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selec_anon/compute_anon_spk_vector/01_run.sh

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+#!/bin/bash
+
+
+UTTDIR=data
+SRCDIR=$PWD/selec_anon/output_ori_spk_vector/
+POOL_XVEC_DIR=libritts_train_other_500
+SCOREDIR=$PWD/selec_anon/output_anon_spk_vector
+mkdir -p ${SCOREDIR}
+
+SPKXVECNAME=spk_xvector
+AFFINITYDIR=affinity
+PSEUDODIR=pseudo_xvectors
+PSEUDODIRBINDIR=xvectors
+
+xvec_dim=192
+rand_level="spk"
+cross_gender="false"
+distance="cosine"
+proximity="farthest"
+#proximity="oldest"
+rand_seed="2020"
+
+#rand_seed=0
+# compute average of each xvector of each speaker
+#  if necessary, concatenate the xvector_*.scp into xvector.scp
+for dirname in `ls ${SRCDIR}`
+do  
+    TMPNAME=`echo ${dirname} | sed 's:xvectors_::g'`
+    SPK2UTT=${UTTDIR}/${TMPNAME}/spk2utt
+    XVEC_SCP=${SRCDIR}/${dirname}/xvector.scp
+
+    OUTPUTDIR=${SCOREDIR}/${dirname}
+    TMPSCP=${SCOREDIR}/${dirname}/xvector.scp
+    mkdir -p ${OUTPUTDIR}
+    
+    if [ ! -e ${XVEC_SCP} ];
+    then
+	SUBFILES=`ls ${SRCDIR}/${dirname}/*.scp`
+	echo "python concatenate_scp.py ${TMPSCP} ${SUBFILES}"
+	# python selec_anon/compute_anon_spk_vector/concatenate_scp.py ${TMPSCP} ${SUBFILES}
+    # python -m ipdb 
+    python selec_anon/compute_anon_spk_vector/concatenate_scp.py ${TMPSCP} ${SUBFILES}
+    else
+	cp ${XVEC_SCP} ${TMPSCP}
+    fi
+
+    echo "python -m ipdb speaker_xvector.py ${SPK2UTT} ${TMPSCP} ${OUTPUTDIR} ${SPKXVECNAME}"
+    # python -m ipdb 
+    python selec_anon/compute_anon_spk_vector/speaker_xvector.py ${SPK2UTT} ${TMPSCP} ${OUTPUTDIR} ${SPKXVECNAME}
+done
+
+
+# compute affinity
+for dirname in `ls ${SRCDIR} | grep -v ${POOL_XVEC_DIR}`
+do   
+    TMP_SRC_PATH=${SCOREDIR}/${dirname}/${SPKXVECNAME}.scp
+    TMP_POOL_PATH=${SCOREDIR}/${POOL_XVEC_DIR}/${SPKXVECNAME}.scp
+    TMP_OUTPUT_DIR=${SCOREDIR}/${dirname}/${AFFINITYDIR}
+    echo "python compute_spk_pool_cosine.py ${TMP_SRC_PATH} ${TMP_POOL_PATH} ${TMP_OUTPUT_DIR}"
+    # python -m ipdb 
+    python selec_anon/compute_anon_spk_vector/compute_spk_pool_cosine.py ${TMP_SRC_PATH} ${TMP_POOL_PATH} ${TMP_OUTPUT_DIR}
+done
+
+# compute pseudo vectors
+for dirname in `ls ${SRCDIR} | grep -v ${POOL_XVEC_DIR}`
+do
+
+    TMPNAME=`echo ${dirname} | sed 's:xvectors_::g'`
+    TMP_SPK2UTT=${UTTDIR}/${TMPNAME}/spk2utt
+    TMP_SPK2GENDER=${UTTDIR}/${TMPNAME}/spk2gender
+    
+    TMPNAME=`echo ${POOL_XVEC_DIR} | sed 's:xvectors_::g'`
+    TMP_POOL_SPK2GENDER=${UTTDIR}/${TMPNAME}/spk2gender
+    
+    TMP_POOL_DATA_PATH=${SCOREDIR}/${POOL_XVEC_DIR}/${SPKXVECNAME}.scp
+    TMP_AFFINITY_PATH=${SCOREDIR}/${dirname}/${AFFINITYDIR}
+    TMP_OUTPUT_DIR=${SCOREDIR}/${dirname}/${PSEUDODIR}
+    mkdir -p ${TMP_OUTPUT_DIR}
+
+    echo "python gen_pseudo_xvecs.py ${TMP_SPK2UTT} ${TMP_SPK2GENDER} ${TMP_POOL_SPK2GENDER} \
+	   ${TMP_AFFINITY_PATH} ${TMP_POOL_DATA_PATH} ${TMP_OUTPUT_DIR} ${PSEUDODIRBINDIR} \
+	   ${rand_level} ${cross_gender} ${proximity} ${rand_seed} ${xvec_dim}"
+    # python -m ipdb  
+    python selec_anon/compute_anon_spk_vector/gen_pseudo_xvecs.py ${TMP_SPK2UTT} ${TMP_SPK2GENDER} ${TMP_POOL_SPK2GENDER} \
+	   ${TMP_AFFINITY_PATH} ${TMP_POOL_DATA_PATH} ${TMP_OUTPUT_DIR} ${PSEUDODIRBINDIR} \
+	   ${rand_level} ${cross_gender} ${proximity} ${rand_seed} ${xvec_dim}
+    
+    #rand_seed=$((rand_seed+1))
+done

