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Fix conv cache error, support async streaming decoding
This commit is contained in:
pkufool
parent
364bccb2e3
commit
b23db42486
118
egs/librispeech/ASR/pruned_transducer_stateless/decode_stream.py
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118
egs/librispeech/ASR/pruned_transducer_stateless/decode_stream.py
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@ -0,0 +1,118 @@
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# 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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# 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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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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self.feature_len: int = 0
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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
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+ ret_chunk_size,
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:,
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]
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self.num_processed_frames += chunk_size
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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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698
egs/librispeech/ASR/pruned_transducer_stateless/streaming_decode.py
Executable file
698
egs/librispeech/ASR/pruned_transducer_stateless/streaming_decode.py
Executable file
@ -0,0 +1,698 @@
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#!/usr/bin/env python3
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# Copyright 2022 Xiaomi Corporation (Authors: Wei Kang, Fangjun Kuang)
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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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"""
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Usage:
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./pruned_transducer_stateless2/streaming_decode.py \
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--epoch 28 \
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--avg 15 \
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--exp-dir ./pruned_transducer_stateless2/exp \
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--decoding_method greedy_search \
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--num-decode-streams 200
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"""
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import argparse
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import logging
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import math
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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import numpy as np
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import k2
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import sentencepiece as spm
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import torch
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import torch.nn as nn
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from asr_datamodule import LibriSpeechAsrDataModule
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from decode_stream import DecodeStream
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from icefall.checkpoint import (
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average_checkpoints,
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find_checkpoints,
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load_checkpoint,
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)
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from icefall.utils import (
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AttributeDict,
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setup_logger,
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store_transcripts,
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str2bool,
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write_error_stats,
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)
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from icefall.decode import Nbest, one_best_decoding
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from icefall.utils import get_texts
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from kaldifeat import FbankOptions, Fbank
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from lhotse import CutSet
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from train import get_params, get_transducer_model
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from torch.nn.utils.rnn import pad_sequence
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LOG_EPS = math.log(1e-10)
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--epoch",
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type=int,
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default=28,
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help="""It specifies the checkpoint to use for decoding.
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Note: Epoch counts from 0.
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You can specify --avg to use more checkpoints for model averaging.""",
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)
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parser.add_argument(
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"--iter",
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type=int,
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default=0,
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help="""If positive, --epoch is ignored and it
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will use the checkpoint exp_dir/checkpoint-iter.pt.
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You can specify --avg to use more checkpoints for model averaging.
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""",
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)
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parser.add_argument(
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"--avg",
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type=int,
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default=15,
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help="Number of checkpoints to average. Automatically select "
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"consecutive checkpoints before the checkpoint specified by "
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"'--epoch' and '--iter'",
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)
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parser.add_argument(
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"--exp-dir",
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type=str,
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default="pruned_transducer_stateless2/exp",
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help="The experiment dir",
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)
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parser.add_argument(
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"--bpe-model",
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type=str,
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default="data/lang_bpe_500/bpe.model",
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help="Path to the BPE model",
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)
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parser.add_argument(
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"--decoding-method",
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type=str,
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default="greedy_search",
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help="""Support only greedy_search and fast_beam_search now.
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""",
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)
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parser.add_argument(
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"--beam",
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type=float,
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default=4,
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help="""A floating point value to calculate the cutoff score during beam
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search (i.e., `cutoff = max-score - beam`), which is the same as the
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`beam` in Kaldi.
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Used only when --decoding-method is fast_beam_search""",
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)
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parser.add_argument(
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"--max-contexts",
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type=int,
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default=4,
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help="""Used only when --decoding-method is
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fast_beam_search""",
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)
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parser.add_argument(
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"--max-states",
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type=int,
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default=32,
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help="""Used only when --decoding-method is
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fast_beam_search""",
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)
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parser.add_argument(
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"--context-size",
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type=int,
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default=2,
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help="The context size in the decoder. 1 means bigram; "
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"2 means tri-gram",
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)
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parser.add_argument(
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"--dynamic-chunk-training",
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type=str2bool,
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default=False,
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help="""Whether to use dynamic_chunk_training, if you want a streaming
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model, this requires to be True.
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Note: not needed for decoding, adding it here to construct transducer model,
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as we reuse the code in train.py.
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""",
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)
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parser.add_argument(
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"--short-chunk-size",
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type=int,
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default=25,
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help="""Chunk length of dynamic training, the chunk size would be either
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max sequence length of current batch or uniformly sampled from (1, short_chunk_size).
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Note: not needed for decoding, adding it here to construct transducer model,
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as we reuse the code in train.py.
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""",
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)
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parser.add_argument(
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"--num-left-chunks",
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type=int,
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default=4,
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help="""How many left context can be seen in chunks when calculating attention.
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Note: not needed for decoding, adding it here to construct transducer model,
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as we reuse the code in train.py.
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""",
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)
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parser.add_argument(
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"--causal-convolution",
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type=str2bool,
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default=True,
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help="""Whether to use causal convolution, this requires to be True when
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using dynamic_chunk_training.
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""",
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)
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parser.add_argument(
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"--decode-chunk-size",
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type=int,
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default=16,
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help="The chunk size for decoding (in frames after subsampling)",
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)
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parser.add_argument(
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"--left-context",
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type=int,
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default=64,
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help="left context can be seen during decoding (in frames after subsampling)",
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)
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parser.add_argument(
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"--num-decode-streams",
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type=int,
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default=2000,
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help="The number of streams that can be decoded parallel.",
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)
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return parser
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def greedy_search(
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model: nn.Module,
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encoder_out: torch.Tensor,
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streams: List[DecodeStream],
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) -> List[List[int]]:
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assert len(streams) == encoder_out.size(0)
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assert encoder_out.ndim == 3
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blank_id = model.decoder.blank_id
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context_size = model.decoder.context_size
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device = model.device
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T = encoder_out.size(1)
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decoder_input = torch.tensor(
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[stream.hyp[-context_size:] for stream in streams],
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device=device,
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dtype=torch.int64,
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)
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# decoder_out is of shape (N, decoder_out_dim)
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decoder_out = model.decoder(decoder_input, need_pad=False)
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for t in range(T):
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# current_encoder_out's shape: (batch_size, 1, encoder_out_dim)
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current_encoder_out = encoder_out[:, t : t + 1, :]
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logits = model.joiner(
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current_encoder_out.unsqueeze(2),
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decoder_out.unsqueeze(1),
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)
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# logits'shape (batch_size, vocab_size)
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logits = logits.squeeze(1).squeeze(1)
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assert logits.ndim == 2, logits.shape
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y = logits.argmax(dim=1).tolist()
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emitted = False
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for i, v in enumerate(y):
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if v != blank_id:
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streams[i].hyp.append(v)
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emitted = True
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if emitted:
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# update decoder output
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decoder_input = torch.tensor(
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[stream.hyp[-context_size:] for stream in streams],
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device=device,
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dtype=torch.int64,
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)
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decoder_out = model.decoder(
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decoder_input,
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need_pad=False,
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)
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hyp_tokens = []
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for stream in streams:
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hyp_tokens.append(stream.hyp)
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return hyp_tokens
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def fast_beam_search(
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model: nn.Module,
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encoder_out: torch.Tensor,
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processed_lens: torch.Tensor,
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decoding_streams: k2.RnntDecodingStreams,
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) -> List[List[int]]:
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B, T, C = encoder_out.shape
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for t in range(T):
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# shape is a RaggedShape of shape (B, context)
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# contexts is a Tensor of shape (shape.NumElements(), context_size)
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shape, contexts = decoding_streams.get_contexts()
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# `nn.Embedding()` in torch below v1.7.1 supports only torch.int64
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contexts = contexts.to(torch.int64)
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# decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim)
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decoder_out = model.decoder(contexts, need_pad=False)
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# current_encoder_out is of shape
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# (shape.NumElements(), 1, joiner_dim)
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# fmt: off
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current_encoder_out = torch.index_select(
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encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64)
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)
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# fmt: on
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logits = model.joiner(
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current_encoder_out.unsqueeze(2),
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decoder_out.unsqueeze(1),
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)
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logits = logits.squeeze(1).squeeze(1)
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log_probs = logits.log_softmax(dim=-1)
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decoding_streams.advance(log_probs)
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decoding_streams.terminate_and_flush_to_streams()
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lattice = decoding_streams.format_output(processed_lens.tolist())
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best_path = one_best_decoding(lattice)
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hyp_tokens = get_texts(best_path)
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return hyp_tokens
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|
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def decode_one_chunk(
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params: AttributeDict,
|
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model: nn.Module,
|
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sp: spm.SentencePieceProcessor,
|
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decode_streams: List[DecodeStream],
|
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) -> List[int]:
|
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"""Decode one chunk frames of features for each decode_streams and
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return the indexes of finished streams in a List.
|
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|
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Args:
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params:
|
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It's the return value of :func:`get_params`.
|
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model:
|
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The neural model.
|
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sp:
|
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The BPE model.
|
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decode_streams:
|
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A List of DecodeStream, each belonging to a utterance.
|
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Returns:
|
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Return a List containing which DecodeStreams are finished.
