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Merge pull request #4 from DigitalPhonetics/speechbrain_asr_eval
Include speechbrain ASR and B2 into main
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anonymization/modules/dsp/anonymise_dir_mcadams_rand_seed.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,142 @@ | ||
| #!/usr/bin/env python3.0 | ||
| # -*- coding: utf-8 -*- | ||
| """ | ||
| @author: Jose Patino, Massimiliano Todisco, Pramod Bachhav, Nicholas Evans | ||
| Audio Security and Privacy Group, EURECOM | ||
| modified version (N.T.) | ||
| """ | ||
| import os | ||
| import librosa | ||
| import numpy as np | ||
| import scipy | ||
| import wave | ||
| import argparse | ||
| from pathlib import Path | ||
| import matplotlib.pyplot as plt | ||
| import random | ||
| from kaldiio import ReadHelper | ||
| import shutil | ||
|
|
||
| def load_utt2spk(path): | ||
| assert os.path.isfile(path), f'File does not exist {path}' | ||
| table = np.genfromtxt(path, dtype='U') | ||
| utt2spk = {utt: spk for utt, spk in table} | ||
| return utt2spk | ||
|
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| def process_data(dataset_path, anon_level, settings): | ||
|
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| utt2spk = None | ||
| if anon_level == 'spk': | ||
| utt2spk = load_utt2spk( dataset_path / 'utt2spk') | ||
|
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| output_path = Path(str(dataset_path) + settings['anon_suffix']) | ||
| if os.path.exists(output_path): | ||
| shutil.rmtree(output_path) | ||
| shutil.copytree(dataset_path, output_path) | ||
| if not os.path.exists(output_path / 'wav'): | ||
| os.makedirs(output_path / 'wav') | ||
| wav_scp = dataset_path / 'wav.scp' | ||
| path_wav_scp_out = output_path / 'wav.scp' | ||
| with open(path_wav_scp_out, 'wt', encoding='utf-8') as writer: | ||
| with ReadHelper(f'scp:{wav_scp}') as reader: | ||
| print(reader) | ||
| for utid, (freq, samples) in reader: | ||
| print(utid) | ||
| output_file = os.path.join(output_path / 'wav', f'{utid}.wav') | ||
| print(output_file) | ||
| if os.path.exists(output_file): | ||
| print('file already exists') | ||
| continue | ||
| samples = samples / (np.iinfo(np.int16).max + 1) | ||
| if anon_level == 'spk': | ||
| assert utid in utt2spk, f'Failed to find speaker ID for utterance {utid}' | ||
| spid = utt2spk[utid] | ||
| random.seed(np.abs(hash(spid))) | ||
| rand_mc_coeff = random.uniform(settings['mc_coeff_min'], settings['mc_coeff_max']) | ||
|
|
||
| samples = anonym(freq=freq, samples=samples, | ||
| winLengthinms=settings['winLengthinms'], | ||
| shiftLengthinms=settings['shiftLengthinms'], | ||
| lp_order=settings['n_coeffs'], mcadams=rand_mc_coeff) | ||
|
|
||
| with wave.open(output_file, 'wb') as stream: | ||
| stream.setframerate(freq) | ||
| stream.setnchannels(1) | ||
| stream.setsampwidth(2) | ||
| stream.writeframes(samples) | ||
| print(f'{utid} {output_file}', file=writer) | ||
| print('Done') | ||
|
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| def anonym(freq, samples, winLengthinms=20, shiftLengthinms=10, lp_order=20, mcadams=0.8): | ||
|
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|
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| print(mcadams) | ||
| eps = np.finfo(np.float32).eps | ||
| samples = samples + eps | ||
|
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| # simulation parameters | ||
| winlen = np.floor(winLengthinms * 0.001 * freq).astype(int) | ||
| shift = np.floor(shiftLengthinms * 0.001 * freq).astype(int) | ||
| length_sig = len(samples) | ||
|
|
||
| # fft processing parameters | ||
| NFFT = 2 ** (np.ceil((np.log2(winlen)))).astype(int) | ||
