def decode_RNN(feat_list, gpu, cvlist=None,
mcd_cvlist_src=None, mcdstd_cvlist_src=None, mcdpow_cvlist_src=None, mcdpowstd_cvlist_src=None,\
mcd_cvlist_cyc=None, mcdstd_cvlist_cyc=None, mcdpow_cvlist_cyc=None, mcdpowstd_cvlist_cyc=None,\
mcd_cvlist=None, mcdstd_cvlist=None, mcdpow_cvlist=None, mcdpowstd_cvlist=None, \
lat_dist_rmse_list=None, lat_dist_cosim_list=None):
with torch.cuda.device(gpu):
# define model and load parameters
with torch.no_grad():
model_encoder = GRU_VAE_ENCODER(
in_dim=config.mcep_dim+config.excit_dim,
n_spk=n_spk,
lat_dim=config.lat_dim,
hidden_layers=config.hidden_layers_enc,
hidden_units=config.hidden_units_enc,
kernel_size=config.kernel_size_enc,
dilation_size=config.dilation_size_enc,
causal_conv=config.causal_conv_enc,
bi=False,
ar=False,
pad_first=True,
right_size=config.right_size_enc)
logging.info(model_encoder)
model_decoder = GRU_SPEC_DECODER(
feat_dim=config.lat_dim,
out_dim=config.mcep_dim,
n_spk=n_spk,
hidden_layers=config.hidden_layers_dec,
hidden_units=config.hidden_units_dec,
kernel_size=config.kernel_size_dec,
dilation_size=config.dilation_size_dec,
causal_conv=config.causal_conv_dec,
bi=False,
ar=False,
pad_first=True,
right_size=config.right_size_dec)
logging.info(model_decoder)
model_post = GRU_POST_NET(
spec_dim=config.mcep_dim,
excit_dim=2,
n_spk=n_spk,
hidden_layers=config.hidden_layers_post,
hidden_units=config.hidden_units_post,
kernel_size=config.kernel_size_post,
dilation_size=config.dilation_size_post,
causal_conv=config.causal_conv_post,
pad_first=True,
right_size=config.right_size_post)
#excit_dim=config.excit_dim,
#excit_dim=None,
logging.info(model_post)
model_encoder.load_state_dict(torch.load(args.model)["model_encoder"])
model_decoder.load_state_dict(torch.load(args.model)["model_decoder"])
model_post.load_state_dict(torch.load(args.model)["model_post"])
model_encoder.remove_weight_norm()
model_decoder.remove_weight_norm()
model_post.remove_weight_norm()
model_encoder.cuda()
model_decoder.cuda()
model_post.cuda()
model_encoder.eval()
model_decoder.eval()
model_post.eval()
for param in model_encoder.parameters():
param.requires_grad = False
for param in model_decoder.parameters():
param.requires_grad = False
for param in model_post.parameters():
param.requires_grad = False
count = 0
pad_left = (model_encoder.pad_left + model_decoder.pad_left + model_post.pad_left)*2
pad_right = (model_encoder.pad_right + model_decoder.pad_right + model_post.pad_right)*2
outpad_lefts = [None]*5
outpad_rights = [None]*5
outpad_lefts[0] = pad_left-model_encoder.pad_left
outpad_rights[0] = pad_right-model_encoder.pad_right
outpad_lefts[1] = outpad_lefts[0]-model_decoder.pad_left
outpad_rights[1] = outpad_rights[0]-model_decoder.pad_right
outpad_lefts[2] = outpad_lefts[1]-model_post.pad_left
outpad_rights[2] = outpad_rights[1]-model_post.pad_right
outpad_lefts[3] = outpad_lefts[2]-model_encoder.pad_left
outpad_rights[3] = outpad_rights[2]-model_encoder.pad_right
outpad_lefts[4] = outpad_lefts[3]-model_decoder.pad_left
outpad_rights[4] = outpad_rights[3]-model_decoder.pad_right
logging.info(f'{pad_left} {pad_right}')
logging.info(outpad_lefts)
logging.info(outpad_rights)
for feat_file in feat_list:
# convert mcep
spk_src = os.path.basename(os.path.dirname(feat_file))
src_idx = spk_list.index(spk_src)
logging.info('%s --> %s' % (spk_src, args.spk_trg))
file_trg = os.path.join(os.path.dirname(os.path.dirname(feat_file)), args.spk_trg, os.path.basename(feat_file))
trg_exist = False
if os.path.exists(file_trg):
logging.info('exist: %s' % (file_trg))
feat_trg = read_hdf5(file_trg, config.string_path)
mcep_trg = feat_trg[:,-config.mcep_dim:]
