def write_preprocessed_target_data(_id: int, key: str, mel: np.ndarray,
filename: str):
raw_mel = mel.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'id': int64_feature([_id]),
'key': bytes_feature([key.encode('utf-8')]),
'mel': bytes_feature([raw_mel]),
'target_length': int64_feature([len(mel)]),
'mel_width': int64_feature([mel.shape[1]]),
}))
write_tfrecord(example, filename)
def write_preprocessed_source_data(_id: int, key: str, source: np.ndarray,
text, filename: str):
raw_source = source.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'id': int64_feature([_id]),
'key': bytes_feature([key.encode('utf-8')]),
'source': bytes_feature([raw_source]),
'source_length': int64_feature([len(source)]),
'text': bytes_feature([text.encode('utf-8')]),
}))
write_tfrecord(example, filename)
def write_preprocessed_target_data(_id: int, key: str, codes: np.ndarray,
codes_length: int, lang, filename: str):
raw_codes = codes.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'id': int64_feature([_id]),
'key': bytes_feature([key.encode('utf-8')]),
'lang': bytes_feature([lang.encode('utf-8')]),
'codes': bytes_feature([raw_codes]),
'codes_length': int64_feature([codes_length]),
'codes_width': int64_feature([codes.shape[1]]),
}))
write_tfrecord(example, filename)
def write_prediction_result(id_: int, key: str, alignments: List[np.ndarray], mel: np.ndarray,
ground_truth_mel: np.ndarray,
text: str, source: np.ndarray, filename: str):
example = tf.train.Example(features=tf.train.Features(feature={
'id': int64_feature([id_]),
'key': bytes_feature([key.encode('utf-8')]),
'mel': bytes_feature([mel.tostring()]),
'mel_length': int64_feature([mel.shape[0]]),
'mel_width': int64_feature([mel.shape[1]]),
'ground_truth_mel': bytes_feature([ground_truth_mel.tostring()]),
'ground_truth_mel_length': int64_feature([ground_truth_mel.shape[0]]),
'alignment': bytes_feature([alignment.tostring() for alignment in alignments]),
'text': bytes_feature([text.encode('utf-8')]),
'source': bytes_feature([source.tostring()]),
'source_length': int64_feature([source.shape[0]]),
}))
write_tfrecord(example, filename)
def write_preprocessed_source_data(_id: int, key: str, source: np.ndarray,
text, phones: np.ndarray, phone_txt,
speaker_id, age, gender, lang,
filename: str):
raw_source = source.tostring()
example = tf.train.Example(features=tf.train.Features(
feature={
'id': int64_feature([_id]),
'key': bytes_feature([key.encode('utf-8')]),
'source': bytes_feature([raw_source]),
'source_length': int64_feature([len(source)]),
'text': bytes_feature([text.encode('utf-8')]),
'phone': bytes_feature([phones.tostring()]),
'phone_length': int64_feature([len(phones)]),
'phone_txt': bytes_feature([phone_txt.encode('utf-8')]),
'speaker_id': int64_feature([speaker_id]),
'age': int64_feature([age]),
'gender': int64_feature([gender]),
'lang': bytes_feature([lang.encode('utf-8')]),
}))
write_tfrecord(example, filename)