def main(*argv): argv = argv if argv else sys.argv[1:] # Options for python description = 'estimate joint feature of source and target speakers' parser = argparse.ArgumentParser(description=description) parser.add_argument('speaker', type=str, help='Input speaker label') parser.add_argument('list_file', type=str, help='List file of the input speaker') parser.add_argument('pair_dir', type=str, help='Statistics directory of the speaker') args = parser.parse_args(argv) # open h5 files h5_dir = os.path.join(args.pair_dir, 'h5') statspath = os.path.join(args.pair_dir, 'stats', args.speaker + '.h5') h5 = HDF5(statspath, mode='a') # estimate and save F0 statistics f0stats = F0statistics() f0s = read_feats(args.list_file, h5_dir, ext='f0') f0stats = f0stats.estimate(f0s) h5.save(f0stats, ext='f0stats') print("f0stats save into " + statspath) # estimate and save GV of orginal and target speakers gv = GV() mceps = read_feats(args.list_file, h5_dir, ext='mcep') gvstats = gv.estimate(mceps) h5.save(gvstats, ext='gv') print("gvstats save into " + statspath) h5.close()
def main(*argv): argv = argv if argv else sys.argv[1:] # Options for python description = 'estimate joint feature of source and target speakers' parser = argparse.ArgumentParser(description=description) parser.add_argument('speaker', type=str, help='Input speaker label') parser.add_argument('list_file', type=str, help='List file of the input speaker') parser.add_argument('pair_dir', type=str, help='Statistics directory of the speaker') args = parser.parse_args(argv) # open h5 files h5_dir = os.path.join(args.pair_dir, 'h5') statspath = os.path.join(args.pair_dir, 'stats', args.speaker + '.h5') h5 = HDF5(statspath, mode='w') # estimate and save F0 statistics f0stats = F0statistics() f0s = read_feats(args.list_file, h5_dir, ext='f0') f0stats = f0stats.estimate(f0s) h5.save(f0stats, ext='f0stats') print("f0stats save into " + statspath) # estimate and save GV of orginal and target speakers gv = GV() mceps = read_feats(args.list_file, h5_dir, ext='mcep') gvstats = gv.estimate(mceps) h5.save(gvstats, ext='gv') print("gvstats save into " + statspath) h5.close()
def main(*argv): argv = argv if argv else sys.argv[1:] # Options for python description = 'Train GMM and converted GV statistics' parser = argparse.ArgumentParser(description=description) parser.add_argument('org_list_file', type=str, help='List file of original speaker') parser.add_argument('pair_yml', type=str, help='Yml file of the speaker pair') parser.add_argument('pair_dir', type=str, help='Directory path of h5 files') args = parser.parse_args(argv) # read pair-dependent yml file pconf = PairYML(args.pair_yml) # read joint feature vector jntf = os.path.join(args.pair_dir, 'jnt', 'it' + str(pconf.jnt_n_iter + 1) + '_jnt.h5') jnth5 = HDF5(jntf, mode='r') jnt = jnth5.read(ext='jnt') # train GMM for mcep using joint feature vector gmm = GMMTrainer(n_mix=pconf.GMM_mcep_n_mix, n_iter=pconf.GMM_mcep_n_iter, covtype=pconf.GMM_mcep_covtype) gmm.train(jnt) # save GMM gmm_dir = os.path.join(args.pair_dir, 'model') if not os.path.exists(gmm_dir): os.makedirs(gmm_dir) gmmpath = os.path.join(gmm_dir, 'GMM.pkl') joblib.dump(gmm.param, gmmpath) print("Conversion model save into " + gmmpath) # calculate GV statistics of converted feature h5_dir = os.path.join(args.pair_dir, 'h5') org_mceps = read_feats(args.org_list_file, h5_dir, ext='mcep') cv_mceps = feature_conversion(pconf, org_mceps, gmm, gmmmode=None) diffcv_mceps = feature_conversion(pconf, org_mceps, gmm, gmmmode='diff') gv = GV() cvgvstats = gv.estimate(cv_mceps) diffcvgvstats = gv.estimate(diffcv_mceps) # open h5 files statspath = os.path.join(gmm_dir, 'cvgv.h5') cvgvh5 = HDF5(statspath, mode='w') cvgvh5.save(cvgvstats, ext='cvgv') cvgvh5.save(diffcvgvstats, ext='diffcvgv') print("Converted gvstats save into " + statspath)
