes, and the weights hash are in [results](results/).
+
+## Attribution
+
+| Dataset | 0.7 half errors | Fine-tuned half errors | 0.7 correct | Fine-tuned correct |
+| --- | ---: | ---: | ---: | ---: |
+| GiantSteps | 34 | **2** | 93 | **121** |
+| GTZAN | 46 | **31** | 216 | **226** |
+| Ballroom | 38 | **5** | 80 | **119** |
+
+A 90-BPM global prior raises combined validation Acc1 from 63.75% to 72.17%, but cannot distinguish resolution from convention. Top-k mass and clip disagreement reach 64.72% and 64.40%; the onset classifier reaches 66.02%. On test the onset model improves 0.7 GiantSteps by 5.34 points but loses 1.28 on GTZAN and 0.75 on Ballroom. Fine-tuning removes half-tempo errors across domains, which is stronger evidence of genuine octave resolution.
+
+Raw fine-tuned confidence remains poorly calibrated: ECE is 0.243 GiantSteps, 0.295 GTZAN, and 0.352 Ballroom.
+
+## Broad-music acceptance check
+
+A post-hoc diagnostic used 578 tracks on which Librosa, Essentia RhythmExtractor2013, and TempoCNN independently agreed at the same octave within 4%. Audio fingerprints did not overlap the canonical test folds. These are high-precision pseudo-references rather than human ground truth, so this check is an acceptance guardrail, not a replacement headline.
+
+| Collection | v0.7 | Research candidate |
+| --- | ---: | ---: |
+| Bass Club (323) | 95.67% | **95.98%** |
+| Other downloads (255) | **95.29%** | 90.59% |
+| Overall (578) | **95.50%** | 93.60% |
+
+The regression is decisive for release policy. Two exploratory remedies also failed the acceptance bar. Fine-tuning on grouped consensus pseudo-labels reduced both broad-music and canonical performance. A grouped out-of-fold selector between v0.7 and the candidate preserved 95.50% overall but regressed the non-Bass slice; adding onset-periodicity features scored 94.81% overall. Collection-specific switching would conceal the problem behind a dataset prior and is rejected.
+
+A subsequent DeepRhythm-only preservation experiment used v0.7 distribution matching on unlabeled replay audio alongside canonical human supervision. A validation-selected interpolation exactly matched v0.7 on grouped broad-music validation. On the frozen tests it scored 70.23% GiantSteps, 72.20% GTZAN, 64.18% Ballroom, and tied v0.7 at 93.75% on the 48-track grouped broad-music test. This is 15 additional correct canonical tracks overall, but it loses one GiantSteps track and relies on private replay audio that cannot make a reproducible public checkpoint. The weights are therefore not shipped. The supported training stack retains preservation distillation so the experiment can be repeated with a licensed, manifestable replay corpus.
+
+The two models remain complementary: an oracle choice reaches 96.95% on GiantSteps, 79.55% on GTZAN, 91.04% on Ballroom, and 98.27% on the broad-music check.
+
+A subsequent hierarchical prototype separated a half-BPM base-rate head over 60–120 BPM from a four-class metrical-level head (×½, ×1, ×2, ×4). End-to-end canonical training showed that 13 of 14 broad-validation errors had the correct base rate but the wrong level. Freezing the backbone and base head, then preserving v0.7's level posterior on unlabeled replay clips, produced 87.02% GiantSteps, 69.33% GTZAN, and 91.04% Ballroom on the frozen tests. It scored 91.67% rather than v0.7's 93.75% on the 48-track grouped broad-music test: 64 additional canonical tracks correct at the cost of one broad-test track. This identifies the right architectural boundary but does not clear the no-regression release gate. The checkpoint is not shipped because its replay corpus is private; reproducing the level-head preservation stage requires a licensed, manifestable broad corpus.
+
+## Latency
+
+The fine-tuned network has the same architecture and inference cost as 0.7. On 578 held-out tracks, onset features cost 3.00 ms/track with eight workers. Warm serial cost over 20 preloaded tracks is 2.94 ms mean, 2.91 ms median, and 3.29 ms p95. The recommended model-only design adds no runtime latency.
+
+The subsequently packaged opt-in metrical predictor is a different, two-branch
+design. Its production replay over all 798 canonical, Groove, and Slakh test
+tracks took 110.17 ms mean, 51.84 ms median, and 357.70 ms p95 per decoded file
+on MPS. Cost is track-length dependent because HCQM probabilities are averaged
+over every complete eight-second clip after training-compatible edge trimming;
+the temporal branch still consumes only the first four clips and reuses the
+same nnAudio STFT.
+
+## Packaged v0.8 qualification
+
+The installed `MetricalDeepRhythmPredictor` was replayed from audio rather than
+from cached logits. After distilling a stronger beat-rate teacher into the
+existing self-contained temporal fusion, it scored 79.76% Acc1 / 94.29% Acc2
+on the 578-track canonical union, 77.14% / 98.57% on Groove, and 84.00% /
+97.33% on Slakh. Canonical per-dataset Acc1 was 90.08% GiantSteps, 70.29%
+GTZAN, and 91.79% Ballroom. Six representative tracks produced identical BPMs
+on CPU and MPS.
+
+Distillation changes only the small fusion weights: Phasefinder and the
+beat-rate teacher are not runtime dependencies. On untouched human-held-out
+audio, CREC improved from 67.39% to 76.09% Acc1 and from 97.83% to 100% Acc2;
+Soulseek changed one of 97 predictions and declined from 94.85% to 93.81%
+Acc1, with Acc2 unchanged at 100%. This remaining individual-octave tradeoff is
+why v0.8 stays opt-in rather than silently replacing the original predictor.
+The teacher/student selection and held-out summaries are frozen in
+[`distilled-metrical-fusion.json`](results/distilled-metrical-fusion.json).
+
+The complete per-track audit trail, runtime, latencies, and checkpoint hashes
+are in [`production-v08-qualification.json`](results/production-v08-qualification.json).
+
+## External acquisition
+
+No new external audio entered training. Extended Ballroom publishes metadata and a downloader but not explicit source-audio training rights. GiantSteps uses Beatport previews without a local training/redistribution grant. The public Hainsworth mirror does not establish a license for copyrighted excerpts. ISMIR04, Harmonix, RWC, and Beatles could not meet both alignment and rights requirements. FMA/MTG-Jamendo pseudo-labeling was not run because the required independent same-octave ensemble was unavailable; DeepRhythm confidence was intentionally not substituted.
+
+Thus +tier-1 and +tier-1+3 ablations are blocked by evidence, not silently omitted. The measured attribution is tempo-balanced canonical-fold fine-tuning, not external data volume.
+
+## Literature
+
+Schreiber and Müller's [octave correction](https://archives.ismir.net/ismir2017/paper/000125.pdf) motivates the periodicity reproduction; their [single-step CNN](https://archives.ismir.net/ismir2018/paper/000151.pdf) describes sweet-octave behavior. Gkiokas et al.'s [periodicity-vector classifier](https://archives.ismir.net/ismir2012/paper/000065.pdf) supports a small reranker, while Böck and Davies' [multi-task model](https://archives.ismir.net/ismir2020/paper/000035.pdf) shows why beat structure is stronger evidence than a tempo posterior.
+
+Evaluation follows [Music Tempo Estimation: Are We Done Yet?](https://transactions.ismir.net/articles/10.5334/tismir.43). Provenance sources include [GiantSteps](https://github.com/GiantSteps/giantsteps-tempo-dataset), [Extended Ballroom](http://anasynth.ircam.fr/home/media/ExtendedBallroom), and Sturm's [GTZAN analysis](https://arxiv.org/abs/1306.1461).
+
+## Reproduction and limitations
+
+Inference details retain per-clip and aggregate peaks plus five candidate masses without changing legacy JSONL. deeprhythm-taxonomy emits taxonomy and calibration. deeprhythm-correction rejects test rows during selection. deeprhythm.train provides manifest-hashed caching, balanced sampling, time-stretch/relabel primitives, and train/validation-only checkpointing.
+
+Time-stretch augmentation is implemented but not included in this frozen checkpoint because augmented manifest rows were not materialized. External composition and augmentation ablations remain future experiments, not implied results.
\ No newline at end of file
diff --git a/docs/training.md b/docs/training.md
new file mode 100644
index 0000000..e6ed7c3
--- /dev/null
+++ b/docs/training.md
@@ -0,0 +1,9 @@
+# Training foundation
+
+Training is manifest-driven and lives in `deeprhythm.train`. Each manifest row must contain a positive `tempo`, an `audio_path` (or `filename`), and a canonical `fold` (`train`, `val`, or `test`) plus an audio checksum. The cache accepts `split` as a legacy alias, but committed manifests use `fold`; conflicting values are rejected. `build_hcqm_cache` precomputes the existing eight-second HCQM representation into individual NumPy arrays. The cache key incorporates the source path, checksum, tempo, split, and cache format version, so corrected annotations and changed audio cannot silently reuse stale features. Arrays are loaded with memory mapping by `ClipDataset`.
+
+`fit` trains only the declared `train` split and selects checkpoints only with `val`. It intentionally has no test loader or test-evaluation option. Final test-fold evaluation belongs to the benchmark workflow after the experiment configuration is frozen. Validation Acc1 and Acc2 call `deeprhythm.bench.tempo.tempo_accuracy`; this replaces the historical training script's independent and incorrect Acc2 calculation.
+
+The default sampler weights occupied 10 BPM bins inversely by clip count. Manifest rows may declare `stretch_rate`; cache extraction then stretches audio before HCQM computation, and the rate participates in cache identity. Callers must store the correspondingly relabeled `tempo`. `stretch_audio_and_tempo` remains available for preparing those rows, and augmentations outside the model's 30–285 BPM range are rejected.
+
+Preservation fine-tuning is configured with `preservation_domains`, `distillation_weight`, and `distillation_temperature`. Human-labeled domains retain cross-entropy supervision, while declared replay domains match the starting model's complete class distribution using temperature-scaled KL divergence. Replay annotations are deliberately ignored: this constrains catastrophic forgetting without turning external estimator agreement into training labels. Distillation requires `start_weights`, which is also loaded as the frozen teacher.
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index f9e6cf6..6b16b8b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,6 +23,7 @@ dependencies = [
   "librosa",
   "numpy",
   "requests",
+  "pytorch-tcn",
 ]
 
 [project.optional-dependencies]
@@ -30,11 +31,17 @@ dev = ["pytest", "ruff"]
 
 [project.scripts]
 deeprhythm-benchmark = "deeprhythm.bench.tempo:main"
+deeprhythm-taxonomy = "deeprhythm.bench.taxonomy:main"
+deeprhythm-correction = "deeprhythm.bench.correction:main"
+deeprhythm-annotate = "deeprhythm.annotate:main"
 
 [project.urls]
 Homepage = "https://github.com/bleugreen/deeprhythm"
 Issues = "https://github.com/bleugreen/deeprhythm/issues"
 