selec_anon/compute_anon_spk_vector/compute_spk_pool_cosine.py

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+#!/usr/bin/env python
+""" Compute cosince distance between
+"""
+import sys
+from kaldiio import WriteHelper, ReadHelper
+import os
+from os.path import join, isdir
+
+from scipy.spatial import distance
+
+args = sys.argv
+
+src_xvec_file = args[1]
+pool_xvec_file = args[2]
+scores_dir = args[3]
+
+if not isdir(scores_dir):
+    os.makedirs(scores_dir)
+
+pool_xvectors = {}
+c = 0
+with ReadHelper('scp:'+pool_xvec_file) as reader:
+    for key, xvec in reader:
+        #print key, mat.shape
+        pool_xvectors[key] = xvec
+        c += 1
+print("Read ", c, "pool xvectors")
+
+with ReadHelper('scp:'+src_xvec_file) as reader:
+    for sspk, sxvec in reader:
+        print("Computing cosine measure for " + sspk)
+        with open(join(scores_dir, 'affinity_'+sspk), 'w') as sf:
+            for pspk, pxvec in pool_xvectors.items():
+                # compute cosine distance between src and pool spk
+                # Multiplying by -1 to ensure compatibility with affinity
+                # Now lower value will indicate less affinity as compared
+                # to original cosine distance
+                dist = -1.0 * distance.cosine(sxvec.squeeze(), pxvec.squeeze())
+                sf.write(sspk + ' ' + pspk + ' ' + str(dist) + '\n')
+

selec_anon/compute_anon_spk_vector/concatenate_scp.py

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+#!/usr/bin/env python
+""" Concatenate multiple files into a single one
+
+$: python concatenate_scp.py output_file input_file_1 input_file_2 ...
+
+"""
+import os
+import sys
+from os.path import join, isdir
+import numpy as np
+
+import local_lib
+
+def main(file_list, output_file):
+    
+    dic_buf = {}
+    for file_path in file_list:
+        data_dic = local_lib.load_scp(file_path)
+        dic_buf = {**dic_buf, **data_dic}
+    print("Concatenate {:d} data items".format(len(dic_buf)))
+    local_lib.write_scp(dic_buf, output_file)
+    return
+
+if __name__ == "__main__":
+
+    # parse input
+    args = sys.argv
+    if len(args) < 3:
+        print(__doc__)
+        exit(1)
+
+    output_file = args[1]
+    input_file = args[2:]
+    main(input_file, output_file)
+

