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import collections
import json
import os
import pathlib
import random
import re
import shutil
import typing
from datetime import datetime
import numpy as np
import sqlalchemy
from montreal_forced_aligner import config
config.TEMPORARY_DIRECTORY = pathlib.Path(os.path.dirname(os.path.abspath(__file__)))
config.USE_POSTGRES = False
import montreal_forced_aligner.utils
from montreal_forced_aligner.data import PhoneSetType, voiced_variants, voiceless_variants
from montreal_forced_aligner.db import Phone, PhoneType, Pronunciation, Word
from montreal_forced_aligner.dictionary.multispeaker import MultispeakerDictionary
from montreal_forced_aligner.models import MODEL_TYPES
rng = np.random.default_rng(1234)
random.seed(1234)
root_dir = pathlib.Path(__file__).resolve().parent
template_dir = root_dir.joinpath("templates")
CURRENT_MODEL_VERSION = "3.3.0"
# Get corpus information
current_corpora = {
"english": [
"Common Voice English v8_0",
"LibriSpeech English",
"Corpus of Regional African American Language v2021_07",
"Google Nigerian English",
"Google UK and Ireland English",
"NCHLT English",
"ARU English corpus",
"ICE-Nigeria",
"A Scripted Pakistani English Daily-use Speech Corpus",
"L2-ARCTIC",
],
"czech": [
"Common Voice Czech v9_0",
"GlobalPhone Czech v3_1",
"Large Corpus of Czech Parliament Plenary Hearings",
"Czech Parliament Meetings",
],
"hausa": ["Common Voice Hausa v9_0", "GlobalPhone Hausa v3_1"],
"swahili": ["Common Voice Swahili v9_0", "ALFFA Swahili", "GlobalPhone Swahili v3_1"],
"korean": [
"GlobalPhone Korean v3_1",
"Deeply Korean read speech corpus public sample",
"Pansori TEDxKR",
"Zeroth Korean",
"Seoul Corpus",
"ASR-KCSC: A Korean Conversational Speech Corpus",
"ASR-SKDuSC: A Scripted Korean Daily-use Speech Corpus",
"Korean Single Speaker Speech Dataset",
"Common Voice Korean v16_1",
],
"mandarin": [
"Common Voice Chinese (China) v9_0",
"Common Voice Chinese (Taiwan) v9_0",
"AI-DataTang Corpus",
"AISHELL-3",
"THCHS-30",
"GlobalPhone Chinese-Mandarin v3_1",
],
"japanese": [
"Common Voice Japanese v9_0",
"GlobalPhone Japanese v3_1",
"Microsoft Speech Language Translation Japanese",
"Japanese Versatile Speech",
"TEDxJP-10K v1_1",
],
"thai": ["Common Voice Thai v9_0", "GlobalPhone Thai v3_1"],
"vietnamese": ["Common Voice Vietnamese v9_0", "VIVOS", "GlobalPhone Vietnamese v3_1"],
}
model_corpus_mapping = {
"Abkhaz CV acoustic model v2_0_0": ["Common Voice Abkhaz v7_0"],
"Armenian CV acoustic model v2_0_0": ["Common Voice Armenian v7_0"],
"Bashkir CV acoustic model v2_0_0": ["Common Voice Bashkir v7_0"],
"Basque CV acoustic model v2_0_0": ["Common Voice Basque v7_0"],
"Belarusian CV acoustic model v2_0_0": ["Common Voice Belarusian v7_0"],
"Bulgarian CV acoustic model v2_0_0": ["Common Voice Bulgarian v7_0"],
"Chuvash CV acoustic model v2_0_0": ["Common Voice Chuvash v7_0"],
"Czech CV acoustic model v2_0_0": ["Common Voice Czech v7_0"],
"Dutch CV acoustic model v2_0_0": ["Common Voice Dutch v7_0"],
"Georgian CV acoustic model v2_0_0": ["Common Voice Georgian v7_0"],
"Greek CV acoustic model v2_0_0": ["Common Voice Greek v7_0"],
"Guarani CV acoustic model v2_0_0": ["Common Voice Guarani v7_0"],
"Hausa CV acoustic model v2_0_0": ["Common Voice Hausa v7_0"],
"Hungarian CV acoustic model v2_0_0": ["Common Voice Hungarian v7_0"],
"Italian CV acoustic model v2_0_0": ["Common Voice Italian v7_0"],
"Kazakh CV acoustic model v2_0_0": ["Common Voice Kazakh v7_0"],
"Kurmanji CV acoustic model v2_0_0": ["Common Voice Kurmanji v7_0"],
"Kyrgyz CV acoustic model v2_0_0": ["Common Voice Kyrgyz v7_0"],
"Polish CV acoustic model v2_0_0": ["Common Voice Polish v7_0"],
"Portuguese CV acoustic model v2_0_0": ["Common Voice Portuguese v7_0"],
"Romanian CV acoustic model v2_0_0": ["Common Voice Romanian v7_0"],
"Russian CV acoustic model v2_0_0": ["Common Voice Russian v7_0"],
"Sorbian (Upper) CV acoustic model v2_0_0": ["Common Voice Sorbian Upper v7_0"],
"Swedish CV acoustic model v2_0_0": ["Common Voice Swedish v7_0"],
"Tamil CV acoustic model v2_0_0": ["Common Voice Tamil v7_0"],
"Tatar CV acoustic model v2_0_0": ["Common Voice Tatar v7_0"],
"Thai CV acoustic model v2_0_0": ["Common Voice Thai v7_0"],
"Turkish CV acoustic model v2_0_0": ["Common Voice Turkish v7_0"],
"Ukrainian CV acoustic model v2_0_0": ["Common Voice Ukrainian v7_0"],
"Uyghur CV acoustic model v2_0_0": ["Common Voice Uyghur v7_0"],
"Uzbek CV acoustic model v2_0_0": ["Common Voice Uzbek v7_0"],
"Vietnamese CV acoustic model v2_0_0": ["Common Voice Vietnamese v7_0"],
"English (US) ARPA acoustic model v2_0_0": ["LibriSpeech English"],
"English (US) ARPA acoustic model v2_0_0a": ["LibriSpeech English"],
"English (US) ARPA acoustic model v3_0_0": ["LibriSpeech English"],
"English MFA acoustic model v2_0_0": [
"Common Voice English v8_0",
"LibriSpeech English",
"Corpus of Regional African American Language v2021_07",
"Google Nigerian English",
"Google UK and Ireland English",
"NCHLT English",
"ARU English corpus",
],
"English MFA acoustic model v2_0_0a": [
"Common Voice English v8_0",
"LibriSpeech English",
"Corpus of Regional African American Language v2021_07",
"Google Nigerian English",
"Google UK and Ireland English",
"NCHLT English",
"ARU English corpus",
],
"English MFA acoustic model v2_2_1": [
"Common Voice English v8_0",
"LibriSpeech English",
"Corpus of Regional African American Language v2021_07",
"Google Nigerian English",
"Google UK and Ireland English",
"NCHLT English",
"ARU English corpus",
"ICE-Nigeria",
"A Scripted Pakistani English Daily-use Speech Corpus",
"L2-ARCTIC",
],
"English MFA acoustic model v3_0_0": [
"Common Voice English v8_0",
"LibriSpeech English",
"Corpus of Regional African American Language v2021_07",
"Google Nigerian English",
"Google UK and Ireland English",
"NCHLT English",
"ARU English corpus",
"ICE-Nigeria",
"A Scripted Pakistani English Daily-use Speech Corpus",
"L2-ARCTIC",
],
"English MFA acoustic model v3_1_0": [
"Common Voice English v17_0",
"LibriSpeech English",
"Corpus of Regional African American Language v2021_07",
"Google Nigerian English",
"Google UK and Ireland English",
"NCHLT English",
"ARU English corpus",
"ICE-Nigeria",
"A Scripted Pakistani English Daily-use Speech Corpus",
"L2-ARCTIC",
],
"English MFA ivector extractor v2_1_0": current_corpora["english"],
"Multilingual MFA ivector extractor v2_1_0": [
x
for k in [
"english",
"czech",
"hausa",
"swahili",
"thai",
"vietnamese",
"japanese",
"mandarin",
]
for x in current_corpora[k]
],
"French MFA acoustic model v2_0_0": [
"Common Voice French v8_0",
"Multilingual LibriSpeech French",
"GlobalPhone French v3_1",
"African-accented French",
],
"French MFA acoustic model v2_0_0a": [
"Common Voice French v8_0",
"Multilingual LibriSpeech French",
"GlobalPhone French v3_1",
"African-accented French",
],
"French MFA acoustic model v3_0_0": [
"Common Voice French v16_1",
"GlobalPhone French v3_1",
"African-accented French",
],
"German MFA acoustic model v2_0_0": [
"Common Voice German v8_0",
"Multilingual LibriSpeech German",
"GlobalPhone German v3_1",
],
"German MFA acoustic model v3_0_0": ["Common Voice German v16_1", "GlobalPhone German v3_1"],
"German MFA acoustic model v2_0_0a": [
"Common Voice German v8_0",
"Multilingual LibriSpeech German",
"GlobalPhone German v3_1",
],
"Japanese MFA acoustic model v2_0_1a": [
"Common Voice Japanese v12_0",
"GlobalPhone Japanese v3_1",
"Microsoft Speech Language Translation Japanese",
"Japanese Versatile Speech",
"TEDxJP-10K v1_1",
],
"Japanese MFA acoustic model v3_0_0": [
"Common Voice Japanese v12_0",
"GlobalPhone Japanese v3_1",
"Microsoft Speech Language Translation Japanese",
"Japanese Versatile Speech",
"TEDxJP-10K v1_1",
],
"Hausa MFA acoustic model v2_0_0": ["Common Voice Hausa v8_0", "GlobalPhone Hausa v3_1"],
"Hausa MFA acoustic model v2_0_0a": ["Common Voice Hausa v9_0", "GlobalPhone Hausa v3_1"],
"Hausa MFA acoustic model v3_0_0": ["Common Voice Hausa v9_0", "GlobalPhone Hausa v3_1"],
"Mandarin MFA acoustic model v2_0_0": [
"Common Voice Chinese (China) v8_0",
"Common Voice Chinese (Taiwan) v8_0",
"AI-DataTang Corpus",
"AISHELL-3",
"THCHS-30",
],
"Mandarin MFA acoustic model v2_0_0a": [
"Common Voice Chinese (China) v9_0",
"Common Voice Chinese (Taiwan) v9_0",
"AI-DataTang Corpus",
"AISHELL-3",
"THCHS-30",
"GlobalPhone Chinese-Mandarin v3_1",
],
"Mandarin MFA acoustic model v3_0_0": [
"Common Voice Chinese (China) v16_1",
"Common Voice Chinese (Taiwan) v16_1",
"AI-DataTang Corpus",
"AISHELL-3",
"THCHS-30",
"GlobalPhone Chinese-Mandarin v3_1",
],
"Korean MFA acoustic model v2_0_0": [
"GlobalPhone Korean v3_1",
"Deeply Korean read speech corpus public sample",
"Pansori TEDxKR",
"Zeroth Korean",
"Seoul Corpus",
],
"Korean MFA acoustic model v2_0_0a": [
"GlobalPhone Korean v3_1",
"Deeply Korean read speech corpus public sample",
"Pansori TEDxKR",
"Zeroth Korean",
"Seoul Corpus",
],
"Korean MFA acoustic model v3_0_0": [
"GlobalPhone Korean v3_1",
"Deeply Korean read speech corpus public sample",
"Pansori TEDxKR",
"Zeroth Korean",
"ASR-KCSC A Korean Conversational Speech Corpus",
"ASR-SKDuSC A Scripted Korean Daily-use Speech Corpus",
"Korean Single Speaker Speech Dataset",
"Common Voice Korean v16_1",
],
"Polish MFA acoustic model v2_0_0": [
"Common Voice Polish v8_0",
"Multilingual LibriSpeech Polish",
"M-AILABS Polish",
"GlobalPhone Polish v3_1",
],
"Polish MFA acoustic model v2_0_0a": [
"Common Voice Polish v8_0",
"Multilingual LibriSpeech Polish",
"M-AILABS Polish",
"GlobalPhone Polish v3_1",
],
"Portuguese MFA acoustic model v2_0_0": [
"Common Voice Portuguese v8_0",
"Multilingual LibriSpeech Portuguese",
"GlobalPhone Portuguese (Brazilian) v3_1",
],
"Portuguese MFA acoustic model v2_0_0a": [
"Common Voice Portuguese v8_0",
"Multilingual LibriSpeech Portuguese",
"GlobalPhone Portuguese (Brazilian) v3_1",
],
"Russian MFA acoustic model v2_0_0": [
"Common Voice Russian v8_0",
"Russian LibriSpeech",
"M-AILABS Russian",
"GlobalPhone Russian v3_1",
],
"Russian MFA acoustic model v2_0_0a": [
"Common Voice Russian v9_0",
"Russian LibriSpeech",
"M-AILABS Russian",
"GlobalPhone Russian v3_1",
],
"Russian MFA acoustic model v3_1_0": [
"Common Voice Russian v17_0",
"Russian LibriSpeech",
"M-AILABS Russian",
"Multilingual TEDx Russian",
"GlobalPhone Russian v3_1",
],
"Spanish MFA acoustic model v2_0_0": [
"Common Voice Spanish v8_0",
"Multilingual LibriSpeech Spanish",
"Google i18n Chile",
"Google i18n Columbia",
"Google i18n Peru",
"Google i18n Puerto Rico",
"Google i18n Venezuela",
"M-AILABS Spanish",
"GlobalPhone Spanish (Latin American) v3_1",
],
"Spanish MFA acoustic model v2_0_0a": [
"Common Voice Spanish v8_0",
"Multilingual LibriSpeech Spanish",
"Google i18n Chile",
