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Parent(s):
9fc3590
Add Gujarati
Browse filesThis view is limited to 50 files because it contains too many changes.
See raw diff
- README.md +5 -5
- app.py +26 -0
- packages.txt +1 -0
- requirements.txt +19 -0
- ttsv/.gitignore +132 -0
- ttsv/LICENSE.md +21 -0
- ttsv/README.md +220 -0
- ttsv/__init__.py +0 -0
- ttsv/checkpoints/glow/.gitkeep +0 -0
- ttsv/checkpoints/hifi/.gitkeep +0 -0
- ttsv/config/.gitkeep +0 -0
- ttsv/config/glow/base.json +54 -0
- ttsv/config/glow/base_blank.json +55 -0
- ttsv/config/hifi/config_v1.json +37 -0
- ttsv/config/hifi/config_v2.json +37 -0
- ttsv/config/hifi/config_v3.json +37 -0
- ttsv/data/.gitkeep +0 -0
- ttsv/install.sh +6 -0
- ttsv/logs/glow/.gitkeep +0 -0
- ttsv/logs/hifi/.gitkeep +0 -0
- ttsv/notebooks/vakyansh_tts_demo.ipynb +0 -0
- ttsv/results/api/.gitkeep +0 -0
- ttsv/scripts/data/duration.sh +9 -0
- ttsv/scripts/data/resample.sh +14 -0
- ttsv/scripts/glow/prepare_data.sh +12 -0
- ttsv/scripts/glow/train_glow.sh +17 -0
- ttsv/scripts/hifi/prepare_data.sh +10 -0
- ttsv/scripts/hifi/train_hifi.sh +21 -0
- ttsv/scripts/inference/advanced_infer.sh +22 -0
- ttsv/scripts/inference/api.sh +8 -0
- ttsv/scripts/inference/gradio.sh +8 -0
- ttsv/scripts/inference/infer.sh +15 -0
- ttsv/setup.py +55 -0
- ttsv/src/glow_tts/attentions.py +378 -0
- ttsv/src/glow_tts/audio_processing.py +100 -0
- ttsv/src/glow_tts/commons.py +273 -0
- ttsv/src/glow_tts/data_utils.py +274 -0
- ttsv/src/glow_tts/generate_mels.py +70 -0
- ttsv/src/glow_tts/hifi/__init__.py +5 -0
- ttsv/src/glow_tts/hifi/env.py +15 -0
- ttsv/src/glow_tts/hifi/models.py +403 -0
- ttsv/src/glow_tts/hifi/utils.py +57 -0
- ttsv/src/glow_tts/init.py +79 -0
- ttsv/src/glow_tts/models.py +403 -0
- ttsv/src/glow_tts/modules.py +276 -0
- ttsv/src/glow_tts/monotonic_align/monotonic_align/__init__.py +5 -0
- ttsv/src/glow_tts/monotonic_align/monotonic_align/core.pyx +45 -0
- ttsv/src/glow_tts/monotonic_align/monotonic_align/mas.py +57 -0
- ttsv/src/glow_tts/monotonic_align/pyproject.toml +7 -0
- ttsv/src/glow_tts/monotonic_align/setup.py +23 -0
README.md
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---
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title: Vakyansh
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emoji:
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colorFrom:
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colorTo:
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sdk: gradio
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sdk_version: 2.
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app_file: app.py
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pinned: false
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license: apache-2.0
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---
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title: Vakyansh Hindi TTS
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emoji: 🐨
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colorFrom: indigo
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colorTo: purple
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sdk: gradio
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sdk_version: 2.8.13
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app_file: app.py
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pinned: false
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license: apache-2.0
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app.py
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import os
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os.system('wget -q https://storage.googleapis.com/vakyansh-open-models/tts/gujarati/gu-IN/male_voice_1/glow.zip && unzip -q glow.zip -d ttsv/checkpoints/male')
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os.system('wget -q https://storage.googleapis.com/vakyansh-open-models/tts/gujarati/gu-IN/male_voice_1/hifi.zip && unzip -q hifi.zip -d ttsv/checkpoints/male')
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os.system('wget -q https://storage.googleapis.com/vakyansh-open-models/translit_models.zip -P ttsv/checkpoints/ && unzip -q ttsv/checkpoints/translit_models.zip -d ttsv/checkpoints/')
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for path, subdirs, files in os.walk('ttsv/checkpoints/'):
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print(subdirs)
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for name in files:
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print(os.path.join(path, name))
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from ttsv.utils.inference.run_gradio import *
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from argparse import Namespace
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#os.system('python ttsv/utils/inference/run_gradio.py -a ttsv/checkpoints/glow/male -v ttsv/checkpoints/hifi/male -d cpu -L hi')
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args = {
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'acoustic':'/home/user/app/ttsv/checkpoints/male/glow_ckp',
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'vocoder':'/home/user/app/ttsv/checkpoints/male/hifi_ckp',
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'device':'cpu',
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'lang':'hi'
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}
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build_gradio(Namespace(**args))
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packages.txt
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libsndfile1
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requirements.txt
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Cython==0.29.24
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layers==0.1.5
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librosa==0.8.1
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matplotlib==3.3.4
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numpy==1.21.0
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scipy==1.5.4
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tensorboardX==2.4
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tensorboard==2.7.0
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tqdm==4.62.3
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fastapi==0.70.0
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uvicorn==0.15.0
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gradio==2.5.2
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wavio==0.0.4
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mosestokenizer==1.2.1
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indic-nlp-library==0.81
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inflect==5.3.0
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Unidecode==1.3.2
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torch
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pydload
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ttsv/.gitignore
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# Byte-compiled / optimized / DLL files
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.DS_Store
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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60 |
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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66 |
+
instance/
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+
.webassets-cache
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68 |
+
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69 |
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# Scrapy stuff:
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70 |
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.scrapy
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# Sphinx documentation
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73 |
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docs/_build/
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# PyBuilder
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76 |
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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|
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# pyenv
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.python-version
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|
88 |
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# pipenv
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89 |
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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90 |
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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91 |
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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103 |
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*.sage.py
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104 |
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# Environments
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106 |
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.env
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107 |
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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115 |
+
.spyderproject
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116 |
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.spyproject
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+
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118 |
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# Rope project settings
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119 |
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.ropeproject
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# mkdocs documentation
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122 |
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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.idea/
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ttsv/LICENSE.md
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MIT License
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Copyright (c) 2022 Open-Speech-EkStep
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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ttsv/README.md
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# vakyansh-tts
|
2 |
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Text to Speech for Indic languages
|
3 |
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|
4 |
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## 1. Installation and Setup for training
|
5 |
+
|
6 |
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Clone repo
|
7 |
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Note : for multspeaker glow-tts training use branch [multispeaker](https://github.com/Open-Speech-EkStep/vakyansh-tts/tree/multispeaker)
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8 |
+
```
|
9 |
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git clone https://github.com/Open-Speech-EkStep/vakyansh-tts
|
10 |
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```
|
11 |
+
Build conda virtual environment
|
12 |
+
```
|
13 |
+
cd ./vakyansh-tts
|
14 |
+
conda create --name <env_name> python=3.7
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15 |
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conda activate <env_name>
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16 |
+
pip install -r requirements.txt
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17 |
+
```
|
18 |
+
Install [apex](https://github.com/NVIDIA/apex); commit: 37cdaf4 for Mixed-precision training
|
19 |
+
|
20 |
+
Note : used only for glow-tts
|
21 |
+
```
|
22 |
+
cd ..
