"""
Segmenter
=========
"""
from __future__ import annotations
import collections
import logging
import os
import typing
from pathlib import Path
from typing import Dict, List, Optional
import sqlalchemy
from kalpy.fstext.lexicon import Pronunciation as KalpyPronunciation
from sqlalchemy.orm import joinedload, selectinload
from tqdm.rich import tqdm
from montreal_forced_aligner import config
from montreal_forced_aligner.abc import FileExporterMixin, MetaDict, TopLevelMfaWorker
from montreal_forced_aligner.corpus.acoustic_corpus import AcousticCorpusMixin
from montreal_forced_aligner.corpus.features import VadConfigMixin
from montreal_forced_aligner.data import Language, TextFileType, WorkflowType
from montreal_forced_aligner.db import (
CorpusWorkflow,
File,
Pronunciation,
Utterance,
Word,
full_load_utterance,
)
from montreal_forced_aligner.exceptions import KaldiProcessingError
from montreal_forced_aligner.helper import load_configuration
from montreal_forced_aligner.models import AcousticModel
from montreal_forced_aligner.tokenization.spacy import generate_language_tokenizer
from montreal_forced_aligner.transcription.transcriber import TranscriberMixin
from montreal_forced_aligner.utils import log_kaldi_errors, run_kaldi_function
from montreal_forced_aligner.vad.models import SegmenterMixin, SpeechbrainSegmenterMixin
from montreal_forced_aligner.vad.multiprocessing import (
SegmentTranscriptArguments,
SegmentTranscriptFunction,
SegmentVadArguments,
SegmentVadFunction,
segment_utterance,
segment_utterance_transcript,
)
SegmentationType = List[Dict[str, float]]
__all__ = ["VadSegmenter", "TranscriptionSegmenter"]
logger = logging.getLogger("mfa")
[docs]
class VadSegmenter(
VadConfigMixin,
AcousticCorpusMixin,
FileExporterMixin,
SegmenterMixin,
TopLevelMfaWorker,
):
"""
Class for performing speaker classification, parameters are passed to
`speechbrain.pretrained.interfaces.VAD.get_speech_segments
<https://speechbrain.readthedocs.io/en/latest/API/speechbrain.pretrained.interfaces.html#speechbrain.pretrained.interfaces.VAD.get_speech_segments>`_
Parameters
----------
segment_padding: float
Size of padding on both ends of a segment
large_chunk_size: float
Size (in seconds) of the large chunks that are read sequentially
from the input audio file.
small_chunk_size: float
Size (in seconds) of the small chunks extracted from the large ones.
The audio signal is processed in parallel within the small chunks.
Note that large_chunk_size/small_chunk_size must be an integer.
overlap_small_chunk: bool
If True, it creates overlapped small chunks (with 50% overal).
The probabilities of the overlapped chunks are combined using
hamming windows.
apply_energy_VAD: bool
If True, a energy-based VAD is used on the detected speech segments.
The neural network VAD often creates longer segments and tends to
merge close segments together. The energy VAD post-processes can be
useful for having a fine-grained voice activity detection.
The energy thresholds is managed by activation_th and
deactivation_th (see below).
double_check: bool
If True, double checks (using the neural VAD) that the candidate
speech segments actually contain speech. A threshold on the mean
posterior probabilities provided by the neural network is applied
based on the speech_th parameter (see below).
activation_th: float
Threshold of the neural posteriors above which starting a speech segment.
deactivation_th: float
Threshold of the neural posteriors below which ending a speech segment.
en_activation_th: float
A new speech segment is started it the energy is above activation_th.
This is active only if apply_energy_VAD is True.
en_deactivation_th: float
The segment is considered ended when the energy is <= deactivation_th.
This is active only if apply_energy_VAD is True.
speech_th: float
Threshold on the mean posterior probability within the candidate
speech segment. Below that threshold, the segment is re-assigned to
a non-speech region. This is active only if double_check is True.
close_th: float
If the distance between boundaries is smaller than close_th, the
segments will be merged.
len_th: float
If the length of the segment is smaller than len_th, the segments
will be merged.
