MFA

.gitattributes

@@ -33,3 +33,19 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+dictionary/german_prosodylab.dict filter=lfs diff=lfs merge=lfs -text
+dictionary/german/prosodylab/v2.0.0/german_prosodylab.dict filter=lfs diff=lfs merge=lfs -text
+dictionary/japanese/mfa/japanese_mfa.dict filter=lfs diff=lfs merge=lfs -text
+dictionary/russian/mfa/russian_mfa.dict filter=lfs diff=lfs merge=lfs -text
+docs/source/_static/favicon.ico filter=lfs diff=lfs merge=lfs -text
+docs/source/_static/fonts/GentiumPlus-Bold.woff filter=lfs diff=lfs merge=lfs -text
+docs/source/_static/fonts/GentiumPlus-Bold.woff2 filter=lfs diff=lfs merge=lfs -text
+docs/source/_static/fonts/GentiumPlus-BoldItalic.woff filter=lfs diff=lfs merge=lfs -text
+docs/source/_static/fonts/GentiumPlus-BoldItalic.woff2 filter=lfs diff=lfs merge=lfs -text
+docs/source/_static/fonts/GentiumPlus-Italic.woff filter=lfs diff=lfs merge=lfs -text
+docs/source/_static/fonts/GentiumPlus-Italic.woff2 filter=lfs diff=lfs merge=lfs -text
+docs/source/_static/fonts/GentiumPlus-Regular.woff filter=lfs diff=lfs merge=lfs -text
+docs/source/_static/fonts/GentiumPlus-Regular.woff2 filter=lfs diff=lfs merge=lfs -text
+manuals/how-i-used-montreal-forced-aligner-for-a-new-language-sinhalese.pdf filter=lfs diff=lfs merge=lfs -text
+manuals/montreal-forced-alignment-hebrew.pdf filter=lfs diff=lfs merge=lfs -text
+paper/Montreal[[:space:]]Forced[[:space:]]Aligner.[[:space:]]Trainable[[:space:]]text-speech[[:space:]]alignment[[:space:]]using[[:space:]]Kaldi.pdf filter=lfs diff=lfs merge=lfs -text

.gitignore

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+
+.idea/
+
+g2p_models_backup/
+
+docs/build/
+
+dictionary/temp/
+
+training/
+staging/
+generated/
+
+dictionary/filter_lists/
+
+scripts/temp/
+/scripts/token
+
+*.db
+
+*.pyc
+
+*.pclprof
+.Rproj.user
+
+*.Rproj
+
+scripts/corpus_prep/temp/
+
+.Rhistory
+
+.RData

.pre-commit-config.yaml

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+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.0.1  # Use the ref you want to point at
+    hooks:
+      - id: check-ast
+      - id: check-builtin-literals
+      - id: check-docstring-first
+      - id: check-merge-conflict
+      - id: check-yaml
+      - id: check-toml
+      - id: file-contents-sorter
+        files: ^(dictionary/.*.txt|dictionary/.*.dict|acoustic/index.txt|g2p/index.txt|ivector/index.txt)$
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: debug-statements
+      - id: mixed-line-ending
+      - id: check-added-large-files
+        args: ['--maxkb=3000']
+  - repo: https://github.com/psf/black
+    rev: 23.9.1
+    hooks:
+      - id: black
+  #- repo: https://github.com/pycqa/flake8
+  #  rev: 7.0.0
+  #  hooks:
+  #    - id: flake8
+  #      additional_dependencies:
+  #        - pyproject-flake8
+  - repo: https://github.com/pre-commit/mirrors-isort
+    rev: v5.10.1
+    hooks:
+      - id: isort
+        additional_dependencies: [toml]
+  - repo: https://github.com/asottile/setup-cfg-fmt
+    rev: v2.2.0
+    hooks:
+      - id: setup-cfg-fmt
+        args:
+          - --min-py3-version
+          - "3.8"

.readthedocs.yaml

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+version: 2
+
+build:
+  os: "ubuntu-20.04"
+  tools:
+    python: "mambaforge-4.10"
+
+sphinx:
+   configuration: docs/source/conf.py
+
+conda:
+  environment: rtd_environment.yml

LICENSE

@@ -0,0 +1,395 @@
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README.md

@@ -0,0 +1,5 @@
+# Montreal Forced Aligner Models
+
+Collection of pretrained models and pronunciation dictionaries for use with [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/).
+
+See also https://mfa-models.readthedocs.io/en/latest/ for full model details and how to use and cite them.

acoustic/abkhaz/cv/v2.0.0/README.md

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+
+# Abkhaz CV acoustic model v2.0.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/abkhaz_cv.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Training data](#training-data)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+
+## Model details
+
+- **Maintainer:** [Vox Communis](https://osf.io/t957v/)
+- **Language:** [Abkhaz](https://en.wikipedia.org/wiki/Abkhaz_language)
+- **Dialect:** N/A
+- **Phone set:** [XPF](https://github.com/CohenPr-XPF/XPF)
+- **Model type:** `Acoustic model`
+- **Features:** `MFCC`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0`
+- **Trained date:** `02-11-2022`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC-0](https://creativecommons.org/publicdomain/zero/1.0/)
+- **Citation:**
+
+```bibtex
+@misc{Ahn_Chodroff_2022,
+	author={Ahn, Emily and Chodroff, Eleanor},
+	title={VoxCommunis Corpus},
+	address={\url{https://osf.io/t957v}},
+	publisher={OSF},
+	year={2022},
+	month={Jan}
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Abkhaz+CV+acoustic+model+v2.0.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic abkhaz_cv
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-abkhaz_cv-v2.0.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Abkhaz](https://en.wikipedia.org/wiki/Abkhaz_language) transcripts.
+
+This model uses the [XPF](https://github.com/CohenPr-XPF/XPF) phone set for Abkhaz, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Abkhaz](../../../../corpus/abkhaz/common_voice_abkhaz/7.0/README.md):
+     * **Hours:** `2.00`
+     * **Speakers:** `28`
+     * **Utterances:** `1,166`

acoustic/abkhaz/cv/v2.0.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "abkhaz_cv",
+    "language": "Abkhaz",
+    "dialect": "",
+    "phone_set": "CV",
+    "version": "2.0.0",
+    "maintainer": "[Vox Communis](https://osf.io/t957v/)",
+    "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}",
+    "license": "CC-0",
+    "license_link": "[CC-0](https://creativecommons.org/publicdomain/zero/1.0/)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "02-11-2022"
+}

acoustic/arabic.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:399954e19f3ccdded9821b1b12ac371201a0f881bbd7c5bba8cb8cd73fbc99cb
+size 14681790

