Add cross-attention to output hypotheses

[mgaido91]

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What does this PR do ?

The PR adds the encoder-decoder cross-attention to the output hypotheses returned by ASR models.

Collection: ASR

Changelog

• Returns the cross-attention scores in the output of the greedy generator
• Returns the cross-attention scores in the output of the beam search generator

Usage

• You can potentially add a usage example below

from nemo.collections.asr.models import ASRModel
from nemo.collections.asr.models.aed_multitask_models import MultiTaskTranscriptionConfig
model = ASRModel.from_pretrained(model_name="nvidia/canary-1b-v2")
config = MultiTaskTranscriptionConfig(
    batch_size=4,
    return_hypotheses=True,
    num_workers=0,
    verbose=False,
    prompt={'source_lang': 'en', 'target_lang': 'en'},
    enable_chunking=False
)
output = model.transcribe("/Users/mgaido/Downloads/vp-test/aa.wav", override_config=config)
assert output[0].xatt_scores is not None

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@nithinraok @andrusenkoau

Additional Information

• Related to Encoder-decoder attention extraction in ASR transcribe #14961 .

[nithinraok]

Thanks Marco. great work. Added comments. Also,
Could you add an option something like preserve_xattn_scores, so when enabled through

decoding_cfg = MultiTaskDecodingConfig(
    strategy="beam",  # or "greedy"
    preserve_xattn_scores=True,
)

only store and return xattn_scores (to save memory by default)

[nithinraok]

Shouldn;t shape is List[BxHxT1xT2] . Also best to add: this is used with AED models

[nithinraok]

what about condition when new_xattn_scores_list is None? cat would fail

[nithinraok]

could you update here as well and also include in returns tuple

[nithinraok]

check for xatt_scores_list if None

[nithinraok]

we might also return xatt_scores_list here as return_beam_scores is independent of return xattn_scores.