"""
def __init__(
self,
**kwargs,
):
super().__init__(**kwargs)
self.transcriptions_required = False
[docs]
@classmethod
def parse_parameters(
cls,
config_path: Optional[Path] = None,
args: Optional[Dict[str, typing.Any]] = None,
unknown_args: Optional[typing.Iterable[str]] = None,
) -> MetaDict:
"""
Parse parameters for segmentation from a config path or command-line arguments
Parameters
----------
config_path: :class:`~pathlib.Path`
Config path
args: dict[str, Any]
Parsed arguments
unknown_args: list[str]
Optional list of arguments that were not parsed
Returns
-------
dict[str, Any]
Configuration parameters
"""
global_params = {}
if config_path and os.path.exists(config_path):
data = load_configuration(config_path)
for k, v in data.items():
if k == "features":
if "type" in v:
v["feature_type"] = v["type"]
del v["type"]
global_params.update(v)
else:
if v is None and k in cls.nullable_fields:
v = []
global_params[k] = v
global_params.update(cls.parse_args(args, unknown_args))
return global_params
[docs]
def segment_vad_arguments(self) -> List[SegmentVadArguments]:
"""
Generate Job arguments for :class:`~montreal_forced_aligner.segmenter.SegmentVadFunction`
Returns
-------
list[SegmentVadArguments]
Arguments for processing
"""
options = self.segmentation_options
options.update(
{
"large_chunk_size": self.max_segment_length,
"frame_shift": getattr(self, "export_frame_shift", 0.01),
"small_chunk_size": self.min_segment_length,
"overlap_small_chunk": self.min_pause_duration,
}
)
return [
SegmentVadArguments(
j.id,
getattr(self, "session" if config.USE_THREADING else "db_string", ""),
self.working_log_directory.joinpath(f"segment_vad.{j.id}.log"),
j.construct_path(self.split_directory, "vad", "scp"),
options,
)
for j in self.jobs
]
[docs]
def segment_vad(self) -> None:
"""
Run segmentation based off of VAD.
See Also
--------
:class:`~montreal_forced_aligner.segmenter.SegmentVadFunction`
Multiprocessing helper function for each job
segment_vad_arguments
Job method for generating arguments for helper function
"""
arguments = self.segment_vad_arguments()
old_utts = set()
new_utts = []
with self.session() as session:
utterances = session.query(
Utterance.id, Utterance.channel, Utterance.speaker_id, Utterance.file_id
)
utterance_cache = {}
for u_id, channel, speaker_id, file_id in utterances:
utterance_cache[u_id] = (channel, speaker_id, file_id)
for utt, segments in run_kaldi_function(
SegmentVadFunction, arguments, total_count=self.num_utterances
):
old_utts.add(utt)
channel, speaker_id, file_id = utterance_cache[utt]
for seg in segments:
new_utts.append(
{
"begin": seg.begin,
"end": seg.end,
"text": "speech",
"speaker_id": speaker_id,
"file_id": file_id,
"oovs": "",
"normalized_text": "",
"features": "",
"in_subset": False,
"ignored": False,
"channel": channel,
}
)
session.query(Utterance).filter(Utterance.id.in_(old_utts)).delete()
session.bulk_insert_mappings(
Utterance, new_utts, return_defaults=False, render_nulls=True
)
session.commit()
[docs]
def setup(self) -> None:
"""Setup segmentation"""
super().setup()
self.create_new_current_workflow(WorkflowType.segmentation)
log_dir = self.working_directory.joinpath("log")
os.makedirs(log_dir, exist_ok=True)
try:
self.load_corpus()
except Exception as e:
if isinstance(e, KaldiProcessingError):
log_kaldi_errors(e.error_logs)
e.update_log_file()
raise
[docs]
def segment(self) -> None:
"""
Performs VAD and segmentation into utterances
Raises
------
:class:`~montreal_forced_aligner.exceptions.KaldiProcessingError`
If there were any errors in running Kaldi binaries
"""
self.setup()
self.create_new_current_workflow(WorkflowType.segmentation)
wf = self.current_workflow
if wf.done:
logger.info("Segmentation already done, skipping.")
