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# ################################
# Model: Whisper (Encoder-Decoder) + NLL
# Augmentation: TimeDomainSpecAugment
# Authors: Pooneh Mousavi 2022
# ################################
# URL for the biggest Fairseq english whisper model.
whisper_hub: openai/whisper-large-v2
# Normalize inputs with
# the same normalization done in the paper. Refer to Appendix C for further information.
normalized_transcripts: True
language: persian
auto_mix_prec: False
sample_rate: 16000
# These values are only used for the searchers.
# They needs to be hardcoded and should not be changed with Whisper.
# They are used as part of the searching process.
# The bos token of the searcher will be timestamp_index
# and will be concatenated with the bos, language and task tokens.
timestamp_index: 50363
eos_index: 50257
bos_index: 50258
# Decoding parameters
min_decode_ratio: 0.0
max_decode_ratio: 0.1
test_beam_size: 8
# Model parameters
freeze_whisper: True
freeze_encoder: True
whisper: !new:speechbrain.lobes.models.huggingface_transformers.whisper.Whisper
source: !ref <whisper_hub>
freeze: !ref <freeze_whisper>
freeze_encoder: !ref <freeze_encoder>
save_path: whisper_checkpoints
encoder_only: False
decoder: !new:speechbrain.decoders.seq2seq.S2SWhisperGreedySearch
model: !ref <whisper>
bos_index: !ref <timestamp_index>
eos_index: !ref <eos_index>
min_decode_ratio: !ref <min_decode_ratio>
max_decode_ratio: !ref <max_decode_ratio>
# test_beam_searcher: !new:speechbrain.decoders.seq2seq.S2SWhisperBeamSearch
# module: [!ref <whisper>]
# bos_index: !ref <timestamp_index>
# eos_index: !ref <eos_index>
# min_decode_ratio: !ref <min_decode_ratio>
# max_decode_ratio: !ref <max_decode_ratio>
# beam_size: !ref <test_beam_size>
modules:
whisper: !ref <whisper>
decoder: !ref <decoder>
pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
loadables:
whisper: !ref <whisper>
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