"""
Model classes
=============
"""
from __future__ import annotations
import json
import logging
import os
import re
import shutil
import typing
from pathlib import Path
from shutil import copy, copyfile, make_archive, move, rmtree, unpack_archive
from typing import TYPE_CHECKING, Collection, Dict, List, Optional, Tuple, Union
import pynini
import pywrapfst
import requests
import yaml
from _kalpy.gmm import AmDiagGmm
from _kalpy.hmm import TransitionModel
from _kalpy.matrix import FloatMatrix
from kalpy.feat.mfcc import MfccComputer
from kalpy.feat.pitch import PitchComputer
from kalpy.fstext.lexicon import LexiconCompiler
from kalpy.gmm.utils import read_gmm_model
from kalpy.utils import read_kaldi_object
from rich.pretty import pprint
from montreal_forced_aligner.abc import MfaModel, ModelExporterMixin
from montreal_forced_aligner.data import Language, PhoneSetType
from montreal_forced_aligner.exceptions import (
LanguageModelNotFoundError,
ModelLoadError,
ModelsConnectionError,
PronunciationAcousticMismatchError,
RemoteModelNotFoundError,
RemoteModelVersionNotFoundError,
)
from montreal_forced_aligner.helper import EnhancedJSONEncoder, mfa_open
if TYPE_CHECKING:
from dataclasses import dataclass
from montreal_forced_aligner.abc import MetaDict
from montreal_forced_aligner.dictionary.mixins import DictionaryMixin
from montreal_forced_aligner.g2p.trainer import G2PTrainer
from montreal_forced_aligner.tokenization.trainer import TokenizerTrainer
else:
from dataclassy import dataclass
# default format for output
FORMAT = "zip"
logger = logging.getLogger("mfa")
__all__ = [
"Archive",
"LanguageModel",
"AcousticModel",
"IvectorExtractorModel",
"DictionaryModel",
"G2PModel",
"ModelManager",
"ModelRelease",
"MODEL_TYPES",
"guess_model_type",
]
def guess_model_type(path: Path) -> List[str]:
"""
Guess a model type given a path
Parameters
----------
path: :class:`~pathlib.Path`
Model archive to guess
Returns
-------
list[str]
Possible model types that use that extension
"""
ext = os.path.splitext(path)[1]
if not ext:
return []
possible = []
for m, mc in MODEL_TYPES.items():
if ext in mc.extensions:
possible.append(m)
return possible
[docs]
class Archive(MfaModel):
"""
Class representing data in a directory or archive file (zip, tar,
tar.gz/tgz)
Based on the prosodylab-aligner
(https://github.com/prosodylab/Prosodylab-Aligner) archive class.
Parameters
----------
source: :class:`~pathlib.Path`
Source path
root_directory: :class:`~pathlib.Path`
Root directory to unpack and store temporary files
"""
extensions = [".zip"]
model_type = None
def __init__(
self,
source: typing.Union[str, Path],
root_directory: Optional[typing.Union[str, Path]] = None,
):
from .config import get_temporary_directory
if isinstance(source, str):
source = Path(source)
source = source.resolve()
if root_directory is None:
root_directory = get_temporary_directory().joinpath(
"extracted_models", self.model_type
)
if isinstance(root_directory, str):
root_directory = Path(root_directory)
self.root_directory = root_directory
self.source = source
self._meta = {}
self.name = source.stem
if os.path.isdir(source):
self.dirname = source
else:
self.dirname = root_directory.joinpath(f"{self.name}_{self.model_type}")
if self.dirname.exists():
shutil.rmtree(self.dirname, ignore_errors=True)
os.makedirs(root_directory, exist_ok=True)
unpack_archive(source, self.dirname)
files = [x for x in self.dirname.iterdir()]
old_dir_path = self.dirname.joinpath(files[0])
if len(files) == 1 and old_dir_path.is_dir(): # Backwards compatibility
for f in old_dir_path.iterdir():
f = f.relative_to(old_dir_path)
move(old_dir_path.joinpath(f), self.dirname.joinpath(f))
old_dir_path.rmdir()
[docs]
def parse_old_features(self) -> None:
"""
Parse MFA model's features and ensure that they are up-to-date with current functionality
"""
if "features" not in self._meta:
return
feature_key_remapping = {
"type": "feature_type",
"deltas": "uses_deltas",
"fmllr": "uses_speaker_adaptation",
}
for key, new_key in feature_key_remapping.items():
if key in self._meta["features"]:
self._meta["features"][new_key] = self._meta["features"][key]
del self._meta["features"][key]
if "uses_splices" not in self._meta["features"]: # Backwards compatibility
self._meta["features"]["uses_splices"] = os.path.exists(
self.dirname.joinpath("lda.mat")
)
if "uses_speaker_adaptation" not in self._meta["features"]:
self._meta["features"]["uses_speaker_adaptation"] = os.path.exists(
self.dirname.joinpath("final.alimdl")
)
[docs]
def get_subclass_object(
self,
) -> Union[AcousticModel, G2PModel, LanguageModel, TokenizerModel, IvectorExtractorModel]:
"""
Instantiate subclass models based on files contained in the archive
Returns
-------
:class:`~montreal_forced_aligner.models.AcousticModel`, :class:`~montreal_forced_aligner.models.G2PModel`, :class:`~montreal_forced_aligner.models.LanguageModel`, or :class:`~montreal_forced_aligner.models.IvectorExtractorModel`
Subclass model that was auto detected
Raises
------
:class:`~montreal_forced_aligner.exceptions.ModelLoadError`
If the model type cannot be determined
"""
files = [x.name for x in self.dirname.iterdir()]
if "tree" in files:
return AcousticModel(self.dirname, self.root_directory)
if "phones.sym" in files or "phones.txt" in files:
return G2PModel(self.dirname, self.root_directory)
if any(f.endswith(".arpa") for f in files):
return LanguageModel(self.dirname, self.root_directory)
if "final.ie" in files:
return IvectorExtractorModel(self.dirname, self.root_directory)
if "tokenizer.fst" in files:
return TokenizerModel(self.dirname, self.root_directory)
raise ModelLoadError(self.source)
[docs]
@classmethod
def valid_extension(cls, filename: Path) -> bool:
"""
Check whether a file has a valid extension for the given model archive
Parameters
----------
filename: :class:`~pathlib.Path`
File name to check
Returns
-------
bool
True if the extension matches the models allowed extensions
"""
if filename.suffix in cls.extensions:
return True
return False
[docs]
@classmethod
def generate_path(
cls, root: Path, name: str, enforce_existence: bool = True
) -> Optional[Path]:
"""
Generate a path for a given model from the root directory and the name of the model
Parameters
----------
root: :class:`~pathlib.Path`
Root directory for the full path
name: str
Name of the model
enforce_existence: bool
Flag to return None if the path doesn't exist, defaults to True
Returns
-------
Path
Full path in the root directory for the model
"""
for ext in cls.extensions:
path = root.joinpath(name + ext)
if path.exists() or not enforce_existence:
return path
return None
[docs]
def pretty_print(self) -> None:
"""
Pretty print the archive's meta data using rich
"""
pprint({"Archive": {"name": self.name, "data": self.meta}})
@property
def meta(self) -> dict:
"""
Get the meta data associated with the model