return
try:
self.compute_vad()
self.segment_vad()
with self.session() as session:
session.query(CorpusWorkflow).filter(CorpusWorkflow.id == wf.id).update(
{"done": True}
)
session.commit()
except Exception as e:
with self.session() as session:
session.query(CorpusWorkflow).filter(CorpusWorkflow.id == wf.id).update(
{"dirty": True}
)
session.commit()
if isinstance(e, KaldiProcessingError):
log_kaldi_errors(e.error_logs)
e.update_log_file()
raise
[docs]
def export_files(self, output_directory: str, output_format: Optional[str] = None) -> None:
"""
Export the results of segmentation as TextGrids
Parameters
----------
output_directory: str
Directory to save segmentation TextGrids
output_format: str, optional
Format to force output files into
"""
if output_format is None:
output_format = TextFileType.TEXTGRID.value
os.makedirs(output_directory, exist_ok=True)
with self.session() as session:
for f in session.query(File).options(
selectinload(File.utterances).joinedload(Utterance.speaker, innerjoin=True),
joinedload(File.sound_file, innerjoin=True),
joinedload(File.text_file),
):
f.save(output_directory, output_format=output_format)
def segment_utterance(self, utterance_id: int, allow_empty: bool = True):
with self.session() as session:
utterance = full_load_utterance(session, utterance_id)
new_utterances = segment_utterance(
utterance.to_kalpy().segment,
None,
self.segmentation_options,
mfcc_options=self.mfcc_options,
vad_options=self.vad_options,
allow_empty=allow_empty,
)
return new_utterances
class SpeechbrainVadSegmenter(
VadConfigMixin,
AcousticCorpusMixin,
FileExporterMixin,
SpeechbrainSegmenterMixin,
TopLevelMfaWorker,
):
"""
Class for performing speaker classification, parameters are passed to
`speechbrain.pretrained.interfaces.VAD.get_speech_segments
<https://speechbrain.readthedocs.io/en/latest/API/speechbrain.pretrained.interfaces.html#speechbrain.pretrained.interfaces.VAD.get_speech_segments>`_
Parameters
----------
segment_padding: float
Size of padding on both ends of a segment
large_chunk_size: float
Size (in seconds) of the large chunks that are read sequentially
from the input audio file.
small_chunk_size: float
Size (in seconds) of the small chunks extracted from the large ones.
The audio signal is processed in parallel within the small chunks.
Note that large_chunk_size/small_chunk_size must be an integer.
overlap_small_chunk: bool
If True, it creates overlapped small chunks (with 50% overal).
The probabilities of the overlapped chunks are combined using
hamming windows.
apply_energy_VAD: bool
If True, a energy-based VAD is used on the detected speech segments.
The neural network VAD often creates longer segments and tends to
merge close segments together. The energy VAD post-processes can be
useful for having a fine-grained voice activity detection.
The energy thresholds is managed by activation_th and
deactivation_th (see below).
double_check: bool
If True, double checks (using the neural VAD) that the candidate
speech segments actually contain speech. A threshold on the mean
posterior probabilities provided by the neural network is applied
based on the speech_th parameter (see below).
activation_th: float
Threshold of the neural posteriors above which starting a speech segment.
deactivation_th: float
Threshold of the neural posteriors below which ending a speech segment.
en_activation_th: float
A new speech segment is started it the energy is above activation_th.
This is active only if apply_energy_VAD is True.
en_deactivation_th: float
The segment is considered ended when the energy is <= deactivation_th.
This is active only if apply_energy_VAD is True.
speech_th: float
Threshold on the mean posterior probability within the candidate
speech segment. Below that threshold, the segment is re-assigned to
a non-speech region. This is active only if double_check is True.
close_th: float
If the distance between boundaries is smaller than close_th, the
segments will be merged.
len_th: float
If the length of the segment is smaller than len_th, the segments
will be merged.