"Google i18n Columbia",
"Google i18n Peru",
"Google i18n Puerto Rico",
"Google i18n Venezuela",
"M-AILABS Spanish",
"GlobalPhone Spanish (Latin American) v3_1",
],
"Spanish MFA acoustic model v3_3_0": [
"Common Voice Spanish v8_0",
"Multilingual LibriSpeech Spanish",
"Google i18n Chile",
"Google i18n Columbia",
"Google i18n Peru",
"Google i18n Puerto Rico",
"Google i18n Venezuela",
"M-AILABS Spanish",
"GlobalPhone Spanish (Latin American) v3_1",
"Multilingual TEDx Spanish",
],
"Swahili MFA acoustic model v2_0_0": [
"Common Voice Swahili v8_0",
"ALFFA Swahili",
"GlobalPhone Swahili v3_1",
],
"Swahili MFA acoustic model v2_0_0a": [
"Common Voice Swahili v9_0",
"ALFFA Swahili",
"GlobalPhone Swahili v3_1",
],
"Swedish MFA acoustic model v2_0_0": [
"Common Voice Swedish v8_0",
"NST Swedish",
"GlobalPhone Swedish v3_1",
],
"Swedish MFA acoustic model v2_0_0a": [
"Common Voice Swedish v8_0",
"NST Swedish",
"GlobalPhone Swedish v3_1",
],
"Swedish MFA acoustic model v3_0_0": [
"Common Voice Swedish v8_0",
"NST Swedish",
"GlobalPhone Swedish v3_1",
],
"Thai MFA acoustic model v2_0_0": ["Common Voice Thai v8_0", "GlobalPhone Thai v3_1"],
"Thai MFA acoustic model v2_0_0a": ["Common Voice Thai v9_0", "GlobalPhone Thai v3_1"],
"Thai MFA acoustic model v3_0_0": [
"Common Voice Thai v16_1",
"GlobalPhone Thai v3_1",
"Lotus Corpus v1_0",
"Gowajee Corpus v0_9_3",
"Thai Elderly Speech dataset by Data Wow and VISAI v1_0_0",
],
"Bulgarian MFA acoustic model v2_0_0": [
"Common Voice Bulgarian v8_0",
"GlobalPhone Bulgarian v3_1",
],
"Bulgarian MFA acoustic model v2_0_0a": [
"Common Voice Bulgarian v9_0",
"GlobalPhone Bulgarian v3_1",
],
"Bulgarian MFA acoustic model v3_0_0": [
"Common Voice Bulgarian v16_1",
"GlobalPhone Bulgarian v3_1",
],
"Croatian MFA acoustic model v2_0_0": [
"Common Voice Serbian v8_0",
"GlobalPhone Croatian v3_1",
],
"Croatian MFA acoustic model v2_0_0a": [
"Common Voice Serbian v9_0",
"GlobalPhone Croatian v3_1",
],
"Croatian MFA acoustic model v3_3_0": [
"Common Voice Serbian v9_0",
"GlobalPhone Croatian v3_1",
],
"Czech MFA acoustic model v2_0_0": [
"Common Voice Czech v8_0",
"GlobalPhone Czech v3_1",
"Large Corpus of Czech Parliament Plenary Hearings",
"Czech Parliament Meetings",
],
"Czech MFA acoustic model v2_0_0a": [
"Common Voice Czech v9_0",
"GlobalPhone Czech v3_1",
"Large Corpus of Czech Parliament Plenary Hearings",
"Czech Parliament Meetings",
],
"Czech MFA acoustic model v3_3_0": [
"Common Voice Czech v9_0",
"GlobalPhone Czech v3_1",
"Large Corpus of Czech Parliament Plenary Hearings",
"Czech Parliament Meetings",
],
"Turkish MFA acoustic model v3_0_0": [
"Common Voice Turkish v16_1",
"GlobalPhone Turkish v3_1",
],
"Turkish MFA acoustic model v2_0_0": [
"Common Voice Turkish v8_0",
"MediaSpeech Turkish v1_1",
"GlobalPhone Turkish v3_1",
],
"Turkish MFA acoustic model v2_0_0a": [
"Common Voice Turkish v8_0",
"MediaSpeech Turkish v1_1",
"GlobalPhone Turkish v3_1",
],
"Ukrainian MFA acoustic model v2_0_0": [
"Common Voice Ukrainian v8_0",
"M-AILABS Ukrainian",
"GlobalPhone Ukrainian v3_1",
],
"Ukrainian MFA acoustic model v2_0_0a": [
"Common Voice Ukrainian v9_0",
"M-AILABS Ukrainian",
"GlobalPhone Ukrainian v3_1",
],
"Ukrainian MFA acoustic model v3_0_0": [
"Common Voice Ukrainian v16_1",
"M-AILABS Ukrainian",
"GlobalPhone Ukrainian v3_1",
],
"Vietnamese MFA acoustic model v2_0_0": [
"Common Voice Vietnamese v8_0",
"VIVOS",
"GlobalPhone Vietnamese v3_1",
],
"Vietnamese MFA acoustic model v2_0_0a": [
"Common Voice Vietnamese v9_0",
"VIVOS",
"GlobalPhone Vietnamese v3_1",
],
"Vietnamese MFA acoustic model v3_0_0": [
"Common Voice Vietnamese v17_0",
"VIVOS",
"GlobalPhone Vietnamese v3_1",
],
}
model_dictionary_mapping = {
"English MFA acoustic model v2_0_0": [
"English (US) MFA dictionary v2_0_0",
"English (UK) MFA dictionary v2_0_0",
"English (Nigeria) MFA dictionary v2_0_0",
],
"English MFA acoustic model v3_0_0": [
"English (US) MFA dictionary v3_0_0",
"English (UK) MFA dictionary v3_0_0",
"English (Nigeria) MFA dictionary v3_0_0",
"English (India) MFA dictionary v3_0_0",
],
"English MFA acoustic model v3_1_0": [
"English (US) MFA dictionary v3_1_0",
"English (UK) MFA dictionary v3_1_0",
"English (Nigeria) MFA dictionary v3_1_0",
"English (India) MFA dictionary v3_1_0",
],
"Vietnamese MFA acoustic model v2_0_0": [
"Vietnamese (Hanoi) MFA dictionary v2_0_0",
"Vietnamese (Ho Chi Minh City) MFA dictionary v2_0_0",
"Vietnamese (Hue) MFA dictionary v2_0_0",
"Vietnamese MFA dictionary v2_0_0",
],
"Spanish MFA acoustic model v2_0_0": [
"Spanish (Latin America) MFA dictionary v2_0_0",
"Spanish (Spain) MFA dictionary v2_0_0",
"Spanish MFA dictionary v2_0_0",
],
"Spanish MFA acoustic model v3_3_0": [
"Spanish (Latin America) MFA dictionary v3_3_0",
"Spanish (Spain) MFA dictionary v3_3_0",
],
"Portuguese MFA acoustic model v2_0_0": [
"Portuguese (Brazil) MFA dictionary v2_0_0",
"Portuguese (Portugal) MFA dictionary v2_0_0",
"Portuguese MFA dictionary v2_0_0",
],
"Mandarin MFA acoustic model v2_0_0": [
"Mandarin (China) MFA dictionary v2_0_0",
"Mandarin (Erhua) MFA dictionary v2_0_0",
"Mandarin (Taiwan) MFA dictionary v2_0_0",
],
}
def make_path_safe(string):
s = re.sub(r"[- .:()]+", "_", string.lower())
if s.endswith("_"):
s = s[:-1]
return s
def get_model_card_directory(model_type, meta_data):
model_directory = os.path.join(mfa_model_root, model_type)
if model_type == "language_model":
language, version = meta_data["language"], meta_data["version"]
directory = os.path.join(model_directory, language.lower(), "mfa", f"v{version}")
elif model_type in {"ivector", "tokenizer"}:
language, version = meta_data["language"], meta_data["version"]
directory = os.path.join(model_directory, language.lower(), f"v{version}")
elif model_type == "corpus":
language, name = meta_data["language"], meta_data["name"]
name = make_path_safe(name)
if "version" in meta_data:
version = meta_data["version"]
directory = os.path.join(model_directory, language.lower(), name, f"{version}")
else:
directory = os.path.join(model_directory, language.lower(), name)
else:
language, phone_set, dialect, version = (
meta_data["language"],
meta_data["phone_set"],
meta_data["dialect"],
meta_data["version"],
)
if dialect:
phoneset_folder = f"{dialect}_{phone_set}".replace(" ", "_").lower()
else:
phoneset_folder = phone_set.lower()
directory = os.path.join(model_directory, language.lower(), phoneset_folder, f"v{version}")
return directory
mfa_model_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
OVERWRITE_METADATA = False
OVERWRITE_MD = False
mfa_citation_template = (
"@techreport{{{id},\n\tauthor={{{extra_authors}McAuliffe, Michael and Sonderegger, Morgan}},"
"\n\ttitle={{{title}}},"
"\n\taddress={{\\url{{https://mfa-models.readthedocs.io/{model_type}/{language}/{link_safe_title}.html}}}},"
"\n\tyear={{{year}}},\n\tmonth={{{month}}},"
"\n}}"
)
cv_citation = (
"@misc{Ahn_Chodroff_2022,\n\tauthor={Ahn, Emily and Chodroff, Eleanor},"
"\n\ttitle={VoxCommunis Corpus},"
"\n\taddress={\\url{https://osf.io/t957v}},"
"\n\tpublisher={OSF},"
"\n\tyear={2022}, \n\tmonth={Jan}\n}"
)
prosodylab_citation = (
"@article{gorman2011prosodylab,\n\tauthor={Gorman, Kyle and Howell, Jonathan and Wagner, Michael},"
"\n\ttitle={Prosodylab-aligner: A tool for forced alignment of laboratory speech},"
"\n\tjournal={Canadian Acoustics},"
"\n\tvolume={39},\n\tnumber={3},\n\tpages={192--193},\n\tyear={2011}\n}"
)
language_link_template = "[{}]({})"
license_links = {
"CC-0": "https://creativecommons.org/publicdomain/zero/1.0/",
"CC BY 4.0": "https://creativecommons.org/licenses/by/4.0/",
"CC BY 3.0": "https://creativecommons.org/licenses/by/3.0/",
"CC BY-SA-NC 3.0": "https://creativecommons.org/licenses/by-nc-sa/3.0/",
"CC BY-NC-SA 4.0": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
"CC BY-NC-SA 3.0": "https://creativecommons.org/licenses/by-nc-sa/3.0/",
"CC BY-NC 4.0": "https://creativecommons.org/licenses/by-nc/4.0/",
"CC BY-SA 4.0": "https://creativecommons.org/licenses/by-sa/4.0/",
"CC BY-NC-ND 4.0": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
"CC BY-NC 2.0": "https://creativecommons.org/licenses/by-nc/2.0/",
"CC BY-NC-ND 3.0": "https://creativecommons.org/licenses/by-nc-nd/3.0/",
"Microsoft Research Data License": "https://msropendata-web-api.azurewebsites.net/licenses/2f933be3-284d-500b-7ea3-2aa2fd0f1bb2/view",
"Apache 2.0": "https://www.apache.org/licenses/LICENSE-2.0",
"O-UDA v1.0": "https://msropendata-web-api.azurewebsites.net/licenses/f1f352a6-243f-4905-8e00-389edbca9e83/view",
"MIT": "https://opensource.org/licenses/MIT",
"Public domain in the USA": "https://creativecommons.org/share-your-work/public-domain/cc0/",
"M-AILABS License": "https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/",
"ELRA": "https://www.elra.info/en/services-around-lrs/distribution/licensing/",
"Buckeye License": "https://buckeyecorpus.osu.edu/php/registration.php",
"LDC License": "https://www.ldc.upenn.edu/data-management/using/licensing",
"LaboroTV Non-commercial": "https://laboro.ai/activity/column/engineer/eg-laboro-tv-corpus-jp/",
}
mfa_maintainer = "[Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)"
cv_maintainer = "[Vox Communis](https://osf.io/t957v/)"
corpus_detail_template = """
* {link}:
* **Hours:** `{num_hours:.2f}`
* **Speakers:** `{num_speakers:,}`
* **Utterances:** `{num_utterances:,}`"""
g2p_training_detail_template = """
* **Words:** `{num_words:,}`
* **Phones:** `{num_phones:,}`
* **Graphemes:** `{num_graphemes:,}`"""
g2p_evaluation_detail_template = """
* **Words:** `{num_words:,}`
* **WER:** `{word_error_rate:.2f}%`
* **PER:** `{phone_error_rate:.2f}%`"""
tokenizer_training_detail_template = """
* **Utterances:** `{num_utterances:,}`
* **Graphemes:** `{num_graphemes:,}`"""
tokenizer_evaluation_detail_template = """
* **Utterances:** `{num_utterances:,}`
* **UER:** `{utterance_error_rate:.2f}%`
* **CER:** `{character_error_rate:.2f}%`"""
lm_training_detail_template = """
* **Words:** `{num_words:,}`
* **OOVs:** `{num_oovs:,}`"""
lm_evaluation_detail_template = """
* **Large model:** `{large_perplexity:.2f}`
* **Medium model:** `{medium_perplexity:.2f}`
* **Small model:** `{small_perplexity:.2f}`"""
link_template = "* {{ref}}`{}`"
see_also_template = """```{{admonition}} {model_type_name}
{links}
```"""
mfa_acoustic_model_card_template = template_dir.joinpath(
"mfa_acoustic_model_card_template.md"
).read_text("utf8")
ivector_card_template = template_dir.joinpath("ivector_card_template.md").read_text("utf8")
other_acoustic_model_card_template = template_dir.joinpath(
"other_acoustic_model_card_template.md"
).read_text("utf8")
g2p_model_card_template = template_dir.joinpath("g2p_model_card_template.md").read_text("utf8")