selec_anon/compute_anon_spk_vector/gen_pseudo_xvecs.py

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+#!/usr/bin/env python
+"""Compute pseudo xvectos
+
+$: python gen_pseudo_xvecs.py 
+
+"""
+import os
+import sys
+from os.path import basename, join
+import operator
+
+import numpy as np
+import random
+from kaldiio import WriteHelper, ReadHelper
+import pickle
+import local_lib
+
+REGION = 100
+WORLD = 200
+
+g_gender_rev = {'M': 'F', 'F': 'M'}
+
+# Core logic of anonymization by randomization
+def select_random_xvec(top500, pool_xvectors, xvec_dim):
+    # number of random xvectors to select out of pool
+    #random100mask = np.random.random_integers(0, 199, NR)
+    choose_spk = []
+    random100mask = random.sample(range(WORLD), REGION)
+    pseudo_spk_list = [x for i, x in enumerate(top500) if i in
+                           random100mask]
+    pseudo_spk_matrix = np.zeros((REGION, xvec_dim), dtype='float64')
+    for i, spk_aff in enumerate(pseudo_spk_list):
+        pseudo_spk_matrix[i, :] = pool_xvectors[spk_aff[0]]
+        choose_spk.append(spk_aff[0])
+    # Take mean of 100 randomly selected xvectors
+    pseudo_xvec = np.mean(pseudo_spk_matrix, axis=0)
+    return pseudo_xvec, choose_spk
+
+
+def get_top_K(spk, affinity_scores_dir, gender, pool_spk2gender, 
+              cross_gender, proximity):
+
+    # Filter the affinity pool by gender
+    affinity_pool = {}
+
+    # If we are doing cross-gender VC, reverse the gender else gender remains same
+    if cross_gender:
+        gender = g_gender_rev[gender]
+
+    #print("Filtering pool for spk: "+spk)
+    with open(join(affinity_scores_dir, 'affinity_'+spk)) as f:
+        for line in f.read().splitlines():
+            sp = line.split()
+            pool_spk = sp[1]
+            af_score = float(sp[2])
+            if pool_spk2gender[pool_spk] == gender:
+                affinity_pool[pool_spk] = af_score
+
+    # Sort the filtered affinity pool by scores
+    if proximity == "farthest":
+        sorted_aff = sorted(affinity_pool.items(), key=operator.itemgetter(1))
+    elif proximity == "nearest":
+        sorted_aff = sorted(affinity_pool.items(), key=operator.itemgetter(1),
+                           reverse=True)
+    elif proximity== "oldest" or proximity== "youngest":
+        with open("/home/smg/miao/speech-re-facebook/japa_company_2022/data/pool_1300/spk_age_pool.pkl",'rb') as tf:
+            new_dic = pickle.load(tf)
+        same_age_dic= {}       
+        for line in new_dic:
+            pool_spk = line
+            age = new_dic[line]
+            if pool_spk2gender[pool_spk] == gender:
+                same_age_dic[pool_spk] = age
+        if proximity== "oldest":
+            sorted_aff = sorted(same_age_dic.items(), key=operator.itemgetter(1),
+                           reverse=True)
+        elif proximity== "youngest":
+            sorted_aff = sorted(same_age_dic.items(), key=operator.itemgetter(1))
+        #WORLD=100
+    # Select WORLD least affinity speakers and then randomly select REGION out of
+    # them
+    top_spk = sorted_aff[:WORLD]
+    return top_spk, gender
+
+def write_pseudo_vector(pseudo_xvec_map, pseudo_gender_map, 
+                        pseudo_xvecs_dir, pseudo_bin_dir):
+    # Write features as ark,scp
+    print("Writing pseud-speaker xvectors to: " + pseudo_xvecs_dir)
+    ark_scp_output = 'ark,scp:{}/{}.ark,{}/{}.scp'.format(
+        pseudo_xvecs_dir, 'pseudo_xvector',
+        pseudo_xvecs_dir, 'pseudo_xvector')
+    with WriteHelper(ark_scp_output) as writer:
+        for uttid, xvec in pseudo_xvec_map.items():