|
23 |
+
git clone https://github.com/NVIDIA/apex
|
24 |
+
cd apex
|
25 |
+
git checkout 37cdaf4
|
26 |
+
pip install -v --disable-pip-version-check --no-cache-dir ./
|
27 |
+
cd ../vakyansh-tts
|
28 |
+
```
|
29 |
+
Build Monotonic Alignment Search Code (Cython)
|
30 |
+
|
31 |
+
Note : used only for glow-tts
|
32 |
+
```
|
33 |
+
bash install.sh
|
34 |
+
```
|
35 |
+
|
36 |
+
## 2. Data Resampling
|
37 |
+
|
38 |
+
The data format should have a folder containing all the .wav files for glow-tts and a text file containing filenames with their sentences.
|
39 |
+
|
40 |
+
Directory structure:
|
41 |
+
|
42 |
+
langauge_folder_name
|
43 |
+
```
|
44 |
+
language_folder_name
|
45 |
+
|-- ./wav/*.wav
|
46 |
+
|-- ./text_file_name.txt
|
47 |
+
```
|
48 |
+
The format for text_file_name.txt (Text file is only needed for glow-tts training)
|
49 |
+
|
50 |
+
```
|
51 |
+
( audio1.wav "Sentence1." )
|
52 |
+
( audio2.wav "Sentence2." )
|
53 |
+
```
|
54 |
+
|
55 |
+
To resample the .wav files to 22050 sample rate, change the following parameters in the vakyansh-tts/scripts/data/resample.sh
|
56 |
+
|
57 |
+
```
|
58 |
+
input_wav_path : absolute path to wav file folder in vakyansh_tts/data/
|
59 |
+
output_wav_path : absolute path to vakyansh_tts/data/resampled_wav_folder_name
|
60 |
+
output_sample_rate : 22050 (or any other desired sample rate)
|
61 |
+
```
|
62 |
+
|
63 |
+
To run:
|
64 |
+
```bash
|
65 |
+
cd scripts/data/
|
66 |
+
bash resample.sh
|
67 |
+
```
|
68 |
+
|
69 |
+
|
70 |
+
## 3. Spectogram Training (glow-tts)
|
71 |
+
|
72 |
+
### 3.1 Data Preparation
|
73 |
+
|
74 |
+
|
75 |
+
To prepare the data edit the vakyansh-tts/scripts/glow/prepare_data.sh file and change the following parameters
|
76 |
+
```
|
77 |
+
input_text_path : absolute path to vakyansh_tts/data/text_file_name.txt
|
78 |
+
input_wav_path : absolute path to vakyansh_tts/data/resampled_wav_folder_name
|
79 |
+
gender : female or male voice
|
80 |
+
```
|
81 |
+
To run:
|
82 |
+
```bash
|
83 |
+
cd scripts/glow/
|
84 |
+
bash prepare_data.sh
|
85 |
+
```
|
86 |
+
### 3.2 Training glow-tts
|
87 |
+
|
88 |
+
To start the spectogram-training edit the vakyansh-tts/scripts/glow/train_glow.sh file and change the following parameter:
|
89 |
+
```
|
90 |
+
gender : female or male voice
|
91 |
+
```
|
92 |
+
Make sure that the gender is same as that of the prepare_data.sh file
|
93 |
+
|
94 |
+
To start the training, run:
|
95 |
+
```bash
|
96 |
+
cd scripts/glow/
|
97 |
+
bash train_glow.sh
|
98 |
+
```
|
99 |
+
## 4. Vocoder Training (hifi-gan)
|
100 |
+
|
101 |
+
### 4.1 Data Preparation
|
102 |
+
|
103 |
+
To prepare the data edit the vakyansh-tts/scripts/hifi/prepare_data.sh file and change the following parameters
|
104 |
+
```
|
105 |
+
input_wav_path : absolute path to vakyansh_tts/data/resampled_wav_folder_name
|
106 |
+
gender : female or male voice
|
107 |
+
```
|
108 |
+
To run:
|
109 |
+
```bash
|
110 |
+
cd scripts/hifi/
|
111 |
+
bash prepare_data.sh
|
112 |
+
```
|
113 |
+
### 4.2 Training hifi-gan
|
114 |
+
|
115 |
+
To start the spectogram-training edit the vakyansh-tts/scripts/hifi/train_hifi.sh file and change the following parameter:
|
116 |
+
```
|
117 |
+
gender : female or male voice
|
118 |
+
```
|
119 |
+
Make sure that the gender is same as that of the prepare_data.sh file
|
120 |
+
|
121 |
+
To start the training, run:
|
122 |
+
```bash
|
123 |
+
cd scripts/hifi/
|
124 |
+
bash train_hifi.sh
|
125 |
+
```
|
126 |
+
|
127 |
+
## 5. Inference
|
128 |
+
|
129 |
+
### 5.1 Using Gradio
|
130 |
+
|
131 |
+
To use the gradio link edit the following parameters in the vakyansh-tts/scripts/inference/gradio.sh file:
|
132 |
+
```
|
133 |
+
gender : female or male voice
|
134 |
+
device : cpu or cuda
|
135 |
+
lang : langauge code
|
136 |
+
```
|
137 |
+
|
138 |
+
To run:
|
139 |
+
```bash
|
140 |
+
cd scripts/inference/
|
141 |
+
bash gradio.sh
|
142 |
+
```
|
143 |
+
### 5.2 Using fast API
|
144 |
+
To use the fast api link edit the parameters in the vakyansh-tts/scripts/inference/api.sh file similar to section 5.1
|
145 |
+
|
146 |
+
To run:
|
147 |
+
```bash
|
148 |
+
cd scripts/inference/
|
149 |
+
bash api.sh
|
150 |
+
```
|
151 |
+
|
152 |
+
### 5.3 Direct Inference using text
|
153 |
+
To infer, edit the parameters in the vakyansh-tts/scripts/inference/infer.sh file similar to section 5.1 and set the text to the text variable
|
154 |
+
|
155 |
+
To run:
|
156 |
+
```bash
|
157 |
+
cd scripts/inference/
|
158 |
+
bash infer.sh
|
159 |
+
```
|
160 |
+
|
161 |
+
To configure other parameters there is a version that runs the advanced inference as well. Additional Parameters:
|
162 |
+
```
|
163 |
+
noise_scale : can vary from 0 to 1 for noise factor
|
164 |
+
length_scale : can vary from 0 to 2 for changing the speed of the generated audio
|
165 |
+
transliteration : whether to switch on/off transliteration. 1: ON, 0: OFF
|
166 |
+
number_conversion : whether to switch on/off number to words conversion. 1: ON, 0: OFF
|
167 |
+
split_sentences : whether to switch on/off splitting of sentences. 1: ON, 0: OFF
|
168 |
+
```
|
169 |
+
To run:
|
170 |
+
```
|
171 |
+
cd scripts/inference/
|
172 |
+
bash advanced_infer.sh
|
173 |
+
```
|
174 |
+
|
175 |
+
### 5.4 Installation of tts_infer package
|
176 |
+
|
177 |
+
In tts_infer package, we currently have two components:
|
178 |
+
|
179 |
+
1. Transliteration (AI4bharat's open sourced models) (Languages supported: {'hi', 'gu', 'mr', 'bn', 'te', 'ta', 'kn', 'pa', 'gom', 'mai', 'ml', 'sd', 'si', 'ur'} )
|
180 |
+
|
181 |
+
2. Num to Word (Languages supported: {'en', 'hi', 'gu', 'mr', 'bn', 'te', 'ta', 'kn', 'or', 'pa'} )
|
182 |
+
```
|
183 |
+
git clone https://github.com/Open-Speech-EkStep/vakyansh-tts
|
184 |
+
cd vakyansh-tts
|
185 |
+
bash install.sh
|
186 |
+
python setup.py bdist_wheel
|
187 |
+
pip install -e .