language_model_card_template = template_dir.joinpath("language_model_card_template.md").read_text(
"utf8"
)
mfa_dictionary_card_template = template_dir.joinpath("mfa_dictionary_card_template.md").read_text(
"utf8"
)
other_dictionary_card_template = template_dir.joinpath(
"other_dictionary_card_template.md"
).read_text("utf8")
corpus_card_template = template_dir.joinpath("corpus_card_template.md").read_text("utf8")
tokenizer_model_card_template = template_dir.joinpath(
"tokenizer_model_card_template.md"
).read_text("utf8")
corpus_docs_md_template = template_dir.joinpath("corpus_docs_md_template.md").read_text("utf8")
acoustic_docs_md_template = template_dir.joinpath("acoustic_docs_md_template.md").read_text("utf8")
ivector_docs_md_template = template_dir.joinpath("ivector_docs_md_template.md").read_text("utf8")
g2p_docs_md_template = template_dir.joinpath("g2p_docs_md_template.md").read_text("utf8")
lm_docs_md_template = template_dir.joinpath("lm_docs_md_template.md").read_text("utf8")
tokenizer_docs_md_template = template_dir.joinpath("tokenizer_docs_md_template.md").read_text(
"utf8"
)
mfa_dictionary_docs_md_template = template_dir.joinpath(
"mfa_dictionary_docs_md_template.md"
).read_text("utf8")
other_dictionary_docs_md_template = template_dir.joinpath(
"other_dictionary_docs_md_template.md"
).read_text("utf8")
language_links = {
"Abkhaz": ("Abkhaz", "https://en.wikipedia.org/wiki/Abkhaz_language"),
"Arabic": ("Arabic", "https://en.wikipedia.org/wiki/Arabic"),
"Armenian": ("Armenian", "https://en.wikipedia.org/wiki/Armenian_language"),
"Bashkir": ("Bashkir", "https://en.wikipedia.org/wiki/Bashkir_language"),
"Basque": ("Basque", "https://en.wikipedia.org/wiki/Basque_language"),
"Belarusian": ("Belarusian", "https://en.wikipedia.org/wiki/Belarusian_language"),
"Bulgarian": ("Bulgarian", "https://en.wikipedia.org/wiki/Bulgarian_language"),
"Chuvash": ("Chuvash", "https://en.wikipedia.org/wiki/Chuvash_language"),
"Croatian": ("Serbo-Croatian", "https://en.wikipedia.org/wiki/Serbo-Croatian"),
"Serbocroatian": ("Serbo-Croatian", "https://en.wikipedia.org/wiki/Serbo-Croatian"),
"Czech": ("Czech", "https://en.wikipedia.org/wiki/Czech_language"),
"Dutch": ("Dutch", "https://en.wikipedia.org/wiki/Dutch_language"),
"English": ("English", "https://en.wikipedia.org/wiki/English_language"),
("English", "US"): (
"General American English",
"https://en.wikipedia.org/wiki/General_American_English",
),
("English", "UK"): ("British English", "https://en.wikipedia.org/wiki/British_English"),
("English", "Nigeria"): ("Nigerian English", "https://en.wikipedia.org/wiki/Nigerian_English"),
("English", "India"): ("Indian English", "Japanese tokenizer v2_1_0.md"),
"French": ("French", "https://en.wikipedia.org/wiki/French_language"),
"Georgian": ("Georgian", "https://en.wikipedia.org/wiki/Georgian_language"),
"German": ("German", "https://en.wikipedia.org/wiki/German_language"),
"Greek": ("Greek", "https://en.wikipedia.org/wiki/Greek_language"),
"Guarani": ("Guarani", "https://en.wikipedia.org/wiki/Guarani_language"),
"Hungarian": ("Hungarian", "https://en.wikipedia.org/wiki/Hungarian_language"),
"Italian": ("Italian", "https://en.wikipedia.org/wiki/Italian_language"),
"Indonesian": ("Indonesian", "https://en.wikipedia.org/wiki/Indonesian_language"),
"Hausa": ("Hausa", "https://en.wikipedia.org/wiki/Hausa_language"),
"Kazakh": ("Kazakh", "https://en.wikipedia.org/wiki/Kazakh_language"),
"Kyrgyz": ("Kyrgyz", "https://en.wikipedia.org/wiki/Kyrgyz_language"),
"Kurmanji": ("Kurmanji", "https://en.wikipedia.org/wiki/Kurmanji"),
"Maltese": ("Maltese", "https://en.wikipedia.org/wiki/Maltese_language"),
"Uzbek": ("Uzbek", "https://en.wikipedia.org/wiki/Uzbek_language"),
"Uyghur": ("Uyghur", "https://en.wikipedia.org/wiki/Uyghur_language"),
"Punjabi": ("Punjabi", "https://en.wikipedia.org/wiki/Punjabi_language"),
"Hindi": ("Hindi", "https://en.wikipedia.org/wiki/Hindi_language"),
"Hindi-Urdu": ("Hindi-Urdu", "https://en.wikipedia.org/wiki/Hindustani_language"),
"Japanese": ("Japanese", "https://en.wikipedia.org/wiki/Japanese_language"),
"Korean": ("Korean", "https://en.wikipedia.org/wiki/Korean_language"),
"Polish": ("Polish", "https://en.wikipedia.org/wiki/Polish_language"),
"Portuguese": ("Portuguese", "https://en.wikipedia.org/wiki/Portuguese_language"),
("Portuguese", "Brazil"): (
"Brazilian Portuguese",
"https://en.wikipedia.org/wiki/Brazilian_Portuguese",
),
("Portuguese", "Portugal"): (
"European Portuguese",
"https://en.wikipedia.org/wiki/European_Portuguese",
),
"Romanian": ("Romanian", "https://en.wikipedia.org/wiki/Romanian_language"),
"Russian": ("Russian", "https://en.wikipedia.org/wiki/Russian_language"),
"Spanish": ("Spanish", "https://en.wikipedia.org/wiki/Spanish_language"),
("Spanish", "Latin America"): (
"Spanish in the Americas",
"https://en.wikipedia.org/wiki/Spanish_language_in_the_Americas",
),
("Spanish", "Spain"): (
"Peninsular Spanish",
"https://en.wikipedia.org/wiki/Peninsular_Spanish",
),
"Swahili": ("Swahili", "https://en.wikipedia.org/wiki/Swahili_language"),
"Swedish": ("Swedish", "https://en.wikipedia.org/wiki/Swedish_language"),
"Tamil": ("Tamil", "https://en.wikipedia.org/wiki/Tamil_language"),
"Tatar": ("Tatar", "https://en.wikipedia.org/wiki/Tatar_language"),
"Thai": ("Thai", "https://en.wikipedia.org/wiki/Thai_language"),
"Turkish": ("Turkish", "https://en.wikipedia.org/wiki/Turkish_language"),
"Ukrainian": ("Ukrainian", "https://en.wikipedia.org/wiki/Ukrainian_language"),
"Vietnamese": ("Vietnamese", "https://en.wikipedia.org/wiki/Vietnamese_language"),
("Vietnamese", "Ho Chi Minh City"): (
"Southern Vietnamese",
"https://en.wikipedia.org/wiki/Vietnamese_language#Language_variation",
),
("Vietnamese", "Hanoi"): (
"Northern Vietnamese",
"https://en.wikipedia.org/wiki/Vietnamese_language#Language_variation",
),
"Sorbian": ("Sorbian", "https://en.wikipedia.org/wiki/Sorbian_languages"),
("Sorbian", "Upper"): (
"Upper Sorbian",
"https://en.wikipedia.org/wiki/Upper_Sorbian_language",
),
"Mandarin": ("Mandarin Chinese", "https://en.wikipedia.org/wiki/Mandarin_Chinese"),
("Mandarin", "Taiwan"): (
"Taiwanese Mandarin",
"https://en.wikipedia.org/wiki/Taiwanese_Mandarin",
),
("Mandarin", "Erhua"): ("Beijing Mandarin", "https://en.wikipedia.org/wiki/Beijing_dialect"),
("Mandarin", "China"): (
"Standard Mandarin Chinese",
"https://en.wikipedia.org/wiki/Standard_Chinese",
),
"Urdu": ("Urdu", "https://en.wikipedia.org/wiki/Urdu"),
}
cv_phone_set_mapping = {
"abkhaz": "XPF",
"armenian": "XPF",
"bashkir": "XPF",
"basque": "XPF",
"belarusian": "XPF",
"bulgarian": "XPF",
"chuvash": "XPF",
"czech": "XPF",
"dutch": "Epitran",
"georgian": "XPF",
"greek": "XPF",
"guarani": "XPF",
"hausa": "Epitran",
"hindi": "Epitran",
"hungarian": "XPF",
"indonesian": "Epitran",
"italian": "Epitran",
"kazakh": "Epitran",
"kurmanji": "Epitran",
"kyrgyz": "Epitran",
"maltese": "Epitran",
"polish": "Epitran",
"punjabi": "Epitran",
"portuguese": "Epitran",
"romanian": "XPF",
"russian": "Epitran",
"sorbian_upper": "XPF",
"sorbian": "XPF",
"swedish": "XPF",
"tamil": "XPF",
"tatar": "Epitran",
"thai": "XPF",
"turkish": "XPF",
"ukrainian": "XPF",
"uyghur": "Epitran",
"uzbek": "Epitran",
"urdu": "Epitran",
"vietnamese": "XPF",
}
phone_set_templates = {
"Epitran": "[Epitran](https://github.com/dmort27/epitran)",
"XPF": "[XPF](https://github.com/CohenPr-XPF/XPF)",
"ARPA": "[ARPA](https://en.wikipedia.org/wiki/ARPABET)",
"PINYIN": "[PINYIN](https://en.wikipedia.org/wiki/Pinyin)",
"PROSODYLAB": "[PROSODYLAB](https://github.com/prosodylab/prosodylab.dictionaries)",
"MFA": "[MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#{language})",
}
model_id_templates = {
"acoustic": "{language}{dialect_title_string} {phone_set} acoustic model{version_string}",
"dictionary": "{language}{dialect_title_string} {phone_set} dictionary{version_string}",
"g2p": "{language}{dialect_title_string} {phone_set} G2P model{version_string}",
"language_model": "{language}{dialect_title_string} language model{version_string}",
"corpus": "{corpus_name}{version_string}",
"ivector": "{language} {phone_set} ivector extractor{version_string}",
"tokenizer": "{language} tokenizer{version_string}",
}
pronunciation_dictionaries = {}
def load_dict(dictionary_path, dict_name, phone_set_type) -> MultispeakerDictionary:
if dict_name not in pronunciation_dictionaries:
pronunciation_dictionaries[dict_name] = MultispeakerDictionary(
dictionary_path, phone_set_type=phone_set_type, position_dependent_phones=False
)
if os.path.exists(pronunciation_dictionaries[dict_name].output_directory):
shutil.rmtree(pronunciation_dictionaries[dict_name].output_directory)
pronunciation_dictionaries[dict_name].dictionary_setup()
return pronunciation_dictionaries[dict_name]
def generate_id(meta_data, model_type):
if "dialect" in meta_data and meta_data["dialect"]:
dialect_title_string = f' ({meta_data["dialect"]})'
else:
dialect_title_string = ""
if "version" in meta_data and meta_data["version"]:
version_string = f' v{meta_data["version"]}'
else:
version_string = ""
template = model_id_templates[model_type]
if model_type == "corpus":
fields = {"corpus_name": meta_data["name"], "version_string": version_string}
else:
fields = {
"language": meta_data["language"].title(),
"dialect_title_string": dialect_title_string,
"version_string": version_string,
}
if model_type not in {"language_model"}:
fields["phone_set"] = meta_data["phone_set"]
if model_type == "ivector":
fields["phone_set"] = "MFA"
return template.format(**fields).replace(".", "_")
def generate_meta_data(model, model_type, language, dialect, version, phone_set):
citation_details = {
"model_name": model.name,
"version": version,
"extra_authors": "",
"model_type": model_type,
"language": language.title(),
"phone_set": phone_set.upper(),
}
citation_template = mfa_citation_template
if language in {"Arabic"}:
citation_details["extra_authors"] = "Shmueli, Natalia and "
maintainer = mfa_maintainer
if dialect:
phone_set_folder = f"{dialect}_{phone_set}".replace(" ", "_").lower()
citation_details["dialect"] = dialect
else:
phone_set_folder = phone_set.lower()
license = "CC BY 4.0"
license_link = f"[CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/{model_type}/{language.lower()}/{phone_set_folder}/v{version}/LICENSE)"
if model_type == "acoustic":
if model.source.name.endswith("_cv.zip"):
citation = cv_citation
maintainer = cv_maintainer
license = "CC-0"
license_link = "[CC-0](https://creativecommons.org/publicdomain/zero/1.0/)"
train_date = "02-11-2022"
else:
train_date = datetime.fromisoformat(model.meta["train_date"]).date()
citation_details["year"] = train_date.year
citation_details["month"] = train_date.strftime("%b")
citation_details["title"] = generate_id(citation_details, model_type).replace("_", ".")
