def speech_to_text():
n_mfcc = 60
batch_size = 1
n_epoch = 100
speech_loader = SpeechLoader(batch_size=batch_size, n_mfcc=n_mfcc)
n_out = speech_loader.vocab_size
model = WaveNet(n_out, batch_size=batch_size, n_mfcc=n_mfcc)
model.build_graph()
chpt = tf.train.get_checkpoint_state(TRAIN_DIR)
if chpt:
print("restore model paramters from %s" % chpt.model_checkpoint_path)
model.restore(chpt.model_checkpoint_path)
else:
print("init a new model.")
model.init_sess()
file_names = os.listdir(TEXT_DIR)
file_list = [os.path.join(TEXT_DIR, file_name) for file_name in file_names]
step = 0
for file in file_list:
step += 1
mfcc_features = speech_loader.load_one_file(file)
output = model.predict(mfcc_features)
# transfer to word
words = speech_loader.index2str(output[0])
print("Input(%d): %s" % (step, file))
print("Output(%d): %s" % (step, words))
def train():
batch_size = 32
n_epoch = 100
n_mfcc = 60
speech_loader = SpeechLoader(batch_size=batch_size, n_mfcc=n_mfcc)
n_out = speech_loader.vocab_size
model = WaveNet(n_out, batch_size=batch_size, n_mfcc=n_mfcc)
model.build_graph()
chpt = tf.train.get_checkpoint_state(TRAIN_DIR)
if chpt:
print("restore model paramters from %s" % chpt.model_checkpoint_path)
model.restore(chpt.model_checkpoint_path)
else:
print("init a new model.")
model.init_sess()
speech_loader.create_batches()
model.train_val(speech_loader.mfcc_tensor,
speech_loader.label_tensor,
ckpt_dir=TRAIN_DIR,
n_epoch=n_epoch,
val_rate=0.15)
def train():
# setting parameters
batch_size = 32
n_epoch = 100
n_mfcc = 60
# load speech data
wav_path = os.path.join(os.getcwd(),'data','wav','train')
label_file = os.path.join(os.getcwd(),'data','doc','trans','train.word.txt')
speech_loader = SpeechLoader(wav_path, label_file, batch_size, n_mfcc)
n_out = speech_loader.vocab_size
# load model
model = Model(n_out, batch_size=batch_size, n_mfcc=n_mfcc)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
for epoch in range(n_epoch):
speech_loader.create_batches() # random shuffle data
speech_loader.reset_batch_pointer()
for batch in range(speech_loader.n_batches):
start = time.time()
batches_wav, batches_label = speech_loader.next_batch()
feed = {model.input_data: batches_wav, model.targets: batches_label}
train_loss, _ = sess.run([model.cost, model.optimizer_op], feed_dict=feed)
end = time.time()
print("epoch: %d/%d, batch: %d/%d, loss: %s, time: %.3f."%(epoch, n_epoch, batch, speech_loader.n_batches, train_loss, end-start))
# save models
if epoch % 5 ==0:
saver.save(sess, os.path.join(os.getcwd(), 'model','speech.module'), global_step=epoch)
def speech_to_text():
n_mfcc = 60
# load data
wav_path = os.path.join(os.getcwd(), 'data', 'wav', 'train')
label_file = os.path.join(os.getcwd(), 'data', 'doc', 'trans',
'train.word.txt')
speech_loader = SpeechLoader(wav_path,
label_file=label_file,
batch_size=1,
n_mfcc=n_mfcc)
# load model
model = Model(speech_loader.vocab_size, n_mfcc=n_mfcc, is_training=False)
saver = tf.train.Saver(tf.trainable_variables())
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('model'))
for j in range(750, 755):
# extract feature
wav_file = os.path.join(os.getcwd(), 'data', 'wav', 'test', 'D4',
'D4_' + str(j) + '.wav')
wav, sr = librosa.load(wav_file, mono=True)
mfcc = np.transpose(
np.expand_dims(librosa.feature.mfcc(wav, sr, n_mfcc=n_mfcc),
axis=0), [0, 2, 1])
mfcc = mfcc.tolist()
# fill 0
while len(mfcc[0]) < speech_loader.wav_max_len:
mfcc[0].append([0] * n_mfcc)
# word dict
wmap = {value: key for key, value in speech_loader.wordmap.items()}
# recognition
decoded = tf.transpose(model.logit, perm=[1, 0, 2])
decoded, probs = tf.nn.ctc_beam_search_decoder(decoded,
model.seq_len,
top_paths=1,
merge_repeated=True)
predict = tf.sparse_to_dense(decoded[0].indices,
decoded[0].dense_shape,
decoded[0].values) + 1
output, probs = sess.run([predict, probs],
feed_dict={model.input_data: mfcc})
# print result
words = ''
for i in range(len(output[0])):
words += wmap.get(output[0][i], -1)
print("---------------------------")
print("Input: " + wav_file)
print("Output: " + words)
def speech_to_text(wav_files, labels_dict):
n_mfcc = 60
# load data
speech_loader = SpeechLoader(n_mfcc=n_mfcc, is_training=False)
wav_max_len = 673
# load model
model = WaveNet(speech_loader.vocab_size, n_mfcc=n_mfcc, is_training=False)
saver = tf.train.Saver(tf.trainable_variables())
test_wav = wav_files[:10]
# word dict
word_map = {value: key for key, value in speech_loader.wordmap.items()}
print(word_map)
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('../model'))
for wav_path in test_wav:
wav, sr = librosa.load(wav_path, mono=True)
mfcc = np.transpose(np.expand_dims(librosa.feature.mfcc(wav, sr, n_mfcc=n_mfcc),