"""
def __init__(
self,
**kwargs,
):
super().__init__(**kwargs)
self.transcriptions_required = False
@classmethod
def parse_parameters(
cls,
config_path: Optional[Path] = None,
args: Optional[Dict[str, typing.Any]] = None,
unknown_args: Optional[typing.Iterable[str]] = None,
) -> MetaDict:
"""
Parse parameters for segmentation from a config path or command-line arguments
Parameters
----------
config_path: :class:`~pathlib.Path`
Config path
args: dict[str, Any]
Parsed arguments
unknown_args: list[str]
Optional list of arguments that were not parsed
Returns
-------
dict[str, Any]
Configuration parameters
"""
global_params = {}
if config_path and os.path.exists(config_path):
data = load_configuration(config_path)
for k, v in data.items():
if k == "features":
if "type" in v:
v["feature_type"] = v["type"]
del v["type"]
global_params.update(v)
else:
if v is None and k in cls.nullable_fields:
v = []
global_params[k] = v
global_params.update(cls.parse_args(args, unknown_args))
return global_params
def segment_vad(self) -> None:
"""
Run segmentation based off of VAD.
See Also
--------
:class:`~montreal_forced_aligner.segmenter.SegmentVadFunction`
Multiprocessing helper function for each job
segment_vad_arguments
Job method for generating arguments for helper function
"""
old_utts = set()
new_utts = []
kwargs = self.segmentation_options
with tqdm(
total=self.num_utterances, disable=config.QUIET
) as pbar, self.session() as session:
utt_index = session.query(sqlalchemy.func.max(Utterance.id)).scalar()
if not utt_index:
utt_index = 0
utt_index += 1
files: List[File] = (
session.query(File, Utterance)
.options(joinedload(File.sound_file))
.join(Utterance.file)
)
for f, u in files:
audio = f.sound_file.load_audio()
segments = self.vad_model.segment_utterance(audio, **kwargs)
for seg in segments:
old_utts.add(u.id)
new_utts.append(
{
"id": utt_index,
"begin": seg.begin,
"end": seg.end,
"text": "speech",
"speaker_id": u.speaker_id,
"file_id": u.file_id,
"oovs": "",
"normalized_text": "",
"features": "",
"in_subset": False,
"ignored": False,
"channel": u.channel,
}
)
utt_index += 1
pbar.update(1)
session.query(Utterance).filter(Utterance.id.in_(old_utts)).delete()
session.bulk_insert_mappings(
Utterance, new_utts, return_defaults=False, render_nulls=True
)
session.commit()
def setup(self) -> None:
"""Setup segmentation"""
super().setup()
self.create_new_current_workflow(WorkflowType.segmentation)
log_dir = self.working_directory.joinpath("log")
os.makedirs(log_dir, exist_ok=True)
try:
self.initialize_database()
self._load_corpus()
except Exception as e:
if isinstance(e, KaldiProcessingError):
log_kaldi_errors(e.error_logs)
e.update_log_file()
raise
def segment(self) -> None:
"""
Performs VAD and segmentation into utterances
Raises
------
:class:`~montreal_forced_aligner.exceptions.KaldiProcessingError`
If there were any errors in running Kaldi binaries
"""
self.setup()
self.create_new_current_workflow(WorkflowType.segmentation)
wf = self.current_workflow
if wf.done:
logger.info("Segmentation already done, skipping.")