citation_details["link_safe_title"] = generate_id(citation_details, model_type)
citation_details["id"] = f'mfa_{model.name}_acoustic_{citation_details["year"]}'
citation = mfa_citation_template.format(**citation_details)
features = "MFCC"
if model.meta["features"].get("use_pitch", False):
features += " + pitch"
return {
"name": model.name,
"language": language.title(),
"dialect": dialect,
"phone_set": phone_set,
"version": version,
"maintainer": maintainer,
"citation": citation,
"license": license,
"license_link": license_link,
"architecture": model.meta["architecture"],
"features": features,
"evaluation": {},
"decode": {},
"train_date": str(train_date),
}
if model_type == "dictionary":
train_date = datetime.today().date()
citation_details["model_type"] = "pronunciation dictionary"
citation_details["year"] = train_date.year
citation_details["month"] = train_date.strftime("%b")
citation_details["link_safe_title"] = generate_id(citation_details, model_type)
citation_details["id"] = f"mfa_{model.name}_dictionary_{train_date.year}"
citation_details["title"] = generate_id(citation_details, model_type).replace("_", ".")
citation = citation_template.format(**citation_details)
phone_set = phone_set.upper()
if model.path.name.endswith("_cv.dict"):
citation = cv_citation
maintainer = cv_maintainer
license_link = "[CC-0](https://creativecommons.org/publicdomain/zero/1.0/)"
dictionary_phone_set = "IPA"
elif model.path.name.endswith("_mfa.dict"):
dictionary_phone_set = "IPA"
else:
if model.path.name.endswith("_prosodylab.dict") or model.path.name.endswith(
"us_arpa.dict"
):
citation = prosodylab_citation
try:
dictionary_phone_set = montreal_forced_aligner.data.PhoneSetType[phone_set].name
except KeyError:
dictionary_phone_set = "UNKNOWN"
dictionary = load_dict(model.path, model.name, phone_set_type=dictionary_phone_set)
word_count = len(dictionary.actual_words)
data = {
"name": model.name,
"language": language.title(),
"dialect": dialect,
"maintainer": maintainer,
"license_link": license_link,
"license": license,
"phone_set": phone_set,
"phones": sorted(dictionary.non_silence_phones),
"word_count": word_count,
"train_date": str(train_date),
"version": version,
"citation": citation,
}
output_path = os.path.join(
os.path.dirname(get_model_card_directory("dictionary", data)),
dictionary.name + ".dict",
)
dictionary.export_lexicon(1, output_path)
return data
if model_type == "g2p":
train_date = datetime.fromisoformat(model.meta["train_date"]).date()
citation_details["model_type"] = "G2P model"
citation_details["year"] = train_date.year
citation_details["month"] = train_date.strftime("%b")
citation_details["link_safe_title"] = generate_id(citation_details, model_type)
citation_details["title"] = generate_id(citation_details, model_type).replace("_", ".")
citation_details["id"] = f"mfa_{model.name}_g2p_{train_date.year}"
return {
"name": model.name,
"language": language.title(),
"dialect": dialect,
"maintainer": maintainer,
"license_link": license_link,
"license": license,
"architecture": model.meta["architecture"],
"training": model.meta["training"],
"evaluation": {
k: v if v is not None else 100 for k, v in model.meta["evaluation"].items()
},
"phone_set": phone_set,
"phones": sorted(model.meta["phones"]),
"version": version,
"train_date": str(train_date),
"citation": citation_template.format(**citation_details),
}
if model_type == "language_model":
train_date = datetime.fromisoformat(model.meta["train_date"]).date()
citation_details["model_type"] = "language model"
citation_details["year"] = train_date.year
citation_details["title"] = generate_id(citation_details, model_type).replace("_", ".")
citation_details["link_safe_title"] = generate_id(citation_details, model_type)
citation_details["month"] = train_date.strftime("%b")
citation_details["id"] = f"mfa_{model.name}_lm_{train_date.year}"
return {
"name": model.name,
"language": language.title(),
"dialect": dialect,
"phone_set": "MFA",
"maintainer": maintainer,
"license_link": license_link,
"license": license,
"architecture": model.meta["architecture"],
"version": version,
"train_date": str(train_date),
"training": {
"num_words": model.meta["training"]["num_words"],
"num_oovs": model.meta["training"]["num_oovs"],
},
"evaluation": {
"large_perplexity": model.meta["evaluation_training"]["large_perplexity"],
"medium_perplexity": model.meta["evaluation_training"]["medium_perplexity"],
"small_perplexity": model.meta["evaluation_training"]["small_perplexity"],
},
"citation": citation_template.format(**citation_details),
}
if model_type == "tokenizer":
train_date = datetime.fromisoformat(model.meta["train_date"]).date()
citation_details["model_type"] = "tokenizer"
citation_details["year"] = train_date.year
citation_details["title"] = generate_id(citation_details, model_type).replace("_", ".")
citation_details["link_safe_title"] = generate_id(citation_details, model_type)
citation_details["month"] = train_date.strftime("%b")
citation_details["id"] = f"mfa_{model.name}_tokenizer_{train_date.year}"
return {
"name": model.name,
"language": language.title(),
"dialect": dialect,
"phone_set": "MFA",
"maintainer": maintainer,
"license_link": license_link,
"license": license,
"architecture": model.meta["architecture"],
"version": version,
"train_date": str(train_date),
"training": {
"num_utterances": model.meta["training"]["num_utterances"],
"num_graphemes": model.meta["training"]["num_graphemes"],
},
"evaluation": {
k: v if v is not None else 100 for k, v in model.meta["evaluation"].items()
},
"citation": citation_template.format(**citation_details),
}
if model_type == "ivector":
print(model.meta)
if "train_date" in model.meta:
train_date = datetime.fromisoformat(model.meta["train_date"]).date()
else:
train_date = datetime.now().date()
citation_details["model_type"] = "ivector"
citation_details["year"] = train_date.year
citation_details["title"] = generate_id(citation_details, model_type).replace("_", ".")
citation_details["link_safe_title"] = generate_id(citation_details, model_type)
citation_details["month"] = train_date.strftime("%b")
citation_details["id"] = f"mfa_{model.name}_ivector_{train_date.year}"
return {
"name": model.name,
"language": language.title(),
"dialect": dialect,
"phone_set": "MFA",
"maintainer": maintainer,
"license_link": license_link,
"license": license,
"version": version,
"train_date": str(train_date),
"citation": citation_template.format(**citation_details),
}
return {}
def extract_model_card_fields(meta_data, model_type):
dialect_link = "N/A"
if "dialect" in meta_data and meta_data["dialect"]:
key = (meta_data["language"], meta_data["dialect"])
if key in language_links:
dialect_link = language_link_template.format(*language_links[key])
if meta_data["language"] != "Multilingual":
language_link = language_link_template.format(*language_links[meta_data["language"]])
else:
language_link = meta_data["language"]
if "dialects" in meta_data and meta_data["dialects"]:
dialect_links = []
for d in meta_data["dialects"]:
key = (meta_data["language"], d)
if key in language_links:
dialect_links.append(language_link_template.format(*language_links[key]))
dialect_link = ", ".join(dialect_links)
if "phone_set" in meta_data:
phone_set = meta_data["phone_set"]
if phone_set == "CV":
phone_set = cv_phone_set_mapping[language.lower()]
phone_set_link = phone_set_templates[phone_set]
if phone_set == "MFA":
phone_set_link = phone_set_link.format(language=meta_data["language"].lower())
name = generate_id(meta_data, model_type)
discussion_title = name.replace(" ", "+").replace(")", "").replace("(", "").replace("_", ".")
