Source code for openspeech.models.conformer.model

# MIT License
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# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
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from omegaconf import DictConfig
from torch import Tensor
from typing import Dict
from collections import OrderedDict

from openspeech.decoders import LSTMAttentionDecoder, RNNTransducerDecoder
from openspeech.models import register_model, OpenspeechEncoderDecoderModel, OpenspeechTransducerModel
from openspeech.models import OpenspeechCTCModel
from openspeech.encoders import ConformerEncoder
from openspeech.modules.wrapper import Linear
from openspeech.tokenizers.tokenizer import Tokenizer
from openspeech.models.conformer.configurations import (
    ConformerConfigs,
    ConformerLSTMConfigs,
    ConformerTransducerConfigs,
    JointCTCConformerLSTMConfigs,
)


@register_model('conformer', dataclass=ConformerConfigs)
class ConformerModel(OpenspeechCTCModel):
    r"""
    Conformer Encoder Only Model.

    Args:
        configs (DictConfig): configuration set.
        tokenizer (Tokenizer): tokenizer is in charge of preparing the inputs for a model.

    Inputs:
        inputs (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
        input_lengths (torch.LongTensor): The length of input tensor. ``(batch)``

    Returns:
        outputs (dict): Result of model predictions that contains `y_hats`, `logits`, `output_lengths`
    """
    def __init__(self, configs: DictConfig, tokenizer: Tokenizer) -> None:
        super(ConformerModel, self).__init__(configs, tokenizer)
        self.fc = Linear(self.configs.model.encoder_dim, self.num_classes, bias=False)

    def build_model(self):
        self.encoder = ConformerEncoder(
            num_classes=self.num_classes,
            input_dim=self.configs.audio.num_mels,
            encoder_dim=self.configs.model.encoder_dim,
            num_layers=self.configs.model.num_encoder_layers,
            num_attention_heads=self.configs.model.num_attention_heads,
            feed_forward_expansion_factor=self.configs.model.feed_forward_expansion_factor,
            conv_expansion_factor=self.configs.model.conv_expansion_factor,
            input_dropout_p=self.configs.model.input_dropout_p,
            feed_forward_dropout_p=self.configs.model.feed_forward_dropout_p,
            attention_dropout_p=self.configs.model.attention_dropout_p,
            conv_dropout_p=self.configs.model.conv_dropout_p,
            conv_kernel_size=self.configs.model.conv_kernel_size,
            half_step_residual=self.configs.model.half_step_residual,
            joint_ctc_attention=False,
        )

    def forward(self, inputs: Tensor, input_lengths: Tensor) -> Dict[str, Tensor]:
        r"""
        Forward propagate a `inputs` and `targets` pair for inference.

        Inputs:
            inputs (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
            input_lengths (torch.LongTensor): The length of input tensor. ``(batch)``

        Returns:
            outputs (dict): Result of model predictions that contains `y_hats`, `logits`, `output_lengths`
        """
        return super(ConformerModel, self).forward(inputs, input_lengths)

    def training_step(self, batch: tuple, batch_idx: int) -> OrderedDict:
        r"""
        Forward propagate a `inputs` and `targets` pair for training.

        Inputs:
            batch (tuple): A train batch contains `inputs`, `targets`, `input_lengths`, `target_lengths`
            batch_idx (int): The index of batch

        Returns:
            loss (torch.Tensor): loss for training
        """
        inputs, targets, input_lengths, target_lengths = batch
        encoder_outputs, encoder_logits, output_lengths = self.encoder(inputs, input_lengths)
        logits = self.fc(encoder_outputs).log_softmax(dim=-1)
        return self.collect_outputs(
            stage='train',
            logits=logits,
            output_lengths=output_lengths,
            targets=targets,
            target_lengths=target_lengths,
        )

    def validation_step(self, batch: tuple, batch_idx: int) -> OrderedDict:
        r"""
        Forward propagate a `inputs` and `targets` pair for validation.

