Merge branch 'master' of gitlab.idiap.ch:bob/bob.learn.pytorch

bob/learn/pytorch/preprocessor/audio/DltResNet.py

@@ -13,6 +13,8 @@ from bob.extension import rc
 from bob.extension.download import download_file
 import os
 from bob.ap import Spectrogram
+from scipy.signal import resample
+
 
 class DltResNetExtractor(Preprocessor):
   """ The class for implementing the feature extraction of DltResNet embeddings.
@@ -25,7 +27,7 @@ class DltResNetExtractor(Preprocessor):
       Use gpu for extracting the embeddings.
 
   """
-  
+
   def __init__(self, model_file=None, num_classes=1211, bn_dim=128, cuda_flag=0, test=False, use_res=False):
     """ Init method
 
@@ -43,11 +45,11 @@ class DltResNetExtractor(Preprocessor):
         Called for unit test (Default: False).
     use_res: bool
         Use ResNet model instead of DltResNet model (Default: False).
-    
+
     """
-        
+
     Preprocessor.__init__(self, min_preprocessed_file_size=bn_dim)
-    
+
     # model
     model_type = "emb"
     if use_res:
@@ -77,9 +79,9 @@ class DltResNetExtractor(Preprocessor):
         model_file = model_path
 
       else:
-        # do nothing (used mainly for unit testing) 
+        # do nothing (used mainly for unit testing)
         pass
-    
+
     if test:
       self.network = torch.nn.DataParallel(self.network)
     else:
@@ -118,8 +120,8 @@ class DltResNetExtractor(Preprocessor):
     Returns
     -------
     feature : 2D :py:class:`numpy.ndarray` (floats)
-      The extracted features as a 1d array of size 128 
-    
+      The extracted features as a 1d array of size 128
+
     """
 
     feats = []
@@ -139,10 +141,10 @@ class DltResNetExtractor(Preprocessor):
             k = specs[i*L:(i+1)*L,:]
         else:
             k = specs[i*L:,:]
-        
+
         X = (k - k.mean(axis=0)) / (k.std(axis=0) + 1e-7)
         batch.append(X)
-        if len(batch) == batch_size or i == embedding_count - 2 or i == embedding_count - 1: 
+        if len(batch) == batch_size or i == embedding_count - 2 or i == embedding_count - 1:
           with torch.no_grad():
             in_batch = numpy.array(batch)
             if cuda_flag == 1:
@@ -152,7 +154,7 @@ class DltResNetExtractor(Preprocessor):
             fX = model(input_var).data.cpu().numpy()
             feats.append(fX)
             batch = []
-  
+
     return numpy.vstack(feats)
 
   def __call__(self, audio, annotations=None):
@@ -161,22 +163,29 @@ class DltResNetExtractor(Preprocessor):
     Parameters
     ----------
     audio : :py:class:`numpy.ndarray` (floats)
-      The audio file to extract the features from. 
+      The audio file to extract the features from.
     annotations : None
       Apply annotations if needed
 
     Returns
     -------
     feature : 2D :py:class:`numpy.ndarray` (floats)
-      The extracted features as a 1d array of size 128 
-    
+      The extracted features as a 1d array of size 128
+
     """
     data = audio[1]
     if data.dtype =='int16':
       data = numpy.cast['float'](data)
     if numpy.max(numpy.abs(data)) < 1:
       data = data * 2**15
+
     rate = audio[0]
+    # resample audio to 16KHz so it can work with the model
+    if rate != 16000:
+      samps = round(len(data) * 16000 / rate)  # Number of samples to resample
+      data = resample(data, samps)
+      rate = 16000
+
     win_length_ms = 25
     win_shift_ms = 10
     normalize_mean = True
@@ -204,4 +213,4 @@ class DltResNetExtractor(Preprocessor):
     """
     f= bob.io.base.HDF5File(data_file)
     feats = f.read("feats")
-    return feats
\ No newline at end of file
+    return feats

doc/guide_audio_extractor.rst

@@ -2,7 +2,7 @@
 Audio Embedding Extractor
 =========================
 
-This subpackage is part of ``bob.learn.pytorch``  package to extract features from an input audio using CNN models which 
+This subpackage is part of ``bob.learn.pytorch``  package to extract features from an input audio using CNN models which
 trained with pytorch_.
 
 For this purpose, you can specify your feature extractor in configuration
@@ -18,10 +18,10 @@ DltResNet Model
 
 .. note::
 
-   The models will automatically download to the data folder of this package and save it in 
+   The models will automatically download to the data folder of this package and save it in
    ``[env-path]./bob/learn/pytorch/preprocessor/audio/data/drn34.pth.tar``.
    If you want to set another path for this model do::
-   
+
    $ bob config set bob.learn.pytorch.extractor.audio.drn_modelpath /path/to/mymodel
 
 
@@ -34,18 +34,18 @@ ResNet Model
 
 .. note::
 
-   The models will automatically download to the data folder of this package and save it in 
+   The models will automatically download to the data folder of this package and save it in
    ``[env-path]./bob/learn/pytorch/preprocessor/audio/data/rn34.pth.tar``.
    If you want to set another path for this model do::
-   
+
    $ bob config set bob.learn.pytorch.extractor.audio.rn_modelpath /path/to/mymodel
 
 
 A concrete example
 ------------------
 
-Imagine that you have the DltResNet model and you would 
-like to use the embedding layer as a feature to encode identity. 
+Imagine that you have the DltResNet model and you would
+like to use the embedding layer as a feature to encode identity.
 
 Your ``preprocessor`` in bob_ pipe-lines should be defined this way in the configuration file:
 
@@ -56,16 +56,18 @@ Your ``preprocessor`` in bob_ pipe-lines should be defined this way in the confi
   _model = 'path/to/your/model.pth'
   _num_classes = 1211
   _use_res = False
-  preprocessor = ResNetDltExtractor(_model, _num_classes, use_res=_use_res)
+  preprocessor = DltResNetExtractor(_model, _num_classes, use_res=_use_res)
 
-Note that the number of classes is irrelevant here, but is required to build the 
+Note that the number of classes is irrelevant here, but is required to build the
 network (before loading it). ``_model``, ``_num_classes`` and ``_use_res`` are optional input arguments and will be set automatically. If you want to use ResNet model instead of DltResNet model, set the ``use_res`` input argument to ``True``. This class is the embedding extractor and this style of naming (``preprocessor``), is for compatibility with bob_ framework. In this set, we just need a dummy ``extractor`` in bob_ framework which can be defined in the configuration file in this way:
 
 .. code:: python
 
-  from bob.learn.pytorch.extractor.audio import DummyExtractor
+  from bob.bio.base.extractor import CallableExtractor
 
-  extractor = DummyExtractor()
+  extractor = CallableExtractor(lambda x: x)
+  extracted_directory = "preprocessed"
+  skip_extraction = True
 
 
 You can easily implement your own extractor based on your own network too. Just have
@@ -73,4 +75,4 @@ a look at the code in ``bob/learn/pytorch/preprocessor/audio``.
 
 
 .. _bob: http://idiap.github.io/bob/
-.. _pytorch: http://pytorch.org/
\ No newline at end of file
+.. _pytorch: http://pytorch.org/