if model_type == "acoustic":
corpora_details = ""
if "corpus" in meta_data:
for corpus in meta_data["corpus"]:
if "version" in corpus and corpus["version"]:
corpus_link_template = "[{name}](../../../../corpus/{language}/{corpus_safe_name}/{version}/README.md)"
link = corpus_link_template.format(
name=corpus["name"],
language=make_path_safe(corpus["language"]),
corpus_safe_name=make_path_safe(corpus["name"]),
version=corpus["version"],
)
else:
corpus_link_template = (
"[{name}](../../../../corpus/{language}/{corpus_safe_name}/README.md)"
)
link = corpus_link_template.format(
name=corpus["name"],
language=make_path_safe(corpus["language"]),
corpus_safe_name=make_path_safe(corpus["name"]),
)
data = {
"name": corpus["name"],
"link": link,
"num_hours": corpus["num_hours"],
"num_speakers": corpus["num_speakers"],
"num_utterances": corpus["num_utterances"],
}
corpora_details += "\n" + corpus_detail_template.format(**data)
return {
"model_name": meta_data["name"],
"title": name.replace("_", "."),
"discussion_title": discussion_title,
"language": meta_data["language"],
"language_link": language_link,
"dialect": meta_data["dialect"],
"dialect_link": dialect_link,
"version": meta_data["version"],
"maintainer": meta_data["maintainer"],
"features": meta_data["features"],
"architecture": meta_data["architecture"],
"mfa_version": CURRENT_MODEL_VERSION,
"date": meta_data["train_date"],
"citation": meta_data["citation"],
"license_link": meta_data["license_link"],
"corpora_details": corpora_details,
"phone_set_link": phone_set_link,
}
if model_type == "ivector":
corpora_details = ""
if "corpus" in meta_data:
for corpus in meta_data["corpus"]:
if "version" in corpus and corpus["version"]:
corpus_link_template = "[{name}](../../../corpus/{language}/{corpus_safe_name}/{version}/README.md)"
link = corpus_link_template.format(
name=corpus["name"],
language=make_path_safe(corpus["language"]),
corpus_safe_name=make_path_safe(corpus["name"]),
version=corpus["version"],
)
else:
corpus_link_template = (
"[{name}](../../../corpus/{language}/{corpus_safe_name}/README.md)"
)
link = corpus_link_template.format(
name=corpus["name"],
language=make_path_safe(corpus["language"]),
corpus_safe_name=make_path_safe(corpus["name"]),
)
data = {
"name": corpus["name"],
"link": link,
"num_hours": corpus["num_hours"],
"num_speakers": corpus["num_speakers"],
"num_utterances": corpus["num_utterances"],
}
corpora_details += "\n" + corpus_detail_template.format(**data)
return {
"model_name": meta_data["name"],
"title": name.replace("_", "."),
"discussion_title": discussion_title,
"language": meta_data["language"],
"language_link": language_link,
"version": meta_data["version"],
"maintainer": meta_data["maintainer"],
"features": meta_data.get("features", "MFCC"),
"architecture": meta_data.get("architecture", "ivector"),
"mfa_version": CURRENT_MODEL_VERSION,
"date": meta_data["train_date"],
"citation": meta_data["citation"],
"license_link": meta_data["license_link"],
"corpora_details": corpora_details,
}
if model_type == "corpus":
citation = meta_data.get("citation", "")
version = meta_data.get("version", "")
dialects = meta_data.get("dialects", [])
if dialects:
dialects = ", ".join(dialects)
else:
dialects = "N/A"
if version:
version = f"- **Version:** `{version}`"
return {
"corpus_name": meta_data["name"],
"title": meta_data["id"].replace("_", "."),
"corpus_id": meta_data["id"],
"language": meta_data["language"],
"language_link": language_link,
"discussion_title": discussion_title,
"corpus_link": f"[{meta_data['name']}]({meta_data['link']})",
"dialects": dialects,
"dialect_link": dialect_link,
"num_hours": meta_data["num_hours"],
"num_utterances": meta_data["num_utterances"],
"num_speakers": meta_data["num_speakers"],
"num_female": meta_data.get("num_female", 0),
"num_male": meta_data.get("num_male", 0),
"num_other": meta_data.get("num_other", meta_data["num_speakers"]),
"license_link": meta_data["license_link"],
"version": version,
"citation": citation,
}
if model_type == "dictionary":
data = {
"model_name": meta_data["name"],
"title": name.replace("_", "."),
"language": meta_data["language"],
"language_link": language_link,
"dialect": meta_data["dialect"],
"dialect_link": dialect_link,
"discussion_title": discussion_title,
"version": meta_data["version"],
"maintainer": meta_data["maintainer"],
"license_link": meta_data["license_link"],
"mfa_version": CURRENT_MODEL_VERSION,
"date": meta_data["train_date"],
"citation": meta_data["citation"],
"phone_set": meta_data["phone_set"],
"phones": " ".join(sorted(meta_data["phones"])),
"word_count": meta_data["word_count"],
"phone_set_link": phone_set_link,
}
if meta_data["phone_set"] in {"MFA", "ARPA"}:
data[
"plain_link"
] = f"https://raw.githubusercontent.com/MontrealCorpusTools/mfa-models/main/dictionary/{language.lower()}/mfa/{model_name}.dict"
return data
if model_type == "g2p":
training_details = g2p_training_detail_template.format(**meta_data["training"])
evaluation_details = g2p_evaluation_detail_template.format(**meta_data["evaluation"])
return {
"model_name": meta_data["name"],
"title": name.replace("_", "."),
"language": meta_data["language"],
"language_link": language_link,
"dialect": meta_data["dialect"],
"dialect_link": dialect_link,
"discussion_title": discussion_title,
"architecture": meta_data["architecture"],
"maintainer": meta_data["maintainer"],
"version": meta_data["version"],
"license_link": meta_data["license_link"],
"mfa_version": CURRENT_MODEL_VERSION,
"date": meta_data["train_date"],
"citation": meta_data["citation"],
"phone_set": meta_data["phone_set"],
"phones": ", ".join(f"{{ipa_inline}}`{x}`" for x in meta_data["phones"]),
"training_details": training_details,
"evaluation_details": evaluation_details,
"phone_set_link": phone_set_link,
}
if model_type == "tokenizer":
training_details = tokenizer_training_detail_template.format(**meta_data["training"])
evaluation_details = tokenizer_evaluation_detail_template.format(**meta_data["evaluation"])
return {
"model_name": meta_data["name"],
"title": name.replace("_", "."),
"language": meta_data["language"],
"language_link": language_link,
"discussion_title": discussion_title,
"architecture": meta_data["architecture"],
"maintainer": meta_data["maintainer"],
"version": meta_data["version"],
"license_link": meta_data["license_link"],
"mfa_version": CURRENT_MODEL_VERSION,
"date": meta_data["train_date"],
"citation": meta_data["citation"],
"training_details": training_details,
"evaluation_details": evaluation_details,
}
if model_type == "language_model":
training_details = lm_training_detail_template.format(**meta_data["training"])
evaluation_details = lm_evaluation_detail_template.format(**meta_data["evaluation"])
return {
"model_name": meta_data["name"],
"title": name.replace("_", "."),
"language": meta_data["language"],
"language_link": language_link,
"dialect": meta_data["dialect"],
"dialect_link": dialect_link,
"discussion_title": discussion_title,
"architecture": meta_data["architecture"],
"maintainer": meta_data["maintainer"],
"version": meta_data["version"],
"license_link": meta_data["license_link"],
"mfa_version": CURRENT_MODEL_VERSION,
"date": meta_data["train_date"],
"citation": meta_data["citation"],
"training_details": training_details,
"evaluation_details": evaluation_details,
}
def extract_doc_card_fields(meta_data, model_type):
tags = [meta_data["language"]]
if model_type not in {"language_model", "corpus"}:
tags.append(meta_data["phone_set"].upper())
see_also = ""
links = []
for k in ["corpus", "dictionary", "g2p", "acoustic", "language_model", "tokenizer"]:
if k == "corpus" and model_type in {"acoustic", "ivector"}:
continue
if k in meta_data:
if k == "corpus":
links.append(
see_also_template.format(
links="\n".join(
link_template.format(x["id"].lower().replace(" ", "_"))
for x in meta_data[k]
),
model_type_name=model_type_names[k],
)
)
else:
print(meta_data[k])
links.append(
see_also_template.format(
links="\n".join(
link_template.format(x.lower().replace(" ", "_")) for x in meta_data[k]
),
model_type_name=model_type_names[k],
)
)
if links:
see_also = "\n\n".join(links)
try:
license_link = f"[{meta_data['license']}]({license_links[meta_data['license']]})"
except KeyError:
license_link = meta_data["license"]
layout_type = "not_mfa"
if "phone_set" in meta_data:
phone_set = meta_data["phone_set"]
if phone_set == "CV":
phone_set = cv_phone_set_mapping[meta_data["language"].lower()]
elif phone_set in {"MFA", "ARPA", "PROSODYLAB"}:
layout_type = "mfa"
try:
phone_set_link = phone_set_templates[phone_set]
if phone_set == "MFA":
phone_set_link = phone_set_link.format(language=meta_data["language"].lower())
except KeyError:
phone_set_link = phone_set
if "dialect" in meta_data and meta_data["dialect"]:
language_sub_folder = f"{meta_data['dialect']}_{meta_data['phone_set']}".replace(
" ", "_"
).lower()
dialect_title_string = f" ({meta_data['dialect']})"
else:
language_sub_folder = meta_data["phone_set"].lower()
dialect_title_string = ""
name = generate_id(meta_data, model_type)
if model_type == "acoustic":
corpora_details = ""
corpus_link_template = "{{ref}}`{corpus_id}`"
dialects = []
if "corpus" in meta_data:
for corpus in meta_data["corpus"]:
if "dialects" in corpus:
dialects.extend(corpus["dialects"])
data = {
"name": corpus["name"],
"link": corpus_link_template.format(corpus_id=corpus["id"].replace(" ", "_")),
"num_hours": corpus["num_hours"],
"num_speakers": corpus["num_speakers"],
"num_utterances": corpus["num_utterances"],
}
corpora_details += "\n" + corpus_detail_template.format(**data)
if not dialects and "dialect" in meta_data and meta_data["dialect"]:
dialects = [meta_data["dialect"]]
if meta_data["phone_set"] in {"CV", "MFA"}:
tags.append("IPA")
return {
"model_name": name,
"ref": name.replace(" ", "_"),
"title": name.replace("_", "."),
"model_type": model_type,
"architecture": meta_data["architecture"],
"version": meta_data["version"],
"corpora_details": corpora_details,
"see_also": see_also,
"tags": ";".join(tags),
"dialects": ";".join(sorted(set(dialects))) if dialects else "N/A",
"language": meta_data["language"].lower(),
"language_name": meta_data["language"].title(),
"license": meta_data["license"],
"phone_set": phone_set,
"layout_type": layout_type,
"license_link": license_link,
"phone_set_link": phone_set_link,
"dialect_title_string": dialect_title_string,
"language_sub_folder": language_sub_folder,
"phone_set_name": meta_data["phone_set"].upper(),
}
if model_type == "ivector":
corpora_details = ""
corpus_link_template = "{{ref}}`{corpus_id}`"
if "corpus" in meta_data:
for corpus in meta_data["corpus"]:
data = {
"name": corpus["name"],
"link": corpus_link_template.format(corpus_id=corpus["id"].replace(" ", "_")),
"num_hours": corpus["num_hours"],
"num_speakers": corpus["num_speakers"],
"num_utterances": corpus["num_utterances"],
}
corpora_details += "\n" + corpus_detail_template.format(**data)
return {
"model_name": name,
"ref": name.replace(" ", "_"),
"title": name.replace("_", "."),
"model_type": model_type,
"architecture": meta_data.get("architecture", "ivector"),
"version": meta_data["version"],
"corpora_details": corpora_details,
"see_also": see_also,
"tags": ";".join(tags),
"language": meta_data["language"].lower(),
"language_name": meta_data["language"].title(),
"license": meta_data["license"],
"layout_type": layout_type,
"license_link": license_link,
}
if model_type == "corpus":
if "tags" in meta_data:
tags.extend(meta_data["tags"])
dialects = []
if "dialects" in meta_data:
dialects = meta_data["dialects"]
version_subdirectory = meta_data.get("version", "")
if version_subdirectory:
version_subdirectory = f"/{version_subdirectory}"
return {
"corpus_id": name,
"ref": name.replace(" ", "_"),
"title": name.replace("_", "."),
"corpus_name": meta_data["name"],
"corpus_name_safe": make_path_safe(meta_data["name"]),
"license": meta_data["license"],
"see_also": see_also,
"layout_type": layout_type,
"tags": ";".join(tags),
"version_subdirectory": version_subdirectory,
"language": meta_data["language"].lower(),
"dialects": ";".join(sorted(set(dialects))) if dialects else "N/A",
"language_name": meta_data["language"].title(),
}
if model_type == "dictionary":
if meta_data["name"].endswith("_cv") or meta_data["name"].endswith("_mfa"):
tags.append("IPA")
elif meta_data["name"].endswith("_prosodylab") or meta_data["name"].endswith("us_arpa"):
tags.append("PROSODYLAB")
tags.append("MFA")
data = {
"model_name": name,
"ref": name.replace(" ", "_"),
"title": name.replace("_", "."),
"model_type": model_type,
"version": meta_data["version"],
"see_also": see_also,
"tags": ";".join(tags),
"layout_type": layout_type,
"language": meta_data["language"].lower(),
"license": meta_data["license"],
"language_name": meta_data["language"].title(),
"dialects": meta_data["dialect"] if meta_data["dialect"] else "N/A",
"phone_set": phone_set,
"language_sub_folder": language_sub_folder,
"dialect_title_string": dialect_title_string,
"phone_set_name": meta_data["phone_set"].upper(),
}
if meta_data["name"] in phone_charts:
charts = phone_charts[meta_data["name"]]
data["consonant_chart"] = charts["consonant_chart"]
data["vowel_section"] = charts["oral_vowel_chart"]
if charts["nasal_vowel_chart"]:
data["vowel_section"] = "#### Oral Vowels\n\n" + data["vowel_section"]
data["vowel_section"] += "\n\n#### Nasal Vowels\n\n" + charts["nasal_vowel_chart"]
if charts["diphthongs"]:
data["vowel_section"] += "\n\n#### Diphthongs\n\n* " + "\n* ".join(
f"{{ipa_inline}}`{x}`" for x in sorted(charts["diphthongs"])
)
if charts["triphthongs"]:
data["vowel_section"] += "\n\n#### Triphthongs\n\n* " + "\n* ".join(
f"{{ipa_inline}}`{x}`" for x in sorted(charts["triphthongs"])
)
if "tones" in charts and charts["tones"]:
data["vowel_section"] += "\n\n#### Tones\n\n* " + "\n* ".join(
f"{{ipa_inline}}`{x}`" for x in sorted(charts["tones"])
)
if "stress" in charts and charts["stress"]:
data["vowel_section"] += "\n\n#### Stress\n\n* " + "\n* ".join(
f"{{ipa_inline}}`{x}`" for x in sorted(charts["stress"])
)
if "other" in charts and charts["other"]:
data["vowel_section"] += "\n\n### Other phones\n\n* " + "\n* ".join(
f"{{ipa_inline}}`{x}`" for x in sorted(charts["other"])
)
return data
if model_type == "g2p":
if meta_data["phone_set"] in {"CV", "MFA"}:
tags.append("IPA")
return {
"model_name": name,
"ref": name.replace(" ", "_"),
"title": name.replace("_", "."),
"model_type": model_type,
"architecture": meta_data["architecture"],
"version": meta_data["version"],
"see_also": see_also,
"layout_type": layout_type,
"language_sub_folder": language_sub_folder,
"dialect_title_string": dialect_title_string,
"tags": ";".join(tags),
"license": meta_data["license"],
"language": meta_data["language"].lower(),
"dialects": meta_data["dialect"] if meta_data["dialect"] else "N/A",
"language_name": meta_data["language"].title(),
"phone_set": phone_set,
"phone_set_name": meta_data["phone_set"].upper(),
}
if model_type == "language_model":
tags = ["MFA"]
return {
"model_name": name,
"ref": name.replace(" ", "_"),
"title": name.replace("_", "."),
"model_type": model_type,
"layout_type": layout_type,
"language": meta_data["language"].lower(),
"dialects": meta_data["dialect"] if meta_data["dialect"] else "N/A",
"architecture": meta_data["architecture"],
"language_name": meta_data["language"].title(),
"dialect_title_string": dialect_title_string,
"license": meta_data["license"],
"version": meta_data["version"],
"source": "mfa",
"see_also": see_also,
"tags": ";".join(tags),
}
if model_type == "tokenizer":
tags = ["MFA"]
return {
"model_name": name,
"ref": name.replace(" ", "_"),
"title": name.replace("_", "."),
"model_type": model_type,
"layout_type": layout_type,
"language": meta_data["language"].lower(),
"architecture": meta_data["architecture"],
"language_name": meta_data["language"].title(),
"license": meta_data["license"],
"version": meta_data["version"],
"source": "mfa",
"see_also": see_also,
"tags": ";".join(tags),
}
model_card_templates = {
"acoustic": {
"mfa": mfa_acoustic_model_card_template,
"other": other_acoustic_model_card_template,
},
"dictionary": {"mfa": mfa_dictionary_card_template, "other": other_dictionary_card_template},
"g2p": {"mfa": g2p_model_card_template, "other": g2p_model_card_template},
"language_model": {"mfa": language_model_card_template, "other": language_model_card_template},
"corpus": {"mfa": corpus_card_template, "other": corpus_card_template},
"ivector": {"mfa": ivector_card_template, "other": ivector_card_template},
"tokenizer": {"mfa": tokenizer_model_card_template, "other": tokenizer_model_card_template},
}
docs_card_templates = {
"acoustic": {"mfa": acoustic_docs_md_template, "other": acoustic_docs_md_template},
"dictionary": {
"mfa": mfa_dictionary_docs_md_template,
"other": other_dictionary_docs_md_template,
},
"g2p": {"mfa": g2p_docs_md_template, "other": g2p_docs_md_template},
"language_model": {"mfa": lm_docs_md_template, "other": lm_docs_md_template},
"corpus": {"mfa": corpus_docs_md_template, "other": corpus_docs_md_template},
"ivector": {"mfa": ivector_docs_md_template, "other": ivector_docs_md_template},
"tokenizer": {"mfa": tokenizer_docs_md_template, "other": tokenizer_docs_md_template},
}
model_type_names = {
"acoustic": "Acoustic models",
"dictionary": "Pronunciation dictionaries",
"g2p": "G2P models",
"language_model": "Language models",
"corpus": "Corpora",
"ivector": "Ivector extractors",
"tokenizer": "Tokenizer models",
}
model_type_columns = {
"acoustic": "ID;language;dialect;phoneset;license",
"ivector": "ID;language;license",
"dictionary": "ID;language;dialect;phoneset;license",
"g2p": "ID;language;dialect;phoneset;license",
"language_model": "ID;language;dialect;license",
"corpus": "ID;language;dialect;license",
"tokenizer": "ID;language;license",
}
model_type_column_widths = {
"acoustic": "40;20;20;10;10",
"dictionary": "40;20;20;10;10",
"g2p": "40;20;20;10;10",
"language_model": "50;20;20;10",
"ivector": "50;25;25",
"tokenizer": "50;25;25",
"corpus": "40;20;25;15",
}
meta_datas = {}
chart_template = """``````{{list-table}}
:header-rows: 1
:stub-columns: {stub_column_count}
:class: {type}_chart table-striped table-bordered
* - {header_data}
* - {row_data}
``````
"""
def generate_extra_data(dictionary, base_indent):
lines = []
for key, value in dictionary.items():
if isinstance(value, dict):
lines.append(f"{base_indent}* {key}")
if len(value) > 4:
value = {k: value[k] for k in rng.choice(list(value.keys()), 4, replace=False)}
lines.extend(generate_extra_data(value, base_indent=" " + base_indent))
else:
lines.append(f"{base_indent}* {key}: {value}")
return lines
def format_ipa_cell(
phone_data: dict[str, list[str]],
extra_data: dict[str, dict[str, typing.Any]] = None,
base_indent: typing.Optional[str] = "",
) -> str:
cell_lines = [f"```{{ipa_cell}}"]
for phone_class, v in phone_data.items():
if not v:
continue
cell_lines.append(f"{base_indent}* {phone_class}")
for phone in v:
cell_lines.append(f"{base_indent} * {phone}")
if phone in extra_data:
cell_lines.extend(
generate_extra_data(extra_data[phone], base_indent=base_indent + " ")
)
cell_lines.append(f"{base_indent}```")
cell_content = "\n".join(cell_lines)
return cell_content
def check_phone(phone, feature_set, phone_set_type):
if phone_set_type is PhoneSetType.ARPA:
return phone in feature_set
else:
return any(x in phone for x in feature_set)
def analyze_dictionary(dictionary_path, name, phone_set_type):
d = load_dict(dictionary_path, name, phone_set_type=phone_set_type)
dictionary_mapping = collections.defaultdict(set)
if d.phone_set_type is PhoneSetType.ARPA:
super_segmentals = {"stress": re.compile(r"[0-2]+")}
ipa_mapping = {
"stops": d.phone_set_type.stops,
"voiced": d.phone_set_type.voiced_obstruents,
"voiceless": d.phone_set_type.voiceless_obstruents,
"fricative": d.phone_set_type.fricatives,
"affricates": d.phone_set_type.affricates,
"sibilant": d.phone_set_type.sibilants,
"lateral": d.phone_set_type.laterals,
"nasal": d.phone_set_type.nasals,
"approximant": d.phone_set_type.approximants,
"labial": d.phone_set_type.labials,
"labiodental": d.phone_set_type.labiodental,
"dental": d.phone_set_type.dental,
"alveolar": d.phone_set_type.alveolar,
"alveopalatal": d.phone_set_type.alveopalatal,
"velar": d.phone_set_type.velar,
"glottal": d.phone_set_type.glottal,
"implosive": set(),
"lateral_tap": set(),
"tap": set(),
"palatal": d.phone_set_type.palatal,
"trill": set(),
"pharyngeal": set(),
"epiglottal": set(),
"uvular": set(),
"retroflex": set(),
"lateral_fricative": set(),
"close": d.phone_set_type.close_vowels,
"close-mid": d.phone_set_type.close_mid_vowels,
"open-mid": d.phone_set_type.open_mid_vowels,
"open": d.phone_set_type.open_vowels,
"front": d.phone_set_type.front_vowels - {"IH"},
"near-front": {"IH"},
"central": d.phone_set_type.central_vowels,
"back": d.phone_set_type.back_vowels - {"UH"},
"near-back": {"UH"},
"rounded": d.phone_set_type.rounded_vowels,
"unrounded": d.phone_set_type.unrounded_vowels,
"lax": {"IH", "UH", "AH", "AE", "ER"},
"other": set(),
}
else:
ipa_mapping = {
"stops": d.phone_set_type.stops,
"voiced": d.phone_set_type.voiced_obstruents,
"voiceless": d.phone_set_type.voiceless_obstruents,
"implosive": d.phone_set_type.implosive_obstruents,
"fricative": d.phone_set_type.fricatives,
"sibilant": d.phone_set_type.sibilants,
"lateral": d.phone_set_type.laterals,
"lateral_fricative": d.phone_set_type.lateral_fricatives,
"nasal": d.phone_set_type.nasals,
"nasal_approximants": d.phone_set_type.nasal_approximants,
"trill": d.phone_set_type.trills,
"tap": d.phone_set_type.taps,
"lateral_tap": d.phone_set_type.lateral_taps,
"approximant": d.phone_set_type.approximants - d.phone_set_type.nasal_approximants,
"labial": d.phone_set_type.labials,
"labiodental": d.phone_set_type.labiodental,
"dental": d.phone_set_type.dental,
"alveolar": d.phone_set_type.alveolar,
"retroflex": d.phone_set_type.retroflex,
"alveopalatal": d.phone_set_type.alveopalatal,
"palatal": d.phone_set_type.palatal,
"velar": d.phone_set_type.velar,
"uvular": d.phone_set_type.uvular,
"pharyngeal": d.phone_set_type.pharyngeal,
"epiglottal": d.phone_set_type.epiglottal,
"glottal": d.phone_set_type.glottal,
"close": d.phone_set_type.close_vowels,
"close-mid": d.phone_set_type.close_mid_vowels,
"open-mid": d.phone_set_type.open_mid_vowels,
"open": d.phone_set_type.open_vowels,
"front": d.phone_set_type.front_vowels - {"ɪ", "ʏ", "ɛ̈", "ʏ̈"},
"near-front": {"ɪ", "ʏ", "ɛ̈", "ʏ̈"},
"central": d.phone_set_type.central_vowels,
"back": d.phone_set_type.back_vowels - {"ʊ", "ɔ̈"},
"near-back": {"ʊ", "ɔ̈"},
"rounded": d.phone_set_type.rounded_vowels,
"unrounded": d.phone_set_type.unrounded_vowels,
"lax": {"ɪ", "ʏ", "ʊ", "ə", "ɐ", "æ", "ɚ"},
"nasalized": {"ã", "õ", "ĩ", "ũ", "ẽ"},
"other": {"kp", "ɧ", "ŋm"},
}
super_segmentals = {"tones": re.compile(r"[˩˨˧˦˥ˀ]+")}
for k, v in ipa_mapping.items():