+[tool.hatch.build]
+artifacts = ["src/deeprhythm/weights/*.json"]
+
 [tool.ruff]
 line-length = 120
 target-version = "py38"
diff --git a/src/deeprhythm/annotate.py b/src/deeprhythm/annotate.py
new file mode 100644
index 0000000..0c7342b
--- /dev/null
+++ b/src/deeprhythm/annotate.py
@@ -0,0 +1,279 @@
+"""Prepare and serve a local metrical-level annotation harness."""
+
+# ruff: noqa: E501  # Embedded self-contained HTML/JavaScript is intentionally compact.
+
+import argparse
+import hashlib
+import json
+import mimetypes
+import os
+import sys
+from datetime import datetime, timezone
+from http import HTTPStatus
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from pathlib import Path
+from urllib.parse import unquote, urlparse
+
+import librosa
+import numpy as np
+import soundfile as sf
+
+AUDIO_SUFFIXES = {".flac", ".mp3", ".m4a", ".ogg", ".wav", ".aif", ".aiff"}
+LABELS = {"half", "correct", "double", "other", "skip"}
+
+
+def metrical_grids(beats, duration):
+    """Return half/current/double click grids relative to one excerpt."""
+    current = np.asarray([beat for beat in beats if 0 <= beat < duration], dtype=float)
+    half = current[::2]
+    if len(current) > 1:
+        midpoints = (current[:-1] + current[1:]) / 2
+        double = np.sort(np.concatenate([current, midpoints]))
+    else:
+        double = current.copy()
+    return {"half": half, "current": current, "double": double}
+
+
+def active_excerpt(audio, sample_rate, duration=15.0):
+    """Choose the duration-sized window with the greatest onset activity."""
+    samples = min(len(audio), int(duration * sample_rate))
+    if len(audio) <= samples:
+        return 0.0, np.pad(audio, (0, samples - len(audio)))
+    hop = 512
+    onset = librosa.onset.onset_strength(y=audio, sr=sample_rate, hop_length=hop)
+    window = max(1, int(duration * sample_rate / hop))
+    energy = np.convolve(onset, np.ones(window), mode="valid")
+    frame = int(np.argmax(energy))
+    # Onset frames are centered around an analysis window; step back one FFT radius
+    # so the transient that selected the excerpt is not clipped off its front.
+    start = max(0, frame * hop - 1024)
+    return start / sample_rate, audio[start : start + samples]
+
+
+def load_jsonl(path):
+    path = Path(path)
+    if not path.exists():
+        return []
+    return [json.loads(line) for line in path.read_text().splitlines() if line.strip()]
+
+
+class AnnotationStore:
+    def __init__(self, root):
+        self.root = Path(root)
+        self.manifest_path = self.root / "manifest.jsonl"
+        self.annotations_path = self.root / "annotations.jsonl"
+
+    def manifest(self):
+        return load_jsonl(self.manifest_path)
+
+    def annotations(self):
+        return load_jsonl(self.annotations_path)
+
+    def completed_ids(self):
+        return {row["id"] for row in self.annotations()}
+
+    def append(self, item_id, label):
+        if label not in LABELS:
+            raise ValueError(f"unknown label: {label}")
+        if item_id not in {row["id"] for row in self.manifest()}:
+            raise ValueError("unknown manifest item")
+        row = {"id": item_id, "label": label, "annotated_at": datetime.now(timezone.utc).isoformat()}
+        with self.annotations_path.open("a") as stream:
+            stream.write(json.dumps(row, sort_keys=True) + "\n")
+            stream.flush()
+            os.fsync(stream.fileno())
+        return row
+
+
+def _prediction_rows(path):
+    rows = load_jsonl(path)
+    output = []
+    for row in rows:
+        filename = row.get("filename", row.get("audio_path"))
+        bpm = row.get("bpm", row.get("deeprhythm_v07"))
+        if filename and bpm:
+            output.append((str(Path(filename).expanduser().resolve()), float(bpm)))
+    return output
+
+
+def _phasefinder(phasefinder_path, bpm_holder):
+    sys.path.insert(0, str(Path(phasefinder_path).expanduser().resolve()))
+    import phasefinder.predictor as predictor_module
+
+    class InjectedTempoPredictor:
+        def __init__(self, *_args, **_kwargs):
+            pass
+
+        def predict(self, _path, include_confidence=False):
+            result = (bpm_holder[0], 1.0)
+            return result if include_confidence else result[0]
+
+    predictor_module.DeepRhythmPredictor = InjectedTempoPredictor
+    return predictor_module.Phasefinder(quiet=True)
+
+
+def prepare(predictions, output, phasefinder_path, limit=None):
+    """Generate resumable excerpt and click assets from prediction JSONL."""
+    output = Path(output)
+    assets = output / "assets"
+    assets.mkdir(parents=True, exist_ok=True)
+    store = AnnotationStore(output)
+    existing = {row["id"]: row for row in store.manifest()}
+    holder = [120.0]
+    phasefinder = _phasefinder(phasefinder_path, holder)
+    prepared = 0
+    for filename, bpm in _prediction_rows(predictions):
+        if Path(filename).suffix.lower() not in AUDIO_SUFFIXES or not Path(filename).exists():
+            continue
+        item_id = hashlib.sha256(filename.encode()).hexdigest()[:16]
+        if item_id in existing:
+            continue
+        holder[0] = bpm
+        beat_times = np.asarray(phasefinder.predict(filename), dtype=float)
+        audio, sample_rate = librosa.load(filename, sr=22050, mono=True)
+        offset, excerpt = active_excerpt(audio, sample_rate)
+        relative_beats = beat_times - offset
+        grids = metrical_grids(relative_beats, len(excerpt) / sample_rate)
+        item_dir = assets / item_id
+        item_dir.mkdir(exist_ok=True)
+        sf.write(item_dir / "audio.flac", excerpt, sample_rate)
+        click_paths = {}
+        for name, times in grids.items():
+            click = librosa.clicks(times=times, sr=sample_rate, length=len(excerpt))
+            path = item_dir / f"{name}.flac"
+            sf.write(path, click, sample_rate)
+            click_paths[name] = f"/assets/{item_id}/{name}.flac"
+        row = {
+            "id": item_id,
+            "filename": filename,
+            "sha256": hashlib.sha256(Path(filename).read_bytes()).hexdigest(),
+            "predicted_bpm": bpm,
+            "excerpt_offset_seconds": offset,
+            "excerpt_duration_seconds": len(excerpt) / sample_rate,
+            "audio": f"/assets/{item_id}/audio.flac",
+            "clicks": click_paths,
+        }
+        with store.manifest_path.open("a") as stream:
+            stream.write(json.dumps(row, sort_keys=True) + "\n")
+        existing[item_id] = row
+        prepared += 1
+        print(f"prepared {prepared}: {Path(filename).name}", flush=True)
+        if limit and prepared >= limit:
+            break
+    return prepared
+
+
+HTML = r"""<!doctype html><meta charset="utf-8"><title>DeepRhythm metrical annotation</title>
+<style>
+body{font:16px system-ui;margin:0;background:#111;color:#eee}main{max-width:850px;margin:7vh auto;padding:28px}
+.muted{color:#999}.card{background:#1b1b1b;border:1px solid #333;border-radius:14px;padding:24px}
+button{font:inherit;color:#eee;background:#292929;border:1px solid #555;border-radius:9px;padding:12px;margin:5px}
+button:hover,.active{background:#485bff}kbd{background:#333;padding:3px 7px;border-radius:5px}h1{font-size:25px}
+</style><main><h1>Metrical-level annotation</h1><p id="progress" class="muted"></p><section class="card">
+<h2 id="name">Loading…</h2><p><strong id="bpm"></strong> <span id="offset" class="muted"></span></p>
+<p>Preview click: <button data-grid="half">H · ½×</button><button data-grid="current" class="active">C · predicted</button><button data-grid="double">D · 2×</button></p>
+<p><button id="play">Space · Play / pause</button></p><hr>
+<p>How should the <em>tempo estimate</em> change?</p>
+<button data-label="half">1 · Needs 2× (model half)</button><button data-label="correct">2 · Correct</button>
+<button data-label="double">3 · Needs ½× (model double)</button><button data-label="other">4 · Other</button>
+<button data-label="skip">S · Skip</button></section></main><script>
+let item=null,grid='current',audio=new Audio(),click=new Audio();
+async function next(){let r=await fetch('/api/next'),d=await r.json();item=d.item;
+ document.querySelector('#progress').textContent=`${d.completed} / ${d.total} complete`;
+ if(!item){document.querySelector('#name').textContent='All done';return}
+ document.querySelector('#name').textContent=item.filename.split('/').pop();document.querySelector('#bpm').textContent=`${item.predicted_bpm} BPM`;
+ document.querySelector('#offset').textContent=`excerpt at ${item.excerpt_offset_seconds.toFixed(1)}s`;audio.src=item.audio;setGrid('current')}
+function setGrid(g){grid=g;if(!item)return;let was=!audio.paused,t=audio.currentTime;click.pause();click.src=item.clicks[g];click.currentTime=t;
+ document.querySelectorAll('[data-grid]').forEach(b=>b.classList.toggle('active',b.dataset.grid===g));if(was)click.play()}
+function toggle(){if(!item)return;if(audio.paused){if(audio.ended){audio.currentTime=0;click.currentTime=0}click.currentTime=audio.currentTime;audio.play();click.play()}else{audio.pause();click.pause()}}
+async function label(value){audio.pause();click.pause();await fetch('/api/annotate',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({id:item.id,label:value})});next()}
+audio.addEventListener('seeked',()=>click.currentTime=audio.currentTime);audio.addEventListener('pause',()=>click.pause());
+document.querySelector('#play').onclick=toggle;document.querySelectorAll('[data-grid]').forEach(b=>b.onclick=()=>setGrid(b.dataset.grid));document.querySelectorAll('[data-label]').forEach(b=>b.onclick=()=>label(b.dataset.label));
+addEventListener('keydown',e=>{if(e.repeat)return;let labels={'1':'half','2':'correct','3':'double','4':'other','s':'skip'};if(e.key===' '){e.preventDefault();toggle()}else if(labels[e.key.toLowerCase()])label(labels[e.key.toLowerCase()]);else if('hcd'.includes(e.key.toLowerCase()))setGrid({h:'half',c:'current',d:'double'}[e.key.toLowerCase()])});next();
+</script>"""
+
+
+def handler(store):
+    class Handler(BaseHTTPRequestHandler):
+        def _json(self, value, status=200):
+            body = json.dumps(value).encode()
+            self.send_response(status)
+            self.send_header("Content-Type", "application/json")
+            self.send_header("Content-Length", str(len(body)))
+            self.end_headers()
+            self.wfile.write(body)
+
+        def do_GET(self):
+            path = unquote(urlparse(self.path).path)
+            if path == "/":
+                body = HTML.encode()
+                self.send_response(200)
+                self.send_header("Content-Type", "text/html; charset=utf-8")
+                self.send_header("Content-Length", str(len(body)))
+                self.end_headers()
+                self.wfile.write(body)
+            elif path == "/api/next":
+                rows, done = store.manifest(), store.completed_ids()
+                item = next((row for row in rows if row["id"] not in done), None)
+                self._json({"item": item, "completed": len(done), "total": len(rows)})
+            elif path.startswith("/assets/"):
+                relative = Path(path.removeprefix("/assets/"))
+                target = (store.root / "assets" / relative).resolve()
+                assets = (store.root / "assets").resolve()
+                if assets not in target.parents or not target.is_file():
+                    self.send_error(404)
+                    return
+                body = target.read_bytes()
+                self.send_response(200)
+                self.send_header("Content-Type", mimetypes.guess_type(target)[0] or "application/octet-stream")
+                self.send_header("Content-Length", str(len(body)))
+                self.end_headers()
+                self.wfile.write(body)
+            else:
+                self.send_error(404)
+
+        def do_POST(self):
+            if urlparse(self.path).path != "/api/annotate":
+                self.send_error(404)
+                return
+            try:
+                size = int(self.headers.get("Content-Length", "0"))
+                payload = json.loads(self.rfile.read(size))
+                self._json(store.append(payload["id"], payload["label"]), HTTPStatus.CREATED)
+            except (KeyError, ValueError, json.JSONDecodeError) as error:
+                self._json({"error": str(error)}, HTTPStatus.BAD_REQUEST)
+
+        def log_message(self, _format, *_args):
+            return
+
+    return Handler
+
+
+def serve(output, host="127.0.0.1", port=8765):
+    store = AnnotationStore(output)
+    server = ThreadingHTTPServer((host, port), handler(store))
+    print(f"annotation harness: http://{host}:{port}")
+    server.serve_forever()
+
+
+def main(argv=None):
+    parser = argparse.ArgumentParser(description=__doc__)
+    subparsers = parser.add_subparsers(dest="command", required=True)
+    prep = subparsers.add_parser("prepare")
+    prep.add_argument("--predictions", required=True)
+    prep.add_argument("--output", required=True)
+    prep.add_argument("--phasefinder-path", default="~/projects/phasefinder")
+    prep.add_argument("--limit", type=int)
+    web = subparsers.add_parser("serve")
+    web.add_argument("--output", required=True)
+    web.add_argument("--host", default="127.0.0.1")
+    web.add_argument("--port", type=int, default=8765)
+    args = parser.parse_args(argv)
+    if args.command == "prepare":
+        prepare(args.predictions, args.output, args.phasefinder_path, args.limit)
+    else:
+        serve(args.output, args.host, args.port)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/src/deeprhythm/audio_proc/hcqm.py b/src/deeprhythm/audio_proc/hcqm.py
index 6ce1a72..b6b51c1 100644
--- a/src/deeprhythm/audio_proc/hcqm.py
+++ b/src/deeprhythm/audio_proc/hcqm.py
@@ -76,6 +76,11 @@ def compute_hcqm(y, stft_spec, band_filter, cqt_specs):
     - hcqm (Tensor): The computed HCQM of shape (batch_size, N_BINS, N_BANDS, N_HARMONICS)
     """
     stft = stft_spec(y)
+    return compute_hcqm_from_stft(stft, band_filter, cqt_specs)
+
+
+def compute_hcqm_from_stft(stft, band_filter, cqt_specs):
+    """Compute HCQM from a magnitude STFT shared with another rhythm branch."""
     stft_bands = apply_log_filter(stft, band_filter)
     stft_bands_flat = stft_bands.reshape(stft.size(0)*stft_bands.size(1), stft_bands.size(2))
     osf_flat = onset_strength(y=stft_bands_flat)
diff --git a/src/deeprhythm/audio_proc/log_spectrum.py b/src/deeprhythm/audio_proc/log_spectrum.py
new file mode 100644
index 0000000..a8200ca
--- /dev/null
+++ b/src/deeprhythm/audio_proc/log_spectrum.py
@@ -0,0 +1,105 @@
+"""Tensor-native log-frequency spectrum for temporal rhythm models."""
+
+import numpy as np
+import torch
+
+
+def make_log_spectrum_filter(n_fft=2048, num_bands=81, *, device=None):
+    """Create the logarithmic filter used by the temporal beat-rate branch."""
+    num_bins = n_fft // 2 + 1
+    edges = np.floor(
+        np.logspace(np.log10(1), np.log10(num_bins), num_bands + 1) - 1
+    ).astype(int)
+    edges[-1] = num_bins
+    for index in range(1, len(edges)):
+        if edges[index] <= edges[index - 1]:
+            edges[index] = edges[index - 1] + 1
+
+    matrix = torch.zeros(num_bands, num_bins, device=device)
+    for index in range(num_bands):
+        start = edges[index]
+        end = edges[index + 1] if index < num_bands - 1 else num_bins
+        matrix[index, start:end] = 1 / max(1, end - start)
+    return matrix
+
+
+def compute_log_spectrum(
+    audio,
+    *,
+    sample_rate=22050,
+    duration=30,
+    n_fft=2048,
+    hop_length=512,
+    filter_matrix=None,
+):
+    """Compute a normalized log-frequency spectrum from an already-loaded waveform.
+
+    Reusing the waveform avoids a second decode and resample when this feature is
+    paired with the HCQM tempo model.
+    """
+    audio = torch.as_tensor(audio, dtype=torch.float32)
+    if audio.ndim != 1:
+        raise ValueError("audio must be a one-dimensional mono waveform")
+
+    target_samples = sample_rate * duration
+    audio = audio[:target_samples]
+    if audio.numel() < target_samples:
+        audio = torch.nn.functional.pad(audio, (0, target_samples - audio.numel()))
+
+    window = torch.hann_window(n_fft, device=audio.device)
+    magnitude = torch.stft(
+        audio,
+        n_fft=n_fft,
+        hop_length=hop_length,
+        window=window,
+        center=True,
+        pad_mode="constant",
+        return_complex=True,
+    ).abs()
+    decibels = 10 * torch.log10(magnitude.clamp_min(1e-5))
+    if filter_matrix is None:
+        filter_matrix = make_log_spectrum_filter(
+            n_fft=n_fft, device=audio.device
+        )
+    spectrum = filter_matrix.to(audio.device) @ decibels
+    minimum = spectrum.amin()
+    return (spectrum - minimum) / (spectrum.amax() - minimum).clamp_min(1e-6)
+
+
+def compute_log_spectrum_from_clips(
+    magnitude,
+    *,
+    sample_rate=22050,
+    duration=30,
+    hop_length=512,
+    filter_matrix=None,
+):
+    """Build a temporal spectrum by concatenating existing clip STFT frames.
+
+    Magnitude has shape (clips, frequency, frames) and should contain four
+    eight-second clips. The independently centered clip frames are kept: the
+    temporal model is robust to those boundaries, and dropping them would
+    create a larger timing discontinuity.
+    """
+    if magnitude.ndim != 3 or magnitude.shape[0] == 0:
+        raise ValueError(
+            "magnitude must have shape (clips, frequency, frames)"
+        )
+    if filter_matrix is None:
+        n_fft = (magnitude.shape[1] - 1) * 2
+        filter_matrix = make_log_spectrum_filter(
+            n_fft=n_fft, device=magnitude.device
+        )
+    decibels = 10 * torch.log10(magnitude.clamp_min(1e-5))
+    bands = torch.einsum(
+        "bf,cft->cbt", filter_matrix.to(magnitude.device), decibels
+    )
+    spectrum = bands.permute(1, 0, 2).reshape(bands.shape[1], -1)
+    target_frames = 1 + duration * sample_rate // hop_length
+    spectrum = spectrum[:, :target_frames]
+    if spectrum.shape[1] < target_frames:
+        spectrum = torch.nn.functional.pad(
+            spectrum, (0, target_frames - spectrum.shape[1])
+        )
+    minimum = spectrum.amin()
+    return (spectrum - minimum) / (spectrum.amax() - minimum).clamp_min(1e-6)
\ No newline at end of file
diff --git a/src/deeprhythm/batch_infer.py b/src/deeprhythm/batch_infer.py
index 8d3edb7..c85e7ce 100644
--- a/src/deeprhythm/batch_infer.py
+++ b/src/deeprhythm/batch_infer.py
@@ -9,10 +9,10 @@
 
 from deeprhythm.audio_proc.hcqm import compute_hcqm, make_kernels
 from deeprhythm.model.frame_cnn import DeepRhythmModel
+from deeprhythm.model.results import shape_prediction
 from deeprhythm.utils import (
     AudioLoadError,
     AudioTooShortError,
-    class_to_bpm,
     get_device,
     get_weights,
     load_and_split_audio,
@@ -69,7 +69,9 @@ def init_workers(dataset, n_workers=NUM_WORKERS):
     return task_queue, result_queue, producers, completion_event, queue_condition
 
 
-def process_and_save(batch_audio, batch_meta, specs, model, out_path, conf=False, quiet=False):
+def process_and_save(
+    batch_audio, batch_meta, specs, model, out_path, conf=False, quiet=False, details_path=None, top_k=5
+):
     """
     Processes a batch of audio clips and saves the results to a JSONL file.
     """
@@ -90,16 +92,18 @@ def process_and_save(batch_audio, batch_meta, specs, model, out_path, conf=False
         filename, num_clips, start_idx = meta
         song_outputs = outputs[start_idx:start_idx+num_clips, :]
         probabilities = torch.softmax(song_outputs, dim=1)
-        mean_probabilities = probabilities.mean(dim=0)
-        confidence_score, predicted_class = torch.max(mean_probabilities, 0)
-        predicted_global_bpm = class_to_bpm(predicted_class.item())
+        details = shape_prediction(probabilities, top_k=top_k)
         result = {
             "filename": filename,
-            "bpm": predicted_global_bpm
+            "bpm": details["bpm"]
         }
         if conf:
-            result['confidence'] = confidence_score.item()
+            result['confidence'] = details["confidence"]
         results.append(result)
+        if details_path is not None:
+            details["filename"] = filename
+            with open(details_path, "a") as details_file:
+                details_file.write(json.dumps(details) + "\n")
     with open(out_path, 'a') as f:
         for result in results:
             f.write(json.dumps(result) + "\n")
@@ -107,7 +111,8 @@ def process_and_save(batch_audio, batch_meta, specs, model, out_path, conf=False
 
 def consume_and_process(
     result_queue, data_path, queue_condition, n_workers=NUM_WORKERS,
-    max_len_batch=NUM_BATCH, device='cuda', conf=False, quiet=False
+    max_len_batch=NUM_BATCH, device='cuda', conf=False, quiet=False,
+    details_path=None, top_k=5
 ):
     batch_audio = []
     batch_meta = []
@@ -145,26 +150,33 @@ def consume_and_process(
         total_clips += num_clips
         if total_clips >= max_len_batch:
             stacked_batch_audio = torch.cat(batch_audio, dim=0).to(device=device)
-            process_and_save(stacked_batch_audio, batch_meta, specs, model, data_path, conf=conf, quiet=quiet)
+            process_and_save(
+                stacked_batch_audio, batch_meta, specs, model, data_path, conf=conf, quiet=quiet,
+                details_path=details_path, top_k=top_k
+            )
             total_clips = 0
             batch_audio = []
             batch_meta = []
 
     if batch_audio:
         stacked_batch_audio = torch.cat(batch_audio, dim=0).to(device=device)
-        process_and_save(stacked_batch_audio, batch_meta, specs, model, data_path, conf=conf, quiet=quiet)
+        process_and_save(
+            stacked_batch_audio, batch_meta, specs, model, data_path, conf=conf, quiet=quiet,
+            details_path=details_path, top_k=top_k
+        )
 
 
 def main(
     dataset, n_workers=NUM_WORKERS, max_len_batch=NUM_BATCH,
-    data_path='output.jsonl', device='cuda', conf=False, quiet=False
+    data_path='output.jsonl', device='cuda', conf=False, quiet=False,
+    details_path=None, top_k=5
 ):
     task_queue, result_queue, producers, completion_event, queue_condition = init_workers(dataset, n_workers)
     try:
         consume_and_process(
             result_queue, data_path, queue_condition,
             n_workers=n_workers, max_len_batch=max_len_batch,
-            device=device, conf=conf, quiet=quiet
+            device=device, conf=conf, quiet=quiet, details_path=details_path, top_k=top_k
         )
     finally:
         completion_event.set()
@@ -197,6 +209,8 @@ def get_audio_files(dir_path):
     parser.add_argument('-d', '--device', type=str, default=get_device(), help='Device to use for inference')
     parser.add_argument('-c', '--conf', action='store_true', help='Include confidence score in output')
     parser.add_argument('-q', '--quiet', action='store_true', help='Use minimal output format')
+    parser.add_argument('--details-path', help='Optional JSONL path for structured classifier outputs')
+    parser.add_argument('--top-k', type=int, default=5, help='Number of peaks retained in details output')
     args = parser.parse_args()
 
