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# Profile: pretrained SpeechBrain model

# Benchmark: real-time factors + memory peaks

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# Authors: Andreas Nautsch 2022

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# Which model to profile? What is its type?

pretrained_model:

source: speechbrain/asr-wav2vec2-commonvoice-fr

type: EncoderASR

# Which settings should be benchmarked?

# by audio duration (random values) -> simulation for nominal level of data

# by batch size -> simulation for nominal level of environment

# take a long example_audio, the profiling script will break it down to its first 1s and then add more duration

# example_audio: None # this will simulate random data for a batch as outlined by audio_mockup_secs & batch_sizes

# triangle_only: True # expecting more VRAM than your GPU can handle? This profiling recipe has Gb overheads, too.

# export_logs: True # store tracing data for visualisation in `log` folder - e.g. tensorboard (reports might differ)

profiling_dimensions:

audio_mockup_secs: [1, 2, 5, 8, 32]

batch_sizes: [1, 4, 8]

example_audio: ../../samples/audio_samples/example2.flac

triangle_only: False

# export_logs: True