# ############################################################################
# Profile: pretrained SpeechBrain model
# Benchmark: real-time factors + memory peaks
#
# Authors:  Andreas Nautsch 2022
# # ############################################################################

# 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