logging.info(mcep_trg.shape)
trg_exist = True
feat_org = read_hdf5(feat_file, config.string_path)
mcep = np.array(feat_org[:,-config.mcep_dim:])
codeap = np.array(np.rint(feat_org[:,2:3])*(-np.exp(feat_org[:,3:config.excit_dim])))
sp = np.array(ps.mc2sp(mcep, args.mcep_alpha, args.fftl))
ap = pw.decode_aperiodicity(codeap, args.fs, args.fftl)
feat_cvf0_lin = np.expand_dims(convert_f0(np.exp(feat_org[:,1]), src_f0_mean, src_f0_std, trg_f0_mean, trg_f0_std), axis=-1)
feat_cv = np.c_[feat_org[:,:1], np.log(feat_cvf0_lin), feat_org[:,2:config.excit_dim]]
logging.info("generate")
with torch.no_grad():
feat = F.pad(torch.FloatTensor(feat_org).cuda().unsqueeze(0).transpose(1,2), (pad_left,pad_right), "replicate").transpose(1,2)
feat_excit = torch.FloatTensor(feat_org[:,:config.excit_dim]).cuda().unsqueeze(0)
feat_excit_cv = torch.FloatTensor(feat_cv).cuda().unsqueeze(0)
spk_logits, _, lat_src, _ = model_encoder(feat, sampling=False)
logging.info('input spkpost')
if outpad_rights[0] > 0:
logging.info(torch.mean(F.softmax(spk_logits[:,outpad_lefts[0]:-outpad_rights[0]], dim=-1), 1))
else:
logging.info(torch.mean(F.softmax(spk_logits[:,outpad_lefts[0]:], dim=-1), 1))
if trg_exist:
spk_trg_logits, _, lat_trg, _ = model_encoder(F.pad(torch.FloatTensor(feat_trg).cuda().unsqueeze(0).transpose(1,2), \
(model_encoder.pad_left,model_encoder.pad_right), "replicate").transpose(1,2), sampling=False)
logging.info('target spkpost')
logging.info(torch.mean(F.softmax(spk_trg_logits, dim=-1), 1))
cvmcep_src, _ = model_decoder((torch.ones((1, lat_src.shape[1]))*src_idx).cuda().long(), lat_src)
cvmcep_src_post, _ = model_post(cvmcep_src, y=(torch.ones((1, cvmcep_src.shape[1]))*src_idx).cuda().long(),
e=F.pad(feat_excit[:,:,:2].transpose(1,2), (outpad_lefts[1],outpad_rights[1]), "replicate").transpose(1,2))
#e=F.pad(feat_excit.transpose(1,2), (outpad_lefts[1],outpad_rights[1]), "replicate").transpose(1,2))
if model_post.pad_right > 0:
spk_logits, _, lat_rec, _ = model_encoder(torch.cat((F.pad(feat_excit.transpose(1,2), \
(outpad_lefts[2],outpad_rights[2]), "replicate").transpose(1,2), cvmcep_src[:,model_post.pad_left:-model_post.pad_right]), 2),
sampling=False)
else:
spk_logits, _, lat_rec, _ = model_encoder(torch.cat((F.pad(feat_excit.transpose(1,2), \
(outpad_lefts[2],outpad_rights[2]), "replicate").transpose(1,2), cvmcep_src[:,model_post.pad_left:]), 2),
sampling=False)
logging.info('rec spkpost')
if outpad_rights[3] > 0:
logging.info(torch.mean(F.softmax(spk_logits[:,outpad_lefts[3]:-outpad_rights[3]], dim=-1), 1))
else:
logging.info(torch.mean(F.softmax(spk_logits[:,outpad_lefts[3]:], dim=-1), 1))
cvmcep, _ = model_decoder((torch.ones((1, lat_src.shape[1]))*trg_idx).cuda().long(), lat_src)
cvmcep_post, _ = model_post(cvmcep, y=(torch.ones((1, cvmcep.shape[1]))*trg_idx).cuda().long(),
e=F.pad(feat_excit_cv[:,:,:2].transpose(1,2), (outpad_lefts[1],outpad_rights[1]), "replicate").transpose(1,2))
#e=F.pad(feat_excit_cv.transpose(1,2), (outpad_lefts[1],outpad_rights[1]), "replicate").transpose(1,2))
if model_post.pad_right > 0:
spk_logits, _, lat_cv, _ = model_encoder(torch.cat((F.pad(feat_excit_cv.transpose(1,2), \
(outpad_lefts[2],outpad_rights[2]), "replicate").transpose(1,2), cvmcep[:,model_post.pad_left:-model_post.pad_right]), 2),
sampling=False)
else:
spk_logits, _, lat_cv, _ = model_encoder(torch.cat((F.pad(feat_excit_cv.transpose(1,2), \
(outpad_lefts[2],outpad_rights[2]), "replicate").transpose(1,2), cvmcep[:,model_post.pad_left:]), 2),
sampling=False)
logging.info('cv spkpost')
if outpad_rights[3] > 0:
logging.info(torch.mean(F.softmax(spk_logits[:,outpad_lefts[3]:-outpad_rights[3]], dim=-1), 1))
else:
logging.info(torch.mean(F.softmax(spk_logits[:,outpad_lefts[3]:], dim=-1), 1))
cvmcep_cyc, _ = model_decoder((torch.ones((1, lat_cv.shape[1]))*src_idx).cuda().long(), lat_cv)
cvmcep_cyc_post, _ = model_post(cvmcep_cyc, y=(torch.ones((1, cvmcep_cyc.shape[1]))*src_idx).cuda().long(),
e=F.pad(feat_excit[:,:,:2].transpose(1,2), (outpad_lefts[4],outpad_rights[4]), "replicate").transpose(1,2))
#e=F.pad(feat_excit.transpose(1,2), (outpad_lefts[4],outpad_rights[4]), "replicate").transpose(1,2))
if outpad_rights[2] > 0:
cvmcep_src = np.array(cvmcep_src_post[0,outpad_lefts[2]:-outpad_rights[2]].cpu().data.numpy(), dtype=np.float64)
cvmcep = np.array(cvmcep_post[0,outpad_lefts[2]:-outpad_rights[2]].cpu().data.numpy(), dtype=np.float64)
else:
cvmcep_src = np.array(cvmcep_src_post[0,outpad_lefts[2]:].cpu().data.numpy(), dtype=np.float64)
cvmcep = np.array(cvmcep_post[0,outpad_lefts[2]:].cpu().data.numpy(), dtype=np.float64)
cvmcep_cyc = np.array(cvmcep_cyc_post[0].cpu().data.numpy(), dtype=np.float64)
if trg_exist:
if outpad_rights[0] > 0:
lat_src = lat_src[:,outpad_lefts[0]:-outpad_rights[0]]
else:
lat_src = lat_src[:,outpad_lefts[0]:]
logging.info(cvmcep_src.shape)
logging.info(cvmcep.shape)
logging.info(cvmcep_cyc.shape)
if trg_exist:
logging.info(lat_src.shape)
logging.info(lat_trg.shape)
cvlist.append(np.var(cvmcep[:,1:], axis=0))
logging.info("cvf0lin")
f0_range = read_hdf5(feat_file, "/f0_range")
cvf0_range_lin = convert_f0(f0_range, src_f0_mean, src_f0_std, trg_f0_mean, trg_f0_std)
uv_range_lin, cont_f0_range_lin = convert_continuos_f0(np.array(cvf0_range_lin))
unique, counts = np.unique(uv_range_lin, return_counts=True)
logging.info(dict(zip(unique, counts)))
cont_f0_lpf_range_lin = \
low_pass_filter(cont_f0_range_lin, int(1.0 / (args.shiftms * 0.001)), cutoff=20)
uv_range_lin = np.expand_dims(uv_range_lin, axis=-1)
cont_f0_lpf_range_lin = np.expand_dims(cont_f0_lpf_range_lin, axis=-1)
# plain converted feat for neural vocoder
feat_cv = np.c_[uv_range_lin, np.log(cont_f0_lpf_range_lin), feat_cv[:,2:config.excit_dim], cvmcep]
logging.info(feat_cv.shape)
logging.info("mcd acc")
spcidx = np.array(read_hdf5(feat_file, "/spcidx_range")[0])
_, mcdpow_arr = dtw.calc_mcd(np.array(mcep[spcidx], dtype=np.float64), np.array(cvmcep_src[spcidx], dtype=np.float64))
_, mcd_arr = dtw.calc_mcd(np.array(mcep[spcidx,1:], dtype=np.float64), np.array(cvmcep_src[spcidx,1:], dtype=np.float64))
mcdpow_mean = np.mean(mcdpow_arr)
mcdpow_std = np.std(mcdpow_arr)
mcd_mean = np.mean(mcd_arr)
mcd_std = np.std(mcd_arr)
logging.info("mcdpow_src_cv: %.6f dB +- %.6f" % (mcdpow_mean, mcdpow_std))
logging.info("mcd_src_cv: %.6f dB +- %.6f" % (mcd_mean, mcd_std))
mcdpow_cvlist_src.append(mcdpow_mean)
mcdpowstd_cvlist_src.append(mcdpow_std)
mcd_cvlist_src.append(mcd_mean)
mcdstd_cvlist_src.append(mcd_std)
if trg_exist:
spcidx_trg = np.array(read_hdf5(file_trg, "/spcidx_range")[0])
_, _, _, mcdpow_arr = dtw.dtw_org_to_trg(np.array(cvmcep[spcidx], \
dtype=np.float64), np.array(mcep_trg[spcidx_trg], dtype=np.float64))
_, _, _, mcd_arr = dtw.dtw_org_to_trg(np.array(cvmcep[spcidx,1:], \
dtype=np.float64), np.array(mcep_trg[spcidx_trg,1:], dtype=np.float64))
mcdpow_mean = np.mean(mcdpow_arr)
mcdpow_std = np.std(mcdpow_arr)
mcd_mean = np.mean(mcd_arr)
mcd_std = np.std(mcd_arr)
logging.info("mcdpow_trg: %.6f dB +- %.6f" % (mcdpow_mean, mcdpow_std))
logging.info("mcd_trg: %.6f dB +- %.6f" % (mcd_mean, mcd_std))
mcdpow_cvlist.append(mcdpow_mean)
mcdpowstd_cvlist.append(mcdpow_std)
mcd_cvlist.append(mcd_mean)
mcdstd_cvlist.append(mcd_std)
spcidx_src = torch.LongTensor(spcidx).cuda()
spcidx_trg = torch.LongTensor(spcidx_trg).cuda()