|
188 |
+
cd tts_infer
|
189 |
+
gsutil -m cp -r gs://vakyaansh-open-models/translit_models .
|
190 |
+
```
|
191 |
+
|
192 |
+
Usage: Refer to example file in tts_infer/
|
193 |
+
```
|
194 |
+
from tts_infer.tts import TextToMel, MelToWav
|
195 |
+
from tts_infer.transliterate import XlitEngine
|
196 |
+
from tts_infer.num_to_word_on_sent import normalize_nums
|
197 |
+
|
198 |
+
import re
|
199 |
+
from scipy.io.wavfile import write
|
200 |
+
|
201 |
+
text_to_mel = TextToMel(glow_model_dir='/path/to/glow-tts/checkpoint/dir', device='cuda')
|
202 |
+
mel_to_wav = MelToWav(hifi_model_dir='/path/to/hifi/checkpoint/dir', device='cuda')
|
203 |
+
|
204 |
+
def translit(text, lang):
|
205 |
+
reg = re.compile(r'[a-zA-Z]')
|
206 |
+
engine = XlitEngine(lang)
|
207 |
+
words = [engine.translit_word(word, topk=1)[lang][0] if reg.match(word) else word for word in text.split()]
|
208 |
+
updated_sent = ' '.join(words)
|
209 |
+
return updated_sent
|
210 |
+
|
211 |
+
def run_tts(text, lang):
|
212 |
+
text = text.replace('।', '.') # only for hindi models
|
213 |
+
text_num_to_word = normalize_nums(text, lang) # converting numbers to words in lang
|
214 |
+
text_num_to_word_and_transliterated = translit(text_num_to_word, lang) # transliterating english words to lang
|
215 |
+
|
216 |
+
mel = text_to_mel.generate_mel(text_num_to_word_and_transliterated)
|
217 |
+
audio, sr = mel_to_wav.generate_wav(mel)
|
218 |
+
write(filename='temp.wav', rate=sr, data=audio) # for saving wav file, if needed
|
219 |
+
return (sr, audio)
|
220 |
+
```
|
ttsv/__init__.py
ADDED
File without changes
|
ttsv/checkpoints/glow/.gitkeep
ADDED
File without changes
|
ttsv/checkpoints/hifi/.gitkeep
ADDED
File without changes
|
ttsv/config/.gitkeep
ADDED
File without changes
|
ttsv/config/glow/base.json
ADDED
@@ -0,0 +1,54 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"train": {
|
3 |
+
"use_cuda": true,
|
4 |
+
"log_interval": 20,
|
5 |
+
"seed": 1234,
|
6 |
+
"epochs": 10000,
|
7 |
+
"learning_rate": 1e0,
|
8 |
+
"betas": [0.9, 0.98],
|
9 |
+
"eps": 1e-9,
|
10 |
+
"warmup_steps": 4000,
|
11 |
+
"scheduler": "noam",
|
12 |
+
"batch_size": 16,
|
13 |
+
"ddi": true,
|
14 |
+
"fp16_run": true,
|
15 |
+
"save_epoch": 1
|
16 |
+
},
|
17 |
+
"data": {
|
18 |
+
"load_mel_from_disk": false,
|
19 |
+
"training_files":"../data/training/train.txt",
|
20 |
+
"validation_files":"../data/training/valid.txt",
|
21 |
+
"chars":"",
|
22 |
+
"punc":"",
|
23 |
+
"text_cleaners":["basic_indic_cleaners"],
|
24 |
+
"max_wav_value": 32768.0,
|
25 |
+
"sampling_rate": 22050,
|
26 |
+
"filter_length": 1024,
|
27 |
+
"hop_length": 256,
|
28 |
+
"win_length": 1024,
|
29 |
+
"n_mel_channels": 80,
|
30 |
+
"mel_fmin": 80.0,
|
31 |
+
"mel_fmax": 7600.0,
|
32 |
+
"add_noise": true
|
33 |
+
},
|
34 |
+
"model": {
|
35 |
+
"hidden_channels": 192,
|
36 |
+
"filter_channels": 768,
|
37 |
+
"filter_channels_dp": 256,
|
38 |
+
"kernel_size": 3,
|
39 |
+
"p_dropout": 0.1,
|
40 |
+
"n_blocks_dec": 12,
|
41 |
+
"n_layers_enc": 6,
|
42 |
+
"n_heads": 2,
|
43 |
+
"p_dropout_dec": 0.05,
|
44 |
+
"dilation_rate": 1,
|
45 |
+
"kernel_size_dec": 5,
|
46 |
+
"n_block_layers": 4,
|
47 |
+
"n_sqz": 2,
|
48 |
+
"prenet": true,
|
49 |
+
"mean_only": true,
|
50 |
+
"hidden_channels_enc": 192,
|
51 |
+
"hidden_channels_dec": 192,
|
52 |
+
"window_size": 4
|
53 |
+
}
|
54 |
+
}
|
ttsv/config/glow/base_blank.json
ADDED
@@ -0,0 +1,55 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"train": {
|
3 |
+
"use_cuda": true,
|
4 |
+
"log_interval": 20,
|
5 |
+
"seed": 1234,
|
6 |
+
"epochs": 10000,
|
7 |
+
"learning_rate": 1e0,
|
8 |
+
"betas": [0.9, 0.98],
|
9 |
+
"eps": 1e-9,
|
10 |
+
"warmup_steps": 4000,
|
11 |
+
"scheduler": "noam",
|
12 |
+
"batch_size": 16,
|
13 |
+
"ddi": true,
|
14 |
+
"fp16_run": true,
|
15 |
+
"save_epoch": 1
|
16 |
+
},
|
17 |
+
"data": {
|
18 |
+
"load_mel_from_disk": false,
|
19 |
+
"training_files":"../data/training/train.txt",
|
20 |
+
"validation_files":"../data/training/valid.txt",
|
21 |
+
"chars":"",
|
22 |
+
"punc":"",
|
23 |
+
"text_cleaners":["basic_indic_cleaners"],
|
24 |
+
"max_wav_value": 32768.0,
|
25 |
+
"sampling_rate": 22050,
|
26 |
+
"filter_length": 1024,
|
27 |
+
"hop_length": 256,
|
28 |
+
"win_length": 1024,
|