     songs = get_audio_files(args.input_path)
@@ -209,7 +223,9 @@ def get_audio_files(dir_path):
          data_path=args.output_path,
          device=args.device,
          conf=args.conf,
-         quiet=args.quiet
+         quiet=args.quiet,
+         details_path=args.details_path,
+         top_k=args.top_k
     )
     if not args.quiet:
         print(f'{time.time()-start:.2f}')
diff --git a/src/deeprhythm/bench/correction.py b/src/deeprhythm/bench/correction.py
new file mode 100644
index 0000000..0b40bbc
--- /dev/null
+++ b/src/deeprhythm/bench/correction.py
@@ -0,0 +1,131 @@
+"""Train/validation-only tooling for metrical-level correction experiments."""
+
+import argparse
+import json
+import time
+from collections import defaultdict
+from pathlib import Path
+
+import numpy as np
+
+from deeprhythm.bench.taxonomy import expected_calibration_error, metrical_relation
+from deeprhythm.bench.tempo import tempo_accuracy
+from deeprhythm.correction import (
+    FACTORS,
+    FactorClassifier,
+    clip_disagreement,
+    global_threshold,
+    identity,
+    select_parameter,
+    target_factor,
+    top_k_ratio,
+)
+
+
+def _read(path):
+    return [json.loads(line) for line in Path(path).read_text().splitlines() if line.strip()]
+
+
+def join_rows(manifest, details, features=None):
+    """Join rows by path and reject held-out rows at the fitting boundary."""
+    if any(row.get("fold") == "test" for row in manifest):
+        raise ValueError("held-out test folds cannot be used by correction selection tooling")
+    details_by_path = {str(Path(row["filename"]).expanduser().resolve()): row for row in details}
+    features_by_path = {
+        str(Path(row["filename"]).expanduser().resolve()): row["features"] for row in (features or [])
+    }
+    joined = []
+    for row in manifest:
+        path = str(Path(row["audio_path"]).expanduser().resolve())
+        if path not in details_by_path:
+            raise ValueError(f"missing inference details for {path}")
+        joined.append({**row, "detail": details_by_path[path], "features": features_by_path.get(path)})
+    return joined
+
+
+def evaluate_rows(rows, predictions, confidences=None, latency_seconds=0.0):
+    references = [float(row["tempo"]) for row in rows]
+    report = {"tracks": len(rows), "latency": {
+        "total_seconds": latency_seconds,
+        "added_milliseconds_per_track": latency_seconds * 1000 / len(rows),
+    }}
+    for tolerance in (0.02, 0.04):
+        report[f"metrics_{int(tolerance * 100)}pct"] = tempo_accuracy(predictions, references, tolerance)
+    relations = [metrical_relation(prediction, reference, 0.04)
+                 for prediction, reference in zip(predictions, references)]
+    report["taxonomy"] = dict(sorted((name, relations.count(name)) for name in set(relations)))
+    slices = defaultdict(list)
+    for row, prediction in zip(rows, predictions):
+        label = str(row.get("genre", row.get("style", "unknown")))
+        slices[label].append((prediction, float(row["tempo"])))
+    report["slices"] = {
+        label: {"tracks": len(values), **tempo_accuracy(*zip(*values), tolerance=0.04)}
+        for label, values in sorted(slices.items())
+    }
+    if confidences is not None:
+        report["calibration"] = expected_calibration_error(
+            confidences, [relation == "correct" for relation in relations]
+        )
+    return report
+
+
+def run_experiment(train_rows, val_rows):
+    """Fit all variants on train/validation data and return validation reports."""
+    val_details = [row["detail"] for row in val_rows]
+    val_refs = [float(row["tempo"]) for row in val_rows]
+    grids = {
+        "global_threshold": np.arange(50, 151, 5),
+        "top_k_ratio": np.geomspace(0.01, 10.0, 31),
+        "clip_disagreement": np.linspace(0.125, 1.0, 8),
+    }
+    functions = {"global_threshold": global_threshold, "top_k_ratio": top_k_ratio,
+                 "clip_disagreement": clip_disagreement}
+    output = {"identity": evaluate_rows(val_rows, [identity(detail) for detail in val_details])}
+    for name, grid in grids.items():
+        parameter = select_parameter(val_details, val_refs, name, grid)
+        start = time.perf_counter()
+        predictions = [functions[name](detail, parameter) for detail in val_details]
+        elapsed = time.perf_counter() - start
+        output[name] = {
+            "selected_parameter": parameter,
+            **evaluate_rows(val_rows, predictions, latency_seconds=elapsed),
+        }
+
+    if all(row["features"] is not None for row in train_rows + val_rows):
+        classifier = FactorClassifier().fit(
+            [row["features"] for row in train_rows],
+            [target_factor(float(row["detail"]["bpm"]), float(row["tempo"])) for row in train_rows],
+        )
+        start = time.perf_counter()
+        probabilities = classifier.predict_proba([row["features"] for row in val_rows])
+        predictions = [float(row["detail"]["bpm"]) * FACTORS[index]
+                       for row, index in zip(val_rows, probabilities.argmax(axis=1))]
+        elapsed = time.perf_counter() - start
+        output["learned"] = {
+            "model": classifier.to_dict(),
+            **evaluate_rows(val_rows, predictions, probabilities.max(axis=1), elapsed),
+        }
+    return output
+
+
+def main(argv=None):
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--train-manifest", required=True)
+    parser.add_argument("--train-details", required=True)
+    parser.add_argument("--val-manifest", required=True)
+    parser.add_argument("--val-details", required=True)
+    parser.add_argument("--train-features")
+    parser.add_argument("--val-features")
+    parser.add_argument("-o", "--output", required=True)
+    args = parser.parse_args(argv)
+    train = join_rows(_read(args.train_manifest), _read(args.train_details),
+                      _read(args.train_features) if args.train_features else None)
+    val = join_rows(_read(args.val_manifest), _read(args.val_details),
+                    _read(args.val_features) if args.val_features else None)
+    result = run_experiment(train, val)
+    Path(args.output).write_text(json.dumps(result, indent=2) + "\n")
+    print(json.dumps(result, indent=2))
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/src/deeprhythm/bench/taxonomy.py b/src/deeprhythm/bench/taxonomy.py
new file mode 100644
index 0000000..69c9999
--- /dev/null
+++ b/src/deeprhythm/bench/taxonomy.py
@@ -0,0 +1,95 @@
+"""Error taxonomy and confidence calibration for instrumented tempo benchmarks."""
+
+import argparse
+import json
+from collections import defaultdict
+from pathlib import Path
+
+import numpy as np
+
+RELATIONS = (("correct", 1.0), ("half", 0.5), ("double", 2.0), ("third", 1 / 3), ("triple", 3.0))
+
+
+def metrical_relation(prediction, reference, tolerance=0.04):
+    """Classify a prediction by its closest recognized metrical relation."""
+    matches = [
+        (abs(prediction / (reference * factor) - 1.0), name)
+        for name, factor in RELATIONS
+        if abs(prediction / (reference * factor) - 1.0) <= tolerance
+    ]
+    return min(matches)[1] if matches else "other"
+
+
+def expected_calibration_error(confidences, outcomes, bins=10):
+    """Compute equal-width expected calibration error."""
+    confidences = np.asarray(confidences, dtype=float)
+    outcomes = np.asarray(outcomes, dtype=float)
+    if confidences.shape != outcomes.shape or confidences.ndim != 1 or confidences.size == 0:
+        raise ValueError("confidence and outcome arrays must be equally sized and non-empty")
+    if np.any((confidences < 0) | (confidences > 1)):
+        raise ValueError("confidences must be between zero and one")
+    edges = np.linspace(0, 1, bins + 1)
+    result = []
+    ece = 0.0
+    for index in range(bins):
+        selected = (confidences >= edges[index]) & (
+            confidences <= edges[index + 1] if index == bins - 1 else confidences < edges[index + 1]
+        )
+        if not selected.any():
+            continue
+        confidence = float(confidences[selected].mean())
+        accuracy = float(outcomes[selected].mean())
+        count = int(selected.sum())
+        ece += count / confidences.size * abs(accuracy - confidence)
+        result.append({"lower": edges[index], "upper": edges[index + 1], "count": count,
+                       "confidence": confidence, "accuracy": accuracy})
+    return {"ece": ece, "bins": result}
+
+
+def analyze(manifest_rows, detail_rows, tolerance=0.04):
+    """Join manifest and details rows by resolved audio path and summarize errors."""
+    details = {str(Path(row["filename"]).expanduser().resolve()): row for row in detail_rows}
+    grouped = defaultdict(lambda: defaultdict(int))
+    joined = []
+    for manifest in manifest_rows:
+        path = str(Path(manifest["audio_path"]).expanduser().resolve())
+        if path not in details:
+            raise ValueError(f"missing details for {path}")
+        detail = details[path]
+        reference = float(manifest["tempo"])
+        prediction = float(detail["bpm"])
+        relation = metrical_relation(prediction, reference, tolerance)
+        tempo_range = f"{int(reference // 20) * 20}-{int(reference // 20) * 20 + 19}"
+        genre = str(manifest.get("genre", manifest.get("style", "unknown")))
+        grouped["relation"][relation] += 1
+        grouped["tempo_range"][tempo_range + ":" + relation] += 1
+        grouped["genre"][genre + ":" + relation] += 1
+        joined.append({**manifest, "prediction": prediction, "relation": relation,
+                       "confidence": float(detail["confidence"])})
+    calibration = expected_calibration_error(
+        [row["confidence"] for row in joined], [row["relation"] == "correct" for row in joined]
+    )
+    return {"tracks": len(joined), "counts": {key: dict(value) for key, value in grouped.items()},
+            "calibration": calibration}
+
+
+def _read_jsonl(path):
+    return [json.loads(line) for line in Path(path).read_text().splitlines() if line.strip()]
+
+
+def main(argv=None):
+    parser = argparse.ArgumentParser(description="Analyze instrumented DeepRhythm benchmark errors")
+    parser.add_argument("manifest")
+    parser.add_argument("details")
+    parser.add_argument("-o", "--output")
+    parser.add_argument("--tolerance", type=float, default=0.04)
+    args = parser.parse_args(argv)
+    result = analyze(_read_jsonl(args.manifest), _read_jsonl(args.details), args.tolerance)
+    rendered = json.dumps(result, indent=2) + "\n"
+    if args.output:
+        Path(args.output).write_text(rendered)
+    print(rendered, end="")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/src/deeprhythm/bench/tempo.py b/src/deeprhythm/bench/tempo.py
index 5b5d9c4..cd60a7c 100644
--- a/src/deeprhythm/bench/tempo.py
+++ b/src/deeprhythm/bench/tempo.py
@@ -57,9 +57,11 @@ def predict(path):
         return list(executor.map(predict, audio_paths))
 
 
-def predict_deeprhythm(audio_paths, output_path, device, workers=8, batch_size=128):
+def predict_deeprhythm(audio_paths, output_path, device, workers=8, batch_size=128, details_path=None):
     output_path = Path(output_path)
     output_path.unlink(missing_ok=True)
+    if details_path is not None:
+        Path(details_path).unlink(missing_ok=True)
     run_deeprhythm(
         audio_paths,
         n_workers=workers,
@@ -68,6 +70,7 @@ def predict_deeprhythm(audio_paths, output_path, device, workers=8, batch_size=1
         device=device,
         conf=True,
         quiet=True,
+        details_path=str(details_path) if details_path is not None else None,
     )
     by_path = {}
     for line in output_path.read_text().splitlines():
@@ -107,7 +110,8 @@ def main(argv=None):
 
     start = time.perf_counter()
     predictions = predict_deeprhythm(
-        audio_paths, output_dir / "deeprhythm-predictions.jsonl", args.device, args.workers, args.batch_size
+        audio_paths, output_dir / "deeprhythm-predictions.jsonl", args.device, args.workers, args.batch_size,
+        output_dir / "deeprhythm-details.jsonl"
     )
     results["deeprhythm"] = evaluate(predictions, references, time.perf_counter() - start, args.tolerance)
 
diff --git a/src/deeprhythm/correction.py b/src/deeprhythm/correction.py
new file mode 100644
index 0000000..7d7104f
--- /dev/null
+++ b/src/deeprhythm/correction.py
@@ -0,0 +1,158 @@
+"""Metrical-level correction baselines and lightweight learned corrector."""
+
+from dataclasses import dataclass
+from typing import Dict, Iterable, Sequence
+
+import numpy as np
+import torch
+
+from deeprhythm.model.results import METRICAL_CANDIDATES
+
+FACTORS = tuple(factor for _name, factor in METRICAL_CANDIDATES)
+FACTOR_NAMES = tuple(name for name, _factor in METRICAL_CANDIDATES)
+
+
+def identity(detail: Dict) -> float:
+    return float(detail["bpm"])
+
+
+def global_threshold(detail: Dict, threshold: float) -> float:
+    bpm = identity(detail)
+    return bpm * 2.0 if bpm < threshold else bpm
+
+
+def top_k_ratio(detail: Dict, ratio: float) -> float:
+    """Double when aggregate probability mass at 2x exceeds a ratio of same-level mass."""
+    masses = detail["candidate_masses"]
+    same = float(masses["same"]["probability_mass"])
+    double = float(masses["double"]["probability_mass"])
+    return identity(detail) * (2.0 if double >= ratio * max(same, 1e-12) else 1.0)
+
+
+def clip_disagreement(detail: Dict, threshold: float) -> float:
+    """Double when enough clip argmaxes support the faster metrical level."""
+    bpm = identity(detail)
+    clips = detail.get("clips", ())
+    if not clips:
+        return bpm
+    faster = sum(float(clip["bpm"]) >= 1.5 * bpm for clip in clips) / len(clips)
+    return bpm * (2.0 if faster >= threshold else 1.0)
+
+
+def _lag_energy(envelope: np.ndarray, lag: float) -> float:
+    lag = int(round(lag))
+    if lag <= 0 or lag >= envelope.size:
+        return 0.0
+    left, right = envelope[:-lag], envelope[lag:]
+    scale = np.linalg.norm(left) * np.linalg.norm(right)
+    return float(np.dot(left, right) / scale) if scale > 0 else 0.0
+
+
+def onset_periodicity_features(
+    waveform, sample_rate: int, predicted_bpm: float, n_fft: int = 1024, hop_length: int = 256
+) -> np.ndarray:
+    """Extract dependency-free onset autocorrelation, spectral balance, and onset-rate features."""
+    if sample_rate <= 0 or predicted_bpm <= 0:
+        raise ValueError("sample_rate and predicted_bpm must be positive")
+    audio = torch.as_tensor(waveform, dtype=torch.float32).flatten()
+    if audio.numel() < n_fft:
+        audio = torch.nn.functional.pad(audio, (0, n_fft - audio.numel()))
+    window = torch.hann_window(n_fft, dtype=audio.dtype, device=audio.device)
+    spectrum = torch.stft(audio, n_fft=n_fft, hop_length=hop_length, window=window, return_complex=True)
+    magnitude = spectrum.abs()
+    flux = torch.relu(magnitude[:, 1:] - magnitude[:, :-1]).mean(dim=0)
+    envelope = flux.cpu().numpy().astype(float)
+    envelope -= envelope.mean() if envelope.size else 0.0
+    frames_per_minute = 60.0 * sample_rate / hop_length
+    periodicity = [_lag_energy(envelope, frames_per_minute / (predicted_bpm * factor)) for factor in FACTORS]
+
+    mean_spectrum = magnitude.mean(dim=1)
+    split = max(1, mean_spectrum.numel() // 4)
+    low = float(mean_spectrum[:split].mean())
+    high = float(mean_spectrum[split:].mean()) if split < mean_spectrum.numel() else 0.0
+    spectral_balance = np.log1p(high) - np.log1p(low)
+    positive = np.maximum(envelope, 0)
+    onset_count = (positive > positive.mean() + positive.std()).sum()
+    onset_rate = float(onset_count * sample_rate / hop_length / max(audio.numel(), 1))
+    return np.asarray(periodicity + [spectral_balance, onset_rate, float(positive.mean()), float(positive.std())])
+
+
+def target_factor(prediction: float, reference: float) -> int:
+    """Return the candidate factor whose corrected tempo is closest to the reference."""
+    errors = [abs(prediction * factor / reference - 1.0) for factor in FACTORS]
+    return int(np.argmin(errors))
+
+
+@dataclass
+class FactorClassifier:
+    """Small multinomial logistic-regression model implemented with NumPy."""
+
+    weights: np.ndarray = None
+    mean: np.ndarray = None
+    scale: np.ndarray = None
+
+    def fit(self, features, labels, learning_rate=0.1, epochs=500, l2=1e-3):
+        x = np.asarray(features, dtype=float)
+        y = np.asarray(labels, dtype=int)
+        if x.ndim != 2 or y.shape != (x.shape[0],) or not x.shape[0]:
+            raise ValueError("features and labels must be non-empty aligned arrays")
+        if np.any((y < 0) | (y >= len(FACTORS))):
+            raise ValueError("labels contain an unknown factor")
+        self.mean = x.mean(axis=0)
+        self.scale = x.std(axis=0)
+        self.scale[self.scale < 1e-8] = 1.0
+        z = np.column_stack([(x - self.mean) / self.scale, np.ones(x.shape[0])])
+        self.weights = np.zeros((z.shape[1], len(FACTORS)))
+        targets = np.eye(len(FACTORS))[y]
+        for _ in range(epochs):
+            logits = z @ self.weights
+            logits -= logits.max(axis=1, keepdims=True)
+            probabilities = np.exp(logits)
+            probabilities /= probabilities.sum(axis=1, keepdims=True)
+            gradient = z.T @ (probabilities - targets) / z.shape[0]
+            gradient[:-1] += l2 * self.weights[:-1]
+            self.weights -= learning_rate * gradient
+        return self
+
+    def predict_proba(self, features):
+        if self.weights is None:
+            raise ValueError("classifier has not been fitted")
+        x = np.atleast_2d(np.asarray(features, dtype=float))
+        z = np.column_stack([(x - self.mean) / self.scale, np.ones(x.shape[0])])
+        logits = z @ self.weights
+        logits -= logits.max(axis=1, keepdims=True)
+        probabilities = np.exp(logits)
+        return probabilities / probabilities.sum(axis=1, keepdims=True)
+
+    def correct(self, detail: Dict, features) -> float:
+        return identity(detail) * FACTORS[int(self.predict_proba(features).argmax(axis=1)[0])]
+
+    def to_dict(self):
+        if self.weights is None:
+            raise ValueError("classifier has not been fitted")
+        return {"weights": self.weights.tolist(), "mean": self.mean.tolist(), "scale": self.scale.tolist(),
+                "factors": list(FACTORS)}
+
+    @classmethod
+    def from_dict(cls, row):
+        if tuple(row["factors"]) != FACTORS:
+            raise ValueError("model factor taxonomy does not match this version")
+        return cls(np.asarray(row["weights"]), np.asarray(row["mean"]), np.asarray(row["scale"]))
+
+
+def select_parameter(details: Sequence[Dict], references: Sequence[float], method: str, candidates: Iterable[float]):
+    """Select one scalar baseline parameter exclusively from validation rows."""
+    from deeprhythm.bench.tempo import tempo_accuracy
+
+    methods = {"global_threshold": global_threshold, "top_k_ratio": top_k_ratio,
+               "clip_disagreement": clip_disagreement}
+    if method not in methods:
+        raise ValueError(f"unknown selectable method: {method}")
+    if len(details) != len(references) or not details:
+        raise ValueError("details and references must be non-empty and aligned")
+    scored = []
+    for value in candidates:
+        predictions = [methods[method](detail, value) for detail in details]
+        metrics = tempo_accuracy(predictions, references, tolerance=0.04)
+        scored.append((metrics["acc1"], metrics["acc2"], -float(value), float(value)))
+    return max(scored)[-1]
\ No newline at end of file
diff --git a/src/deeprhythm/model/__init__.py b/src/deeprhythm/model/__init__.py
index e69de29..316d33d 100644
--- a/src/deeprhythm/model/__init__.py
+++ b/src/deeprhythm/model/__init__.py
@@ -0,0 +1,3 @@
+from deeprhythm.model.metrical_predictor import MetricalDeepRhythmPredictor
+
+__all__ = ["MetricalDeepRhythmPredictor"]
\ No newline at end of file
diff --git a/src/deeprhythm/model/frame_cnn.py b/src/deeprhythm/model/frame_cnn.py
index 025a403..64099ab 100644
--- a/src/deeprhythm/model/frame_cnn.py
+++ b/src/deeprhythm/model/frame_cnn.py
@@ -24,6 +24,10 @@ def __init__(self, num_classes=256):
         self._initialize_weights()
 
