Fixes ASR numpy > 2.x compatibility issues while replicating existing behavior

[andylamp]

What does this PR do ?

The PR attempts to restore functionality with recent numpy versions; numpy 2.0 removed sctypes and this is breaking asr functionality.

Collection: asr

Changelog

• As said above numpy from version 2.0 onwards removed sctypes functionality, which breaks asr functionality in various places (e.g. transcribe_speech, example notebooks etc).
• The changes reside in the _convert_samples_to_float32 function residing in both feature_loader.py and segment.py.
• There have been other issues relating to this (e.g. Replace usage of np.sctypes with np.issubdtype #10839 and Replace usage of np.sctypes with np.issubdtype #10180), however these do not replicate existing functionality.

Usage

The usage is that ASR tasks that call _convert_samples_to_float32 functions now succeed. As noted above, while this has been attempted to be addressed in the PRs mentioned above, I believe that the functionality is not replicated accurately.

More concretely, if you check the output of sctype for the types of int and float in supported versions of numpy you'd get the following:

print(np.sctypes["int"])
[<class 'numpy.int8'>, <class 'numpy.int16'>, <class 'numpy.int32'>, <class 'numpy.int64'>]
print(np.sctypes["float"])
[<class 'numpy.float16'>, <class 'numpy.float32'>, <class 'numpy.float64'>]

However, if we use the issubdtype to perform this the set will be wider, case on point for floating point:

import numpy as np
float128 = np.floating[128]
np.issubdtype(float128, np.floating)
Out[6]: True

And, also for integers the set mostly covers the output of sctype for signed ones. However any subclass from signedinteger or unsignedinteger will return true. A more concrete example would be,

import numpy as np
int96 = np.signedinteger[96]
np.issubdtype(int96, np.integer)
Out[4]: True
uint96 = np.unsignedinteger[96]
np.issubdtype(uint96, np.integer)
Out[5]: True

Therefore, in this case not only we consider int{8,16,32,64} but also uint{8,16,32,64} which is not the expected result when querying sctype['int'] leading to potentially unexpected behavior. Case on point,

import numpy as np
np.issubdtype(np.uint8, np.integer)
Out[3]: True

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• New Feature
• Bugfix
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Additional Information

The fix is backwards compatible with all supported numpy 1.x versions as well. Therefore, it should pose minimal risk wrt to integration.

[andylamp]

Note: I cannot seem to be able to request review for this PR, therefore based on contributing guidelines I am asking @titu1994 for one as that username was the first one in the list ;).

[github-actions]

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[JanetVictorious]

Maybe @redoctopus, @jbalam-nv, or @okuchaiev could review?

[andylamp]

Would be great to get this merged as numpy 2.x is being iffy and always have to apply that patch ;)

[github-actions]

This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.

[JanetVictorious]

@oyilmaz-nvidia could you review this PR? It would be really nice if we could get support for numpy>2

[andylamp]

Yeah, it'd be great if that's the case :). As I said, I have to apply this patch in order to get everything working properly when using numpy version >= 2.x.

[andylamp]

Any updates on this one? :)

[andylamp]

Pinging again in this thread given no review has been given...

[github-actions]

This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.

[andylamp]

tagging @redoctopus, @jbalam-nv, or @okuchaiev requesting a review 🙏