# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # 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. """U2 ASR Model Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recognition (https://arxiv.org/pdf/2012.05481.pdf) """ import sys import time from collections import defaultdict from typing import Dict from typing import List from typing import Optional from typing import Tuple import paddle from paddle import jit from paddle import nn from yacs.config import CfgNode from deepspeech.frontend.utility import IGNORE_ID from deepspeech.frontend.utility import load_cmvn from deepspeech.modules.cmvn import GlobalCMVN from deepspeech.modules.ctc import CTCDecoder from deepspeech.modules.decoder import TransformerDecoder from deepspeech.modules.encoder import ConformerEncoder from deepspeech.modules.encoder import TransformerEncoder from deepspeech.modules.loss import LabelSmoothingLoss from deepspeech.modules.mask import make_pad_mask from deepspeech.modules.mask import mask_finished_preds from deepspeech.modules.mask import mask_finished_scores from deepspeech.modules.mask import subsequent_mask from deepspeech.utils import checkpoint from deepspeech.utils import layer_tools from deepspeech.utils.ctc_utils import remove_duplicates_and_blank from deepspeech.utils.log import Log from deepspeech.utils.tensor_utils import add_sos_eos from deepspeech.utils.tensor_utils import pad_sequence from deepspeech.utils.tensor_utils import th_accuracy from deepspeech.utils.utility import log_add __all__ = ["U2Model", "U2InferModel"] logger = Log(__name__).getlog() class U2BaseModel(nn.Layer): """CTC-Attention hybrid Encoder-Decoder model""" @classmethod def params(cls, config: Optional[CfgNode]=None) -> CfgNode: # network architecture default = CfgNode() # allow add new item when merge_with_file default.cmvn_file = "" default.cmvn_file_type = "json" default.input_dim = 0 default.output_dim = 0 # encoder related default.encoder = 'transformer' default.encoder_conf = CfgNode( dict( output_size=256, # dimension of attention attention_heads=4, linear_units=2048, # the number of units of position-wise feed forward num_blocks=12, # the number of encoder blocks dropout_rate=0.1, positional_dropout_rate=0.1, attention_dropout_rate=0.0, input_layer='conv2d', # encoder input type, you can chose conv2d, conv2d6 and conv2d8 normalize_before=True, # use_cnn_module=True, # cnn_module_kernel=15, # activation_type='swish', # pos_enc_layer_type='rel_pos', # selfattention_layer_type='rel_selfattn', )) # decoder related default.decoder = 'transformer' default.decoder_conf = CfgNode( dict( attention_heads=4, linear_units=2048, num_blocks=6, dropout_rate=0.1, positional_dropout_rate=0.1, self_attention_dropout_rate=0.0, src_attention_dropout_rate=0.0, )) # hybrid CTC/attention default.model_conf = CfgNode( dict( ctc_weight=0.3, lsm_weight=0.1, # label smoothing option length_normalized_loss=False, )) if config is not None: config.merge_from_other_cfg(default) return default def __init__(self, vocab_size: int, encoder: TransformerEncoder, decoder: TransformerDecoder, ctc: CTCDecoder, ctc_weight: float=0.5, ignore_id: int=IGNORE_ID, lsm_weight: float=0.0, length_normalized_loss: bool=False): assert 0.0 <= ctc_weight <= 1.0, ctc_weight super().