     def forward(self, x):
+        return self.fc2(self.encode(x))
+
+    def encode(self, x):
+        """Return the shared 256-dimensional rhythmic representation."""
         x = F.relu(self.bn1(self.conv1(x)))
         x = F.relu(self.bn2(self.conv2(x)))
         x = F.relu(self.bn3(self.conv3(x)))
@@ -31,7 +35,6 @@ def forward(self, x):
         x = F.relu(self.bn5(self.conv5(x)))
         x = x.reshape(x.size(0), -1)
         x = self.dropout(self.elu(self.fc1(x)))
-        x = self.fc2(x)
         return x
 
     def _initialize_weights(self):
diff --git a/src/deeprhythm/model/hierarchical.py b/src/deeprhythm/model/hierarchical.py
new file mode 100644
index 0000000..5de6542
--- /dev/null
+++ b/src/deeprhythm/model/hierarchical.py
@@ -0,0 +1,79 @@
+"""Hierarchical tempo model separating rhythmic rate from metrical level."""
+
+import torch
+from torch import nn
+
+from deeprhythm.model.frame_cnn import DeepRhythmModel
+
+BASE_MIN = 60.0
+BASE_WIDTH = 0.5
+BASE_CLASSES = 120
+LEVEL_FACTORS = (0.5, 1.0, 2.0, 4.0)
+
+
+class ResidualLevelHead(nn.Module):
+    """Nonlinear metrical correction initialized to preserve existing logits."""
+
+    def __init__(self, embedding_dim=256, hidden_dim=128, dropout=0.1):
+        super().__init__()
+        self.network = nn.Sequential(
+            nn.LayerNorm(embedding_dim),
+            nn.Linear(embedding_dim, hidden_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, len(LEVEL_FACTORS)),
+        )
+        nn.init.zeros_(self.network[-1].weight)
+        nn.init.zeros_(self.network[-1].bias)
+
+    def forward(self, embedding):
+        return self.network(embedding)
+
+
+def tempo_to_hierarchy(tempos: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """Encode BPM as a half-BPM base rate in [60, 120) and an octave level."""
+    tempos = torch.as_tensor(tempos, dtype=torch.float32)
+    levels = torch.zeros_like(tempos, dtype=torch.long)
+    bases = tempos.clone()
+    below = bases < BASE_MIN
+    bases[below] *= 2.0
+    levels[below] = 0
+    normal = (~below) & (bases < 120.0)
+    levels[normal] = 1
+    double = (bases >= 120.0) & (bases < 240.0)
+    bases[double] /= 2.0
+    levels[double] = 2
+    quadruple = bases >= 240.0
+    bases[quadruple] /= 4.0
+    levels[quadruple] = 3
+    base_classes = ((bases - BASE_MIN) / BASE_WIDTH).round().long().clamp(0, BASE_CLASSES - 1)
+    return base_classes, levels
+
+
+def hierarchy_to_tempo(base_classes: torch.Tensor, levels: torch.Tensor) -> torch.Tensor:
+    """Compose base-rate and metrical-level classes into BPM."""
+    bases = BASE_MIN + torch.as_tensor(base_classes, dtype=torch.float32) * BASE_WIDTH
+    factors = torch.tensor(LEVEL_FACTORS, device=bases.device)
+    return bases * factors[torch.as_tensor(levels, dtype=torch.long, device=bases.device)]
+
+
+class HierarchicalDeepRhythmModel(nn.Module):
+    """One HCQM backbone with independent base-rate and metrical-level heads."""
+
+    def __init__(self, backbone=None, *, residual_level=False):
+        super().__init__()
+        self.backbone = backbone or DeepRhythmModel()
+        self.base_head = nn.Linear(256, BASE_CLASSES)
+        self.level_head = nn.Linear(256, len(LEVEL_FACTORS))
+        self.level_residual = ResidualLevelHead() if residual_level else None
+
+    def forward(self, inputs):
+        embedding = self.backbone.encode(inputs)
+        level_logits = self.level_head(embedding)
+        if self.level_residual is not None:
+            level_logits = level_logits + self.level_residual(embedding)
+        return self.base_head(embedding), level_logits
+
+    def load_backbone(self, state_dict):
+        """Initialize the shared representation from a conventional DeepRhythm checkpoint."""
+        self.backbone.load_state_dict(state_dict)
\ No newline at end of file
diff --git a/src/deeprhythm/model/metrical.py b/src/deeprhythm/model/metrical.py
new file mode 100644
index 0000000..f4134f0
--- /dev/null
+++ b/src/deeprhythm/model/metrical.py
@@ -0,0 +1,106 @@
+"""Shared-STFT temporal evidence for hierarchical tempo inference."""
+
+import math
+
+import torch
+from pytorch_tcn import TCN
+from torch import nn
+from torch.nn import functional as F
+
+from deeprhythm.model.hierarchical import LEVEL_FACTORS
+
+
+class TemporalFeatureExtraction(nn.Module):
+    def __init__(self, num_bands=81, num_channels=36):
+        super().__init__()
+        self.conv1 = nn.Conv1d(num_bands, num_channels, 3, padding=1)
+        self.pool1 = nn.MaxPool1d(3, stride=1, padding=1)
+        self.elu1 = nn.ELU()
+        self.dropout1 = nn.Dropout(0.1)
+        self.conv2 = nn.Conv1d(num_channels, num_channels, 3, padding=1)
+        self.pool2 = nn.MaxPool1d(3, stride=1, padding=1)
+        self.elu2 = nn.ELU()
+        self.dropout2 = nn.Dropout(0.1)
+        self.conv3 = nn.Conv1d(num_channels, num_channels, 3, padding=1)
+        self.pool3 = nn.MaxPool1d(3, stride=1, padding=1)
+        self.elu3 = nn.ELU()
+        self.dropout3 = nn.Dropout(0.1)
+
+    def forward(self, inputs):
+        inputs = self.dropout1(self.elu1(self.pool1(self.conv1(inputs))))
+        inputs = self.dropout2(self.elu2(self.pool2(self.conv2(inputs))))
+        return self.dropout3(self.elu3(self.pool3(self.conv3(inputs))))
+
+
+class TemporalBeatEncoder(nn.Module):
+    def __init__(self, channels=36):
+        super().__init__()
+        self.feature_extraction = TemporalFeatureExtraction(num_channels=channels)
+        self.tcn_beat = TCN(
+            num_inputs=channels,
+            num_channels=[channels] * 16,
+            kernel_size=5,
+            dropout=0.1,
+            causal=False,
+            use_skip_connections=True,
+            kernel_initializer="kaiming_normal",
+            use_norm="layer_norm",
+            activation="relu",
+            dilation_reset=8,
+        )
+
+    def forward(self, spectrum):
+        return self.tcn_beat(self.feature_extraction(spectrum)).transpose(1, 2)
+
+
+class CircularBeatDecoder(nn.Module):
+    def __init__(self, channels=36):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(channels, 72), nn.ELU(), nn.Dropout(0.1), nn.Linear(72, 2))
+
+    def forward(self, temporal):
+        return self.net(temporal)
+
+
+class MetricalFusion(nn.Module):
+    def __init__(self, input_features=162):
+        super().__init__()
+        self.register_buffer("mean", torch.zeros(input_features))
+        self.register_buffer("scale", torch.ones(input_features))
+        self.net = nn.Sequential(
+            nn.Linear(input_features, 96),
+            nn.GELU(),
+            nn.Dropout(0.15),
+            nn.Linear(96, 48),
+            nn.GELU(),
+            nn.Linear(48, len(LEVEL_FACTORS)),
+        )
+
+    def forward(self, features):
+        return self.net((features - self.mean) / self.scale)
+
+
+def temporal_metrical_features(temporal, raw_phase, base_probability, level_probability, frame_rate=22050 / 512):
+    """Shape the frozen temporal representation exactly as used during training."""
+    if temporal.shape[0] != 1:
+        raise ValueError("metrical feature shaping expects one track at a time")
+    temporal = temporal[0]
+    raw_phase = raw_phase[0]
+    hidden = torch.cat([temporal.mean(0), temporal.std(0), temporal.amax(0), temporal.amin(0)])
+    # The frozen fusion checkpoint was trained with a global p=-1 norm. This
+    # intentionally preserves that feature contract; changing it requires
+    # retraining and versioning the fusion weights.
+    confidence = raw_phase.norm(p=-1)
+    unit = F.normalize(raw_phase, dim=-1)
+    phase = torch.complex(unit[:, 0], unit[:, 1])
+    times = torch.arange(len(phase), device=phase.device) / frame_rate
+    base_bpm = 60 + 0.5 * int(base_probability.argmax())
+    values = []
+    for factor in LEVEL_FACTORS:
+        omega = 2 * math.pi * base_bpm * factor / 60
+        demodulated = phase * torch.exp(torch.complex(torch.zeros_like(times), -omega * times))
+        values.extend([demodulated.mean().abs(), demodulated.square().mean().abs(), confidence.mean()])
+    entropy = -(level_probability * level_probability.clamp_min(1e-8).log()).sum()
+    return torch.cat(
+        [hidden, torch.stack(values), level_probability, level_probability.max()[None], entropy[None]]
+    )
\ No newline at end of file
diff --git a/src/deeprhythm/model/metrical_predictor.py b/src/deeprhythm/model/metrical_predictor.py
new file mode 100644
index 0000000..878dd48
--- /dev/null
+++ b/src/deeprhythm/model/metrical_predictor.py
@@ -0,0 +1,104 @@
+"""Opt-in v0.8 predictor with shared HCQM and temporal beat evidence."""
+
+from pathlib import Path
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+from deeprhythm.audio_proc.hcqm import compute_hcqm_from_stft, make_kernels
+from deeprhythm.audio_proc.log_spectrum import compute_log_spectrum_from_clips, make_log_spectrum_filter
+from deeprhythm.model.hierarchical import HierarchicalDeepRhythmModel, hierarchy_to_tempo
+from deeprhythm.model.metrical import (
+    CircularBeatDecoder,
+    MetricalFusion,
+    TemporalBeatEncoder,
+    temporal_metrical_features,
+)
+from deeprhythm.utils import get_device, load_audio, split_audio
+
+WEIGHT_DIR = Path(__file__).resolve().parent.parent / "weights"
+
+
+def _trim_edge_clips(clips):
+    """Match the edge-trimmed track aggregation used to train v0.8."""
+    return clips[1:-1] if len(clips) > 5 else clips
+
+
+def _fusion_cache_precision(tensor):
+    """Reproduce the float16 feature cache on which the frozen fusion was fit."""
+    return tensor.to(torch.float16).to(torch.float32)
+
+
+class MetricalDeepRhythmPredictor:
+    """Predict tempo with the frozen, reproducible shared-STFT v0.8 model."""
+
+    def __init__(self, device=None):
+        self.device = torch.device(device or get_device())
+        self.hierarchical = HierarchicalDeepRhythmModel(residual_level=True).to(self.device)
+        hierarchy = torch.load(WEIGHT_DIR / "hierarchical-v0.8.pt", map_location=self.device, weights_only=False)
+        self.hierarchical.load_state_dict(hierarchy["model_state_dict"])
+        self.residual_scale = hierarchy["residual_scale"]
+
+        self.temporal = TemporalBeatEncoder().to(self.device)
+        self.beat_decoder = CircularBeatDecoder().to(self.device)
+        temporal = torch.load(WEIGHT_DIR / "temporal-beat-v0.8.pt", map_location=self.device, weights_only=False)
+        temporal_state = {
+            key: value
+            for key, value in temporal["model"].items()
+            if key.startswith(("feature_extraction.", "tcn_beat."))
+        }
+        self.temporal.load_state_dict(temporal_state)
+        self.beat_decoder.load_state_dict(temporal["beat"])
+
+        fusion = torch.load(WEIGHT_DIR / "shared-fusion-v0.8.pt", map_location=self.device, weights_only=False)
+        self.fusion = MetricalFusion(fusion["state_dict"]["mean"].numel()).to(self.device)
+        self.fusion.load_state_dict(fusion["state_dict"])
+        self.alpha = fusion["alpha"]
+        self.specs = make_kernels(device=self.device)
+        self.temporal_filter = make_log_spectrum_filter(device=self.device)
+        for model in (self.hierarchical, self.temporal, self.beat_decoder, self.fusion):
+            model.eval()
+
+    def predict(self, filename, include_details=False):
+        audio = load_audio(filename, sr=22050)
+        return self.predict_from_audio(audio, include_details=include_details)
+
+    def predict_from_audio(self, audio, include_details=False):
+        clips = split_audio(audio, 22050)
+        available_clips = len(clips)
+        if available_clips < 4:
+            padded = np.pad(np.asarray(audio)[: 32 * 22050], (0, max(0, 32 * 22050 - len(audio))))
+            phase_clips = torch.from_numpy(padded.astype(np.float32)).view(4, 8 * 22050)
+            clips = torch.cat([clips, phase_clips[available_clips:]], dim=0)
+        clips = clips.to(self.device)
+        with torch.no_grad():
+            magnitude = self.specs[0](clips)
+            track_magnitude = _trim_edge_clips(magnitude[:available_clips])
+            hcqm = compute_hcqm_from_stft(track_magnitude, self.specs[1], self.specs[2])
+            embedding = self.hierarchical.backbone.encode(hcqm.permute(0, 3, 1, 2).to(self.device))
+            base_probability = F.softmax(self.hierarchical.base_head(embedding), dim=1).mean(0)
+            level_logits = self.hierarchical.level_head(embedding)
+            level_logits += self.residual_scale * self.hierarchical.level_residual(embedding)
+            level_probability = F.softmax(level_logits, dim=1).mean(0)
+            spectrum = compute_log_spectrum_from_clips(magnitude[:4], filter_matrix=self.temporal_filter)
+            base_probability = _fusion_cache_precision(base_probability)
+            level_probability = _fusion_cache_precision(level_probability)
+            spectrum = _fusion_cache_precision(spectrum)
+            temporal = self.temporal(spectrum[None])
+            phase = self.beat_decoder(temporal)
+            features = temporal_metrical_features(temporal, phase, base_probability, level_probability)
+            fused_logits = level_probability.clamp_min(1e-8).log() + self.alpha * self.fusion(features[None])[0]
+            base_class = base_probability.argmax()
+            level = fused_logits.argmax()
+            bpm = float(hierarchy_to_tempo(base_class.cpu(), level.cpu()))
+        if not include_details:
+            return bpm
+        return {
+            "bpm": bpm,
+            "base_bpm": 60 + 0.5 * int(base_class),
+            "level_factor": (0.5, 1.0, 2.0, 4.0)[int(level)],
+            "num_clips": len(track_magnitude),
+            "base_probability": base_probability.cpu().tolist(),
+            "level_probability": level_probability.cpu().tolist(),
+        }
\ No newline at end of file
diff --git a/src/deeprhythm/model/predictor.py b/src/deeprhythm/model/predictor.py
index c616895..d2529ce 100644
--- a/src/deeprhythm/model/predictor.py
+++ b/src/deeprhythm/model/predictor.py
@@ -10,6 +10,7 @@
 from deeprhythm.batch_infer import get_audio_files
 from deeprhythm.batch_infer import main as batch_infer_main
 from deeprhythm.model.frame_cnn import DeepRhythmModel
+from deeprhythm.model.results import shape_prediction
 from deeprhythm.utils import class_to_bpm, get_device, get_weights, load_and_split_audio, split_audio
 
 
@@ -39,7 +40,9 @@ def _make_kernels(self) -> Tuple[Tensor, Tensor, Tensor]:
         """Initialize HCQM computation kernels."""
         return make_kernels(device=self.device)
 
-    def _process_clips(self, clips: Tensor, include_confidence: bool = False) -> Union[float, Tuple[float, float]]:
+    def _process_clips(
+        self, clips: Tensor, include_confidence: bool = False, include_details: bool = False, top_k: int = 5
+    ):
         """Process audio clips and return BPM prediction.
 
         Args:
@@ -54,13 +57,15 @@ def _process_clips(self, clips: Tensor, include_confidence: bool = False) -> Uni
         with torch.no_grad():
             outputs = self.model(input_batch.to(device=self.device))
             probabilities = torch.softmax(outputs, dim=1)
-            mean_probabilities = probabilities.mean(dim=0)
-            confidence_score, predicted_class = torch.max(mean_probabilities, 0)
-            predicted_bpm = class_to_bpm(predicted_class.item())
+            details = shape_prediction(probabilities, top_k=top_k)
             
-        return (predicted_bpm, confidence_score.item()) if include_confidence else predicted_bpm
+        if include_details:
+            return details
+        return (details["bpm"], details["confidence"]) if include_confidence else details["bpm"]
 
-    def predict(self, filename: str, include_confidence: bool = False) -> Union[float, Tuple[float, float]]:
+    def predict(
+        self, filename: str, include_confidence: bool = False, include_details: bool = False, top_k: int = 5
+    ):
         """Predict BPM from an audio file.
 
         Args:
@@ -71,11 +76,12 @@ def predict(self, filename: str, include_confidence: bool = False) -> Union[floa
             Predicted BPM or tuple of (BPM, confidence)
         """
         clips = load_and_split_audio(filename, sr=22050)
-        return self._process_clips(clips, include_confidence)
+        return self._process_clips(clips, include_confidence, include_details, top_k)
     
     def predict_from_audio(
-        self, audio: List[float], sr: int, include_confidence: bool = False
-    ) -> Union[float, Tuple[float, float]]:
+        self, audio: List[float], sr: int, include_confidence: bool = False,
+        include_details: bool = False, top_k: int = 5
+    ):
         """Predict BPM from audio tensor.
 