29 |
+
"n_mel_channels": 80,
|
30 |
+
"mel_fmin": 80.0,
|
31 |
+
"mel_fmax": 7600.0,
|
32 |
+
"add_noise": true,
|
33 |
+
"add_blank": true
|
34 |
+
},
|
35 |
+
"model": {
|
36 |
+
"hidden_channels": 192,
|
37 |
+
"filter_channels": 768,
|
38 |
+
"filter_channels_dp": 256,
|
39 |
+
"kernel_size": 3,
|
40 |
+
"p_dropout": 0.1,
|
41 |
+
"n_blocks_dec": 12,
|
42 |
+
"n_layers_enc": 6,
|
43 |
+
"n_heads": 2,
|
44 |
+
"p_dropout_dec": 0.05,
|
45 |
+
"dilation_rate": 1,
|
46 |
+
"kernel_size_dec": 5,
|
47 |
+
"n_block_layers": 4,
|
48 |
+
"n_sqz": 2,
|
49 |
+
"prenet": true,
|
50 |
+
"mean_only": true,
|
51 |
+
"hidden_channels_enc": 192,
|
52 |
+
"hidden_channels_dec": 192,
|
53 |
+
"window_size": 4
|
54 |
+
}
|
55 |
+
}
|
ttsv/config/hifi/config_v1.json
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"resblock": "1",
|
3 |
+
"num_gpus": 0,
|
4 |
+
"batch_size": 24,
|
5 |
+
"learning_rate": 0.0002,
|
6 |
+
"adam_b1": 0.8,
|
7 |
+
"adam_b2": 0.99,
|
8 |
+
"lr_decay": 0.999,
|
9 |
+
"seed": 1234,
|
10 |
+
|
11 |
+
"upsample_rates": [8,8,2,2],
|
12 |
+
"upsample_kernel_sizes": [16,16,4,4],
|
13 |
+
"upsample_initial_channel": 512,
|
14 |
+
"resblock_kernel_sizes": [3,7,11],
|
15 |
+
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
16 |
+
|
17 |
+
"segment_size": 8192,
|
18 |
+
"num_mels": 80,
|
19 |
+
"num_freq": 1025,
|
20 |
+
"n_fft": 1024,
|
21 |
+
"hop_size": 256,
|
22 |
+
"win_size": 1024,
|
23 |
+
|
24 |
+
"sampling_rate": 22050,
|
25 |
+
|
26 |
+
"fmin": 80,
|
27 |
+
"fmax": 7600,
|
28 |
+
"fmax_for_loss": null,
|
29 |
+
|
30 |
+
"num_workers": 4,
|
31 |
+
|
32 |
+
"dist_config": {
|
33 |
+
"dist_backend": "nccl",
|
34 |
+
"dist_url": "tcp://localhost:54321",
|
35 |
+
"world_size": 1
|
36 |
+
}
|
37 |
+
}
|
ttsv/config/hifi/config_v2.json
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"resblock": "1",
|
3 |
+
"num_gpus": 0,
|
4 |
+
"batch_size": 24,
|
5 |
+
"learning_rate": 0.0002,
|
6 |
+
"adam_b1": 0.8,
|
7 |
+
"adam_b2": 0.99,
|
8 |
+
"lr_decay": 0.999,
|
9 |
+
"seed": 1234,
|
10 |
+
|
11 |
+
"upsample_rates": [8,8,2,2],
|
12 |
+
"upsample_kernel_sizes": [16,16,4,4],
|
13 |
+
"upsample_initial_channel": 128,
|
14 |
+
"resblock_kernel_sizes": [3,7,11],
|
15 |
+
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
16 |
+
|
17 |
+
"segment_size": 8192,
|
18 |
+
"num_mels": 80,
|
19 |
+
"num_freq": 1025,
|
20 |
+
"n_fft": 1024,
|
21 |
+
"hop_size": 256,
|
22 |
+
"win_size": 1024,
|
23 |
+
|
24 |
+
"sampling_rate": 22050,
|
25 |
+
|
26 |
+
"fmin": 80,
|
27 |
+
"fmax": 7600,
|
28 |
+
"fmax_for_loss": null,
|
29 |
+
|
30 |
+
"num_workers": 4,
|
31 |
+
|
32 |
+
"dist_config": {
|
33 |
+
"dist_backend": "nccl",
|
34 |
+
"dist_url": "tcp://localhost:54321",
|
35 |
+
"world_size": 1
|
36 |
+
}
|
37 |
+
}
|
ttsv/config/hifi/config_v3.json
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"resblock": "2",
|
3 |
+
"num_gpus": 0,
|
4 |
+
"batch_size": 24,
|
5 |
+
"learning_rate": 0.0002,
|
6 |
+
"adam_b1": 0.8,
|
7 |
+
"adam_b2": 0.99,
|
8 |
+
"lr_decay": 0.999,
|
9 |
+
"seed": 1234,
|
10 |
+
|
11 |
+
"upsample_rates": [8,8,4],
|
12 |
+
"upsample_kernel_sizes": [16,16,8],
|
13 |
+
"upsample_initial_channel": 256,
|
14 |
+
"resblock_kernel_sizes": [3,5,7],
|
15 |
+
"resblock_dilation_sizes": [[1,2], [2,6], [3,12]],
|
16 |
+
|
17 |
+
"segment_size": 8192,
|
18 |
+
"num_mels": 80,
|
19 |
+
"num_freq": 1025,
|
20 |
+
"n_fft": 1024,
|
21 |
+
"hop_size": 256,
|
22 |
+
"win_size": 1024,
|
23 |
+
|
24 |
+
"sampling_rate": 22050,
|
25 |
+
|
26 |
+
"fmin": 80,
|
27 |
+
"fmax": 7600,
|
28 |
+
"fmax_for_loss": null,
|
29 |
+
|
30 |
+
"num_workers": 4,
|
31 |
+
|
32 |
+
"dist_config": {
|
33 |
+
"dist_backend": "nccl",
|
34 |
+
"dist_url": "tcp://localhost:54321",
|
35 |
+
"world_size": 1
|
36 |
+
}
|
37 |
+
}
|
ttsv/data/.gitkeep
ADDED
File without changes
|
ttsv/install.sh
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
cd src/glow_tts/monotonic_align/
|
2 |
+
pip install .
|
3 |
+
cd ../../../
|
4 |
+
|
5 |
+
# torch
|
6 |
+
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
|
ttsv/logs/glow/.gitkeep
ADDED
File without changes
|
ttsv/logs/hifi/.gitkeep
ADDED
File without changes
|
ttsv/notebooks/vakyansh_tts_demo.ipynb
ADDED
The diff for this file is too large to render.
