LSTM | Voice activity detection of noisy speech files | Machine Learning library

by jefflai108 Python Version: Current License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | LSTM Summary

LSTM is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Neural Network applications. LSTM has no bugs, it has no vulnerabilities and it has low support. However LSTM build file is not available. You can download it from GitHub.

Voice activity detection of noisy speech files with LSTM. LSTM is implemented with Keras. Data processing is done with Python, MATLAB, and Bash. Experiments are done on Johns Hopkins CLSP GPUs.

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LSTM has a low active ecosystem.

It has 6 star(s) with 4 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 688 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of LSTM is current.

Quality

LSTM has 0 bugs and 0 code smells.

Security

LSTM has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

LSTM code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

LSTM does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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LSTM releases are not available. You will need to build from source code and install.

LSTM has no build file. You will be need to create the build yourself to build the component from source.

It has 1501 lines of code, 37 functions and 19 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed LSTM and discovered the below as its top functions. This is intended to give you an instant insight into LSTM implemented functionality, and help decide if they suit your requirements.

Open a VAD file
Sort the vad according to the vad
Open a MFCC file
LSTM model
Compares two VADs
Checks the order of vad and mfcc
Saves training data to a file
Check new VAD files
Process a VAD data file

Get all kandi verified functions for this library.

LSTM Key Features

No Key Features are available at this moment for LSTM.

LSTM Examples and Code Snippets

Wrapper for LSTM .

python

Lines of Code : 133

License : Non-SPDX (Apache License 2.0)

Copy

def lstm_with_backend_selection(inputs, init_h, init_c, kernel,
                                recurrent_kernel, bias, mask, time_major,
                                go_backwards, sequence_lengths,
                                zero_output_for_

Compute the LSTM .

python

Lines of Code : 123

License : Non-SPDX (Apache License 2.0)

Copy

def gpu_lstm(inputs, init_h, init_c, kernel, recurrent_kernel, bias, mask,
             time_major, go_backwards, sequence_lengths):
  """LSTM with either CuDNN or ROCm implementation which is only available for GPU.

  Note that currently only right

Standard LSTM .

python

Lines of Code : 83

License : Non-SPDX (Apache License 2.0)

Copy

def standard_lstm(inputs, init_h, init_c, kernel, recurrent_kernel, bias,
                  mask, time_major, go_backwards, sequence_lengths,
                  zero_output_for_mask):
  """LSTM with standard kernel implementation.

  This implementati

Community Discussions

Trending Discussions on LSTM

Using RNN Trained Model without pytorch installed

Getting optimal vocab size and embedding dimensionality using GridSearchCV

How can Keras predict sequences of sales (individually) of 11106 distinct customers, each a series of varying length (anyway from 1 to 15 periods)

Deep Learning how to split 5 dimensions timeseries and pass some dimensions through embedding layer

Problem with inputs when building a model with TFBertModel and AutoTokenizer from HuggingFace's transformers

Pytorch with CUDA throws RuntimeError when using pack_padded_sequence

LSTM occurs ValueError: Shapes (5, 2, 3) and (5, 3) are incompatible

NotImplementedError: Cannot convert a symbolic Tensor (lstm_2/strided_slice:0) to a numpy array. T

Unknown error/crash - TensorFlow LSTM with GPU (no output after start of 1st epoch)

Keras LSTM loading data from CSV "expected ndim=3, found ndim=2. Full shape received: (None, 150)"

QUESTION

Using RNN Trained Model without pytorch installed

Asked 2022-Feb-28 at 20:17

I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.

I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.

I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.

...

ANSWER

Answered 2022-Feb-17 at 10:47

You should try to export the model using torch.onnx. The page gives you an example that you can start with.

An alternative is to use TorchScript, but that requires torch libraries.

Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html

ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)

A running example

Just modifying a little your example to go over the errors I found

Notice that via tracing any if/elif/else, for, while will be unrolled

Source https://stackoverflow.com/questions/71146140

QUESTION

Getting optimal vocab size and embedding dimensionality using GridSearchCV

Asked 2022-Feb-06 at 09:13

I'm trying to use GridSearchCV to find the best hyperparameters for an LSTM model, including the best parameters for vocab size and the word embeddings dimension. First, I prepared my testing and training data.

...

ANSWER

Answered 2022-Feb-02 at 08:53

I tried with scikeras but I got errors because it doesn't accept not-numerical inputs (in our case the input is in str format). So I came back to the standard keras wrapper.

The focal point here is that the model is not built correctly. The TextVectorization must be put inside the Sequential model like shown in the official documentation.

So the build_model function becomes:

Source https://stackoverflow.com/questions/70884608

QUESTION

How can Keras predict sequences of sales (individually) of 11106 distinct customers, each a series of varying length (anyway from 1 to 15 periods)

Asked 2022-Feb-01 at 21:07

I am approaching a problem that Keras must offer an excellent solution for, but I am having problems developing an approach (because I am such a neophyte concerning anything for deep learning). I have sales data. It contains 11106 distinct customers, each with its time series of purchases, of varying length (anyway from 1 to 15 periods).

I want to develop a single model to predict each customer's purchase amount for the next period. I like the idea of an LSTM, but clearly, I cannot make one for each customer; even if I tried, there would not be enough data for an LSTM in any case---the longest individual time series only has 15 periods.