+            writer(uttid, xvec)
+
+    print("Writing pseudo-speaker spk2gender.")
+    with open(join(pseudo_xvecs_dir, 'spk2gender'), 'w') as f:
+        spk2gen_arr = [spk+' '+gender for spk, gender in pseudo_gender_map.items()]
+        sorted_spk2gen = sorted(spk2gen_arr)
+        f.write('\n'.join(sorted_spk2gen) + '\n')
+
+    # write to binary format
+    output_dir = os.path.join(pseudo_xvecs_dir, pseudo_bin_dir)
+    if not os.path.isdir(output_dir):
+        os.mkdir(output_dir)
+    for uttid, xvec in pseudo_xvec_map.items():
+        output_path = os.path.join(output_dir, uttid) + '.xvector'
+        local_lib.f_write_raw_mat(xvec, output_path)
+
+    return
+
+def main(args):
+    
+    ## args
+    src_spk2utt_file = args[1]
+    src_spk2gender_file = args[2]
+    pool_spk2gender_file = args[3]
+
+    affinity_scores_dir = args[4]
+    pool_xvec_scp = args[5]
+    output_pseudo_xvecs_dir = args[6]
+    output_pseudo_xvecs_bin_dir = args[7]
+    
+    rand_level = args[8]
+    cross_gender = args[9] == "true"
+    proximity = args[10]
+    rand_seed = args[11]
+    xvec_dim = int(args[12])
+    
+    ## 
+    random.seed(rand_seed)
+
+    if cross_gender:
+        print("Opposite gender speakers will be selected.")
+    else:
+        print("Same gender speakers will be selected.")
+
+    print("Randomization level: " + rand_level)
+    print("Proximity: " + proximity)
+    
+    
+    ##
+    src_spk2gender = local_lib.load_gender(src_spk2gender_file)
+    src_spk2utt = local_lib.load_utt(src_spk2utt_file)
+    pool_spk2gender = local_lib.load_gender(pool_spk2gender_file)
+    
+    # 
+    pool_xvectors = local_lib.load_scp(pool_xvec_scp)
+    
+    
+    pseudo_xvec_map = {}
+    pseudo_gender_map = {}
+    pseudo_spk_map = {}
+    
+    for spk, gender in src_spk2gender.items():
+        gender = gender.upper()
+        print(spk,gender)
+        
+        # get the top_K
+        top_spk, gender = get_top_K(spk, affinity_scores_dir, gender,
+                                    pool_spk2gender, cross_gender, proximity)
+        pseudo_gender_map[spk] = gender
+        
+        # get the pseudo vector
+        if rand_level == 'spk':
+            # For rand_level = spk, one xvector is assigned to all the utterances
+            # of a speaker
+            pseudo_xvec, choose_spks = select_random_xvec(top_spk, pool_xvectors, xvec_dim)
+            # Assign it to all utterances of the current speaker
+            for uttid in src_spk2utt[spk]:
+                pseudo_xvec_map[uttid] = pseudo_xvec
+        elif rand_level == 'utt':
+            # For rand_level = utt, random xvector is assigned to all the utterances
+            # of a speaker
+            for uttid in src_spk2utt[spk]:
+                # Compute random vector for every utt
+                pseudo_xvec, choose_spks = select_random_xvec(top_spk, pool_xvectors, xvec_dim)
+                # Assign it to all utterances of the current speaker
+                pseudo_xvec_map[uttid] = pseudo_xvec
+        else:
+            print("rand_level not supported! Errors will happen!")
+
+        pseudo_spk_map[spk] = choose_spks
+        #print(choose_spks)
+
+    with open(join(output_pseudo_xvecs_dir, 'choose-spks'), 'w') as f:
+        spk2spks_arr = [spk+' '+' '.join(spks) for spk, spks in pseudo_spk_map.items()]
+        f.write('\n'.join(spk2spks_arr) + '\n')
+    f.close()
+
+    write_pseudo_vector(pseudo_xvec_map, pseudo_gender_map, 
+                        output_pseudo_xvecs_dir, output_pseudo_xvecs_bin_dir)
+    return
+
+if __name__ == "__main__":
+    args = sys.argv
+    main(args)