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"""
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device = model.device
|
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|
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features = []
|
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feature_lens = []
|
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states = []
|
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|
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rnnt_stream_list = []
|
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processed_feature_lens = []
|
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|
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for stream in decode_streams:
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feat, feat_len = stream.get_feature_frames(
|
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params.decode_chunk_size * params.subsampling_factor
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)
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features.append(feat)
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feature_lens.append(feat_len)
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states.append(stream.states)
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if params.decoding_method == "fast_beam_search":
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processed_feature_lens.append(stream.feature_len)
|
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rnnt_stream_list.append(stream.rnnt_decoding_stream)
|
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|
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feature_lens = torch.tensor(feature_lens, device=device)
|
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features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS)
|
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|
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# if T is less than 7 there will be an error in time reduction layer,
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# because we subsample features with ((x_len - 1) // 2 - 1) // 2
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if features.size(1) < 7:
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feature_lens += 7 - features.size(1)
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features = torch.cat(
|
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[
|
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features,
|
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torch.tensor(
|
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LOG_EPS, dtype=features.dtype, device=device
|
||||
).expand(
|
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features.size(0), 7 - features.size(1), features.size(2)
|
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),
|
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],
|
||||
dim=1,
|
||||
)
|
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|
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states = [
|
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torch.stack([x[0] for x in states], dim=2),
|
||||
torch.stack([x[1] for x in states], dim=2),
|
||||
]
|
||||
|
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# Note: states will be modified in streaming_forward.
|
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encoder_out, encoder_out_lens = model.encoder.streaming_forward(
|
||||
x=features,
|
||||
x_lens=feature_lens,
|
||||
states=states,
|
||||
left_context=params.left_context,
|
||||
)
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
hyp_tokens = greedy_search(model, encoder_out, decode_streams)
|
||||
elif params.decoding_method == "fast_beam_search":
|
||||
config = k2.RnntDecodingConfig(
|
||||
vocab_size=params.vocab_size,
|
||||
decoder_history_len=params.context_size,
|
||||
beam=params.beam,
|
||||
max_contexts=params.max_contexts,
|
||||
max_states=params.max_states,
|
||||
)
|
||||
processed_feature_lens = torch.tensor(
|
||||
processed_feature_lens, device=device
|
||||
)
|
||||
decoding_streams = k2.RnntDecodingStreams(rnnt_stream_list, config)
|
||||
processed_lens = processed_feature_lens + encoder_out_lens
|
||||
hyp_tokens = fast_beam_search(
|
||||
model, encoder_out, processed_lens, decoding_streams
|
||||
)
|
||||
else:
|
||||
assert False
|
||||
|
||||
states = [torch.unbind(states[0], dim=2), torch.unbind(states[1], dim=2)]
|
||||
|
||||
finished_streams = []
|
||||
for i in range(len(decode_streams)):
|
||||
decode_streams[i].states = [states[0][i], states[1][i]]
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
decode_streams[i].feature_len += encoder_out_lens[i]
|
||||
decode_streams[i].hyp = hyp_tokens[i]
|
||||
if decode_streams[i].done:
|
||||
finished_streams.append(i)
|
||||
|
||||
return finished_streams
|
||||
|
||||
|
||||
def decode_dataset(
|
||||
cuts: CutSet,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
sp: spm.SentencePieceProcessor,
|
||||
decoding_graph: Optional[k2.Fsa] = None,
|
||||
) -> Dict[str, List[Tuple[List[str], List[str]]]]:
|
||||
"""Decode dataset.
|
||||
|
||||
Args:
|
||||
cuts:
|
||||
Lhotse Cutset containing the dataset to decode.
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
sp:
|
||||
The BPE model.
|
||||
decoding_graph:
|
||||
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
|
||||
only when --decoding_method is fast_beam_search.
|
||||
Returns:
|
||||
Return a dict, whose key may be "greedy_search" if greedy search
|
||||
is used, or it may be "beam_7" if beam size of 7 is used.
|
||||
Its value is a list of tuples. Each tuple contains two elements:
|
||||
The first is the reference transcript, and the second is the
|
||||
predicted result.
|
||||
"""
|
||||
device = model.device
|
||||
|
||||
opts = FbankOptions()
|
||||
opts.device = device
|
||||
opts.frame_opts.dither = 0
|
||||
opts.frame_opts.snip_edges = False
|
||||
opts.frame_opts.samp_freq = 16000
|
||||
opts.mel_opts.num_bins = 80
|
||||
|
||||
log_interval = 300
|
||||
|
||||
decode_results = []
|
||||
# Contain decode streams currently running.
|
||||
decode_streams = []
|
||||
for num, cut in enumerate(cuts):
|
||||
# each utterance has a DecodeStream.
|
||||
decode_stream = DecodeStream(
|
||||
params=params,
|
||||
decoding_graph=decoding_graph,
|
||||
device=device,
|
||||
)
|
||||
|
||||
audio: np.ndarray = cut.load_audio()
|
||||
# audio.shape: (1, num_samples)
|
||||
assert len(audio.shape) == 2
|
||||
assert audio.shape[0] == 1, "Should be single channel"
|
||||
assert audio.dtype == np.float32, audio.dtype
|
||||
|
||||
# The trained model is using normalized samples
|
||||
assert audio.max() <= 1, "Should be normalized to [-1, 1])"
|
||||
|
||||
samples = torch.from_numpy(audio).squeeze(0)
|
||||
|
||||
fbank = Fbank(opts)
|
||||
decode_stream.set_features(fbank(samples.to(device)))
|
||||
decode_stream.ground_truth = cut.supervisions[0].text
|
||||
|
||||
decode_streams.append(decode_stream)
|
||||
|
||||
while len(decode_streams) >= params.num_decode_streams:
|
||||
finished_streams = decode_one_chunk(
|
||||
params, model, sp, decode_streams
|
||||
)
|
||||
for i in sorted(finished_streams, reverse=True):
|
||||
hyp = decode_streams[i].hyp
|
||||
if params.decoding_method == "greedy_search":
|
||||
hyp = hyp[params.context_size :]
|
||||
decode_results.append(
|
||||
(
|
||||
decode_streams[i].ground_truth.split(),
|
||||
sp.decode(hyp).split(),
|
||||
)
|
||||
)
|
||||
del decode_streams[i]
|
||||
|
||||
if num % log_interval == 0:
|
||||
logging.info(f"Cuts processed until now is {num}.")
|
||||
|
||||
# decode final chunks of last sequences
|
||||
while len(decode_streams):
|
||||
finished_streams = decode_one_chunk(params, model, sp, decode_streams)
|
||||
for i in sorted(finished_streams, reverse=True):
|
||||
hyp = decode_streams[i].hyp
|
||||
if params.decoding_method == "greedy_search":
|
||||
hyp = hyp[params.context_size :]
|
||||
decode_results.append(
|
||||
(
|
||||
decode_streams[i].ground_truth.split(),
|
||||
sp.decode(hyp).split(),
|
||||
)
|
||||
)
|
||||
del decode_streams[i]
|
||||
|
||||
key = "greedy_search"
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
key = (
|
||||
f"beam_{params.beam}_"
|
||||
f"max_contexts_{params.max_contexts}_"
|
||||
f"max_states_{params.max_states}"
|
||||
)
|
||||
return {key: decode_results}
|
||||
|
||||
|
||||
def save_results(
|
||||
params: AttributeDict,
|
||||
test_set_name: str,
|
||||
results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
|
||||
):
|
||||
test_set_wers = dict()
|
||||
for key, results in results_dict.items():
