TensorFlow-io package not possible on computer
@sushreebarsa Could you please try to install the tensorflow_io and try again. 
Try with the following
```
!pip install tensorflow_io
```
I was able to run the code successfully after installing this module. Please have a look at this colab [gist](https://colab.research.google.com/gist/sushreebarsa/8adeb47ab958017b60d93c514ff6d932/61264.ipynb) for reference. 
Please find the log below;
```
Downloading data from https://storage.googleapis.com/audioset/miaow_16k.wav
215546/215546 [==============================] - 0s 0us/step
./test_data/miaow_16k.wav
WARNING:tensorflow:Using a while_loop for converting IO>AudioResample cause there is no registered converter for this op.
Speech
Child speech, kid speaking
Conversation
Narration, monologue
Babbling
Speech synthesizer
Shout
Bellow
Whoop
Yell
Children shouting
Screaming
Whispering
Laughter
Baby laughter
Giggle
Snicker
Belly laugh
Chuckle, chortle
Crying, sobbing
...
The main sound is: Animal
The embeddings shape: (13, 1024)
Downloading data from https://github.com/karoldvl/ESC-50/archive/master.zip
   8192/Unknown - 0s 0us/stepWARNING:tensorflow:Using a while_loop for converting IO>AudioResample cause there is no registered converter for this op.
Model: "my_model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense (Dense)               (None, 512)               524800    
                                                                 
 dense_1 (Dense)             (None, 2)                 1026      
                                                                 
=================================================================
Total params: 525,826
Trainable params: 525,826
Non-trainable params: 0
_________________________________________________________________
Epoch 1/20
15/15 [==============================] - 11s 72ms/step - loss: 1.2337 - accuracy: 0.8229 - val_loss: 0.9781 - val_accuracy: 0.8687
Epoch 2/20
15/15 [==============================] - 0s 22ms/step - loss: 0.5836 - accuracy: 0.8771 - val_loss: 0.5496 - val_accuracy: 0.9187
Epoch 3/20
15/15 [==============================] - 0s 22ms/step - loss: 0.5533 - accuracy: 0.8958 - val_loss: 0.4514 - val_accuracy: 0.9187
Epoch 4/20
15/15 [==============================] - 0s 22ms/step - loss: 0.2929 - accuracy: 0.9083 - val_loss: 0.2570 - val_accuracy: 0.8750
Epoch 5/20
15/15 [==============================] - 0s 20ms/step - loss: 0.1942 - accuracy: 0.9146 - val_loss: 0.2387 - val_accuracy: 0.9125
Epoch 6/20
15/15 [==============================] - 0s 13ms/step - loss: 0.2205 - accuracy: 0.9229 - val_loss: 0.2080 - val_accuracy: 0.9187
Epoch 7/20
15/15 [==============================] - 0s 14ms/step - loss: 0.2351 - accuracy: 0.9167 - val_loss: 0.3459 - val_accuracy: 0.9187
Epoch 8/20
15/15 [==============================] - 0s 13ms/step - loss: 0.3472 - accuracy: 0.9125 - val_loss: 0.6668 - val_accuracy: 0.8687
5/5 [==============================] - 0s 11ms/step - loss: 0.2761 - accuracy: 0.9000
Loss:  0.27613919973373413
Accuracy:  0.8999999761581421
The main sound is: cat
```

Thank you!
 @sushreebarsa 
I tried that and it didn't work... This is what I get every time I try to install the TensorFlow_io: 

ERROR: Could not find a version that satisfies the requirement tensorflow==2.11.1 (from versions: 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0)
ERROR: No matching distribution found for tensorflow==2.11.1 @sushreebarsa 
Any other recommendations?
I found out that TensorFlow_io doesn't work on apple computers that have the m1 chip... Is there another way to resolve this or is there something other than that package that I could use?
 Please switch to using TF 2.13 instead of 2.11. That could possibly solve the issue. @mihaimaruseac 

I am currently using 2.13 > @mihaimaruseac
> 
> I am currently using 2.13

Sorry, was confused by the error message that is looking at `tensorflow==2.11.1`:

> @sushreebarsa I tried that and it didn't work... This is what I get every time I try to install the TensorFlow_io:
> 
> ERROR: Could not find a version that satisfies the requirement tensorflow==2.11.1 (from versions: 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0) ERROR: No matching distribution found for tensorflow==2.11.1


Can you post the output of `pip list`, please? > > @mihaimaruseac
> > I am currently using 2.13
> 
> Sorry, was confused by the error message that is looking at `tensorflow==2.11.1`:
> 
> > @sushreebarsa I tried that and it didn't work... This is what I get every time I try to install the TensorFlow_io:
> > ERROR: Could not find a version that satisfies the requirement tensorflow==2.11.1 (from versions: 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0) ERROR: No matching distribution found for tensorflow==2.11.1
> 
> Can you post the output of `pip list`, please?