See raw diff
|
|
ttsv/results/api/.gitkeep
ADDED
File without changes
|
ttsv/scripts/data/duration.sh
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
wav_path='/home/harveen/en/iitm_data/english/wav_22k'
|
2 |
+
#######################
|
3 |
+
|
4 |
+
dir=$PWD
|
5 |
+
parentdir="$(dirname "$dir")"
|
6 |
+
parentdir="$(dirname "$parentdir")"
|
7 |
+
|
8 |
+
|
9 |
+
python $parentdir/utils/data/duration.py $wav_path
|
ttsv/scripts/data/resample.sh
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
input_wav_path='/home/harveen/en/iitm_data/english/wav/'
|
2 |
+
output_wav_path='/home/harveen/en/iitm_data/english/wav_22k/'
|
3 |
+
output_sample_rate=22050
|
4 |
+
|
5 |
+
#######################
|
6 |
+
|
7 |
+
dir=$PWD
|
8 |
+
parentdir="$(dirname "$dir")"
|
9 |
+
parentdir="$(dirname "$parentdir")"
|
10 |
+
|
11 |
+
mkdir -p $output_wav_path
|
12 |
+
python $parentdir/utils/data/resample.py -i $input_wav_path -o $output_wav_path -s $output_sample_rate
|
13 |
+
|
14 |
+
python $parentdir/utils/data/duration.py $output_wav_path
|
ttsv/scripts/glow/prepare_data.sh
ADDED
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
input_text_path='/home/harveen/en/iitm_data/english/txt.done.data'
|
2 |
+
input_wav_path='/home/harveen/en/iitm_data/english/wav_22k'
|
3 |
+
gender='male'
|
4 |
+
|
5 |
+
|
6 |
+
output_data_path='../../data/glow/'$gender
|
7 |
+
|
8 |
+
valid_samples=100
|
9 |
+
test_samples=10
|
10 |
+
|
11 |
+
mkdir -p $output_data_path
|
12 |
+
python ../../utils/glow/prepare_iitm_data_glow_en.py -i $input_text_path -o $output_data_path -w $input_wav_path -v $valid_samples -t $test_samples
|
ttsv/scripts/glow/train_glow.sh
ADDED
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/bin/bash
|
2 |
+
|
3 |
+
gender='male'
|
4 |
+
|
5 |
+
config='../../config/glow/'$gender'.json'
|
6 |
+
modeldir='../../checkpoints/glow/'$gender
|
7 |
+
logdir='../../logs/glow/'$gender
|
8 |
+
init=1 # 1 if start from scratch. 0 if start from last checkpoint
|
9 |
+
|
10 |
+
|
11 |
+
####################################################
|
12 |
+
|
13 |
+
if [[ $init -eq 1 ]]
|
14 |
+
then
|
15 |
+
python ../../src/glow_tts/init.py -c $config -m $modeldir -l $logdir
|
16 |
+
fi
|
17 |
+
python ../../src/glow_tts/train.py -c $config -m $modeldir -l $logdir
|
ttsv/scripts/hifi/prepare_data.sh
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
input_wav_path='/home/harveen/en/iitm_data/english/wav_22k' #give multiple folders separated by comma(,)
|
2 |
+
gender='male'
|
3 |
+
|
4 |
+
output_data_path='../../data/hifi/'$gender
|
5 |
+
|
6 |
+
valid_samples=100
|
7 |
+
test_samples=10
|
8 |
+
|
9 |
+
mkdir -p $output_data_path
|
10 |
+
python ../../utils/hifi/prepare_iitm_data_hifi.py -i $input_wav_path -v $valid_samples -t $test_samples -d $output_data_path
|
ttsv/scripts/hifi/train_hifi.sh
ADDED
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/bin/bash
|
2 |
+
|
3 |
+
gender='male'
|
4 |
+
|
5 |
+
config='../../config/hifi/config_v1.json'
|
6 |
+
modeldir='../../checkpoints/hifi/'$gender
|
7 |
+
logdir='../../logs/hifi/'$gender
|
8 |
+
|
9 |
+
|
10 |
+
####################################################
|
11 |
+
|
12 |
+
|
13 |
+
|
14 |
+
python ../../src/hifi_gan/train.py \
|
15 |
+
--config $config \
|
16 |
+
--input_training_file '../../data/hifi/'$gender'/train.txt' \
|
17 |
+
--input_validation_file '../../data/hifi/'$gender'/valid.txt' \
|
18 |
+
--checkpoint_path $modeldir \
|
19 |
+
--logs_path $logdir \
|
20 |
+
--checkpoint_interval 10000 \
|
21 |
+
--stdout_interval 50
|
ttsv/scripts/inference/advanced_infer.sh
ADDED
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
gender='male'
|
2 |
+
glowdir='../../checkpoints/glow/'$gender'/'
|
3 |
+
hifidir='../../checkpoints/hifi/'$gender'/'
|
4 |
+
device='cpu'
|
5 |
+
text='Hey mr. I am testing this one. Now on multiple sentences. Just want to see the flow.'
|
6 |
+
noise_scale='0.667'
|
7 |
+
length_scale='1.0'
|
8 |
+
transliteration=1
|
9 |
+
number_conversion=1
|
10 |
+
split_sentences=1
|
11 |
+
lang='en'
|
12 |
+
|
13 |
+
|
14 |
+
timestamp=$(date +%s)
|
15 |
+
wav='../../results/'$gender'/'
|
16 |
+
wav_file=$wav/$timestamp'.wav'
|
17 |
+
|
18 |
+
|
19 |
+
mkdir -p $wav
|
20 |
+
|
21 |
+
python ../../utils/inference/advanced_tts.py -a $glowdir -v $hifidir -d $device -t "$text" -w $wav_file -L $lang -n $noise_scale -l $length_scale -T $transliteration -N $number_conversion -S $split_sentences
|
22 |
+
echo "File saved at: "$wav_file
|
ttsv/scripts/inference/api.sh
ADDED
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
gender='male'
|
2 |
+
glowdir='../../checkpoints/glow/'$gender'/'
|
3 |
+
hifidir='../../checkpoints/hifi/'$gender'/'
|
4 |
+
device='cpu'
|
5 |
+
lang='en'
|
6 |
+
|
7 |
+
|
8 |
+
python ../../utils/inference/api.py -a $glowdir -v $hifidir -d $device -L $lang
|
ttsv/scripts/inference/gradio.sh
ADDED
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
gender='male'
|
2 |
+
glowdir='../../checkpoints/glow/'$gender'/'
|
3 |
+
hifidir='../../checkpoints/hifi/'$gender'/'
|
4 |
+
device='cpu'
|
5 |
+
lang='en'
|
6 |
+
|
7 |
+
|
8 |
+
python ../../utils/inference/run_gradio.py -a $glowdir -v $hifidir -d $device -L $lang
|
ttsv/scripts/inference/infer.sh
ADDED
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
gender='male'
|
2 |
+
glowdir='../../checkpoints/glow/'$gender'/'
|
3 |
+
hifidir='../../checkpoints/hifi/'$gender'/'
|
4 |
+
device='cpu'
|
5 |
+
text='testing this one'
|
6 |
+
|
7 |
+
|
8 |
+
timestamp=$(date +%s)
|
