Skip to main content

Why does slicing lazy-loaded AudioIOTensor fail with stereo FLAC?

TensorFlow tutorial on audio data preparation (https://www.tensorflow.org/io/tutorials/audio) provides the following example:

import tensorflow as tf
import tensorflow_io as tfio
audio = tfio.audio.AudioIOTensor('gs://cloud-samples-tests/speech/brooklyn.flac')
print(audio)

...and then states "The content of the audio clip will only be read as needed, either by converting AudioIOTensor to Tensor through to_tensor(), or though slicing (emphasis added). Slicing is especially useful when only a small portion of a large audio clip is needed:"

audio_slice = audio[100:]
# remove last dimension
audio_tensor = tf.squeeze(audio_slice, axis=[-1])
print(audio_tensor)

This works as advertised, and it prints:

<AudioIOTensor: shape=[28979     1], dtype=<dtype: 'int16'>, rate=16000>
tf.Tensor([16 39 66 ... 56 81 83], shape=(28879,), dtype=int16)

So far, so good. Now I try this with a stereo FLAC:

audio = tfio.audio.AudioIOTensor('./audio/stereo_file.flac')
print(audio.shape)

...which prints

tf.Tensor([12371520        2], shape=(2,), dtype=int64)

I see here there are two channels as expected. Again, so far, so good.

Now I'd like to extract one channel only and take some number of samples, say 512. So I try:

audio_slice = audio[0:512, 0:1]

...and this fails and crashes Python.

Check failed: 1 == NumElements() (1 vs. 2)Must have a one element tensor
...
Process finished with exit code 134 (interrupted by signal 6: SIGABRT)

However, if I make a copy first with empty slice notation, everything works as I'd hope.

audio_slice = audio[:]
print(audio_slice.shape)
audio_slice = audio_slice[0:512, 0:1]
print(audio_slice.shape)
audio_tensor = tf.squeeze(audio_slice, axis=[-1])
print(audio_tensor.shape)

...which prints:

tf.Tensor([12371520        2], shape=(2,), dtype=int64)
(12371520, 2)
(512, 1)
(512,)

I assume the first fails on account of the audio being lazy-loaded, but I'm not sure why it works in the tutorial but fails with my stereo file. Shouldn't the slicing load data as needed?

tensorflow>=2.8.0
tensorflow-io>=0.25.0


source https://stackoverflow.com/questions/71991390/why-does-slicing-lazy-loaded-audioiotensor-fail-with-stereo-flac

Comments

Popular posts from this blog

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings

How to load Javascript with imported modules?

I am trying to import modules from tensorflowjs, and below is my code. test.html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Document</title </head> <body> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <script type="module" src="./test.js"></script> </body> </html> test.js import * as tf from "./node_modules/@tensorflow/tfjs"; import {loadGraphModel} from "./node_modules/@tensorflow/tfjs-converter"; const MODEL_URL = './model.json'; const model = await loadGraphModel(MODEL_URL); const cat = document.getElementById('cat'); model.execute(tf.browser.fromPixels(cat)); Besides, I run the server using python -m http.server in my command prompt(Windows 10), and this is the error prompt in the console log of my browser: Failed to loa...