trj_lat_src = np.array(torch.index_select(lat_src[0],0,spcidx_src).cpu().data.numpy(), dtype=np.float64)
trj_lat_trg = np.array(torch.index_select(lat_trg[0],0,spcidx_trg).cpu().data.numpy(), dtype=np.float64)
aligned_lat_srctrg, _, _, _ = dtw.dtw_org_to_trg(trj_lat_src, trj_lat_trg)
lat_dist_srctrg = np.mean(np.sqrt(np.mean((aligned_lat_srctrg-trj_lat_trg)**2, axis=0)))
_, _, lat_cdist_srctrg, _ = dtw.dtw_org_to_trg(trj_lat_trg, trj_lat_src, mcd=0)
aligned_lat_trgsrc, _, _, _ = dtw.dtw_org_to_trg(trj_lat_trg, trj_lat_src)
lat_dist_trgsrc = np.mean(np.sqrt(np.mean((aligned_lat_trgsrc-trj_lat_src)**2, axis=0)))
_, _, lat_cdist_trgsrc, _ = dtw.dtw_org_to_trg(trj_lat_src, trj_lat_trg, mcd=0)
logging.info("%lf %lf %lf %lf" % (lat_dist_srctrg, lat_cdist_srctrg, lat_dist_trgsrc, lat_cdist_trgsrc))
lat_dist_rmse = (lat_dist_srctrg+lat_dist_trgsrc)/2
lat_dist_cosim = (lat_cdist_srctrg+lat_cdist_trgsrc)/2
lat_dist_rmse_list.append(lat_dist_rmse)
lat_dist_cosim_list.append(lat_dist_cosim)
logging.info("lat_dist: %.6f %.6f" % (lat_dist_rmse, lat_dist_cosim))
_, mcdpow_arr = dtw.calc_mcd(np.array(mcep[spcidx], dtype=np.float64), np.array(cvmcep_cyc[spcidx], dtype=np.float64))
_, mcd_arr = dtw.calc_mcd(np.array(mcep[spcidx,1:], dtype=np.float64), np.array(cvmcep_cyc[spcidx,1:], dtype=np.float64))
mcdpow_mean = np.mean(mcdpow_arr)
mcdpow_std = np.std(mcdpow_arr)
mcd_mean = np.mean(mcd_arr)
mcd_std = np.std(mcd_arr)
logging.info("mcdpow_cyc_cv: %.6f dB +- %.6f" % (mcdpow_mean, mcdpow_std))
logging.info("mcd_cyc_cv: %.6f dB +- %.6f" % (mcd_mean, mcd_std))
mcdpow_cvlist_cyc.append(mcdpow_mean)
mcdpowstd_cvlist_cyc.append(mcdpow_std)
mcd_cvlist_cyc.append(mcd_mean)
mcdstd_cvlist_cyc.append(mcd_std)
logging.info("synth anasyn")
wav = np.clip(pw.synthesize(f0_range, sp, ap, args.fs, frame_period=args.shiftms), -1, 1)
wavpath = os.path.join(args.outdir,os.path.basename(feat_file).replace(".h5","_anasyn.wav"))
sf.write(wavpath, wav, args.fs, 'PCM_16')
logging.info(wavpath)
logging.info("synth voco rec")
cvsp_src = ps.mc2sp(cvmcep_src, args.mcep_alpha, args.fftl)
logging.info(cvsp_src.shape)
wav = np.clip(pw.synthesize(f0_range, cvsp_src, ap, args.fs, frame_period=args.shiftms), -1, 1)
wavpath = os.path.join(args.outdir, os.path.basename(feat_file).replace(".h5", "_rec.wav"))
sf.write(wavpath, wav, args.fs, 'PCM_16')
logging.info(wavpath)
logging.info("synth voco cv")
cvsp = ps.mc2sp(cvmcep, args.mcep_alpha, args.fftl)
logging.info(cvsp.shape)
wav = np.clip(pw.synthesize(cvf0_range_lin, cvsp, ap, args.fs, frame_period=args.shiftms), -1, 1)
wavpath = os.path.join(args.outdir, os.path.basename(feat_file).replace(".h5", "_cv.wav"))
sf.write(wavpath, wav, args.fs, 'PCM_16')
logging.info(wavpath)
logging.info("synth voco cv GV")
datamean = np.mean(cvmcep[:,1:], axis=0)
cvmcep_gv = np.c_[cvmcep[:,0], args.gv_coeff*(np.sqrt(gv_mean_trg/cvgv_mean) * \
(cvmcep[:,1:]-datamean) + datamean) + (1-args.gv_coeff)*cvmcep[:,1:]]
cvmcep_gv = mod_pow(cvmcep_gv, cvmcep, alpha=args.mcep_alpha, irlen=IRLEN)
cvsp_gv = ps.mc2sp(cvmcep_gv, args.mcep_alpha, args.fftl)
logging.info(cvsp_gv.shape)
wav = np.clip(pw.synthesize(cvf0_range_lin, cvsp_gv, ap, args.fs, frame_period=args.shiftms), -1, 1)
wavpath = os.path.join(args.outdir, os.path.basename(feat_file).replace(".h5", "_cvGV.wav"))
sf.write(wavpath, wav, args.fs, 'PCM_16')
logging.info(wavpath)
#logging.info("synth diffGV")
#shiftl = int(args.fs/1000*args.shiftms)
#mc_cv_diff = cvmcep_gv-mcep
#b = np.apply_along_axis(ps.mc2b, 1, mc_cv_diff, args.mcep_alpha)
#logging.info(b.shape)
#assert np.isfinite(b).all
#mlsa_fil = ps.synthesis.Synthesizer(MLSADF(mcep_dim, alpha=args.mcep_alpha), shiftl)