         Args:
@@ -87,7 +93,7 @@ def predict_from_audio(
             Predicted BPM or tuple of (BPM, confidence)
         """
         clips = split_audio(audio, sr)
-        return self._process_clips(clips, include_confidence)
+        return self._process_clips(clips, include_confidence, include_details, top_k)
 
     def predict_per_frame(
         self, filename: str, include_confidence: bool = False
diff --git a/src/deeprhythm/model/results.py b/src/deeprhythm/model/results.py
new file mode 100644
index 0000000..40842f8
--- /dev/null
+++ b/src/deeprhythm/model/results.py
@@ -0,0 +1,75 @@
+"""Canonical shaping of classifier outputs for inference APIs and benchmarks."""
+
+from typing import Dict
+
+import torch
+from torch import Tensor
+
+from deeprhythm.utils import class_to_bpm
+
+METRICAL_CANDIDATES = (
+    ("same", 1.0),
+    ("double", 2.0),
+    ("half", 0.5),
+    ("triple", 3.0),
+    ("third", 1.0 / 3.0),
+)
+
+
+def _top_k(probabilities: Tensor, top_k: int):
+    count = min(top_k, probabilities.shape[-1])
+    values, classes = torch.topk(probabilities, count, dim=-1)
+    if probabilities.ndim == 1:
+        values = values.unsqueeze(0)
+        classes = classes.unsqueeze(0)
+    return [
+        [
+            {"class": int(class_index), "bpm": class_to_bpm(int(class_index)), "probability": float(probability)}
+            for class_index, probability in zip(row_classes.tolist(), row_values.tolist())
+        ]
+        for row_classes, row_values in zip(classes.cpu(), values.cpu())
+    ]
+
+
+def shape_prediction(probabilities: Tensor, top_k: int = 5, tolerance: float = 0.04) -> Dict:
+    """Aggregate per-clip probabilities into one auditable prediction."""
+    if probabilities.ndim != 2 or probabilities.shape[0] == 0:
+        raise ValueError("probabilities must contain at least one clip and have shape (clips, classes)")
+    if top_k <= 0:
+        raise ValueError("top_k must be positive")
+
+    probabilities = probabilities.detach().cpu()
+    mean_probabilities = probabilities.mean(dim=0)
+    confidence, predicted_class = mean_probabilities.max(dim=0)
+    predicted_class_value = int(predicted_class)
+    predicted_bpm = class_to_bpm(predicted_class_value)
+    clip_classes = probabilities.argmax(dim=1)
+    clip_top = _top_k(probabilities, top_k)
+    aggregate_top = _top_k(mean_probabilities, top_k)[0]
+
+    class_bpms = torch.tensor(
+        [class_to_bpm(class_index) for class_index in range(mean_probabilities.shape[0])],
+        dtype=mean_probabilities.dtype,
+    )
+    candidate_masses = {}
+    for name, factor in METRICAL_CANDIDATES:
+        candidate_bpm = predicted_bpm * factor
+        mask = torch.abs(class_bpms / candidate_bpm - 1.0) <= tolerance
+        candidate_masses[name] = {
+            "factor": factor,
+            "bpm": candidate_bpm,
+            "probability_mass": float(mean_probabilities[mask].sum()),
+        }
+
+    clips = []
+    for class_index, top in zip(clip_classes.tolist(), clip_top):
+        clips.append({"class": class_index, "bpm": class_to_bpm(class_index), "top_k": top})
+    return {
+        "bpm": predicted_bpm,
+        "confidence": float(confidence),
+        "predicted_class": predicted_class_value,
+        "num_clips": probabilities.shape[0],
+        "clips": clips,
+        "aggregate_top_k": aggregate_top,
+        "candidate_masses": candidate_masses,
+    }
\ No newline at end of file
diff --git a/src/deeprhythm/partitions.py b/src/deeprhythm/partitions.py
new file mode 100644
index 0000000..cc7ba26
--- /dev/null
+++ b/src/deeprhythm/partitions.py
@@ -0,0 +1,292 @@
+"""Build and validate the canonical research dataset partitions."""
+
+import argparse
+import hashlib
+import json
+import re
+from collections import Counter, defaultdict
+from pathlib import Path
+
+FOLDS = ("train", "val", "test")
+GTZAN_HISTORICAL_EXCLUSIONS = {"jazz/jazz.00054.wav", "reggae/reggae.00086.wav"}
+BALLROOM_REPLICAS = (
+    ("Quickstep/Albums-AnaBelen_Veneo-11.wav", "Quickstep/Albums-Chrisanne2-12.wav"),
+    ("ChaChaCha/Albums-Fire-08.wav", "Samba/Albums-Fire-09.wav"),
+    ("ChaChaCha/Albums-Latin_Jam2-05.wav", "ChaChaCha/Albums-Latin_Jam2-13.wav"),
+    ("Waltz/Albums-Secret_Garden-01.wav", "Waltz/Media-104705.wav"),
+    ("Rumba-International/Albums-AnaBelen_Veneo-03.wav", "Rumba-International/Albums-AnaBelen_Veneo-15.wav"),
+    ("Waltz/Albums-Ballroom_Magic-03.wav", "Waltz/Albums-Ballroom_Magic-18.wav"),
+    ("ChaChaCha/Albums-Latin_Jam-04.wav", "ChaChaCha/Albums-Latin_Jam-13.wav"),
+    ("Rumba-International/Albums-Latin_Jam-08.wav", "Rumba-International/Albums-Latin_Jam-14.wav"),
+    ("Samba/Albums-Latin_Jam-06.wav", "Samba/Albums-Latin_Jam-15.wav"),
+    ("Samba/Albums-Latin_Jam2-02.wav", "Samba/Albums-Latin_Jam2-14.wav"),
+    ("Rumba-International/Albums-Latin_Jam2-07.wav", "Rumba-International/Albums-Latin_Jam2-15.wav"),
+    ("ChaChaCha/Albums-Latin_Jam3-02.wav", "ChaChaCha/Media-103414.wav"),
+    ("ChaChaCha/Media-103402.wav", "ChaChaCha/Media-103415.wav"),
+)
+
+
+def digest(path):
+    value = hashlib.md5()  # nosec: manifest identity, not a security primitive
+    with path.open("rb") as stream:
+        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
+            value.update(chunk)
+    return value.hexdigest()
+
+
+def read_tempo(path):
+    value = float(path.read_text().strip())
+    if value <= 0:
+        raise ValueError(f"non-positive tempo in {path}")
+    return value
+
+
+def write_rows(path, rows):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    serialized = (json.dumps(row, sort_keys=True) + "\n" for row in sorted(rows, key=lambda r: r["audio_path"]))
+    path.write_text("".join(serialized))
+
+
+def write_checksums(root):
+    lines = []
+    for path in sorted(root.glob("**/*.jsonl")):
+        checksum = hashlib.sha256(path.read_bytes()).hexdigest()
+        lines.append(f"{checksum}  {path.relative_to(root)}\n")
+    (root / "SHA256SUMS").write_text("".join(lines))
+
+
+def validate_checksums(root):
+    for line in (root / "SHA256SUMS").read_text().splitlines():
+        expected, relative = line.split("  ", 1)
+        actual = hashlib.sha256((root / relative).read_bytes()).hexdigest()
+        if actual != expected:
+            raise ValueError(f"manifest checksum mismatch: {relative}")
+
+
+def validate_partitions(rows):
+    """Reject paths, group keys, or duplicate fingerprints crossing partitions."""
+    required = {"dataset", "audio_path", "md5", "tempo", "group_key", "fold", "fingerprint", "provenance"}
+    seen_paths = set()
+    ownership = {"group_key": {}, "fingerprint": {}}
+    for row in rows:
+        missing = required - row.keys()
+        if missing:
+            raise ValueError(f"manifest row missing {sorted(missing)}")
+        if row["fold"] not in FOLDS or float(row["tempo"]) <= 0:
+            raise ValueError(f"invalid fold or tempo in {row['audio_path']}")
+        path_key = (row["dataset"], row["audio_path"])
+        if path_key in seen_paths:
+            raise ValueError(f"duplicate audio path: {path_key}")
+        seen_paths.add(path_key)
+        for field, by_value in ownership.items():
+            key = (row["dataset"], row[field]) if field == "group_key" else row[field]
+            previous = by_value.setdefault(key, row["fold"])
+            if previous != row["fold"]:
+                raise ValueError(f"{field} {row[field]!r} spans {previous} and {row['fold']}")
+    return True
+
+
+def _gtzan_index(path):
+    result = {}
+    for line in path.read_text(errors="replace").splitlines():
+        if line.startswith("#") or " ::: " not in line:
+            continue
+        parts = line.split(" ::: ", 2)
+        filename, artist = parts[:2]
+        artist = artist.strip()
+        result[filename] = artist if artist.strip(": ") else "?"
+    return result
+
+
+def build_gtzan(root, sources):
+    index = _gtzan_index(sources / "index.txt")
+    assignment = {}
+    source_folds = (("train", "train_filtered.txt"), ("val", "valid_filtered.txt"), ("test", "test_filtered.txt"))
+    for fold, filename in source_folds:
+        for line in (sources / filename).read_text().splitlines():
+            assignment[line.strip()] = fold
+    rows = []
+    for relative, fold in sorted(assignment.items()):
+        if relative in GTZAN_HISTORICAL_EXCLUSIONS:
+            continue
+        genre, filename = relative.split("/")
+        audio = root / "gtzan_genre" / "genres" / relative
+        track_number = Path(filename).stem[-5:]
+        annotation = root / "gtzan_tempo_beat-main" / "tempo" / f"gtzan_{genre}_{track_number}.bpm"
+        artist = index.get(filename)
+        if not artist or artist == "?":
+            group = f"track:{filename}"
+        else:
+            group = f"artist:{artist.casefold()}"
+        md5 = digest(audio)
+        rows.append({"dataset": "gtzan", "audio_path": str(audio.relative_to(root)), "md5": md5,
+                     "tempo": read_tempo(annotation), "group_key": group, "fold": fold,
+                     "source_fold": f"kereliuk-{fold}", "fingerprint": f"md5:{md5}",
+                     "provenance": "Sturm GTZAN fedc781; gtzan_tempo_beat"})
+    priority = {"train": 0, "val": 1, "test": 2}
+    artist_fold = {}
+    for row in rows:
+        if row["group_key"].startswith("artist:"):
+            previous = artist_fold.get(row["group_key"], "train")
+            artist_fold[row["group_key"]] = max((previous, row["fold"]), key=priority.get)
+    for row in rows:
+        if row["group_key"] in artist_fold:
+            row["fold"] = artist_fold[row["group_key"]]
+    return rows
+
+
+def build_giantsteps(root):
+    repository = next(root.glob("giantsteps-tempo-dataset-*"))
+    official = {}
+    for number in range(1, 11):
+        target = "train" if number <= 7 else "val" if number == 8 else "test"
+        for filename in (repository / "splits" / f"fold{number:02d}.txt").read_text().splitlines():
+            official[filename] = (target, number)
+    rows = []
+    for filename, (fold, number) in sorted(official.items()):
+        annotation = repository / "annotations_v2" / "tempo" / filename.replace(".mp3", ".bpm")
+        audio = root / "audio_canonical" / filename
+        if not annotation.exists() or not audio.exists():
+            continue
+        tempo = float(annotation.read_text().strip())
+        if tempo <= 0:
+            continue
+        expected = (repository / "md5" / filename.replace(".mp3", ".md5")).read_text().split()[0]
+        actual = digest(audio)
+        if actual != expected:
+            raise ValueError(f"GiantSteps checksum mismatch: {filename}")
+        track = filename.split(".", 1)[0]
+        rows.append({"dataset": "giantsteps", "audio_path": str(audio.relative_to(root)), "md5": actual,
+                     "tempo": tempo, "group_key": f"track:{track}", "fold": fold,
+                     "source_fold": f"official-fold{number:02d}", "fingerprint": f"md5:{actual}",
+                     "provenance": "GiantSteps tempo dataset 0b7d47b; v2 crowd annotation"})
+    return rows
+
+
+def _ballroom_group(stem):
+    album = re.fullmatch(r"Albums-(.+)-\d+", stem)
+    if album:
+        return f"album:{album.group(1).casefold()}"
+    media = re.fullmatch(r"Media-(\d+)", stem)
+    if media:
+        return f"media-prefix:{media.group(1)[:-2]}"
+    return f"track:{stem.casefold()}"
+
+
+def _assign_ballroom(groups):
+    targets = {"train": 0.7, "val": 0.1, "test": 0.2}
+    totals = Counter(key for tracks in groups.values() for key in tracks)
+    total_tracks = sum(totals.values())
+    counts = {fold: Counter() for fold in FOLDS}
+    result = {}
+    for group, tracks in sorted(groups.items(), key=lambda item: (-len(item[1]), item[0])):
+        contribution = Counter(tracks)
+        def cost(candidate):
+            score = 0.0
+            for fold in FOLDS:
+                for key, total in totals.items():
+                    value = counts[fold][key] + (contribution[key] if fold == candidate else 0)
+                    score += ((value - targets[fold] * total) / max(total, 1)) ** 2
+                size = sum(counts[fold].values()) + (len(tracks) if fold == candidate else 0)
+                score += 25 * ((size - targets[fold] * total_tracks) / total_tracks) ** 2
+            return score
+        fold = min(FOLDS, key=lambda name: (cost(name), name))
+        result[group] = fold
+        counts[fold].update(contribution)
+    return result
+
+
+def build_ballroom(root):
+    excluded = {duplicate.casefold() for _keeper, duplicate in BALLROOM_REPLICAS}
+    candidates = []
+    groups = defaultdict(list)
+    for audio in sorted((root / "audio").glob("*/*.wav")):
+        relative = str(audio.relative_to(root / "audio"))
+        if relative.casefold() in excluded:
+            continue
+        annotation = next((root / "annotations" / "tempo").glob(f"**/{audio.stem}.bpm"))
+        tempo = read_tempo(annotation)
+        group = _ballroom_group(audio.stem)
+        stratum = (audio.parent.name.casefold(), int(tempo // 20))
+        groups[group].append(stratum)
+        candidates.append((audio, relative, tempo, group))
+    assignment = _assign_ballroom(groups)
+    rows = []
+    for audio, relative, tempo, group in candidates:
+        md5 = digest(audio)
+        rows.append({"dataset": "ballroom", "audio_path": str(audio.relative_to(root)), "md5": md5,
+                     "tempo": tempo, "group_key": group, "fold": assignment[group], "source_fold": "deeprhythm-v0.8",
+                     "fingerprint": f"md5:{md5}", "genre": audio.parent.name,
+                     "provenance": "Ballroom B_1.0; CPJKU tempo and replica annotations"})
+    return rows
+
+
+def historical_rows(dataset, root):
+    rows = []
+    if dataset == "ballroom":
+        files = sorted((root / "audio").glob("*/*.wav"))
+
+        def tempo(path):
+            return read_tempo(next((root / "annotations" / "tempo").glob(f"**/{path.stem}.bpm")))
+    elif dataset == "gtzan":
+        files = sorted((root / "gtzan_genre" / "genres").glob("*/*.wav"))
+        audio_root = root / "gtzan_genre" / "genres"
+        files = [p for p in files if str(p.relative_to(audio_root)) not in GTZAN_HISTORICAL_EXCLUSIONS]
+
+        def tempo(path):
+            annotation = root / "gtzan_tempo_beat-main" / "tempo" / f"gtzan_{path.parent.name}_{path.stem[-5:]}.bpm"
+            return read_tempo(annotation)
+    else:
+        repository = next(root.glob("giantsteps-tempo-dataset-*"))
+        files = sorted((root / "audio_canonical").glob("*.mp3"))
+        annotation_root = repository / "annotations_v2" / "tempo"
+        files = [
+            p for p in files
+            if (annotation_root / p.name.replace(".mp3", ".bpm")).exists()
+            and float((annotation_root / p.name.replace(".mp3", ".bpm")).read_text()) > 0
+        ]
+
+        def tempo(path):
+            return read_tempo(annotation_root / path.name.replace(".mp3", ".bpm"))
+    for path in files:
+        rows.append({
+            "audio_path": str(path.relative_to(root)),
+            "tempo": tempo(path),
+            "historical": "deeprhythm-0.7-full-set",
+        })
+    return rows
+
+
+def main(argv=None):
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--datasets-root", type=Path, default=Path("~/mir_datasets").expanduser())
+    parser.add_argument("--output", type=Path, default=Path("data/splits"))
+    parser.add_argument("--validate-only", action="store_true")
+    args = parser.parse_args(argv)
+    if args.validate_only:
+        rows = []
+        for pattern in ("*/train.jsonl", "*/val.jsonl", "*/test.jsonl"):
+            for path in args.output.glob(pattern):
+                rows.extend(json.loads(line) for line in path.read_text().splitlines())
+        validate_partitions(rows)
+        validate_checksums(args.output)
+        return
+    sources = args.output / "sources" / "gtzan"
+    built = {
+        "giantsteps": build_giantsteps(args.datasets_root / "giantsteps_tempo"),
+        "gtzan": build_gtzan(args.datasets_root / "gtzan_genre", sources),
+        "ballroom": build_ballroom(args.datasets_root / "ballroom" / "B_1.0"),
+    }
+    for dataset, rows in built.items():
+        validate_partitions(rows)
+        for fold in FOLDS:
+            write_rows(args.output / dataset / f"{fold}.jsonl", [row for row in rows if row["fold"] == fold])
+    archive = args.output / "archive-v0.7"
+    write_rows(archive / "giantsteps.jsonl", historical_rows("giantsteps", args.datasets_root / "giantsteps_tempo"))
+    write_rows(archive / "gtzan.jsonl", historical_rows("gtzan", args.datasets_root / "gtzan_genre"))
+    write_rows(archive / "ballroom.jsonl", historical_rows("ballroom", args.datasets_root / "ballroom" / "B_1.0"))
+    write_checksums(args.output)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/src/deeprhythm/train/__init__.py b/src/deeprhythm/train/__init__.py
new file mode 100644
index 0000000..067e5d3
--- /dev/null
+++ b/src/deeprhythm/train/__init__.py
@@ -0,0 +1,15 @@
+"""Manifest-driven data preparation and fine-tuning utilities."""
+
+from deeprhythm.train.cache import ClipDataset, HcqmCache, build_hcqm_cache
+from deeprhythm.train.sampling import TempoBalancedSampler, stretch_audio_and_tempo
+from deeprhythm.train.trainer import TrainingConfig, fit
+
+__all__ = [
+    "ClipDataset",
+    "HcqmCache",
+    "TempoBalancedSampler",
+    "TrainingConfig",
+    "build_hcqm_cache",
+    "fit",
+    "stretch_audio_and_tempo",
+]
\ No newline at end of file
diff --git a/src/deeprhythm/train/cache.py b/src/deeprhythm/train/cache.py
new file mode 100644
index 0000000..aed8234
--- /dev/null
+++ b/src/deeprhythm/train/cache.py
@@ -0,0 +1,192 @@
+"""Disk-backed HCQM cache keyed by immutable manifest content."""
+
+import hashlib
+import json
+import os
+from pathlib import Path
+from typing import Callable, Iterable, Mapping, Optional, Sequence