"""
if not self._meta:
meta_path = self.dirname.joinpath("meta.json")
format = "json"
if not os.path.exists(meta_path):
meta_path = self.dirname.joinpath("meta.yaml")
format = "yaml"
with mfa_open(meta_path, "r") as f:
if format == "yaml":
self._meta = yaml.load(f, Loader=yaml.Loader)
else:
self._meta = json.load(f)
self.parse_old_features()
return self._meta
[docs]
@classmethod
def empty(
cls, head: str, root_directory: Optional[typing.Union[str, Path]] = None
) -> Union[
Archive, IvectorExtractorModel, AcousticModel, G2PModel, TokenizerModel, LanguageModel
]:
"""
Initialize an archive using an empty directory
Parameters
----------
head: str
Directory name to create
root_directory: str, optional
Root directory to create temporary data, defaults to the MFA temporary directory
Returns
-------
:class:`~montreal_forced_aligner.models.Archive`, :class:`~montreal_forced_aligner.models.AcousticModel`, :class:`~montreal_forced_aligner.models.G2PModel`, :class:`~montreal_forced_aligner.models.LanguageModel`, :class:`~montreal_forced_aligner.models.TokenizerModel`, or :class:`~montreal_forced_aligner.models.IvectorExtractorModel`
Model constructed from the empty directory
"""
from .config import get_temporary_directory
if root_directory is None:
root_directory = get_temporary_directory().joinpath("temp_models", cls.model_type)
source = root_directory.joinpath(head)
source.mkdir(parents=True, exist_ok=True)
return cls(source, root_directory)
[docs]
def add(self, source: str):
"""
Add file into archive
Parameters
----------
source: str
Path to file to copy into the directory
"""
copy(source, self.dirname)
def __repr__(self) -> str:
"""Representation string of a model"""
return f"{self.__class__.__name__}(dirname={self.dirname!r})"
[docs]
def clean_up(self) -> None:
"""Remove temporary directory"""
rmtree(self.dirname)
[docs]
def dump(self, path: Path, archive_fmt: str = FORMAT) -> str:
"""
Write archive to disk, and return the name of final archive
Parameters
----------
path: :class:`~pathlib.Path`
Path to write to
archive_fmt: str, optional
Archive extension to use, defaults to ".zip"
Returns
-------
str
Path of constructed archive
"""
return make_archive(os.path.splitext(path)[0], archive_fmt, *os.path.split(self.dirname))
[docs]
class AcousticModel(Archive):
"""
Class for storing acoustic models in MFA, exported as zip files containing the necessary Kaldi files
to be reused
"""
files = [
"final.mdl",
"final.alimdl",
"lda.mat",
"phone_pdf.counts",
# "rules.yaml",
"tokenizer.fst",
"phone_lm.fst",
"tree",
"rules.yaml",
"phones.txt",
"graphemes.txt",
]
extensions = [".zip", ".am"]
model_type = "acoustic"
def __init__(
self,
source: typing.Union[str, Path],
root_directory: Optional[typing.Union[str, Path]] = None,
):
if source in AcousticModel.get_available_models():
source = AcousticModel.get_pretrained_path(source)
super().__init__(source, root_directory)
self._am = None
self._tm = None
@property
def version(self):
return self.meta["version"]
@property
def uses_cmvn(self):
return self.meta["features"]["uses_cmvn"]
@property
def language(self) -> Language:
return Language[self.meta.get("language", "unknown")]
@property
def parameters(self) -> MetaDict:
"""Parameters to pass to top-level workers"""
params = {**self.meta["features"]}
params["non_silence_phones"] = {x for x in self.meta["phones"]}
params["oov_phone"] = self.meta["oov_phone"]
params["language"] = self.meta["language"]
params["optional_silence_phone"] = self.meta["optional_silence_phone"]
params["phone_set_type"] = self.meta["phone_set_type"]
params["silence_probability"] = self.meta.get("silence_probability", 0.5)
params["initial_silence_probability"] = self.meta.get("initial_silence_probability", 0.5)
params["final_non_silence_correction"] = self.meta.get(
"final_non_silence_correction", None
)
params["final_silence_correction"] = self.meta.get("final_silence_correction", None)
if "other_noise_phone" in self.meta:
params["other_noise_phone"] = self.meta["other_noise_phone"]
if (
"dictionaries" in self.meta
and "position_dependent_phones" in self.meta["dictionaries"]
):
params["position_dependent_phones"] = self.meta["dictionaries"][
"position_dependent_phones"
]
else:
params["position_dependent_phones"] = self.meta.get("position_dependent_phones", True)
return params
@property
def tree_path(self) -> Path:
"""Current acoustic model path"""
return self.dirname.joinpath("tree")
@property
def lda_mat_path(self) -> Path:
"""Current acoustic model path"""
return self.dirname.joinpath("lda.mat")
@property
def model_path(self) -> Path:
"""Current acoustic model path"""
return self.dirname.joinpath("final.mdl")
@property
def phone_symbol_path(self) -> Path:
"""Path to phone symbol table"""
return self.dirname.joinpath("phones.txt")
@property
def rules_path(self) -> Path:
"""Path to phone symbol table"""
return self.dirname.joinpath("rules.yaml")
@property
def alignment_model_path(self) -> Path:
"""Alignment model path"""
path = self.model_path.with_suffix(".alimdl")
if os.path.exists(path):
return path
return self.model_path
@property
def acoustic_model(self) -> AmDiagGmm:
if self._am is None:
self._tm, self._am = read_gmm_model(self.alignment_model_path)
return self._am
@property
def transition_model(self) -> TransitionModel:
if self._tm is None:
self._tm, self._am = read_gmm_model(self.alignment_model_path)
return self._tm
@property
def lexicon_compiler(self):
lc = LexiconCompiler(
silence_probability=self.meta.get("silence_probability", 0.5),
initial_silence_probability=self.meta.get("initial_silence_probability", 0.5),
final_silence_correction=self.meta.get("final_silence_correction", None),
final_non_silence_correction=self.meta.get("final_non_silence_correction", None),
silence_phone=self.meta.get("optional_silence_phone", "sil"),
oov_phone=self.meta.get("oov_phone", "sil"),
position_dependent_phones=self.meta.get("position_dependent_phones", False),
phones={x for x in self.meta["phones"]},
)
if self.meta.get("phone_mapping", None):
lc.phone_table = pywrapfst.SymbolTable()
for k, v in self.meta["phone_mapping"].items():
lc.phone_table.add_symbol(k, v)
elif self.phone_symbol_path.exists():
lc.phone_table = pywrapfst.SymbolTable.read_text(self.phone_symbol_path)
return lc
@property
def mfcc_computer(self) -> MfccComputer:
return MfccComputer(**self.mfcc_options)
@property
def pitch_computer(self) -> typing.Optional[PitchComputer]:
if self.meta["features"].get("use_pitch", False):
return PitchComputer(**self.pitch_options)
return
@property
def lda_mat(self) -> FloatMatrix:
lda_mat_path = self.dirname.joinpath("lda.mat")
lda_mat = None
if lda_mat_path.exists():
lda_mat = read_kaldi_object(FloatMatrix, lda_mat_path)
return lda_mat
@property
def mfcc_options(self) -> MetaDict:
"""Parameters to use in computing MFCC features."""