acoustic/armenian/cv/v2.0.0/README.md

@@ -0,0 +1,110 @@
+
+# Armenian CV acoustic model v2.0.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/armenian_cv.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Training data](#training-data)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+
+## Model details
+
+- **Maintainer:** [Vox Communis](https://osf.io/t957v/)
+- **Language:** [Armenian](https://en.wikipedia.org/wiki/Armenian_language)
+- **Dialect:** N/A
+- **Phone set:** [XPF](https://github.com/CohenPr-XPF/XPF)
+- **Model type:** `Acoustic model`
+- **Features:** `MFCC`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0`
+- **Trained date:** `02-11-2022`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC-0](https://creativecommons.org/publicdomain/zero/1.0/)
+- **Citation:**
+
+```bibtex
+@misc{Ahn_Chodroff_2022,
+	author={Ahn, Emily and Chodroff, Eleanor},
+	title={VoxCommunis Corpus},
+	address={\url{https://osf.io/t957v}},
+	publisher={OSF},
+	year={2022},
+	month={Jan}
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Armenian+CV+acoustic+model+v2.0.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic armenian_cv
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-armenian_cv-v2.0.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Armenian](https://en.wikipedia.org/wiki/Armenian_language) transcripts.
+
+This model uses the [XPF](https://github.com/CohenPr-XPF/XPF) phone set for Armenian, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Armenian](../../../../corpus/armenian/common_voice_armenian/7.0/README.md):
+     * **Hours:** `1.00`
+     * **Speakers:** `22`
+     * **Utterances:** `767`

acoustic/armenian/cv/v2.0.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "armenian_cv",
+    "language": "Armenian",
+    "dialect": "",
+    "phone_set": "CV",
+    "version": "2.0.0",
+    "maintainer": "[Vox Communis](https://osf.io/t957v/)",
+    "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}",
+    "license": "CC-0",
+    "license_link": "[CC-0](https://creativecommons.org/publicdomain/zero/1.0/)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "02-11-2022"
+}

acoustic/bashkir/cv/v2.0.0/README.md

@@ -0,0 +1,110 @@
+
+# Bashkir CV acoustic model v2.0.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/bashkir_cv.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Training data](#training-data)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+
+## Model details
+
+- **Maintainer:** [Vox Communis](https://osf.io/t957v/)
+- **Language:** [Bashkir](https://en.wikipedia.org/wiki/Bashkir_language)
+- **Dialect:** N/A
+- **Phone set:** [XPF](https://github.com/CohenPr-XPF/XPF)
+- **Model type:** `Acoustic model`
+- **Features:** `MFCC`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0`
+- **Trained date:** `02-11-2022`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC-0](https://creativecommons.org/publicdomain/zero/1.0/)
+- **Citation:**
+
+```bibtex
+@misc{Ahn_Chodroff_2022,
+	author={Ahn, Emily and Chodroff, Eleanor},
+	title={VoxCommunis Corpus},
+	address={\url{https://osf.io/t957v}},
+	publisher={OSF},
+	year={2022},
+	month={Jan}
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Bashkir+CV+acoustic+model+v2.0.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic bashkir_cv
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-bashkir_cv-v2.0.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Bashkir](https://en.wikipedia.org/wiki/Bashkir_language) transcripts.
+
+This model uses the [XPF](https://github.com/CohenPr-XPF/XPF) phone set for Bashkir, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Bashkir](../../../../corpus/bashkir/common_voice_bashkir/7.0/README.md):
+     * **Hours:** `247.00`
+     * **Speakers:** `835`
+     * **Utterances:** `200,869`

acoustic/bashkir/cv/v2.0.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "bashkir_cv",
+    "language": "Bashkir",
+    "dialect": "",
+    "phone_set": "CV",
+    "version": "2.0.0",
+    "maintainer": "[Vox Communis](https://osf.io/t957v/)",
+    "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}",
+    "license": "CC-0",
+    "license_link": "[CC-0](https://creativecommons.org/publicdomain/zero/1.0/)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "02-11-2022"
+}

acoustic/basque/cv/v2.0.0/README.md

@@ -0,0 +1,110 @@
+
+# Basque CV acoustic model v2.0.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/basque_cv.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Training data](#training-data)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+
+## Model details
+
+- **Maintainer:** [Vox Communis](https://osf.io/t957v/)
+- **Language:** [Basque](https://en.wikipedia.org/wiki/Basque_language)
+- **Dialect:** N/A
+- **Phone set:** [XPF](https://github.com/CohenPr-XPF/XPF)
+- **Model type:** `Acoustic model`
+- **Features:** `MFCC`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0`
+- **Trained date:** `02-11-2022`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC-0](https://creativecommons.org/publicdomain/zero/1.0/)
+- **Citation:**
+
+```bibtex
+@misc{Ahn_Chodroff_2022,
+	author={Ahn, Emily and Chodroff, Eleanor},
+	title={VoxCommunis Corpus},
+	address={\url{https://osf.io/t957v}},
+	publisher={OSF},
+	year={2022},
+	month={Jan}
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Basque+CV+acoustic+model+v2.0.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic basque_cv
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-basque_cv-v2.0.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Basque](https://en.wikipedia.org/wiki/Basque_language) transcripts.
+
+This model uses the [XPF](https://github.com/CohenPr-XPF/XPF) phone set for Basque, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Basque](../../../../corpus/basque/common_voice_basque/7.0/README.md):
+     * **Hours:** `91.00`
+     * **Speakers:** `842`
+     * **Utterances:** `63,916`

acoustic/basque/cv/v2.0.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "basque_cv",
+    "language": "Basque",
+    "dialect": "",
+    "phone_set": "CV",
+    "version": "2.0.0",
+    "maintainer": "[Vox Communis](https://osf.io/t957v/)",
+    "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}",
+    "license": "CC-0",
+    "license_link": "[CC-0](https://creativecommons.org/publicdomain/zero/1.0/)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "02-11-2022"
+}

acoustic/belarusian/cv/v2.0.0/README.md

@@ -0,0 +1,110 @@
+
+# Belarusian CV acoustic model v2.0.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/belarusian_cv.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Training data](#training-data)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+
+## Model details
+
+- **Maintainer:** [Vox Communis](https://osf.io/t957v/)
+- **Language:** [Belarusian](https://en.wikipedia.org/wiki/Belarusian_language)
+- **Dialect:** N/A
+- **Phone set:** [XPF](https://github.com/CohenPr-XPF/XPF)
+- **Model type:** `Acoustic model`
+- **Features:** `MFCC`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0`
+- **Trained date:** `02-11-2022`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC-0](https://creativecommons.org/publicdomain/zero/1.0/)
+- **Citation:**
+
+```bibtex
+@misc{Ahn_Chodroff_2022,
+	author={Ahn, Emily and Chodroff, Eleanor},
+	title={VoxCommunis Corpus},
+	address={\url{https://osf.io/t957v}},
+	publisher={OSF},
+	year={2022},
+	month={Jan}
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Belarusian+CV+acoustic+model+v2.0.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic belarusian_cv
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-belarusian_cv-v2.0.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Belarusian](https://en.wikipedia.org/wiki/Belarusian_language) transcripts.
+
+This model uses the [XPF](https://github.com/CohenPr-XPF/XPF) phone set for Belarusian, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Belarusian](../../../../corpus/belarusian/common_voice_belarusian/7.0/README.md):
+     * **Hours:** `91.00`
+     * **Speakers:** `3,620`
+     * **Utterances:** `182,840`