voiceless = [x for x in v if x in ipa_mapping["voiceless"]]
voiced = [x for x in v if x not in ipa_mapping["voiceless"]]
mod_phones = set()
for p in voiceless:
mod_phones |= voiceless_variants(p)
for p in voiced:
mod_phones |= voiced_variants(p)
ipa_mapping[k] = mod_phones | v
extra_data = {}
with d.session() as session:
phones = session.query(Phone).filter(Phone.phone_type == PhoneType.non_silence)
phone_counts = collections.Counter()
pronunciations = session.query(Pronunciation.pronunciation)
for (p,) in pronunciations:
p = p.split()
phone_counts.update(p)
total_phones = set()
triphthongs = d.phone_set_type.triphthong_phones
diphthongs = d.phone_set_type.diphthong_phones
for phone in phones:
words = (
session.query(Word.word, Pronunciation.pronunciation)
.join(Word.pronunciations)
.filter(
sqlalchemy.func.length(Word.word) > 2,
sqlalchemy.func.length(Word.word) < 6,
Pronunciation.probability != None, # noqa
Pronunciation.pronunciation.regexp_match(rf"\b{phone.phone}(?=\s|$)"),
)
.distinct()
.order_by(sqlalchemy.func.random())
.limit(4)
)
for super_seg, pattern in super_segmentals.items():
phone_m = pattern.search(phone.phone)
if phone_m:
dictionary_mapping[super_seg].add(phone_m.group(0))
counts = phone_counts[phone.phone]
examples = {}
for w, pron in words:
examples[w] = f"[{pron}]"
phone = phone.phone.replace(phone_m.group(0), "")
if phone not in extra_data:
extra_data[phone] = {"Occurrences": 0, "Examples": {}}
if isinstance(extra_data[phone]["Occurrences"], str):
try:
extra_data[phone]["Occurrences"] = int(extra_data[phone]["Occurrences"])
except ValueError:
extra_data[phone]["Occurrences"] = 0
extra_data[phone]["Occurrences"] += counts
extra_data[phone]["Examples"].update(examples)
break
else:
extra_data[phone.phone] = {"Occurrences": phone_counts[phone.phone], "Examples": {}}
phone = phone.phone
for w, pron in words:
extra_data[phone]["Examples"][w] = f"[{pron}]"
base_phone = d.get_base_phone(phone)
query_set = {phone, base_phone}
if base_phone in ipa_mapping["other"]:
dictionary_mapping["other"].add(phone)
continue
if "ʲ" in phone:
dictionary_mapping["palatalized"].add(phone)
if "ʷ" in phone:
dictionary_mapping["labialized"].add(phone)
if "̃" in phone:
dictionary_mapping["nasalized"].add(phone)
base_phone = base_phone.replace("̃", "")
if "͈" in phone:
dictionary_mapping["tense"].add(phone)
dictionary_mapping["voiceless"].add(phone)
if "̪" in phone:
dictionary_mapping["dental"].add(phone)
if any(x in phone for x in ["ⁿ", "ᵑ", "ᵐ"]):
dictionary_mapping["prenasalized"].add(phone)
dictionary_mapping["voiced"].add(phone)
elif "ʱ" in phone or "̤" in phone:
dictionary_mapping["aspirated"].add(phone)
dictionary_mapping["voiced"].add(phone)
elif check_phone(phone, ipa_mapping["voiced"], d.phone_set_type):
dictionary_mapping["voiced"].add(phone)
elif check_phone(phone, ipa_mapping["implosive"], d.phone_set_type):
dictionary_mapping["implosive"].add(phone)
dictionary_mapping["voiced"].add(phone)
elif "ʰ" in phone:
dictionary_mapping["aspirated"].add(phone)
dictionary_mapping["voiceless"].add(phone)
elif "ʼ" in phone:
dictionary_mapping["ejective"].add(phone)
dictionary_mapping["voiceless"].add(phone)
elif check_phone(phone, ipa_mapping["voiceless"], d.phone_set_type):
dictionary_mapping["voiceless"].add(phone)
if "̚" in phone:
dictionary_mapping["unreleased"].add(phone)
if any(x in diphthongs for x in query_set):
dictionary_mapping["diphthong"].add(phone)
elif any(x in triphthongs for x in query_set):
dictionary_mapping["triphthong"].add(phone)
elif any(x in d.phone_set_type.affricates for x in query_set):
dictionary_mapping["affricate"].add(phone)
elif any(x in d.phone_set_type.stops for x in query_set):
dictionary_mapping["stop"].add(phone)
for k, v in ipa_mapping.items():
if base_phone in v:
dictionary_mapping[k].add(phone)
total_phones.add(phone)
for v in dictionary_mapping.values():
if phone in v:
break
else:
dictionary_mapping["other"].add(phone)
places = [
"labial",
"labiodental",
"dental",
"alveolar",
"alveopalatal",
"retroflex",
"palatal",
"velar",
"uvular",
"pharyngeal",
"epiglottal",
"glottal",
]
columns = []
for p in places:
if p in dictionary_mapping:
columns.append(p)
sub_manners = ["tense", "aspirated", "implosive", "ejective", "unreleased", "prenasalized"]
rows = []
plotted = set()
for manner in [
"nasal",
"stop",
"affricate",
"sibilant",
"fricative",
"approximant",
"tap",
"trill",
"lateral_fricative",
"lateral",
"lateral_tap",
]:
if manner not in dictionary_mapping:
continue
realized_submanner_rows = {}
for x in sub_manners:
if dictionary_mapping[manner] & dictionary_mapping[x]:
realized_submanner_rows[x] = [f"{{submanner}}`{x.title()}`"]
row_title = f"{{manner}}`{manner.replace('_',' ').title()}`"
if realized_submanner_rows:
row_title += " {submanner}`Plain`"
row = [row_title]
for place in columns:
cell_set = dictionary_mapping[manner] & dictionary_mapping[place]
base_set = dictionary_mapping[manner] & dictionary_mapping[place]
for x in sub_manners:
cell_set -= dictionary_mapping[x]
base_set -= dictionary_mapping[x]
voiced_set = base_set & dictionary_mapping["voiced"]
voiceless_set = base_set & dictionary_mapping["voiceless"]
other_set = base_set - dictionary_mapping["voiceless"] - dictionary_mapping["voiced"]
plotted.update(voiceless_set)
plotted.update(voiced_set)
plotted.update(other_set)
cell_data = {
"voiceless": sorted(voiceless_set),
"voiced": sorted(voiced_set),
"other": sorted(other_set),
}
cell_contents = format_ipa_cell(cell_data, extra_data, base_indent=" ")
row.append(cell_contents)
rows.append(row)
if realized_submanner_rows:
for place in columns:
for sub_manner in realized_submanner_rows.keys():
cell_set = (
dictionary_mapping[manner]
& dictionary_mapping[place]
& dictionary_mapping[sub_manner]
)
for s in realized_submanner_rows.keys():
if s == sub_manner:
continue
cell_set -= dictionary_mapping[s]
voiced_set = cell_set & dictionary_mapping["voiced"]
voiceless_set = cell_set & dictionary_mapping["voiceless"]
other_set = (
cell_set - dictionary_mapping["voiceless"] - dictionary_mapping["voiced"]
)
plotted.update(voiceless_set)
plotted.update(voiced_set)
plotted.update(other_set)
cell_data = {
"voiceless": sorted(voiceless_set),
"voiced": sorted(voiced_set),
"other": sorted(other_set),
}
cell_contents = format_ipa_cell(cell_data, extra_data, base_indent=" ")
realized_submanner_rows[sub_manner].append(cell_contents)
rows.extend(realized_submanner_rows.values())
row_headers = ["Manner"]
columns = row_headers + columns
consonants = {"header": columns, "rows": rows}
oral_rows = []
nasal_rows = []
headers = ["front", "near-front", "central", "near-back", "back"]
has_nasal = False
for height in ["close", "close-mid", "open-mid", "open"]:
for on in ["nasalized", "oral"]:
main_row = [height.title()]
lax_row = [""]
for column in headers:
cell_set = dictionary_mapping[height] & dictionary_mapping[column]
if on in dictionary_mapping: # nasalized
cell_set &= dictionary_mapping["nasalized"]
if cell_set and not has_nasal:
has_nasal = True
else:
cell_set -= dictionary_mapping["nasalized"]
if height == "close" and column in {"front", "back"}:
lax_set = set()
tense_set = cell_set - dictionary_mapping["lax"]
elif height == "close" and column in {"near-front", "near-back"}:
tense_set = set()
lax_set = cell_set & dictionary_mapping["lax"]
else:
tense_set = cell_set - dictionary_mapping["lax"]
lax_set = cell_set & dictionary_mapping["lax"]
tense_rounded = tense_set & dictionary_mapping["rounded"]
tense_unrounded = tense_set & dictionary_mapping["unrounded"]
cell_data = {
"unrounded": sorted(tense_unrounded),
"rounded": sorted(tense_rounded),
}
plotted.update(tense_unrounded)
plotted.update(tense_rounded)
tense_cell_contents = format_ipa_cell(cell_data, extra_data, base_indent=" ")
lax_rounded = lax_set & dictionary_mapping["rounded"]
lax_unrounded = lax_set & dictionary_mapping["unrounded"]
plotted.update(lax_rounded)
plotted.update(lax_unrounded)
cell_data = {
"unrounded": sorted(lax_unrounded),
"rounded": sorted(lax_rounded),
}
lax_cell_contents = format_ipa_cell(cell_data, extra_data, base_indent=" ")
main_row.append(tense_cell_contents)
lax_row.append(lax_cell_contents)
if on in dictionary_mapping: # nasalized
nasal_rows.append(main_row)
if height != "open":
nasal_rows.append(lax_row)
else:
oral_rows.append(main_row)
if height != "open":
oral_rows.append(lax_row)
headers = [""] + [x.title() for x in headers]
if not has_nasal:
nasal_rows = None
header_row_string = "\n - ".join(x.title() for x in consonants["header"])
row_strings = "\n* - ".join("\n - ".join(x) for x in consonants["rows"])
stub_column_count = 1
consonant_chart = chart_template.format(
header_data=header_row_string,
row_data=row_strings,
type="consonant",
stub_column_count=stub_column_count,
)
vowels = {
"oral_rows": oral_rows,
"nasal_rows": nasal_rows,
"header": headers,
}
header_row_string = "\n - ".join(vowels["header"])
row_strings = "\n* - ".join("\n - ".join(x) for x in vowels["oral_rows"])
oral_chart = chart_template.format(
header_data=header_row_string, row_data=row_strings, type="vowel", stub_column_count=1
)
nasal_chart = None
if nasal_rows:
header_row_string = "\n - ".join(vowels["header"])
row_strings = "\n* - ".join("\n - ".join(x) for x in vowels["nasal_rows"])
nasal_chart = chart_template.format(
header_data=header_row_string, row_data=row_strings, type="vowel", stub_column_count=1
)
data = {
"consonant_chart": consonant_chart,
"oral_vowel_chart": oral_chart,
"nasal_vowel_chart": nasal_chart,
"diphthongs": dictionary_mapping["diphthong"],
"other": dictionary_mapping["other"] & (total_phones - plotted),
"triphthongs": dictionary_mapping["triphthong"],
}
for k in super_segmentals.keys():
if k in dictionary_mapping:
data[k] = dictionary_mapping[k]
return data
phone_charts = {}
model_mappings = {}
for model_type, model_class in MODEL_TYPES.items():
# if model_type != 'ivector':
# continue
meta_datas[model_type] = {}
model_mappings[model_type] = {}
model_directory = os.path.join(mfa_model_root, model_type)
staging_directory = os.path.join(model_directory, "staging")
models_to_stage = os.listdir(staging_directory)
for file_name in models_to_stage:
if not os.path.isfile(os.path.join(staging_directory, file_name)):
continue
if model_type == "dictionary" and not file_name.endswith(".dict"):
continue
print(file_name)
model = model_class(os.path.join(staging_directory, file_name))
print(model.meta)
s = model.name.split("_")
dialect = ""
if model_type == "language_model":
if "_mfa" in model.name:
s = model.name.replace("_mfa", "").split("_")
language = "_".join(s[:-1])
dialect = " ".join(s[1:-1])
phone_set = "MFA"
elif model_type == "ivector":
language = model.name.replace("_mfa", "")
dialect = ""
phone_set = ""
elif model_type == "tokenizer":
language = model.name.replace("_mfa", "")
dialect = ""
phone_set = ""
elif len(s) == 1:
language = s[0]
phone_set = "Unknown"
dialect = ""
elif len(s) == 2:
language, phone_set = s
phone_set = phone_set.upper()
dialect = ""
else:
language = s[0]
phone_set = s[-1].upper()
dialect = " ".join(s[1:-1])
try:
version = model.meta["version"]
except KeyError:
version = montreal_forced_aligner.utils.get_mfa_version()
if version.startswith("2.") or version.startswith("3."):
version = CURRENT_MODEL_VERSION
language = language.title()
if len(dialect) == 2:
dialect = dialect.upper()
else:
dialect = dialect.title()
print(model_directory, language, phone_set, version)
if dialect:
phone_set_folder = f"{dialect}_{phone_set}".replace(" ", "_").lower()
else:
phone_set_folder = phone_set.lower()
if phone_set_folder:
output_directory = os.path.join(
model_directory, language.lower(), phone_set_folder, f"v{version}"
)
else:
output_directory = os.path.join(model_directory, language.lower(), f"v{version}")
os.makedirs(output_directory, exist_ok=True)
license_path = os.path.join(output_directory, "LICENSE")
if phone_set != "CV" and not os.path.exists(license_path):
shutil.copyfile(os.path.join(mfa_model_root, "LICENSE"), license_path)
meta_path = os.path.join(output_directory, "meta.json")
if OVERWRITE_METADATA or not os.path.exists(meta_path):
meta_data = generate_meta_data(
model, model_type, language, dialect, version, phone_set
)
with open(meta_path, "w", encoding="utf8") as f:
json.dump(meta_data, f, indent=4, ensure_ascii=False)
else:
with open(meta_path, "r", encoding="utf8") as f:
meta_data = json.load(f)
meta_datas[model_type][generate_id(meta_data, model_type)] = meta_data
keys = [language]
if model_type in {"language_model", "ivector", "tokenizer"}:
if dialect:
keys.append((language, dialect))
key = (language, dialect)
else:
if dialect:
keys.append((language, dialect))
keys.append((language, dialect, phone_set))
key = (language, dialect, phone_set)
dialect_key = (language, dialect)
else:
keys.append((language, phone_set))
for key in keys:
if key not in model_mappings[model_type]:
model_mappings[model_type][key] = []
model_mappings[model_type][key].append(generate_id(meta_data, model_type))
if model_type == "dictionary" and phone_set in {"MFA", "CV", "ARPA"}:
phone_set_type = "IPA"
if phone_set == "ARPA":
phone_set_type = "ARPA"
phone_charts[meta_data["name"]] = analyze_dictionary(
model.path, model.name, phone_set_type
)
# if language == 'hindi':
# err
existing_models = []
for language in os.listdir(model_directory):
if language in {"staging", "training", "filter_lists", "1.0"}:
continue
language_directory = os.path.join(model_directory, language)
if not os.path.isdir(language_directory):
continue
language = language.title()
if model_type in {"ivector", "tokenizer"}:
for version in os.listdir(language_directory):
meta_path = os.path.join(language_directory, version, "meta.json")
if not os.path.exists(meta_path):
continue
with open(meta_path, "r", encoding="utf8") as f:
meta_data = json.load(f)
meta_datas[model_type][generate_id(meta_data, model_type)] = meta_data
keys = [language]
for key in keys:
if key not in model_mappings[model_type]:
model_mappings[model_type][key] = []
model_mappings[model_type][key].append(generate_id(meta_data, model_type))
else:
for phone_set in os.listdir(language_directory):
print(phone_set)
phone_set_dir = os.path.join(language_directory, phone_set)
if "_" in phone_set:
dialect, phone_set = phone_set.rsplit("_", maxsplit=1)
else:
dialect = ""
for version in os.listdir(phone_set_dir):
meta_path = os.path.join(phone_set_dir, version, "meta.json")
if not os.path.exists(meta_path):
continue
with open(meta_path, "r", encoding="utf8") as f:
meta_data = json.load(f)
meta_datas[model_type][generate_id(meta_data, model_type)] = meta_data
keys = [language]
if model_type == "language_model":
if dialect:
keys.append((language, dialect))
key = (language, dialect)
else:
if dialect:
keys.append((language, dialect))
keys.append((language, dialect, phone_set))
key = (language, dialect, phone_set)
dialect_key = (language, dialect)
else:
keys.append((language, phone_set))
for key in keys:
if key not in model_mappings[model_type]:
model_mappings[model_type][key] = []
model_mappings[model_type][key].append(generate_id(meta_data, model_type))
if "dictionary" in meta_datas:
for k in model_corpus_mapping.keys():
dict_id = k.replace("acoutic model", "dictionary")
if dict_id in meta_datas["dictionary"]:
model_dictionary_mapping[k] = [dict_id]
if "g2p" in meta_datas:
for v in model_corpus_mapping.values():
for d_id in v:
g2p_id = d_id.replace("dictionary", "G2P model")
if g2p_id in meta_datas["g2p"]:
model_dictionary_mapping[g2p_id] = [d_id]
if "language_model" in meta_datas:
for k, v in model_dictionary_mapping.items():
lm_id = k.replace("acoustic", "language")
if lm_id in meta_datas["language_model"]:
model_dictionary_mapping[lm_id] = v
if "tokenizer" in meta_datas:
for k, v in model_dictionary_mapping.items():
tokenizer_id = k.replace("tokenizer", "language")
if tokenizer_id in meta_datas["tokenizer"]:
model_dictionary_mapping[tokenizer_id] = v
corpora_metadata = {}
model_mappings["corpus"] = {}
corpus_metadata_file = os.path.join(mfa_model_root, "corpus", "staging", "corpus_data.json")
if os.path.exists(corpus_metadata_file):
with open(corpus_metadata_file, "r", encoding="utf8") as f:
data = json.load(f)
for language, c_list in data.items():
if language == "Hindi-Urdu":
continue
for c in c_list:
name = c["name"]
if "version" in c:
name += f'_{c["version"]}'
id = make_path_safe(name)
c["language"] = language
c["id"] = generate_id(c, "corpus")
c["license_link"] = f"[{c['license']}]({license_links[c['license']]})"
if "dialects" not in c:
c["dialects"] = []
c["dialects"] = [x.title() if len(x) > 2 else x.upper() for x in c["dialects"]]
corpora_metadata[c["id"]] = c
print(c)
print(generate_id(c, "corpus"))
language_key = language
if language_key not in model_mappings["corpus"]:
model_mappings["corpus"][language_key] = []
model_mappings["corpus"][language_key].append(c["id"])
if c["dialects"]:
for d in c["dialects"]:
key = (language, d)
if key not in model_mappings["corpus"]:
model_mappings["corpus"][key] = []
model_mappings["corpus"][key].append(c["id"])
meta_datas["corpus"] = corpora_metadata
# Add links
for model_type, data in meta_datas.items():
for model_name, meta_data in data.items():
model_id = generate_id(meta_data, model_type)
if model_type in {"acoustic", "language_model", "ivector", "tokenizer"}:
print("HELLO!?", model_id, model_corpus_mapping.keys())
if model_id in model_corpus_mapping:
print(model_corpus_mapping[model_id])
print(corpora_metadata.keys())
meta_data["corpus"] = [corpora_metadata[x] for x in model_corpus_mapping[model_id]]
for corpus_id in model_corpus_mapping[model_id]:
if model_type not in meta_datas["corpus"][corpus_id]:
meta_datas["corpus"][corpus_id][model_type] = []
meta_datas["corpus"][corpus_id][model_type].append(model_id)
if model_type in {"language_model", "corpus", "ivector"}:
if "dialect" in meta_data and meta_data["dialect"]:
key = (meta_data["language"], meta_data["dialect"])
else:
key = meta_data["language"]
else:
if "dialect" in meta_data and meta_data["dialect"]:
key = (meta_data["language"], meta_data["dialect"], meta_data["phone_set"])
else:
key = (meta_data["language"], meta_data["phone_set"])
if model_type in {"acoustic", "language_model", "g2p"}:
print(meta_data["name"])
print(key)
print(model_mappings["dictionary"])
if key in model_mappings["dictionary"]:
if "dictionary" not in meta_data:
meta_data["dictionary"] = []
meta_data["dictionary"].extend(model_mappings["dictionary"][key])
if model_id in model_dictionary_mapping:
if "dictionary" not in meta_data:
meta_data["dictionary"] = []
meta_data["dictionary"].extend(
[
x
for x in model_dictionary_mapping[model_id]
if x not in meta_data["dictionary"]
]
)
elif model_type == "dictionary":
for t in ["acoustic", "g2p", "language_model", "corpus"]:
if key in model_mappings[t]:
if t not in meta_data:
meta_data[t] = []
meta_data[t].extend(model_mappings[t][key])
elif model_type == "corpus":
meta_data["dictionary"] = []
if "dialects" in meta_data and meta_data["dialects"]:
for dialect in meta_data["dialects"]:
key = (meta_data["language"], dialect)
if key in model_mappings["dictionary"]:
meta_data["dictionary"].extend(model_mappings["dictionary"][key])
else:
print(
meta_data["language"],
model_mappings["dictionary"],
meta_data["language"] in model_mappings["dictionary"],
)
if meta_data["language"] in model_mappings["dictionary"]:
for dictionary_id in model_mappings["dictionary"][meta_data["language"]]:
m = meta_datas["dictionary"][dictionary_id]
meta_data["dictionary"].append(dictionary_id)
for model_type, data in meta_datas.items():
docs_dir = os.path.join(mfa_model_root, "docs", "source", model_type)
os.makedirs(docs_dir, exist_ok=True)
language_model_doc_mds = {}
for model_name, meta_data in data.items():
print(model_name, meta_data)
if model_type not in {"language_model", "corpus"} and meta_data["phone_set"] in {
"PROSODYLAB",
"PINYIN",
}:
model_card_template = model_card_templates[model_type]["other"]
docs_md_template = docs_card_templates[model_type]["other"]
elif model_type not in {"language_model", "corpus"} and meta_data["phone_set"] in {"CV"}:
model_card_template = model_card_templates[model_type]["other"]
docs_md_template = docs_card_templates[model_type]["mfa"]
else:
model_card_template = model_card_templates[model_type]["mfa"]
docs_md_template = docs_card_templates[model_type]["mfa"]
if model_type == "language_model":
language, version = meta_data["language"], meta_data["version"]
elif model_type == "corpus":
language, name = meta_data["language"], meta_data["name"]
name = make_path_safe(name)
else:
language, phone_set, dialect, version = (
meta_data["language"],
meta_data["phone_set"],
meta_data["dialect"],
meta_data["version"],
)
output_directory = get_model_card_directory(model_type, meta_data)
os.makedirs(output_directory, exist_ok=True)
model_card_path = os.path.join(output_directory, "README.md")
rst_path = model_name + ".md"
docs_language_dir = os.path.join(docs_dir, language)
if language not in language_model_doc_mds:
language_model_doc_mds[language] = []
os.makedirs(docs_language_dir, exist_ok=True)
docs_card_path = os.path.join(docs_language_dir, rst_path)
language_model_doc_mds[language].append(rst_path)
if OVERWRITE_MD or not os.path.exists(model_card_path):
with open(model_card_path, "w", encoding="utf8") as f:
print(meta_data)
fields = extract_model_card_fields(meta_data, model_type)
f.write(model_card_template.format(**fields))
if OVERWRITE_MD or not os.path.exists(docs_card_path):
with open(docs_card_path, "w", encoding="utf8") as f:
print(meta_data)
fields = extract_doc_card_fields(meta_data, model_type)
f.write(docs_md_template.format(**fields))
index_path = os.path.join(docs_dir, "index.rst")
rst_string = " " + "\n ".join(
f"{x}/index.rst" for x in sorted(language_model_doc_mds.keys())
)
if model_type == "dictionary":
rst_string = " ../mfa_phone_set.md\n" + rst_string
model_type_name = model_type_names[model_type]
columns = model_type_columns[model_type]
widths = model_type_column_widths[model_type]
with open(index_path, "w", encoding="utf8") as f:
f.write(
f"""
.. _{model_type}:
{model_type_name}
{'='* len(model_type_name)}
.. needtable::
:types: {model_type}
:style: datatable
:columns: {columns}
:class: table-striped
:colwidths: {widths}
.. toctree::
:hidden:
{rst_string}
"""
)
for language, model_doc_mds in sorted(language_model_doc_mds.items()):
index_path = os.path.join(docs_dir, language, "index.rst")
rst_string = " " + "\n ".join(model_doc_mds)
with open(index_path, "w", encoding="utf8") as f:
f.write(
f"""
.. _{model_type}_{language.lower()}:
{language.title()}
{'='* len(language)}
.. needtable::
:types: {model_type}
:filter: language == "{language.title()}"
:style: datatable
:columns: {columns}
:class: table-striped
:colwidths: {widths}
.. toctree::
:hidden:
{rst_string}
"""
)
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