Update hyperparams.yaml

4118f45 verified about 1 year ago

1.79 kB

# URL for the biggest Fairseq english whisper model.
whisper_hub: openai/whisper-tiny

# Normalize inputs with
# the same normalization done in the paper. Refer to Appendix C for further information.
normalized_transcripts: True


language: english

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
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: pretrained_models
    encoder_only:  False


decoder: !new:speechbrain.decoders.seq2seq.S2SWhisperGreedySearcher
    model: !ref <whisper>
    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>
    paths:
        whisper: !ref <whisper_hub>/pytorch_model.bin

	# URL for the biggest Fairseq english whisper model.
	whisper_hub: openai/whisper-tiny

	# Normalize inputs with
	# the same normalization done in the paper. Refer to Appendix C for further information.
	normalized_transcripts: True


	language: english

	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
	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: pretrained_models
	encoder_only: False


	decoder: !new:speechbrain.decoders.seq2seq.S2SWhisperGreedySearcher
	model: !ref <whisper>
	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>
	paths:
	whisper: !ref <whisper_hub>/pytorch_model.bin