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#
# 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.
"""Contains the speech segment class."""
import numpy as np
from deepspeech.frontend.audio import AudioSegment
class SpeechSegment(AudioSegment):
"""Speech Segment with Text
Args:
AudioSegment (AudioSegment): Audio Segment
"""
def __init__(self,
samples,
sample_rate,
transcript,
tokens=None,
token_ids=None):
"""Speech segment abstraction, a subclass of AudioSegment,
with an additional transcript.
Args:
samples (ndarray.float32): Audio samples [num_samples x num_channels].
sample_rate (int): Audio sample rate.
transcript (str): Transcript text for the speech.
tokens (List[str], optinal): Transcript tokens for the speech.
token_ids (List[int], optional): Transcript token ids for the speech.
"""
AudioSegment.__init__(self, samples, sample_rate)
self._transcript = transcript
# must init `tokens` with `token_ids` at the same time
self._tokens = tokens
self._token_ids = token_ids
def __eq__(self, other):
"""Return whether two objects are equal.
Returns:
bool: True, when equal to other
"""
if not AudioSegment.__eq__(self, other):
return False
if self._transcript != other._transcript:
return False
if self.has_token and other.has_token:
if self._tokens != other._tokens:
return False
if self._token_ids != other._token_ids:
return False
return True
def __ne__(self, other):
"""Return whether two objects are unequal."""
return not self.__eq__(other)
@classmethod
def from_file(cls, filepath, transcript, tokens=None, token_ids=None):
"""Create speech segment from audio file and corresponding transcript.
Args:
filepath (str|file): Filepath or file object to audio file.
transcript (str): Transcript text for the speech.
tokens (List[str], optional): text tokens. Defaults to None.
token_ids (List[int], optional): text token ids. Defaults to None.
Returns:
SpeechSegment: Speech segment instance.
"""
audio = AudioSegment.from_file(filepath)
return cls(audio.samples, audio.sample_rate, transcript, tokens,
token_ids)
@classmethod
def from_bytes(cls, bytes, transcript, tokens=None, token_ids=None):
"""Create speech segment from a byte string and corresponding
Args:
filepath (str|file): Filepath or file object to audio file.
transcript (str): Transcript text for the speech.
tokens (List[str], optional): text tokens. Defaults to None.
token_ids (List[int], optional): text token ids. Defaults to None.
Returns:
SpeechSegment: Speech segment instance.
"""
audio = AudioSegment.from_bytes(bytes)
return cls(audio.samples, audio.sample_rate, transcript, tokens,
token_ids)
@classmethod
def concatenate(cls, *segments):
"""Concatenate an arbitrary number of speech segments together, both
audio and transcript will be concatenated.
:param *segments: Input speech segments to be concatenated.
:type *segments: tuple of SpeechSegment
:return: Speech segment instance.
:rtype: SpeechSegment
:raises ValueError: If the number of segments is zero, or if the
sample_rate of any two segments does not match.
:raises TypeError: If any segment is not SpeechSegment instance.
"""
if len(segments) == 0:
raise ValueError("No speech segments are given to concatenate.")
sample_rate = segments[0]._sample_rate
transcripts = ""
tokens = []
token_ids = []
for seg in segments:
if sample_rate != seg._sample_rate:
raise ValueError("Can't concatenate segments with "
"different sample rates")
if type(seg) is not cls:
raise TypeError("Only speech segments of the same type "
"instance can be concatenated.")
transcripts += seg._transcript
if self.has_token:
tokens += seg._tokens
token_ids += seg._token_ids
samples = np.concatenate([seg.samples for seg in segments])
return cls(samples, sample_rate, transcripts, tokens, token_ids)
@classmethod
def slice_from_file(cls,
filepath,
transcript,
tokens=None,
token_ids=None,
start=None,
end=None):
"""Loads a small section of an speech without having to load
the entire file into the memory which can be incredibly wasteful.
:param filepath: Filepath or file object to audio file.
:type filepath: str|file
:param start: Start time in seconds. If start is negative, it wraps
around from the end. If not provided, this function
reads from the very beginning.
:type start: float
:param end: End time in seconds. If end is negative, it wraps around
from the end. If not provided, the default behvaior is
to read to the end of the file.
:type end: float
:param transcript: Transcript text for the speech. if not provided,
the defaults is an empty string.
:type transript: str
:return: SpeechSegment instance of the specified slice of the input
speech file.
:rtype: SpeechSegment
"""
audio = AudioSegment.slice_from_file(filepath, start, end)
return cls(audio.samples, audio.sample_rate, transcript, tokens,
token_ids)
@classmethod
def make_silence(cls, duration, sample_rate):
"""Creates a silent speech segment of the given duration and
sample rate, transcript will be an empty string.
Args:
duration (float): Length of silence in seconds.
sample_rate (float): Sample rate.
Returns:
SpeechSegment: Silence of the given duration.
"""
audio = AudioSegment.make_silence(duration, sample_rate)
return cls(audio.samples, audio.sample_rate, "")
@property
def has_token(self):
if self._tokens and self._token_ids:
return True
return False
@property
def transcript(self):
"""Return the transcript text.
Returns:
str: Transcript text for the speech.
"""
return self._transcript
@property
def tokens(self):
"""Return the transcript text tokens.
Returns:
List[str]: text tokens.
"""
return self._tokens
@property
def token_ids(self):
"""Return the transcript text token ids.
Returns:
List[int]: text token ids.
"""
return self._token_ids