From 1eebe806fa79620fbc1fd5e93647419bab2aa3c1 Mon Sep 17 00:00:00 2001 From: genisplaja Date: Thu, 30 Nov 2023 12:02:18 +0000 Subject: [PATCH] deploy: b0645867664fe91cddda63064fff41f6aedc8266 --- .../separation/singing-voice-extraction.ipynb | 36 +++- melodic_analysis/exploring-raga.html | 64 +++---- melodic_analysis/raga-recognition.html | 17 +- searchindex.js | 2 +- separation/singing-voice-extraction.html | 167 +++++++++++++++++- 5 files changed, 223 insertions(+), 63 deletions(-) diff --git a/_sources/separation/singing-voice-extraction.ipynb b/_sources/separation/singing-voice-extraction.ipynb index ba89a1b..2b1df6a 100644 --- a/_sources/separation/singing-voice-extraction.ipynb +++ b/_sources/separation/singing-voice-extraction.ipynb @@ -85,16 +85,36 @@ "outputs": [], "source": [ "# Importing and initializing a melodia instance\n", - "from compiam import load_model\n", + "import soundfile as sf\n", + "from compiam import load_model, load_dataset\n", "separation_model = load_model('separation:cold-diff-sep')\n", "\n", - "# Running extraction for an example track\n", + "# Loading an normalizing an example track\n", + "saraga_carnatic = load_dataset(\n", + " \"saraga_carnatic\",\n", + " data_home=os.path.join(\"..\", \"audio\", \"mir_datasets\")\n", + ")\n", + "saraga_tracks = saraga_carnatic.load_tracks()\n", + "example = saraga_tracks[\"109_Sri_Raghuvara_Sugunaalaya\"]\n", + "input_mixture, sr = sf.read(example.audio_path)\n", + "\n", + "input_mixture = input_mixture.T\n", + "mean = np.mean(input_mixture, keepdims=True)\n", + "std = np.std(input_mixture, keepdims=True)\n", + "input_mixture = (input_mixture - mean) / (1e-6 + std)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Getting 30 seconds and separating\n", + "input_mixture = input_mixture[:, :44100*30]\n", "separation = separation_model.separate(\n", - " input_data=os.path.join(\n", - " \"..\", \"audio\", \"mir_datasets\", \"saraga1.5_carnatic\",\n", - " \"Live at Vani Mahal by Sanjay Subrahmanyan\", \"Sri Raghuvara Sugunaalaya\",\n", - " \"Sanjay Subrahmanyan - Sri Raghuvara Sugunaalaya.mp3\"\n", - " ),\n", + " input_data=input_mixture,\n", + " input_sr=sr\n", ")" ] }, @@ -109,7 +129,7 @@ "# And we play it!\n", "ipd.Audio(\n", " data=separation,\n", - " rate=22050,\n", + " rate=separation_model.sample_rate,\n", ")" ] } diff --git a/melodic_analysis/exploring-raga.html b/melodic_analysis/exploring-raga.html index fcfb3ef..e1d83d2 100644 --- a/melodic_analysis/exploring-raga.html +++ b/melodic_analysis/exploring-raga.html @@ -1432,14 +1432,14 @@

3.1 Predominant pitch extraction -
2023-11-29 22:48:31.864978: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/cv2/../../lib64:
-2023-11-29 22:48:31.865007: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+
2023-11-30 11:53:38.008753: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/cv2/../../lib64:
+2023-11-30 11:53:38.008780: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
 

 

-
2023-11-29 22:48:33.012095: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/cv2/../../lib64:
-2023-11-29 22:48:33.012123: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
-2023-11-29 22:48:33.012148: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (fv-az1247-986): /proc/driver/nvidia/version does not exist
-2023-11-29 22:48:33.012329: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+
2023-11-30 11:53:39.158627: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/cv2/../../lib64:
+2023-11-30 11:53:39.158653: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
+2023-11-30 11:53:39.158679: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (fv-az888-914): /proc/driver/nvidia/version does not exist
+2023-11-30 11:53:39.158847: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
 

 

