# Copyright 2022 Xiaomi Corp. (author: 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. from typing import List from typing import Tuple import k2 import torch import _k2 from k2 import Fsa from k2 import RaggedShape from k2 import RaggedTensor from torch import Tensor from .ops import index_select from _k2 import RnntDecodingConfig class RnntDecodingStream(object): def __init__(self, fsa: Fsa) -> None: """Create a new rnnt decoding stream. Every sequence(wave data) needs a decoding stream, this function is expected to be called when a new sequence comes. We support different decoding graphs for different streams. Args: graph: The decoding graph used in this stream. Returns: A rnnt decoding stream object, which will be combined into :class:`RnntDecodingStreams` to do decoding together with other sequences in parallel. """ self.fsa = fsa self.stream = _k2.create_rnnt_decoding_stream(fsa.arcs) self.device = fsa.device def __str__(self) -> str: """Return a string representation of this object For visualization and debug only. """ return f"{self.stream}, device : {self.device}\n" class RnntDecodingStreams(object): """See https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py # noqa for how this class is used in RNN-T decoding. """ def __init__( self, src_streams: List[RnntDecodingStream], config: RnntDecodingConfig ) -> None: """ Combines multiple RnntDecodingStream objects to create a RnntDecodingStreams object, then all these RnntDecodingStreams can do decoding in parallel. Args: src_streams: A list of RnntDecodingStream object to be combined. config: A configuration object which contains decoding parameters like `vocab-size`, `decoder_history_len`, `beam`, `max_states`, `max_contexts` etc. Returns: Return a RnntDecodingStreams object. """ assert len(src_streams) > 0 self.num_streams = len(src_streams) self.src_streams = src_streams self.device = self.src_streams[0].device streams = [x.stream for x in self.src_streams] self.streams = _k2.RnntDecodingStreams(streams, config) def __str__(self) -> str: """Return a string representation of this object For visualization and debug only. """ s = f"num_streams : {self.num_streams}\n" for i in range(self.num_streams): s += f"stream[{i}] : {self.src_streams[i]}" return s def get_contexts(self) -> Tuple[RaggedShape, Tensor]: """ This function must be called prior to evaluating the joiner network for a particular frame. It tells the calling code for which contexts it must evaluate the joiner network. Returns: Return a two-element tuple containing a RaggedShape and a tensor. shape: A RaggedShape with 2 axes, representing [stream][context]. contexts: A tensor of shape [tot_contexts][decoder_history_len], where tot_contexts == shape->TotSize(1) and decoder_history_len comes from the config, it represents the number of symbols in the context of the decoder network (assumed to be finite). It contains the token ids into the vocabulary(i.e. `0 <= value < vocab_size`). Its dtype is torch.int32. """ return self.streams.get_contexts() def advance(self, logprobs: Tensor) -> None: """ Advance decoding streams by one frame. Args: logprobs: A tensor of shape [tot_contexts][num_symbols], containing log-probs of symbols given the contexts output by `get_contexts()`. It satisfies `logprobs.Dim0() == shape.TotSize(1)`, shape is returned by `get_contexts()`. """ self.streams.advance(logprobs) def terminate_and_flush_to_streams(self) -> None: """ Terminate the decoding process of current RnntDecodingStreams object. It will update the decoding states and store the decoding results currently got to each of the individual streams. Note: We can not decode with this object anymore after calling terminate_and_flush_to_streams(). """ self.streams.terminate_and_flush_to_streams() def format_output( self, num_frames: List[int], allow_partial: bool = False ) -> Fsa: """ Generate the lattice Fsa currently got. Note: The attributes of the generated lattice is a union of the attributes of all the decoding graphs. For example, if `self` contains three individual stream, each stream has its own decoding graphs, graph[0] has attributes attr1, attr2; graph[1] has attributes attr1, attr3; graph[2] has attributes attr3, attr4; then the generated lattice has attributes attr1, attr2, attr3, attr4. Args: num_frames: A List containing the number of frames we want to gather for each stream (note: the frames we have ever received for the corresponding stream). It MUST satisfy `len(num_frames) == self.num_streams`. allow_partial: If true and there is no final state active, we will treat all the states on the last frame to be final state. If false, we only care about the real final state in the decoding graph on the last frame when generating lattice. Default False. Returns: Return the lattice Fsa with all the attributes propagated. The returned Fsa has 3 axes with `fsa.dim0==self.num_streams`. """ assert len(num_frames) == self.num_streams ragged_arcs, out_map = self.streams.format_output( num_frames, allow_partial ) fsa = Fsa(ragged_arcs) # propagate attributes tensor_attr_info = dict() # gather the attributes info of all the decoding graphs, for i in range(self.num_streams): src = self.src_streams[i].fsa for name, value in src.named_tensor_attr(include_scores=False): if name not in tensor_attr_info: filler = 0 if isinstance(value, Tensor): filler = float(src.get_filler(name)) dtype = value.dtype tensor_type = "Tensor" else: assert isinstance(value, k2.RaggedTensor) # Only integer types ragged attributes are supported now assert value.dtype == torch.int32 assert value.num_axes == 2 dtype = torch.int32 tensor_type = "RaggedTensor" tensor_attr_info[name] = { "filler": filler, "dtype": dtype, "tensor_type": tensor_type, } # combine the attributes propagating from different decoding graphs for name, info in tensor_attr_info.items(): values = list() start = 0 for i in range(self.num_streams): src = self.src_streams[i].fsa device = self.device num_arcs = fsa[i].num_arcs arc_map = out_map[start: start + num_arcs] start = start + num_arcs if hasattr(src, name): value = getattr(src, name) if info["tensor_type"] == "Tensor": assert isinstance(value, Tensor) new_value = index_select( value, arc_map, default_value=filler ) else: assert isinstance(value, RaggedTensor) # Only integer types ragged attributes are supported now assert value.num_axes == 2 assert value.dtype == torch.int32 new_value, _ = value.index( arc_map, axis=0, need_value_indexes=False ) else: if info["tensor_type"] == "Tensor": # fill with filler value new_value = torch.tensor( [filler] * num_arcs, dtype=info["dtype"], device=device, ) else: # fill with empty RaggedTensor new_value = RaggedTensor( torch.empty( (num_arcs, 0), dtype=info["dtype"], device=device, ) ) values.append(new_value) if info["tensor_type"] == "Tensor": new_value = torch.cat(values) else: new_value = k2.ragged.cat(values, axis=0) setattr(fsa, name, new_value) # set non_tensor_attrs for i in range(self.num_streams): src = self.src_streams[i].fsa for name, value in src.named_non_tensor_attr(): setattr(fsa, name, value) return fsa