selec_anon/compute_anon_spk_vector/local_lib.py

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+#!/usr/bin/env python
+""" Compute average of x-vectors as speaker xvectors
+
+$: python speaker_xvector.py spk2utt xvector.scp outputdir output_name_prefix
+
+"""
+import os
+import sys
+from os.path import join, isdir
+import numpy as np
+from kaldiio import WriteHelper, ReadHelper
+
+
+def load_scp(scp_file):
+    pool_xvectors = {}
+    c = 0
+    with ReadHelper('scp:' + scp_file) as reader:
+        for key, xvec in reader:
+            #print key, mat.shape
+            pool_xvectors[key] = xvec
+            c += 1
+        print("Read ", c, "pool xvectors")
+    return pool_xvectors
+
+def write_scp(data_dic, output_file_path):
+    output_file_path = os.path.splitext(output_file_path)[0]
+    print("Writing to: " + output_file_path)
+    ark_scp_output = 'ark,scp:{:s}.ark,{:s}.scp'.format(
+        output_file_path, output_file_path)
+    with WriteHelper(ark_scp_output) as writer:
+        for spk, xvec in data_dic.items():
+            writer(spk, xvec)
+    return
+
+
+def load_utt(spk2utt_file):
+    spk2utt = {}
+    with open(spk2utt_file) as f:
+        for line in f.read().splitlines():
+            sp = line.split()
+            spk2utt[sp[0]] = sp[1:]
+    return spk2utt
+
+
+def load_gender(spk2gender_file):
+    spk2gender = {}
+    with open(spk2gender_file) as f:
+        for line in f.read().splitlines():
+            sp = line.split()
+            spk2gender[sp[0]] = sp[1]
+    return spk2gender
+
+def f_write_raw_mat(data, filename, data_format='f4', end='l'):
+    """flag = write_raw_mat(data, filename, data_format='f4', end='l')
+    Write data to file on the file system as binary data
+
+    input
+    -----
+      data:     np.array, data to be saved
+      filename: str, path of the file to save the data
+      data_format:   str, data_format for numpy
+                 default: 'f4', float32
+      end: str   little endian 'l' or big endian 'b'?
+                 default: 'l'
+
+    output   
+    ------
+      flag: bool, whether the writing is done or not
+    """
+    if not isinstance(data, np.ndarray):
+        print("Error write_raw_mat: input should be np.array")
+        return False
+    f = open(filename,'wb')
+    if len(data_format)>0:
+        if end=='l':
+            data_format = '<'+data_format
+        elif end=='b':
+            data_format = '>'+data_format
+        else:
+            data_format = '='+data_format
+        datatype = np.dtype(data_format)
+        temp_data = data.astype(datatype)
+    else:
+        temp_data = data
+    temp_data.tofile(f,'')
+    f.close()
+    return True

selec_anon/compute_anon_spk_vector/speaker_xvector.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python
+""" Compute average of x-vectors as speaker xvectors
+
+$: python speaker_xvector.py spk2utt xvector.scp outputdir output_name_prefix
+
+"""
+import os
+import sys
+from os.path import join, isdir
+import numpy as np
+
+import local_lib
+
+def transform_data(data):
+    return data
+
+def average_vectors(data_buf):
+    ave_buf = np.zeros(transform_data(data_buf[0]).shape)
+    # average, online algorithm
+    for idx, data in enumerate(data_buf):
+        ave_buf = ave_buf + (transform_data(data) - ave_buf) / (idx + 1)
+    return ave_buf
+    
+def collect_data_spk(data_pool_dic, spk2utt_dic, spk):
+    if spk not in spk2utt_dic:
+        print("Cannot find {:s} in sp2utt".format(spk))
+        exit(1)
+
+    spk_utt_list = spk2utt_dic[spk]
+    data_buf = [data_pool_dic[x] for x in spk_utt_list]
+    
+    return data_buf
+
+def main(spk2utt_file, xvec_file, output_dir, output_name):
+    
+    
+    # load spk2utt
+    spk2utt = local_lib.load_utt(spk2utt_file)
+    
+    # load all the xvectors
+    pool_xvectors = local_lib.load_scp(xvec_file)
+
+    ave_data_dic = {}
+    # compute average for each spk
+    for spk in spk2utt.keys():
+        # get data
+        spk_data_buf = collect_data_spk(pool_xvectors, spk2utt, spk)
+        
+        if len(spk_data_buf) < 1:
+            print("Cannot find data for {:s}".format(spk))
+            exit(1)
+        else:
+            print("{:s} has {:d} data vectors".format(spk, len(spk_data_buf)))
+    
+        # get average
+        ave_data = average_vectors(spk_data_buf)
+
+        # save 
+        ave_data_dic[spk] = ave_data
+    
+    # save
+    output_file = os.path.join(output_dir, output_name)
+    local_lib.write_scp(ave_data_dic, output_file)
+    return
+
+if __name__ == "__main__":
+
+    # parse input
+    args = sys.argv
+    if len(args) != 4 and len(args) != 5:
+        print(__doc__)
+        exit(1)
+
+    spk2utt_file = args[1]
+    xvec_file = args[2]
+    output_dir = args[3]
+    if len(args) == 5:
+        output_name = args[4]
+    else:
+        output_name = 'spk_xvector'
+
+    main(spk2utt_file, xvec_file, output_dir, output_name) 
+