| # anaysis and synth window which satisfies the constraint | ||
| wPR = np.hanning(winlen) | ||
| K = np.sum(wPR) / shift | ||
| win = np.sqrt(wPR / K) | ||
| Nframes = 1 + np.floor((length_sig - winlen) / shift).astype(int) # nr of complete frames | ||
|
|
||
| # carry out the overlap - add FFT processing | ||
| sig_rec = np.zeros([length_sig]) # allocate output+'ringing' vector | ||
|
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| for m in np.arange(1, Nframes): | ||
| # indices of the mth frame | ||
| index = np.arange(m * shift, np.minimum(m * shift + winlen, length_sig)) | ||
| # windowed mth frame (other than rectangular window) | ||
| frame = samples[index] * win | ||
| # get lpc coefficients | ||
| a_lpc = librosa.core.lpc(frame + eps, order=lp_order) | ||
| # get poles | ||
| poles = scipy.signal.tf2zpk(np.array([1]), a_lpc)[1] | ||
| #index of imaginary poles | ||
| ind_imag = np.where(np.isreal(poles) == False)[0] | ||
| #index of first imaginary poles | ||
| ind_imag_con = ind_imag[np.arange(0, np.size(ind_imag), 2)] | ||
|
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| # here we define the new angles of the poles, shifted accordingly to the mcadams coefficient | ||
| # values >1 expand the spectrum, while values <1 constract it for angles>1 | ||
| # values >1 constract the spectrum, while values <1 expand it for angles<1 | ||
| # the choice of this value is strongly linked to the number of lpc coefficients | ||
| # a bigger lpc coefficients number constraints the effect of the coefficient to very small variations | ||
| # a smaller lpc coefficients number allows for a bigger flexibility | ||
| new_angles = np.angle(poles[ind_imag_con]) ** mcadams | ||
| #new_angles = np.angle(poles[ind_imag_con])**path[m] | ||
|
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| # make sure new angles stay between 0 and pi | ||
| new_angles[np.where(new_angles >= np.pi)] = np.pi | ||
| new_angles[np.where(new_angles <= 0)] = 0 | ||
|
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||
| # copy of the original poles to be adjusted with the new angles | ||
| new_poles = poles | ||
| for k in np.arange(np.size(ind_imag_con)): | ||
| # compute new poles with the same magnitued and new angles | ||
| new_poles[ind_imag_con[k]] = np.abs(poles[ind_imag_con[k]]) * np.exp(1j * new_angles[k]) | ||
| # applied also to the conjugate pole | ||
| new_poles[ind_imag_con[k] + 1] = np.abs(poles[ind_imag_con[k] + 1]) * np.exp(-1j * new_angles[k]) | ||
|
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| # recover new, modified lpc coefficients | ||
| a_lpc_new = np.real(np.poly(new_poles)) | ||
| # get residual excitation for reconstruction | ||
| res = scipy.signal.lfilter(a_lpc,np.array(1),frame) | ||
| # reconstruct frames with new lpc coefficient | ||
| frame_rec = scipy.signal.lfilter(np.array([1]),a_lpc_new,res) | ||
| frame_rec = frame_rec * win | ||
|
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| outindex = np.arange(m * shift, m * shift + len(frame_rec)) | ||
| # overlap add | ||
| sig_rec[outindex] = sig_rec[outindex] + frame_rec | ||
| sig_rec = (sig_rec / np.max(np.abs(sig_rec)) * (np.iinfo(np.int16).max - 1)).astype(np.int16) | ||
| return sig_rec | ||
| #scipy.io.wavfile.write(output_file, freq, np.float32(sig_rec)) | ||
| #awk -F'[/.]' '{print $5 " sox " $0 " -t wav -R -b 16 - |"}' > data/$dset$anon_data_suffix/wav.scp |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -15,7 +15,7 @@ pyworld | |
| scipy | ||
| segments | ||
| sentencepiece | ||
| sklearn | ||
| scikit-learn | ||
| sounddevice | ||
| SoundFile | ||
| speechbrain==0.5.10 | ||
|
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