# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, Wei Kang) # # See ../../../../LICENSE for clarification regarding multiple authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random import k2 import torch import torch.nn as nn from encoder_interface import EncoderInterface from scaling import penalize_abs_values_gt from icefall.utils import add_sos, torch_autocast class Transducer(nn.Module): """It implements https://arxiv.org/pdf/1211.3711.pdf "Sequence Transduction with Recurrent Neural Networks" """ def __init__( self, encoder: EncoderInterface, decoder: nn.Module, joiner: nn.Module, encoder_dim: int, decoder_dim: int, joiner_dim: int, vocab_size: int, ): """ Args: encoder: It is the transcription network in the paper. Its accepts two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,). It returns two tensors: `logits` of shape (N, T, encoder_dm) and `logit_lens` of shape (N,). decoder: It is the prediction network in the paper. Its input shape is (N, U) and its output shape is (N, U, decoder_dim). It should contain one attribute: `blank_id`. joiner: It has two inputs with shapes: (N, T, encoder_dim) and (N, U, decoder_dim). Its output shape is (N, T, U, vocab_size). Note that its output contains unnormalized probs, i.e., not processed by log-softmax. """ super().__init__() assert isinstance(encoder, EncoderInterface), type(encoder) assert hasattr(decoder, "blank_id") self.encoder = encoder self.decoder = decoder self.joiner = joiner self.simple_am_proj = nn.Linear( encoder_dim, vocab_size, ) self.simple_lm_proj = nn.Linear(decoder_dim, vocab_size) def forward( self, x: torch.Tensor, x_lens: torch.Tensor, y: k2.RaggedTensor, prune_range: int = 5, am_scale: float = 0.0, lm_scale: float = 0.0, ) -> torch.Tensor: """ Args: x: A 3-D tensor of shape (N, T, C). x_lens: A 1-D tensor of shape (N,). It contains the number of frames in `x` before padding. y: A ragged tensor with 2 axes [utt][label]. It contains labels of each utterance. prune_range: The prune range for rnnt loss, it means how many symbols(context) we are considering for each frame to compute the loss. am_scale: The scale to smooth the loss with am (output of encoder network) part lm_scale: The scale to smooth the loss with lm (output of predictor network) part Returns: Return the transducer loss. Note: Regarding am_scale & lm_scale, it will make the loss-function one of the form: lm_scale * lm_probs + am_scale * am_probs + (1-lm_scale-am_scale) * combined_probs """ assert x.ndim == 3, x.shape assert x_lens.ndim == 1, x_lens.shape assert y.num_axes == 2, y.num_axes assert x.size(0) == x_lens.size(0) == y.dim0 # x.T_dim == max(x_len) assert x.size(1) == x_lens.max().item(), (x.shape, x_lens, x_lens.max()) encoder_out, x_lens = self.encoder(x, x_lens) assert torch.all(x_lens > 0) # Now for the decoder, i.e., the prediction network row_splits = y.shape.row_splits(1) y_lens = row_splits[1:] - row_splits[:-1] blank_id = self.decoder.blank_id sos_y = add_sos(y, sos_id=blank_id) # sos_y_padded: [B, S + 1], start with SOS. sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id) # decoder_out: [B, S + 1, decoder_dim] decoder_out = self.decoder(sos_y_padded) # Note: y does not start with SOS # y_padded : [B, S] y_padded = y.pad(mode="constant", padding_value=0) y_padded = y_padded.to(torch.int64) boundary = torch.zeros((x.size(0), 4), dtype=torch.int64, device=x.device) boundary[:, 2] = y_lens boundary[:, 3] = x_lens lm = self.simple_lm_proj(decoder_out) am = self.simple_am_proj(encoder_out) # if self.training and random.random() < 0.25: # lm = penalize_abs_values_gt(lm, 100.0, 1.0e-04) # if self.training and random.random() < 0.25: # am = penalize_abs_values_gt(am, 30.0, 1.0e-04) with torch_autocast(enabled=False): simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed( lm=lm.float(), am=am.float(), symbols=y_padded, termination_symbol=blank_id, lm_only_scale=lm_scale, am_only_scale=am_scale, boundary=boundary, reduction="sum", return_grad=True, ) # ranges : [B, T, prune_range] ranges = k2.get_rnnt_prune_ranges( px_grad=px_grad, py_grad=py_grad, boundary=boundary, s_range=prune_range, ) # am_pruned : [B, T, prune_range, encoder_dim] # lm_pruned : [B, T, prune_range, decoder_dim] am_pruned, lm_pruned = k2.do_rnnt_pruning( am=self.joiner.encoder_proj(encoder_out), lm=self.joiner.decoder_proj(decoder_out), ranges=ranges, ) # logits : [B, T, prune_range, vocab_size] # project_input=False since we applied the decoder's input projections # prior to do_rnnt_pruning (this is an optimization for speed). logits = self.joiner(am_pruned, lm_pruned, project_input=False) with torch_autocast(enabled=False): pruned_loss = k2.rnnt_loss_pruned( logits=logits.float(), symbols=y_padded, ranges=ranges, termination_symbol=blank_id, boundary=boundary, reduction="sum", ) return (simple_loss, pruned_loss)