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120 lines
4.5 KiB
120 lines
4.5 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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 dataclasses import dataclass
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import numpy as np
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from paddlespeech.cli.log import logger
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@dataclass
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class OnlineCTCEndpointRule:
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must_contain_nonsilence: bool = True
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min_trailing_silence: int = 1000
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min_utterance_length: int = 0
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@dataclass
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class OnlineCTCEndpoingOpt:
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frame_shift_in_ms: int = 10
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blank: int = 0 # blank id, that we consider as silence for purposes of endpointing.
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blank_threshold: float = 0.8 # above blank threshold is silence
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# We support three rules. We terminate decoding if ANY of these rules
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# evaluates to "true". If you want to add more rules, do it by changing this
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# code. If you want to disable a rule, you can set the silence-timeout for
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# that rule to a very large number.
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# rule1 times out after 5 seconds of silence, even if we decoded nothing.
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rule1: OnlineCTCEndpointRule = OnlineCTCEndpointRule(False, 5000, 0)
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# rule2 times out after 1.0 seconds of silence after decoding something,
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# even if we did not reach a final-state at all.
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rule2: OnlineCTCEndpointRule = OnlineCTCEndpointRule(True, 1000, 0)
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# rule3 times out after the utterance is 20 seconds long, regardless of
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# anything else.
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rule3: OnlineCTCEndpointRule = OnlineCTCEndpointRule(False, 0, 20000)
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class OnlineCTCEndpoint:
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"""
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[END-TO-END AUTOMATIC SPEECH RECOGNITION INTEGRATED WITH CTC-BASED VOICE ACTIVITY DETECTION](https://arxiv.org/pdf/2002.00551.pdf)
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"""
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def __init__(self, opts: OnlineCTCEndpoingOpt):
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self.opts = opts
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logger.info(f"Endpont Opts: {opts}")
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self.frame_shift_in_ms = opts.frame_shift_in_ms
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self.num_frames_decoded = 0
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self.trailing_silence_frames = 0
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self.reset()
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def reset(self):
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self.num_frames_decoded = 0
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self.trailing_silence_frames = 0
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def rule_activated(self,
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rule: OnlineCTCEndpointRule,
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rule_name: str,
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decoding_something: bool,
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trailine_silence: int,
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utterance_length: int) -> bool:
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ans = (
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decoding_something or (not rule.must_contain_nonsilence)
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) and trailine_silence >= rule.min_trailing_silence and utterance_length >= rule.min_utterance_length
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if (ans):
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logger.info(f"Endpoint Rule: {rule_name} activated: {rule}")
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return ans
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def endpoint_detected(self,
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ctc_log_probs: np.ndarray,
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decoding_something: bool) -> bool:
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"""detect endpoint.
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Args:
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ctc_log_probs (np.ndarray): (T, D)
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decoding_something (bool): contain nonsilince.
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Returns:
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bool: whether endpoint detected.
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"""
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for logprob in ctc_log_probs:
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blank_prob = np.exp(logprob[self.opts.blank])
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self.num_frames_decoded += 1
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if blank_prob > self.opts.blank_threshold:
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self.trailing_silence_frames += 1
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else:
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self.trailing_silence_frames = 0
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assert self.num_frames_decoded >= self.trailing_silence_frames
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assert self.frame_shift_in_ms > 0
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decoding_something = (self.num_frames_decoded > self.trailing_silence_frames) and decoding_something
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utterance_length = self.num_frames_decoded * self.frame_shift_in_ms
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trailing_silence = self.trailing_silence_frames * self.frame_shift_in_ms
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if self.rule_activated(self.opts.rule1, 'rule1', decoding_something,
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trailing_silence, utterance_length):
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return True
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if self.rule_activated(self.opts.rule2, 'rule2', decoding_something,
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trailing_silence, utterance_length):
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return True
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if self.rule_activated(self.opts.rule3, 'rule3', decoding_something,
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trailing_silence, utterance_length):
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return True
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return False
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