selec_anon/compute_ori_spk_vector/00_extract_emb_fbank.sh

@@ -0,0 +1,38 @@
+#!/bin/bash
+#SBATCH --gres=gpu:tesla_a100:1
+#SBATCH --time=3-00:00:00
+#SBATCH -w gpuhost14
+#source_dir=home/smg/miao/speech-re-facebook/unifi_fbank_ecapa_hifigan/scp/aishell/aishell_dev.lst
+#outdir=aishell1_fbank_xvectors/anon
+
+
+source ./env.sh
+
+config=configs/extract_ecapa_f_ecapa_vox.yaml
+echo ${config}
+## extract libritts_train_other_500
+source_dir=data
+outdir=selec_anon/output_ori_spk_vector
+anon_pool="libritts_train_other_500"
+xv_flag=provide
+if [[ ${xv_flag} == "extract" ]]; then
+	#Download libritts_train_other_500 by yourself
+	python selec_anon/compute_ori_spk_vector/extract_emb.py ${config} $source_dir/$anon_pool/wav.scp $outdir/$anon_pool fbank
+ elif [[ ${xv_flag} == 'provide' ]]; then
+	#  wget https://github.com/nii-yamagishilab/SSL-SAS/releases/download/provided_xvector/libritts_train_other_500_xvector.tar.gz
+	 tar -xvf  libritts_train_other_500_xvector.tar.gz
+fi
+
+
+
+## extract vpc eval
+for dset in libri_dev_{enrolls,trials_f,trials_m} \
+              libri_test_{enrolls,trials_f,trials_m}; do
+python selec_anon/compute_ori_spk_vector/extract_emb.py ${config} $source_dir/$dset/wav.scp $outdir/$dset fbank
+done
+
+
+## extract librispeech-360
+#outdir=selec_anon/output_ori_spk_vector
+#dset="libri_360"
+#python selec_anon/compute_ori_spk_vector/extract_emb.py ${config} $source_dir/$dset/wav.scp $outdir/$dset fbank

