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espnet2.svs.naive_rnn.naive_rnn.NaiveRNN

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espnet2.svs.naive_rnn.naive_rnn.NaiveRNN

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class espnet2.svs.naive_rnn.naive_rnn.NaiveRNN(idim: int, odim: int, midi_dim: int = 129, embed_dim: int = 512, eprenet_conv_layers: int = 3, eprenet_conv_chans: int = 256, eprenet_conv_filts: int = 5, elayers: int = 3, eunits: int = 1024, ebidirectional: bool = True, midi_embed_integration_type: str = 'add', dlayers: int = 3, dunits: int = 1024, dbidirectional: bool = True, postnet_layers: int = 5, postnet_chans: int = 256, postnet_filts: int = 5, use_batch_norm: bool = True, reduction_factor: int = 1, spks: int | None = None, langs: int | None = None, spk_embed_dim: int | None = None, spk_embed_integration_type: str = 'add', eprenet_dropout_rate: float = 0.5, edropout_rate: float = 0.1, ddropout_rate: float = 0.1, postnet_dropout_rate: float = 0.5, init_type: str = 'xavier_uniform', use_masking: bool = False, use_weighted_masking: bool = False, loss_type: str = 'L1')

Bases: AbsSVS

NaiveRNN-SVS module.

This is an implementation of naive RNN for singing voice synthesis The features are processed directly over time-domain from music score and predict the singing voice features

Initialize NaiveRNN module.

  • Parameters:
    • idim (int) – Dimension of the label inputs.
    • odim (int) – Dimension of the outputs.
    • midi_dim (int) – Dimension of the midi inputs.
    • embed_dim (int) – Dimension of the token embedding.
    • eprenet_conv_layers (int) – Number of prenet conv layers.
    • eprenet_conv_filts (int) – Number of prenet conv filter size.
    • eprenet_conv_chans (int) – Number of prenet conv filter channels.
    • elayers (int) – Number of encoder layers.
    • eunits (int) – Number of encoder hidden units.
    • ebidirectional (bool) – If bidirectional in encoder.
    • midi_embed_integration_type (str) – how to integrate midi information, (“add” or “cat”).
    • dlayers (int) – Number of decoder lstm layers.
    • dunits (int) – Number of decoder lstm units.
    • dbidirectional (bool) – if bidirectional in decoder.
    • postnet_layers (int) – Number of postnet layers.
    • postnet_filts (int) – Number of postnet filter size.
    • postnet_chans (int) – Number of postnet filter channels.
    • use_batch_norm (bool) – Whether to use batch normalization.
    • reduction_factor (int) – Reduction factor.
    • related ( # extra embedding)
    • spks (Optional *[*int ]) – Number of speakers. If set to > 1, assume that the sids will be provided as the input and use sid embedding layer.
    • langs (Optional *[*int ]) – Number of languages. If set to > 1, assume that the lids will be provided as the input and use sid embedding layer.
    • spk_embed_dim (Optional *[*int ]) – Speaker embedding dimension. If set to > 0, assume that spembs will be provided as the input.
    • spk_embed_integration_type (str) – How to integrate speaker embedding.
    • eprenet_dropout_rate (float) – Prenet dropout rate.
    • edropout_rate (float) – Encoder dropout rate.
    • ddropout_rate (float) – Decoder dropout rate.
    • postnet_dropout_rate (float) – Postnet dropout_rate.
    • init_type (str) – How to initialize transformer parameters.
    • use_masking (bool) – Whether to mask padded part in loss calculation.
    • use_weighted_masking (bool) – Whether to apply weighted masking in loss calculation.
    • loss_type (str) – Loss function type (“L1”, “L2”, or “L1+L2”).

forward(text: Tensor, text_lengths: Tensor, feats: Tensor, feats_lengths: Tensor, label: Dict[str, Tensor] | None = None, label_lengths: Dict[str, Tensor] | None = None, melody: Dict[str, Tensor] | None = None, melody_lengths: Dict[str, Tensor] | None = None, pitch: Tensor | None = None, pitch_lengths: Tensor | None = None, duration: Dict[str, Tensor] | None = None, duration_lengths: Dict[str, Tensor] | None = None, slur: LongTensor | None = None, slur_lengths: Tensor | None = None, spembs: Tensor | None = None, sids: Tensor | None = None, lids: Tensor | None = None, flag_IsValid=False) → Tuple[Tensor, Dict[str, Tensor], Tensor]

Calculate forward propagation.