acoustic/belarusian/cv/v2.0.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "belarusian_cv",
+    "language": "Belarusian",
+    "dialect": "",
+    "phone_set": "CV",
+    "version": "2.0.0",
+    "maintainer": "[Vox Communis](https://osf.io/t957v/)",
+    "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}",
+    "license": "CC-0",
+    "license_link": "[CC-0](https://creativecommons.org/publicdomain/zero/1.0/)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "02-11-2022"
+}

acoustic/bulgarian.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d08c109d1bc790542533bc4d8eb4422656bce9e75832fa292c6a718df0481a60
+size 14665607

acoustic/bulgarian/cv/v2.0.0/README.md

@@ -0,0 +1,110 @@
+
+# Bulgarian CV acoustic model v2.0.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/bulgarian_cv.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Training data](#training-data)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+
+## Model details
+
+- **Maintainer:** [Vox Communis](https://osf.io/t957v/)
+- **Language:** [Bulgarian](https://en.wikipedia.org/wiki/Bulgarian_language)
+- **Dialect:** N/A
+- **Phone set:** [XPF](https://github.com/CohenPr-XPF/XPF)
+- **Model type:** `Acoustic model`
+- **Features:** `MFCC`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0`
+- **Trained date:** `02-11-2022`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC-0](https://creativecommons.org/publicdomain/zero/1.0/)
+- **Citation:**
+
+```bibtex
+@misc{Ahn_Chodroff_2022,
+	author={Ahn, Emily and Chodroff, Eleanor},
+	title={VoxCommunis Corpus},
+	address={\url{https://osf.io/t957v}},
+	publisher={OSF},
+	year={2022},
+	month={Jan}
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Bulgarian+CV+acoustic+model+v2.0.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic bulgarian_cv
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-bulgarian_cv-v2.0.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Bulgarian](https://en.wikipedia.org/wiki/Bulgarian_language) transcripts.
+
+This model uses the [XPF](https://github.com/CohenPr-XPF/XPF) phone set for Bulgarian, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Bulgarian](../../../../corpus/bulgarian/common_voice_bulgarian/7.0/README.md):
+     * **Hours:** `5.00`
+     * **Speakers:** `35`
+     * **Utterances:** `3,459`

acoustic/bulgarian/cv/v2.0.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "bulgarian_cv",
+    "language": "Bulgarian",
+    "dialect": "",
+    "phone_set": "CV",
+    "version": "2.0.0",
+    "maintainer": "[Vox Communis](https://osf.io/t957v/)",
+    "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}",
+    "license": "CC-0",
+    "license_link": "[CC-0](https://creativecommons.org/publicdomain/zero/1.0/)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "02-11-2022"
+}

acoustic/bulgarian/mfa/v2.0.0/LICENSE

@@ -0,0 +1,395 @@
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acoustic/bulgarian/mfa/v2.0.0/README.md

@@ -0,0 +1,128 @@
+
+# Bulgarian MFA acoustic model v2.0.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/bulgarian_mfa.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Metrics](#metrics)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+- [Training data](#training-data)
+- [Evaluation data](#evaluation-data)
+
+## Model details
+
+- **Maintainer:** [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)
+- **Language:** [Bulgarian](https://en.wikipedia.org/wiki/Bulgarian_language)
+- **Dialect:** N/A
+- **Phone set:** [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#bulgarian)
+- **Model type:** `Acoustic`
+- **Features:** `MFCC + pitch`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0`
+- **Trained date:** `2022-03-21`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/bulgarian/mfa/v2.0.0/LICENSE)
+- **Citation:**
+
+```bibtex
+@techreport{mfa_bulgarian_mfa_acoustic_2022,
+	author={McAuliffe, Michael and Sonderegger, Morgan},
+	title={Bulgarian MFA acoustic model v2.0.0},
+	address={\url{https://mfa-models.readthedocs.io/acoustic/Bulgarian/Bulgarian MFA acoustic model v2_0_0.html}},
+	year={2022},
+	month={Mar},
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Bulgarian+MFA+acoustic+model+v2.0.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic bulgarian_mfa
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-bulgarian_mfa-v2.0.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Bulgarian](https://en.wikipedia.org/wiki/Bulgarian_language) transcripts.
+
+This model uses the [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#bulgarian) phone set for Bulgarian, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Metrics
+
+Acoustic models are typically generated as one component of a larger ASR system where the metric is word error rate (WER). For forced alignment, there is typically not the same sort of gold standard measure for most languages.
+
+As a rough approximation of the acoustic model quality, we evaluated it against the corpus it was trained on alongside a language model trained from the same data.  Key caveat here is that this is not a typical WER measure on held out data, so it should not be taken as a hard measure of how well an acoustic model will generalize to your data, but rather is more of a sanity check that the training data quality was sufficiently high.
+
+Using the pronunciation dictionary above and a language model trained on the GlobalPhone Bulgarian corpus (see the [GlobalPhone benchmark analysis page](https://montreal-forced-aligner.readthedocs.io/en/latest/benchmarks/globalphone_transcriptions.html) for more details):
+
+- **WER:** `2.5%`
+- **CER:** `0.8%`
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Bulgarian](../../../../corpus/bulgarian/common_voice_bulgarian/8.0/README.md):
+     * **Hours:** `7.98`
+     * **Speakers:** `53`
+     * **Utterances:** `5,387`
+
+   * [GlobalPhone Bulgarian](../../../../corpus/bulgarian/globalphone_bulgarian/3.1/README.md):
+     * **Hours:** `7.98`
+     * **Speakers:** `53`
+     * **Utterances:** `5,387`

acoustic/bulgarian/mfa/v2.0.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "bulgarian_mfa",
+    "language": "Bulgarian",
+    "dialect": "",
+    "phone_set": "MFA",
+    "version": "2.0.0",
+    "maintainer": "[Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)",
+    "citation": "@techreport{mfa_bulgarian_mfa_acoustic_2022,\n\tauthor={McAuliffe, Michael and Sonderegger, Morgan},\n\ttitle={Bulgarian MFA acoustic model v2.0.0},\n\taddress={\\url{https://mfa-models.readthedocs.io/acoustic/Bulgarian/Bulgarian MFA acoustic model v2_0_0.html}},\n\tyear={2022},\n\tmonth={Mar},\n}",
+    "license": "CC BY 4.0",
+    "license_link": "[CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/bulgarian/mfa/v2.0.0/LICENSE)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC + pitch",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "2022-03-21"
+}

acoustic/bulgarian/mfa/v2.0.0a/LICENSE

@@ -0,0 +1,395 @@
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acoustic/bulgarian/mfa/v2.0.0a/README.md

