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inference_9.py
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inference_9.py
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import matplotlib
matplotlib.use("Agg")
import matplotlib.pylab as plt
import os
import librosa
import numpy as np
import torch
from torch.utils.data import DataLoader
from reader.reader import TextMelIDLoader, TextMelIDCollate
from reader.symbols import id2ph, id2sp
from hparams import create_hparams
from model.model_9 import Parrot
from model import lcm
from train import load_model
import scipy.io.wavfile
import torch.nn.functional as F
########### Configuration ###########
hparams = create_hparams()
#generation list
# hlist = '/home/jxzhang/Documents/DataSets/VCTK/list/hold_english.list'
tlist = '/home/hk/voice_conversion/nonparaSeq2seqVC_text-dependent_SE/reader/inference_255to315.txt'
# use seen (tlist) or unseen list (hlist)
test_list = tlist
checkpoint_path='outdir_100speaker_ref_attention_addition_normed/checkpoint_74000'
# TTS or VC task?
input_text= False
# number of utterances for generation
NUM=10
ISMEL=(not hparams.predict_spectrogram)
#####################################
def plot_data(data, fn, figsize=(12, 4)):
fig, axes = plt.subplots(1, len(data), figsize=figsize)
for i in range(len(data)):
if len(data) == 1:
ax = axes
else:
ax = axes[i]
g = ax.imshow(data[i], aspect='auto', origin='bottom',
interpolation='none')
plt.colorbar(g, ax=ax)
plt.savefig(fn)
model = load_model(hparams)
model.load_state_dict(torch.load(checkpoint_path)['state_dict'])
_ = model.eval()
test_set = TextMelIDLoader(test_list,hparams.mel_mean, hparams.mel_std, shuffle=True)
sample_list = test_set.file_path_list
collate_fn = TextMelIDCollate(lcm(hparams.n_frames_per_step_encoder,
hparams.n_frames_per_step_decoder))
test_loader = DataLoader(test_set, num_workers=1, shuffle=False,
sampler=None,
batch_size=1, pin_memory=False,
drop_last=True, collate_fn=collate_fn)
task = 'tts' if input_text else 'vc'
path_save = os.path.join(checkpoint_path.replace('checkpoint', 'test'), task)
path_save += '_seen' if test_list == tlist else '_unseen'
if not os.path.exists(path_save):
os.makedirs(path_save)
print(path_save)
def recover_wav(mel, wav_path, ismel=False,
n_fft=2048, win_length=800,hop_length=200):
if ismel:
mean = np.load(hparams.mel_mean)
std = np.load(hparams.mel_std)
else:
mean, std = np.load(hparams.mel_mean_std.replace('mel','spec'))
mean = mean[:,None]
std = std[:,None]
mel = 1.2 * mel * std + mean
#np.save(os.path.join("/home/hk/voice_conversion/nonparaSeq2seqVC_code/pre-train/outdir_mean_std_trimmed/test_116000/vc_seen", 'Mel.npy'), mel)
#import pdb
#pdb.set_trace()
mel = np.exp(mel)
if ismel:
filters = librosa.filters.mel(sr=22050, n_fft=2048, n_mels=80)
inv_filters = np.linalg.pinv(filters)
spec = np.dot(inv_filters, mel)
else:
spec = mel
def _griffin_lim(stftm_matrix, shape, max_iter=50):
y = np.random.random(shape)
for i in range(max_iter):
stft_matrix = librosa.core.stft(y, n_fft=n_fft, win_length=win_length, hop_length=hop_length)
stft_matrix = stftm_matrix * stft_matrix / np.abs(stft_matrix)
y = librosa.core.istft(stft_matrix, win_length=win_length, hop_length=hop_length)
return y
shape = spec.shape[1] * hop_length - hop_length + 1
y = _griffin_lim(spec, shape)
scipy.io.wavfile.write(wav_path, 22050, y)
return y