#x, fs_ = sf.read(os.path.join(os.path.dirname(feat_file).replace("hdf5", "wav_filtered"), os.path.basename(feat_file).replace(".h5", ".wav")))
#assert(fs_ == args.fs)
#wav = mlsa_fil.synthesis(x, b)
#wav = np.clip(wav, -1, 1)
#wavpath = os.path.join(args.outdir, os.path.basename(feat_file).replace(".h5", "_DiffGV.wav"))
#sf.write(wavpath, wav, args.fs, 'PCM_16')
#logging.info(wavpath)
#logging.info("synth diffGVF0")
#time_axis = read_hdf5(feat_file, "/time_axis")
#sp_diff = pw.cheaptrick(wav, f0_range, time_axis, args.fs, fft_size=args.fftl)
#logging.info(sp_diff.shape)
#ap_diff = pw.d4c(wav, f0_range, time_axis, args.fs, fft_size=args.fftl)
#logging.info(ap_diff.shape)
#wav = pw.synthesize(cvf0_range_lin, sp_diff, ap_diff, args.fs, frame_period=args.shiftms)
#wav = np.clip(wav, -1, 1)
#wavpath = os.path.join(args.outdir,os.path.basename(feat_file).replace(".h5", "_DiffGVF0.wav"))
#sf.write(wavpath, wav, args.fs, 'PCM_16')
#logging.info(wavpath)
#logging.info("analysis diffGVF0")
#sp_diff_anasyn = pw.cheaptrick(wav, cvf0_range_lin, time_axis, args.fs, fft_size=args.fftl)
#logging.info(sp_diff_anasyn.shape)
#mc_cv_diff_anasyn = ps.sp2mc(sp_diff_anasyn, mcep_dim, args.mcep_alpha)
#ap_diff_anasyn = pw.d4c(wav, cvf0_range_lin, time_axis, args.fs, fft_size=args.fftl)
#code_ap_diff_anasyn = pw.code_aperiodicity(ap_diff_anasyn, args.fs)
## convert to continouos codeap with uv
#for i in range(code_ap_diff_anasyn.shape[-1]):
# logging.info('codeap: %d' % (i+1))
# uv_codeap_i, cont_codeap_i = convert_continuos_codeap(np.array(code_ap_diff_anasyn[:,i]))
# cont_codeap_i = np.log(-np.clip(cont_codeap_i, a_min=np.amin(cont_codeap_i), a_max=MAX_CODEAP))
# if i > 0:
# cont_codeap = np.c_[cont_codeap, np.expand_dims(cont_codeap_i, axis=-1)]
# else:
# uv_codeap = np.expand_dims(uv_codeap_i, axis=-1)
# cont_codeap = np.expand_dims(cont_codeap_i, axis=-1)
# uv_codeap_i = np.expand_dims(uv_codeap_i, axis=-1)
# unique, counts = np.unique(uv_codeap_i, return_counts=True)
# logging.info(dict(zip(unique, counts)))
## postprocessed converted feat for neural vocoder
#feat_diffgv_anasyn = np.c_[feat_cv[:,:2], uv_codeap, cont_codeap, mc_cv_diff_anasyn]
#logging.info("write lat")
#outTxtDir = os.path.join(args.outdir, os.path.basename(os.path.dirname(feat_file)))
#if not os.path.exists(outTxtDir):
# os.mkdir(outTxtDir)
#outTxt = os.path.join(outTxtDir, os.path.basename(feat_file).replace(".wav", ".txt"))
#logging.info(outTxt)
#g = open(outTxt, "wt")
#idx_frm = 0
#nfrm = trj_lat_src.shape[0]
#dim = trj_lat_src.shape[1]
#if not args.time_flag:
##if True:
# while idx_frm < nfrm:
# idx_elmt = 1
# for elmt in trj_lat_src[idx_frm]:
# if idx_elmt < dim:
# g.write("%lf " % (elmt))
# else:
# g.write("%lf\n" % (elmt))
# idx_elmt += 1
# idx_frm += 1
#else:
# while idx_frm < nfrm:
# idx_elmt = 1
# for elmt in trj_lat_src[idx_frm]:
# if idx_elmt < dim:
# if idx_elmt > 1:
# g.write("%lf " % (elmt))
# else:
# g.write("%lf %lf " % (time_axis[idx_frm], elmt))
# else:
# g.write("%lf\n" % (elmt))
# idx_elmt += 1
# idx_frm += 1
#g.close()
logging.info('write to h5')
outh5dir = os.path.join(os.path.dirname(os.path.dirname(feat_file)), spk_src+"-"+args.spk_trg)
if not os.path.exists(outh5dir):
os.makedirs(outh5dir)
feat_file = os.path.join(outh5dir, os.path.basename(feat_file))
# cv
write_path = args.string_path
logging.info(feat_file + ' ' + write_path)
logging.info(feat_cv.shape)
write_hdf5(feat_file, write_path, feat_cv)
## diffGVF0
#write_path = args.string_path+"_diffgvf0"
#logging.info(feat_file + ' ' + write_path)
#logging.info(feat_diffgv_anasyn.shape)
#write_hdf5(feat_file, write_path, feat_diffgv_anasyn)
count += 1