return {
"sample_frequency": self._meta["features"].get("sample_frequency", 16000),
"frame_shift": self._meta["features"].get("frame_shift", 10),
"frame_length": self._meta["features"].get("frame_length", 25),
"dither": self._meta["features"].get("dither", 0.0001),
"preemphasis_coefficient": self._meta["features"].get("preemphasis_coefficient", 0.97),
"snip_edges": self._meta["features"].get("snip_edges", True),
"num_mel_bins": self._meta["features"].get("num_mel_bins", 23),
"low_frequency": self._meta["features"].get("low_frequency", 20),
"high_frequency": self._meta["features"].get("high_frequency", 7800),
"num_coefficients": self._meta["features"].get("num_coefficients", 13),
"use_energy": self._meta["features"].get("use_energy", False),
"energy_floor": self._meta["features"].get("energy_floor", 1.0),
"raw_energy": self._meta["features"].get("raw_energy", True),
"cepstral_lifter": self._meta["features"].get("cepstral_lifter", 22),
}
@property
def pitch_options(self) -> MetaDict:
"""Parameters to use in computing MFCC features."""
use_pitch = self._meta["features"].get("use_pitch", False)
use_voicing = self._meta["features"].get("use_voicing", False)
use_delta_pitch = self._meta["features"].get("use_delta_pitch", False)
normalize = self._meta["features"].get("normalize_pitch", True)
options = {
"frame_shift": self._meta["features"].get("frame_shift", 10),
"frame_length": self._meta["features"].get("frame_length", 25),
"min_f0": self._meta["features"].get("min_f0", 50),
"max_f0": self._meta["features"].get("max_f0", 800),
"sample_frequency": self._meta["features"].get("sample_frequency", 16000),
"penalty_factor": self._meta["features"].get("penalty_factor", 0.1),
"delta_pitch": self._meta["features"].get("delta_pitch", 0.005),
"snip_edges": self._meta["features"].get("snip_edges", True),
"add_normalized_log_pitch": False,
"add_delta_pitch": False,
"add_pov_feature": False,
}
if use_pitch:
options["add_normalized_log_pitch"] = normalize
options["add_raw_log_pitch"] = not normalize
options["add_delta_pitch"] = use_delta_pitch
options["add_pov_feature"] = use_voicing
return options
@property
def lda_options(self) -> MetaDict:
"""Parameters to use in computing MFCC features."""
return {
"splice_left_context": self._meta["features"].get("splice_left_context", 3),
"splice_right_context": self._meta["features"].get("splice_right_context", 3),
}
@property
def meta(self) -> MetaDict:
"""
Metadata information for the acoustic model
"""
default_features = {
"feature_type": "mfcc",
"use_energy": False,
"frame_shift": 10,
"snip_edges": True,
"low_frequency": 20,
"high_frequency": 7800,
"sample_frequency": 16000,
"allow_downsample": True,
"allow_upsample": True,
"use_pitch": False,
"use_voicing": False,
"uses_cmvn": True,
"uses_deltas": True,
"uses_splices": False,
"uses_voiced": False,
"uses_speaker_adaptation": False,
"silence_weight": 0.0,
"fmllr_update_type": "full",
"splice_left_context": 3,
"splice_right_context": 3,
}
if not self._meta:
meta_path = self.dirname.joinpath("meta.json")
format = "json"
if not os.path.exists(meta_path):
meta_path = self.dirname.joinpath("meta.yaml")
format = "yaml"
if not os.path.exists(meta_path):
self._meta = {
"version": "0.9.0",
"architecture": "gmm-hmm",
"features": default_features,
}
else:
with mfa_open(meta_path, "r") as f:
if format == "yaml":
self._meta = yaml.load(f, Loader=yaml.Loader)
else:
self._meta = json.load(f)
if self._meta["features"] == "mfcc+deltas":
self._meta["features"] = default_features
if "pitch" in self._meta["features"]:
self._meta["features"]["use_pitch"] = self._meta["features"].pop("pitch")
if (
self._meta["features"].get("use_pitch", False)
and self._meta["version"] < "2.0.6"
):
self._meta["features"]["use_delta_pitch"] = True
if "phone_type" not in self._meta:
self._meta["phone_type"] = "triphone"
if "optional_silence_phone" not in self._meta:
self._meta["optional_silence_phone"] = "sil"
if "oov_phone" not in self._meta:
self._meta["oov_phone"] = "spn"
if format == "yaml":
self._meta["other_noise_phone"] = "sp"
if "phone_set_type" not in self._meta:
self._meta["phone_set_type"] = "UNKNOWN"
if "language" not in self._meta:
self._meta["language"] = "unknown"
self._meta["phones"] = set(self._meta.get("phones", []))
if (
"uses_speaker_adaptation" not in self._meta["features"]
or not self._meta["features"]["uses_speaker_adaptation"]
):
self._meta["features"]["uses_speaker_adaptation"] = os.path.exists(
self.dirname.joinpath("final.alimdl")
)
if self._meta["version"] in {"0.9.0", "1.0.0"}:
self._meta["features"]["uses_speaker_adaptation"] = True
if (
"uses_splices" not in self._meta["features"]
or not self._meta["features"]["uses_splices"]
):
self._meta["features"]["uses_splices"] = os.path.exists(
self.dirname.joinpath("lda.mat")
)
if self._meta["features"]["uses_splices"]:
self._meta["features"]["uses_deltas"] = False
if (
self._meta["features"].get("use_pitch", False)
and "use_voicing" not in self._meta["features"]
):
self._meta["features"]["use_voicing"] = True
if (
"dictionaries" in self._meta
and "position_dependent_phones" not in self._meta["dictionaries"]
):
if self._meta["version"] < "2.0":
default_value = True
else:
default_value = False
self._meta["dictionaries"]["position_dependent_phones"] = self._meta.get(
"position_dependent_phones", default_value
)
self.parse_old_features()
return self._meta
[docs]
def pretty_print(self) -> None:
"""
Prints the metadata information to the terminal
"""
configuration_data = {"Acoustic model": {"name": self.name, "data": {}}}
configuration_data["Acoustic model"]["data"]["Version"] = (self.meta["version"],)
if "citation" in self.meta:
configuration_data["Acoustic model"]["data"]["Citation"] = self.meta["citation"]