9 |
+
wav='../../results/'$gender'/'
|
10 |
+
wav_file=$wav/$timestamp'.wav'
|
11 |
+
|
12 |
+
|
13 |
+
mkdir -p $wav
|
14 |
+
python ../../utils/inference/tts.py -a $glowdir -v $hifidir -d $device -t "$text" -w $wav_file
|
15 |
+
echo "File saved at: "$wav_file
|
ttsv/setup.py
ADDED
@@ -0,0 +1,55 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from setuptools import setup, find_packages
|
2 |
+
|
3 |
+
with open("README.md", "r") as f:
|
4 |
+
long_description = f.read()
|
5 |
+
|
6 |
+
setup(
|
7 |
+
name="vakyansh-tts",
|
8 |
+
version="0.0.5",
|
9 |
+
description="Text to speech for Indic languages",
|
10 |
+
long_description=long_description,
|
11 |
+
long_description_content_type="text/markdown",
|
12 |
+
url="https://github.com/Open-Speech-EkStep/vakyansh-tts.git",
|
13 |
+
keywords="nlp, tts, Indic languages, deep learning, text to speech",
|
14 |
+
# package_dir={'': 'src'},
|
15 |
+
# packages=find_packages(where='src'),
|
16 |
+
packages=["tts_infer"],
|
17 |
+
python_requires=">=3.7, <4",
|
18 |
+
install_requires=[
|
19 |
+
"Cython==0.29.24",
|
20 |
+
"layers==0.1.5",
|
21 |
+
"librosa==0.8.1",
|
22 |
+
"matplotlib==3.3.4",
|
23 |
+
"numpy==1.20.2",
|
24 |
+
"scipy==1.5.4",
|
25 |
+
"tensorboardX==2.4",
|
26 |
+
"tensorboard==2.7.0",
|
27 |
+
"tqdm==4.62.3",
|
28 |
+
"fastapi==0.70.0",
|
29 |
+
"uvicorn==0.15.0",
|
30 |
+
"gradio==2.5.2",
|
31 |
+
"wavio==0.0.4",
|
32 |
+
"pydload==1.0.9",
|
33 |
+
"mosestokenizer==1.2.1",
|
34 |
+
"indic-nlp-library==0.81"
|
35 |
+
],
|
36 |
+
classifiers=[
|
37 |
+
# How mature is this project? Common values are
|
38 |
+
# 3 - Alpha
|
39 |
+
# 4 - Beta
|
40 |
+
# 5 - Production/Stable
|
41 |
+
"Development Status :: 3 - Alpha",
|
42 |
+
# Indicate who your project is intended for
|
43 |
+
"Intended Audience :: Developers",
|
44 |
+
"Intended Audience :: Education",
|
45 |
+
"Intended Audience :: Science/Research",
|
46 |
+
"Topic :: Scientific/Engineering :: Artificial Intelligence",
|
47 |
+
"Topic :: Text Processing :: Linguistic",
|
48 |
+
# Pick your license as you wish (should match "license" above)
|
49 |
+
"License :: OSI Approved :: MIT License",
|
50 |
+
# Specify the Python versions you support here. In particular, ensure
|
51 |
+
# that you indicate whether you support Python 2, Python 3 or both.
|
52 |
+
"Programming Language :: Python :: 3.7",
|
53 |
+
],
|
54 |
+
include_package_data=True,
|
55 |
+
)
|
ttsv/src/glow_tts/attentions.py
ADDED
@@ -0,0 +1,378 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
import copy
|
2 |
+
import math
|
3 |
+
import numpy as np
|
4 |
+
import torch
|
5 |
+
from torch import nn
|
6 |
+
from torch.nn import functional as F
|
7 |
+
|
8 |
+
import commons
|
9 |
+
import modules
|
10 |
+
from modules import LayerNorm
|
11 |
+
|
12 |
+
|
13 |
+
class Encoder(nn.Module):
|
14 |
+
def __init__(
|
15 |
+
self,
|
16 |
+
hidden_channels,
|
17 |
+
filter_channels,
|
18 |
+
n_heads,
|
19 |
+
n_layers,
|
20 |
+
kernel_size=1,
|
21 |
+
p_dropout=0.0,
|
22 |
+
window_size=None,
|
23 |
+
block_length=None,
|
24 |
+
**kwargs
|
25 |
+
):
|
26 |
+
super().__init__()
|
27 |
+
self.hidden_channels = hidden_channels
|
28 |
+
self.filter_channels = filter_channels
|
29 |
+
self.n_heads = n_heads
|
30 |
+
self.n_layers = n_layers
|
31 |
+
self.kernel_size = kernel_size
|
32 |
+
self.p_dropout = p_dropout
|
33 |
+
self.window_size = window_size
|
34 |
+
self.block_length = block_length
|
35 |
+
|
36 |
+
self.drop = nn.Dropout(p_dropout)
|
37 |
+
self.attn_layers = nn.ModuleList()
|
38 |
+
self.norm_layers_1 = nn.ModuleList()
|
39 |
+
self.ffn_layers = nn.ModuleList()
|
40 |
+
self.norm_layers_2 = nn.ModuleList()
|
41 |
+
for i in range(self.n_layers):
|
42 |
+
self.attn_layers.append(
|
43 |
+
MultiHeadAttention(
|
44 |
+
hidden_channels,
|
45 |
+
hidden_channels,
|
46 |
+
n_heads,
|
47 |
+
window_size=window_size,
|
48 |
+
p_dropout=p_dropout,
|
49 |
+
block_length=block_length,
|
50 |
+
)
|
51 |
+
)
|
52 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
53 |
+
self.ffn_layers.append(
|
54 |
+
FFN(
|
55 |
+
hidden_channels,
|
56 |
+
hidden_channels,
|
57 |
+
filter_channels,
|
58 |
+
kernel_size,
|
59 |
+
p_dropout=p_dropout,
|
60 |
+
)
|
61 |
+
)
|
62 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
63 |
+
|
64 |
+
def forward(self, x, x_mask):
|
65 |
+
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
66 |
+
for i in range(self.n_layers):
|
67 |
+
x = x * x_mask
|
68 |
+
y = self.attn_layers[i](x, x, attn_mask)
|
69 |
+
y = self.drop(y)
|
70 |
+
x = self.norm_layers_1[i](x + y)
|
71 |
+
|
72 |
+
y = self.ffn_layers[i](x, x_mask)
|
73 |
+
y = self.drop(y)
|
74 |
+
x = self.norm_layers_2[i](x + y)
|
75 |
+
x = x * x_mask
|
76 |
+
return x
|
77 |
+
|
78 |
+
|
79 |
+
class CouplingBlock(nn.Module):
|
80 |
+
def __init__(
|
81 |
+
self,
|
82 |
+
in_channels,
|
83 |
+
hidden_channels,
|
84 |
+
kernel_size,
|
85 |
+
dilation_rate,
|
86 |
+
n_layers,
|
87 |
+
gin_channels=0,
|
88 |
+
p_dropout=0,
|
89 |
+
sigmoid_scale=False,
|
90 |
+
):
|
91 |
+
super().__init__()
|
92 |
+
self.in_channels = in_channels
|
93 |
+
self.hidden_channels = hidden_channels
|
94 |
+
self.kernel_size = kernel_size
|
95 |
+
self.dilation_rate = dilation_rate
|
96 |
+
self.n_layers = n_layers
|
97 |
+
self.gin_channels = gin_channels
|
98 |
+
self.p_dropout = p_dropout
|
99 |
+
self.sigmoid_scale = sigmoid_scale
|
100 |
+
|
101 |
+
start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1)
|
102 |
+
start = torch.nn.utils.weight_norm(start)