return
try:
self.segment_vad()
with self.session() as session:
session.query(CorpusWorkflow).filter(CorpusWorkflow.id == wf.id).update(
{"done": True}
)
session.commit()
except Exception as e:
with self.session() as session:
session.query(CorpusWorkflow).filter(CorpusWorkflow.id == wf.id).update(
{"dirty": True}
)
session.commit()
if isinstance(e, KaldiProcessingError):
log_kaldi_errors(e.error_logs)
e.update_log_file()
raise
def export_files(self, output_directory: str, output_format: Optional[str] = None) -> None:
"""
Export the results of segmentation as TextGrids
Parameters
----------
output_directory: str
Directory to save segmentation TextGrids
output_format: str, optional
Format to force output files into
"""
if output_format is None:
output_format = TextFileType.TEXTGRID.value
os.makedirs(output_directory, exist_ok=True)
with self.session() as session:
for f in session.query(File).options(
selectinload(File.utterances).joinedload(Utterance.speaker, innerjoin=True),
joinedload(File.sound_file, innerjoin=True),
joinedload(File.text_file),
):
f.save(output_directory, output_format=output_format)
def segment_utterance(self, utterance_id: int, allow_empty: bool = True):
with self.session() as session:
utterance = full_load_utterance(session, utterance_id)
new_utterances = segment_utterance(
utterance.to_kalpy().segment,
self.vad_model,
self.segmentation_options,
allow_empty=allow_empty,
)
return new_utterances
[docs]
class TranscriptionSegmenter(
VadConfigMixin, TranscriberMixin, SpeechbrainSegmenterMixin, TopLevelMfaWorker
):
def __init__(self, acoustic_model_path: Path = None, **kwargs):
self.acoustic_model = AcousticModel(acoustic_model_path)
kw = self.acoustic_model.parameters
kw["apply_energy_vad"] = True
kw.update(kwargs)
super().__init__(**kw)
[docs]
def setup(self) -> None:
TopLevelMfaWorker.setup(self)
self.create_new_current_workflow(WorkflowType.segmentation)
self.setup_acoustic_model()
self.dictionary_setup()
self._load_corpus()
self.initialize_jobs()
self.normalize_text()
self.write_lexicon_information(write_disambiguation=False)
def setup_acoustic_model(self):
self.acoustic_model.validate(self)
self.acoustic_model.export_model(self.model_directory)
self.acoustic_model.export_model(self.working_directory)
self.acoustic_model.log_details()
[docs]
def segment(self):
"""
Performs VAD and segmentation into utterances
Raises
------
:class:`~montreal_forced_aligner.exceptions.KaldiProcessingError`
If there were any errors in running Kaldi binaries
"""
self.setup()
self.create_new_current_workflow(WorkflowType.segmentation)
wf = self.current_workflow
if wf.done:
logger.info("Segmentation already done, skipping.")
return
try:
self.segment_transcripts()
with self.session() as session:
session.query(CorpusWorkflow).filter(CorpusWorkflow.id == wf.id).update(
{"done": True}
)
session.commit()
except Exception as e:
with self.session() as session:
session.query(CorpusWorkflow).filter(CorpusWorkflow.id == wf.id).update(
{"dirty": True}
)
session.commit()
if isinstance(e, KaldiProcessingError):
log_kaldi_errors(e.error_logs)
e.update_log_file()
raise
[docs]
def segment_transcript_arguments(self) -> List[SegmentTranscriptArguments]:
"""
Generate Job arguments for :class:`~montreal_forced_aligner.segmenter.SegmentTranscriptFunction`
Returns
-------
list[SegmentTranscriptArguments]
Arguments for processing
"""
decode_options = self.decode_options
boost_silence = decode_options.pop("boost_silence", 1.0)
if boost_silence != 1.0:
self.acoustic_model.acoustic_model.boost_silence(
self.acoustic_model.transition_model, self.silence_symbols, boost_silence
)
return [
SegmentTranscriptArguments(
j.id,
getattr(self, "session" if config.USE_THREADING else "db_string", ""),
self.working_log_directory.joinpath(f"segment_vad.{j.id}.log"),
self.acoustic_model,
self.vad_model,
self.lexicon_compilers,
self.mfcc_options,
self.vad_options,
self.segmentation_options,
self.decode_options,
)
for j in self.jobs
]
def segment_transcripts(self) -> None:
arguments = self.segment_transcript_arguments()
old_utts = set()
new_utterance_mapping = []
with self.session() as session:
utterances = session.query(Utterance.id, Utterance.speaker_id, Utterance.file_id)
utterance_cache = {}
for u_id, speaker_id, file_id in utterances:
utterance_cache[u_id] = (speaker_id, file_id)
for utt, new_utts in run_kaldi_function(
SegmentTranscriptFunction, arguments, total_count=self.num_utterances
):
old_utts.add(utt)
speaker_id, file_id = utterance_cache[utt]
for new_utt in new_utts:
new_utterance_mapping.append(
{
"begin": new_utt.segment.begin,
"end": new_utt.segment.end,
"speaker_id": speaker_id,
"file_id": file_id,
"oovs": "",
"text": new_utt.transcript,
"normalized_text": new_utt.transcript,
"features": "",
"in_subset": False,
"ignored": False,
"channel": new_utt.segment.channel,
}
)
with self.session() as session:
session.query(Utterance).filter(Utterance.id.in_(old_utts)).delete()
session.bulk_insert_mappings(
Utterance, new_utterance_mapping, return_defaults=False, render_nulls=True
)
session.commit()
def segment_transcript(self, utterance_id: int):
with self.session() as session:
# interjection_words = [x for x, in session.query(Word.word).filter(Word.word_type == WordType.interjection)]
interjection_words = []
utterance = full_load_utterance(session, utterance_id)
if self.acoustic_model.language is not Language.unknown:
tokenizer = generate_language_tokenizer(self.acoustic_model.language)
utterance.normalized_text, utterance.normalized_character_text = tokenizer(
utterance.text
)
session.flush()
if self.g2p_model is None:
lexicon_compiler = self.lexicon_compilers[utterance.speaker.dictionary_id]
else:
lexicon_compiler = self.acoustic_model.lexicon_compiler
lexicon_compiler.disambiguation = bool(interjection_words)
words = set(utterance.normalized_text.split())
query = (
session.query(Word, Pronunciation)
.join(Pronunciation.word)
.filter(Word.dictionary_id == utterance.speaker.dictionary_id)
.filter(Word.word.in_(words))
.order_by(Word.word)
)
for w, p in query:
lexicon_compiler.word_table.add_symbol(w.word)
lexicon_compiler.pronunciations.append(
KalpyPronunciation(
w.word,
p.pronunciation,
p.probability,
p.silence_after_probability,
p.silence_before_correction,
p.non_silence_before_correction,
None,
)
)
to_g2p = set()
word_to_g2p_mapping = collections.defaultdict(set)
for w, pron in zip(
utterance.normalized_text.split(), utterance.normalized_character_text.split()
):
if not lexicon_compiler.word_table.member(w):
word_to_g2p_mapping[w].add(pron)
to_g2p.add(pron)
if to_g2p:
from montreal_forced_aligner.g2p.generator import PyniniGenerator
gen = PyniniGenerator(
g2p_model_path=self.g2p_model.source,
word_list=to_g2p,
num_pronunciations=1,
strict_graphemes=True,
)
g2pped = gen.generate_pronunciations()
for w, ps in word_to_g2p_mapping.items():
pronunciations = [g2pped[x][0] for x in ps if x in g2pped and g2pped[x]]
if not pronunciations:
pronunciations = [self.oov_phone]
lexicon_compiler.word_table.add_symbol(w)
for p in pronunciations:
lexicon_compiler.pronunciations.append(
KalpyPronunciation(
w,
p,
None,
None,
None,
None,
None,
)
)
lexicon_compiler.compute_disambiguation_symbols()
lexicon_compiler.create_fsts()
new_utterances = segment_utterance_transcript(
self.acoustic_model,
utterance.to_kalpy(),
lexicon_compiler,
self.vad_model if self.speechbrain else None,
self.segmentation_options,
interjection_words=interjection_words,
mfcc_options=self.mfcc_options if not self.speechbrain else None,
vad_options=self.vad_options if not self.speechbrain else None,
**self.decode_options,
)
return new_utterances
[docs]
def export_files(self, output_directory: str, output_format: Optional[str] = None) -> None:
"""
Export the results of segmentation as TextGrids
Parameters
----------
output_directory: str
Directory to save segmentation TextGrids
output_format: str, optional
Format to force output files into
"""
if output_format is None:
output_format = TextFileType.TEXTGRID.value
os.makedirs(output_directory, exist_ok=True)
with self.session() as session:
for f in session.query(File).options(
selectinload(File.utterances).joinedload(Utterance.speaker, innerjoin=True),
joinedload(File.sound_file, innerjoin=True),
joinedload(File.text_file),
):
f.save(output_directory, output_format=output_format)