def decode_RNN(wav_list, gpu, cvlist=None, cvlist_src=None, \
mcd_cvlist_src=None, mcdstd_cvlist_src=None, mcdpow_cvlist_src=None, mcdpowstd_cvlist_src=None):
with torch.cuda.device(gpu):
mean_trg = torch.FloatTensor(
read_hdf5(args.stats_jnt,
"/mean_feat_org_lf0")[config.stdim:]).cuda()
std_trg = torch.FloatTensor(
read_hdf5(args.stats_jnt,
"/scale_feat_org_lf0")[config.stdim:]).cuda()
# define model and load parameters
logging.info(config)
logging.info("model")
with torch.no_grad():
model_encoder = GRU_RNN_STOCHASTIC(
in_dim=config.in_dim,
out_dim=config.lat_dim,
hidden_layers=config.hidden_layers,
hidden_units=config.hidden_units,
kernel_size=config.kernel_size_enc,
dilation_size=config.dilation_size_enc,
arparam=config.arparam,
spk_dim=n_spk,
causal_conv=config.causal_conv,
scale_out_flag=False)
model_decoder = GRU_RNN(in_dim=config.lat_dim + n_spk,
out_dim=config.out_dim,
hidden_layers=config.hidden_layers,
hidden_units=config.hidden_units,
kernel_size=config.kernel_size_dec,
dilation_size=config.dilation_size_dec,
causal_conv=config.causal_conv,
scale_in_flag=False)
logging.info(model_encoder)
logging.info(model_decoder)
model_encoder.load_state_dict(
torch.load(args.model)["model_encoder"])
model_decoder.load_state_dict(
torch.load(args.model)["model_decoder"])
model_encoder.cuda()
model_decoder.cuda()
model_encoder.eval()
model_decoder.eval()
for param in model_encoder.parameters():
param.requires_grad = False
for param in model_decoder.parameters():
param.requires_grad = False
if config.arparam:
init_pp = np.zeros((1, 1, config.lat_dim * 2 + n_spk))
else:
init_pp = np.zeros((1, 1, config.lat_dim + n_spk))
y_in_pp = torch.FloatTensor(init_pp).cuda()
y_in_src = y_in_trg = torch.unsqueeze(
torch.unsqueeze((0 - mean_trg) / std_trg, 0), 0)
fs = args.fs
fft_size = args.fftl
mcep_dim = model_decoder.out_dim - 1
for wav_file in wav_list:
# convert mcep
feat_file = os.path.join(
args.h5outdir,
os.path.basename(wav_file).replace(".wav", ".h5"))
logging.info("cvmcep " + feat_file + " " + wav_file)
fs, x = read_wav(wav_file, cutoff=70)
time_axis, f0, sp, ap = analyze_range(x, fs=fs, minf0=args.minf0, maxf0=args.maxf0, \
fperiod=args.shiftms, fftl=args.fftl)
logging.info(sp.shape)
mcep = ps.sp2mc(sp, mcep_dim, args.mcep_alpha)
logging.info(mcep.shape)
codeap = pw.code_aperiodicity(ap, fs)
logging.info(codeap.shape)
npow = spc2npow(sp)
logging.info(npow.shape)
_, spcidx = extfrm(mcep, npow, power_threshold=args.pow)
spcidx = spcidx[0]
logging.info(spcidx.shape)
uv, contf0 = convert_continuos_f0(np.array(f0))
uv = np.expand_dims(uv, axis=-1)
logging.info(uv.shape)
cont_f0_lpf = low_pass_filter(contf0,
int(1.0 /
(args.shiftms * 0.001)),
cutoff=LP_CUTOFF)
logcontf0 = np.expand_dims(np.log(cont_f0_lpf), axis=-1)
logging.info(logcontf0.shape)
feat = np.c_[uv, logcontf0, codeap, mcep]
logging.info(feat.shape)
logging.info("generate")
with torch.no_grad():
lat_feat_src, _, _, _, _ = \
model_encoder(torch.FloatTensor(feat).cuda(), y_in_pp, sampling=False)
src_code = np.zeros((lat_feat_src.shape[0], n_spk))
src_code[:, src_code_idx] = 1
src_code = torch.FloatTensor(src_code).cuda()
trg_code = np.zeros((lat_feat_src.shape[0], n_spk))
trg_code[:, trg_code_idx] = 1
trg_code = torch.FloatTensor(trg_code).cuda()
cvmcep_src, _, _ = model_decoder(
torch.cat((src_code, lat_feat_src), 1), y_in_src)
cvmcep_src = np.array(cvmcep_src.cpu().data.numpy(),
dtype=np.float64)
cvmcep, _, _ = model_decoder(
torch.cat((trg_code, lat_feat_src), 1), y_in_trg)
cvmcep = np.array(cvmcep.cpu().data.numpy(),
dtype=np.float64)
logging.info(lat_feat_src.shape)
logging.info(cvmcep_src.shape)
logging.info(cvmcep.shape)