if "train_date" in self.meta:
configuration_data["Acoustic model"]["data"]["Train date"] = self.meta["train_date"]
configuration_data["Acoustic model"]["data"]["Architecture"] = self.meta["architecture"]
configuration_data["Acoustic model"]["data"]["Phone type"] = self.meta["phone_type"]
configuration_data["Acoustic model"]["data"]["Features"] = {
"Feature type": self.meta["features"]["feature_type"],
"Frame shift": self.meta["features"]["frame_shift"],
"Performs speaker adaptation": self.meta["features"]["uses_speaker_adaptation"],
"Performs LDA on features": self.meta["features"]["uses_splices"],
}
if self.meta["phones"]:
configuration_data["Acoustic model"]["data"]["Phones"] = self.meta["phones"]
else:
configuration_data["Acoustic model"]["data"]["Phones"] = "None found!"
pprint(configuration_data)
[docs]
def add_model(self, source: Path) -> None:
"""
Add file into archive
Parameters
----------
source: str
File to add
"""
for f in self.files:
source_path = source.joinpath(f)
dest_path = self.dirname.joinpath(f)
if source_path.exists():
if f == "phones.txt":
with mfa_open(source_path, "r") as in_f, mfa_open(dest_path, "w") as out_f:
for line in in_f:
if re.match(r"#\d+", line):
continue
out_f.write(line)
else:
copyfile(source_path, dest_path)
[docs]
def add_pronunciation_models(
self, source: Path, dictionary_base_names: Collection[str]
) -> None:
"""
Add file into archive
Parameters
----------
source: str
File to add
dictionary_base_names: list[str]
Base names of dictionaries to add pronunciation models
"""
for base_name in dictionary_base_names:
for f in [f"{base_name}.fst", f"{base_name}_align.fst"]:
if source.joinpath(f).exists():
copyfile(source.joinpath(f), self.dirname.joinpath(f))
[docs]
def export_model(self, destination: Path) -> None:
"""
Extract the model files to a new directory
Parameters
----------
destination: str
Destination directory to extract files to
"""
destination.mkdir(parents=True, exist_ok=True)
for f in self.files:
if os.path.exists(self.dirname.joinpath(f)):
copyfile(self.dirname.joinpath(f), destination.joinpath(f))
[docs]
def log_details(self) -> None:
"""
Log metadata information to a logger
"""
logger.debug("")
logger.debug("====ACOUSTIC MODEL INFO====")
logger.debug("Acoustic model root directory: " + str(self.root_directory))
logger.debug("Acoustic model dirname: " + str(self.dirname))
meta_path = self.dirname.joinpath("meta.json")
if not os.path.exists(meta_path):
meta_path = self.dirname.joinpath("meta.yaml")
logger.debug("Acoustic model meta path: " + str(meta_path))
if not os.path.exists(meta_path):
logger.debug("META.YAML DOES NOT EXIST, this may cause issues in validating the model")
logger.debug("Acoustic model meta information:")
stream = yaml.dump(self.meta)
logger.debug(stream)
logger.debug("")
[docs]
def validate(self, dictionary: DictionaryMixin) -> None:
"""
Validate this acoustic model against a pronunciation dictionary to ensure their
phone sets are compatible
Parameters
----------
dictionary: :class:`~montreal_forced_aligner.dictionary.mixins.DictionaryMixin`
DictionaryMixin to compare phone sets with
Raises
------
:class:`~montreal_forced_aligner.exceptions.PronunciationAcousticMismatchError`
If there are phones missing from the acoustic model
"""
if isinstance(dictionary, G2PModel):
missing_phones = dictionary.meta["phones"] - set(self.meta["phones"])
else:
missing_phones = dictionary.non_silence_phones - set(self.meta["phones"])
missing_phones -= {"sp", "<eps>"}
if missing_phones: # Compatibility
raise (PronunciationAcousticMismatchError(missing_phones))
[docs]
class G2PModel(Archive):
"""
Class for G2P models
Parameters
----------
source: str
Path to source archive
root_directory: str
Path to save exported model
"""
extensions = [".zip", ".g2p"]
model_type = "g2p"
def __init__(
self,
source: typing.Union[str, Path],
root_directory: Optional[typing.Union[str, Path]] = None,
):
if source in G2PModel.get_available_models():
source = G2PModel.get_pretrained_path(source)
super().__init__(source, root_directory)
@property
def fst(self):
return pynini.Fst.read(self.fst_path)
@property
def phone_table(self):
return pywrapfst.SymbolTable.read_text(self.sym_path)
@property
def grapheme_table(self):
return pywrapfst.SymbolTable.read_text(self.grapheme_sym_path)
@property
def rewriter(self):
if not self.grapheme_sym_path.exists():
return None
if self.meta["architecture"] == "phonetisaurus":
from montreal_forced_aligner.g2p.generator import PhonetisaurusRewriter
rewriter = PhonetisaurusRewriter(
self.fst,
self.grapheme_table,
self.phone_table,
num_pronunciations=1,
grapheme_order=self.meta["grapheme_order"],
graphemes=self.meta["graphemes"],
sequence_separator=self.meta["sequence_separator"],
strict=True,
)
else:
from montreal_forced_aligner.g2p.generator import Rewriter
rewriter = Rewriter(
self.fst, self.grapheme_table, self.phone_table, num_pronunciations=1, strict=True
)
return rewriter
@property
def meta(self) -> dict:
"""Metadata for the G2P model"""
if not self._meta:
meta_path = self.dirname.joinpath("meta.json")
format = "json"
if not os.path.exists(meta_path):
meta_path = self.dirname.joinpath("meta.yaml")
format = "yaml"
if not os.path.exists(meta_path):
self._meta = {"version": "0.9.0", "architecture": "phonetisaurus"}
else:
with mfa_open(meta_path, "r") as f:
if format == "json":
self._meta = json.load(f)
else:
self._meta = yaml.load(f, Loader=yaml.Loader)
self._meta["phones"] = set(self._meta.get("phones", []))
self._meta["graphemes"] = set(self._meta.get("graphemes", []))
self._meta["evaluation"] = self._meta.get("evaluation", [])
self._meta["training"] = self._meta.get("training", [])
return self._meta
@property
def fst_path(self) -> Path:
"""G2P model's FST path"""