def test_GVstatistics(self): gvstats = GV() datalist = [] for i in range(1, 4): datalist.append(np.random.rand(100 * i).reshape(100 * i // 2, 2)) gv = gvstats.estimate(datalist) data = np.random.rand(100 * 5).reshape(100 * 5 // 2, 2) odata = gvstats.postfilter(data, gv, startdim=0) assert data.shape[0] == odata.shape[0] odata = gvstats.postfilter(data, gv, alpha=0.0, startdim=0) assert np.all(data == odata)
def test_GVstatistics(self): gvstats = GV() datalist = [] for i in range(1, 4): datalist.append(np.random.rand(100 * i).reshape(100 * i // 2, 2)) gv = gvstats.estimate(datalist) data = np.random.rand(100 * 5).reshape(100 * 5 // 2, 2) odata = gvstats.postfilter(data, gv, startdim=0) assert data.shape[0] == odata.shape[0] odata = gvstats.postfilter(data, gv, alpha=0.0, startdim=0) assert np.all(data == odata)
def main(*argv): argv = argv if argv else sys.argv[1:] # Options for python description = 'estimate joint feature of source and target speakers' parser = argparse.ArgumentParser(description=description) parser.add_argument('-gmmmode', '--gmmmode', type=str, default=None, help='mode of the GMM [None, diff, or intra]') parser.add_argument('org', type=str, help='Original speaker') parser.add_argument('tar', type=str, help='Target speaker') parser.add_argument('org_yml', type=str, help='Yml file of the original speaker') parser.add_argument('pair_yml', type=str, help='Yml file of the speaker pair') parser.add_argument('eval_list_file', type=str, help='List file for evaluation') parser.add_argument('wav_dir', type=str, help='Directory path of source spekaer') parser.add_argument('pair_dir', type=str, help='Directory path of pair directory') args = parser.parse_args(argv) # read parameters from speaker yml sconf = SpeakerYML(args.org_yml) pconf = PairYML(args.pair_yml) # read GMM for mcep mcepgmmpath = os.path.join(args.pair_dir, 'model/GMM_mcep.pkl') mcepgmm = GMMConvertor( n_mix=pconf.GMM_mcep_n_mix, covtype=pconf.GMM_mcep_covtype, gmmmode=args.gmmmode, ) param = joblib.load(mcepgmmpath) mcepgmm.open_from_param(param) print("GMM for mcep conversion mode: {}".format(args.gmmmode)) # read F0 statistics stats_dir = os.path.join(args.pair_dir, 'stats') orgstatspath = os.path.join(stats_dir, args.org + '.h5') orgstats_h5 = HDF5(orgstatspath, mode='r') orgf0stats = orgstats_h5.read(ext='f0stats') orgstats_h5.close() # read F0 and GV statistics for target tarstatspath = os.path.join(stats_dir, args.tar + '.h5') tarstats_h5 = HDF5(tarstatspath, mode='r') tarf0stats = tarstats_h5.read(ext='f0stats') targvstats = tarstats_h5.read(ext='gv') tarstats_h5.close() # read GV statistics for converted mcep cvgvstatspath = os.path.join(args.pair_dir, 'model', 'cvgv.h5') cvgvstats_h5 = HDF5(cvgvstatspath, mode='r') cvgvstats = cvgvstats_h5.read(ext='cvgv') diffcvgvstats = cvgvstats_h5.read(ext='diffcvgv') cvgvstats_h5.close() mcepgv = GV() f0stats = F0statistics() # constract FeatureExtractor class feat = FeatureExtractor(analyzer=sconf.analyzer, fs=sconf.wav_fs, fftl=sconf.wav_fftl, shiftms=sconf.wav_shiftms, minf0=sconf.f0_minf0, maxf0=sconf.f0_maxf0) # constract Synthesizer class synthesizer = Synthesizer(fs=sconf.wav_fs, fftl=sconf.wav_fftl, shiftms=sconf.wav_shiftms) # test directory test_dir = os.path.join(args.pair_dir, 'test') os.makedirs(os.path.join(test_dir, args.org), exist_ok=True) # conversion