__init__() # note that eos is the same as sos (equivalent ID) self.sos = vocab_size - 1 self.eos = vocab_size - 1 self.vocab_size = vocab_size self.ignore_id = ignore_id self.ctc_weight = ctc_weight self.encoder = encoder self.decoder = decoder self.ctc = ctc self.criterion_att = LabelSmoothingLoss( size=vocab_size, padding_idx=ignore_id, smoothing=lsm_weight, normalize_length=length_normalized_loss, ) def forward( self, speech: paddle.Tensor, speech_lengths: paddle.Tensor, text: paddle.Tensor, text_lengths: paddle.Tensor, ) -> Tuple[Optional[paddle.Tensor], Optional[paddle.Tensor], Optional[ paddle.Tensor]]: """Frontend + Encoder + Decoder + Calc loss Args: speech: (Batch, Length, ...) speech_lengths: (Batch, ) text: (Batch, Length) text_lengths: (Batch,) Returns: total_loss, attention_loss, ctc_loss """ assert text_lengths.dim() == 1, text_lengths.shape # Check that batch_size is unified assert (speech.shape[0] == speech_lengths.shape[0] == text.shape[0] == text_lengths.shape[0]), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape) # 1. Encoder start = time.time() encoder_out, encoder_mask = self.encoder(speech, speech_lengths) encoder_time = time.time() - start #logger.debug(f"encoder time: {encoder_time}") #TODO(Hui Zhang): sum not support bool type #encoder_out_lens = encoder_mask.squeeze(1).sum(1) #[B, 1, T] -> [B] encoder_out_lens = encoder_mask.squeeze(1).cast(paddle.int64).sum( 1) #[B, 1, T] -> [B] # 2a. Attention-decoder branch loss_att = None if self.ctc_weight != 1.0: start = time.time() loss_att, acc_att = self._calc_att_loss(encoder_out, encoder_mask, text, text_lengths) decoder_time = time.time() - start #logger.debug(f"decoder time: {decoder_time}") # 2b. CTC branch loss_ctc = None if self.ctc_weight != 0.0: start = time.time() loss_ctc = self.ctc(encoder_out, encoder_out_lens, text, text_lengths) ctc_time = time.time() - start #logger.debug(f"ctc time: {ctc_time}") if loss_ctc is None: loss = loss_att elif loss_att is None: loss = loss_ctc else: loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att return loss, loss_att, loss_ctc def _calc_att_loss( self, encoder_out: paddle.Tensor, encoder_mask: paddle.Tensor, ys_pad: paddle.Tensor, ys_pad_lens: paddle.Tensor, ) -> Tuple[paddle.Tensor, float]: """Calc attention loss. Args: encoder_out (paddle.Tensor): [B, Tmax, D] encoder_mask (paddle.Tensor): [B, 1, Tmax] ys_pad (paddle.Tensor): [B, Umax] ys_pad_lens (paddle.Tensor): [B] Returns: Tuple[paddle.Tensor, float]: attention_loss, accuracy rate """ ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id) ys_in_lens = ys_pad_lens + 1 # 1. Forward decoder decoder_out, _ = self.decoder(encoder_out, encoder_mask, ys_in_pad, ys_in_lens) # 2. Compute attention loss loss_att = self.criterion_att(decoder_out, ys_out_pad) acc_att = th_accuracy( decoder_out.view(-1, self.vocab_size), ys_out_pad, ignore_label=self.ignore_id, ) return loss_att, acc_att def _forward_encoder( self, speech: paddle.Tensor, speech_lengths: paddle.Tensor, decoding_chunk_size: int=-1, num_decoding_left_chunks: int=-1, simulate_streaming: bool=False, ) -> Tuple[paddle.Tensor, paddle.Tensor]: """Encoder pass. Args: speech (paddle.Tensor): [B, Tmax, D] speech_lengths (paddle.Tensor): [B] decoding_chunk_size (int, optional): chuck size. Defaults to -1. num_decoding_left_chunks (int, optional): nums chunks. Defaults to -1. simulate_streaming (bool, optional): streaming or not. Defaults to False. Returns: Tuple[paddle.Tensor, paddle.Tensor]: encoder hiddens (B, Tmax, D), encoder hiddens mask (B, 1, Tmax). """ # Let's assume B = batch_size # 1. Encoder if simulate_streaming and decoding_chunk_size > 0: encoder_out, encoder_mask = self.encoder.forward_chunk_by_chunk( speech, decoding_chunk_size=decoding_chunk_size, num_decoding_left_chunks=num_decoding_left_chunks ) # (B, maxlen, encoder_dim) else: encoder_out, encoder_mask = self.encoder( speech, speech_lengths, decoding_chunk_size=decoding_chunk_size, num_decoding_left_chunks=num_decoding_left_chunks ) # (B, maxlen, encoder_dim) return encoder_out, encoder_mask def recognize( self, speech: paddle.Tensor, speech_lengths: paddle.Tensor, beam_size: int=10, decoding_chunk_size: int=-1, num_decoding_left_chunks: int=-1, simulate_streaming: bool=False, ) -> paddle.Tensor: """ Apply beam search on attention decoder Args: speech (paddle.Tensor): (batch, max_len, feat_dim) speech_length (paddle.Tensor): (batch, ) beam_size (int): beam size for beam search decoding_chunk_size (int): decoding chunk for dynamic chunk trained model. <0: for decoding, use full chunk. >0: for decoding, use fixed chunk size as set. 0: used for training, it's prohibited here simulate_streaming (bool): whether do encoder forward in a streaming fashion Returns: paddle.Tensor: decoding result, (batch, max_result_len) """ assert speech.shape[0] == speech_lengths.shape[0] assert decoding_chunk_size != 0 device = speech.place batch_size = speech.shape[0] # Let's assume B = batch_size and N = beam_size # 1. Encoder encoder_out, encoder_mask = self._forward_encoder( speech, speech_lengths, decoding_chunk_size, num_decoding_left_chunks, simulate_streaming) # (B, maxlen, encoder_dim) maxlen = encoder_out.size(1) encoder_dim = encoder_out.size(2) running_size = batch_size * beam_size encoder_out = encoder_out.unsqueeze(1).repeat(1, beam_size, 1, 1).view( running_size, maxlen, encoder_dim) # (B*N, maxlen, encoder_dim) encoder_mask = encoder_mask.unsqueeze(1).repeat( 1, beam_size, 1, 1).view(running_size, 1, maxlen) # (B*N, 1, max_len) hyps = paddle.ones( [running_size, 1], dtype=paddle.long).fill_(self.sos) # (B*N, 1) # log scale score scores = paddle.to_tensor( [0.0] + [-float('inf')] * (beam_size - 1), dtype=paddle.float) scores = scores.to(device).repeat(batch_size).unsqueeze(1).to( device) # (B*N, 1) end_flag = paddle.zeros_like(scores, dtype=paddle.bool) # (B*N, 1) cache: Optional[List[paddle.Tensor]] = None # 2. Decoder forward step by step for i in range(1, maxlen + 1): # Stop if all batch and all beam produce eos # TODO(Hui Zhang): if end_flag.sum() == running_size: if end_flag.cast(paddle.int64).sum() == running_size: break # 2.1 Forward decoder step hyps_mask = subsequent_mask(i).unsqueeze(0).repeat( running_size, 1, 1).to(device) # (B*N, i, i) # logp: (B*N, vocab) logp, cache = self.decoder.forward_one_step( encoder_out, encoder_mask, hyps, hyps_mask, cache) # 2.2 First beam prune: select topk best prob at current time top_k_logp, top_k_index = logp.topk(beam_size) # (B*N, N) top_k_logp = mask_finished_scores(top_k_logp, end_flag) top_k_index = mask_finished_preds(top_k_index, end_flag, self.eos) # 2.3 Seconde beam prune: select topk score with history scores = scores + top_k_logp # (B*N, N), broadcast add scores = scores.view(batch_size, beam_size * beam_size) # (B, N*N) scores, offset_k_index = scores.topk(k=beam_size) # (B, N) scores = scores.view(-1, 1) # (B*N, 1) # 2.4. Compute base index in top_k_index, # regard top_k_index as (B*N*N),regard offset_k_index as (B*N), # then find offset_k_index in top_k_index base_k_index = paddle.arange(batch_size).view(-1, 1).repeat( 1, beam_size) # (B, N) base_k_index = base_k_index * beam_size * beam_size best_k_index = base_k_index.view(-1) + offset_k_index.view( -1) # (B*N) # 2.5 Update best hyps best_k_pred = paddle.index_select( top_k_index.view(-1), index=best_k_index, axis=0) # (B*N) best_hyps_index = best_k_index // beam_size last_best_k_hyps = paddle.index_select( hyps, index=best_hyps_index, axis=0) # (B*N, i) hyps = paddle.cat( (last_best_k_hyps, best_k_pred.view(-1, 1)), dim=1) # (B*N, i+1) # 2.6 Update end flag end_flag = paddle.eq(hyps[:, -1], self.eos).view(-1, 1) # 3. Select best of best scores = scores.view(batch_size, beam_size) # TODO: length normalization best_index = paddle.argmax(scores, axis=-1).long() # (B) best_hyps_index = best_index + paddle.arange( batch_size, dtype=paddle.long) * beam_size best_hyps = paddle.index_select(hyps, index=best_hyps_index, axis=0) best_hyps = best_hyps[:, 1:] return best_hyps def ctc_greedy_search( self, speech: paddle.Tensor, speech_lengths: paddle.Tensor, decoding_chunk_size: int=-1, num_decoding_left_chunks: int=-1, simulate_streaming: bool=False, ) -> List[List[int]]: """ Apply CTC greedy search Args: speech (paddle.Tensor): (batch, max_len, feat_dim) speech_length (paddle.Tensor): (batch, ) beam_size (int): beam size for beam search decoding_chunk_size (int): decoding chunk for dynamic chunk trained model. <0: for decoding, use full chunk. >0: for decoding, use fixed chunk size as set. 0: used for training, it's prohibited here simulate_streaming (bool): whether do encoder forward in a streaming fashion Returns: List[List[int]]: best path result """ assert speech.shape[0] == speech_lengths.shape[0] assert decoding_chunk_size != 0 batch_size = speech.shape[0] # Let's assume B = batch_size # encoder_out: (B, maxlen, encoder_dim) # encoder_mask: (B, 1, Tmax) encoder_out, encoder_mask = self._forward_encoder( speech, speech_lengths, decoding_chunk_size, num_decoding_left_chunks, simulate_streaming) maxlen = encoder_out.size(1) # (TODO Hui Zhang): bool no support reduce_sum # encoder_out_lens = encoder_mask.squeeze(1).sum(1) encoder_out_lens = encoder_mask.squeeze(1).astype(paddle.int).sum(1) ctc_probs = self.ctc.log_softmax(encoder_out) # (B, maxlen, vocab_size) topk_prob, topk_index = ctc_probs.topk(1, axis=2) # (B, maxlen, 1) topk_index = topk_index.view(batch_size, maxlen) # (B, maxlen) pad_mask = make_pad_mask(encoder_out_lens) # (B, maxlen) topk_index = topk_index.masked_fill_(pad_mask, self.eos) # (B, maxlen) hyps = [hyp.tolist() for hyp in topk_index] hyps = [remove_duplicates_and_blank(hyp) for hyp in hyps] return hyps def _ctc_prefix_beam_search( self, speech: paddle.Tensor, speech_lengths: paddle.Tensor, beam_size: int, decoding_chunk_size: int=-1, num_decoding_left_chunks: int=-1, simulate_streaming: bool=False, blank_id: int=0, ) -> Tuple[List[Tuple[int, float]], paddle.Tensor]: """ CTC prefix beam search inner implementation Args: speech (paddle.Tensor): (batch, max_len, feat_dim) speech_length (paddle.Tensor): (batch, ) beam_size (int): beam size for beam search decoding_chunk_size (int): decoding chunk for dynamic chunk trained model. <0: for decoding, use full chunk. >0: for decoding, use fixed chunk size as set. 0: used for training, it's prohibited here simulate_streaming (bool): whether do encoder forward in a streaming fashion Returns: List[Tuple[int, float]]: nbest results, (N,1), (text, likelihood) paddle.Tensor: encoder output, (1, max_len, encoder_dim), it will be used for rescoring in attention rescoring mode """ assert speech.shape[0] == speech_lengths.shape[0] assert decoding_chunk_size != 0 batch_size = speech.shape[0] # For CTC prefix beam search, we only support batch_size=1 assert batch_size == 1 # Let's assume B = batch_size and N = beam_size # 1. Encoder forward and get CTC score encoder_out, encoder_mask = self._forward_encoder( speech, speech_lengths, decoding_chunk_size, num_decoding_left_chunks, simulate_streaming) # (B, maxlen, encoder_dim) maxlen = encoder_out.size(1) ctc_probs = self.ctc.log_softmax(encoder_out) # (1, maxlen, vocab_size) ctc_probs = ctc_probs.squeeze(0) # cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score)) # blank_ending_score and none_blank_ending_score in ln domain cur_hyps = [(tuple(), (0.0, -float('inf')))] # 2. CTC beam search step by step for t in range(0, maxlen): logp = ctc_probs[t] # (vocab_size,) # key: prefix, value (pb, pnb), default value(-inf, -inf) next_hyps = defaultdict(lambda: (-float('inf'), -float('inf'))) # 2.1 First beam prune: select topk best top_k_logp, top_k_index = logp.topk(beam_size) # (beam_size,) for s in top_k_index: s = s.item() ps = logp[s].item() for prefix, (pb, pnb) in cur_hyps: last = prefix[-1] if len(prefix) > 0 else None if s == blank_id: # blank n_pb, n_pnb = next_hyps[prefix] n_pb = log_add([n_pb, pb + ps, pnb + ps]) next_hyps[prefix] = (n_pb, n_pnb) elif s == last: # Update *ss -> *s; n_pb, n_pnb = next_hyps[prefix] n_pnb = log_add([n_pnb, pnb + ps]) next_hyps[prefix] = (n_pb, n_pnb) # Update *s-s -> *ss, - is for blank n_prefix = prefix + (s, ) n_pb, n_pnb = next_hyps[n_prefix] n_pnb = log_add([n_pnb, pb + ps]) next_hyps[n_prefix] = (n_pb, n_pnb) else: n_prefix = prefix + (s, ) n_pb, n_pnb = next_hyps[n_prefix] n_pnb = log_add([n_pnb, pb + ps, pnb + ps]) next_hyps[n_prefix] = (n_pb, n_pnb) # 2.2 Second beam prune next_hyps = sorted( next_hyps.items(), key=lambda x: log_add(list(x[1])), reverse=True) cur_hyps = next_hyps[:beam_size] hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in cur_hyps] return hyps, encoder_out def ctc_prefix_beam_search( self, speech: paddle.Tensor, speech_lengths: paddle.Tensor, beam_size: int, decoding_chunk_size: int=-1, num_decoding_left_chunks: int=-1, simulate_streaming: bool=False, ) -> List[int]: """ Apply CTC prefix beam search Args: speech (paddle.Tensor): (batch, max_len, feat_dim) speech_length (paddle.Tensor): (batch, ) beam_size (int): beam size for beam search decoding_chunk_size (int): decoding chunk for dynamic chunk trained model. <0: for decoding, use full chunk. >0: for decoding, use fixed chunk size as set. 0: used for training, it's prohibited here simulate_streaming (bool): whether do encoder forward in a streaming fashion Returns: List[int]: CTC prefix beam search nbest results """ hyps, _ = self._ctc_prefix_beam_search( speech, speech_lengths, beam_size, decoding_chunk_size, num_decoding_left_chunks, simulate_streaming) return hyps[0][0] def attention_rescoring( self, speech: paddle.Tensor, speech_lengths: paddle.Tensor, beam_size: int, decoding_chunk_size: int=-1, num_decoding_left_chunks: int=-1, ctc_weight: float=0.0, simulate_streaming: bool=False, ) -> List[int]: """ Apply attention rescoring decoding, CTC prefix beam search is applied first to get nbest, then we resoring the nbest on attention decoder with corresponding encoder out Args: speech (paddle.Tensor): (batch, max_len, feat_dim) speech_length (paddle.Tensor): (batch, ) beam_size (int): beam size for beam search decoding_chunk_size (int): decoding chunk for dynamic chunk trained model. <0: for decoding, use full chunk. >0: for decoding, use fixed chunk size as set. 0: used for training, it's prohibited here simulate_streaming (bool): whether do encoder forward in a streaming fashion Returns: List[int]: Attention rescoring result """ assert speech.shape[0] == speech_lengths.shape[0] assert decoding_chunk_size != 0 device = speech.place batch_size = speech.shape[0] # For attention rescoring we only support batch_size=1 