return self.dirname.joinpath("model.fst")
@property
def sym_path(self) -> Path:
"""G2P model's symbols path"""
path = self.dirname.joinpath("phones.txt")
if path.exists():
return path
return self.dirname.joinpath("phones.sym")
@property
def grapheme_sym_path(self) -> Path:
"""G2P model's grapheme symbols path"""
path = self.dirname.joinpath("graphemes.txt")
if path.exists():
return path
return self.dirname.joinpath("graphemes.sym")
[docs]
def add_sym_path(self, source_directory: Path) -> None:
"""
Add symbols file into archive
Parameters
----------
source_directory: str
Source directory path
"""
if not os.path.exists(self.sym_path):
copyfile(os.path.join(source_directory, "phones.txt"), self.sym_path)
if not os.path.exists(self.grapheme_sym_path) and os.path.exists(
os.path.join(source_directory, "graphemes.txt")
):
copyfile(os.path.join(source_directory, "graphemes.txt"), self.grapheme_sym_path)
[docs]
def add_fst_model(self, source_directory: Path) -> None:
"""
Add FST file into archive
Parameters
----------
source_directory: str
Source directory path
"""
if not self.fst_path.exists():
copyfile(os.path.join(source_directory, "model.fst"), self.fst_path)
[docs]
def export_fst_model(self, destination: str) -> None:
"""
Extract FST model path to destination
Parameters
----------
destination: str
Destination directory
"""
os.makedirs(destination, exist_ok=True)
copy(self.fst_path, destination)
[docs]
def validate(self, word_list: Collection[str]) -> bool:
"""
Validate the G2P model against a word list to ensure that all graphemes are known
Parameters
----------
word_list: Collection[str]
Word list to validate against
Returns
-------
bool
False if missing graphemes were found
"""
graphemes = set()
for w in word_list:
graphemes.update(w)
missing_graphemes = graphemes - self.meta["graphemes"]
if missing_graphemes:
return False
else:
return True
[docs]
class TokenizerModel(Archive):
"""
Class for Tokenizer models
Parameters
----------
source: str
Path to source archive
root_directory: str
Path to save exported model
"""
extensions = [".zip", ".tkn"]
model_type = "tokenizer"
def __init__(
self,
source: typing.Union[str, Path],
root_directory: Optional[typing.Union[str, Path]] = None,
):
if source in TokenizerModel.get_available_models():
source = TokenizerModel.get_pretrained_path(source)
super().__init__(source, root_directory)
@property
def meta(self) -> dict:
"""Metadata for the G2P model"""
if not self._meta:
meta_path = self.dirname.joinpath("meta.json")
format = "json"
if not os.path.exists(meta_path):
meta_path = self.dirname.joinpath("meta.yaml")
format = "yaml"
if not os.path.exists(meta_path):
self._meta = {"version": "0.9.0", "architecture": "pynini"}
else:
with mfa_open(meta_path, "r") as f:
if format == "json":
self._meta = json.load(f)
else:
self._meta = yaml.load(f, Loader=yaml.Loader)
self._meta["evaluation"] = self._meta.get("evaluation", [])
self._meta["training"] = self._meta.get("training", [])
return self._meta
@property
def fst_path(self) -> Path:
"""Tokenizer model's FST path"""
return self.dirname.joinpath("tokenizer.fst")
@property
def sym_path(self) -> Path:
"""Tokenizer model's grapheme symbols path"""
path = self.dirname.joinpath("graphemes.txt")
if path.exists():
return path
path = self.dirname.joinpath("graphemes.sym")
if path.exists():
return path
return self.dirname.joinpath("graphemes.syms")
@property
def input_sym_path(self) -> Path:
"""Tokenizer model's input symbols path"""
path = self.dirname.joinpath("input.txt")
if path.exists():
return path
return self.dirname.joinpath("input.syms")
@property
def output_sym_path(self) -> Path:
"""Tokenizer model's output symbols path"""
path = self.dirname.joinpath("output.txt")
if path.exists():
return path
return self.dirname.joinpath("output.syms")
[docs]
def add_graphemes_path(self, source_directory: Path) -> None:
"""
Add symbols file into archive
Parameters
----------
source_directory: :class:`~pathlib.Path`
Source directory path
"""
for p in [self.sym_path, self.output_sym_path, self.input_sym_path]:
source_p = source_directory.joinpath(p.name)
if not p.exists() and source_p.exists():
copyfile(source_p, p)
[docs]
def add_tokenizer_model(self, source_directory: Path) -> None:
"""
Add FST file into archive
Parameters
----------
source_directory: :class:`~pathlib.Path`
Source directory path
"""
if not self.fst_path.exists():
copyfile(source_directory.joinpath("tokenizer.fst"), self.fst_path)
[docs]
def export_fst_model(self, destination: Path) -> None:
"""
Extract FST model path to destination
Parameters
----------
destination: :class:`~pathlib.Path`
Destination directory
"""
destination.mkdir(parents=True, exist_ok=True)
copy(self.fst_path, destination)
[docs]
def validate(self, *args) -> None:
"""
Placeholder
"""
pass
[docs]
class LanguageModel(Archive):
"""
Class for MFA language models
Parameters
----------
source: str
Path to source archive
root_directory: str
Path to save exported model
"""
model_type = "language_model"
arpa_extension = ".arpa"
extensions = [f".{FORMAT}", arpa_extension, ".lm"]
def __init__(
self,
source: typing.Union[str, Path],
root_directory: Optional[typing.Union[str, Path]] = None,
):
if source in LanguageModel.get_available_models():
source = LanguageModel.get_pretrained_path(source)
from .config import get_temporary_directory
if isinstance(source, str):
source = Path(source)
if root_directory is None:
root_directory = get_temporary_directory().joinpath(
"extracted_models", self.model_type
)
if isinstance(root_directory, str):
source = Path(root_directory)
if source.suffix == self.arpa_extension:
self.root_directory = root_directory
self._meta = {}
self.name = source.stem
self.dirname = root_directory.joinpath(f"{self.name}_{self.model_type}")