@@ -0,0 +1,128 @@
+
+# Bulgarian MFA acoustic model v2.0.0a
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/bulgarian_mfa.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Metrics](#metrics)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+- [Training data](#training-data)
+- [Evaluation data](#evaluation-data)
+
+## Model details
+
+- **Maintainer:** [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)
+- **Language:** [Bulgarian](https://en.wikipedia.org/wiki/Bulgarian_language)
+- **Dialect:** N/A
+- **Phone set:** [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#bulgarian)
+- **Model type:** `Acoustic`
+- **Features:** `MFCC + pitch`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0a`
+- **Trained date:** `2022-05-29`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/bulgarian/mfa/v2.0.0a/LICENSE)
+- **Citation:**
+
+```bibtex
+@techreport{mfa_bulgarian_mfa_acoustic_2022,
+	author={McAuliffe, Michael and Sonderegger, Morgan},
+	title={Bulgarian MFA acoustic model v2.0.0a},
+	address={\url{https://mfa-models.readthedocs.io/acoustic/Bulgarian/Bulgarian MFA acoustic model v2_0_0a.html}},
+	year={2022},
+	month={May},
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Bulgarian+MFA+acoustic+model+v2.0.0a) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic bulgarian_mfa
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-bulgarian_mfa-v2.0.0a).
+
+## Intended use
+
+This model is intended for forced alignment of [Bulgarian](https://en.wikipedia.org/wiki/Bulgarian_language) transcripts.
+
+This model uses the [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#bulgarian) phone set for Bulgarian, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Metrics
+
+Acoustic models are typically generated as one component of a larger ASR system where the metric is word error rate (WER). For forced alignment, there is typically not the same sort of gold standard measure for most languages.
+
+As a rough approximation of the acoustic model quality, we evaluated it against the corpus it was trained on alongside a language model trained from the same data.  Key caveat here is that this is not a typical WER measure on held out data, so it should not be taken as a hard measure of how well an acoustic model will generalize to your data, but rather is more of a sanity check that the training data quality was sufficiently high.
+
+Using the pronunciation dictionaries and language models above:
+
+- **WER:** `0%`
+- **CER:** `0%`
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Bulgarian](../../../../corpus/bulgarian/common_voice_bulgarian/9.0/README.md):
+     * **Hours:** `12.06`
+     * **Speakers:** `62`
+     * **Utterances:** `7,921`
+
+   * [GlobalPhone Bulgarian](../../../../corpus/bulgarian/globalphone_bulgarian/3.1/README.md):
+     * **Hours:** `7.98`
+     * **Speakers:** `53`
+     * **Utterances:** `5,387`

acoustic/bulgarian/mfa/v2.0.0a/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "bulgarian_mfa",
+    "language": "Bulgarian",
+    "dialect": "",
+    "phone_set": "MFA",
+    "version": "2.0.0a",
+    "maintainer": "[Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)",
+    "citation": "@techreport{mfa_bulgarian_mfa_acoustic_2022,\n\tauthor={McAuliffe, Michael and Sonderegger, Morgan},\n\ttitle={Bulgarian MFA acoustic model v2.0.0a},\n\taddress={\\url{https://mfa-models.readthedocs.io/acoustic/Bulgarian/Bulgarian MFA acoustic model v2_0_0a.html}},\n\tyear={2022},\n\tmonth={May},\n}",
+    "license": "CC BY 4.0",
+    "license_link": "[CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/bulgarian/mfa/v2.0.0a/LICENSE)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC + pitch",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "2022-05-29"
+}

acoustic/bulgarian/mfa/v3.0.0/LICENSE

@@ -0,0 +1,395 @@
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acoustic/bulgarian/mfa/v3.0.0/README.md

@@ -0,0 +1,127 @@
+# Bulgarian MFA acoustic model v3.0.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/bulgarian_mfa.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Metrics](#metrics)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+- [Training data](#training-data)
+- [Evaluation data](#evaluation-data)
+
+## Model details
+
+- **Maintainer:** [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)
+- **Language:** [Bulgarian](https://en.wikipedia.org/wiki/Bulgarian_language)
+- **Dialect:** N/A
+- **Phone set:** [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#bulgarian)
+- **Model type:** `Acoustic`
+- **Features:** `MFCC`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v3.0.0`
+- **Trained date:** `2024-02-26`
+- **Compatible MFA version:** `v3.0.0`
+- **License:** [CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/bulgarian/mfa/v3.0.0/LICENSE)
+- **Citation:**
+
+```bibtex
+@techreport{mfa_bulgarian_mfa_acoustic_2024,
+	author={McAuliffe, Michael and Sonderegger, Morgan},
+	title={Bulgarian MFA acoustic model v3.0.0},
+	address={\url{https://mfa-models.readthedocs.io/acoustic/Bulgarian/Bulgarian MFA acoustic model v3_0_0.html}},
+	year={2024},
+	month={Feb},
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Bulgarian+MFA+acoustic+model+v3.0.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic bulgarian_mfa
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-bulgarian_mfa-v3.0.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Bulgarian](https://en.wikipedia.org/wiki/Bulgarian_language) transcripts.
+
+This model uses the [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#bulgarian) phone set for Bulgarian, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Metrics
+
+Acoustic models are typically generated as one component of a larger ASR system where the metric is word error rate (WER). For forced alignment, there is typically not the same sort of gold standard measure for most languages.
+
+As a rough approximation of the acoustic model quality, we evaluated it against the corpus it was trained on alongside a language model trained from the same data.  Key caveat here is that this is not a typical WER measure on held out data, so it should not be taken as a hard measure of how well an acoustic model will generalize to your data, but rather is more of a sanity check that the training data quality was sufficiently high.
+
+Using the pronunciation dictionaries and language models above:
+
+- **WER:** `0%`
+- **CER:** `0%`
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Bulgarian](../../../../corpus/bulgarian/common_voice_bulgarian/16.1/README.md):
+     * **Hours:** `13.72`
+     * **Speakers:** `95`
+     * **Utterances:** `9,126`
+
+   * [GlobalPhone Bulgarian](../../../../corpus/bulgarian/globalphone_bulgarian/3.1/README.md):
+     * **Hours:** `7.98`
+     * **Speakers:** `53`
+     * **Utterances:** `5,387`

acoustic/bulgarian/mfa/v3.0.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "bulgarian_mfa",
+    "language": "Bulgarian",
+    "dialect": "",
+    "phone_set": "MFA",
+    "version": "3.0.0",
+    "maintainer": "[Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)",
+    "citation": "@techreport{mfa_bulgarian_mfa_acoustic_2024,\n\tauthor={McAuliffe, Michael and Sonderegger, Morgan},\n\ttitle={Bulgarian MFA acoustic model v3.0.0},\n\taddress={\\url{https://mfa-models.readthedocs.io/acoustic/Bulgarian/Bulgarian MFA acoustic model v3_0_0.html}},\n\tyear={2024},\n\tmonth={Feb},\n}",
+    "license": "CC BY 4.0",
+    "license_link": "[CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/bulgarian/mfa/v3.0.0/LICENSE)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "2024-02-26"
+}