|
103 |
+
self.start = start
|
104 |
+
# Initializing last layer to 0 makes the affine coupling layers
|
105 |
+
# do nothing at first. It helps to stabilze training.
|
106 |
+
end = torch.nn.Conv1d(hidden_channels, in_channels, 1)
|
107 |
+
end.weight.data.zero_()
|
108 |
+
end.bias.data.zero_()
|
109 |
+
self.end = end
|
110 |
+
|
111 |
+
self.wn = modules.WN(
|
112 |
+
in_channels,
|
113 |
+
hidden_channels,
|
114 |
+
kernel_size,
|
115 |
+
dilation_rate,
|
116 |
+
n_layers,
|
117 |
+
gin_channels,
|
118 |
+
p_dropout,
|
119 |
+
)
|
120 |
+
|
121 |
+
def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs):
|
122 |
+
b, c, t = x.size()
|
123 |
+
if x_mask is None:
|
124 |
+
x_mask = 1
|
125 |
+
x_0, x_1 = x[:, : self.in_channels // 2], x[:, self.in_channels // 2 :]
|
126 |
+
|
127 |
+
x = self.start(x_0) * x_mask
|
128 |
+
x = self.wn(x, x_mask, g)
|
129 |
+
out = self.end(x)
|
130 |
+
|
131 |
+
z_0 = x_0
|
132 |
+
m = out[:, : self.in_channels // 2, :]
|
133 |
+
logs = out[:, self.in_channels // 2 :, :]
|
134 |
+
if self.sigmoid_scale:
|
135 |
+
logs = torch.log(1e-6 + torch.sigmoid(logs + 2))
|
136 |
+
|
137 |
+
if reverse:
|
138 |
+
z_1 = (x_1 - m) * torch.exp(-logs) * x_mask
|
139 |
+
logdet = None
|
140 |
+
else:
|
141 |
+
z_1 = (m + torch.exp(logs) * x_1) * x_mask
|
142 |
+
logdet = torch.sum(logs * x_mask, [1, 2])
|
143 |
+
|
144 |
+
z = torch.cat([z_0, z_1], 1)
|
145 |
+
return z, logdet
|
146 |
+
|
147 |
+
def store_inverse(self):
|
148 |
+
self.wn.remove_weight_norm()
|
149 |
+
|
150 |
+
|
151 |
+
class MultiHeadAttention(nn.Module):
|
152 |
+
def __init__(
|
153 |
+
self,
|
154 |
+
channels,
|
155 |
+
out_channels,
|
156 |
+
n_heads,
|
157 |
+
window_size=None,
|
158 |
+
heads_share=True,
|
159 |
+
p_dropout=0.0,
|
160 |
+
block_length=None,
|
161 |
+
proximal_bias=False,
|
162 |
+
proximal_init=False,
|
163 |
+
):
|
164 |
+
super().__init__()
|
165 |
+
assert channels % n_heads == 0
|
166 |
+
|
167 |
+
self.channels = channels
|
168 |
+
self.out_channels = out_channels
|
169 |
+
self.n_heads = n_heads
|
170 |
+
self.window_size = window_size
|
171 |
+
self.heads_share = heads_share
|
172 |
+
self.block_length = block_length
|
173 |
+
self.proximal_bias = proximal_bias
|
174 |
+
self.p_dropout = p_dropout
|
175 |
+
self.attn = None
|
176 |
+
|
177 |
+
self.k_channels = channels // n_heads
|
178 |
+
self.conv_q = nn.Conv1d(channels, channels, 1)
|
179 |
+
self.conv_k = nn.Conv1d(channels, channels, 1)
|
180 |
+
self.conv_v = nn.Conv1d(channels, channels, 1)
|
181 |
+
if window_size is not None:
|
182 |
+
n_heads_rel = 1 if heads_share else n_heads
|
183 |
+
rel_stddev = self.k_channels ** -0.5
|
184 |
+
self.emb_rel_k = nn.Parameter(
|
185 |
+
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
|
186 |
+
* rel_stddev
|
187 |
+
)
|
188 |
+
self.emb_rel_v = nn.Parameter(
|
189 |
+
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
|
190 |
+
* rel_stddev
|
191 |
+
)
|
192 |
+
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
193 |
+
self.drop = nn.Dropout(p_dropout)
|
194 |
+
|
195 |
+
nn.init.xavier_uniform_(self.conv_q.weight)
|
196 |
+
nn.init.xavier_uniform_(self.conv_k.weight)
|
197 |
+
if proximal_init:
|
198 |
+
self.conv_k.weight.data.copy_(self.conv_q.weight.data)
|
199 |
+
self.conv_k.bias.data.copy_(self.conv_q.bias.data)
|
200 |
+
nn.init.xavier_uniform_(self.conv_v.weight)
|
201 |
+
|
202 |
+
def forward(self, x, c, attn_mask=None):
|
203 |
+
q = self.conv_q(x)
|
204 |
+
k = self.conv_k(c)
|
205 |
+
v = self.conv_v(c)
|
206 |
+
|
207 |
+
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
208 |
+
|
209 |
+
x = self.conv_o(x)
|
210 |
+
return x
|
211 |
+
|
212 |
+
def attention(self, query, key, value, mask=None):
|
213 |
+
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
214 |
+
b, d, t_s, t_t = (*key.size(), query.size(2))
|
215 |
+
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
216 |
+
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
217 |
+
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
218 |
+
|
219 |
+
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
|
220 |
+
if self.window_size is not None:
|
221 |
+
assert (
|
222 |
+
t_s == t_t
|
223 |
+
), "Relative attention is only available for self-attention."
|
224 |
+
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
225 |
+
rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings)
|
226 |
+
rel_logits = self._relative_position_to_absolute_position(rel_logits)
|
227 |
+
scores_local = rel_logits / math.sqrt(self.k_channels)
|
228 |
+
scores = scores + scores_local
|
229 |
+
if self.proximal_bias:
|
230 |
+
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
231 |
+
scores = scores + self._attention_bias_proximal(t_s).to(
|
232 |
+
device=scores.device, dtype=scores.dtype
|
233 |
+
)
|
234 |
+
if mask is not None:
|
235 |
+
scores = scores.masked_fill(mask == 0, -1e4)
|
236 |
+
if self.block_length is not None:
|
237 |
+
block_mask = (
|
238 |
+
torch.ones_like(scores)
|
239 |
+
.triu(-self.block_length)
|
240 |
+
.tril(self.block_length)
|
241 |
+
)
|
242 |
+
scores = scores * block_mask + -1e4 * (1 - block_mask)
|
243 |
+
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
244 |
+
p_attn = self.drop(p_attn)
|
245 |
+
output = torch.matmul(p_attn, value)
|
246 |
+
if self.window_size is not None:
|
247 |
+
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
248 |
+
value_relative_embeddings = self._get_relative_embeddings(
|
249 |
+
self.emb_rel_v, t_s
|
250 |
+
)
|
251 |
+
output = output + self._matmul_with_relative_values(
|
252 |
+
relative_weights, value_relative_embeddings
|
253 |
+
)
|
254 |
+
output = (
|
255 |
+
output.transpose(2, 3).contiguous().view(b, d, t_t)
|
256 |
+
) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
257 |
+
return output, p_attn
|
258 |
+
|
259 |
+
def _matmul_with_relative_values(self, x, y):
|
260 |
+
"""
|
261 |
+
x: [b, h, l, m]
|
262 |
+
y: [h or 1, m, d]
|
263 |
+
ret: [b, h, l, d]
|
264 |
+
"""
|
265 |
+
ret = torch.matmul(x, y.unsqueeze(0))
|
266 |
+
return ret
|
267 |
+
|
268 |
+
def _matmul_with_relative_keys(self, x, y):
|
269 |
+
"""
|
270 |
+
x: [b, h, l, d]
|
271 |
+
y: [h or 1, m, d]
|
272 |
+
ret: [b, h, l, m]
|
273 |
+
"""
|
274 |
+
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
275 |
+
return ret
|
276 |
+
|
277 |
+
def _get_relative_embeddings(self, relative_embeddings, length):