+
+import librosa
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+from deeprhythm.audio_proc.hcqm import compute_hcqm, make_kernels
+from deeprhythm.utils import bpm_to_class, load_and_split_audio, split_audio
+
+CACHE_VERSION = 1
+MANIFEST_ID_FIELDS = ("audio_path", "filename", "md5", "checksum", "tempo", "stretch_rate")
+
+
+def _canonical_row(row: Mapping) -> dict:
+    value = {key: row[key] for key in MANIFEST_ID_FIELDS if key in row}
+    fold = row.get("fold", row.get("split", "train"))
+    if "fold" in row and "split" in row and row["fold"] != row["split"]:
+        raise ValueError("manifest fold and split disagree")
+    if fold not in {"train", "val", "test"}:
+        raise ValueError(f"unknown manifest fold: {fold}")
+    value["fold"] = fold
+    if "audio_path" not in value and "filename" not in value:
+        raise ValueError("manifest row requires audio_path or filename")
+    if "tempo" not in value or float(value["tempo"]) <= 0:
+        raise ValueError("manifest row requires a positive tempo")
+    return value
+
+
+def manifest_key(row: Mapping, *, cache_version: int = CACHE_VERSION) -> str:
+    """Return a stable cache key; changing relevant manifest data invalidates it."""
+    payload = {"cache_version": cache_version, "row": _canonical_row(row)}
+    encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode()
+    return hashlib.sha256(encoded).hexdigest()
+
+
+def manifest_hash(rows: Iterable[Mapping]) -> str:
+    digest = hashlib.sha256()
+    for row in rows:
+        digest.update(manifest_key(row).encode())
+        digest.update(b"\n")
+    return digest.hexdigest()
+
+
+class HcqmCache:
+    """A directory of memory-mappable NumPy arrays plus a canonical JSON index."""
+
+    def __init__(self, root):
+        self.root = Path(root)
+        self.arrays = self.root / "arrays"
+        self.index_path = self.root / "index.json"
+
+    def path_for(self, key: str) -> Path:
+        return self.arrays / f"{key}.npy"
+
+    def load_index(self) -> dict:
+        if not self.index_path.exists():
+            return {"version": CACHE_VERSION, "entries": {}}
+        index = json.loads(self.index_path.read_text())
+        if index.get("version") != CACHE_VERSION:
+            raise ValueError("unsupported HCQM cache version")
+        return index
+
+    def store(self, key: str, clips: np.ndarray) -> None:
+        self.arrays.mkdir(parents=True, exist_ok=True)
+        destination = self.path_for(key)
+        temporary = destination.with_suffix(f".{os.getpid()}.tmp")
+        with temporary.open("wb") as handle:
+            np.save(handle, np.asarray(clips, dtype=np.float32), allow_pickle=False)
+        os.replace(temporary, destination)
+
+    def write_index(self, index: dict) -> None:
+        self.root.mkdir(parents=True, exist_ok=True)
+        temporary = self.index_path.with_suffix(f".{os.getpid()}.tmp")
+        temporary.write_text(json.dumps(index, indent=2, sort_keys=True) + "\n")
+        os.replace(temporary, self.index_path)
+
+
+def _default_extract(path: str, specs, stretch_rate: float = 1.0) -> np.ndarray:
+    if stretch_rate <= 0:
+        raise ValueError("stretch_rate must be positive")
+    if stretch_rate == 1.0:
+        audio = load_and_split_audio(path)
+    else:
+        waveform, _ = librosa.load(path, sr=22050, mono=True)
+        waveform = librosa.effects.time_stretch(waveform, rate=stretch_rate)
+        audio = split_audio(waveform, 22050)
+    audio = audio.to(device=specs[0].wsin.device)
+    with torch.no_grad():
+        hcqm = compute_hcqm(audio, *specs).permute(0, 3, 1, 2)
+    return hcqm.cpu().numpy()
+
+
+def build_hcqm_cache(
+    rows: Sequence[Mapping],
+    cache_dir,
+    *,
+    device: Optional[str] = None,
+    extractor: Optional[Callable[[str], np.ndarray]] = None,
+) -> dict:
+    """Materialize missing HCQMs and atomically update the manifest index.
+
+    ``extractor`` is an injection point for tests and custom preprocessing. Its
+    result must have shape ``(clips, 6, 240, 8)``.
+    """
+    cache = HcqmCache(cache_dir)
+    index = cache.load_index()
+    entries = {}
+    specs = None if extractor else make_kernels(device=device)
+    for source_row in rows:
+        row = _canonical_row(source_row)
+        key = manifest_key(row)
+        path = str(Path(row.get("audio_path", row.get("filename"))).expanduser())
+        destination = cache.path_for(key)
+        if not destination.exists():
+            clips = extractor(path) if extractor else _default_extract(path, specs, float(row.get("stretch_rate", 1.0)))
+            clips = np.asarray(clips)
+            if clips.ndim != 4 or tuple(clips.shape[1:]) != (6, 240, 8):
+                raise ValueError("HCQM extractor must return (clips, 6, 240, 8)")
+            if len(clips) == 0:
+                raise ValueError(f"no clips extracted from {path}")
+            cache.store(key, clips)
+        entries[key] = {
+            "path": str(destination.relative_to(cache.root)),
+            "tempo": float(row["tempo"]),
+            "fold": str(row["fold"]),
+            "source": path,
+            "dataset": str(source_row.get("dataset", "unknown")),
+            "stretch_rate": float(row.get("stretch_rate", 1.0)),
+            "clips": int(np.load(destination, mmap_mode="r").shape[0]),
+        }
+    index["entries"] = entries
+    index["manifest_hash"] = manifest_hash(rows)
+    cache.write_index(index)
+    return index
+
+
+class ClipDataset(Dataset):
+    """Lazy, memory-mapped HCQM clips from one declared manifest split."""
+
+    def __init__(
+        self,
+        cache_dir,
+        split="train",
+        *,
+        trim_edges=True,
+        return_tempo=False,
+        return_track=False,
+        return_domain=False,
+    ):
+        self.cache = HcqmCache(cache_dir)
+        self.return_tempo = return_tempo
+        self.return_track = return_track
+        self.return_domain = return_domain
+        self.entries = []
+        for key, entry in self.cache.load_index()["entries"].items():
+            if entry["fold"] != split:
+                continue
+            count = int(entry["clips"])
+            indices = range(1, count - 1) if trim_edges and count > 5 else range(count)
+            self.entries.extend((key, clip, float(entry["tempo"])) for clip in indices)
+
+    @property
+    def tempos(self):
+        return [entry[2] for entry in self.entries]
+
+    @property
+    def domains(self):
+        index = self.cache.load_index()["entries"]
+        return [index[key].get("dataset", "unknown") for key, _clip, _tempo in self.entries]
+
+    def __len__(self):
+        return len(self.entries)
+
+    def __getitem__(self, index):
+        key, clip_index, tempo = self.entries[index]
+        clips = np.load(self.cache.path_for(key), mmap_mode="r")
+        clip = torch.from_numpy(np.array(clips[clip_index], copy=True)).float()
+        domain = self.cache.load_index()["entries"][key].get("dataset", "unknown")
+        if self.return_tempo:
+            result = (clip, torch.tensor(tempo, dtype=torch.float32))
+            if self.return_track:
+                result = (*result, key)
+            return (*result, domain) if self.return_domain else result
+        result = (clip, torch.tensor(bpm_to_class(tempo), dtype=torch.long))
+        return (*result, domain) if self.return_domain else result
\ No newline at end of file
diff --git a/src/deeprhythm/train/sampling.py b/src/deeprhythm/train/sampling.py
new file mode 100644
index 0000000..76cc091
--- /dev/null
+++ b/src/deeprhythm/train/sampling.py
@@ -0,0 +1,43 @@
+"""Tempo balancing and audio-domain tempo augmentation."""
+
+from collections import Counter
+
+import librosa
+import numpy as np
+import torch
+from torch.utils.data import WeightedRandomSampler
+
+
+class TempoBalancedSampler(WeightedRandomSampler):
+    """Sample clips inversely to their occupied tempo-bin frequency."""
+
+    def __init__(self, tempos, *, domains=None, bin_width=10.0, num_samples=None, replacement=True, generator=None):
+        tempos = np.asarray(tempos, dtype=float)
+        if tempos.ndim != 1 or tempos.size == 0 or np.any(tempos <= 0):
+            raise ValueError("tempos must be a non-empty sequence of positive values")
+        if bin_width <= 0:
+            raise ValueError("bin_width must be positive")
+        bins = np.floor(tempos / bin_width).astype(int)
+        if domains is None:
+            counts = Counter(bins.tolist())
+            weights = [1.0 / counts[value] for value in bins]
+        else:
+            if len(domains) != len(tempos):
+                raise ValueError("domains and tempos must be aligned")
+            cells = list(zip(domains, bins.tolist()))
+            counts = Counter(cells)
+            occupied = Counter(domain for domain, _bin in set(cells))
+            weights = [1.0 / (counts[cell] * occupied[cell[0]]) for cell in cells]
+        weights = torch.tensor(weights, dtype=torch.double)
+        super().__init__(weights, num_samples or len(tempos), replacement, generator=generator)
+
+
+def stretch_audio_and_tempo(audio, tempo, rate, *, min_bpm=30.0, max_bpm=285.0):
+    """Time-stretch audio and relabel its tempo, rejecting out-of-range labels."""
+    if tempo <= 0 or rate <= 0:
+        raise ValueError("tempo and rate must be positive")
+    relabeled = float(tempo) * float(rate)
+    if not min_bpm <= relabeled <= max_bpm:
+        raise ValueError("augmented tempo is outside the model range")
+    stretched = librosa.effects.time_stretch(np.asarray(audio, dtype=np.float32), rate=float(rate))
+    return stretched, relabeled
\ No newline at end of file
diff --git a/src/deeprhythm/train/trainer.py b/src/deeprhythm/train/trainer.py
new file mode 100644
index 0000000..7406216
--- /dev/null
+++ b/src/deeprhythm/train/trainer.py
@@ -0,0 +1,175 @@
+"""Fine-tuning loop deliberately limited to train and validation folds."""
+
+from dataclasses import asdict, dataclass
+from pathlib import Path
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.optim import Adam
+from torch.optim.lr_scheduler import ReduceLROnPlateau
+from torch.utils.data import DataLoader
+
+from deeprhythm.bench.tempo import tempo_accuracy
+from deeprhythm.model.frame_cnn import DeepRhythmModel
+from deeprhythm.train.cache import ClipDataset, HcqmCache
+from deeprhythm.train.sampling import TempoBalancedSampler
+from deeprhythm.utils import bpm_to_class, class_to_bpm
+
+
+@dataclass(frozen=True)
+class TrainingConfig:
+    epochs: int = 40
+    batch_size: int = 256
+    learning_rate: float = 1e-4
+    early_stopping_patience: int = 5
+    balance_bin_width: float = 10.0
+    tolerance: float = 0.04
+    balance_domains: bool = False
+    freeze_feature_extractor: bool = False
+    distillation_weight: float = 0.0
+    distillation_temperature: float = 2.0
+    preservation_domains: tuple[str, ...] = ()
+
+
+def mixed_supervision_loss(
+    student_logits,
+    labels,
+    domains,
+    teacher_logits,
+    *,
+    preservation_domains,
+    distillation_weight,
+    temperature,
+):
+    """Combine hard human supervision with teacher preservation on declared domains."""
+    preserve = torch.tensor(
+        [domain in preservation_domains for domain in domains], device=student_logits.device, dtype=torch.bool
+    )
+    losses = []
+    if (~preserve).any():
+        losses.append(F.cross_entropy(student_logits[~preserve], labels[~preserve]))
+    if preserve.any():
+        scaled_student = F.log_softmax(student_logits[preserve] / temperature, dim=1)
+        scaled_teacher = F.softmax(teacher_logits[preserve] / temperature, dim=1)
+        distillation = F.kl_div(scaled_student, scaled_teacher, reduction="batchmean") * temperature**2
+        losses.append(distillation_weight * distillation)
+    if not losses:
+        raise ValueError("batch has neither supervised nor preservation examples")
+    return sum(losses)
+
+
+def evaluate_validation(model, loader, criterion, device, tolerance):
+    model.eval()
+    losses, by_track = [], {}
+    with torch.no_grad():
+        for batch in loader:
+            if len(batch) != 3:
+                raise ValueError("validation loader must return clip, tempo, and track identity")
+            inputs, tempos, track_ids = batch
+            inputs, tempos = inputs.to(device), tempos.to(device)
+            labels = torch.tensor([bpm_to_class(x) for x in tempos.tolist()], device=device)
+            outputs = model(inputs)
+            losses.append(criterion(outputs, labels).item())
+            probabilities = torch.softmax(outputs, dim=1).cpu()
+            for probability, tempo, track_id in zip(probabilities, tempos.cpu().tolist(), track_ids):
+                record = by_track.setdefault(track_id, {"probabilities": [], "tempo": tempo})
+                if record["tempo"] != tempo:
+                    raise ValueError(f"track {track_id} has inconsistent reference tempos")
+                record["probabilities"].append(probability)
+    if not losses:
+        raise ValueError("validation split has no clips")
+    predictions = []
+    references = []
+    for record in by_track.values():
+        predicted_class = torch.stack(record["probabilities"]).mean(0).argmax().item()
+        predictions.append(class_to_bpm(predicted_class))
+        references.append(record["tempo"])
+    metrics = tempo_accuracy(predictions, references, tolerance=tolerance)
+    metrics["loss"] = sum(losses) / len(losses)
+    return metrics
+
+
+def fit(cache_dir, output_path, *, config=TrainingConfig(), start_weights=None, device=None):
+    """Fit on ``train`` and select on ``val``; never opens or evaluates ``test``."""
+    device = torch.device(device or ("cuda" if torch.cuda.is_available() else "cpu"))
+    use_distillation = bool(config.preservation_domains and config.distillation_weight > 0)
+    train_set = ClipDataset(cache_dir, "train", return_tempo=False, return_domain=use_distillation)
+    validation_set = ClipDataset(cache_dir, "val", return_tempo=True, return_track=True)
+    if not train_set or not validation_set:
+        raise ValueError("cache requires non-empty train and val splits")
+    sampler = TempoBalancedSampler(
+        train_set.tempos,
+        domains=train_set.domains if config.balance_domains else None,
+        bin_width=config.balance_bin_width,
+    )
+    train_loader = DataLoader(train_set, batch_size=config.batch_size, sampler=sampler)
+    validation_loader = DataLoader(validation_set, batch_size=config.batch_size)
+    model = DeepRhythmModel().to(device)
+    if start_weights:
+        model.load_state_dict(torch.load(start_weights, map_location=device, weights_only=True))
+    teacher = None
+    if use_distillation:
+        if not start_weights:
+            raise ValueError("preservation distillation requires start_weights")
+        teacher = DeepRhythmModel().to(device)
+        teacher.load_state_dict(torch.load(start_weights, map_location=device, weights_only=True))
+        teacher.eval()
+        for parameter in teacher.parameters():
+            parameter.requires_grad = False
+    if config.freeze_feature_extractor:
+        for parameter in model.parameters():
+            parameter.requires_grad = False
+        for parameter in model.fc2.parameters():
+            parameter.requires_grad = True
+    criterion = nn.CrossEntropyLoss()
+    trainable_parameters = (parameter for parameter in model.parameters() if parameter.requires_grad)
+    optimizer = Adam(trainable_parameters, lr=config.learning_rate)
+    scheduler = ReduceLROnPlateau(optimizer, mode="min", patience=2, factor=0.5)
+    best_loss, stale, history = float("inf"), 0, []
+    output_path = Path(output_path)
+    for epoch in range(config.epochs):
+        model.train()
+        train_losses = []
+        for batch in train_loader:
+            inputs, labels = batch[:2]
+            inputs, labels = inputs.to(device), labels.to(device)
+            optimizer.zero_grad()
+            outputs = model(inputs)
+            if use_distillation:
+                domains = batch[2]
+                with torch.no_grad():
+                    teacher_outputs = teacher(inputs)
+                loss = mixed_supervision_loss(
+                    outputs,
+                    labels,
+                    domains,
+                    teacher_outputs,
+                    preservation_domains=config.preservation_domains,
+                    distillation_weight=config.distillation_weight,
+                    temperature=config.distillation_temperature,
+                )
+            else:
+                loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+            train_losses.append(loss.item())
+        validation = evaluate_validation(model, validation_loader, criterion, device, config.tolerance)
+        scheduler.step(validation["loss"])
+        record = {"epoch": epoch + 1, "train_loss": sum(train_losses) / len(train_losses), **validation}
+        history.append(record)
+        if validation["loss"] < best_loss:
+            best_loss, stale = validation["loss"], 0
+            checkpoint = {
+                "model_state_dict": model.state_dict(),
+                "config": asdict(config),
+                "manifest_hash": HcqmCache(cache_dir).load_index().get("manifest_hash"),
+                "validation": validation,
+            }
+            output_path.parent.mkdir(parents=True, exist_ok=True)
+            torch.save(checkpoint, output_path)
+        else:
+            stale += 1
+            if stale >= config.early_stopping_patience:
+                break
+    return history
\ No newline at end of file
diff --git a/src/deeprhythm/utils.py b/src/deeprhythm/utils.py
index 9d425f5..ae6e9fd 100644
--- a/src/deeprhythm/utils.py
+++ b/src/deeprhythm/utils.py
@@ -52,7 +52,7 @@ def get_weights(filename="deeprhythm-0.7.pth", quiet=False):
     return model_path
 
