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Browse files- hyperparams.yaml +60 -163
- model.ckpt +2 -2
hyperparams.yaml
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################################
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# Audio Parameters #
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################################
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sample_rate: 22050
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hop_length: 256
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win_length: 1024
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n_mel_channels: 80
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- B
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- CH
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- D
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- DH
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- EH
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- ER
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- EY
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- F
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- G
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- HH
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- IH
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- IY
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- JH
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- K
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- L
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- M
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- N
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- NG
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- OW
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- OY
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- P
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- R
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- S
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- SH
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- T
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- TH
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- UH
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- UW
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- V
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- W
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- Y
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- Z
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- ZH
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- ' '
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n_symbols: 42 #fixed depending on symbols in the lexicon +1 for a dummy symbol used for padding
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padding_idx: 0
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# Define model architecture
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d_model: 512
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nhead: 8
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num_encoder_layers: 6
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num_decoder_layers: 6
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dim_feedforward: 2048
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dropout: 0.2
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blank_index: 0 # This special token is for padding
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bos_index: 1
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eos_index: 2
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stop_weight: 0.45
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stop_threshold: 0.5
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###################PRENET#######################
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enc_pre_net: !new:models.EncoderPrenet
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dec_pre_net: !new:models.DecoderPrenet
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encoder_emb: !new:torch.nn.Embedding
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num_embeddings: 128
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embedding_dim: !ref <d_model>
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padding_idx: !ref <blank_index>
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pos_emb_enc: !new:models.ScaledPositionalEncoding
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d_model: !ref <d_model>
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decoder_emb: !new:torch.nn.Embedding
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num_embeddings: 128
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embedding_dim: !ref <d_model>
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padding_idx: !ref <blank_index>
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pos_emb_dec: !new:models.ScaledPositionalEncoding
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d_model: !ref <d_model>
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Seq2SeqTransformer: !new:torch.nn.Transformer
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d_model: !ref <d_model>
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nhead: !ref <nhead>
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num_encoder_layers: !ref <num_encoder_layers>
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num_decoder_layers: !ref <num_decoder_layers>
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dim_feedforward: !ref <dim_feedforward>
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dropout: !ref <dropout>
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batch_first: True
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postnet: !new:models.PostNet
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mel_channels: !ref <n_mel_channels>
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postnet_channels: 512
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kernel_size: 5
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postnet_layers: 5
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mel_lin: !new:speechbrain.nnet.linear.Linear
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input_size: !ref <d_model>
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n_neurons: !ref <n_mel_channels>
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stop_lin: !new:speechbrain.nnet.linear.Linear
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input_size: !ref <d_model>
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n_neurons: 1
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modules:
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enc_pre_net: !ref <enc_pre_net>
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encoder_emb: !ref <encoder_emb>
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pos_emb_enc: !ref <pos_emb_enc>
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dec_pre_net: !ref <dec_pre_net>
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#decoder_emb: !ref <decoder_emb>
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pos_emb_dec: !ref <pos_emb_dec>
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Seq2SeqTransformer: !ref <Seq2SeqTransformer>
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postnet: !ref <postnet>
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mel_lin: !ref <mel_lin>
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stop_lin: !ref <stop_lin>
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model: !ref <model>
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lookahead_mask: !name:speechbrain.lobes.models.transformer.Transformer.get_lookahead_mask
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padding_mask: !name:speechbrain.lobes.models.transformer.Transformer.get_key_padding_mask
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model: !new:torch.nn.ModuleList
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- [!ref <enc_pre_net>, !ref <encoder_emb>, !ref <pos_emb_enc>, !ref <dec_pre_net>, !ref <pos_emb_dec>, !ref <Seq2SeqTransformer>, !ref <postnet>, !ref <mel_lin>, !ref <stop_lin>]
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label_encoder: !new:speechbrain.dataio.encoder.TextEncoder
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pretrained_path: /content/
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pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
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loadables:
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model: !ref <model>
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paths:
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model: !ref <pretrained_path>/model.ckpt
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label_encoder: !ref <pretrained_path>/label_encoder.txt
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# ################################
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# Model: Tacotroon2 for TTS
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# Authors: Artem Ploujnikov, Yingzhi Wang
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# ################################
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mask_padding: True
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n_mel_channels: 80
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n_symbols: 148
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symbols_embedding_dim: 512
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encoder_kernel_size: 5
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encoder_n_convolutions: 3
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encoder_embedding_dim: 512
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attention_rnn_dim: 1024
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attention_dim: 128
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attention_location_n_filters: 32
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attention_location_kernel_size: 31
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n_frames_per_step: 1
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decoder_rnn_dim: 1024
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prenet_dim: 256
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max_decoder_steps: 1000
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gate_threshold: 0.5
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p_attention_dropout: 0.1
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p_decoder_dropout: 0.1
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postnet_embedding_dim: 512
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postnet_kernel_size: 5
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postnet_n_convolutions: 5
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decoder_no_early_stopping: False
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sample_rate: 22050
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# Model
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model: !new:speechbrain.lobes.models.Tacotron2.Tacotron2
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mask_padding: !ref <mask_padding>
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n_mel_channels: !ref <n_mel_channels>
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# symbols
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n_symbols: !ref <n_symbols>
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symbols_embedding_dim: !ref <symbols_embedding_dim>
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# encoder
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encoder_kernel_size: !ref <encoder_kernel_size>
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encoder_n_convolutions: !ref <encoder_n_convolutions>
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encoder_embedding_dim: !ref <encoder_embedding_dim>
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# attention
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attention_rnn_dim: !ref <attention_rnn_dim>
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attention_dim: !ref <attention_dim>
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# attention location
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attention_location_n_filters: !ref <attention_location_n_filters>
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attention_location_kernel_size: !ref <attention_location_kernel_size>
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# decoder
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n_frames_per_step: !ref <n_frames_per_step>
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decoder_rnn_dim: !ref <decoder_rnn_dim>
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prenet_dim: !ref <prenet_dim>
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max_decoder_steps: !ref <max_decoder_steps>
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gate_threshold: !ref <gate_threshold>
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p_attention_dropout: !ref <p_attention_dropout>
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p_decoder_dropout: !ref <p_decoder_dropout>
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# postnet
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postnet_embedding_dim: !ref <postnet_embedding_dim>
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postnet_kernel_size: !ref <postnet_kernel_size>
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postnet_n_convolutions: !ref <postnet_n_convolutions>
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decoder_no_early_stopping: !ref <decoder_no_early_stopping>
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# Function that converts the text into a sequence of valid characters.
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text_to_sequence: !name:speechbrain.utils.text_to_sequence.text_to_sequence
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modules:
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model: !ref <model>
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pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
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loadables:
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model: !ref <model>
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model.ckpt
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:02d1aa3fc1cc8fb0981895c765e9d0017416bb78861b5450e458dda92e567856
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size 112830206
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