acoustic/chuvash/cv/v2.0.0/README.md

@@ -0,0 +1,110 @@
+
+# Chuvash CV acoustic model v2.0.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/chuvash_cv.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Training data](#training-data)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+
+## Model details
+
+- **Maintainer:** [Vox Communis](https://osf.io/t957v/)
+- **Language:** [Chuvash](https://en.wikipedia.org/wiki/Chuvash_language)
+- **Dialect:** N/A
+- **Phone set:** [XPF](https://github.com/CohenPr-XPF/XPF)
+- **Model type:** `Acoustic model`
+- **Features:** `MFCC`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0`
+- **Trained date:** `02-11-2022`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC-0](https://creativecommons.org/publicdomain/zero/1.0/)
+- **Citation:**
+
+```bibtex
+@misc{Ahn_Chodroff_2022,
+	author={Ahn, Emily and Chodroff, Eleanor},
+	title={VoxCommunis Corpus},
+	address={\url{https://osf.io/t957v}},
+	publisher={OSF},
+	year={2022},
+	month={Jan}
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Chuvash+CV+acoustic+model+v2.0.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic chuvash_cv
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-chuvash_cv-v2.0.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Chuvash](https://en.wikipedia.org/wiki/Chuvash_language) transcripts.
+
+This model uses the [XPF](https://github.com/CohenPr-XPF/XPF) phone set for Chuvash, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Chuvash](../../../../corpus/chuvash/common_voice_chuvash/7.0/README.md):
+     * **Hours:** `5.00`
+     * **Speakers:** `82`
+     * **Utterances:** `3,748`

acoustic/chuvash/cv/v2.0.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "chuvash_cv",
+    "language": "Chuvash",
+    "dialect": "",
+    "phone_set": "CV",
+    "version": "2.0.0",
+    "maintainer": "[Vox Communis](https://osf.io/t957v/)",
+    "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}",
+    "license": "CC-0",
+    "license_link": "[CC-0](https://creativecommons.org/publicdomain/zero/1.0/)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "02-11-2022"
+}

acoustic/croatian.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:103cf14062d937cab272b5db959918e0f911b2a3f66286edbdcda4bb11aa15d9
+size 14668100

acoustic/croatian/mfa/v2.0.0/LICENSE

@@ -0,0 +1,395 @@
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acoustic/croatian/mfa/v2.0.0/README.md

@@ -0,0 +1,128 @@
+
+# Croatian MFA acoustic model v2.0.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/croatian_mfa.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Metrics](#metrics)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+- [Training data](#training-data)
+- [Evaluation data](#evaluation-data)
+
+## Model details
+
+- **Maintainer:** [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)
+- **Language:** [Serbo-Croatian](https://en.wikipedia.org/wiki/Serbo-Croatian)
+- **Dialect:** N/A
+- **Phone set:** [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#croatian)
+- **Model type:** `Acoustic`
+- **Features:** `MFCC + pitch`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0`
+- **Trained date:** `2022-03-21`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/croatian/mfa/v2.0.0/LICENSE)
+- **Citation:**
+
+```bibtex
+@techreport{mfa_croatian_mfa_acoustic_2022,
+	author={McAuliffe, Michael and Sonderegger, Morgan},
+	title={Croatian MFA acoustic model v2.0.0},
+	address={\url{https://mfa-models.readthedocs.io/acoustic/Croatian/Croatian MFA acoustic model v2_0_0.html}},
+	year={2022},
+	month={Mar},
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Croatian+MFA+acoustic+model+v2.0.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic croatian_mfa
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-croatian_mfa-v2.0.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Serbo-Croatian](https://en.wikipedia.org/wiki/Serbo-Croatian) transcripts.
+
+This model uses the [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#croatian) phone set for Croatian, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Metrics
+
+Acoustic models are typically generated as one component of a larger ASR system where the metric is word error rate (WER). For forced alignment, there is typically not the same sort of gold standard measure for most languages.
+
+As a rough approximation of the acoustic model quality, we evaluated it against the corpus it was trained on alongside a language model trained from the same data.  Key caveat here is that this is not a typical WER measure on held out data, so it should not be taken as a hard measure of how well an acoustic model will generalize to your data, but rather is more of a sanity check that the training data quality was sufficiently high.
+
+Using the pronunciation dictionary above and a language model trained on the GlobalPhone Croatian corpus (see the [GlobalPhone benchmark analysis page](https://montreal-forced-aligner.readthedocs.io/en/latest/benchmarks/globalphone_transcriptions.html) for more details):
+
+- **WER:** `6.5%`
+- **CER:** `3.6%`
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Serbian](../../../../corpus/croatian/common_voice_serbian/8.0/README.md):
+     * **Hours:** `1.50`
+     * **Speakers:** `50`
+     * **Utterances:** `1,878`
+
+   * [GlobalPhone Croatian](../../../../corpus/croatian/globalphone_croatian/3.1/README.md):
+     * **Hours:** `15.89`
+     * **Speakers:** `92`
+     * **Utterances:** `4,499`

acoustic/croatian/mfa/v2.0.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "croatian_mfa",
+    "language": "Croatian",
+    "dialect": "",
+    "phone_set": "MFA",
+    "version": "2.0.0",
+    "maintainer": "[Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)",
+    "citation": "@techreport{mfa_croatian_mfa_acoustic_2022,\n\tauthor={McAuliffe, Michael and Sonderegger, Morgan},\n\ttitle={Croatian MFA acoustic model v2.0.0},\n\taddress={\\url{https://mfa-models.readthedocs.io/acoustic/Croatian/Croatian MFA acoustic model v2_0_0.html}},\n\tyear={2022},\n\tmonth={Mar},\n}",
+    "license": "CC BY 4.0",
+    "license_link": "[CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/croatian/mfa/v2.0.0/LICENSE)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC + pitch",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "2022-03-21"
+}

acoustic/croatian/mfa/v2.0.0a/LICENSE

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+material is shared under a Creative Commons public license or as
+otherwise permitted by the Creative Commons policies published at
+creativecommons.org/policies, Creative Commons does not authorize the
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+public licenses.
+
+Creative Commons may be contacted at creativecommons.org.