Here is output of pip list:

```
Package                 Version
----------------------- --------
absl-py                 1.4.0
aiohttp                 3.8.4
aiosignal               1.3.1
astunparse              1.6.3
async-timeout           4.0.2
attrs                   22.2.0
cachetools              5.3.1
certifi                 2023.5.7
charset-normalizer      3.1.0
cmake                   3.25.2
contourpy               1.0.7
cycler                  0.11.0
face-recognition-models 0.3.0
flatbuffers             23.5.26
fonttools               4.38.0
frozenlist              1.3.3
gast                    0.4.0
google-auth             2.21.0
google-auth-oauthlib    1.0.0
google-pasta            0.2.0
grpcio                  1.56.0
h5py                    3.9.0
idna                    3.4
keras                   2.13.1
kiwisolver              1.4.4
libclang                16.0.0
Markdown                3.4.3
MarkupSafe              2.1.3
matplotlib              3.6.3
multidict               6.0.4
numpy                   1.24.2
oauthlib                3.2.2
opt-einsum              3.3.0
packaging               23.0
pandas                  1.5.3
Pillow                  9.4.0
pip                     23.1.2
protobuf                4.23.3
pyasn1                  0.5.0
pyasn1-modules          0.3.0
pyparsing               3.0.9
PySimpleGUI             4.60.5
python-dateutil         2.8.2
pytz                    2022.7.1
requests                2.31.0
requests-oauthlib       1.3.1
rsa                     4.9
setuptools              65.5.0
six                     1.16.0
tensorboard             2.13.0
tensorboard-data-server 0.7.1
tensorflow              2.13.0
tensorflow-estimator    2.13.0
tensorflow-macos        2.13.0
termcolor               2.3.0
typing_extensions       4.5.0
urllib3                 1.26.16
Werkzeug                2.3.6
wheel                   0.40.0
wrapt                   1.15.0
yarl                    1.8.2
``` Oh, I think the issue is that TF-IO did not have a release since Mar: https://pypi.org/project/tensorflow-io/0.32.0/#history, so the latest one is expecting TF 2.11.

Tagging @yongtang for a new release now that TF 2.13 has just been released. (sorry, closed by mistake, reopened) > Oh, I think the issue is that TF-IO did not have a release since Mar: https://pypi.org/project/tensorflow-io/0.32.0/#history, so the latest one is expecting TF 2.11.
> 
> Tagging @yongtang for a new release now that TF 2.13 has just been released.

Ohhh I see. Thank you, I know a couple people have been having the same issue as me. Would you recommend I install TF 2.11 and try to work from that for right now? (I'm just curious as I need it for my job) Let's try with that one, though I also see you are on a Mac with M1 and I think full support for that is coming only in 2.13. But hopefully it works?

If not, I'd recommend trying the Google Colab Notebooks, if possible, as those run on Linux VMs and should work 99.999% of the time (plus, are very similar to Jupyter Notebooks, so experience transfers nicely). Of course, this is only if job requirements allow it. > Let's try with that one, though I also see you are on a Mac with M1 and I think full support for that is coming only in 2.13. But hopefully it works?
> 
> If not, I'd recommend trying the Google Colab Notebooks, if possible, as those run on Linux VMs and should work 99.999% of the time (plus, are very similar to Jupyter Notebooks, so experience transfers nicely). Of course, this is only if job requirements allow it.

yeah it won't even let me install the 2.11 version haha. I can try working on colab or something for now. When do you think the TF-io release for 2.13 will be completed? That depends on when @yongtang and other maintainers of TF IO can do the next release.
### Issue type

Feature Request

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

source

### TensorFlow version

newest

### Custom code

No

### OS platform and distribution

_No response_

### Mobile device

_No response_

### Python version

3.9 and 3.11

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I have an Apple Mac Pro with an M1 chip and have been trying to get the example code from your website to compile on both my terminal and pycharm. I have installed the packed for tf metal and for macOS but when I attempt to install the TensorFlow-io it doesn't work at all. After research online I saw this is an issue for others as well. Is there a way that the code at this link (https://www.tensorflow.org/tutorials/audio/transfer_learning_audio ) would be able to be done without TensorFlow-io. In other words, is there an alternative so that it works the same? Or is there a solution to the package installation problem?

