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https://github.com/k2-fsa/icefall.git
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280 lines
9.6 KiB
Python
Executable File
280 lines
9.6 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (Author: Weiji Zhuang,
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# Liyong Guo)
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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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import argparse
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import copy
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import logging
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from concurrent.futures import ProcessPoolExecutor
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from typing import Tuple
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import numpy as np
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from lhotse.features.io import NumpyHdf5Reader
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from tqdm import tqdm
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from icefall.utils import AttributeDict
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from train import get_params
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from graph import ctc_trivial_decoding_graph
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class Arc:
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def __init__(
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self, src_state: int, dst_state: int, ilabel: int, olabel: int
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) -> None:
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self.src_state = int(src_state)
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self.dst_state = int(dst_state)
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self.ilabel = int(ilabel)
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self.olabel = int(olabel)
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def next_state(self) -> None:
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return self.dst_state
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class State:
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def __init__(self) -> None:
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self.arc_list = list()
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def add_arc(self, arc: Arc) -> None:
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self.arc_list.append(arc)
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class FiniteStateTransducer:
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"""Represents a decoding graph for wake word detection."""
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def __init__(self, graph: str) -> None:
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self.state_list = list()
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for arc_str in graph.split("\n"):
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arc = arc_str.strip().split()
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if len(arc) == 0:
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continue
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# 1 and 2 for final state
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# 4 for non-final state
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assert len(arc) in [1, 2, 4], f"{len(arc)} {arc_str}"
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if len(arc) == 4: # Non-final state
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# FST must be sorted
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if len(self.state_list) <= int(arc[0]):
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new_state = State()
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self.state_list.append(new_state)
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self.state_list[int(arc[0])].add_arc(
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Arc(arc[0], arc[1], arc[2], arc[3])
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)
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else:
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self.final_state_id = int(arc[0])
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def to_str(self) -> None:
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fst_str = ""
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for state_idx in range(len(self.state_list)):
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cur_state = self.state_list[state_idx]
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for arc_idx in range(len(cur_state.arc_list)):
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cur_arc = cur_state.arc_list[arc_idx]
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ilabel = cur_arc.ilabel
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olabel = cur_arc.olabel
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src_state = cur_arc.src_state
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dst_state = cur_arc.dst_state
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fst_str += f"{src_state} {dst_state} {ilabel} {olabel}\n"
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fst_str += f"{dst_state}\n"
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return fst_str
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class Token:
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def __init__(self) -> None:
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self.is_active = False
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self.total_score = -float("inf")
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self.keyword_frames = 0
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self.average_keyword_score = -float("inf")
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self.average_max_keyword_score = 0.0
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def set_token(
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self,
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src_token,
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is_keyword_ilabel: bool,
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acoustic_score: float,
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) -> None:
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"""
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A dynamic programming process computing the highest score for a token
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from all possible paths which could reach this token.
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Args:
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src_token: The source token connected to current token with an arc.
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is_keyword_ilabel: If true, the arc consumes an input label which is
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a part of wake word. Otherwhise, the input label is
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blank or unknown, i.e. current token is still not part of wake word.
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acoustic_score: acoustic score of this arc.
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"""
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if (
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not self.is_active
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or self.total_score < src_token.total_score + acoustic_score
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):
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self.is_active = True
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self.total_score = src_token.total_score + acoustic_score
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if is_keyword_ilabel:
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self.average_keyword_score = (
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acoustic_score
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+ src_token.average_keyword_score * src_token.keyword_frames
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) / (src_token.keyword_frames + 1)
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self.keyword_frames = src_token.keyword_frames + 1
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else:
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self.average_keyword_score = 0.0
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class SingleDecodable:
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def __init__(
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self,
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model_output,
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keyword_ilabel_start,
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graph,
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):
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"""
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Args:
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model_output: log_softmax(logit) with shape [T, C]
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keyword_ilabel_start: index of the first token of the wake word.
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In this recipe, tokens not for wake word has smaller token index,
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i.e. blank 0; unk 1.
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graph: decoding graph of the wake word.