@@ -1727,31 +1727,11 @@ 
3.2 Visualising predominant pitch
-
{54.997834212038505: 'R2',
- 109.99566842407701: 'R2',
- 219.99133684815402: 'R2',
- 439.98267369630804: 'R2',
- 879.965347392616: 'R2',
- 58.268175617345825: 'G2',
+
{58.268175617345825: 'G2',
  116.53635123469165: 'G2',
  233.0727024693833: 'G2',
  466.1454049387666: 'G2',
  932.2908098775332: 'G2',
- 82.40364421278775: 'D2',
- 164.8072884255755: 'D2',
- 329.614576851151: 'D2',
- 659.229153702302: 'D2',
- 1318.458307404604: 'D2',
- 65.40381575469627: 'M1',
- 130.80763150939254: 'M1',
- 261.6152630187851: 'M1',
- 523.2305260375701: 'M1',
- 1046.46105207514: 'M1',
- 73.4133009992652: 'P',
- 146.8266019985304: 'P',
- 293.6532039970608: 'P',
- 587.3064079941216: 'P',
- 1174.6128159882435: 'P',
  87.30361988416256: 'N2',
  174.60723976832512: 'N2',
  349.21447953665023: 'N2',
@@ -1761,7 +1741,27 @@ 
3.2 Visualising predominant pitch
-
[<matplotlib.lines.Line2D at 0x7f5dcda43d00>]
+
[<matplotlib.lines.Line2D at 0x7f1579aca220>]
 

 

 ../_images/exploring-raga_110_1.png
@@ -2317,7 +2317,7 @@ 
Fixing tonic errors in folded plots
-
[<matplotlib.lines.Line2D at 0x7f5dcda3ea00>]
+
[<matplotlib.lines.Line2D at 0x7f1579abedf0>]
 

 

 ../_images/exploring-raga_120_1.png
@@ -2666,8 +2666,8 @@ 
3.5 Pitch density across rāga
-
Rītigauḷa: {'R2', 'G2', 'D2', 'M1', 'P', 'N2', 'S'}
-Bhairavi: {'R2', 'D1', 'G2', 'D2', 'M1', 'P', 'N2', 'S'}
+
Rītigauḷa: {'G2', 'N2', 'S', 'R2', 'P', 'M1', 'D2'}
+Bhairavi: {'G2', 'N2', 'S', 'R2', 'P', 'D2', 'M1', 'D1'}
 

 

 

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diff --git a/searchindex.js b/searchindex.js
index 7dfde12..4dbffd3 100644
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the Dunya Corpora","Datasets","Carnatic Music","Hindustani music","Indian Art Music","compIAM package","Python for audio processing","Setting everything up","Welcome!","Exploring R\u0101ga Performance in Carnatic Music","Melodic pattern dicovery","Melodic transcription","Pitch extraction","Automatic raga recognition","Tonic identification","Acknowledgements","Citing this work","References","Meter analysis","Percussion transcription","Singing voice extraction","Music segmentation","Stroke classification"],titleterms:{"1":9,"2":9,"3":9,"4":9,"5":9,"6":9,"7":9,"8":9,"\u0101vart":3,"ak\u1e63ara":2,"b\u014dl":3,"bh\u0101va":2,"class":3,"do":0,"final":12,"function":5,"import":5,"long":3,"m\u0101tr\u0101":3,"na\u1e0de":2,"pi\u1e0di":2,"r\u0101ga":[2,9],"r\u016bpak":3,"sa\u00f1c\u0101ra":2,"sa\u00f1cara":10,"sa\u1e45g\u012bta":2,"short":[2,3],"solka\u1e6d\u1e6du":2,"t\u0101\u1e37a":2,"t\u0101l":3,"tamb\u016br\u0101":2,"vibh\u0101g":3,A:[9,21],For:3,The:[2,3,5],about:[8,9],access:0,acknowledg:15,acronym:6,across:9,akshara:18,all:0,alli:3,along:2,also:2,an:[2,6,9],analysi:[9,18],annot:9,api:0,ar:[2,3],art:[4,21],audio:6,author:9,automat:13,avail:5,avart:3,awar:20,background:9,bandish:21,base:[2,11],basic:5,beat:2,book:[7,8],brows:0,build:7,canon:3,carnat:[2,4,9,11,12,18],characterist:2,cite:16,classif:22,cloud:7,cluster:9,collaboratori:7,column:[2,3],combin:[2,3],compiam:5,compon:5,compos:2,composit:2,concept:2,conclus:9,consist:3,contribut:[1,5],corpora:0,cycl:2,data:[0,9],dataload:1,dataset:[1,9,18],datastet:9,deepsrgm:13,densiti:9,descript:[2,3],dhrupad:21,dicoveri:10,discret:6,discuss:9,dissert:[2,3],distribut:3,domain:6,drone:2,drum:3,dunya:0,e:3,each:[2,3],epistemolog:3,error:9,evalu:9,everyth:7,exampl:9,explor:9,extent:5,extract:[9,12,20],featur:9,fix:9,fold:9,format:2,fourth:3,frequenc:6,ftanet:12,g:3,gamaka:2,get:[0,7,8],given:2,googl:7,grammar:3,group:[2,3,9],ha:3,hierarchi:3,hindustani:[3,4,18],identif:14,improvis:2,includ:0,indian:[4,21],indic:2,instal:5,instanc:12,instrument:2,integr:5,introduct:9,kalpita:2,khayal:3,last:[2,3],leakag:20,led:2,left:[2,3],length:3,let:8,load:6,load_corpora:5,load_dataset:5,load_model:5,loader:1,local:7,manodharma:2,melod:[2,9,10,11],melodia:12,metadata:0,meter:[2,3,18],mirdata:1,model:20,most:2,motif:3,motiv:5,mridangam:[2,22],multi:14,music:[2,3,4,9,11,18,21],non:12,note:21,number:[2,3],onc:0,packag:5,pars:0,pattern:[9,10],per:2,percuss:[2,3,19],perform:[2,3,9],phrase:[2,3],pitch:[3,9,12,14],plai:3,plot:9,pre:2,predomin:9,process:6,provid:2,puls:18,python:[0,6],raga:[3,13],recognit:13,refer:[8,9,17],relev:6,remind:5,repertoir:3,repres:6,represent:6,requir:9,respect:3,result:9,rhyhtm:18,rhythm:[2,3],right:[2,3],run:7,s:8,saraga:9,sbt:11,search:10,second:3,segment:[9,21],self:9,separ:[20,21],set:7,setup:9,shape:3,show:[2,3],shown:3,signal:6,similar:[3,9],simultan:3,sing:20,singl:9,some:12,sourc:[20,21],spoiler:19,start:8,state:11,stroke:[2,3,22],structur:[2,3,7],svara:2,syllabl:[2,3],symbol:[2,3],tabl:3,tabla:3,term:3,thi:[2,7,8,12,16],third:3,thought:12,three:3,timbr:[2,3],time:6,togeth:3,tonal:3,tonic:[9,14],tool:5,touch:7,track:9,tracker:18,train:12,transcript:[11,19],tutori:8,two:3,up:7,us:[0,2,3],usag:5,util:5,variat:2,visualis:[6,9],vocal:2,voic:20,we:[2,3],websit:0,welcom:8,what:[0,8,9],why:[1,5],work:16,wrapper:5}})
\ No newline at end of file
diff --git a/separation/singing-voice-extraction.html b/separation/singing-voice-extraction.html
index 39cb6fa..727f3ab 100644
--- a/separation/singing-voice-extraction.html
+++ b/separation/singing-voice-extraction.html
@@ -609,20 +609,169 @@ 
Leakage-aware source separation model
 