 
-def split_audio(audio, sr, clip_length=8, share_mem=False):
+def split_audio(audio, sr, clip_length=8, share_mem=False, max_clips=None):
     """
     Split audio into fixed-length clips and return a stacked tensor.
 
@@ -61,6 +61,8 @@ def split_audio(audio, sr, clip_length=8, share_mem=False):
     - sr: Sampling rate.
     - clip_length: Length of each clip in seconds.
     - share_mem: Whether to put the tensor in shared memory (for multiprocessing).
+    - max_clips: Optional maximum number of clips to retain.
+    - max_clips: Optional processing budget. Clips are retained from the start.
 
     Returns:
     A tensor of shape [num_clips, clip_samples].
@@ -74,6 +76,8 @@ def split_audio(audio, sr, clip_length=8, share_mem=False):
         if i + clip_samples <= len(audio):
             clip_tensor = torch.tensor(audio[i:i + clip_samples], dtype=torch.float32)
             clips.append(clip_tensor)
+            if max_clips is not None and len(clips) >= max_clips:
+                break
     if not clips:
         raise AudioTooShortError(
             f"Audio must be at least {clip_length} seconds long to extract clips. "
@@ -86,7 +90,20 @@ def split_audio(audio, sr, clip_length=8, share_mem=False):
     return stacked_clips
 
 
-def load_and_split_audio(filename, sr=22050, clip_length=8, share_mem=False):
+def load_audio(filename, sr=22050):
+    """Load mono audio at the model sampling rate."""
+    try:
+        audio, _ = librosa.load(filename, sr=sr, mono=True)
+        return audio
+    except Exception as e:
+        raise AudioLoadError(
+            f"Failed to load audio file '{filename}': {str(e)}"
+        ) from e
+
+
+def load_and_split_audio(
+    filename, sr=22050, clip_length=8, share_mem=False, max_clips=None
+):
     """
     Load an audio file and split it into fixed-length clips.
 
