diff demos/audio_trim_demo.py @ 2:edee860b9f61

First release on Github

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/demos/audio_trim_demo.py	Thu Sep 17 22:01:30 2015 +0200
@@ -0,0 +1,90 @@
+"""
+@author: Amine SEHILI <[e-mail]>
+September, 2015
+"""
+
+# Trim leading and trailing silence from a record
+
+from auditok import ADSFactory, AudioEnergyValidator, StreamTokenizer, player_for, dataset
+import pyaudio
+
+"""
+The  tokenizer in the following example is set up to remove the silence
+that precedes the first acoustic activity or follows the last activity 
+in a record. It preserves whatever it founds between the two activities.
+In other words, it removes the leading and trailing silence.
+
+Sampling rate is 44100 sample per second, we'll use an analysis window of 100 ms
+(i.e. bloc_ksize == 4410)
+
+Energy threshold is 50.
+
+The tokenizer will start accumulating windows up from the moment it encounters
+the first analysis window of an energy >= 50. ALL the following windows will be 
+kept regardless of their energy. At the end of the analysis, it will drop trailing
+ windows with an energy below 50.
+
+This is an interesting example because the audio file we're analyzing contains a very
+brief noise that occurs within the leading silence. We certainly do want our tokenizer 
+to stop at this point and considers whatever it comes after as a useful signal.
+To force the tokenizer to ignore that brief event we use two other parameters `init_min`
+ans `init_max_silence`. By `init_min`=3 and `init_max_silence`=1 we tell the tokenizer
+that a valid event must start with at least 3 noisy windows, between which there
+is at most 1 silent window.
+
+Still with this configuration we can get the tokenizer detect that noise as a valid event
+(if it actually contains 3 consecutive noisy frames). To circummvent this we use an enough
+large analysis window (here of 100 ms) to ensure that the brief noise be surrounded by a much
+longer silence and hence the energy of the overall analysis window will be below 50.
+
+When using a shorter analysis window (of 10ms for instance, block_size == 441), the brief
+noise contributes more to energy calculation which yields an energy of over 50 for the window.
+Again we can deal with this situation by using a higher energy threshold (55 for example)
+ 
+"""
+
+
+# record = True so that we'll be able to rewind the source.
+asource = ADSFactory.ads(filename=dataset.was_der_mensch_saet_mono_44100_lead_trail_silence,
+          record=True, block_size=4410)
+asource.open()
+
+original_signal = []
+# Read the whole signal
+while True:
+   w = asource.read()
+   if w is None:
+      break
+   original_signal.append(w)
+
+original_signal = ''.join(original_signal)
+
+
+# rewind source
+asource.rewind()
+
+# Create a validator with an energy threshold of 50
+validator = AudioEnergyValidator(sample_width=asource.get_sample_width(), energy_threshold=50)
+
+# Create a tokenizer with an unlimited token length and continuous silence within a token
+# Note the DROP_TRAILING_SILENCE mode that will ensure removing trailing silence
+trimmer = StreamTokenizer(validator, min_length = 20, max_length=99999999,
+                          max_continuous_silence=9999999, mode=StreamTokenizer.DROP_TRAILING_SILENCE, init_min=3, init_max_silence=1)
+
+
+tokens = trimmer.tokenize(asource)
+
+# Make sure we only have one token
+assert len(tokens) == 1, "Should have detected one single token"
+
+trimmed_signal = ''.join(tokens[0][0])
+
+player = player_for(asource)
+
+print("\n ** Playing original signal (with leading and trailing silence)...")
+player.play(original_signal)
+print("\n ** Playing trimmed signal...")
+player.play(trimmed_signal)
+
+player.stop()
+asource.close()