|
||||
recog_path = (
|
||||
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
# ref/hyp pairs.
|
||||
errs_filename = (
|
||||
params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results, enable_log=True
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
logging.info("Wrote detailed error stats to {}".format(errs_filename))
|
||||
|
||||
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
|
||||
errs_info = (
|
||||
params.res_dir
|
||||
/ f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_info, "w") as f:
|
||||
print("settings\tWER", file=f)
|
||||
for key, val in test_set_wers:
|
||||
print("{}\t{}".format(key, val), file=f)
|
||||
|
||||
s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
|
||||
note = "\tbest for {}".format(test_set_name)
|
||||
for key, val in test_set_wers:
|
||||
s += "{}\t{}{}\n".format(key, val, note)
|
||||
note = ""
|
||||
logging.info(s)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
LibriSpeechAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
params.res_dir = params.exp_dir / "streaming" / params.decoding_method
|
||||
|
||||
if params.iter > 0:
|
||||
params.suffix = f"iter-{params.iter}-avg-{params.avg}"
|
||||
else:
|
||||
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
|
||||
|
||||
# for streaming
|
||||
params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
|
||||
params.suffix += f"-left-context-{params.left_context}"
|
||||
|
||||
# for fast_beam_search
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
params.suffix += f"-beam-{params.beam}"
|
||||
params.suffix += f"-max-contexts-{params.max_contexts}"
|
||||
params.suffix += f"-max-states-{params.max_states}"
|
||||
|
||||
setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
|
||||
logging.info("Decoding started")
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"Device: {device}")
|
||||
|
||||
sp = spm.SentencePieceProcessor()
|
||||
sp.load(params.bpe_model)
|
||||
|
||||
# <blk> and <unk> is defined in local/train_bpe_model.py
|
||||
params.blank_id = sp.piece_to_id("<blk>")
|
||||
params.unk_id = sp.piece_to_id("<unk>")
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
|
||||
assert (
|
||||
params.causal_convolution
|
||||
), "Decoding in streaming requires causal convolution"
|
||||
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
|
||||
if params.iter > 0:
|
||||
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
||||
: params.avg
|
||||
]
|
||||
if len(filenames) == 0:
|
||||
raise ValueError(
|
||||
f"No checkpoints found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
elif len(filenames) < params.avg:
|
||||
raise ValueError(
|
||||
f"Not enough checkpoints ({len(filenames)}) found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.to(device)
|
||||
model.load_state_dict(average_checkpoints(filenames, device=device))
|
||||
elif params.avg == 1:
|
||||
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
|
||||
else:
|
||||
start = params.epoch - params.avg + 1
|
||||
filenames = []
|
||||
for i in range(start, params.epoch + 1):
|
||||
if start >= 0:
|
||||
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.to(device)
|
||||
model.load_state_dict(average_checkpoints(filenames, device=device))
|
||||
|
||||
model.to(device)
|
||||
model.eval()
|
||||
model.device = device
|
||||
|
||||
decoding_graph = None
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
librispeech = LibriSpeechAsrDataModule(args)
|
||||
|
||||
test_clean_cuts = librispeech.test_clean_cuts()
|
||||
test_other_cuts = librispeech.test_other_cuts()
|
||||
|
||||
test_sets = ["test-clean", "test-other"]
|
||||
test_cuts = [test_clean_cuts, test_other_cuts]
|
||||
|
||||
for test_set, test_cut in zip(test_sets, test_cuts):
|
||||
results_dict = decode_dataset(
|
||||
cuts=test_cut,
|
||||
params=params,
|
||||
model=model,
|
||||
sp=sp,
|
||||
decoding_graph=decoding_graph,
|
||||
)
|
||||
|
||||
save_results(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -330,7 +330,7 @@ def get_params() -> AttributeDict:
|
||||
|
||||
- subsampling_factor: The subsampling factor for the model.
|
||||
|
||||
- attention_dim: Hidden dim for multi-head attention model.
|
||||
- encoder_dim: Hidden dim for multi-head attention model.
|
||||
|
||||
- num_decoder_layers: Number of decoder layer of transformer decoder.
|
||||
|
||||
@ -350,10 +350,11 @@ def get_params() -> AttributeDict:
|
||||
# parameters for conformer
|
||||
"feature_dim": 80,
|
||||
"subsampling_factor": 4,
|
||||
"attention_dim": 512,
|
||||
"encoder_dim": 512,
|
||||
"nhead": 8,
|
||||
"dim_feedforward": 2048,
|
||||
"num_encoder_layers": 12,
|
||||
"cnn_module_kernel": 31,
|
||||
"vgg_frontend": False,
|
||||
# parameters for decoder
|
||||
"embedding_dim": 512,
|
||||
@ -372,10 +373,11 @@ def get_encoder_model(params: AttributeDict) -> nn.Module:
|
||||
num_features=params.feature_dim,
|
||||
output_dim=params.vocab_size,
|
||||
subsampling_factor=params.subsampling_factor,
|
||||
d_model=params.attention_dim,
|
||||
d_model=params.encoder_dim,
|
||||
nhead=params.nhead,
|
||||
dim_feedforward=params.dim_feedforward,
|
||||
num_encoder_layers=params.num_encoder_layers,
|
||||
cnn_module_kernel=params.cnn_module_kernel,
|
||||
vgg_frontend=params.vgg_frontend,
|
||||
dynamic_chunk_training=params.dynamic_chunk_training,
|
||||
short_chunk_size=params.short_chunk_size,
|
||||
@ -862,7 +864,7 @@ def run(rank, world_size, args):
|
||||
|
||||
optimizer = Noam(
|
||||
model.parameters(),
|
||||
model_size=params.attention_dim,
|
||||
model_size=params.encoder_dim,
|
||||
factor=params.lr_factor,
|
||||
warm_step=params.warm_step,
|
||||
)
|
||||
|
||||
@ -19,7 +19,7 @@ import logging
|
||||
import copy
|
||||
import math
|
||||
import warnings
|
||||
from typing import Optional, Tuple
|
||||
from typing import List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from encoder_interface import EncoderInterface
|
||||
@ -104,11 +104,12 @@ class Conformer(EncoderInterface):
|
||||
|
||||
self.encoder_layers = num_encoder_layers
|
||||
self.d_model = d_model
|
||||
self.cnn_module_kernel = cnn_module_kernel
|
||||
self.causal = causal
|
||||
self.dynamic_chunk_training = dynamic_chunk_training
|
||||
self.short_chunk_threshold = short_chunk_threshold
|
||||
self.short_chunk_size = short_chunk_size
|
||||
self.num_left_chunks = num_left_chunks
|
||||
self.causal = causal
|
||||
|
||||
self.encoder_pos = RelPositionalEncoding(d_model, dropout)
|
||||
|
||||
@ -192,11 +193,11 @@ class Conformer(EncoderInterface):
|
||||
x: torch.Tensor,
|
||||
x_lens: torch.Tensor,
|
||||
warmup: float = 1.0,
|
||||
states: Optional[Tensor] = None,
|
||||
states: Optional[List[Tensor]] = None,
|
||||
chunk_size: int = 16,
|
||||
left_context: int = 64,
|
||||
simulate_streaming: bool = False,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""
|
||||
Args:
|
||||
x:
|
||||
@ -210,8 +211,11 @@ class Conformer(EncoderInterface):
|
||||
to turn modules on sequentially.
|
||||
states:
|
||||
The decode states for previous frames which contains the cached data.
|
||||
It has a shape of (2, encoder_layers, left_context, batch, attention_dim),
|
||||
states[0,...] is the attn_cache, states[1,...] is the conv_cache.
|
||||
It has two elements, the first element is the attn_cache which has
|
||||
a shape of (encoder_layers, left_context, batch, attention_dim),
|
||||
the second element is the conv_cache which has a shape of
|
||||
(encoder_layers, cnn_module_kernel-1, batch, conv_dim).
|
||||
Note: If not None, states will be modified in this function.
|
||||
chunk_size:
|
||||
The chunk size for decoding, this will be used to simulate streaming
|
||||
decoding using masking.
|
||||
@ -234,7 +238,11 @@ class Conformer(EncoderInterface):
|
||||
|
||||
# x: [N, T, C]
|
||||
# Caution: We assume the subsampling factor is 4!
|
||||
lengths = ((x_lens - 1) // 2 - 1) // 2
|
||||
|
||||
# lengths = ((x_lens - 1) // 2 - 1) // 2 # issue an warning
|
||||
#
|
||||
# Note: rounding_mode in torch.div() is available only in torch >= 1.8.0
|
||||
lengths = (((x_lens - 1) >> 1) - 1) >> 1
|
||||
|
||||
if not simulate_streaming:
|
||||
assert (
|
||||
@ -242,11 +250,14 @@ class Conformer(EncoderInterface):
|
||||
), "Require cache when sending data in streaming mode"
|
||||
|
||||
assert (
|
||||
states.shape == (2, self.encoder_layers, left_context, x.size(0), self.d_model)
|
||||
), f"""The shape of states MUST be equal to
|
||||
(2, encoder_layers, left_context, batch, d_model) which is
|
||||
{(2, self.encoder_layers, left_context, x.size(0), self.d_model)}
|
||||
given {states.shape}."""
|
||||
len(states) == 2 and
|
||||
states[0].shape == (self.encoder_layers, left_context, x.size(0), self.d_model) and
|
||||
states[1].shape == (self.encoder_layers, self.cnn_module_kernel - 1, x.size(0), self.d_model)
|
||||
), f"""The length of states MUST be equal to 2, and the shape of
|
||||
first element should be {(self.encoder_layers, left_context, x.size(0), self.d_model)},
|
||||
given {states[0].shape}. the shape of second element should be
|
||||
{(self.encoder_layers, self.cnn_module_kernel - 1, x.size(0), self.d_model)},
|
||||
given {states[1].shape}."""