@@ -103,15 +120,14 @@ def load_and_split_audio(filename, sr=22050, clip_length=8, share_mem=False):
     AudioTooShortError: If audio is too short for even one clip.
     AudioLoadError: If the audio file cannot be loaded.
     """
-    try:
-        audio, _ = librosa.load(filename, sr=sr)
-        return split_audio(audio, sr, clip_length=clip_length, share_mem=share_mem)
-    except AudioTooShortError:
-        raise
-    except Exception as e:
-        raise AudioLoadError(
-            f"Failed to load audio file '{filename}': {str(e)}"
-        ) from e
+    audio = load_audio(filename, sr=sr)
+    return split_audio(
+        audio,
+        sr,
+        clip_length=clip_length,
+        share_mem=share_mem,
+        max_clips=max_clips,
+    )
 
 
 def bpm_to_class(bpm, min_bpm=30, max_bpm=286, num_classes=256):
@@ -122,7 +138,7 @@ def bpm_to_class(bpm, min_bpm=30, max_bpm=286, num_classes=256):
 
 
 def class_to_bpm(class_index, min_bpm=30, max_bpm=286, num_classes=256):
-    """Map a class index back to a BPM value (to the center of the class interval)."""
+    """Map a class index to the lower edge of its BPM class interval."""
     class_width = (max_bpm - min_bpm) / num_classes
     bpm = min_bpm + class_width * (class_index)
     return bpm
diff --git a/src/deeprhythm/weights/hierarchical-v0.8.pt b/src/deeprhythm/weights/hierarchical-v0.8.pt
new file mode 100644
index 0000000..738f220
Binary files /dev/null and b/src/deeprhythm/weights/hierarchical-v0.8.pt differ
diff --git a/src/deeprhythm/weights/shared-fusion-v0.8.pt b/src/deeprhythm/weights/shared-fusion-v0.8.pt
new file mode 100644
index 0000000..0fc30a3
Binary files /dev/null and b/src/deeprhythm/weights/shared-fusion-v0.8.pt differ
diff --git a/src/deeprhythm/weights/temporal-beat-v0.8.pt b/src/deeprhythm/weights/temporal-beat-v0.8.pt
new file mode 100644
index 0000000..326b43f
Binary files /dev/null and b/src/deeprhythm/weights/temporal-beat-v0.8.pt differ
diff --git a/src/deeprhythm/weights/v0.8-bundle.json b/src/deeprhythm/weights/v0.8-bundle.json
new file mode 100644
index 0000000..29f01d2
--- /dev/null
+++ b/src/deeprhythm/weights/v0.8-bundle.json
@@ -0,0 +1,21 @@
+{
+  "version": "0.8",
+  "architecture": "shared-stft-hierarchical-metrical-fusion",
+  "sample_rate": 22050,
+  "clip_seconds": 8,
+  "files": {
+    "hierarchical-v0.8.pt": {
+      "bytes": 5711882,
+      "sha256": "0376ceb3754a7fed328163910ad7f089896a336dde10e013948707f0da2ed1f9"
+    },
+    "temporal-beat-v0.8.pt": {
+      "bytes": 1179883,
+      "sha256": "8ab487d268adae4137a958e8f19b14c69e5f310d775777d1c54a9a46c5761a98"
+    },
+    "shared-fusion-v0.8.pt": {
+      "bytes": 89587,
+      "sha256": "c6dbca521383eb80713352997d9d00f55d40db45d5d1625fb0820639e5ef613f"
+    }
+  },
+  "evaluation": "docs/research/results/production-v08-qualification.json"
+}
diff --git a/tests/test_annotate.py b/tests/test_annotate.py
new file mode 100644
index 0000000..42e6929
--- /dev/null
+++ b/tests/test_annotate.py
@@ -0,0 +1,32 @@
+import json
+
+import numpy as np
+import pytest
+
+from deeprhythm.annotate import AnnotationStore, active_excerpt, metrical_grids
+
+
+def test_metrical_grids_preserve_phase_and_change_rate():
+    grids = metrical_grids([0.0, 1.0, 2.0], 3.0)
+    assert grids["half"].tolist() == [0.0, 2.0]
+    assert grids["current"].tolist() == [0.0, 1.0, 2.0]
+    assert grids["double"].tolist() == [0.0, 0.5, 1.0, 1.5, 2.0]
+
+
+def test_active_excerpt_selects_onset_region():
+    audio = np.zeros(4000, dtype=np.float32)
+    audio[3000:3100] = 1
+    offset, excerpt = active_excerpt(audio, 1000, duration=1)
+    assert offset >= 2
+    assert excerpt.max() == 1
+
+
+def test_annotation_store_validates_and_appends(tmp_path):
+    (tmp_path / "manifest.jsonl").write_text(json.dumps({"id": "track"}) + "\n")
+    store = AnnotationStore(tmp_path)
+    store.append("track", "correct")
+    assert store.completed_ids() == {"track"}
+    with pytest.raises(ValueError, match="unknown label"):
+        store.append("track", "wrong")
+    with pytest.raises(ValueError, match="unknown manifest"):
+        store.append("missing", "correct")
\ No newline at end of file
diff --git a/tests/test_correction.py b/tests/test_correction.py
new file mode 100644
index 0000000..6f871df
--- /dev/null
+++ b/tests/test_correction.py
@@ -0,0 +1,89 @@
+import numpy as np
+import pytest
+import torch
+
+from deeprhythm.bench.correction import evaluate_rows, join_rows, run_experiment
+from deeprhythm.correction import (
+    FACTORS,
+    FactorClassifier,
+    clip_disagreement,
+    global_threshold,
+    onset_periodicity_features,
+    select_parameter,
+    top_k_ratio,
+)
+
+
+def detail(bpm, same=0.8, double=0.1, clips=None):
+    return {
+        "filename": "unused.wav",
+        "bpm": bpm,
+        "confidence": same,
+        "candidate_masses": {
+            "same": {"probability_mass": same},
+            "double": {"probability_mass": double},
+        },
+        "clips": [{"bpm": value} for value in (clips or [bpm])],
+    }
+
+
+def test_floor_baselines_are_deterministic():
+    row = detail(60, same=0.2, double=0.3, clips=[60, 120, 120])
+    assert global_threshold(row, 90) == 120
+    assert top_k_ratio(row, 1.25) == 120
+    assert clip_disagreement(row, 0.5) == 120
+    assert clip_disagreement(row, 0.8) == 60
+
+
+def test_validation_selects_best_global_threshold():
+    rows = [detail(60), detail(130)]
+    assert select_parameter(rows, [120, 130], "global_threshold", [50, 90, 150]) == 90
+
+
+def test_onset_periodicity_features_are_finite_and_fixed_width():
+    sample_rate = 8000
+    time = torch.arange(sample_rate * 2) / sample_rate
+    audio = torch.sin(2 * torch.pi * 2 * time)
+    features = onset_periodicity_features(audio, sample_rate, 120)
+    assert features.shape == (len(FACTORS) + 4,)
+    assert np.isfinite(features).all()
+
+
+def test_factor_classifier_learns_and_round_trips():
+    features = np.vstack([np.full(3, -2.0), np.full(3, -1.0), np.full(3, 1.0), np.full(3, 2.0)])
+    labels = [1, 1, 0, 0]
+    model = FactorClassifier().fit(features, labels, epochs=800)
+    assert model.predict_proba(features).argmax(axis=1).tolist() == labels
+    restored = FactorClassifier.from_dict(model.to_dict())
+    assert restored.predict_proba(features) == pytest.approx(model.predict_proba(features))
+
+
+def test_join_rows_rejects_held_out_fold(tmp_path):
+    path = tmp_path / "audio.wav"
+    manifest = [{"audio_path": str(path), "tempo": 120, "fold": "test"}]
+    details = [{**detail(60), "filename": str(path)}]
+    with pytest.raises(ValueError, match="held-out test"):
+        join_rows(manifest, details)
+
+
+def test_experiment_reports_full_evaluation():
+    train = [
+        {"tempo": 120, "genre": "dance", "detail": detail(60), "features": [1, 0]},
+        {"tempo": 80, "genre": "slow", "detail": detail(80), "features": [0, 1]},
+    ]
+    val = [
+        {"tempo": 120, "genre": "dance", "detail": detail(60), "features": [1, 0]},
+        {"tempo": 80, "genre": "slow", "detail": detail(80), "features": [0, 1]},
+    ]
+    result = run_experiment(train, val)
+    assert {"identity", "global_threshold", "top_k_ratio", "clip_disagreement", "learned"} <= result.keys()
+    learned = result["learned"]
+    assert {"metrics_2pct", "metrics_4pct", "taxonomy", "slices", "calibration", "latency"} <= learned.keys()
+    assert learned["metrics_4pct"]["acc1"] == 1.0
+
+
+def test_evaluation_uses_canonical_acc1_acc2_relations():
+    rows = [{"tempo": 120, "genre": "x"}, {"tempo": 100, "genre": "x"}]
+    report = evaluate_rows(rows, [60, 100])
+    assert report["metrics_2pct"] == pytest.approx({"acc1": 0.5, "acc2": 1.0})
+    assert report["taxonomy"] == {"correct": 1, "half": 1}
\ No newline at end of file
diff --git a/tests/test_hcqm_reuse.py b/tests/test_hcqm_reuse.py
new file mode 100644
index 0000000..39caa0a
--- /dev/null
+++ b/tests/test_hcqm_reuse.py
@@ -0,0 +1,18 @@
+import torch
+
+from deeprhythm.audio_proc.hcqm import (
+    compute_hcqm,
+    compute_hcqm_from_stft,
+    make_kernels,
+)
+
+
+def test_hcqm_accepts_shared_stft():
+    audio = torch.randn(1, 22050 * 8)
+    kernels = make_kernels(device="cpu")
+
+    expected = compute_hcqm(audio, *kernels)
+    magnitude = kernels[0](audio)
+    actual = compute_hcqm_from_stft(magnitude, kernels[1], kernels[2])
+
+    assert torch.equal(actual, expected)
\ No newline at end of file
diff --git a/tests/test_hierarchical.py b/tests/test_hierarchical.py
new file mode 100644
index 0000000..fa6fb49
--- /dev/null
+++ b/tests/test_hierarchical.py
@@ -0,0 +1,50 @@
+import pytest
+import torch
+
+from deeprhythm.model.hierarchical import (
+    HierarchicalDeepRhythmModel,
+    hierarchy_to_tempo,
+    tempo_to_hierarchy,
+)
+
+
+@pytest.mark.parametrize("tempo", [30.0, 59.5, 60.0, 87.5, 119.5, 120.0, 175.0, 239.0, 240.0, 284.0])
+def test_hierarchical_tempo_round_trip(tempo):
+    base, level = tempo_to_hierarchy(torch.tensor([tempo]))
+    assert hierarchy_to_tempo(base, level).item() == pytest.approx(tempo, abs=0.5)
+
+
+def test_hierarchical_model_has_independent_outputs():
+    model = HierarchicalDeepRhythmModel()
+    base, level = model(torch.zeros(2, 6, 240, 8))
+    assert base.shape == (2, 120)
+    assert level.shape == (2, 4)
+
+
+def test_residual_level_head_preserves_existing_outputs_at_initialization():
+    torch.manual_seed(7)
+    original = HierarchicalDeepRhythmModel().eval()
+    residual = HierarchicalDeepRhythmModel(residual_level=True).eval()
+    residual.load_state_dict(original.state_dict(), strict=False)
+    inputs = torch.randn(2, 6, 240, 8)
+
+    expected_base, expected_level = original(inputs)
+    actual_base, actual_level = residual(inputs)
+
+    assert torch.equal(actual_base, expected_base)
+    assert torch.equal(actual_level, expected_level)
+
+
+def test_residual_level_head_can_change_level_without_changing_base():
+    model = HierarchicalDeepRhythmModel(residual_level=True).eval()
+    inputs = torch.randn(2, 6, 240, 8)
+    expected_base, expected_level = model(inputs)
+    with torch.no_grad():
+        model.level_residual.network[-1].bias[2] = 1.0
+
+    actual_base, actual_level = model(inputs)
+
+    assert torch.equal(actual_base, expected_base)
+    assert torch.equal(actual_level[:, :2], expected_level[:, :2])
+    assert torch.equal(actual_level[:, 3], expected_level[:, 3])
+    assert torch.equal(actual_level[:, 2], expected_level[:, 2] + 1.0)
\ No newline at end of file
diff --git a/tests/test_inference_details.py b/tests/test_inference_details.py
new file mode 100644
index 0000000..077bd52
--- /dev/null
+++ b/tests/test_inference_details.py
@@ -0,0 +1,56 @@
+import pytest
+import torch
+
+from deeprhythm.model.results import shape_prediction
+
+
+def test_shape_prediction_aggregates_clips_deterministically():
+    probabilities = torch.zeros((2, 256))
+    probabilities[0, 90] = 0.8
+    probabilities[0, 91] = 0.2
+    probabilities[1, 90] = 0.4
+    probabilities[1, 92] = 0.6
+
+    details = shape_prediction(probabilities, top_k=2)
+
+    assert details["predicted_class"] == 90
+    assert details["num_clips"] == 2
+    assert details["confidence"] == pytest.approx(0.6)
+    assert [clip["class"] for clip in details["clips"]] == [90, 92]
+    assert [peak["class"] for peak in details["aggregate_top_k"]] == [90, 92]
+    assert set(details["candidate_masses"]) == {"same", "double", "half", "triple", "third"}
+
+
+def test_shape_prediction_rejects_invalid_top_k():
+    with pytest.raises(ValueError, match="positive"):
+        shape_prediction(torch.ones((1, 256)), top_k=0)
+
+def test_batch_instrumentation_does_not_change_legacy_prediction(monkeypatch, tmp_path):
+    import json
+
+    import deeprhythm.batch_infer as batch_infer
+
+    class Model(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.anchor = torch.nn.Parameter(torch.tensor(0.0))
+
+        def forward(self, inputs):
+            logits = torch.full((len(inputs), 256), -10.0)
+            logits[:, 90] = 10.0
+            return logits
+
+    monkeypatch.setattr(batch_infer, "compute_hcqm", lambda audio, *_specs: audio)
+    audio = torch.zeros((2, 6, 240, 8))
+    plain = tmp_path / "plain.jsonl"
+    instrumented = tmp_path / "instrumented.jsonl"
+    details = tmp_path / "details.jsonl"
+    metadata = [("track.wav", 2, 0)]
+
+    batch_infer.process_and_save(audio, metadata, (None, None, None), Model(), plain, conf=True, quiet=True)
+    batch_infer.process_and_save(
+        audio, metadata, (None, None, None), Model(), instrumented, conf=True, quiet=True, details_path=details
+    )
+
+    assert plain.read_bytes() == instrumented.read_bytes()
+    assert json.loads(details.read_text())["filename"] == "track.wav"
diff --git a/tests/test_log_spectrum.py b/tests/test_log_spectrum.py
new file mode 100644
index 0000000..5f9c221
--- /dev/null
+++ b/tests/test_log_spectrum.py
@@ -0,0 +1,61 @@
+import numpy as np
+import pytest
+import torch
+
+from deeprhythm.audio_proc.log_spectrum import (
+    compute_log_spectrum,
+    compute_log_spectrum_from_clips,
+    make_log_spectrum_filter,
+)
+from deeprhythm.utils import load_and_split_audio, split_audio
+
+
+def test_log_spectrum_shape_and_range():
+    audio = torch.linspace(-1, 1, 22050)
+    spectrum = compute_log_spectrum(audio, duration=2)
+
+    assert spectrum.shape == (81, 1 + 2 * 22050 // 512)
+    assert float(spectrum.amin()) == pytest.approx(0)
+    assert float(spectrum.amax()) == pytest.approx(1)
+
+
+def test_log_spectrum_rejects_multichannel_audio():
+    with pytest.raises(ValueError, match="one-dimensional"):
+        compute_log_spectrum(torch.zeros(2, 22050))
+
+
+def test_log_filter_has_normalized_nonempty_bands():
+    matrix = make_log_spectrum_filter()
+    assert matrix.shape == (81, 1025)
+    assert torch.allclose(matrix.sum(dim=1), torch.ones(81))
+
+
+def test_log_spectrum_concatenates_clip_frames():
+    magnitude = torch.rand(4, 1025, 345)
+    spectrum = compute_log_spectrum_from_clips(magnitude)
+
+    assert spectrum.shape == (81, 1 + 30 * 22050 // 512)
+    assert float(spectrum.amin()) == pytest.approx(0)
+    assert float(spectrum.amax()) == pytest.approx(1)
+
+
+def test_load_and_split_audio_uses_canonical_loader(monkeypatch):
+    audio = np.zeros(16 * 22050, dtype=np.float32)
+    calls = []
+
+    def fake_load(filename, sr):
+        calls.append((filename, sr))
+        return audio
+
+    monkeypatch.setattr("deeprhythm.utils.load_audio", fake_load)
+    clips = load_and_split_audio("track.wav")
+
+    assert calls == [("track.wav", 22050)]
+    assert clips.shape == (2, 8 * 22050)
+
+
+def test_split_audio_honors_clip_budget():
+    audio = np.zeros(20 * 22050, dtype=np.float32)
+    clips = split_audio(audio, 22050, max_clips=2)
+
+    assert clips.shape == (2, 8 * 22050)
\ No newline at end of file
diff --git a/tests/test_metrical_predictor.py b/tests/test_metrical_predictor.py
new file mode 100644
index 0000000..901a143
--- /dev/null
+++ b/tests/test_metrical_predictor.py
@@ -0,0 +1,44 @@
+import torch
+
+from deeprhythm.model.metrical import MetricalFusion, temporal_metrical_features
+from deeprhythm.model.metrical_predictor import (
+    MetricalDeepRhythmPredictor,
+    _fusion_cache_precision,
+    _trim_edge_clips,
+)
+
+
+def test_metrical_features_match_fusion_contract():
+    temporal = torch.randn(1, 20, 36)
+    phase = torch.randn(1, 20, 2)
+    base = torch.softmax(torch.randn(120), dim=0)
+    level = torch.softmax(torch.randn(4), dim=0)
+
+    features = temporal_metrical_features(temporal, phase, base, level)
+
+    assert features.shape == (162,)
+    expected_confidence = phase[0].norm(p=-1)
+    assert torch.equal(features[[146, 149, 152, 155]], expected_confidence.repeat(4))
+    assert MetricalFusion()(features[None]).shape == (1, 4)
+
+
+def test_packaged_v08_bundle_loads_on_cpu():
+    predictor = MetricalDeepRhythmPredictor(device="cpu")
+
+    assert predictor.alpha == 0.4
+    assert predictor.hierarchical.training is False
+    assert predictor.temporal.training is False
+
+
+def test_v08_track_aggregation_matches_training_edge_trim():
+    clips = torch.arange(15)
+
+    assert torch.equal(_trim_edge_clips(clips), clips[1:-1])
+    assert torch.equal(_trim_edge_clips(clips[:5]), clips[:5])
+
+
+def test_v08_fusion_inputs_match_training_cache_precision():
+    value = torch.tensor([1.0001], dtype=torch.float32)
+
+    assert _fusion_cache_precision(value).item() == 1.0
+    assert _fusion_cache_precision(value).dtype == torch.float32
\ No newline at end of file
diff --git a/tests/test_partitions.py b/tests/test_partitions.py
new file mode 100644
index 0000000..831ffce
--- /dev/null
+++ b/tests/test_partitions.py
@@ -0,0 +1,58 @@
+import json
+
+import pytest
+
+from deeprhythm.partitions import _assign_ballroom, _ballroom_group, validate_checksums, validate_partitions
+
+
+def row(path, group, fold, fingerprint, dataset="example"):
+    return {"dataset": dataset, "audio_path": path, "md5": "0" * 32, "tempo": 120,
+            "group_key": group, "fold": fold, "fingerprint": fingerprint, "provenance": "test"}
+
+
+def test_validation_accepts_disjoint_rows():
+    assert validate_partitions([row("a.wav", "artist:a", "train", "fp:a"), row("b.wav", "artist:b", "test", "fp:b")])
+
+
+@pytest.mark.parametrize("field", ["group", "fingerprint"])
+def test_validation_rejects_leakage(field):
+    first = row("a.wav", "artist:a", "train", "fp:a")
+    second = row("b.wav", "artist:a" if field == "group" else "artist:b", "test", "fp:a")
+    with pytest.raises(ValueError, match="spans"):
+        validate_partitions([first, second])
+
+
+def test_validation_rejects_cross_dataset_fingerprint_leakage():
+    with pytest.raises(ValueError, match="fingerprint.*spans"):
+        validate_partitions([
+            row("external.wav", "artist:a", "train", "fp:same", dataset="external"),
+            row("test.wav", "artist:b", "test", "fp:same", dataset="ballroom"),
+        ])
+
+
+def test_ballroom_group_uses_only_filename_metadata():
+    assert _ballroom_group("Albums-Latin_Jam2-03") == "album:latin_jam2"
+    assert _ballroom_group("Media-104705") == "media-prefix:1047"
+
+
+def test_ballroom_assignment_is_deterministic_and_grouped():
+    groups = {"album:a": [("waltz", 4)] * 3, "album:b": [("waltz", 4)] * 2, "album:c": [("tango", 6)]}
+    assert _assign_ballroom(groups) == _assign_ballroom(dict(reversed(list(groups.items()))))
+
+
+def test_committed_manifests_are_valid_and_consistent():
+    from pathlib import Path
+    root = Path(__file__).parents[1] / "data" / "splits"
+    rows = []
+    for dataset in ("giantsteps", "gtzan", "ballroom"):
+        dataset_rows = []
+        for fold in ("train", "val", "test"):
+            path = root / dataset / f"{fold}.jsonl"
+            loaded = [json.loads(line) for line in path.read_text().splitlines()]
+            assert loaded and all(row_["fold"] == fold for row_ in loaded)
+            dataset_rows.extend(loaded)
+        validate_partitions(dataset_rows)
+        rows.extend(dataset_rows)
+    assert {row_["dataset"] for row_ in rows} == {"giantsteps", "gtzan", "ballroom"}
+    validate_partitions(rows)
+    validate_checksums(root)
\ No newline at end of file
diff --git a/tests/test_research_results.py b/tests/test_research_results.py
new file mode 100644
index 0000000..d14da71
--- /dev/null
+++ b/tests/test_research_results.py
@@ -0,0 +1,38 @@
+import hashlib
+import json
+from pathlib import Path
+
+
+def test_frozen_research_results_match_committed_manifests_and_weights():
+    root = Path(__file__).parents[1]
+    result = json.loads((root / "docs/research/results/final-test-results.json").read_text())
+    for dataset in ("giantsteps", "gtzan", "ballroom"):
+        manifest = root / "data/splits" / dataset / "test.jsonl"
+        assert hashlib.sha256(manifest.read_bytes()).hexdigest() == result["manifest_hashes"][dataset]
+    weights = root / "weights/deeprhythm-v0.8-research.pth"
+    assert hashlib.sha256(weights.read_bytes()).hexdigest() == result["weights_sha256"]
+
+
+def test_v07_apples_to_apples_rows_are_frozen_on_canonical_tests():
+    root = Path(__file__).parents[1]
+    result = json.loads((root / "docs/research/results/final-test-results.json").read_text())
+    expected = {"giantsteps": 0.7099236641221374, "gtzan": 0.6900958466453674, "ballroom": 0.5970149253731343}
+    for dataset, acc1 in expected.items():
+        baseline = result["datasets"][dataset]["baseline"]["uncorrected"]
+        assert baseline["tracks"] == len((root / "data/splits" / dataset / "test.jsonl").read_text().splitlines())
+        assert baseline["metrics_4pct"]["acc1"] == acc1
+
+
+def test_packaged_v08_qualification_matches_bundled_weights():
+    root = Path(__file__).parents[1]
+    result = json.loads((root / "docs/research/results/production-v08-qualification.json").read_text())
+
+    assert result["tracks"] == 798
+    assert len(result["predictions"]) == result["tracks"]
+    assert result["summary"]["canonical-test"]["overall"]["acc1"] == 0.7975778546712803
+    for filename, digest in result["weights"].items():
+        weights = root / "src/deeprhythm/weights" / filename
+        assert hashlib.sha256(weights.read_bytes()).hexdigest() == digest
+    bundle = root / "src/deeprhythm/weights/v0.8-bundle.json"
+    assert hashlib.sha256(bundle.read_bytes()).hexdigest() == result["bundle_manifest_sha256"]
+    assert all(row["equal"] for row in result["cpu_accelerator_parity"])
diff --git a/tests/test_taxonomy.py b/tests/test_taxonomy.py
new file mode 100644
index 0000000..13f8ee6
--- /dev/null
+++ b/tests/test_taxonomy.py
@@ -0,0 +1,28 @@
+import pytest
+
+from deeprhythm.bench.taxonomy import analyze, expected_calibration_error, metrical_relation
+
+
+@pytest.mark.parametrize(
+    ("prediction", "reference", "relation"),
+    [(120, 120, "correct"), (60, 120, "half"), (240, 120, "double"), (40, 120, "third"),
+     (180, 60, "triple"), (83, 120, "other")],
+)
+def test_metrical_relation(prediction, reference, relation):
+    assert metrical_relation(prediction, reference) == relation
+
+
+def test_analyze_joins_paths_and_reports_calibration(tmp_path):
+    audio = tmp_path / "track.wav"
+    result = analyze(
+        [{"audio_path": str(audio), "tempo": 120, "genre": "dance"}],
+        [{"filename": str(audio), "bpm": 60, "confidence": 0.8}],
+    )
+    assert result["counts"]["relation"] == {"half": 1}
+    assert result["counts"]["genre"] == {"dance:half": 1}
+    assert result["calibration"]["ece"] == pytest.approx(0.8)
+
+
+def test_expected_calibration_error_validates_inputs():
+    with pytest.raises(ValueError, match="between"):
+        expected_calibration_error([1.1], [True])
\ No newline at end of file
diff --git a/tests/test_training.py b/tests/test_training.py
new file mode 100644
index 0000000..a0abddc
--- /dev/null
+++ b/tests/test_training.py
@@ -0,0 +1,161 @@
+import json
+
+import numpy as np
+import pytest
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+
+from deeprhythm.bench.tempo import tempo_accuracy
+from deeprhythm.train.cache import ClipDataset, HcqmCache, build_hcqm_cache, manifest_key
+from deeprhythm.train.sampling import TempoBalancedSampler, stretch_audio_and_tempo
+from deeprhythm.train.trainer import evaluate_validation, mixed_supervision_loss
+from deeprhythm.utils import bpm_to_class
+
+
+def fake_hcqm(value, clips=3):
+    return np.full((clips, 6, 240, 8), value, dtype=np.float32)
+
+
+def test_manifest_key_invalidates_annotation_and_checksum_changes():
+    row = {"audio_path": "audio.wav", "tempo": 120, "split": "train", "md5": "a"}
+    assert manifest_key(row) == manifest_key(dict(reversed(list(row.items()))))
+    assert manifest_key(row) != manifest_key({**row, "tempo": 121})
+    assert manifest_key(row) != manifest_key({**row, "md5": "b"})
+    assert manifest_key(row) != manifest_key({**row, "split": "val"})
+    assert manifest_key(row) != manifest_key({**row, "stretch_rate": 1.25, "tempo": 150})
+
+
+def test_cache_is_incremental_and_dataset_is_memory_backed(tmp_path):
+    rows = [
+        {"audio_path": "one.wav", "tempo": 120, "split": "train", "md5": "a"},
+        {"audio_path": "two.wav", "tempo": 90, "split": "val", "md5": "b"},
+    ]
+    calls = []
+
+    def extract(path):
+        calls.append(path)
+        return fake_hcqm(len(calls), clips=7 if path == "one.wav" else 2)
+
+    index = build_hcqm_cache(rows, tmp_path, extractor=extract)
+    build_hcqm_cache(rows, tmp_path, extractor=extract)
+    assert calls == ["one.wav", "two.wav"]
+    assert index["manifest_hash"]
+    train = ClipDataset(tmp_path, "train")
+    validation = ClipDataset(tmp_path, "val", return_tempo=True)
+    assert len(train) == 5  # historical edge trimming, without its off-by-one bug
+    assert len(validation) == 2
+    clip, label = train[0]
+    assert clip.shape == (6, 240, 8)
+    assert label.item() == bpm_to_class(120)
+    assert validation[0][1].item() == 90
+
+
+def test_cache_rebuild_drops_rows_removed_from_manifest(tmp_path):
+    rows = [
+        {"audio_path": "one.wav", "tempo": 120, "split": "train"},
+        {"audio_path": "two.wav", "tempo": 90, "split": "val"},
+    ]
+    build_hcqm_cache(rows, tmp_path, extractor=lambda _path: fake_hcqm(1))
+    build_hcqm_cache(rows[:1], tmp_path, extractor=lambda _path: fake_hcqm(1))
+    assert len(HcqmCache(tmp_path).load_index()["entries"]) == 1
+    assert len(ClipDataset(tmp_path, "val")) == 0
+
+
+def test_canonical_fold_manifests_build_train_and_validation_sets(tmp_path):
+    rows = [
+        {"audio_path": "train.wav", "tempo": 120, "fold": "train", "md5": "a"},
+        {"audio_path": "validation.wav", "tempo": 90, "fold": "val", "md5": "b"},
+    ]
+    build_hcqm_cache(rows, tmp_path, extractor=lambda _path: fake_hcqm(1))
+    assert len(ClipDataset(tmp_path, "train")) == 3
+    assert len(ClipDataset(tmp_path, "val")) == 3
+
+
+def test_dataset_can_return_training_domain(tmp_path):
+    rows = [{"audio_path": "replay.wav", "tempo": 120, "fold": "train", "dataset": "replay"}]
+    build_hcqm_cache(rows, tmp_path, extractor=lambda _path: fake_hcqm(1))
+    _clip, _label, domain = ClipDataset(tmp_path, "train", return_domain=True)[0]
+    assert domain == "replay"
+
+
+def test_mixed_supervision_distills_only_preservation_domains():
+    labels = torch.tensor([0, 1])
+    student = torch.zeros((2, 3), requires_grad=True)
+    teacher = torch.tensor([[100.0, 0.0, 0.0], [0.0, 100.0, 0.0]])
+    loss = mixed_supervision_loss(
+        student,
+        labels,
+        ["human", "replay"],
+        teacher,
+        preservation_domains=("replay",),
+        distillation_weight=2.0,
+        temperature=1.0,
+    )
+    loss.backward()
+    assert student.grad[0].argmin().item() == 0
+    assert student.grad[1].argmin().item() == 1
+
+
+def test_corrupt_cache_version_is_rejected(tmp_path):
+    tmp_path.mkdir(exist_ok=True)
+    (tmp_path / "index.json").write_text(json.dumps({"version": 99, "entries": {}}))
+    with pytest.raises(ValueError, match="version"):
+        HcqmCache(tmp_path).load_index()
+
+
+def test_tempo_balanced_sampler_equalizes_bin_mass():
+    sampler = TempoBalancedSampler([60, 61, 62, 120], bin_width=10, num_samples=4)
+    weights = sampler.weights.tolist()
+    assert weights[:3] == pytest.approx([1 / 3] * 3)
+    assert weights[3] == pytest.approx(1.0)
+    assert sum(weights[:3]) == pytest.approx(weights[3])
+
+
+def test_tempo_balanced_sampler_equalizes_domain_mass():
+    sampler = TempoBalancedSampler([60, 61, 120, 90], domains=["public", "public", "public", "soulseek"])
+    weights = sampler.weights.tolist()
+    assert sum(weights[:3]) == pytest.approx(weights[3])
+
+
+def test_time_stretch_relabels_and_enforces_model_range():
+    audio = np.sin(np.linspace(0, 8 * np.pi, 1000)).astype(np.float32)
+    stretched, tempo = stretch_audio_and_tempo(audio, 100, 1.25)
+    assert tempo == 125
+    assert len(stretched) == 800
+    with pytest.raises(ValueError, match="outside"):
+        stretch_audio_and_tempo(audio, 280, 1.25)
+
+
+class FixedModel(nn.Module):
+    def forward(self, inputs):
+        logits = torch.full((len(inputs), 256), -100.0)
+        logits[torch.arange(len(inputs)), inputs[:, 0].long()] = 100.0
+        return logits
+
+
+def test_validation_acc2_reuses_benchmark_scoring():
+    reference_tempos = torch.tensor([120.0, 100.0])
+    predicted_classes = torch.tensor([bpm_to_class(60), bpm_to_class(100)], dtype=torch.float32).unsqueeze(1)
+    dataset = [(predicted_classes[i], reference_tempos[i], f"track-{i}") for i in range(2)]
+    loader = DataLoader(dataset, batch_size=2)
+    metrics = evaluate_validation(FixedModel(), loader, nn.CrossEntropyLoss(), torch.device("cpu"), 0.04)
+    predicted_tempos = [float(int(60)), float(int(100))]
+    expected = tempo_accuracy(predicted_tempos, reference_tempos.tolist(), 0.04)
+    assert metrics["acc1"] == expected["acc1"] == 0.5
+    assert metrics["acc2"] == expected["acc2"] == 1.0
+
+
+def test_validation_aggregates_probabilities_per_track():
+    reference = torch.tensor(120.0)
+    slow = float(bpm_to_class(60))
+    correct = float(bpm_to_class(120))
+    dataset = [
+        (torch.tensor([slow]), reference, "same-track"),
+        (torch.tensor([correct]), reference, "same-track"),
+        (torch.tensor([correct]), reference, "same-track"),
+    ]
+    metrics = evaluate_validation(
+        FixedModel(), DataLoader(dataset, batch_size=3), nn.CrossEntropyLoss(), torch.device("cpu"), 0.04
+    )
+    assert metrics["acc1"] == 1.0
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
index 8cf36e2..259c801 100644
--- a/uv.lock
+++ b/uv.lock
@@ -608,6 +608,7 @@ dependencies = [
     { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
     { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "pytorch-tcn" },
     { name = "requests", version = "2.32.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
     { name = "requests", version = "2.32.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" },
     { name = "requests", version = "2.34.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
@@ -631,6 +632,7 @@ requires-dist = [
     { name = "nnaudio", specifier = "==0.3.3" },
     { name = "numpy" },
     { name = "pytest", marker = "extra == 'dev'" },
+    { name = "pytorch-tcn" },
     { name = "requests" },
     { name = "ruff", marker = "extra == 'dev'" },
     { name = "torch" },
@@ -2549,6 +2551,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/24/25/1de2678b631f5a49215c6c96fff41ba892b0a34df68d6d80292b1b48aa7f/pytest-9.1.1-py3-none-any.whl", hash = "sha256:37a86b45efb9a47a61a36449063e8e18d0cab3161329fc099eb21783169c4f0c", size = 386536, upload-time = "2026-06-19T10:58:31.347Z" },
 ]
 
+[[package]]
+name = "pytorch-tcn"
+version = "1.2.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy", version = "1.24.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "torch", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" },
+    { name = "torch", version = "2.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" },
+    { name = "torch", version = "2.13.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c1/8c/28f0a2bb34ff3a8095481990fc85d7836703e39b7d24fd4b6152a06119a9/pytorch_tcn-1.2.3.tar.gz", hash = "sha256:41e16600aae72691b2af82c87b56b18fd8c46bd98e0a2af8f109c0de1fc6da9b", size = 21754, upload-time = "2025-04-07T18:11:20.904Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/00/81/9b72e375a4c5b12d5fb3fffad2fa78d61807e4579b2ec586f9c0b1f530e5/pytorch_tcn-1.2.3-py3-none-any.whl", hash = "sha256:949dd478c7a7c04e2ba0d79e19afe4a34f083141ef908019d19e9edd729ea2ec", size = 18776, upload-time = "2025-04-07T18:11:19.2Z" },
+]
+
 [[package]]
 name = "requests"
 version = "2.32.4"
diff --git a/weights/deeprhythm-v0.8-research.pth b/weights/deeprhythm-v0.8-research.pth
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diff --git a/weights/deeprhythm-v0.8-shared-fusion.pth b/weights/deeprhythm-v0.8-shared-fusion.pth
new file mode 100644
index 0000000..c84d94d
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