assert batch_size == 1 # len(hyps) = beam_size, encoder_out: (1, maxlen, encoder_dim) hyps, encoder_out = self._ctc_prefix_beam_search( speech, speech_lengths, beam_size, decoding_chunk_size, num_decoding_left_chunks, simulate_streaming) assert len(hyps) == beam_size hyps_pad = pad_sequence([ paddle.to_tensor(hyp[0], place=device, dtype=paddle.long) for hyp in hyps ], True, self.ignore_id) # (beam_size, max_hyps_len) hyps_lens = paddle.to_tensor( [len(hyp[0]) for hyp in hyps], place=device, dtype=paddle.long) # (beam_size,) hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id) hyps_lens = hyps_lens + 1 # Add at begining encoder_out = encoder_out.repeat(beam_size, 1, 1) encoder_mask = paddle.ones( (beam_size, 1, encoder_out.size(1)), dtype=paddle.bool) decoder_out, _ = self.decoder( encoder_out, encoder_mask, hyps_pad, hyps_lens) # (beam_size, max_hyps_len, vocab_size) # ctc score in ln domain decoder_out = paddle.nn.functional.log_softmax(decoder_out, axis=-1) decoder_out = decoder_out.numpy() # Only use decoder score for rescoring best_score = -float('inf') best_index = 0 # hyps is List[(Text=List[int], Score=float)], len(hyps)=beam_size for i, hyp in enumerate(hyps): score = 0.0 for j, w in enumerate(hyp[0]): score += decoder_out[i][j][w] # last decoder output token is `eos`, for laste decoder input token. score += decoder_out[i][len(hyp[0])][self.eos] # add ctc score (which in ln domain) score += hyp[1] * ctc_weight if score > best_score: best_score = score best_index = i return hyps[best_index][0] #@jit.to_static def subsampling_rate(self) -> int: """ Export interface for c++ call, return subsampling_rate of the model """ return self.encoder.embed.subsampling_rate #@jit.to_static def right_context(self) -> int: """ Export interface for c++ call, return right_context of the model """ return self.encoder.embed.right_context #@jit.to_static def sos_symbol(self) -> int: """ Export interface for c++ call, return sos symbol id of the model """ return self.sos #@jit.to_static def eos_symbol(self) -> int: """ Export interface for c++ call, return eos symbol id of the model """ return self.eos @jit.to_static def forward_encoder_chunk( self, xs: paddle.Tensor, offset: int, required_cache_size: int, subsampling_cache: Optional[paddle.Tensor]=None, elayers_output_cache: Optional[List[paddle.Tensor]]=None, conformer_cnn_cache: Optional[List[paddle.Tensor]]=None, ) -> Tuple[paddle.Tensor, paddle.Tensor, List[paddle.Tensor], List[ paddle.Tensor]]: """ Export interface for c++ call, give input chunk xs, and return output from time 0 to current chunk. Args: xs (paddle.Tensor): chunk input subsampling_cache (Optional[paddle.Tensor]): subsampling cache elayers_output_cache (Optional[List[paddle.Tensor]]): transformer/conformer encoder layers output cache conformer_cnn_cache (Optional[List[paddle.Tensor]]): conformer cnn cache Returns: paddle.Tensor: output, it ranges from time 0 to current chunk. paddle.Tensor: subsampling cache List[paddle.Tensor]: attention cache List[paddle.Tensor]: conformer cnn cache """ return self.encoder.forward_chunk( xs, offset, required_cache_size, subsampling_cache, elayers_output_cache, conformer_cnn_cache) # @jit.to_static([ # paddle.static.InputSpec(shape=[1, None, feat_dim],dtype='float32'), # audio feat, [B,T,D] # ]) def ctc_activation(self, xs: paddle.Tensor) -> paddle.Tensor: """ Export interface for c++ call, apply linear transform and log softmax before ctc Args: xs (paddle.Tensor): encoder output, (B, T, D) Returns: paddle.Tensor: activation before ctc """ return self.ctc.log_softmax(xs) @jit.to_static