cvf0 = convert_f0(f0, f0_range_mean_src, f0_range_std_src,
f0_range_mean_trg, f0_range_std_trg)
uv_cv, contf0_cv = convert_continuos_f0(np.array(cvf0))
uv_cv = np.expand_dims(uv_cv, axis=-1)
logging.info(uv_cv.shape)
cont_f0_lpf_cv = low_pass_filter(contf0_cv,
int(1.0 /
(args.shiftms * 0.001)),
cutoff=LP_CUTOFF)
logcontf0_cv = np.expand_dims(np.log(cont_f0_lpf_cv), axis=-1)
logging.info(logcontf0_cv.shape)
feat_cv = np.c_[uv_cv, logcontf0_cv, codeap]
logging.info(feat_cv.shape)
feat_cvmcep = np.c_[feat_cv, cvmcep]
logging.info(feat_cvmcep.shape)
write_path = '/feat_cvmcep_cycvae-' + model_epoch
logging.info(feat_file + ' ' + write_path)
write_hdf5(feat_file, write_path, feat_cvmcep)
cvlist.append(np.var(cvmcep[:, 1:], axis=0))
_, mcdpow_arr = dtw.calc_mcd(np.array(mcep[np.array(spcidx),:], dtype=np.float64), \
np.array(cvmcep_src[np.array(spcidx),:], dtype=np.float64))
_, mcd_arr = dtw.calc_mcd(np.array(mcep[np.array(spcidx),1:], dtype=np.float64), \
np.array(cvmcep_src[np.array(spcidx),1:], dtype=np.float64))
mcdpow_mean = np.mean(mcdpow_arr)
mcdpow_std = np.std(mcdpow_arr)
mcd_mean = np.mean(mcd_arr)
mcd_std = np.std(mcd_arr)
logging.info("mcdpow_src_cv: %.6f dB +- %.6f" %
(mcdpow_mean, mcdpow_std))
logging.info("mcd_src_cv: %.6f dB +- %.6f" %
(mcd_mean, mcd_std))
mcdpow_cvlist_src.append(mcdpow_mean)
mcdpowstd_cvlist_src.append(mcdpow_std)
mcd_cvlist_src.append(mcd_mean)
mcdstd_cvlist_src.append(mcd_std)
cvlist_src.append(np.var(cvmcep_src[:, 1:], axis=0))
logging.info("synth voco")
cvsp = ps.mc2sp(np.ascontiguousarray(cvmcep), args.mcep_alpha,
fft_size)
logging.info(cvsp.shape)
wav = np.clip(
pw.synthesize(cvf0,
cvsp,
ap,
fs,
frame_period=args.shiftms), -1, 1)
wavpath = os.path.join(
args.outdir,
os.path.basename(wav_file).replace(".wav", "_cv.wav"))
sf.write(wavpath, wav, fs, 'PCM_16')
logging.info(wavpath)
logging.info("synth anasyn")
wav = np.clip(
pw.synthesize(f0, sp, ap, fs, frame_period=args.shiftms),
-1, 1)
wavpath = os.path.join(
args.outdir,
os.path.basename(wav_file).replace(".wav", "_anasyn.wav"))
sf.write(wavpath, wav, fs, 'PCM_16')
logging.info(wavpath)
def gpu_decode(feat_list, gpu, cvlist=None, mcdpow_cvlist=None, mcdpowstd_cvlist=None, mcd_cvlist=None, \
mcdstd_cvlist=None, cvlist_dv=None, mcdpow_cvlist_dv=None, mcdpowstd_cvlist_dv=None, \
mcd_cvlist_dv=None, mcdstd_cvlist_dv=None):
with torch.cuda.device(gpu):
mean_jnt = torch.FloatTensor(
read_hdf5(args.stats_jnt,
"/mean_feat_org_lf0")[config.stdim:]).cuda()
std_jnt = torch.FloatTensor(
read_hdf5(args.stats_jnt,
"/scale_feat_org_lf0")[config.stdim:]).cuda()
# define model and load parameters
logging.info("model")
logging.info(config)
with torch.no_grad():
model_encoder = GRU_RNN_STOCHASTIC(
in_dim=config.in_dim,
out_dim=config.lat_dim,
hidden_layers=config.hidden_layers,
hidden_units=config.hidden_units,
kernel_size=config.kernel_size,
dilation_size=config.dilation_size,
spk_dim=n_spk,
scale_out_flag=False)
model_decoder = GRU_RNN(in_dim=config.lat_dim + n_spk,
out_dim=config.out_dim,
hidden_layers=config.hidden_layers,
hidden_units=config.hidden_units,
kernel_size=config.kernel_size,
dilation_size=config.dilation_size,
scale_in_flag=False)
logging.info(model_encoder)
logging.info(model_decoder)
model_encoder.load_state_dict(
torch.load(args.model)["model_encoder"])
model_decoder.load_state_dict(
torch.load(args.model)["model_decoder"])
model_encoder.cuda()
model_decoder.cuda()
model_encoder.eval()
model_decoder.eval()
for param in model_encoder.parameters():
param.requires_grad = False
for param in model_decoder.parameters():
param.requires_grad = False
init_pp = np.zeros((1, 1, config.lat_dim * 2 + n_spk))
