I am using software reference HM Test Model 16.9, How to get information and output of Sum of Absolute Difference in HM Test Model? Is it possible to configure it via config file and save it into text file? I already read the software manual but can't find any.
Thanks in advance
For SAD, I am not sure.
But I know that at the encoder side, you can print out MSE both at the frame level and sequence level simply by setting the CFG parameters PrintFrameMSE and PrintSequenceMSE.
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I am training the Tacotron2 model using TensorflowTTS for a new language.
I managed to train the model (performed pre-processing, normalization, and decoded the few generated output files)
The files in the output directory are .npy files. Which makes sense as they are mel-spectograms.
I am trying to find a way to convert said files to a .wav file in order to check if my work has been fruitfull.
I used this :
melspectrogram = librosa.feature.melspectrogram(
"/content/prediction/tacotron2-0/paol_wavpaol_8-norm-feats.npy", sr=22050,
window=scipy.signal.hanning, n_fft=1024, hop_length=256)
print('melspectrogram.shape', melspectrogram.shape)
print(melspectrogram)
audio_signal = librosa.feature.inverse.mel_to_audio(
melspectrogram, sr22050, n_fft=1024, hop_length=256, window=scipy.signal.hanning)
print(audio_signal, audio_signal.shape)
sf.write('test.wav', audio_signal, sample_rate)
But it is given me this error : Audio data must be of type numpy.ndarray.
Although I am already giving it a numpy.ndarray file.
Does anyone know where the issue might be, and if anyone knows a better way to do it?
I'm not sure what your error is, but the output of a Tacotron 2 system are log Mel spectral features and you can't just apply the inverse Fourier transform to get a waveform because you are missing the phase information and because the features are not invertible. You can learn about why this is at places like Speech.Zone (https://speech.zone/courses/)
Instead of using librosa like you are doing, you need to use a vocoder like HiFiGan (https://github.com/jik876/hifi-gan) that is trained to reconstruct a waveform from log Mel spectral features. You can use a pre-trained model, and most off-the-shelf vocoders, but make sure that the sample rate, Mel range, FFT, hop size and window size are all the same between your Tacotron2 feature prediction network and whatever vocoder you choose otherwise you'll just get noise!
It is possible to log with mlflow the confusion matrix every step like a simple metrics?
If it is possible it have a visualization like this?
For every possible run, you could get the individual confusion matric values
# get confusion matrix values
conf_matrix = confusion_matrix(y_test,y_pred)
true_positive = conf_matrix[0][0]
true_negative = conf_matrix[1][1]
false_positive = conf_matrix[0][1]
false_negative = conf_matrix[1][0]
mlflow.log_metric("true_positive", true_positive)
mlflow.log_metric("true_negative", true_negative)
mlflow.log_metric("false_positive", false_positive)
mlflow.log_metric("false_negative", false_negative)
And then log_artifact(<your_plot>, "confusion_matrix")
Since you already have the confusion matrix as visualization you can log it with the mlflow.log_artifact() call as file.
The official documenation also has an example even though it is with a txt file it should be no problem to serialize the visualization somehow.
I'm using the ebalance Stata package to calculate post-stratification weights, and I'd like to convert the weights output (_webal, which is generated as a double with format %10.0g) to a matrix.
I'd like to normalize all weights in the "control" group, but I can't seem to convert the variable to a matrix in order to manipulate the weights individually (I'm a novice to Stata, so I was just going to do this using a loop––I'd normally just export and do this in R, but I have to calculate results within a bootstrap). I can, however, view the individual-level weights produced by the output, and I can use them to calculate sample statistics.
Any ideas, anyone? Thanks so much!
This is not an answer, but it doesn't fit within a comment box.
As a self-described novice in Stata, you are asking the wrong question.
Your problem is that you have a variable that you want to do some calculations on, and since you can't just use R and you don't know how to do those (unspecified) calculations directly in Stata, you have decided that the first step is to create a matrix from the variable.
Your question would be better phrased as a simple description of the relevant portions of your data and the calculation you need to do using that data (ebalance is an obscure distraction that probably lost you a few readers) and where you are stuck.
See also https://stackoverflow.com/help/mcve for a discussion of completing a minimal complete example with a description of the results you expect for that example.
I'm using tensorboard (tensorflow 1.1.0) to show the result of my CNN classifier.
I added some output vector as tf.summary.histogram in order to show the counts of output in each bin, but tensorboard seems to automatically compute interpolation and show them as (somehow) smoothed distribution
(and therefore I can not find the exact counts for the bins).
Could someone tell me how can I avoid the interpolation and show usual histograms using bars?
I not sure that there is easy way to do it.
I very unsure in below text, correct me if I wrong.
From this file https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/vz_histogram_timeseries/index.html it seems that histogram comes to tensorboard in double values.
Summary op uses either histogram from https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/python/ops/histogram_ops.py (1) or https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/core/lib/histogram/histogram.cc (2)
I suppose that it uses 2nd because here https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/python/summary/summary.py#L189 it calls function from generated file. In my package code in this generated file there is another function call:
result = _op_def_lib.apply_op("HistogramSummary", tag=tag, values=values,
name=name)
I have grep all repo and seems like there is no other python code which define something with "HistogramSummary", so it seems like it's really defined here https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/core/kernels/summary_op.cc and this code uses code mentioned above (2).
So, it seems to me that histogram which is used now is buried deep inside of framework and I not sure that it's easy to rewrite it.
In this page there is email for support https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/summary . I suppose that it's better to contact this person or make issue on github.
im getting started with tensorflow und using retrain.py to teach it some new categories - this works well - however i have some questions:
In the comments of retrain.py it says:
"This produces a new model file that can be loaded and run by any TensorFlow
program, for example the label_image sample code"
however I havent found where this new model file is saved to ?
also: it does contain the whole model, right ? not just the retrained part ?
Thanks for clearing this up
1)I think you may want to save the new model.
When you want to save a model after some process, you can use
saver.save(sess, 'directory/model-name', *optional-arg).
Check out https://www.tensorflow.org/api_docs/python/tf/train/Saver
If you change model-name by epoch or any measure you would like to use, you can save the new model(otherwise, it may overlap with previous models saved).
You can find the model saved by searching 'checkpoint', '.index', '.meta'.
2)Saving the whole model or just part of it?
It's the part you need to learn bunch of ideas on tf.session and savers. You can save either the whole or just part, it's up to you. Again, start from the above link. The moral is that you put the variables you would like to save in a list quoted as 'var_list' in the link, and you can save only for them. When you call them back, you now also need to specify which variables in your model correspond to the variables in the loaded variables.
While running retrain.py you can give --output_graph and --output_labels parameters which specify the location to save graph (default is /tmp/output_graph.pb) and the labels as well. You can change those as per your requirements.