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"""
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self.init_token_list = [Token() for i in range(len(graph.state_list))]
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self.reset_token_list()
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self.model_output = model_output
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self.T = model_output.shape[0]
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self.utt_score = 0.0
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self.current_frame_index = 0
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self.keyword_ilabel_start = keyword_ilabel_start
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self.graph = graph
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self.number_tokens = len(self.cur_token_list)
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def reset_token_list(self) -> None:
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"""
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Reset all tokens to a condition without consuming any acoustic frames.
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"""
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self.cur_token_list = copy.deepcopy(self.init_token_list)
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self.expand_token_list = copy.deepcopy(self.init_token_list)
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self.cur_token_list[0].is_active = True
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self.cur_token_list[0].total_score = 0
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self.cur_token_list[0].average_keyword_score = 0
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def process_oneframe(self) -> None:
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"""
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Decode a frame and update all tokens.
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"""
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for state_id, cur_token in enumerate(self.cur_token_list):
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if cur_token.is_active:
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for arc_id in self.graph.state_list[state_id].arc_list:
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acoustic_score = self.model_output[self.current_frame_index][
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arc_id.ilabel
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]
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is_keyword_ilabel = arc_id.ilabel >= self.keyword_ilabel_start
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self.expand_token_list[arc_id.next_state()].set_token(
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cur_token,
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is_keyword_ilabel,
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acoustic_score,
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)
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# use best_score to keep total_score in a good range
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self.best_state_id = 0
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best_score = self.expand_token_list[0].total_score
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for state_id in range(self.number_tokens):
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if self.expand_token_list[state_id].is_active:
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if best_score < self.expand_token_list[state_id].total_score:
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best_score = self.expand_token_list[state_id].total_score
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self.best_state_id = state_id
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self.cur_token_list = self.expand_token_list
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for state_id in range(self.number_tokens):
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self.cur_token_list[state_id].total_score -= best_score
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self.expand_token_list = copy.deepcopy(self.init_token_list)
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potential_score = np.exp(
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self.cur_token_list[self.graph.final_state_id].average_keyword_score
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)
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if potential_score > self.utt_score:
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self.utt_score = potential_score
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self.current_frame_index += 1
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def decode_utt(
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params: AttributeDict, utt_id: str, post_file, graph: FiniteStateTransducer
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) -> Tuple[str, float]:
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"""
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Decode a single utterance.
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Args:
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params:
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The return value of :func:`get_params`.
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utt_id: utt_id to be decoded, used to fetch posterior matrix from post_file.
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post_file: file to save posterior for all test set.
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graph: decoding graph.
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Returns:
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utt_id and its corresponding probability to be a wake word.
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"""
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reader = NumpyHdf5Reader(post_file)
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model_output = reader.read(utt_id)
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keyword_ilabel_start = params.wakeup_word_tokens[0]
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decodable = SingleDecodable(
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model_output=model_output,
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keyword_ilabel_start=keyword_ilabel_start,
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graph=graph,
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)
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for t in range(decodable.T):
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decodable.process_oneframe()
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return utt_id, decodable.utt_score
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def get_parser():
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parser = argparse.ArgumentParser(
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description="A simple FST decoder for the wake word detection\n"
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)
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parser.add_argument(
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"--decoding-graph", help="decoding graph", default="himia_ctc_graph.txt"
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)
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parser.add_argument("--post-h5", help="model output in h5 format")
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parser.add_argument("--score-file", help="file to save scores of each utterance")
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return parser
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def main():
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logging.basicConfig(
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level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s"
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)
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parser = get_parser()
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args = parser.parse_args()
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params = get_params()
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params.update(vars(args))
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keys = NumpyHdf5Reader(params.post_h5).hdf.keys()
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graph = FiniteStateTransducer(ctc_trivial_decoding_graph(params.wakeup_word_tokens))
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logging.info(f"Graph used:\n{graph.to_str()}")
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logging.info("About to load data to decoder.")
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with ProcessPoolExecutor() as executor, open(
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params.score_file, "w", encoding="utf8"
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) as fout:
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futures = [
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executor.submit(decode_utt, params, key, params.post_h5, graph)
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for key in tqdm(keys)
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]
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logging.info("Decoding.")
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for future in tqdm(futures):
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k, v = future.result()
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fout.write(str(k) + " " + str(v) + "\n")
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if __name__ == "__main__":
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main()
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