acoustic/croatian/mfa/v2.0.0a/README.md

@@ -0,0 +1,128 @@
+
+# Croatian MFA acoustic model v2.0.0a
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/croatian_mfa.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Metrics](#metrics)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+- [Training data](#training-data)
+- [Evaluation data](#evaluation-data)
+
+## Model details
+
+- **Maintainer:** [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)
+- **Language:** [Serbo-Croatian](https://en.wikipedia.org/wiki/Serbo-Croatian)
+- **Dialect:** N/A
+- **Phone set:** [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#croatian)
+- **Model type:** `Acoustic`
+- **Features:** `MFCC + pitch`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0a`
+- **Trained date:** `2022-05-18`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/croatian/mfa/v2.0.0a/LICENSE)
+- **Citation:**
+
+```bibtex
+@techreport{mfa_croatian_mfa_acoustic_2022,
+	author={McAuliffe, Michael and Sonderegger, Morgan},
+	title={Croatian MFA acoustic model v2.0.0a},
+	address={\url{https://mfa-models.readthedocs.io/acoustic/Croatian/Croatian MFA acoustic model v2_0_0a.html}},
+	year={2022},
+	month={May},
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Croatian+MFA+acoustic+model+v2.0.0a) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic croatian_mfa
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-croatian_mfa-v2.0.0a).
+
+## Intended use
+
+This model is intended for forced alignment of [Serbo-Croatian](https://en.wikipedia.org/wiki/Serbo-Croatian) transcripts.
+
+This model uses the [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#croatian) phone set for Croatian, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Metrics
+
+Acoustic models are typically generated as one component of a larger ASR system where the metric is word error rate (WER). For forced alignment, there is typically not the same sort of gold standard measure for most languages.
+
+As a rough approximation of the acoustic model quality, we evaluated it against the corpus it was trained on alongside a language model trained from the same data.  Key caveat here is that this is not a typical WER measure on held out data, so it should not be taken as a hard measure of how well an acoustic model will generalize to your data, but rather is more of a sanity check that the training data quality was sufficiently high.
+
+Using the pronunciation dictionary above and a language model trained on the GlobalPhone Croatian corpus (see the [GlobalPhone benchmark analysis page](https://montreal-forced-aligner.readthedocs.io/en/latest/benchmarks/globalphone_transcriptions.html) for more details):
+
+- **WER:** `6.4%`
+- **CER:** `3.4%`
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Serbian](../../../../corpus/croatian/common_voice_serbian/8.0/README.md):
+     * **Hours:** `1.50`
+     * **Speakers:** `50`
+     * **Utterances:** `1,878`
+
+   * [GlobalPhone Croatian](../../../../corpus/croatian/globalphone_croatian/3.1/README.md):
+     * **Hours:** `15.89`
+     * **Speakers:** `92`
+     * **Utterances:** `4,499`

acoustic/croatian/mfa/v2.0.0a/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "croatian_mfa",
+    "language": "Croatian",
+    "dialect": "",
+    "phone_set": "MFA",
+    "version": "2.0.0a",
+    "maintainer": "[Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)",
+    "citation": "@techreport{mfa_croatian_mfa_acoustic_2022,\n\tauthor={McAuliffe, Michael and Sonderegger, Morgan},\n\ttitle={Croatian MFA acoustic model v2.0.0a},\n\taddress={\\url{https://mfa-models.readthedocs.io/acoustic/Croatian/Croatian MFA acoustic model v2_0_0a.html}},\n\tyear={2022},\n\tmonth={May},\n}",
+    "license": "CC BY 4.0",
+    "license_link": "[CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/croatian/mfa/v2.0.0a/LICENSE)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC + pitch",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "2022-05-18"
+}

acoustic/czech.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8e7112879afccb6cdbb16864087ad632c7cd18dd9fc45e85b4e457fc8d721f7
+size 14670658

acoustic/czech/cv/v2.0.0/README.md

@@ -0,0 +1,110 @@
+
+# Czech CV acoustic model v2.0.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/czech_cv.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Training data](#training-data)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+
+## Model details
+
+- **Maintainer:** [Vox Communis](https://osf.io/t957v/)
+- **Language:** [Czech](https://en.wikipedia.org/wiki/Czech_language)
+- **Dialect:** N/A
+- **Phone set:** [XPF](https://github.com/CohenPr-XPF/XPF)
+- **Model type:** `Acoustic model`
+- **Features:** `MFCC`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0`
+- **Trained date:** `02-11-2022`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC-0](https://creativecommons.org/publicdomain/zero/1.0/)
+- **Citation:**
+
+```bibtex
+@misc{Ahn_Chodroff_2022,
+	author={Ahn, Emily and Chodroff, Eleanor},
+	title={VoxCommunis Corpus},
+	address={\url{https://osf.io/t957v}},
+	publisher={OSF},
+	year={2022},
+	month={Jan}
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Czech+CV+acoustic+model+v2.0.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic czech_cv
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-czech_cv-v2.0.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Czech](https://en.wikipedia.org/wiki/Czech_language) transcripts.
+
+This model uses the [XPF](https://github.com/CohenPr-XPF/XPF) phone set for Czech, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Czech](../../../../corpus/czech/common_voice_czech/7.0/README.md):
+     * **Hours:** `49.00`
+     * **Speakers:** `475`
+     * **Utterances:** `41,567`

acoustic/czech/cv/v2.0.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "czech_cv",
+    "language": "Czech",
+    "dialect": "",
+    "phone_set": "CV",
+    "version": "2.0.0",
+    "maintainer": "[Vox Communis](https://osf.io/t957v/)",
+    "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}",
+    "license": "CC-0",
+    "license_link": "[CC-0](https://creativecommons.org/publicdomain/zero/1.0/)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "02-11-2022"
+}

acoustic/czech/mfa/v2.0.0/LICENSE

@@ -0,0 +1,395 @@
+Attribution 4.0 International
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acoustic/czech/mfa/v2.0.0/README.md

@@ -0,0 +1,138 @@
+
+# Czech MFA acoustic model v2.0.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/czech_mfa.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Metrics](#metrics)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+- [Training data](#training-data)
+- [Evaluation data](#evaluation-data)
+
+## Model details
+
+- **Maintainer:** [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)
+- **Language:** [Czech](https://en.wikipedia.org/wiki/Czech_language)
+- **Dialect:** N/A
+- **Phone set:** [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#czech)
+- **Model type:** `Acoustic`
+- **Features:** `MFCC + pitch`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0`
+- **Trained date:** `2022-03-31`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/czech/mfa/v2.0.0/LICENSE)
+- **Citation:**
+
+```bibtex
+@techreport{mfa_czech_mfa_acoustic_2022,
+	author={McAuliffe, Michael and Sonderegger, Morgan},
+	title={Czech MFA acoustic model v2.0.0},
+	address={\url{https://mfa-models.readthedocs.io/acoustic/Czech/Czech MFA acoustic model v2_0_0.html}},
+	year={2022},
+	month={Mar},
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Czech+MFA+acoustic+model+v2.0.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic czech_mfa
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-czech_mfa-v2.0.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Czech](https://en.wikipedia.org/wiki/Czech_language) transcripts.
+
+This model uses the [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#czech) phone set for Czech, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Metrics
+
+Acoustic models are typically generated as one component of a larger ASR system where the metric is word error rate (WER). For forced alignment, there is typically not the same sort of gold standard measure for most languages.
+
+As a rough approximation of the acoustic model quality, we evaluated it against the corpus it was trained on alongside a language model trained from the same data.  Key caveat here is that this is not a typical WER measure on held out data, so it should not be taken as a hard measure of how well an acoustic model will generalize to your data, but rather is more of a sanity check that the training data quality was sufficiently high.
+
+Using the pronunciation dictionary above and a language model trained on the GlobalPhone Czech corpus (see the [GlobalPhone benchmark analysis page](https://montreal-forced-aligner.readthedocs.io/en/latest/benchmarks/globalphone_transcriptions.html) for more details):
+
+- **WER:** `3.9%`
+- **CER:** `1.5%`
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Czech](../../../../corpus/czech/common_voice_czech/8.0/README.md):
+     * **Hours:** `66.33`
+     * **Speakers:** `523`
+     * **Utterances:** `55,391`
+
+   * [GlobalPhone Czech](../../../../corpus/czech/globalphone_czech/3.1/README.md):
+     * **Hours:** `31.67`
+     * **Speakers:** `101`
+     * **Utterances:** `12,322`
+
+   * [Large Corpus of Czech Parliament Plenary Hearings](../../../../corpus/czech/large_corpus_of_czech_parliament_plenary_hearings/README.md):
+     * **Hours:** `450.95`
+     * **Speakers:** `84`
+     * **Utterances:** `194,181`
+
+   * [Czech Parliament Meetings](../../../../corpus/czech/czech_parliament_meetings/README.md):
+     * **Hours:** `88.81`
+     * **Speakers:** `67`
+     * **Utterances:** `51,494`