text_input, mel, speaker_id = test_set[0]
reference_mel = mel.cuda().unsqueeze(0)
ref_sp = id2sp[speaker_id.item()]
def levenshteinDistance(s1, s2):
if len(s1) > len(s2):
s1, s2 = s2, s1
distances = list(range(len(s1) + 1))
for i2, c2 in enumerate(s2):
distances_ = [i2+1]
for i1, c1 in enumerate(s1):
if c1 == c2:
distances_.append(distances[i1])
else:
distances_.append(1 + min((distances[i1], distances[i1 + 1], distances_[-1])))
distances = distances_
return distances[-1]
with torch.no_grad():
errs = 0
totalphs = 0
for i, batch in enumerate(test_loader):
if i == NUM:
break
#import pdb
#pdb.set_trace()
sample_id = sample_list[i].split('/')[-1][1:8]
print(('%d index %s, decoding ...'%(i,sample_id)))
x, y = model.parse_batch(batch)
text_input_padded, mel_padded, text_lengths, mel_lengths = x
mel_padded = mel_padded.data.cpu().numpy()[0]
predicted_mel, post_output, predicted_stop, alignments, \
text_hidden, audio_seq2seq_hidden, audio_seq2seq_phids, audio_seq2seq_alignments, \
speaker_id, SE_alignment = model.inference(x, input_text, reference_mel, hparams.beam_width)
post_output = post_output.data.cpu().numpy()[0]
predicted_mel = predicted_mel.data.cpu().numpy()[0]
alignments = alignments.data.cpu().numpy()[0].T
audio_seq2seq_alignments = audio_seq2seq_alignments.data.cpu().numpy()[0].T
SE_alignment = SE_alignment.data.cpu().numpy()[0].T
text_hidden = text_hidden.data.cpu().numpy()[0].T #-> [hidden_dim, max_text_len]
audio_seq2seq_hidden = audio_seq2seq_hidden.data.cpu().numpy()[0].T
audio_seq2seq_phids = audio_seq2seq_phids.data.cpu().numpy()[0] # [T + 1]
speaker_id = speaker_id.data.cpu().numpy()[0] # scalar
predicted_stop = F.sigmoid(predicted_stop[0]).data.cpu().numpy()
predicted_stop = np.argmax(predicted_stop)
#post_output = post_output[:,:predicted_stop]
#alignments = alignments[:,:(predicted_stop)]
# plot_data(plot_spectrogram_to_numpy(mel.data.cpu().numpy()),
# os.path.join(path_save, 'Hid_%s_ref_%s_%s.pdf'%(sample_id, ref_sp, task)))
task = 'TTS' if input_text else 'VC'
#import pdb
#pdb.set_trace()
recover_wav(post_output,
os.path.join(path_save, 'Wav_%s_ref_%s_%s.wav'%(sample_id, ref_sp, task)),
ismel=ISMEL)
post_output_path = os.path.join(path_save, 'Mel_%s_ref_%s_%s.npy'%(sample_id, ref_sp, task))
np.save(post_output_path, post_output)
plot_data([mel], os.path.join(path_save, 'mel_ref_%s.pdf'% ref_sp))
plot_data([mel_padded], os.path.join(path_save, 'mel_src_%s.pdf'% sample_id))
plot_data([predicted_mel], os.path.join(path_save, 'melconverted_src_%s_ref_%s_%s.pdf'% (sample_id, ref_sp,task)))
#import pdb
#pdb.set_trace()
plot_data([alignments, audio_seq2seq_alignments],
os.path.join(path_save, 'Ali_%s_ref_%s_%s.pdf'%(sample_id, ref_sp, task)))
plot_data([alignments,SE_alignment],
os.path.join(path_save, 'SE_alignment%s_ref_%s_%s.pdf'%(sample_id, ref_sp, task)))
plot_data([np.hstack([text_hidden, audio_seq2seq_hidden])],
os.path.join(path_save, 'Hid_%s_ref_%s_%s.pdf'%(sample_id, ref_sp, task)))
audio_seq2seq_phids = [id2ph[id] for id in audio_seq2seq_phids[:-1]]
target_text = y[0].data.cpu().numpy()[0]
target_text = [id2ph[id] for id in target_text[:]]
print('Sounds like %s, Decoded text is '%(id2sp[speaker_id]))
print(audio_seq2seq_phids)
print(target_text)
err = levenshteinDistance(audio_seq2seq_phids, target_text)
print(err, len(target_text))
errs += err
totalphs += len(target_text)
print(float(errs)/float(totalphs))