extract feat

extract_feat.lua

@@ -0,0 +1,78 @@
+require 'torch'
+require 'nn'
+require 'audio'
+require 'hdf5'
+require 'cunn'
+require 'cudnn'
+
+opt = {
+  model = "models/soundnet8_final.t7",   -- which soundnet model to load
+  list = "",                             -- text file listing mp3's to process, one per line
+  layer = 24,                            -- which layer to extract features at
+  force=0,                               -- force overwrite
+}
+
+-- one-line argument parser. parses enviroment variables to override the defaults
+for k,v in pairs(opt) do opt[k] = tonumber(os.getenv(k)) or os.getenv(k) or opt[k] end
+print(opt)
+
+if opt.list == '' then error('you must specify text file of audio files to process') end
+
+-- set good defaults
+torch.manualSeed(0)
+torch.setnumthreads(1)
+torch.setdefaulttensortype('torch.FloatTensor')
+cutorch.setDevice(1)
+
+-- load the network
+print('Loading network: ' .. opt.model)
+local net = torch.load(opt.model) 
+net:cuda()
+
+print('Network:')
+print(net)
+
+print('Extracting layer ' .. opt.layer)
+
+-- remove unnecessary layers
+while #net.modules ~= opt.layer do net:remove(#net.modules) end
+
+net:evaluate()
+
+-- http://stackoverflow.com/questions/4990990/lua-check-if-a-file-exists
+function file_exists(name)
+  local f=io.open(name,"r")
+  if f~=nil then io.close(f) return true else return false end
+end
+
+for line in io.lines(opt.list) do
+  local out_file = line .. '.soundnet.h5'
+
+  if file_exists(out_file) and opt.force ~= 1 then
+    print('Skip ' .. out_file)
+
+  else
+    print('Read ' .. line)
+
+    local sound = audio.load(line)
+
+    -- data preprocessing
+    if sound:size(2) > 1 then sound = sound:select(2,1) end -- select first channel (mono)
+    sound:mul(2^-23)                                        -- make range [-256, 256]
+    sound = sound:view(1, 1, -1, 1)                         -- shape to BatchSize x 1 x DIM x 1
+    sound = sound:cuda()                                    -- ship to GPU
+
+    -- forward pass
+    net:forward(sound)
+
+    -- extract layer
+    local feat = net.modules[opt.layer].output:float()
+
+    print('Write ' .. out_file)
+    local fd = hdf5.open(out_file, 'w')
+    fd:write('layer' .. opt.layer, feat:squeeze())
+    fd:close()
+  end
+
+  net:clearState()
+end