|
||||
|
||||
src_key_padding_mask = make_pad_mask(lengths + left_context)
|
||||
|
||||
@ -254,7 +265,7 @@ class Conformer(EncoderInterface):
|
||||
embed, pos_enc = self.encoder_pos(embed, left_context)
|
||||
embed = embed.permute(1, 0, 2) # (B, T, F) -> (T, B, F)
|
||||
|
||||
x, states = self.encoder(
|
||||
x = self.encoder(
|
||||
embed,
|
||||
pos_enc,
|
||||
src_key_padding_mask=src_key_padding_mask,
|
||||
@ -284,7 +295,7 @@ class Conformer(EncoderInterface):
|
||||
num_left_chunks=num_left_chunks,
|
||||
device=x.device
|
||||
)
|
||||
x, _ = self.encoder(
|
||||
x = self.encoder(
|
||||
x,
|
||||
pos_emb,
|
||||
mask=mask,
|
||||
@ -294,7 +305,7 @@ class Conformer(EncoderInterface):
|
||||
|
||||
x = x.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
|
||||
|
||||
return x, lengths, states
|
||||
return x, lengths
|
||||
|
||||
|
||||
class ConformerEncoderLayer(nn.Module):
|
||||
@ -376,9 +387,9 @@ class ConformerEncoderLayer(nn.Module):
|
||||
src_mask: Optional[Tensor] = None,
|
||||
src_key_padding_mask: Optional[Tensor] = None,
|
||||
warmup: float = 1.0,
|
||||
states: Optional[Tensor] = None,
|
||||
states: Optional[List[Tensor]] = None,
|
||||
left_context: int = 0,
|
||||
) -> Tuple[Tensor, Tensor]:
|
||||
) -> Tuple[Tensor]:
|
||||
"""
|
||||
Pass the input through the encoder layer.
|
||||
|
||||
@ -391,8 +402,11 @@ class ConformerEncoderLayer(nn.Module):
|
||||
bypass layers more frequently.
|
||||
states:
|
||||
The decode states for previous frames which contains the cached data.
|
||||
It has a shape of (2, encoder_layers, left_context, batch, attention_dim),
|
||||
states[0,...] is the attn_cache, states[1,...] is the conv_cache.
|
||||
It has two elements, the first element is the attn_cache which has
|
||||
a shape of (left_context, batch, attention_dim),
|
||||
the second element is the conv_cache which has a shape of
|
||||
(cnn_module_kernel-1, batch, conv_dim).
|
||||
Note: If not None, states will be modified in this function.
|
||||
left_context: left context (in frames) used during streaming decoding.
|
||||
this is used only in real streaming decoding, in other circumstances,
|
||||
it MUST be 0.
|
||||
@ -425,9 +439,9 @@ class ConformerEncoderLayer(nn.Module):
|
||||
val = src
|
||||
if not self.training and states is not None:
|
||||
# src: [chunk_size, N, F] e.g. [8, 41, 512]
|
||||
key = torch.cat([states[0, ...], src], dim=0)
|
||||
key = torch.cat([states[0], src], dim=0)
|
||||
val = key
|
||||
states[0, ...] = key[-left_context:, ...]
|
||||
states[0] = key[-left_context:, ...]
|
||||
else:
|
||||
assert left_context == 0
|
||||
|
||||
@ -445,15 +459,12 @@ class ConformerEncoderLayer(nn.Module):
|
||||
src = src + self.dropout(src_att)
|
||||
|
||||
# convolution module
|
||||
residual = src
|
||||
if not self.training and states is not None:
|
||||
src = torch.cat([states[1, ...], src], dim=0)
|
||||
states[1, ...] = src[-left_context:, ...]
|
||||
|
||||
conv, conv_cache = self.conv_module(src, states[1])
|
||||
states[1] = conv_cache
|
||||
else:
|
||||
conv = self.conv_module(src)
|
||||
conv = conv[-residual.size(0) :, :, :] # noqa: E203
|
||||
|
||||
src = residual + self.dropout(conv)
|
||||
src = src + self.dropout(conv)
|
||||
|
||||
# feed forward module
|
||||
src = src + self.dropout(self.feed_forward(src))
|
||||
@ -463,7 +474,7 @@ class ConformerEncoderLayer(nn.Module):
|
||||
if alpha != 1.0:
|
||||
src = alpha * src + (1 - alpha) * src_orig
|
||||
|
||||
return src, states
|
||||
return src
|
||||
|
||||
|
||||
class ConformerEncoder(nn.Module):
|
||||
@ -495,9 +506,9 @@ class ConformerEncoder(nn.Module):
|
||||
mask: Optional[Tensor] = None,
|
||||
src_key_padding_mask: Optional[Tensor] = None,
|
||||
warmup: float = 1.0,
|
||||
states: Optional[Tensor] = None,
|
||||
states: Optional[List[Tensor]] = None,
|
||||
left_context: int = 0,
|
||||
) -> Tuple[Tensor, Tensor]:
|
||||
) -> Tensor:
|
||||
r"""Pass the input through the encoder layers in turn.
|
||||
|
||||
Args:
|
||||
@ -509,8 +520,11 @@ class ConformerEncoder(nn.Module):
|
||||
bypass layers more frequently.
|
||||
states:
|
||||
The decode states for previous frames which contains the cached data.
|
||||
It has a shape of (2, encoder_layers, left_context, batch, attention_dim),
|
||||
states[0,...] is the attn_cache, states[1,...] is the conv_cache.
|
||||
It has two elements, the first element is the attn_cache which has
|
||||
a shape of (encoder_layers, left_context, batch, attention_dim),
|
||||
the second element is the conv_cache which has a shape of
|
||||
(encoder_layers, cnn_module_kernel-1, batch, conv_dim).
|
||||
Note: If not None, states will be modified in this function.
|
||||
left_context: left context (in frames) used during streaming decoding.
|
||||
this is used only in real streaming decoding, in other circumstances,
|
||||
it MUST be 0.
|
||||
@ -532,19 +546,21 @@ class ConformerEncoder(nn.Module):
|
||||
assert left_context >= 0
|
||||
|
||||
for layer_index, mod in enumerate(self.layers):
|
||||
output, cache = mod(
|
||||
cache = None if states is None else [states[0][layer_index], states[1][layer_index]]
|
||||
output = mod(
|
||||
output,
|
||||
pos_emb,
|
||||
src_mask=mask,
|
||||
src_key_padding_mask=src_key_padding_mask,
|
||||
warmup=warmup,
|
||||
states=None if states is None else states[:, layer_index, ...],
|
||||
states=cache,
|
||||
left_context=left_context,
|
||||
)
|
||||
if states is not None:
|
||||
states[:, layer_index, ...] = cache
|
||||
states[0][layer_index] = cache[0]
|
||||
states[1][layer_index] = cache[1]
|
||||
|
||||
return output, states
|
||||
return output
|
||||
|
||||
|
||||
class RelPositionalEncoding(torch.nn.Module):
|
||||
@ -1180,14 +1196,23 @@ class ConvolutionModule(nn.Module):
|
||||
initial_scale=0.25,
|
||||
)
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
cache: Optional[Tensor] = None
|
||||
) -> Union[Tensor, Tuple[Tensor, Tensor]]:
|
||||
"""Compute convolution module.
|
||||
|
||||
Args:
|
||||
x: Input tensor (#time, batch, channels).
|
||||
cache: The cache of depthwise_conv, only used in real streaming
|
||||
decoding.
|
||||
|
||||
Returns:
|
||||
Tensor: Output tensor (#time, batch, channels).
|
||||
If cache is None return the output tensor (#time, batch, channels).
|
||||
If cache is not None, return a tuple of Tensor, the first one is
|
||||
the output tensor (#time, batch, channels), the second one is the
|
||||
new cache for next chunk (#kernel_size - 1, batch, channels).
|
||||
|
||||
"""
|
||||
# exchange the temporal dimension and the feature dimension
|
||||
@ -1201,9 +1226,15 @@ class ConvolutionModule(nn.Module):
|
||||
|
||||
# 1D Depthwise Conv
|
||||
if self.causal and self.lorder > 0:
|
||||
if cache is None:
|
||||
# Make depthwise_conv causal by
|
||||
# manualy padding self.lorder zeros to the left
|
||||
x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0)
|
||||
else:
|
||||
assert not self.training, "Cache should be None in training time"
|
||||
assert cache.size(0) == self.lorder
|
||||
x = torch.cat([cache.permute(1, 2, 0), x], dim=2)
|
||||
cache = x.permute(2, 0, 1)[-self.lorder:,...]