 
# Importing and initializing a melodia instance
-from compiam import load_model
+import soundfile as sf
+from compiam import load_model, load_dataset
 separation_model = load_model('separation:cold-diff-sep')
 
-# Running extraction for an example track
+# Loading an normalizing an example track
+saraga_carnatic = load_dataset(
+    "saraga_carnatic",
+    data_home=os.path.join("..", "audio", "mir_datasets")
+)
+saraga_tracks = saraga_carnatic.load_tracks()
+example = saraga_tracks["109_Sri_Raghuvara_Sugunaalaya"]
+input_mixture, sr = sf.read(example.audio_path)
+
+input_mixture = input_mixture.T
+mean = np.mean(input_mixture, keepdims=True)
+std = np.std(input_mixture, keepdims=True)
+input_mixture = (input_mixture - mean) / (1e-6 + std)
+

+

+

+

+

+

+
### Getting 30 seconds and separating
+input_mixture = input_mixture[:, :44100*30]
 separation = separation_model.separate(
-    input_data=os.path.join(
-        "..", "audio", "mir_datasets", "saraga1.5_carnatic",
-        "Live at Vani Mahal by Sanjay Subrahmanyan", "Sri Raghuvara Sugunaalaya",
-        "Sanjay Subrahmanyan - Sri Raghuvara Sugunaalaya.mp3"
-    ),
+    input_data=input_mixture,
+    input_sr=sr
 )
 

 

 

+

+
[2023-11-30 11:57:07,539] WARNING [compiam.separation.singing_voice_extraction.cold_diff_sep.separate:117] Resampling... (input sampling rate is assumed 44100Hz,                     make sure this is correct and change input_sr otherwise)
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@@ -631,7 +780,7 @@ 
Leakage-aware source separation model# And we play it!
 ipd.Audio(
     data=separation,
-    rate=22050,
+    rate=separation_model.sample_rate,
 )
 

 

@@ -639,7 +788,7 @@ 
Leakage-aware source separation model