simsamu-transcription / hyperparams.yaml
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make hyperparams.yaml more similar to speechbrain official models
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# modules params
# (nb_tokens, blank_index, bos_index and eos_index must be constistent with
# pretrained tokenizer)
nb_tokens: 500
blank_index: 0
sample_rate: 16000
wav2vec2_hub: LeBenchmark/wav2vec2-FR-7K-large
wav2vec_output_dim: 1024
nb_enc_neurons: 1024
enc_drop_out: 0.25
## modules
tokenizer: !new:sentencepiece.SentencePieceProcessor
wav2vec2: !new:speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWav2Vec2
source: !ref <wav2vec2_hub>
output_norm: True
freeze: True
save_path: pretrained_models
enc: !new:speechbrain.nnet.containers.Sequential
input_shape: [null, null, !ref <wav2vec_output_dim>]
linear1: !name:speechbrain.nnet.linear.Linear
n_neurons: !ref <nb_enc_neurons>
bias: True
bn1: !name:speechbrain.nnet.normalization.BatchNorm1d
activation: !new:torch.nn.LeakyReLU
drop: !new:torch.nn.Dropout
p: !ref <enc_drop_out>
linear2: !name:speechbrain.nnet.linear.Linear
n_neurons: !ref <nb_enc_neurons>
bias: True
bn2: !name:speechbrain.nnet.normalization.BatchNorm1d
activation2: !new:torch.nn.LeakyReLU
drop2: !new:torch.nn.Dropout
p: !ref <enc_drop_out>
linear3: !name:speechbrain.nnet.linear.Linear
n_neurons: !ref <nb_enc_neurons>
bias: True
bn3: !name:speechbrain.nnet.normalization.BatchNorm1d
activation3: !new:torch.nn.LeakyReLU
ctc_lin: !new:speechbrain.nnet.linear.Linear
input_size: !ref <nb_enc_neurons>
n_neurons: !ref <nb_tokens>
decoding_function: !name:speechbrain.decoders.ctc_greedy_decode
blank_id: !ref <blank_index>
encoder: !new:speechbrain.nnet.containers.LengthsCapableSequential
wav2vec2: !ref <wav2vec2>
enc: !ref <enc>
ctc_lin: !ref <ctc_lin>
modules:
encoder: !ref <encoder>
asr_model: !new:torch.nn.ModuleList
- [!ref <enc>, !ref <ctc_lin>]
pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
loadables:
wav2vec2: !ref <wav2vec2>
asr: !ref <asr_model>
tokenizer: !ref <tokenizer>