|
||||
x = self.depthwise_conv(x)
|
||||
|
||||
x = self.deriv_balancer2(x)
|
||||
@ -1211,7 +1242,7 @@ class ConvolutionModule(nn.Module):
|
||||
|
||||
x = self.pointwise_conv2(x) # (batch, channel, time)
|
||||
|
||||
return x.permute(2, 0, 1)
|
||||
return x.permute(2, 0, 1) if cache is None else (x.permute(2, 0, 1), cache)
|
||||
|
||||
|
||||
class Conv2dSubsampling(nn.Module):
|
||||
|
||||
@ -0,0 +1 @@
|
||||
../pruned_transducer_stateless/decode_stream.py
|
||||
705
egs/librispeech/ASR/pruned_transducer_stateless2/streaming_decode.py
Executable file
705
egs/librispeech/ASR/pruned_transducer_stateless2/streaming_decode.py
Executable file
@ -0,0 +1,705 @@
|
||||
#!/usr/bin/env python3
|
||||
# Copyright 2022 Xiaomi Corporation (Authors: Wei Kang, Fangjun Kuang)
|
||||
#
|
||||
# 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.
|
||||
|
||||
"""
|
||||
Usage:
|
||||
./pruned_transducer_stateless2/streaming_decode.py \
|
||||
--epoch 28 \
|
||||
--avg 15 \
|
||||
--exp-dir ./pruned_transducer_stateless2/exp \
|
||||
--decoding_method greedy_search \
|
||||
--num-decode-streams 200
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import math
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
import k2
|
||||
import sentencepiece as spm
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from asr_datamodule import LibriSpeechAsrDataModule
|
||||
from decode_stream import DecodeStream
|
||||
from icefall.checkpoint import (
|
||||
average_checkpoints,
|
||||
find_checkpoints,
|
||||
load_checkpoint,
|
||||
)
|
||||
from icefall.utils import (
|
||||
AttributeDict,
|
||||
setup_logger,
|
||||
store_transcripts,
|
||||
str2bool,
|
||||
write_error_stats,
|
||||
)
|
||||
from icefall.decode import Nbest, one_best_decoding
|
||||
from icefall.utils import get_texts
|
||||
from kaldifeat import FbankOptions, Fbank
|
||||
from lhotse import CutSet
|
||||
from train import get_params, get_transducer_model
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
|
||||
LOG_EPS = math.log(1e-10)
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = argparse.ArgumentParser(
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--epoch",
|
||||
type=int,
|
||||
default=28,
|
||||
help="""It specifies the checkpoint to use for decoding.
|
||||
Note: Epoch counts from 0.
|
||||
You can specify --avg to use more checkpoints for model averaging.""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--iter",
|
||||
type=int,
|
||||
default=0,
|
||||
help="""If positive, --epoch is ignored and it
|
||||
will use the checkpoint exp_dir/checkpoint-iter.pt.
|
||||
You can specify --avg to use more checkpoints for model averaging.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--avg",
|
||||
type=int,
|
||||
default=15,
|
||||
help="Number of checkpoints to average. Automatically select "
|
||||
"consecutive checkpoints before the checkpoint specified by "
|
||||
"'--epoch' and '--iter'",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--exp-dir",
|
||||
type=str,
|
||||
default="pruned_transducer_stateless2/exp",
|
||||
help="The experiment dir",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--bpe-model",
|
||||
type=str,
|
||||
default="data/lang_bpe_500/bpe.model",
|
||||
help="Path to the BPE model",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--decoding-method",
|
||||
type=str,
|
||||
default="greedy_search",
|
||||
help="""Support only greedy_search and fast_beam_search now.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--beam",
|
||||
type=float,
|
||||
default=4,
|
||||
help="""A floating point value to calculate the cutoff score during beam
|
||||
search (i.e., `cutoff = max-score - beam`), which is the same as the
|
||||
`beam` in Kaldi.
|
||||
Used only when --decoding-method is fast_beam_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-contexts",
|
||||
type=int,
|
||||
default=4,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--max-states",
|
||||
type=int,
|
||||
default=32,
|
||||
help="""Used only when --decoding-method is
|
||||
fast_beam_search""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--context-size",
|
||||
type=int,
|
||||
default=2,
|
||||
help="The context size in the decoder. 1 means bigram; "
|
||||
"2 means tri-gram",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--dynamic-chunk-training",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="""Whether to use dynamic_chunk_training, if you want a streaming
|
||||
model, this requires to be True.
|
||||
Note: not needed for decoding, adding it here to construct transducer model,
|
||||
as we reuse the code in train.py.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--short-chunk-size",
|
||||
type=int,
|
||||
default=25,
|
||||
help="""Chunk length of dynamic training, the chunk size would be either
|
||||
max sequence length of current batch or uniformly sampled from (1, short_chunk_size).
|
||||
Note: not needed for decoding, adding it here to construct transducer model,
|
||||
as we reuse the code in train.py.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-left-chunks",
|
||||
type=int,
|
||||
default=4,
|
||||
help="""How many left context can be seen in chunks when calculating attention.
|
||||
Note: not needed for decoding, adding it here to construct transducer model,
|
||||
as we reuse the code in train.py.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--causal-convolution",
|
||||
type=str2bool,
|
||||
default=True,
|
||||
help="""Whether to use causal convolution, this requires to be True when
|
||||
using dynamic_chunk_training.
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--decode-chunk-size",
|
||||
type=int,
|
||||
default=16,
|
||||
help="The chunk size for decoding (in frames after subsampling)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--left-context",
|
||||
type=int,
|
||||
default=64,
|
||||
help="left context can be seen during decoding (in frames after subsampling)",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--num-decode-streams",
|
||||
type=int,
|
||||
default=2000,
|
||||
help="The number of streams that can be decoded parallel.",
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def greedy_search(
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
streams: List[DecodeStream],
|
||||
) -> List[List[int]]:
|
||||
|
||||
assert len(streams) == encoder_out.size(0)
|
||||
assert encoder_out.ndim == 3
|
||||
|
||||
blank_id = model.decoder.blank_id
|
||||
context_size = model.decoder.context_size
|
||||
device = model.device
|
||||
T = encoder_out.size(1)
|
||||
|
||||
decoder_input = torch.tensor(
|
||||
[stream.hyp[-context_size:] for stream in streams],
|
||||
device=device,
|
||||
dtype=torch.int64,
|
||||
)
|
||||
# decoder_out is of shape (N, decoder_out_dim)
|
||||
decoder_out = model.decoder(decoder_input, need_pad=False)
|
||||
decoder_out = model.joiner.decoder_proj(decoder_out)
|
||||
# logging.info(f"decoder_out shape : {decoder_out.shape}")
|
||||
|
||||
for t in range(T):
|
||||
# current_encoder_out's shape: (batch_size, 1, encoder_out_dim)
|
||||
current_encoder_out = encoder_out[:, t : t + 1, :]
|
||||
|
||||
logits = model.joiner(
|
||||
current_encoder_out.unsqueeze(2),
|
||||
decoder_out.unsqueeze(1),
|
||||
project_input=False,
|
||||
)
|
||||
# logits'shape (batch_size, vocab_size)
|
||||
logits = logits.squeeze(1).squeeze(1)
|
||||
|
||||
assert logits.ndim == 2, logits.shape
|
||||
y = logits.argmax(dim=1).tolist()
|
||||
emitted = False
|
||||
for i, v in enumerate(y):
|
||||
if v != blank_id:
|
||||
streams[i].hyp.append(v)
|
||||
emitted = True
|
||||
if emitted:
|
||||
# update decoder output
|
||||
decoder_input = torch.tensor(
|
||||
[stream.hyp[-context_size:] for stream in streams],
|
||||
device=device,
|
||||
dtype=torch.int64,
|
||||
)
|
||||
decoder_out = model.decoder(
|
||||
decoder_input,
|
||||
need_pad=False,
|
||||
)
|
||||
decoder_out = model.joiner.decoder_proj(decoder_out)
|
||||
|
||||
hyp_tokens = []
|
||||
for stream in streams:
|
||||
hyp_tokens.append(stream.hyp)
|
||||
return hyp_tokens
|
||||
|
||||
|
||||
def fast_beam_search(
|
||||
model: nn.Module,
|
||||
encoder_out: torch.Tensor,
|
||||
processed_lens: torch.Tensor,
|
||||
decoding_streams: k2.RnntDecodingStreams,
|
||||
) -> List[List[int]]:
|
||||
|
||||
B, T, C = encoder_out.shape
|
||||
for t in range(T):
|
||||
# shape is a RaggedShape of shape (B, context)
|
||||
# contexts is a Tensor of shape (shape.NumElements(), context_size)
|
||||
shape, contexts = decoding_streams.get_contexts()
|
||||
# `nn.Embedding()` in torch below v1.7.1 supports only torch.int64
|
||||
contexts = contexts.to(torch.int64)
|
||||
# decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim)
|
||||
decoder_out = model.decoder(contexts, need_pad=False)
|
||||
decoder_out = model.joiner.decoder_proj(decoder_out)
|
||||
# current_encoder_out is of shape
|
||||
# (shape.NumElements(), 1, joiner_dim)
|
||||
# fmt: off
|
||||
current_encoder_out = torch.index_select(
|
||||
encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64)
|
||||
)
|
||||
# fmt: on
|
||||
logits = model.joiner(
|
||||
current_encoder_out.unsqueeze(2),
|
||||
decoder_out.unsqueeze(1),
|
||||
project_input=False,
|
||||
)
|
||||
logits = logits.squeeze(1).squeeze(1)
|
||||
log_probs = logits.log_softmax(dim=-1)
|
||||
decoding_streams.advance(log_probs)
|
||||
|
||||
decoding_streams.terminate_and_flush_to_streams()
|
||||
|
||||
lattice = decoding_streams.format_output(processed_lens.tolist())
|
||||
best_path = one_best_decoding(lattice)
|
||||
hyp_tokens = get_texts(best_path)
|
||||
return hyp_tokens
|
||||
|
||||
|
||||
def decode_one_chunk(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
sp: spm.SentencePieceProcessor,
|
||||
decode_streams: List[DecodeStream],
|
||||
) -> List[int]:
|
||||
"""Decode one chunk frames of features for each decode_streams and
|
||||
return the indexes of finished streams in a List.
