Issue in Generate Training Data

[shrutisvani30]

I have followed tutorial "Atomic14" step-by-step but issue in "generate training data"
Need solution for this error.

CODE:

def process_files(file_names, label, repeat=1):
file_names = tf.repeat(file_names, repeat).numpy()
return [(process_file(file_name), label) for file_name in tqdm(file_names, desc=f"{word} ({label})", leave=False)]

def process_word(word, repeat=1):
# the index of the word word we are processing
label = words.index(word)
# get a list of files names for the word
file_names = [file_name for file_name in tqdm(get_files(word), desc="Checking", leave=False) if is_valid_file(file_name)]
# randomly shuffle the filenames
np.random.shuffle(file_names)
# split the files into train, validate and test buckets
train_size=int(TRAIN_SIZElen(file_names))
validation_size=int(VALIDATION_SIZElen(file_names))
test_size=int(TEST_SIZE*len(file_names))
# get the training samples
train.extend(
process_files(
file_names[:train_size],
label,
repeat=repeat
)
)
# and the validation samples
validate.extend(
process_files(
file_names[train_size:train_size+validation_size],
label,
repeat=repeat
)
)
# and the test samples
test.extend(
process_files(
file_names[train_size+validation_size:],
label,
repeat=repeat
)
)
for word in tqdm(words, desc="Processing words"):
if '_' not in word:
# add more examples of marvin to balance our training set
repeat = 70 if word == 'marvin' else 1
process_word(word, repeat=repeat)

print(len(train), len(test), len(validate))

Error :

AttributeError Traceback (most recent call last)
Cell In[11], line 47
44 if '_' not in word:
45 # add more examples of marvin to balance our training set
46 repeat = 70 if word == 'marvin' else 1
---> 47 process_word(word, repeat=repeat)
49 print(len(train), len(test), len(validate))

Cell In[11], line 10, in process_word(word, repeat)
8 label = words.index(word)
9 # get a list of files names for the word
---> 10 file_names = [file_name for file_name in tqdm(get_files(word), desc="Checking", leave=False) if is_valid_file(file_name)]
11 # randomly shuffle the filenames
12 np.random.shuffle(file_names)

Cell In[11], line 10, in (.0)
8 label = words.index(word)
9 # get a list of files names for the word
---> 10 file_names = [file_name for file_name in tqdm(get_files(word), desc="Checking", leave=False) if is_valid_file(file_name)]
11 # randomly shuffle the filenames
12 np.random.shuffle(file_names)

Cell In[9], line 29, in is_valid_file(file_name)
27 def is_valid_file(file_name):
28 #load the audio file
---> 29 audio_tensor = tfio.audio.AudiofIOTensor(file_name)
30 #cehck the file is long enough
31 if not is_correct_length(audio_tensor, EXPECTED_SAMPLES):

AttributeError: module 'tensorflow_io.python.api.audio' has no attribute 'AudiofIOTensor'