        Inputs:
            batch (tuple): A train batch contains `inputs`, `targets`, `input_lengths`, `target_lengths`
            batch_idx (int): The index of batch

        Returns:
            loss (torch.Tensor): loss for training
        """
        inputs, targets, input_lengths, target_lengths = batch
        encoder_outputs, encoder_logits, output_lengths = self.encoder(inputs, input_lengths)
        logits = self.fc(encoder_outputs).log_softmax(dim=-1)
        return self.collect_outputs(
            stage='valid',
            logits=logits,
            output_lengths=output_lengths,
            targets=targets,
            target_lengths=target_lengths,
        )

    def test_step(self, batch: tuple, batch_idx: int) -> OrderedDict:
        r"""
        Forward propagate a `inputs` and `targets` pair for test.

        Inputs:
            batch (tuple): A train batch contains `inputs`, `targets`, `input_lengths`, `target_lengths`
            batch_idx (int): The index of batch

        Returns:
            loss (torch.Tensor): loss for training
        """
        inputs, targets, input_lengths, target_lengths = batch
        encoder_outputs, encoder_logits, output_lengths = self.encoder(inputs, input_lengths)
        logits = self.fc(encoder_outputs).log_softmax(dim=-1)
        return self.collect_outputs(
            stage='test',
            logits=logits,
            output_lengths=output_lengths,
            targets=targets,
            target_lengths=target_lengths,
        )


@register_model('conformer_lstm', dataclass=ConformerLSTMConfigs)
class ConformerLSTMModel(OpenspeechEncoderDecoderModel):
    r"""
    Conformer encoder + LSTM decoder.

    Args:
        configs (DictConfig): configuraion set
        tokenizer (Tokenizer): tokenizer is in charge of preparing the inputs for a model.

    Inputs:
        inputs (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
        input_lengths (torch.LongTensor): The length of input tensor. ``(batch)``

    Returns:
        outputs (dict): Result of model predictions that contains `y_hats`, `logits`,
            `encoder_outputs`, `encoder_logits`, `encoder_output_lengths`.
    """

    def __init__(self, configs: DictConfig, tokenizer: Tokenizer) -> None:
        super(ConformerLSTMModel, self).__init__(configs, tokenizer)

    def build_model(self):
        self.encoder = ConformerEncoder(
            num_classes=self.num_classes,
            input_dim=self.configs.audio.num_mels,
            encoder_dim=self.configs.model.encoder_dim,
            num_layers=self.configs.model.num_encoder_layers,
            num_attention_heads=self.configs.model.num_attention_heads,
            feed_forward_expansion_factor=self.configs.model.feed_forward_expansion_factor,
            conv_expansion_factor=self.configs.model.conv_expansion_factor,
            input_dropout_p=self.configs.model.input_dropout_p,
            feed_forward_dropout_p=self.configs.model.feed_forward_dropout_p,
            attention_dropout_p=self.configs.model.attention_dropout_p,
            conv_dropout_p=self.configs.model.conv_dropout_p,
            conv_kernel_size=self.configs.model.conv_kernel_size,
            half_step_residual=self.configs.model.half_step_residual,
            joint_ctc_attention=False,
        )
        self.decoder = LSTMAttentionDecoder(
            num_classes=self.num_classes,
            max_length=self.configs.model.max_length,
            hidden_state_dim=self.configs.model.encoder_dim,
            pad_id=self.tokenizer.pad_id,
            sos_id=self.tokenizer.sos_id,
            eos_id=self.tokenizer.eos_id,
            num_heads=self.configs.model.num_attention_heads,
            dropout_p=self.configs.model.decoder_dropout_p,
            num_layers=self.configs.model.num_decoder_layers,
            attn_mechanism=self.configs.model.decoder_attn_mechanism,
            rnn_type=self.configs.model.rnn_type,
        )

    def set_beam_decoder(self, beam_size: int = 3):
        """ Setting beam search decoder """
        from openspeech.search import BeamSearchLSTM
        self.decoder = BeamSearchLSTM(
            decoder=self.decoder,
            beam_size=beam_size,
        )


@register_model('conformer_transducer', dataclass=ConformerTransducerConfigs)
class ConformerTransducerModel(OpenspeechTransducerModel):
    r"""
    Conformer: Convolution-augmented Transformer for Speech Recognition
    Paper: https://arxiv.org/abs/2005.08100

    Args:
        configs (DictConfig): configuraion set
        tokenizer (Tokenizer): tokenizer is in charge of preparing the inputs for a model.