selec_anon/compute_ori_spk_vector/extract_emb.py

@@ -0,0 +1,131 @@
+#!/usr/bin/python3
+from kaldiio import WriteHelper
+"""Recipe for training a speaker verification system based on cosine distance.
+The cosine distance is computed on the top of pre-trained embeddings.
+The pre-trained model is automatically downloaded from the web if not specified.
+This recipe is designed to work on a single GPU.
+
+To run this recipe, run the following command:
+    >  python speaker_verification_cosine.py hyperparams/verification_ecapa_tdnn.yaml
+
+Authors
+    * Hwidong Na 2020
+    * Mirco Ravanelli 2020
+"""
+import os
+import sys
+import torch
+import logging
+import torchaudio
+import speechbrain as sb
+from tqdm.contrib import tqdm
+from hyperpyyaml import load_hyperpyyaml
+from speechbrain.utils.metric_stats import EER, minDCF
+from speechbrain.utils.data_utils import download_file
+from speechbrain.utils.distributed import run_on_main
+import numpy as np
+import torch.nn.functional as F
+# Compute embeddings from the waveforms
+def compute_embedding(wavs, wav_lens):
+    """Compute speaker embeddings.
+
+    Arguments
+    ---------
+    wavs : Torch.Tensor
+        Tensor containing the speech waveform (batch, time).
+        Make sure the sample rate is fs=16000 Hz.
+    wav_lens: Torch.Tensor
+        Tensor containing the relative length for each sentence
+        in the length (e.g., [0.8 0.6 1.0])
+    """
+
+    with torch.no_grad():
+        feats = params["compute_features"](wavs)
+        feats = params["mean_var_norm"](feats, wav_lens)
+        embeddings = params["embedding_model"](feats, wav_lens)
+        #embeddings = params["mean_var_norm_emb"](
+        #    embeddings, torch.ones(embeddings.shape[0]).to(embeddings.device)
+        #)
+    return embeddings.squeeze(1)
+
+def compute_embedding_ssl(wavs, wav_lens):
+    """Compute speaker embeddings.
+
+    Arguments
+    ---------
+    wavs : Torch.Tensor
+        Tensor containing the speech waveform (batch, time).
+        Make sure the sample rate is fs=16000 Hz.
+    wav_lens: Torch.Tensor
+        Tensor containing the relative length for each sentence
+        in the length (e.g., [0.8 0.6 1.0])
+    """
+
+    with torch.no_grad():
+        feats = params["wav2vec2"].to(params["device"])(wavs)
+        embeddings = params["embedding_model"].to(params["device"])(feats, wav_lens)
+    return embeddings.squeeze(1)
+
+if __name__ == "__main__":
+    # Logger setup
+    logger = logging.getLogger(__name__)
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    sys.path.append(os.path.dirname(current_dir))
+
+    # Load hyperparameters file with command-line overrides
+    params_file, run_opts, overrides = sb.core.parse_arguments(sys.argv[1:2])
+    with open(params_file) as fin:
+        params = load_hyperpyyaml(fin, overrides)
+
+
+    # We download the pretrained LM from HuggingFace (or elsewhere depending on
+    # the path given in the YAML file). The tokenizer is loaded at the same time.
+    run_on_main(params["pretrainer"].collect_files)
+    params["pretrainer"].load_collected(params["device"])
+    params["embedding_model"].eval()
+    params["embedding_model"].to(params["device"])
+
+    wav_scp = sys.argv[2]
+    outdir = sys.argv[3]
+    feat_type = sys.argv[4]
+    if not os.path.exists(outdir):
+        os.makedirs(outdir)
+
+    with WriteHelper('ark,scp:%s/xvector.ark,%s/xvector.scp'%(outdir,outdir)) as writer:
+        # Computing embeddings
+        for wav_info in open(wav_scp):
+            if '/.' in wav_info:
+                print("skip error wav: %s"%wav_info)
+                continue
+            if ' ' in wav_info.strip():
+                wav_name, wav_path = wav_info.strip().split(' ')
+            elif 'vox' in wav_info.strip():
+                wav_path = wav_info.strip()
+                wav_name = "-".join(wav_info.strip().split('/')[-3:])
+                wav_name = wav_name.replace('.wav','')
+
+            else:
+                wav_path = wav_info.strip()
+                wav_name = wav_info.strip().split('/')[-1].split('.')[0]
+                
+            wav, fs = torchaudio.load(wav_path)
+            wav  = torchaudio.transforms.Resample(fs, 16000)(wav) # Paper didn't use this
+
+            # Manage single waveforms in input
+            if len(wav.shape) == 1:
+                wav = wav.unsqueeze(0)
+            # Assign full length if wav_lens is not assigned
+
+            wav_lens = torch.ones(wav.shape[0], device=params["device"])
+
+            # Storing waveform in the specified device
+            wav, wav_lens = wav.to(params["device"]), wav_lens.to(params["device"])
+            wav = wav.float()
+            if feat_type == 'fbank':
+                print("Extract ori emb for %s"%wav_name)
+                emb = compute_embedding(wav,wav_lens)
+                #print(emb)
+            elif feat_type == 'ssl':
+                emb = compute_embedding_ssl(wav,wav_lens)
+
+            writer(wav_name, emb.cpu().numpy())

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selec_anon/output_anon_spk_vector/libri_test_trials_f/pseudo_xvectors/spk2gender

@@ -0,0 +1,20 @@
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+1580 F
+1995 F
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+237 F
+2961 F
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