axis=0), [0, 2, 1])
mfcc = mfcc.tolist()
while len(mfcc[0]) < wav_max_len:
mfcc[0].append([0] * n_mfcc)
# recognition
decoded = tf.transpose(model.logit, perm=[1, 0, 2])
decoded, probs = tf.nn.ctc_beam_search_decoder(decoded, model.seq_len,
top_paths=1, merge_repeated=True)
predict = tf.sparse_to_dense(decoded[0].indices,
decoded[0].dense_shape,
decoded[0].values) + 1
output, probs = sess.run([predict, probs], feed_dict={model.input_data: mfcc})
# result
words = ''
for i in range(len(output[0])):
words += word_map.get(output[0][i], -1)
wav_name = os.path.basename(wav_path).split('.')[0]
print('-------------------------------------------------------')
print(f'Input: {wav_path}')
print(f'Output: {words}')
print(f'True result: {labels_dict[wav_name]}')
def train():
# setting parameters
batch_size = 2
n_epoch = 100
n_mfcc = 60
# load speech data
wav_path = os.path.join(os.getcwd(), 'data', 'wav', 'train')
label_file = os.path.join(os.getcwd(), 'data', 'doc', 'trans',
'train.word.txt')
speech_loader = SpeechLoader(wav_path, label_file, batch_size, n_mfcc)
n_out = speech_loader.vocab_size
# load model
model = Model(n_out, batch_size=batch_size, n_mfcc=n_mfcc)
tf.summary.scalar('loss', model.cost)
merged = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
if len(os.listdir('./model')) > 3:
print("loading model from checkpoint")
checkpoint = tf.train.latest_checkpoint('./model')
saver.restore(sess, checkpoint)
tf.train.write_graph(sess.graph_def, './model', 'model.pbtxt')
summary_writer = tf.summary.FileWriter('./model', graph=sess.graph)
for epoch in range(n_epoch):
speech_loader.create_batches() # random shuffle data
speech_loader.reset_batch_pointer()
for batch in range(speech_loader.n_batches):
start = time.time()
batches_wav, batches_label = speech_loader.next_batch()
feed = {
model.input_data: batches_wav,
model.targets: batches_label
}
result, train_loss, _ = sess.run(
[merged, model.cost, model.optimizer_op], feed_dict=feed)
end = time.time()
print("epoch: %d/%d, batch: %d/%d, loss: %s, time: %.3f." %
(epoch, n_epoch, batch, speech_loader.n_batches,
train_loss, end - start))
summary_writer.add_summary(result, epoch)
# save models
if epoch % 5 == 0:
saver.save(sess,
os.path.join(os.getcwd(), 'model', 'speech.module'),
global_step=epoch)
def train():
'''
:return:
'''
batch_size = 8
n_mfcc = 60
n_epoch = 100
source_file = '/home/ydf_micro/datasets/data_thchs30'
speech_loader = SpeechLoader(os.path.join(source_file, 'train'),
batch_size, n_mfcc)
n_out = speech_loader.vocab_size
# load model
model = WaveNet(n_out, batch_size=batch_size, n_mfcc=n_mfcc)
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# sess.graph.finalize() # Graph is read-only after this statement
for epoch in range(n_epoch):
speech_loader.create_batches() # random shuffle data
speech_loader.reset_batch_pointer()
for batch in range(speech_loader.n_batches):
batch_start = time.time()
batches_wav, batches_label = speech_loader.next_batch()
feed = {
model.input_data: batches_wav,
model.targets: batches_label
}
train_loss, _ = sess.run([model.cost, model.optimizer_op],
feed_dict=feed)
batch_end = time.time()
print(
f'epoch: {epoch+1}/{n_epoch}, batch: {batch+1}/{speech_loader.n_batches}, '
f'loss: {train_loss:.2f}, time: {(batch_end-batch_start):.2f}s'
)
# save models
if epoch % 5 == 0:
saver.save(sess,
os.path.join(os.path.dirname(os.getcwd()), 'model',
'speech.module'),
global_step=epoch)
def speech_to_text():
n_mfcc = 60
# load data
speech_loader = SpeechLoader(n_mfcc=60, is_test=True)
# load model
model = Model(speech_loader.vocab_size, n_mfcc=n_mfcc, is_training=False)
saver = tf.train.Saver()
with tf.Session() as sess:
# extract feature
wav, sr = librosa.load("data/test-cn.wav", mono=True)
mfcc = np.transpose(
np.expand_dims(librosa.feature.mfcc(wav, sr, n_mfcc=n_mfcc),
axis=0), [0, 2, 1])
mfcc = mfcc.tolist()
# fill 0
while len(mfcc[0]) < speech_loader.wav_max_len:
mfcc[0].append([0] * n_mfcc)
# word dict
wmap = {value: key for key, value in speech_loader.wordmap.items()}
# recognition
#saver.restore(sess, tf.train.latest_checkpoint('model'))
saver.restore(sess, "model/SpeechToTextCN/speech.module.v2-99")
decoded = tf.transpose(model.logit, perm=[1, 0, 2])
decoded, probs = tf.nn.ctc_beam_search_decoder(decoded,
model.seq_len,
top_paths=1,
merge_repeated=True)
predict = tf.sparse_to_dense(
decoded[0].indices, decoded[0].dense_shape, decoded[0].values) + 1
output, probs = sess.run([predict, probs],
feed_dict={model.input_data: mfcc})
# print result
words = ''
for i in range(len(output[0])):
words += wmap.get(output[0][i], -1)
print("---------------------------")
print("Predict: " + words)