mirror of
https://github.com/k2-fsa/icefall.git
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126 lines
4.2 KiB
Python
126 lines
4.2 KiB
Python
# Copyright 2022 Xiaomi Corp. (authors: Wei Kang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List, Optional, Tuple
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import k2
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import torch
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from icefall.utils import AttributeDict
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class DecodeStream(object):
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def __init__(
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self,
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params: AttributeDict,
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decoding_graph: Optional[k2.Fsa] = None,
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device: torch.device = torch.device("cpu"),
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) -> None:
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"""
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Args:
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decoding_graph:
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Decoding graph used for decoding, may be a TrivialGraph or a HLG.
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device:
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The device to run this stream.
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"""
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if decoding_graph is not None:
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assert device == decoding_graph.device
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self.params = params
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# It contains a 2-D tensors representing the feature frames.
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self.features: torch.Tensor = None
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# how many frames are processed. (before subsampling).
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self.num_processed_frames: int = 0
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self._done: bool = False
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# The transcript of current utterance.
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self.ground_truth: str = ""
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# The decoding result (partial or final) of current utterance.
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self.hyp: List = []
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self.feature_len: int = 0
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if params.decoding_method == "greedy_search":
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self.hyp = [params.blank_id] * params.context_size
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elif params.decoding_method == "fast_beam_search":
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# feature_len is needed to get partial results.
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# The rnnt_decoding_stream for fast_beam_search.
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self.rnnt_decoding_stream: k2.RnntDecodingStream = (
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k2.RnntDecodingStream(decoding_graph)
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)
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else:
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assert (
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False
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), f"Decoding method :{params.decoding_method} do not support"
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# The caches for streaming conformer
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# It is a List containing two tensors, the first one is the cache for
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# attention which has a shape of
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# (num_encoder_layers, left_context, encoder_dim),
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# the second one is the cache of conv_module which has a shape of
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# (num_encoder_layers, cnn_module_kernel - 1, encoder_dim).
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self.states: List[torch.Tensor] = [
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torch.zeros(
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(
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params.num_encoder_layers,
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params.left_context,
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params.encoder_dim,
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),
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device=device,
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),
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torch.zeros(
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(
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params.num_encoder_layers,
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params.cnn_module_kernel - 1,
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params.encoder_dim,
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),
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device=device,
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),
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]
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@property
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def done(self) -> bool:
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"""Return True if all the features are processed."""
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return self._done
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def set_features(
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self,
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features: torch.Tensor,
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) -> None:
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"""Set features tensor of current utterance."""
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self.features = features
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def get_feature_frames(self, chunk_size: int) -> Tuple[torch.Tensor, int]:
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"""Consume chunk_size frames of features"""
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ret_chunk_size = min(
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self.features.size(0) - self.num_processed_frames, chunk_size + 3
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)
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ret_features = self.features[
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self.num_processed_frames : self.num_processed_frames # noqa
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+ ret_chunk_size,
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:,
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]
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self.num_processed_frames += (
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chunk_size
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- 2 * self.params.subsampling_factor
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- self.params.right_context * self.params.subsampling_factor
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)
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if self.num_processed_frames >= self.features.size(0):
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self._done = True
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return ret_features, ret_chunk_size
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