Speech_Recognition_Youtube_and_VLC_Project
I'm trying to run your project on colab, but to no avail I'm getting this error>
ModuleNotFoundError Traceback (most recent call last)
in ()
----> 1 import speech_recognition as spr

ModuleNotFoundError: No module named 'speech_recognition'

NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.

To view examples of installing

!apt install libasound2-dev portaudio19-dev libportaudio2 libportaudiocpp0 ffmpeg
!pip install pyaudio
!pip install ConfigParser
!apt-get install python-gnuradio-audio-portaudio
!python -m pip install pyaudio
!pip install SpeechRecognition

Where did you run it?

@nitinkaushik01,

Hi,
I have used Dialogflow for training a chatbot . Which is better for training a bot? What are the advantages of tensorflow over Dialogflow? what is accuracy?
And If i want train large FAQs dataset , Do we have to change the training model?

Thanks

While executing #Generate Captions for a Fresh Image cell I am getting this error.
can anyone please try to resolve it?

When i had executed model.summary(), an unwanted layer named 'NotLayer' also seem to be connected to my input layer which is giving me an error.
I even tried to access it but i can't using its index
On stack overflow similar problem is sovled as describing it as 'Custom Layer' but i haven't added it as a custom layer
please help and guide me to debug it

ValueError: Layer model_19 expects 2 input(s), but it received 3 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, None) dtype=float32>, <tf.Tensor 'IteratorGetNext:1' shape=(None, None) dtype=int32>, <tf.Tensor 'IteratorGetNext:2' shape=(None, None) dtype=float32>]
Getting this when trying to run the model

Image caption generator

For this code:

train the model

dump(tokenizer, open('/content/tokenizer.pkl', 'wb'))
model = define_model(vocab_size, max_length)

train the model, run epochs manually and save after each epoch

epochs = 20
steps = len(train_descriptions)
for i in range(epochs):
# create the data generator
generator = data_generator(train_descriptions, train_features, tokenizer, max_length)
# fit for one epoch
model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
# save model
model.save('model_' + str(i) + '.h5')

This is error

ValueError Traceback (most recent call last)
in ()
9 generator = data_generator(train_descriptions, train_features, tokenizer, max_length)
10 # fit for one epoch
---> 11 model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
12 # save model
13 model.save('model_' + str(i) + '.h5')

12 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise

ValueError: in user code:

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
    return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
    return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
    outputs = model.train_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:757 train_step
    self.trainable_variables)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:2737 _minimize
    trainable_variables))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:562 _aggregate_gradients
    filtered_grads_and_vars = _filter_grads(grads_and_vars)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:1271 _filter_grads
    ([v.name for _, v in grads_and_vars],))

ValueError: No gradients provided for any variable: ['embedding_3/embeddings:0', 'dense_9/kernel:0', 'dense_9/bias:0', 'lstm_3/lstm_cell_3/kernel:0', 'lstm_3/lstm_cell_3/recurrent_kernel:0', 'lstm_3/lstm_cell_3/bias:0', 'dense_10/kernel:0', 'dense_10/bias:0', 'dense_11/kernel:0', 'dense_11/bias:0'].