y_in_pp = torch.FloatTensor(init_pp).cuda()
y_in_src = y_in_trg = torch.unsqueeze(
torch.unsqueeze((0 - mean_jnt) / std_jnt, 0), 0)
for feat_file in feat_list:
# convert mcep
logging.info("recmcep " + feat_file)
feat = read_hdf5(feat_file, "/feat_org_lf0")
logging.info(feat.shape)
f0 = read_hdf5(feat_file, "/f0_range")
cvf0 = convert_f0(f0, f0_range_mean_trg, f0_range_std_trg,
f0_range_mean_src, f0_range_std_src)
cvuv, cont_f0 = convert_continuos_f0(cvf0)
cvuv = np.expand_dims(cvuv, axis=-1)
cont_f0_lpf = low_pass_filter(cont_f0,
int(1.0 /
(args.shiftms * 0.001)),
cutoff=LP_CUTOFF)
if np.min(cont_f0_lpf) <= 0:
length = len(cont_f0_lpf)
for i in range(length):
if cont_f0_lpf[i] <= 0:
if i > 0 and i < length - 1:
for j in range(i - 1, -1, -1):
if cont_f0_lpf[j] > 0:
left_val = cont_f0_lpf[j]
break
for j in range(i + 1, length):
if cont_f0_lpf[j] > 0:
right_val = cont_f0_lpf[j]
break
cont_f0_lpf[i] = (left_val + right_val) / 2
elif i == 0:
for j in range(1, length):
if cont_f0_lpf[j] > 0:
right_val = cont_f0_lpf[j]
break
cont_f0_lpf[i] = right_val
else:
for j in range(i - 1, -1, -1):
if cont_f0_lpf[j] > 0:
left_val = cont_f0_lpf[j]
break
cont_f0_lpf[i] = left_val
cvlogf0fil = np.expand_dims(np.log(cont_f0_lpf), axis=-1)
feat_cv = np.c_[cvuv, cvlogf0fil, feat[:, 2:config.stdim]]
with torch.no_grad():
lat_feat, _, _, _, _ = model_encoder(torch.FloatTensor(feat).cuda(), \
y_in_pp, sampling=False)
src_code = np.zeros((lat_feat.shape[0], n_spk))
src_code[:, src_code_idx] = 1
src_code = torch.FloatTensor(src_code).cuda()
cvmcep, _, _ = model_decoder(
torch.cat((src_code, lat_feat), 1), y_in_src)
lat_feat, _, _, _, _ = model_encoder(torch.cat((torch.FloatTensor(feat_cv).cuda(), cvmcep),1), \
y_in_pp, sampling=False)
trg_code = np.zeros((lat_feat.shape[0], n_spk))
trg_code[:, trg_code_idx] = 1
trg_code = torch.FloatTensor(trg_code).cuda()
cvmcep, _, _ = model_decoder(
torch.cat((trg_code, lat_feat), 1), y_in_trg)
cvmcep = np.array(cvmcep.cpu().data.numpy(), dtype=np.float64)
logging.info(cvmcep.shape)
mcep = feat[:, config.stdim:]
spcidx = read_hdf5(feat_file, "/spcidx_range")[0]
_, mcdpow_arr = dtw.calc_mcd(np.array(mcep[np.array(spcidx),:], dtype=np.float64), \
np.array(cvmcep[np.array(spcidx),:], dtype=np.float64))
_, mcd_arr = dtw.calc_mcd(np.array(mcep[np.array(spcidx),1:], dtype=np.float64), \
np.array(cvmcep[np.array(spcidx),1:], dtype=np.float64))
mcdpow_mean = np.mean(mcdpow_arr)
mcdpow_std = np.std(mcdpow_arr)
mcd_mean = np.mean(mcd_arr)
mcd_std = np.std(mcd_arr)
dataset = feat_file.split('/')[1].split('_')[0]
if 'tr' in dataset:
logging.info('trn')
mcdpow_cvlist.append(mcdpow_mean)
mcdpowstd_cvlist.append(mcdpow_std)
mcd_cvlist.append(mcd_mean)
mcdstd_cvlist.append(mcd_std)
cvlist.append(np.var(cvmcep[:, 1:], axis=0))
logging.info(len(cvlist))
elif 'dv' in dataset:
logging.info('dev')
mcdpow_cvlist_dv.append(mcdpow_mean)
mcdpowstd_cvlist_dv.append(mcdpow_std)
mcd_cvlist_dv.append(mcd_mean)
mcdstd_cvlist_dv.append(mcd_std)
cvlist_dv.append(np.var(cvmcep[:, 1:], axis=0))
logging.info(len(cvlist_dv))
logging.info("mcdpow_rec: %.6f dB +- %.6f" %
(mcdpow_mean, mcdpow_std))
logging.info("mcd_rec: %.6f dB +- %.6f" % (mcd_mean, mcd_std))
feat_cvmcep = np.c_[feat[:, :config.stdim], cvmcep]
logging.info(feat_cvmcep.shape)
write_path = '/feat_recmcep_cycvae-' + model_epoch
outh5dir = os.path.join(
os.path.dirname(os.path.dirname(feat_file)),
spk_trg + "-" + spk_src + "-" + spk_trg)
if not os.path.exists(outh5dir):
os.makedirs(outh5dir)
feat_file_cyc = os.path.join(outh5dir,
os.path.basename(feat_file))
logging.info(feat_file_cyc + ' ' + write_path)
write_hdf5(feat_file_cyc, write_path, feat_cvmcep)