I have used types of Markov chains, clustering, and regression in the past to model this kind of data. I am asking the question here, though, about what type of model in Keras is suited to this type of prediction. A complication is that all customers can be clustered by their overall patterns. Some belong together based on similarity; others do not; e.g., some customers spend with patterns like $100-$100-$100, others like $100-$100-$1000-$10000, and so on.

Can anyone point me to a type of sequential model supported by Keras that might handle this well? Thank you.

I am trying to achieve this in R. Haven't been able to build a model that gives me more than about .3 accuracy.

...

ANSWER

Answered 2022-Jan-31 at 18:55

Hi here's my suggestion and I will edit it later to provide you with more information

Since its a sequence problem you should use RNN based models: LSTM, GRU's

Source https://stackoverflow.com/questions/70646000

QUESTION

Deep Learning how to split 5 dimensions timeseries and pass some dimensions through embedding layer

Asked 2021-Dec-12 at 11:08

I have an input that is a time series of 5 dimensions:

a = [[8,3],[2] , [4,5],[1], [9,1],[2]...] #total 100 timestamps. For each element, dims 0,1 are numerical data and dim 2 is a numerical encoding of a category. This is per sample, 3200 samples

The category has 3 possible values (0,1,2)

I want to build a NN such that the last dimension (the category) will go through an embedding layer with output size 8, and then will be concatenated back to the first two dims (the numerical data).

So, this will be something like:

...

ANSWER

Answered 2021-Dec-12 at 11:08

There are a couple of issues you are having here. First let me give you a working example and explain along the way how to solve your issues.

Imports and Data Generation

Source https://stackoverflow.com/questions/70274978

QUESTION

Problem with inputs when building a model with TFBertModel and AutoTokenizer from HuggingFace's transformers

Asked 2021-Sep-26 at 14:51

I'm trying to build the model illustrated in this picture:

I obtained a pre-trained BERT and respective tokenizer from HuggingFace's transformers in the following way:

...

ANSWER

Answered 2021-Sep-16 at 19:53

text input must of type str (single example), List[str] (batch or single pretokenized example) or List[List[str]] (batch of pretokenized examples).

Solution to the above error:

Just use text_input = 'text'

instead of

text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')

Source https://stackoverflow.com/questions/69195950

QUESTION

Pytorch with CUDA throws RuntimeError when using pack_padded_sequence

Asked 2021-Jun-22 at 15:58

I am trying to train a BiLSTM-CRF on detecting new NER entities with Pytorch. To do so, I am using a snippet of code derivated from the Pytorch Advanced tutorial. This snippet implements batch training.

I followed the READ-ME in order to present data as required. Everything works great on CPU, but when I'm trying to get it to GPU, the following error occur :

...

ANSWER

Answered 2021-Jun-22 at 15:58

Within PadSequence function (which acts as a collate_fn which gathers samples and makes a batch from them) you are explicitly casting to cuda device, namely:

Source https://stackoverflow.com/questions/68086528

QUESTION

LSTM occurs ValueError: Shapes (5, 2, 3) and (5, 3) are incompatible

Asked 2021-Jun-22 at 15:44

I want to do time series multi-class classification with time-series data. Here the data set I have got needs to be preprocessed heavily and that just to get an idea of how to implement the model I have used the IRIS data set(not suitable for LSTM) since it has the exact same structure of the time series data I have( 4 input features,1 output feature, 120 samples). I have the following code implemented but it causes me the invalid shape error when fitting the model with a batch size of 5 (changed the batch size many times but didn't seem to make any change)

...

ANSWER

Answered 2021-Jun-21 at 12:48

Your y_true and y_pred are not in the same shape. You may need to define your LSTM in the following way

Source https://stackoverflow.com/questions/68061611

QUESTION

NotImplementedError: Cannot convert a symbolic Tensor (lstm_2/strided_slice:0) to a numpy array. T

Asked 2021-May-14 at 15:11

tensorflow version 2.3.1 numpy version 1.20

below the code

...

ANSWER

Answered 2021-Feb-15 at 11:55

I solved with numpy downgrade to 1.18.5

Source https://stackoverflow.com/questions/66207609

QUESTION

Unknown error/crash - TensorFlow LSTM with GPU (no output after start of 1st epoch)

Asked 2021-Apr-20 at 23:59

I'm trying to train a model using LSTM layers. I'm using a GPU and all needed libraries are loaded.

When I'm building the model this way:

...

ANSWER

Answered 2021-Apr-20 at 23:59

I found the solution... kinda.

So it works as it should when I downgraded tensorflow to 2.1.0, CUDA to 10.1 and cudnn to 7.6.5 (at the time 4th combination from this list on TensorFlow website)

I don't know why it didn't work at the newest version, or at the valid combination for tensorflow 2.4.0.

It's working well so my issue is solved. Nonetheless it would be nice to know why using LSTM with cudnn on higher versions didn't work for me, as I haven't found this issue anywhere.

Source https://stackoverflow.com/questions/67169344

QUESTION

Keras LSTM loading data from CSV "expected ndim=3, found ndim=2. Full shape received: (None, 150)"

Asked 2021-Apr-19 at 06:54

I am a beginner with LSTMs so sorry if this is a basic question. I've been trying to make a simple LSTM model that loads data from a csv text file for training

...

ANSWER

Answered 2021-Apr-19 at 06:54

trainX.shape = (35, 150) which means that you have 35 samples of 150. But you need to pass the data with the batch_size in the first position according to Keras. So you would have to expand the 2D input to 3D:

Source https://stackoverflow.com/questions/67155624

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install LSTM

You can download it from GitHub.
You can use LSTM like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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