acoustic/czech/mfa/v2.0.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "czech_mfa",
+    "language": "Czech",
+    "dialect": "",
+    "phone_set": "MFA",
+    "version": "2.0.0",
+    "maintainer": "[Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)",
+    "citation": "@techreport{mfa_czech_mfa_acoustic_2022,\n\tauthor={McAuliffe, Michael and Sonderegger, Morgan},\n\ttitle={Czech MFA acoustic model v2.0.0},\n\taddress={\\url{https://mfa-models.readthedocs.io/acoustic/Czech/Czech MFA acoustic model v2_0_0.html}},\n\tyear={2022},\n\tmonth={Mar},\n}",
+    "license": "CC BY 4.0",
+    "license_link": "[CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/czech/mfa/v2.0.0/LICENSE)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC + pitch",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "2022-03-31"
+}

acoustic/czech/mfa/v2.0.0a/LICENSE

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acoustic/czech/mfa/v2.0.0a/README.md

@@ -0,0 +1,138 @@
+
+# Czech MFA acoustic model v2.0.0a
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/czech_mfa.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Metrics](#metrics)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+- [Training data](#training-data)
+- [Evaluation data](#evaluation-data)
+
+## Model details
+
+- **Maintainer:** [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)
+- **Language:** [Czech](https://en.wikipedia.org/wiki/Czech_language)
+- **Dialect:** N/A
+- **Phone set:** [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#czech)
+- **Model type:** `Acoustic`
+- **Features:** `MFCC + pitch`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v2.0.0a`
+- **Trained date:** `2022-05-19`
+- **Compatible MFA version:** `v2.0.0`
+- **License:** [CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/czech/mfa/v2.0.0a/LICENSE)
+- **Citation:**
+
+```bibtex
+@techreport{mfa_czech_mfa_acoustic_2022,
+	author={McAuliffe, Michael and Sonderegger, Morgan},
+	title={Czech MFA acoustic model v2.0.0a},
+	address={\url{https://mfa-models.readthedocs.io/acoustic/Czech/Czech MFA acoustic model v2_0_0a.html}},
+	year={2022},
+	month={May},
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Czech+MFA+acoustic+model+v2.0.0a) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic czech_mfa
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-czech_mfa-v2.0.0a).
+
+## Intended use
+
+This model is intended for forced alignment of [Czech](https://en.wikipedia.org/wiki/Czech_language) transcripts.
+
+This model uses the [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#czech) phone set for Czech, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Metrics
+
+Acoustic models are typically generated as one component of a larger ASR system where the metric is word error rate (WER). For forced alignment, there is typically not the same sort of gold standard measure for most languages.
+
+As a rough approximation of the acoustic model quality, we evaluated it against the corpus it was trained on alongside a language model trained from the same data.  Key caveat here is that this is not a typical WER measure on held out data, so it should not be taken as a hard measure of how well an acoustic model will generalize to your data, but rather is more of a sanity check that the training data quality was sufficiently high.
+
+Using the pronunciation dictionary above and a language model trained on the GlobalPhone Czech corpus (see the [GlobalPhone benchmark analysis page](https://montreal-forced-aligner.readthedocs.io/en/latest/benchmarks/globalphone_transcriptions.html) for more details):
+
+- **WER:** `3.1%`
+- **CER:** `1.2%`
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Czech](../../../../corpus/czech/common_voice_czech/8.0/README.md):
+     * **Hours:** `66.33`
+     * **Speakers:** `523`
+     * **Utterances:** `55,391`
+
+   * [GlobalPhone Czech](../../../../corpus/czech/globalphone_czech/3.1/README.md):
+     * **Hours:** `31.67`
+     * **Speakers:** `101`
+     * **Utterances:** `12,322`
+
+   * [Large Corpus of Czech Parliament Plenary Hearings](../../../../corpus/czech/large_corpus_of_czech_parliament_plenary_hearings/README.md):
+     * **Hours:** `450.95`
+     * **Speakers:** `84`
+     * **Utterances:** `194,181`
+
+   * [Czech Parliament Meetings](../../../../corpus/czech/czech_parliament_meetings/README.md):
+     * **Hours:** `88.81`
+     * **Speakers:** `67`
+     * **Utterances:** `51,494`

acoustic/czech/mfa/v2.0.0a/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "czech_mfa",
+    "language": "Czech",
+    "dialect": "",
+    "phone_set": "MFA",
+    "version": "2.0.0a",
+    "maintainer": "[Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)",
+    "citation": "@techreport{mfa_czech_mfa_acoustic_2022,\n\tauthor={McAuliffe, Michael and Sonderegger, Morgan},\n\ttitle={Czech MFA acoustic model v2.0.0a},\n\taddress={\\url{https://mfa-models.readthedocs.io/acoustic/Czech/Czech MFA acoustic model v2_0_0a.html}},\n\tyear={2022},\n\tmonth={May},\n}",
+    "license": "CC BY 4.0",
+    "license_link": "[CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/czech/mfa/v2.0.0a/LICENSE)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC + pitch",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "2022-05-19"
+}

acoustic/czech/mfa/v3.3.0/LICENSE

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acoustic/czech/mfa/v3.3.0/README.md