  • Parameters:
    • text (LongTensor) – Batch of padded character ids (B, Tmax).
    • text_lengths (LongTensor) – Batch of lengths of each input batch (B,).
    • feats (Tensor) – Batch of padded target features (B, Lmax, odim).
    • feats_lengths (LongTensor) – Batch of the lengths of each target (B,).
    • label (Optional *[*Dict ]) – key is “lab” or “score”; value (LongTensor): Batch of padded label ids (B, Tmax).
    • label_lengths (Optional *[*Dict ]) – key is “lab” or “score”; value (LongTensor): Batch of the lengths of padded label ids (B, ).
    • melody (Optional *[*Dict ]) – key is “lab” or “score”; value (LongTensor): Batch of padded melody (B, Tmax).
    • melody_lengths (Optional *[*Dict ]) – key is “lab” or “score”; value (LongTensor): Batch of the lengths of padded melody (B, ).
    • pitch (FloatTensor) – Batch of padded f0 (B, Tmax).
    • pitch_lengths (LongTensor) – Batch of the lengths of padded f0 (B, ).
    • duration (Optional *[*Dict ]) – key is “lab”, “score”; value (LongTensor): Batch of padded duration (B, Tmax).
    • duration_lengths (Optional *[*Dict ]) – key is “lab” or “score”; value (LongTensor): Batch of the lengths of padded duration (B, ).
    • slur (LongTensor) – Batch of padded slur (B, Tmax).
    • slur_lengths (LongTensor) – Batch of the lengths of padded slur (B, ).
    • spembs (Optional *[*Tensor ]) – Batch of speaker embeddings (B, spk_embed_dim).
    • sids (Optional *[*Tensor ]) – Batch of speaker IDs (B, 1).
    • lids (Optional *[*Tensor ]) – Batch of language IDs (B, 1).

GS Fix: : arguements from forward func. V.S. <br/>

**

<br/> batch from espnet_model.py label == durations ｜ phone sequence melody -> pitch sequence

  • Returns: Loss scalar value. Dict: Statistics to be monitored. Tensor: Weight value if not joint training else model outputs.
  • Return type: Tensor

inference(text: Tensor, feats: Tensor | None = None, label: Dict[str, Tensor] | None = None, melody: Dict[str, Tensor] | None = None, pitch: Tensor | None = None, duration: Dict[str, Tensor] | None = None, slur: Dict[str, Tensor] | None = None, spembs: Tensor | None = None, sids: Tensor | None = None, lids: Tensor | None = None, use_teacher_forcing: Tensor = False) → Tuple[Tensor, Dict[str, Tensor], Tensor]

Calculate forward propagation.

  • Parameters:
    • text (LongTensor) – Batch of padded character ids (Tmax).
    • feats (Tensor) – Batch of padded target features (Lmax, odim).
    • label (Optional *[*Dict ]) – key is “lab” or “score”; value (LongTensor): Batch of padded label ids (Tmax).
    • melody (Optional *[*Dict ]) – key is “lab” or “score”; value (LongTensor): Batch of padded melody (Tmax).
    • pitch (FloatTensor) – Batch of padded f0 (Tmax).
    • slur (LongTensor) – Batch of padded slur (B, Tmax).
    • duration (Optional *[*Dict ]) – key is “lab”, “score”; value (LongTensor): Batch of padded duration (Tmax).
    • spembs (Optional *[*Tensor ]) – Batch of speaker embeddings (spk_embed_dim).
    • sids (Optional *[*Tensor ]) – Batch of speaker IDs (1).
    • lids (Optional *[*Tensor ]) – Batch of language IDs (1).
  • Returns: Output dict including the following items: * feat_gen (Tensor): Output sequence of features (T_feats, odim).
  • Return type: Dict[str, Tensor]
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