|
||||
|
||||
Args:
|
||||
params:
|
||||
It's the return value of :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
sp:
|
||||
The BPE model.
|
||||
decode_streams:
|
||||
A List of DecodeStream, each belonging to a utterance.
|
||||
Returns:
|
||||
Return a List containing which DecodeStreams are finished.
|
||||
"""
|
||||
device = model.device
|
||||
|
||||
features = []
|
||||
feature_lens = []
|
||||
states = []
|
||||
|
||||
rnnt_stream_list = []
|
||||
processed_feature_lens = []
|
||||
|
||||
for stream in decode_streams:
|
||||
feat, feat_len = stream.get_feature_frames(
|
||||
params.decode_chunk_size * params.subsampling_factor
|
||||
)
|
||||
features.append(feat)
|
||||
feature_lens.append(feat_len)
|
||||
states.append(stream.states)
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
processed_feature_lens.append(stream.feature_len)
|
||||
rnnt_stream_list.append(stream.rnnt_decoding_stream)
|
||||
|
||||
feature_lens = torch.tensor(feature_lens, device=device)
|
||||
features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS)
|
||||
|
||||
# if T is less than 7 there will be an error in time reduction layer,
|
||||
# because we subsample features with ((x_len - 1) // 2 - 1) // 2
|
||||
if features.size(1) < 7:
|
||||
feature_lens += 7 - features.size(1)
|
||||
features = torch.cat(
|
||||
[
|
||||
features,
|
||||
torch.tensor(
|
||||
LOG_EPS, dtype=features.dtype, device=device
|
||||
).expand(
|
||||
features.size(0), 7 - features.size(1), features.size(2)
|
||||
),
|
||||
],
|
||||
dim=1,
|
||||
)
|
||||
|
||||
states = [
|
||||
torch.stack([x[0] for x in states], dim=2),
|
||||
torch.stack([x[1] for x in states], dim=2),
|
||||
]
|
||||
|
||||
# Note: states will be modified in streaming_forward.
|
||||
encoder_out, encoder_out_lens = model.encoder.streaming_forward(
|
||||
x=features,
|
||||
x_lens=feature_lens,
|
||||
states=states,
|
||||
left_context=params.left_context,
|
||||
)
|
||||
encoder_out = model.joiner.encoder_proj(encoder_out)
|
||||
|
||||
if params.decoding_method == "greedy_search":
|
||||
hyp_tokens = greedy_search(model, encoder_out, decode_streams)
|
||||
elif params.decoding_method == "fast_beam_search":
|
||||
config = k2.RnntDecodingConfig(
|
||||
vocab_size=params.vocab_size,
|
||||
decoder_history_len=params.context_size,
|
||||
beam=params.beam,
|
||||
max_contexts=params.max_contexts,
|
||||
max_states=params.max_states,
|
||||
)
|
||||
processed_feature_lens = torch.tensor(
|
||||
processed_feature_lens, device=device
|
||||
)
|
||||
decoding_streams = k2.RnntDecodingStreams(rnnt_stream_list, config)
|
||||
processed_lens = processed_feature_lens + encoder_out_lens
|
||||
hyp_tokens = fast_beam_search(
|
||||
model, encoder_out, processed_lens, decoding_streams
|
||||
)
|
||||
else:
|
||||
assert False
|
||||
|
||||
states = [torch.unbind(states[0], dim=2), torch.unbind(states[1], dim=2)]
|
||||
|
||||
finished_streams = []
|
||||
for i in range(len(decode_streams)):
|
||||
decode_streams[i].states = [states[0][i], states[1][i]]
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
decode_streams[i].feature_len += encoder_out_lens[i]
|
||||
decode_streams[i].hyp = hyp_tokens[i]
|
||||
if decode_streams[i].done:
|
||||
finished_streams.append(i)
|
||||
|
||||
return finished_streams
|
||||
|
||||
|
||||
def decode_dataset(
|
||||
cuts: CutSet,
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
sp: spm.SentencePieceProcessor,
|
||||
decoding_graph: Optional[k2.Fsa] = None,
|
||||
) -> Dict[str, List[Tuple[List[str], List[str]]]]:
|
||||
"""Decode dataset.
|
||||
|
||||
Args:
|
||||
cuts:
|
||||
Lhotse Cutset containing the dataset to decode.
|
||||
params:
|
||||
It is returned by :func:`get_params`.
|
||||
model:
|
||||
The neural model.
|
||||
sp:
|
||||
The BPE model.
|
||||
decoding_graph:
|
||||
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
|
||||
only when --decoding_method is fast_beam_search.
|
||||
Returns:
|
||||
Return a dict, whose key may be "greedy_search" if greedy search
|
||||
is used, or it may be "beam_7" if beam size of 7 is used.
|
||||
Its value is a list of tuples. Each tuple contains two elements:
|
||||
The first is the reference transcript, and the second is the
|
||||
predicted result.
|
||||
"""
|
||||
device = model.device
|
||||
|
||||
opts = FbankOptions()
|
||||
opts.device = device
|
||||
opts.frame_opts.dither = 0
|
||||
opts.frame_opts.snip_edges = False
|
||||
opts.frame_opts.samp_freq = 16000
|
||||
opts.mel_opts.num_bins = 80
|
||||
|
||||
log_interval = 300
|
||||
|
||||
decode_results = []
|
||||
# Contain decode streams currently running.
|
||||
decode_streams = []
|
||||
for num, cut in enumerate(cuts):
|
||||
# each utterance has a DecodeStream.
|
||||
decode_stream = DecodeStream(
|
||||
params=params,
|
||||
decoding_graph=decoding_graph,
|
||||
device=device,
|
||||
)
|
||||
|
||||
audio: np.ndarray = cut.load_audio()
|
||||
# audio.shape: (1, num_samples)
|
||||
assert len(audio.shape) == 2
|
||||
assert audio.shape[0] == 1, "Should be single channel"
|
||||
assert audio.dtype == np.float32, audio.dtype
|
||||
|
||||
# The trained model is using normalized samples
|
||||
assert audio.max() <= 1, "Should be normalized to [-1, 1])"
|
||||
|
||||
samples = torch.from_numpy(audio).squeeze(0)
|
||||
|
||||
fbank = Fbank(opts)
|
||||
decode_stream.set_features(fbank(samples.to(device)))
|
||||
decode_stream.ground_truth = cut.supervisions[0].text
|
||||
|
||||
decode_streams.append(decode_stream)
|
||||
|
||||
while len(decode_streams) >= params.num_decode_streams:
|
||||
finished_streams = decode_one_chunk(
|
||||
params, model, sp, decode_streams
|
||||
)
|
||||
for i in sorted(finished_streams, reverse=True):
|
||||
hyp = decode_streams[i].hyp
|
||||
if params.decoding_method == "greedy_search":
|
||||
hyp = hyp[params.context_size :]
|
||||
decode_results.append(
|
||||
(
|
||||
decode_streams[i].ground_truth.split(),
|
||||
sp.decode(hyp).split(),
|
||||
)
|
||||
)
|
||||
del decode_streams[i]
|
||||
|
||||
if num % log_interval == 0:
|
||||
logging.info(f"Cuts processed until now is {num}.")
|
||||
|
||||
# decode final chunks of last sequences
|
||||
while len(decode_streams):
|
||||
finished_streams = decode_one_chunk(params, model, sp, decode_streams)
|
||||
for i in sorted(finished_streams, reverse=True):
|
||||
hyp = decode_streams[i].hyp
|
||||
if params.decoding_method == "greedy_search":
|
||||
hyp = hyp[params.context_size :]
|
||||
decode_results.append(
|
||||
(
|
||||
decode_streams[i].ground_truth.split(),
|
||||
sp.decode(hyp).split(),
|
||||
)
|
||||
)
|
||||
del decode_streams[i]
|
||||
|
||||
key = "greedy_search"
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
key = (
|
||||
f"beam_{params.beam}_"
|
||||
f"max_contexts_{params.max_contexts}_"
|
||||
f"max_states_{params.max_states}"
|
||||
)
|
||||
return {key: decode_results}
|
||||
|
||||
|
||||
def save_results(
|
||||
params: AttributeDict,
|
||||
test_set_name: str,
|
||||
results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
|
||||
):
|
||||
test_set_wers = dict()
|
||||
for key, results in results_dict.items():
|
||||
recog_path = (
|
||||
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
store_transcripts(filename=recog_path, texts=results)
|
||||
logging.info(f"The transcripts are stored in {recog_path}")