def forward_attention_decoder( self, hyps: paddle.Tensor, hyps_lens: paddle.Tensor, encoder_out: paddle.Tensor, ) -> paddle.Tensor: """ Export interface for c++ call, forward decoder with multiple hypothesis from ctc prefix beam search and one encoder output Args: hyps (paddle.Tensor): hyps from ctc prefix beam search, already pad sos at the begining, (B, T) hyps_lens (paddle.Tensor): length of each hyp in hyps, (B) encoder_out (paddle.Tensor): corresponding encoder output, (B=1, T, D) Returns: paddle.Tensor: decoder output, (B, L) """ assert encoder_out.size(0) == 1 num_hyps = hyps.size(0) assert hyps_lens.size(0) == num_hyps encoder_out = encoder_out.repeat(num_hyps, 1, 1) # (B, 1, T) encoder_mask = paddle.ones( [num_hyps, 1, encoder_out.size(1)], dtype=paddle.bool) # (num_hyps, max_hyps_len, vocab_size) decoder_out, _ = self.decoder(encoder_out, encoder_mask, hyps, hyps_lens) decoder_out = paddle.nn.functional.log_softmax(decoder_out, dim=-1) return decoder_out @paddle.no_grad() def decode(self, feats: paddle.Tensor, feats_lengths: paddle.Tensor, text_feature: Dict[str, int], decoding_method: str, lang_model_path: str, beam_alpha: float, beam_beta: float, beam_size: int, cutoff_prob: float, cutoff_top_n: int, num_processes: int, ctc_weight: float=0.0, decoding_chunk_size: int=-1, num_decoding_left_chunks: int=-1, simulate_streaming: bool=False): """u2 decoding. Args: feats (Tenosr): audio features, (B, T, D) feats_lengths (Tenosr): (B) text_feature (TextFeaturizer): text feature object. decoding_method (str): decoding mode, e.g. 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring' lang_model_path (str): lm path. beam_alpha (float): lm weight. beam_beta (float): length penalty. beam_size (int): beam size for search cutoff_prob (float): for prune. cutoff_top_n (int): for prune. num_processes (int): ctc_weight (float, optional): ctc weight for attention rescoring decode mode. Defaults to 0.0. decoding_chunk_size (int, optional): decoding chunk size. Defaults to -1. <0: for decoding, use full chunk. >0: for decoding, use fixed chunk size as set. 0: used for training, it's prohibited here. num_decoding_left_chunks (int, optional): number of left chunks for decoding. Defaults to -1. simulate_streaming (bool, optional): simulate streaming inference. Defaults to False. Raises: ValueError: when not support decoding_method. Returns: List[List[int]]: transcripts. """ batch_size = feats.size(0) if decoding_method in ['ctc_prefix_beam_search', 'attention_rescoring'] and batch_size > 1: logger.fatal( f'decoding mode {decoding_method} must be running with batch_size == 1' ) sys.exit(1) if decoding_method == 'attention': hyps = self.recognize( feats, feats_lengths, beam_size=beam_size, decoding_chunk_size=decoding_chunk_size, num_decoding_left_chunks=num_decoding_left_chunks, simulate_streaming=simulate_streaming) hyps = [hyp.tolist() for hyp in hyps] elif decoding_method == 'ctc_greedy_search': hyps = self.ctc_greedy_search( feats, feats_lengths, decoding_chunk_size=decoding_chunk_size, num_decoding_left_chunks=num_decoding_left_chunks, simulate_streaming=simulate_streaming) # ctc_prefix_beam_search and attention_rescoring only return one # result in List[int], change it to List[List[int]] for compatible # with other batch decoding mode elif decoding_method == 'ctc_prefix_beam_search': assert feats.size(0) == 1 hyp = self.ctc_prefix_beam_search( feats, feats_lengths, beam_size, decoding_chunk_size=decoding_chunk_size, num_decoding_left_chunks=num_decoding_left_chunks, simulate_streaming=simulate_streaming) hyps = [hyp] elif decoding_method == 'attention_rescoring': assert feats.size(0) == 1 hyp = self.attention_rescoring( feats, feats_lengths, beam_size, decoding_chunk_size=decoding_chunk_size, num_decoding_left_chunks=num_decoding_left_chunks, ctc_weight=ctc_weight, simulate_streaming=simulate_streaming) hyps = [hyp] else: raise ValueError(f"Not support decoding method: {decoding_method}") res = [text_feature.defeaturize(hyp) for hyp in hyps] return res class U2Model(U2BaseModel): def __init__(self, configs: dict): vocab_size, encoder, decoder, ctc = U2Model._init_from_config(configs) super().__init__( vocab_size=vocab_size, encoder=encoder, decoder=decoder, ctc=ctc, **configs['model_conf']) @classmethod def _init_from_config(cls, configs: dict): """init sub module for model. Args: configs (dict): config dict. Raises: ValueError: raise when using not support encoder type. Returns: int, nn.Layer, nn.Layer, nn.Layer: vocab size, encoder, decoder, ctc """ if configs['cmvn_file'] is not None: mean, istd = load_cmvn(configs['cmvn_file'], configs['cmvn_file_type']) global_cmvn = GlobalCMVN( paddle.to_tensor(mean, dtype=paddle.float), paddle.to_tensor(istd, dtype=paddle.float)) else: global_cmvn = None input_dim = configs['input_dim'] vocab_size = configs['output_dim'] assert input_dim != 0, input_dim assert vocab_size != 0, vocab_size encoder_type = configs.get('encoder', 'transformer') logger.info(f"U2 Encoder type: {encoder_type}") if encoder_type == 'transformer': encoder = TransformerEncoder( input_dim, global_cmvn=global_cmvn, **configs['encoder_conf']) elif encoder_type == 'conformer': encoder = ConformerEncoder( input_dim, global_cmvn=global_cmvn, **configs['encoder_conf']) else: raise ValueError(f"not support encoder type:{encoder_type}") decoder = TransformerDecoder(vocab_size, encoder.output_size(), **configs['decoder_conf']) ctc = CTCDecoder( odim=vocab_size, enc_n_units=encoder.output_size(), blank_id=0, dropout_rate=0.0, reduction=True, # sum batch_average=True) # sum / batch_size return vocab_size, encoder, decoder, ctc @classmethod def from_config(cls, configs: dict): """init model. Args: configs (dict): config dict. Raises: ValueError: raise when using not support encoder type. Returns: nn.Layer: U2Model """ model = cls(configs) return model @classmethod def from_pretrained(cls, dataloader, config, checkpoint_path): """Build a DeepSpeech2Model model from a pretrained model. Args: dataloader (paddle.io.DataLoader): not used. config (yacs.config.CfgNode): model configs checkpoint_path (Path or str): the path of pretrained model checkpoint, without extension name Returns: DeepSpeech2Model: The model built from pretrained result. """ config.defrost() config.input_dim = dataloader.collate_fn.feature_size config.output_dim = dataloader.collate_fn.vocab_size config.freeze() model = cls.from_config(config) if checkpoint_path: infos = checkpoint.Checkpoint().load_parameters( model, checkpoint_path=checkpoint_path) logger.info(f"checkpoint info: {infos}") layer_tools.summary(model) return model class U2InferModel(U2Model): def __init__(self, configs: dict): super().__init__(configs) def forward(self, feats, feats_lengths, decoding_chunk_size=-1, num_decoding_left_chunks=-1, simulate_streaming=False): """export model function Args: feats (Tensor): [B, T, D] feats_lengths (Tensor): [B] Returns: List[List[int]]: best path result """ return self.ctc_greedy_search( feats, feats_lengths, decoding_chunk_size=decoding_chunk_size, num_decoding_left_chunks=num_decoding_left_chunks, simulate_streaming=simulate_streaming)