if not os.path.exists(self.dirname):
os.makedirs(self.dirname, exist_ok=True)
copy(source, self.large_arpa_path)
else:
super().__init__(source, root_directory)
@property
def decode_arpa_path(self) -> Path:
"""
Uses the smallest language model for decoding
"""
for path in [self.small_arpa_path, self.medium_arpa_path, self.large_arpa_path]:
if path.exists():
return path
raise LanguageModelNotFoundError(self.small_arpa_path)
@property
def carpa_path(self) -> Path:
"""
Uses the largest language model for rescoring
"""
for path in [self.large_arpa_path, self.medium_arpa_path, self.small_arpa_path]:
if path.exists():
return path
raise LanguageModelNotFoundError(self.large_arpa_path)
@property
def small_arpa_path(self) -> Path:
"""Small arpa path"""
for path in self.dirname.iterdir():
if path.name.endswith("_small" + self.arpa_extension):
return path
return self.dirname.joinpath(f"{self.name}_small{self.arpa_extension}")
@property
def medium_arpa_path(self) -> Path:
"""Medium arpa path"""
for path in self.dirname.iterdir():
if path.name.endswith("_med" + self.arpa_extension):
return path
return self.dirname.joinpath(f"{self.name}_med{self.arpa_extension}")
@property
def large_arpa_path(self) -> Path:
"""Large arpa path"""
for path in self.dirname.iterdir():
if (
path.name.endswith(self.arpa_extension)
and "_small" not in path.name
and "_med" not in path.name
):
return path
return self.dirname.joinpath(self.name + self.arpa_extension)
[docs]
def add_arpa_file(self, arpa_path: Path) -> None:
"""
Adds an ARPA file to the model
Parameters
----------
arpa_path: :class:`~pathlib.Path`
Path to ARPA file
"""
output_name = self.large_arpa_path
if arpa_path.name.endswith("_small.arpa"):
output_name = self.small_arpa_path
elif arpa_path.name.endswith("_medium.arpa"):
output_name = self.medium_arpa_path
copyfile(arpa_path, output_name)
[docs]
class DictionaryModel(MfaModel):
"""
Class for representing MFA pronunciation dictionaries
Parameters
----------
path: :class:`~pathlib.Path`
Path to the dictionary file
root_directory: :class:`~pathlib.Path`, optional
Path to working directory (currently not needed, but present to maintain consistency with other MFA Models
"""
model_type = "dictionary"
extensions = [".dict", ".txt", ".yaml", ".yml"]
def __init__(
self,
path: typing.Union[str, Path],
root_directory: Optional[typing.Union[str, Path]] = None,
phone_set_type: typing.Union[str, PhoneSetType] = "UNKNOWN",
):
if path in DictionaryModel.get_available_models():
path = DictionaryModel.get_pretrained_path(path)
if isinstance(path, str):
path = Path(path)
if root_directory is None:
from montreal_forced_aligner.config import get_temporary_directory
root_directory = get_temporary_directory().joinpath(
"extracted_models", self.model_type
)
if isinstance(root_directory, str):
root_directory = Path(root_directory)
self.path = path
self.dirname = root_directory.joinpath(f"{self.name}_{self.model_type}")
self.pronunciation_probabilities = True
self.silence_probabilities = True
self.oov_probabilities = True
if not isinstance(phone_set_type, PhoneSetType):
phone_set_type = PhoneSetType[phone_set_type]
self.phone_set_type = phone_set_type
detect_phone_set = False
if self.phone_set_type == PhoneSetType.AUTO:
detect_phone_set = True
patterns = {
PhoneSetType.ARPA: PhoneSetType.ARPA.regex_detect,
PhoneSetType.IPA: PhoneSetType.IPA.regex_detect,
PhoneSetType.PINYIN: PhoneSetType.PINYIN.regex_detect,
}
counts = {
PhoneSetType.UNKNOWN: 0,
PhoneSetType.ARPA: 0,
PhoneSetType.IPA: 0,
PhoneSetType.PINYIN: 0,
}
count = 0
with mfa_open(self.path, "r") as f:
for line in f:
line = line.strip()
if not line:
continue
if detect_phone_set:
for phone_set, pattern in patterns.items():
if pattern.search(line):
counts[phone_set] += 1
break
else:
counts[PhoneSetType.UNKNOWN] += 1
continue
if counts[phone_set] > 100:
other_sets_max = max(counts[x] for x in counts if x is not phone_set)
if counts[phone_set] - other_sets_max >= 100:
break
else:
count += 1
if count > 15:
break
_, line = line.split(maxsplit=1) # word
try:
next_item, line = line.split(maxsplit=1)
except ValueError:
next_item = line
line = ""
if self.pronunciation_probabilities:
try:
prob = float(next_item)
if prob > 1 or prob < 0:
raise ValueError
except ValueError:
self.pronunciation_probabilities = False
try:
next_item, line = line.split(maxsplit=1)
except ValueError:
self.silence_probabilities = False
continue
if self.silence_probabilities:
try:
prob = float(next_item)
if prob > 1 or prob < 0:
raise ValueError
except ValueError:
self.silence_probabilities = False
self.oov_probabilities = False
if detect_phone_set:
self.phone_set_type = max(counts.keys(), key=lambda x: counts[x])
@property
def meta(self) -> MetaDict:
"""Metadata for the dictionary"""
return {
"phone_set_type": self.phone_set_type,
"pronunciation_probabilities": self.pronunciation_probabilities,
"silence_probabilities": self.silence_probabilities,
}
[docs]
def pretty_print(self) -> None:
"""
Pretty print the dictionary's metadata
"""
from montreal_forced_aligner.dictionary.multispeaker import MultispeakerDictionary
configuration_data = {"Dictionary": {"name": self.name, "data": self.meta}}
temp_directory = self.dirname.joinpath("temp")
if temp_directory.exists():
shutil.rmtree(temp_directory)
dictionary = MultispeakerDictionary(self.path, phone_set_type=self.phone_set_type)
graphemes, phone_counts = dictionary.dictionary_setup()
configuration_data["Dictionary"]["data"]["phones"] = sorted(dictionary.non_silence_phones)
configuration_data["Dictionary"]["data"]["detailed_phone_info"] = {}
if self.phone_set_type.has_base_phone_regex:
for k, v in sorted(dictionary.base_phones.items()):
if k not in configuration_data["Dictionary"]["data"]["detailed_phone_info"]:
