[DEV] update normalisation preoprorcess
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@ -14,7 +14,10 @@ import math
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import json
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import resampy
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import numpy as np
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import scipy.io.wavfile as wavfile
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import scipy.io.wavfile
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import scipy.signal
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parser = argparse.ArgumentParser()
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parser.add_argument("-i", "--input", help="Input directory",
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@ -81,21 +84,137 @@ for elem in audio_corpus_element:
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debug.error(" ==> missing field 'audio_filename' ...")
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filename = os.path.join(os.path.dirname(elem), input_data["audio_filename"])
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if filename[-3:] == "wav":
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sample_rate, audio_data = wavfile.read(filename)
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sample_rate, audio_data = scipy.io.wavfile.read(filename)
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else:
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debug.error("Not supported file type: '" + str(filename[-3:]) + "' suported: [wav]")
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debug.info("Read: " + str(len(audio_data)) + " sample(s)")
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audio_16k = resampy.resample(audio_data, 48000, 16000)
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#######################################################
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## Step 1: Generate speech areas (work on input, because I am sure that the data is not modified
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#######################################################
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audio_data_absolute = np.absolute(audio_data)
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shape = np.copy(audio_data_absolute)
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# average of the anbiant noise
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avg = 0
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# count represent the number of basic sample to etalonate the generic ambiant noise
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count = 0
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count_all = 0
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set_value = 32767
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# number of sampel of the anbiant noise calculation
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ambiant_basic_size = 48000/4 # : 1/4 s ==> the cormus is manage to have 1 second before starting to have real data voice
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for sample_audio in audio_data_absolute:
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#shape[count_all] = 0
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count_all += 1
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if count < ambiant_basic_size:
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# Remove the 0 padding
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if count == 0 and sample_audio == 0:
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continue
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elif count == 0:
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debug.info("start data " + str(count_all) + " samples (at value 0)")
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count += 1
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avg += sample_audio
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elif count == ambiant_basic_size:
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count += 1
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avg /= ambiant_basic_size
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debug.info("basic ambiant is: " + str(avg) + " start annalyse at : " + str(count_all/48000) + " sec")
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if avg <= 327.67:
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avg = int(327.67)
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else:
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avg = int(avg * 1.1)
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## set_value = avg
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debug.info(" inspect at " + str(avg))
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else:
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if sample_audio >= avg:
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shape[count_all-1] = set_value;
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#filename_out = os.path.join(args.output, input_data["audio_filename"] + "_ori.wav")
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#scipy.io.wavfile.write(filename_out, 48000, audio_data)
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#filename_out = os.path.join(args.output, input_data["audio_filename"] + "_abs.wav")
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#scipy.io.wavfile.write(filename_out, 48000, audio_data_absolute)
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#filename_out = os.path.join(args.output, input_data["audio_filename"] + "_shape1.wav")
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#scipy.io.wavfile.write(filename_out, 48000, shape)
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windows = int(48000 * 0.02)
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count_all = 0
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count = 0
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for sample_audio in shape:
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count_all += 1
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if sample_audio == set_value:
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count = windows
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continue
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count -= 1
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if count >= 0:
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shape[count_all-1] = set_value;
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shape = np.flip(shape)
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count_all = 0
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count = 0
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for sample_audio in shape:
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count_all += 1
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if sample_audio == set_value:
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count = windows
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continue
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count -= 1
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if count >= 0:
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shape[count_all-1] = set_value;
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else:
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shape[count_all-1] = 0
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shape = np.flip(shape)
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filename_out = os.path.join(args.output, input_data["audio_filename"] + "_shape.wav")
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scipy.io.wavfile.write(filename_out, 48000, shape)
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count_all = 0
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table_voice_detected = [[0,False]]
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previous = False
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# use numpy slicing: [start:stop:step] https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#basic-slicing-and-indexing
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for sample_audio in shape[3:len(shape):3]:
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count_all += 1
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if previous == False:
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if sample_audio == set_value:
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table_voice_detected.append([count_all, True])
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previous = True
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else:
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if sample_audio == 0:
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table_voice_detected.append([count_all, False])
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previous = False
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#######################################################
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## Step 2: Resample
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#######################################################
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audio_16k = resampy.resample(audio_data, 48000, 16000, filter='kaiser_best')
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#audio_16k = resampy.resample(audio_data, 48000, 16000, filter='kaiser_fast')
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debug.info("write: " + str(len(audio_16k)) + " sample(s)")
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filename_out = os.path.join(args.output, input_data["audio_filename"])
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wavfile.write(filename_out, 16000, audio_16k)
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input_data["audio_sample_rate"] = 16000
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scipy.io.wavfile.write(filename_out, 16000, audio_16k)
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# create new data format:
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output_data = {
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"value": input_data["value"],
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"language": input_data["language"],
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"audio_sample_rate": 16000,
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"audio_filename": input_data["audio_filename"],
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"VAD": table_voice_detected,
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"action": [
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{
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"type": "resampling",
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"tool": "resampy",
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"desc": "48000 ==> 16000",
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"src": elem,
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},{
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"type": "auto VAD",
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"tool": "internal_1",
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"desc": "When control the input data, with small noise, we can detect voice, just with signal power ...",
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"src": elem,
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},
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],
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}
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filename_out_json = os.path.join(args.output, os.path.basename(elem))
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with open(filename_out_json, 'w') as outfile:
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json.dump(input_data, outfile, indent="\t")
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json.dump(output_data, outfile, indent="\t")
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debug.info("Finish")
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