What does it mean that these cache-aware streaming conformer methods *simulate* cache-aware streaming?

[maxeduc]

See these sources:

https://github.com/NVIDIA/NeMo/blob/cda2a637e9c1fefaa419e7b31ab2203d72d9819f/docs/source/asr/models.rst?plain=1#L236

https://github.com/NVIDIA/NeMo/blob/cda2a637e9c1fefaa419e7b31ab2203d72d9819f/nemo/collections/asr/parts/mixins/mixins.py#L590

https://github.com/NVIDIA/NeMo/blob/cda2a637e9c1fefaa419e7b31ab2203d72d9819f/nemo/collections/asr/parts/mixins/mixins.py#L714

Does it "simulate" cache-aware streaming or does it perform it? Models trained natively with cache-aware streaming are available, e.g. here. Does running functions such as conformer_stream_step() repeatedly, like it's done in the notebook here, actually perform the streaming step with the appropriate optimizations? Is it that it somehow logically produces the same output as cache-aware streaming but unoptimized, like you're still feeding in large batches of context into the model or something and they're just thrown out to produce the same output as optimized cache-aware streaming?

[zhenyih]

AI generated solution. please verify

The difference between "simulating" cache-aware streaming and actually performing it in NeMo's conformer models is primarily about the execution environment, not the underlying algorithm or optimizations.

When the documentation mentions that functions like conformer_stream_step() "simulate" cache-aware streaming, it means they're processing pre-recorded audio in chunks while maintaining state between chunks, rather than processing live audio in real-time. The actual algorithm, caching mechanisms, and optimizations are identical to what would be used in a real streaming deployment.

The cache-aware streaming approach in NeMo works by:

1. Training models with limited right context to match inference conditions
2. Using caching to store intermediate activations from previous chunks
3. Maintaining two types of caches:
   • Channel caches for attention layers
   • Time caches for convolution layers
4. Avoiding redundant computation by reusing these cached activations

When you call conformer_stream_step() repeatedly as shown in the notebook example, you're getting the exact same optimized processing that would occur in a real-time streaming implementation. The model processes each chunk while using the cached state from previous chunks, producing identical results to what you'd get in a deployed streaming system.

The term "simulate" is used because the code is running on pre-recorded audio in a controlled environment rather than on live audio input, but the underlying streaming implementation with its caching optimizations is fully functional and production-ready.