# Generated 2023-06-24 from:
# /netscratch/sagar/thesis/speechbrain/recipes/RescueSpeech/Enhancement/joint-training/transformers/hparams/robust_asr_16k.yaml
# yamllint disable
# Model: wav2vec2 + DNN + CTC
# Augmentation: SpecAugment
# Authors: Sangeet Sagar 2023
# ################################

# URL for the biggest Fairseq english whisper model.
whisper_hub: openai/whisper-large-v2
language: german

# Normalize the english inputs with
# the same normalization done in the paper
normalized_transcripts: true
test_only: false # Set it to True if you only want to  do the evaluation

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: 1.0

# 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>

modules:
    whisper: !ref <whisper>
    decoder:  !ref <decoder>


pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
    loadables:
        whisper: !ref <whisper>