diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py
index 39a7219433..4ca0974a17 100644
--- a/gensim/models/word2vec.py
+++ b/gensim/models/word2vec.py
@@ -647,13 +647,19 @@ def build_vocab_from_freq(self, word_freq, keep_raw_vocab=False, corpus_count=No
 
         Examples
         --------
-        >>> build_vocab_from_freq({"Word1":15,"Word2":20}, update=True)
+        >>> from gensim.models.word2vec import Word2Vec
+        >>> model= Word2Vec()
+        >>> model.build_vocab_from_freq({"Word1": 15, "Word2": 20})
         """
         logger.info("Processing provided word frequencies")
-        vocab = defaultdict(int, word_freq)
+        raw_vocab = word_freq  # Instead of scanning text, this will assign provided word frequencies dictionary(word_freq) to be directly the raw vocab
+        logger.info(
+            "collected %i different raw word, with total frequency of %i",
+            len(raw_vocab), sum(itervalues(raw_vocab))
+        )
 
-        self.corpus_count = corpus_count if corpus_count else 0
-        self.raw_vocab = vocab
+        self.corpus_count = corpus_count if corpus_count else 0  # Since no sentences are provided, this is to control the corpus_count
+        self.raw_vocab = raw_vocab
 
         self.scale_vocab(keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, update=update)  # trim by min_count & precalculate downsampling
         self.finalize_vocab(update=update)  # build tables & arrays
@@ -682,7 +688,7 @@ def scan_vocab(self, sentences, progress_per=10000, trim_rule=None):
                 )
             for word in sentence:
                 vocab[word] += 1
-                total_words += 1
+            total_words += len(sentence)
 
             if self.max_vocab_size and len(vocab) > self.max_vocab_size:
                 utils.prune_vocab(vocab, min_reduce, trim_rule=trim_rule)
@@ -694,6 +700,7 @@ def scan_vocab(self, sentences, progress_per=10000, trim_rule=None):
         )
         self.corpus_count = sentence_no + 1
         self.raw_vocab = vocab
+        return total_words
 
     def scale_vocab(self, min_count=None, sample=None, dry_run=False,
                     keep_raw_vocab=False, trim_rule=None, update=False):
diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py
index 4c642ce5d2..242b6d39bd 100644
--- a/gensim/test/test_word2vec.py
+++ b/gensim/test/test_word2vec.py
@@ -97,8 +97,8 @@ def testBuildVocabFromFreq(self):
         model_neg = word2vec.Word2Vec(size=10, min_count=0, seed=42, hs=0, negative=5)
         model_hs.build_vocab_from_freq(freq_dict)
         model_neg.build_vocab_from_freq(freq_dict)
-        self.assertTrue(len(model_hs.wv.vocab), 12)
-        self.assertTrue(len(model_neg.wv.vocab), 12)
+        self.assertEqual(len(model_hs.wv.vocab), 12)
+        self.assertEqual(len(model_neg.wv.vocab), 12)
         self.assertEqual(model_hs.wv.vocab['minors'].count, 2)
         self.assertEqual(model_hs.wv.vocab['graph'].count, 3)
         self.assertEqual(model_hs.wv.vocab['system'].count, 4)
@@ -126,11 +126,42 @@ def testBuildVocabFromFreq(self):
         new_freq_dict = {'computer': 1, 'artificial': 4, 'human': 1, 'graph': 1, 'intelligence': 4, 'system': 1, 'trees': 1}
         model_hs.build_vocab_from_freq(new_freq_dict, update=True)
         model_neg.build_vocab_from_freq(new_freq_dict, update=True)
-        self.assertTrue(model_hs.wv.vocab['graph'].count, 4)
-        self.assertTrue(model_hs.wv.vocab['artificial'].count, 4)
+        self.assertEqual(model_hs.wv.vocab['graph'].count, 4)
+        self.assertEqual(model_hs.wv.vocab['artificial'].count, 4)
         self.assertEqual(len(model_hs.wv.vocab), 14)
         self.assertEqual(len(model_neg.wv.vocab), 14)
 
+    def testPruneVocab(self):
+        """Test Prune vocab while scanning sentences"""
+        sentences = [
+            ["graph", "system"],
+            ["graph", "system"],
+            ["system", "eps"],
+            ["graph", "system"]
+        ]
+        model = word2vec.Word2Vec(sentences, size=10, min_count=0, max_vocab_size=2, seed=42, hs=1, negative=0)
+        self.assertEqual(len(model.wv.vocab), 2)
+        self.assertEqual(model.wv.vocab['graph'].count, 3)
+        self.assertEqual(model.wv.vocab['system'].count, 4)
+
+        sentences = [
+            ["graph", "system"],
+            ["graph", "system"],
+            ["system", "eps"],
+            ["graph", "system"],
+            ["minors", "survey", "minors", "survey", "minors"]
+        ]
+        model = word2vec.Word2Vec(sentences, size=10, min_count=0, max_vocab_size=2, seed=42, hs=1, negative=0)
+        self.assertEqual(len(model.wv.vocab), 3)
+        self.assertEqual(model.wv.vocab['graph'].count, 3)
+        self.assertEqual(model.wv.vocab['minors'].count, 3)
+        self.assertEqual(model.wv.vocab['system'].count, 4)
+
+    def testTotalWordCount(self):
+        model = word2vec.Word2Vec(size=10, min_count=0, seed=42)
+        total_words = model.scan_vocab(sentences)
+        self.assertEqual(total_words, 29)
+
     def testOnlineLearning(self):
         """Test that the algorithm is able to add new words to the
         vocabulary and to a trained model when using a sorted vocabulary"""