|
||||
|
||||
# The following prints out WERs, per-word error statistics and aligned
|
||||
# ref/hyp pairs.
|
||||
errs_filename = (
|
||||
params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_filename, "w") as f:
|
||||
wer = write_error_stats(
|
||||
f, f"{test_set_name}-{key}", results, enable_log=True
|
||||
)
|
||||
test_set_wers[key] = wer
|
||||
|
||||
logging.info("Wrote detailed error stats to {}".format(errs_filename))
|
||||
|
||||
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
|
||||
errs_info = (
|
||||
params.res_dir
|
||||
/ f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
|
||||
)
|
||||
with open(errs_info, "w") as f:
|
||||
print("settings\tWER", file=f)
|
||||
for key, val in test_set_wers:
|
||||
print("{}\t{}".format(key, val), file=f)
|
||||
|
||||
s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
|
||||
note = "\tbest for {}".format(test_set_name)
|
||||
for key, val in test_set_wers:
|
||||
s += "{}\t{}{}\n".format(key, val, note)
|
||||
note = ""
|
||||
logging.info(s)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
LibriSpeechAsrDataModule.add_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
args.exp_dir = Path(args.exp_dir)
|
||||
|
||||
params = get_params()
|
||||
params.update(vars(args))
|
||||
|
||||
params.res_dir = params.exp_dir / "streaming" / params.decoding_method
|
||||
|
||||
if params.iter > 0:
|
||||
params.suffix = f"iter-{params.iter}-avg-{params.avg}"
|
||||
else:
|
||||
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
|
||||
|
||||
# for streaming
|
||||
params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
|
||||
params.suffix += f"-left-context-{params.left_context}"
|
||||
|
||||
# for fast_beam_search
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
params.suffix += f"-beam-{params.beam}"
|
||||
params.suffix += f"-max-contexts-{params.max_contexts}"
|
||||
params.suffix += f"-max-states-{params.max_states}"
|
||||
|
||||
setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
|
||||
logging.info("Decoding started")
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"Device: {device}")
|
||||
|
||||
sp = spm.SentencePieceProcessor()
|
||||
sp.load(params.bpe_model)
|
||||
|
||||
# <blk> and <unk> is defined in local/train_bpe_model.py
|
||||
params.blank_id = sp.piece_to_id("<blk>")
|
||||
params.unk_id = sp.piece_to_id("<unk>")
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
|
||||
assert (
|
||||
params.causal_convolution
|
||||
), "Decoding in streaming requires causal convolution"
|
||||
|
||||
logging.info(params)
|
||||
|
||||
logging.info("About to create model")
|
||||
model = get_transducer_model(params)
|
||||
|
||||
if params.iter > 0:
|
||||
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
|
||||
: params.avg
|
||||
]
|
||||
if len(filenames) == 0:
|
||||
raise ValueError(
|
||||
f"No checkpoints found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
elif len(filenames) < params.avg:
|
||||
raise ValueError(
|
||||
f"Not enough checkpoints ({len(filenames)}) found for"
|
||||
f" --iter {params.iter}, --avg {params.avg}"
|
||||
)
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.to(device)
|
||||
model.load_state_dict(average_checkpoints(filenames, device=device))
|
||||
elif params.avg == 1:
|
||||
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
|
||||
else:
|
||||
start = params.epoch - params.avg + 1
|
||||
filenames = []
|
||||
for i in range(start, params.epoch + 1):
|
||||
if start >= 0:
|
||||
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.to(device)
|
||||
model.load_state_dict(average_checkpoints(filenames, device=device))
|
||||
|
||||
model.to(device)
|
||||
model.eval()
|
||||
model.device = device
|
||||
|
||||
decoding_graph = None
|
||||
if params.decoding_method == "fast_beam_search":
|
||||
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
librispeech = LibriSpeechAsrDataModule(args)
|
||||
|
||||
test_clean_cuts = librispeech.test_clean_cuts()
|
||||
test_other_cuts = librispeech.test_other_cuts()
|
||||
|
||||
test_sets = ["test-clean", "test-other"]
|
||||
test_cuts = [test_clean_cuts, test_other_cuts]
|
||||
|
||||
for test_set, test_cut in zip(test_sets, test_cuts):
|
||||
results_dict = decode_dataset(
|
||||
cuts=test_cut,
|
||||
params=params,
|
||||
model=model,
|
||||
sp=sp,
|
||||
decoding_graph=decoding_graph,
|
||||
)
|
||||
|
||||
save_results(
|
||||
params=params,
|
||||
test_set_name=test_set,
|
||||
results_dict=results_dict,
|
||||
)
|
||||
|
||||
logging.info("Done!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -393,6 +393,7 @@ def get_params() -> AttributeDict:
|
||||
"nhead": 8,
|
||||
"dim_feedforward": 2048,
|
||||
"num_encoder_layers": 12,
|
||||
"cnn_module_kernel": 31,
|
||||
# parameters for decoder
|
||||
"decoder_dim": 512,
|
||||
# parameters for joiner
|
||||
@ -415,6 +416,7 @@ def get_encoder_model(params: AttributeDict) -> nn.Module:
|
||||
nhead=params.nhead,
|
||||
dim_feedforward=params.dim_feedforward,
|
||||
num_encoder_layers=params.num_encoder_layers,
|
||||
cnn_module_kernel=params.cnn_module_kernel,
|
||||
dynamic_chunk_training=params.dynamic_chunk_training,
|
||||
short_chunk_size=params.short_chunk_size,
|
||||
num_left_chunks=params.num_left_chunks,
|
||||
|
||||
@ -18,7 +18,7 @@
|
||||
import copy
|
||||
import math
|
||||
import warnings
|
||||
from typing import Optional, Tuple
|
||||
from typing import List, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
from torch import Tensor, nn
|
||||
@ -97,11 +97,13 @@ class Conformer(Transformer):
|
||||
|
||||
self.encoder_layers = num_encoder_layers
|
||||
self.d_model = d_model
|
||||
self.cnn_module_kernel = cnn_module_kernel
|
||||
self.causal = causal
|
||||
|
||||
self.dynamic_chunk_training = dynamic_chunk_training
|
||||
self.short_chunk_threshold = short_chunk_threshold
|
||||
self.short_chunk_size = short_chunk_size
|
||||
self.num_left_chunks = num_left_chunks
|
||||
self.causal = causal
|
||||
|
||||
self.encoder_pos = RelPositionalEncoding(d_model, dropout)
|
||||
|
||||
@ -188,11 +190,11 @@ class Conformer(Transformer):
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
x_lens: torch.Tensor,
|
||||
states: Optional[torch.Tensor] = None,
|
||||
states: Optional[List[torch.Tensor]] = None,
|
||||
chunk_size: int = 16,
|
||||
left_context: int = 64,
|
||||
simulate_streaming: bool = False,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""
|
||||
Args:
|
||||
x:
|
||||
@ -202,8 +204,11 @@ class Conformer(Transformer):
|
||||
`x` before padding.
|
||||
states:
|
||||
The decode states for previous frames which contains the cached data.
|
||||
It has a shape of (2, encoder_layers, left_context, batch, attention_dim),
|
||||
states[0,...] is the attn_cache, states[1,...] is the conv_cache.
|
||||
It has two elements, the first element is the attn_cache which has
|
||||
a shape of (encoder_layers, left_context, batch, attention_dim),
|
||||
the second element is the conv_cache which has a shape of
|
||||
(encoder_layers, cnn_module_kernel-1, batch, conv_dim).
|
||||
Note: If not None, states will be modified in this function.
|
||||
chunk_size:
|
||||
The chunk size for decoding, this will be used to simulate streaming
|
||||
decoding using masking.
|
||||
@ -226,7 +231,11 @@ class Conformer(Transformer):
|
||||
|
||||
# x: [N, T, C]
|
||||
# Caution: We assume the subsampling factor is 4!
|
||||
lengths = ((x_lens - 1) // 2 - 1) // 2
|
||||
|
||||
# lengths = ((x_lens - 1) // 2 - 1) // 2 # issue an warning
|
||||
#
|
||||
# Note: rounding_mode in torch.div() is available only in torch >= 1.8.0
|
||||
lengths = (((x_lens - 1) >> 1) - 1) >> 1
|
||||
|
||||
if not simulate_streaming:
|
||||
assert (
|
||||
@ -234,11 +243,14 @@ class Conformer(Transformer):
|
||||
), "Require cache when sending data in streaming mode"
|
||||
|
||||
assert (
|
||||
states.shape == (2, self.encoder_layers, left_context, x.size(0), self.d_model)
|
||||
), f"""The shape of states MUST be equal to
|
||||
(2, encoder_layers, left_context, batch, d_model) which is
|
||||
{(2, self.encoder_layers, left_context, x.size(0), self.d_model)}
|
||||
given {states.shape}."""
|
||||
len(states) == 2 and
|
||||
states[0].shape == (self.encoder_layers, left_context, x.size(0), self.d_model) and
|
||||
states[1].shape == (self.encoder_layers, self.cnn_module_kernel - 1, x.size(0), self.d_model)
|
||||
), f"""The length of states MUST be equal to 2, and the shape of
|
||||
first element should be {(self.encoder_layers, left_context, x.size(0), self.d_model)},
|
||||
given {states[0].shape}. the shape of second element should be
|
||||
{(self.encoder_layers, self.cnn_module_kernel - 1, x.size(0), self.d_model)},
|
||||
given {states[1].shape}."""
