torchaudio.prototype.models.conformer_rnnt_model(*, input_dim: int, encoding_dim: int, time_reduction_stride: int, conformer_input_dim: int, conformer_ffn_dim: int, conformer_num_layers: int, conformer_num_heads: int, conformer_depthwise_conv_kernel_size: int, conformer_dropout: float, num_symbols: int, symbol_embedding_dim: int, num_lstm_layers: int, lstm_hidden_dim: int, lstm_layer_norm: int, lstm_layer_norm_epsilon: int, lstm_dropout: int, joiner_activation: str) RNNT[source]

Builds Conformer-based recurrent neural network transducer (RNN-T) model.

  • input_dim (int) – dimension of input sequence frames passed to transcription network.

  • encoding_dim (int) – dimension of transcription- and prediction-network-generated encodings passed to joint network.

  • time_reduction_stride (int) – factor by which to reduce length of input sequence.

  • conformer_input_dim (int) – dimension of Conformer input.

  • conformer_ffn_dim (int) – hidden layer dimension of each Conformer layer’s feedforward network.

  • conformer_num_layers (int) – number of Conformer layers to instantiate.

  • conformer_num_heads (int) – number of attention heads in each Conformer layer.

  • conformer_depthwise_conv_kernel_size (int) – kernel size of each Conformer layer’s depthwise convolution layer.

  • conformer_dropout (float) – Conformer dropout probability.

  • num_symbols (int) – cardinality of set of target tokens.

  • symbol_embedding_dim (int) – dimension of each target token embedding.

  • num_lstm_layers (int) – number of LSTM layers to instantiate.

  • lstm_hidden_dim (int) – output dimension of each LSTM layer.

  • lstm_layer_norm (bool) – if True, enables layer normalization for LSTM layers.

  • lstm_layer_norm_epsilon (float) – value of epsilon to use in LSTM layer normalization layers.

  • lstm_dropout (float) – LSTM dropout probability.

  • joiner_activation (str) – activation function to use in the joiner. Must be one of (“relu”, “tanh”). (Default: “relu”)

  • Returns


    Conformer RNN-T model.


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