### Standalone code to reproduce the issue

```shell
import os

from IPython import display
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_io as tfio

yamnet_model_handle = 'https://tfhub.dev/google/yamnet/1'
yamnet_model = hub.load(yamnet_model_handle)

testing_wav_file_name = tf.keras.utils.get_file('miaow_16k.wav',
                                                'https://storage.googleapis.com/audioset/miaow_16k.wav',
                                                cache_dir='./',
                                                cache_subdir='test_data')

print(testing_wav_file_name)

# Utility functions for loading audio files and making sure the sample rate is correct.

@tf.function
def load_wav_16k_mono(filename):
    """ Load a WAV file, convert it to a float tensor, resample to 16 kHz single-channel audio. """
    file_contents = tf.io.read_file(filename)
    wav, sample_rate = tf.audio.decode_wav(
          file_contents,
          desired_channels=1)
    wav = tf.squeeze(wav, axis=-1)
    sample_rate = tf.cast(sample_rate, dtype=tf.int64)
    wav = tfio.audio.resample(wav, rate_in=sample_rate, rate_out=16000)
    return wav

testing_wav_data = load_wav_16k_mono(testing_wav_file_name)

_ = plt.plot(testing_wav_data)

# Play the audio file.
display.Audio(testing_wav_data, rate=16000)

class_map_path = yamnet_model.class_map_path().numpy().decode('utf-8')
class_names = list(pd.read_csv(class_map_path)['display_name'])

for name in class_names[:20]:
    print(name)
print('...')

scores, embeddings, spectrogram = yamnet_model(testing_wav_data)
class_scores = tf.reduce_mean(scores, axis=0)
top_class = tf.math.argmax(class_scores)
inferred_class = class_names[top_class]

print(f'The main sound is: {inferred_class}')
print(f'The embeddings shape: {embeddings.shape}')

_ = tf.keras.utils.get_file('esc-50.zip',
                            'https://github.com/karoldvl/ESC-50/archive/master.zip',
                            cache_dir='./',
                            cache_subdir='datasets',
                            extract=True)

esc50_csv = './datasets/ESC-50-master/meta/esc50.csv'
base_data_path = './datasets/ESC-50-master/audio/'

pd_data = pd.read_csv(esc50_csv)
pd_data.head()

my_classes = ['dog', 'cat']
map_class_to_id = {'dog': 0, 'cat': 1}

filtered_pd = pd_data[pd_data.category.isin(my_classes)]

class_id = filtered_pd['category'].apply(lambda name: map_class_to_id[name])
filtered_pd = filtered_pd.assign(target=class_id)

full_path = filtered_pd['filename'].apply(lambda row: os.path.join(base_data_path, row))
filtered_pd = filtered_pd.assign(filename=full_path)

filtered_pd.head(10)

filenames = filtered_pd['filename']
targets = filtered_pd['target']
folds = filtered_pd['fold']

main_ds = tf.data.Dataset.from_tensor_slices((filenames, targets, folds))
main_ds.element_spec

def load_wav_for_map(filename, label, fold):
    return load_wav_16k_mono(filename), label, fold

main_ds = main_ds.map(load_wav_for_map)
main_ds.element_spec

# applies the embedding extraction model to a wav data
def extract_embedding(wav_data, label, fold):
 # run YAMNet to extract embedding from the wav data
    scores, embeddings, spectrogram = yamnet_model(wav_data)
    num_embeddings = tf.shape(embeddings)[0]
    return (embeddings,
            tf.repeat(label, num_embeddings),
            tf.repeat(fold, num_embeddings))

# extract embedding
main_ds = main_ds.map(extract_embedding).unbatch()
main_ds.element_spec

cached_ds = main_ds.cache()
train_ds = cached_ds.filter(lambda embedding, label, fold: fold < 4)
val_ds = cached_ds.filter(lambda embedding, label, fold: fold == 4)
test_ds = cached_ds.filter(lambda embedding, label, fold: fold == 5)

# remove the folds column now that it's not needed anymore
remove_fold_column = lambda embedding, label, fold: (embedding, label)

train_ds = train_ds.map(remove_fold_column)
val_ds = val_ds.map(remove_fold_column)
test_ds = test_ds.map(remove_fold_column)

train_ds = train_ds.cache().shuffle(1000).batch(32).prefetch(tf.data.AUTOTUNE)
val_ds = val_ds.cache().batch(32).prefetch(tf.data.AUTOTUNE)
test_ds = test_ds.cache().batch(32).prefetch(tf.data.AUTOTUNE)

my_model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=1024, dtype=tf.float32,
                          name='input_embedding'),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(len(my_classes))
], name='my_model')

my_model.summary()

my_model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                 optimizer="adam",
                 metrics=['accuracy'])

callback = tf.keras.callbacks.EarlyStopping(monitor='loss',
                                            patience=3,
                                            restore_best_weights=True)

history = my_model.fit(train_ds,
                       epochs=20,
                       validation_data=val_ds,
                       callbacks=callback)

loss, accuracy = my_model.evaluate(test_ds)

print("Loss: ", loss)
print("Accuracy: ", accuracy)

scores, embeddings, spectrogram = yamnet_model(testing_wav_data)
result = my_model(embeddings).numpy()

inferred_class = my_classes[result.mean(axis=0).argmax()]
print(f'The main sound is: {inferred_class}')
```


### Relevant log output

```shell
Traceback (most recent call last):
  File "/Users/oliviafranken/Documents/birds/yamnet.py", line 10, in <module>
    import tensorflow_io as tfio
ModuleNotFoundError: No module named 'tensorflow_io'
```