|
||||
|
||||
src_key_padding_mask = make_pad_mask(lengths + left_context)
|
||||
|
||||
@ -246,7 +258,7 @@ class Conformer(Transformer):
|
||||
embed, pos_enc = self.encoder_pos(embed, left_context)
|
||||
embed = embed.permute(1, 0, 2) # (B, T, F) -> (T, B, F)
|
||||
|
||||
x, states = self.encoder(
|
||||
x = self.encoder(
|
||||
embed,
|
||||
pos_enc,
|
||||
src_key_padding_mask=src_key_padding_mask,
|
||||
@ -275,7 +287,7 @@ class Conformer(Transformer):
|
||||
num_left_chunks=num_left_chunks,
|
||||
device=x.device
|
||||
)
|
||||
x, _ = self.encoder(
|
||||
x = self.encoder(
|
||||
x,
|
||||
pos_emb,
|
||||
mask=mask,
|
||||
@ -288,7 +300,7 @@ class Conformer(Transformer):
|
||||
logits = self.encoder_output_layer(x)
|
||||
logits = logits.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
|
||||
|
||||
return logits, lengths, states
|
||||
return logits, lengths
|
||||
|
||||
|
||||
class ConformerEncoderLayer(nn.Module):
|
||||
@ -369,9 +381,9 @@ class ConformerEncoderLayer(nn.Module):
|
||||
pos_emb: Tensor,
|
||||
src_mask: Optional[Tensor] = None,
|
||||
src_key_padding_mask: Optional[Tensor] = None,
|
||||
states: Optional[Tensor] = None,
|
||||
states: Optional[List[Tensor]] = None,
|
||||
left_context: int = 0,
|
||||
) -> Tuple[Tensor, Tensor]:
|
||||
) -> Tensor:
|
||||
"""
|
||||
Pass the input through the encoder layer.
|
||||
|
||||
@ -380,9 +392,13 @@ class ConformerEncoderLayer(nn.Module):
|
||||
pos_emb: Positional embedding tensor (required).
|
||||
src_mask: the mask for the src sequence (optional).
|
||||
src_key_padding_mask: the mask for the src keys per batch (optional).
|
||||
states: The decode states for previous frames which contains the cached data.
|
||||
It has a shape of (2, left_context, batch, attention_dim),
|
||||
states[0,...] is the attn_cache, states[1,...] is the conv_cache.
|
||||
states:
|
||||
The decode states for previous frames which contains the cached data.
|
||||
It has two elements, the first element is the attn_cache which has
|
||||
a shape of (encoder_layers, left_context, batch, attention_dim),
|
||||
the second element is the conv_cache which has a shape of
|
||||
(encoder_layers, cnn_module_kernel-1, batch, conv_dim).
|
||||
Note: If not None, states will be modified in this function.
|
||||
left_context: left context (in frames) used during streaming decoding.
|
||||
this is used only in real streaming decoding, in other circumstances,
|
||||
it MUST be 0.
|
||||
@ -413,9 +429,9 @@ class ConformerEncoderLayer(nn.Module):
|
||||
val = src
|
||||
if not self.training and states is not None:
|
||||
# src: [chunk_size, N, F] e.g. [8, 41, 512]
|
||||
key = torch.cat([states[0, ...], src], dim=0)
|
||||
key = torch.cat([states[0], src], dim=0)
|
||||
val = key
|
||||
states[0, ...] = key[-left_context:, ...]
|
||||
states[0] = key[-left_context:, ...]
|
||||
else:
|
||||
assert left_context == 0
|
||||
|
||||
@ -438,13 +454,12 @@ class ConformerEncoderLayer(nn.Module):
|
||||
src = self.norm_conv(src)
|
||||
|
||||
if not self.training and states is not None:
|
||||
src = torch.cat([states[1, ...], src], dim=0)
|
||||
states[1, ...] = src[-left_context:, ...]
|
||||
|
||||
src, conv_cache = self.conv_module(src, states[1])
|
||||
states[1] = conv_cache
|
||||
else:
|
||||
src = self.conv_module(src)
|
||||
src = src[-residual.size(0) :, :, :] # noqa: E203
|
||||
|
||||
src = residual + self.dropout(src)
|
||||
|
||||
if not self.normalize_before:
|
||||
src = self.norm_conv(src)
|
||||
|
||||
@ -459,7 +474,7 @@ class ConformerEncoderLayer(nn.Module):
|
||||
if self.normalize_before:
|
||||
src = self.norm_final(src)
|
||||
|
||||
return src, states
|
||||
return src
|
||||
|
||||
|
||||
class ConformerEncoder(nn.Module):
|
||||
@ -490,9 +505,9 @@ class ConformerEncoder(nn.Module):
|
||||
pos_emb: Tensor,
|
||||
mask: Optional[Tensor] = None,
|
||||
src_key_padding_mask: Optional[Tensor] = None,
|
||||
states: Optional[Tensor] = None,
|
||||
states: Optional[List[Tensor]] = None,
|
||||
left_context: int = 0,
|
||||
) -> Tuple[Tensor, Tensor]:
|
||||
) -> Tensor:
|
||||
r"""Pass the input through the encoder layers in turn.
|
||||
|
||||
Args:
|
||||
@ -500,9 +515,13 @@ class ConformerEncoder(nn.Module):
|
||||
pos_emb: Positional embedding tensor (required).
|
||||
mask: the mask for the src sequence (optional).
|
||||
src_key_padding_mask: the mask for the src keys per batch (optional).
|
||||
states: The decode states for previous frames which contains the cached data.
|
||||
It has a shape of (2, encoder_layers, left_context, batch, attention_dim),
|
||||
states[0,...] is the attn_cache, states[1,...] is the conv_cache.
|
||||
states:
|
||||
The decode states for previous frames which contains the cached data.
|
||||
It has two elements, the first element is the attn_cache which has
|
||||
a shape of (encoder_layers, left_context, batch, attention_dim),
|
||||
the second element is the conv_cache which has a shape of
|
||||
(encoder_layers, cnn_module_kernel-1, batch, conv_dim).
|
||||
Note: If not None, states will be modified in this function.
|
||||
left_context: left context (in frames) used during streaming decoding.
|
||||
this is used only in real streaming decoding, in other circumstances,
|
||||
it MUST be 0.
|
||||
@ -525,18 +544,20 @@ class ConformerEncoder(nn.Module):
|
||||
assert left_context >= 0
|
||||
|
||||
for layer_index, mod in enumerate(self.layers):
|
||||
output, cache = mod(
|
||||
cache = None if states is None else [states[0][layer_index], states[1][layer_index]]
|
||||
output = mod(
|
||||
output,
|
||||
pos_emb,
|
||||
src_mask=mask,
|
||||
src_key_padding_mask=src_key_padding_mask,
|
||||
states=None if states is None else states[:,layer_index, ...],
|
||||
states=cache,
|
||||
left_context=left_context,
|
||||
)
|
||||
if states is not None:
|
||||
states[:, layer_index, ...] = cache
|
||||
states[0][layer_index] = cache[0]
|
||||
states[1][layer_index] = cache[1]
|
||||
|
||||
return output, states
|
||||
return output
|
||||
|
||||
|
||||
class RelPositionalEncoding(torch.nn.Module):
|
||||
@ -1146,7 +1167,11 @@ class ConvolutionModule(nn.Module):
|
||||
)
|
||||
self.activation = Swish()
|
||||
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
cache: Optional[Tensor] = None
|
||||
) -> Union[Tensor, Tuple[Tensor, Tensor]]:
|
||||
"""Compute convolution module.
|
||||
|
||||
Args:
|
||||
@ -1165,9 +1190,15 @@ class ConvolutionModule(nn.Module):
|
||||
|
||||
# 1D Depthwise Conv
|
||||
if self.causal and self.lorder > 0:
|
||||
if cache is None:
|
||||
# Make depthwise_conv causal by
|
||||
# manualy padding self.lorder zeros to the left
|
||||
x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0)
|
||||
else:
|
||||
assert not self.training, "Cache should be None in training time"
|
||||
assert cache.size(0) == self.lorder
|
||||
x = torch.cat([cache.permute(1, 2, 0), x], dim=2)
|
||||
cache = x.permute(2, 0, 1)[-self.lorder:,...]
|
||||
|
||||
x = self.depthwise_conv(x)
|
||||
# x is (batch, channels, time)
|
||||
@ -1179,7 +1210,7 @@ class ConvolutionModule(nn.Module):
|
||||
|
||||
x = self.pointwise_conv2(x) # (batch, channel, time)
|
||||
|
||||
return x.permute(2, 0, 1)
|
||||
return x.permute(2, 0, 1) if cache is None else (x.permute(2, 0, 1), cache)
|
||||
|
||||
|
||||
class Swish(torch.nn.Module):
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user