configuration_data["Dictionary"]["data"]["detailed_phone_info"][k] = []
for p2 in sorted(v, key=lambda x: -phone_counts[x]):
detail_string = f"{p2} ({phone_counts[p2]})"
configuration_data["Dictionary"]["data"]["detailed_phone_info"][k].append(
detail_string
)
else:
configuration_data["Dictionary"]["data"]["detailed_phone_info"] = {}
for phone in sorted(dictionary.non_silence_phones):
configuration_data["Dictionary"]["data"]["detailed_phone_info"][
phone
] = phone_counts[phone]
if len(graphemes) < 50:
configuration_data["Dictionary"]["data"]["graphemes"] = sorted(graphemes)
else:
configuration_data["Dictionary"]["data"]["graphemes"] = f"{len(graphemes)} graphemes"
pprint(configuration_data)
[docs]
@classmethod
def valid_extension(cls, filename: Path) -> bool:
"""
Check whether a file has a valid extension for the given model archive
Parameters
----------
filename: :class:`~pathlib.Path`
File name to check
Returns
-------
bool
True if the extension matches the models allowed extensions
"""
if filename.suffix in cls.extensions:
return True
return False
[docs]
@classmethod
def generate_path(
cls, root: Path, name: str, enforce_existence: bool = True
) -> Optional[Path]:
"""
Generate a path for a given model from the root directory and the name of the model
Parameters
----------
root: :class:`~pathlib.Path`
Root directory for the full path
name: str
Name of the model
enforce_existence: bool
Flag to return None if the path doesn't exist, defaults to True
Returns
-------
Path
Full path in the root directory for the model
"""
for ext in cls.extensions:
path = root.joinpath(name + ext)
if path.exists() or not enforce_existence:
return path
return None
@property
def is_multiple(self) -> bool:
"""Flag for whether the dictionary contains multiple lexicons"""
return self.path.suffix in [".yaml", ".yml"]
@property
def name(self) -> str:
"""Name of the dictionary"""
return self.path.stem
[docs]
def load_dictionary_paths(self) -> Dict[str, Tuple[DictionaryModel, typing.Set[str]]]:
"""
Load the pronunciation dictionaries
Returns
-------
dict[str, :class:`~montreal_forced_aligner.models.DictionaryModel`]
Mapping of component pronunciation dictionaries
"""
mapping = {}
if self.is_multiple:
with mfa_open(self.path, "r") as f:
data = yaml.load(f, Loader=yaml.Loader)
for speaker, path in data.items():
if path not in mapping:
mapping[path] = (DictionaryModel(path), set())
mapping[path][1].add(speaker)
else:
mapping[str(self.path)] = (self, {"default"})
return mapping
MODEL_TYPES = {
"acoustic": AcousticModel,
"g2p": G2PModel,
"dictionary": DictionaryModel,
"language_model": LanguageModel,
"ivector": IvectorExtractorModel,
"tokenizer": TokenizerModel,
}
@dataclass(slots=True)
class ModelRelease:
"""
Dataclas for model releases
Parameters
----------
model_name: str
Name of the model
tag_name: str
Tag on GitHub
version: str
Version of the model
download_link: str
Link to download the model
download_file_name: str
File name to save as
release_id: int
Release ID on GitHub
"""
model_name: str
tag_name: str
version: str
download_link: str
download_file_name: str
release_id: int = None
@property
def release_link(self) -> Optional[str]:
"""Generate link pointing to the release on GitHub"""
if not self.release_id:
return None
return ModelManager.base_url + f"/{self.release_id}"
class ModelManager:
"""
Class for managing the currently available models on the local system and the models available to be downloaded
Parameters
----------
token: str, optional
GitHub authentication token to use to increase release limits
ignore_cache: bool
Flag to ignore previously downloaded files
"""
base_url = "https://api.github.com/repos/MontrealCorpusTools/mfa-models/releases"
def __init__(self, token: typing.Optional[str] = None, ignore_cache: bool = False):
from montreal_forced_aligner.config import get_temporary_directory
pretrained_dir = get_temporary_directory().joinpath("pretrained_models")
pretrained_dir.mkdir(parents=True, exist_ok=True)
self.local_models = {k: [] for k in MODEL_TYPES.keys()}
self.remote_models: Dict[str, Dict[str, Dict[str, ModelRelease]]] = {
k: {} for k in MODEL_TYPES.keys()
}
self.token = token
environment_token = os.environ.get("GITHUB_TOKEN", None)
if self.token is None:
self.token = environment_token
self.synced_remote = False
self.ignore_cache = ignore_cache
self._cache_info = {}
self.refresh_local()
@property
def cache_path(self) -> Path:
"""Path to json file with cached etags and download links"""
from montreal_forced_aligner.config import get_temporary_directory
return get_temporary_directory().joinpath("pretrained_models", "cache.json")
def reset_local(self) -> None:
"""Reset cached models"""
from montreal_forced_aligner.config import get_temporary_directory
pretrained_dir = get_temporary_directory().joinpath("pretrained_models")
if pretrained_dir.exists():
shutil.rmtree(pretrained_dir, ignore_errors=True)
def refresh_local(self) -> None:
"""Refresh cached information with the latest list of local model"""
if self.cache_path.exists() and not self.ignore_cache:
reset_cache = False
with mfa_open(self.cache_path, "r") as f:
self._cache_info = json.load(f)
for (
model_type,
model_releases,
) in self._cache_info.items(): # Backward compatibility
if model_type not in MODEL_TYPES:
continue
for version_data in model_releases.values():
if not isinstance(version_data, dict):
reset_cache = True
break
if reset_cache:
break
if reset_cache:
self._cache_info = {}
if "list_etags" in self._cache_info:
self._cache_info["list_etags"] = {
int(k): v for k, v in self._cache_info["list_etags"].items()
}
self.local_models = {
model_type: model_class.get_available_models()
for model_type, model_class in MODEL_TYPES.items()
}