in each evaluation file with open(args.eval_list_file, 'r') as fp: for line in fp: # open wav file f = line.rstrip() wavf = os.path.join(args.wav_dir, f + '.wav') fs, x = wavfile.read(wavf) x = x.astype(np.float) x = low_cut_filter(x, fs, cutoff=70) assert fs == sconf.wav_fs # analyze F0, mcep, and ap f0, spc, ap = feat.analyze(x) mcep = feat.mcep(dim=sconf.mcep_dim, alpha=sconf.mcep_alpha) mcep_0th = mcep[:, 0] # convert F0 cvf0 = f0stats.convert(f0, orgf0stats, tarf0stats) # convert mcep cvmcep_wopow = mcepgmm.convert(static_delta(mcep[:, 1:]), cvtype=pconf.GMM_mcep_cvtype) cvmcep = np.c_[mcep_0th, cvmcep_wopow] # synthesis VC w/ GV if args.gmmmode is None: cvmcep_wGV = mcepgv.postfilter(cvmcep, targvstats, cvgvstats=cvgvstats, alpha=pconf.GV_morph_coeff, startdim=1) wav = synthesizer.synthesis( cvf0, cvmcep_wGV, ap, rmcep=mcep, alpha=sconf.mcep_alpha, ) wavpath = os.path.join(test_dir, f + '_VC.wav') # synthesis DIFFVC w/ GV if args.gmmmode == 'diff': cvmcep[:, 0] = 0.0 cvmcep_wGV = mcepgv.postfilter(mcep + cvmcep, targvstats, cvgvstats=diffcvgvstats, alpha=pconf.GV_morph_coeff, startdim=1) - mcep wav = synthesizer.synthesis_diff( x, cvmcep_wGV, rmcep=mcep, alpha=sconf.mcep_alpha, ) wavpath = os.path.join(test_dir, f + '_DIFFVC.wav') # write waveform if not os.path.exists(os.path.join(test_dir, f)): os.makedirs(os.path.join(test_dir, f)) wav = np.clip(wav, -32768, 32767) wavfile.write(wavpath, fs, wav.astype(np.int16)) print(wavpath)
def main(*argv): argv = argv if argv else sys.argv[1:] # Options for python description = 'Train GMM and converted GV statistics' parser = argparse.ArgumentParser(description=description) parser.add_argument('org_list_file', type=str, help='List file of original speaker') parser.add_argument('pair_yml', type=str, help='Yml file of the speaker pair') parser.add_argument('pair_dir', type=str, help='Directory path of h5 files') args = parser.parse_args(argv) # read pair-dependent yml file pconf = PairYML(args.pair_yml) # read joint feature vector jntf = os.path.join(args.pair_dir, 'jnt', 'it' + str(pconf.jnt_n_iter + 1) + '_jnt.h5') jnth5 = HDF5(jntf, mode='r') jnt = jnth5.read(ext='mcep') jnt_codeap = jnth5.read(ext='codeap') # train GMM for mcep using joint feature vector gmm = GMMTrainer(n_mix=pconf.GMM_mcep_n_mix, n_iter=pconf.GMM_mcep_n_iter, covtype=pconf.GMM_mcep_covtype) gmm.train(jnt) # train GMM for codeap using joint feature vector gmm_codeap = GMMTrainer(n_mix=pconf.GMM_codeap_n_mix, n_iter=pconf.GMM_codeap_n_iter, covtype=pconf.GMM_codeap_covtype) gmm_codeap.train(jnt_codeap) # save GMM gmm_dir = os.path.join(args.pair_dir, 'model') if not os.path.exists(gmm_dir): os.makedirs(gmm_dir) gmmpath = os.path.join(gmm_dir, 'GMM_mcep.pkl') joblib.dump(gmm.param, gmmpath) print("Conversion model for mcep save into " + gmmpath) gmmpath_codeap = os.path.join(gmm_dir, 'GMM_codeap.pkl') joblib.dump(gmm_codeap.param, gmmpath_codeap) print("Conversion model for codeap save into " + gmmpath_codeap) # calculate GV statistics of converted feature h5_dir = os.path.join(args.pair_dir, 'h5') org_mceps = read_feats(args.org_list_file, h5_dir, ext='mcep') cv_mceps = feature_conversion(pconf, org_mceps, gmm, gmmmode=None) diffcv_mceps = feature_conversion(pconf, org_mceps, gmm, gmmmode='diff') gv = GV() cvgvstats = gv.estimate(cv_mceps) diffcvgvstats = gv.estimate(diffcv_mceps) # open h5 files statspath = os.path.join(gmm_dir, 'cvgv.h5') cvgvh5 = HDF5(statspath, mode='w') cvgvh5.save(cvgvstats, ext='cvgv') cvgvh5.save(diffcvgvstats, ext='diffcvgv') print("Converted gvstats save into " + statspath)