@@ -0,0 +1,137 @@
+# Czech MFA acoustic model v3.3.0
+
+[Link to documentation on mfa-models](https://mfa-models.readthedocs.io/en/main/acoustic/czech_mfa.html)
+
+Jump to section:
+
+- [Model details](#model-details)
+- [Installation](#installation)
+- [Intended use](#intended-use)
+- [Performance Factors](#performance-factors)
+- [Metrics](#metrics)
+- [Ethical considerations](#ethical-considerations)
+- [Troubleshooting issues](#troubleshooting-issues)
+- [Training data](#training-data)
+- [Evaluation data](#evaluation-data)
+
+## Model details
+
+- **Maintainer:** [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)
+- **Language:** [Czech](https://en.wikipedia.org/wiki/Czech_language)
+- **Dialect:** N/A
+- **Phone set:** [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#czech)
+- **Model type:** `Acoustic`
+- **Features:** `MFCC`
+- **Architecture:** `gmm-hmm`
+- **Model version:** `v3.3.0`
+- **Trained date:** `2025-05-05`
+- **Compatible MFA version:** `v3.3.0`
+- **License:** [CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/czech/mfa/v3.3.0/LICENSE)
+- **Citation:**
+
+```bibtex
+@techreport{mfa_czech_mfa_acoustic_2025,
+	author={McAuliffe, Michael and Sonderegger, Morgan},
+	title={Czech MFA acoustic model v3.3.0},
+	address={\url{https://mfa-models.readthedocs.io/acoustic/Czech/Czech MFA acoustic model v3_3_0.html}},
+	year={2025},
+	month={May},
+}
+```
+
+- If you have comments or questions about this model, you can check [previous MFA model discussion posts](https://github.com/MontrealCorpusTools/mfa-models/discussions?discussions_q=Czech+MFA+acoustic+model+v3.3.0) or create [a new one](https://github.com/MontrealCorpusTools/mfa-models/discussions/new).
+
+## Installation
+
+Install from the [MFA command line](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/models/index.html):
+
+```
+mfa model download acoustic czech_mfa
+```
+
+Or download from [the release page](https://github.com/MontrealCorpusTools/mfa-models/releases/tag/acoustic-czech_mfa-v3.3.0).
+
+## Intended use
+
+This model is intended for forced alignment of [Czech](https://en.wikipedia.org/wiki/Czech_language) transcripts.
+
+This model uses the [MFA](https://mfa-models.readthedocs.io/en/refactor/mfa_phone_set.html#czech) phone set for Czech, and was trained with the pronunciation dictionaries above. Pronunciations can be added on top of the dictionary, as long as no additional phones are introduced.
+
+## Performance Factors
+
+As forced alignment is a relatively well-constrained problem (given accurate transcripts), this model should be applicable to a range of recording conditions and speakers. However, please note that it was trained on read speech in low-noise environments, so as your data diverges from that, you may run into alignment issues or need to [increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands) or see other recommendations in the [troubleshooting section below](#troubleshooting-issues).
+
+Please note as well that MFA does not use state-of-the-art ASR models for forced alignment. You may get better performance (especially on speech-to-text tasks) using other frameworks like [Coqui](https://coqui.ai/).
+
+## Metrics
+
+Acoustic models are typically generated as one component of a larger ASR system where the metric is word error rate (WER). For forced alignment, there is typically not the same sort of gold standard measure for most languages.
+
+As a rough approximation of the acoustic model quality, we evaluated it against the corpus it was trained on alongside a language model trained from the same data.  Key caveat here is that this is not a typical WER measure on held out data, so it should not be taken as a hard measure of how well an acoustic model will generalize to your data, but rather is more of a sanity check that the training data quality was sufficiently high.
+
+Using the pronunciation dictionaries and language models above:
+
+- **WER:** `0%`
+- **CER:** `0%`
+
+## Ethical considerations
+
+Deploying any Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
+
+### Demographic Bias
+
+You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
+
+### Surveillance
+
+Speech-to-Text technologies may be misused to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in many countries. You should not assume consent to record and analyze private speech.
+
+
+## Troubleshooting issues
+
+Machine learning models (like this acoustic model) perform best on data that is similar to the data on which they were trained.
+
+The primary sources of variability in forced alignment will be the applicability of the pronunciation dictionary and how similar the speech, demographics, and recording conditions are. If you encounter issues in alignment, there are couple of avenues to improve performance:
+
+1. [Increase the beam size of MFA](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/configuration/#configuring-specific-commands)
+
+   * MFA defaults to a narrow beam to ensure quick alignment and also as a way to detect potential issues in your dataset, but depending on your data, you might benefit from boosting the beam to 100 or higher.
+
+2. Add pronunciations to the pronunciation dictionary
+
+   * This model was trained a particular dialect/style, and so adding pronunciations more representative of the variety spoken in your dataset will help alignment.
+
+3. Check the quality of your data
+
+   * MFA includes a [validator utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/data_validation.html), which aims to detect issues in the dataset.
+   * Use MFA's [anchor utility](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/anchor.html) to visually inspect your data as MFA sees it and correct issues in transcription or OOV items.
+
+4. Adapt the model to your data
+
+   * MFA has an [adaptation command](https://montreal-forced-aligner.readthedocs.io/en/latest/user_guide/workflows/adapt_acoustic_model.html) to adapt some of the model to your data based on an initial alignment, and then run another alignment with the adapted model.
+
+## Training data
+
+This model was trained on the following corpora:
+
+
+
+   * [Common Voice Czech](../../../../corpus/czech/common_voice_czech/9.0/README.md):
+     * **Hours:** `66.33`
+     * **Speakers:** `523`
+     * **Utterances:** `55,391`
+
+   * [GlobalPhone Czech](../../../../corpus/czech/globalphone_czech/3.1/README.md):
+     * **Hours:** `31.67`
+     * **Speakers:** `101`
+     * **Utterances:** `12,322`
+
+   * [Large Corpus of Czech Parliament Plenary Hearings](../../../../corpus/czech/large_corpus_of_czech_parliament_plenary_hearings/README.md):
+     * **Hours:** `450.95`
+     * **Speakers:** `84`
+     * **Utterances:** `194,181`
+
+   * [Czech Parliament Meetings](../../../../corpus/czech/czech_parliament_meetings/README.md):
+     * **Hours:** `88.81`
+     * **Speakers:** `67`
+     * **Utterances:** `51,494`

acoustic/czech/mfa/v3.3.0/meta.json

@@ -0,0 +1,16 @@
+{
+    "name": "czech_mfa",
+    "language": "Czech",
+    "dialect": "",
+    "phone_set": "MFA",
+    "version": "3.3.0",
+    "maintainer": "[Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/)",
+    "citation": "@techreport{mfa_czech_mfa_acoustic_2025,\n\tauthor={McAuliffe, Michael and Sonderegger, Morgan},\n\ttitle={Czech MFA acoustic model v3.3.0},\n\taddress={\\url{https://mfa-models.readthedocs.io/acoustic/Czech/Czech MFA acoustic model v3_3_0.html}},\n\tyear={2025},\n\tmonth={May},\n}",
+    "license": "CC BY 4.0",
+    "license_link": "[CC BY 4.0](https://github.com/MontrealCorpusTools/mfa-models/tree/main/acoustic/czech/mfa/v3.3.0/LICENSE)",
+    "architecture": "gmm-hmm",
+    "features": "MFCC",
+    "evaluation": {},
+    "decode": {},
+    "train_date": "2025-05-05"
+}