def refresh_remote(self) -> None:
"""Refresh cached information with the latest list of downloadable models"""
self.remote_models = {k: {} for k in MODEL_TYPES.keys()}
data_count = 100
headers = {"Accept": "application/vnd.github.v3+json"}
if self.token:
headers["Authorization"] = f"token {self.token}"
else:
logger.debug("No Github Token supplied")
page = 1
etags = {}
if "list_etags" in self._cache_info:
etags = self._cache_info["list_etags"]
else:
self._cache_info["list_etags"] = {}
while data_count == 100:
if page in etags:
headers["If-None-Match"] = etags[page]
r = requests.get(
self.base_url, params={"per_page": 100, "page": page}, headers=headers
)
if r.status_code >= 400:
raise ModelsConnectionError(r.status_code, r.json(), r.headers)
if r.status_code >= 300: # Using cached releases
for model_type, model_releases in self._cache_info.items():
if model_type not in MODEL_TYPES:
continue
for model_name, version_data in model_releases.items():
for version, data in version_data.items():
if model_name not in self.remote_models[model_type]:
self.remote_models[model_type][model_name] = {}
self.remote_models[model_type][model_name][version] = ModelRelease(
*data
)
return
self._cache_info["list_etags"][page] = r.headers["etag"]
data = r.json()
data_count = len(data)
for d in data:
tag = d["tag_name"]
model_type, model_name, version = tag.split(
"-"
) # tag format "{model_type}-{model_name}-v{version}"
if model_type not in self.remote_models: # Other releases, archived, etc
continue
if not tag.startswith(model_type):
continue
if "archive" in tag:
continue
download_url = d["assets"][0]["url"]
file_name = d["assets"][0]["name"]
if model_name not in self.remote_models[model_type]:
self.remote_models[model_type][model_name] = {}
self.remote_models[model_type][model_name][version] = ModelRelease(
model_name, tag, version, download_url, file_name, d["id"]
)
if model_type not in self._cache_info:
self._cache_info[model_type] = {}
if model_name not in self._cache_info[model_type]:
self._cache_info[model_type][model_name] = {}
self._cache_info[model_type][model_name][version] = [
model_name,
tag,
version,
download_url,
file_name,
d["id"],
]
page += 1
with mfa_open(self.cache_path, "w") as f:
json.dump(self._cache_info, f, ensure_ascii=False)
self.synced_remote = True
def has_local_model(self, model_type: str, model_name: str) -> bool:
"""Check for local model"""
return model_name in self.local_models[model_type]
def print_local_models(self, model_type: typing.Optional[str] = None) -> None:
"""
List all local pretrained models
Parameters
----------
model_type: str, optional
Model type, will list models of all model types if None
"""
self.refresh_local()
if model_type is None:
logger.info("Available local models")
data = {}
for model_type, model_class in MODEL_TYPES.items():
data[model_type] = model_class.get_available_models()
pprint(data)
else:
logger.info(f"Available local {model_type} models")
model_class = MODEL_TYPES[model_type]
names = model_class.get_available_models()
if names:
pprint(names)
else:
logger.error("No models found")
def print_remote_models(self, model_type: typing.Optional[str] = None) -> None:
"""
Print of models available for download
Parameters
----------
model_type: str
Model type to look up
"""
if not self.synced_remote:
self.refresh_remote()
if model_type is None:
logger.info("Available models for download")
data = {}
for model_type, release_data in self.remote_models.items():
data[model_type] = sorted(release_data.keys())
pprint(data)
else:
logger.info(f"Available {model_type} models for download")
names = {
x: sorted(self.remote_models[model_type][x].keys())
for x in sorted(self.remote_models[model_type].keys())
}
if names:
pprint(names)
else:
logger.error("No models found")
def download_model(
self,
model_type: str,
model_name: typing.Optional[str],
version: typing.Optional[str] = None,
) -> None:
"""
Download a model to MFA's temporary directory
Parameters
----------
model_type: str
Model type
model_name: str
Name of model
version: str, optional
Version of model to download, optional
"""
if not model_name:
return self.print_remote_models(model_type)
if not self.synced_remote:
self.refresh_remote()
ignore_cache = self.ignore_cache
if model_name not in self.remote_models[model_type]:
raise RemoteModelNotFoundError(
model_name, model_type, sorted(self.remote_models[model_type].keys())
)
if version is None:
version = sorted(self.remote_models[model_type][model_name].keys())[-1]
else:
if not version.startswith("v"):
version = f"v{version}"
ignore_cache = True
if version not in self.remote_models[model_type][model_name]:
raise RemoteModelVersionNotFoundError(
model_name,
model_type,
version,
sorted(self.remote_models[model_type][model_name].keys()),
)
release = self.remote_models[model_type][model_name][version]
local_path = (
MODEL_TYPES[model_type].pretrained_directory().joinpath(release.download_file_name)
)
if local_path.exists() and not ignore_cache:
logger.warning(
f"Local version of model already exists ({local_path}). "
f"Use the --ignore_cache flag to force redownloading."
)
return
headers = {"Accept": "application/octet-stream"}
if self.token:
headers["Authorization"] = f"token {self.token}"
if release.download_link in self._cache_info:
headers["If-None-Match"] = self._cache_info[release.download_link]
r = requests.get(release.download_link, headers=headers)
if r.status_code >= 400:
raise ModelsConnectionError(r.status_code, r.json(), r.headers)
self._cache_info[release.download_link] = r.headers["etag"]
with mfa_open(local_path, "wb") as f:
f.write(r.content)
self.refresh_local()
logger.info(
f"Saved model to {local_path}, you can now use {model_name} in place of {model_type} paths in mfa commands."
)
