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self.encoder = keras.Sequential( |
[self.enc_input] |
+ [ |
TransformerEncoder(num_hid, num_head, num_feed_forward) |
for _ in range(num_layers_enc) |
] |
) |
for i in range(num_layers_dec): |
setattr( |
self, |
f\"dec_layer_{i}\", |
TransformerDecoder(num_hid, num_head, num_feed_forward), |
) |
self.classifier = layers.Dense(num_classes) |
def decode(self, enc_out, target): |
y = self.dec_input(target) |
for i in range(self.num_layers_dec): |
y = getattr(self, f\"dec_layer_{i}\")(enc_out, y) |
return y |
def call(self, inputs): |
source = inputs[0] |
target = inputs[1] |
x = self.encoder(source) |
y = self.decode(x, target) |
return self.classifier(y) |
@property |
def metrics(self): |
return [self.loss_metric] |
def train_step(self, batch): |
\"\"\"Processes one batch inside model.fit().\"\"\" |
source = batch[\"source\"] |
target = batch[\"target\"] |
dec_input = target[:, :-1] |
dec_target = target[:, 1:] |
with tf.GradientTape() as tape: |
preds = self([source, dec_input]) |
one_hot = tf.one_hot(dec_target, depth=self.num_classes) |
mask = tf.math.logical_not(tf.math.equal(dec_target, 0)) |
loss = self.compiled_loss(one_hot, preds, sample_weight=mask) |
trainable_vars = self.trainable_variables |
gradients = tape.gradient(loss, trainable_vars) |
self.optimizer.apply_gradients(zip(gradients, trainable_vars)) |
self.loss_metric.update_state(loss) |
return {\"loss\": self.loss_metric.result()} |
def test_step(self, batch): |
source = batch[\"source\"] |
target = batch[\"target\"] |
dec_input = target[:, :-1] |
dec_target = target[:, 1:] |
preds = self([source, dec_input]) |
one_hot = tf.one_hot(dec_target, depth=self.num_classes) |
mask = tf.math.logical_not(tf.math.equal(dec_target, 0)) |
loss = self.compiled_loss(one_hot, preds, sample_weight=mask) |
self.loss_metric.update_state(loss) |
return {\"loss\": self.loss_metric.result()} |
def generate(self, source, target_start_token_idx): |
\"\"\"Performs inference over one batch of inputs using greedy decoding.\"\"\" |
bs = tf.shape(source)[0] |
enc = self.encoder(source) |
dec_input = tf.ones((bs, 1), dtype=tf.int32) * target_start_token_idx |
dec_logits = [] |
for i in range(self.target_maxlen - 1): |
dec_out = self.decode(enc, dec_input) |
logits = self.classifier(dec_out) |
logits = tf.argmax(logits, axis=-1, output_type=tf.int32) |
last_logit = tf.expand_dims(logits[:, -1], axis=-1) |
dec_logits.append(last_logit) |
dec_input = tf.concat([dec_input, last_logit], axis=-1) |
return dec_input |
Download the dataset |
Note: This requires ~3.6 GB of disk space and takes ~5 minutes for the extraction of files. |
keras.utils.get_file( |
os.path.join(os.getcwd(), \"data.tar.gz\"), |
\"https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2\", |
extract=True, |
archive_format=\"tar\", |
cache_dir=\".\", |
) |
saveto = \"./datasets/LJSpeech-1.1\" |
wavs = glob(\"{}/**/*.wav\".format(saveto), recursive=True) |
id_to_text = {} |
with open(os.path.join(saveto, \"metadata.csv\"), encoding=\"utf-8\") as f: |
for line in f: |
id = line.strip().split(\"|\")[0] |
text = line.strip().split(\"|\")[2] |
id_to_text[id] = text |