    Inputs:
        inputs (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
        input_lengths (torch.LongTensor): The length of input tensor. ``(batch)``

    Returns:
        outputs (dict): Result of model predictions.
    """

    def __init__(self, configs: DictConfig, tokenizer: Tokenizer) -> None:
        super(ConformerTransducerModel, self).__init__(configs, tokenizer)

    def build_model(self):
        self.encoder = ConformerEncoder(
            num_classes=self.num_classes,
            input_dim=self.configs.audio.num_mels,
            encoder_dim=self.configs.model.encoder_dim,
            num_layers=self.configs.model.num_encoder_layers,
            num_attention_heads=self.configs.model.num_attention_heads,
            feed_forward_expansion_factor=self.configs.model.feed_forward_expansion_factor,
            conv_expansion_factor=self.configs.model.conv_expansion_factor,
            input_dropout_p=self.configs.model.input_dropout_p,
            feed_forward_dropout_p=self.configs.model.feed_forward_dropout_p,
            attention_dropout_p=self.configs.model.attention_dropout_p,
            conv_dropout_p=self.configs.model.conv_dropout_p,
            conv_kernel_size=self.configs.model.conv_kernel_size,
            half_step_residual=self.configs.model.half_step_residual,
            joint_ctc_attention=False,
        )
        self.decoder = RNNTransducerDecoder(
            num_classes=self.num_classes,
            hidden_state_dim=self.configs.model.decoder_hidden_state_dim,
            output_dim=self.configs.model.decoder_output_dim,
            num_layers=self.configs.model.num_decoder_layers,
            rnn_type=self.configs.model.rnn_type,
            pad_id=self.tokenizer.pad_id,
            sos_id=self.tokenizer.sos_id,
            eos_id=self.tokenizer.eos_id,
            dropout_p=self.configs.model.decoder_dropout_p,
        )


@register_model('joint_ctc_conformer_lstm', dataclass=JointCTCConformerLSTMConfigs)
class JointCTCConformerLSTMModel(OpenspeechEncoderDecoderModel):
    r"""
    Conformer encoder + LSTM decoder.

    Args:
        configs (DictConfig): configuraion set
        tokenizer (Tokeizer): tokenizer is in charge of preparing the inputs for a model.

    Inputs:
        inputs (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
        input_lengths (torch.LongTensor): The length of input tensor. ``(batch)``

    Returns:
        outputs (torch.FloatTensor): Result of model predictions.
    """

    def __init__(self, configs: DictConfig, tokenizer: Tokenizer) -> None:
        super(JointCTCConformerLSTMModel, self).__init__(configs, tokenizer)

    def build_model(self):
        self.encoder = ConformerEncoder(
            num_classes=self.num_classes,
            input_dim=self.configs.audio.num_mels,
            encoder_dim=self.configs.model.encoder_dim,
            num_layers=self.configs.model.num_encoder_layers,
            num_attention_heads=self.configs.model.num_attention_heads,
            feed_forward_expansion_factor=self.configs.model.feed_forward_expansion_factor,
            conv_expansion_factor=self.configs.model.conv_expansion_factor,
            input_dropout_p=self.configs.model.input_dropout_p,
            feed_forward_dropout_p=self.configs.model.feed_forward_dropout_p,
            attention_dropout_p=self.configs.model.attention_dropout_p,
            conv_dropout_p=self.configs.model.conv_dropout_p,
            conv_kernel_size=self.configs.model.conv_kernel_size,
            half_step_residual=self.configs.model.half_step_residual,
            joint_ctc_attention=True,
        )
        self.decoder = LSTMAttentionDecoder(
            num_classes=self.num_classes,
            max_length=self.configs.model.max_length,
            hidden_state_dim=self.configs.model.encoder_dim,
            pad_id=self.tokenizer.pad_id,
            sos_id=self.tokenizer.sos_id,
            eos_id=self.tokenizer.eos_id,
            num_heads=self.configs.model.num_decoder_attention_heads,
            dropout_p=self.configs.model.decoder_dropout_p,
            num_layers=self.configs.model.num_decoder_layers,
            attn_mechanism=self.configs.model.decoder_attn_mechanism,
            rnn_type=self.configs.model.rnn_type,
        )

    def set_beam_decoder(self, beam_size: int = 3):
        """ Setting beam search decoder """
        from openspeech.search import BeamSearchLSTM
        self.decoder = BeamSearchLSTM(
            decoder=self.decoder,
            beam_size=beam_size,
        )