|
278 |
+
max_relative_position = 2 * self.window_size + 1
|
279 |
+
# Pad first before slice to avoid using cond ops.
|
280 |
+
pad_length = max(length - (self.window_size + 1), 0)
|
281 |
+
slice_start_position = max((self.window_size + 1) - length, 0)
|
282 |
+
slice_end_position = slice_start_position + 2 * length - 1
|
283 |
+
if pad_length > 0:
|
284 |
+
padded_relative_embeddings = F.pad(
|
285 |
+
relative_embeddings,
|
286 |
+
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
|
287 |
+
)
|
288 |
+
else:
|
289 |
+
padded_relative_embeddings = relative_embeddings
|
290 |
+
used_relative_embeddings = padded_relative_embeddings[
|
291 |
+
:, slice_start_position:slice_end_position
|
292 |
+
]
|
293 |
+
return used_relative_embeddings
|
294 |
+
|
295 |
+
def _relative_position_to_absolute_position(self, x):
|
296 |
+
"""
|
297 |
+
x: [b, h, l, 2*l-1]
|
298 |
+
ret: [b, h, l, l]
|
299 |
+
"""
|
300 |
+
batch, heads, length, _ = x.size()
|
301 |
+
# Concat columns of pad to shift from relative to absolute indexing.
|
302 |
+
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
|
303 |
+
|
304 |
+
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
305 |
+
x_flat = x.view([batch, heads, length * 2 * length])
|
306 |
+
x_flat = F.pad(
|
307 |
+
x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
|
308 |
+
)
|
309 |
+
|
310 |
+
# Reshape and slice out the padded elements.
|
311 |
+
x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
|
312 |
+
:, :, :length, length - 1 :
|
313 |
+
]
|
314 |
+
return x_final
|
315 |
+
|
316 |
+
def _absolute_position_to_relative_position(self, x):
|
317 |
+
"""
|
318 |
+
x: [b, h, l, l]
|
319 |
+
ret: [b, h, l, 2*l-1]
|
320 |
+
"""
|
321 |
+
batch, heads, length, _ = x.size()
|
322 |
+
# padd along column
|
323 |
+
x = F.pad(
|
324 |
+
x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
|
325 |
+
)
|
326 |
+
x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)])
|
327 |
+
# add 0's in the beginning that will skew the elements after reshape
|
328 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
329 |
+
x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
|
330 |
+
return x_final
|
331 |
+
|
332 |
+
def _attention_bias_proximal(self, length):
|
333 |
+
"""Bias for self-attention to encourage attention to close positions.
|
334 |
+
Args:
|
335 |
+
length: an integer scalar.
|
336 |
+
Returns:
|
337 |
+
a Tensor with shape [1, 1, length, length]
|
338 |
+
"""
|
339 |
+
r = torch.arange(length, dtype=torch.float32)
|
340 |
+
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
341 |
+
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
342 |
+
|
343 |
+
|
344 |
+
class FFN(nn.Module):
|
345 |
+
def __init__(
|
346 |
+
self,
|
347 |
+
in_channels,
|
348 |
+
out_channels,
|
349 |
+
filter_channels,
|
350 |
+
kernel_size,
|
351 |
+
p_dropout=0.0,
|
352 |
+
activation=None,
|
353 |
+
):
|
354 |
+
super().__init__()
|
355 |
+
self.in_channels = in_channels
|
356 |
+
self.out_channels = out_channels
|
357 |
+
self.filter_channels = filter_channels
|
358 |
+
self.kernel_size = kernel_size
|
359 |
+
self.p_dropout = p_dropout
|
360 |
+
self.activation = activation
|
361 |
+
|
362 |
+
self.conv_1 = nn.Conv1d(
|
363 |
+
in_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
364 |
+
)
|
365 |
+
self.conv_2 = nn.Conv1d(
|
366 |
+
filter_channels, out_channels, kernel_size, padding=kernel_size // 2
|
367 |
+
)
|
368 |
+
self.drop = nn.Dropout(p_dropout)
|
369 |
+
|
370 |
+
def forward(self, x, x_mask):
|
371 |
+
x = self.conv_1(x * x_mask)
|
372 |
+
if self.activation == "gelu":
|
373 |
+
x = x * torch.sigmoid(1.702 * x)
|
374 |
+
else:
|
375 |
+
x = torch.relu(x)
|
376 |
+
x = self.drop(x)
|
377 |
+
x = self.conv_2(x * x_mask)
|
378 |
+
return x * x_mask
|
ttsv/src/glow_tts/audio_processing.py
ADDED
@@ -0,0 +1,100 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
|
|
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|
|
|
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|
|
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|
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|
|
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|
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|
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|
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|
|
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|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import numpy as np
|
3 |
+
from scipy.signal import get_window
|
4 |
+
import librosa.util as librosa_util
|
5 |
+
|
6 |
+
|
7 |
+
def window_sumsquare(
|
8 |
+
window,
|
9 |
+
n_frames,
|
10 |
+
hop_length=200,
|
11 |
+
win_length=800,
|
12 |
+
n_fft=800,
|
13 |
+
dtype=np.float32,
|
14 |
+
norm=None,
|
15 |
+
):
|
16 |
+
"""
|
17 |
+
# from librosa 0.6
|
18 |
+
Compute the sum-square envelope of a window function at a given hop length.
|
19 |
+
|
20 |
+
This is used to estimate modulation effects induced by windowing
|
21 |
+
observations in short-time fourier transforms.
|
22 |
+
|
23 |
+
Parameters
|
24 |
+
----------
|
25 |
+
window : string, tuple, number, callable, or list-like
|
26 |
+
Window specification, as in `get_window`
|
27 |
+
|
28 |
+
n_frames : int > 0
|
29 |
+
The number of analysis frames
|
30 |
+
|
31 |
+
hop_length : int > 0
|
32 |
+
The number of samples to advance between frames
|
33 |
+
|
34 |
+
win_length : [optional]
|
35 |
+
The length of the window function. By default, this matches `n_fft`.
|
36 |
+
|
37 |
+
n_fft : int > 0
|
38 |
+
The length of each analysis frame.
|
39 |
+
|
40 |
+
dtype : np.dtype
|
41 |
+
The data type of the output
|
42 |
+
|
43 |
+
Returns
|
44 |
+
-------
|
45 |
+
wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
|
46 |
+
The sum-squared envelope of the window function
|
47 |
+
"""
|
48 |
+
if win_length is None:
|
49 |
+
win_length = n_fft
|
50 |
+
|
51 |
+
n = n_fft + hop_length * (n_frames - 1)
|
52 |
+
x = np.zeros(n, dtype=dtype)
|
53 |
+
|
54 |
+
# Compute the squared window at the desired length
|
55 |
+
win_sq = get_window(window, win_length, fftbins=True)
|
56 |
+
win_sq = librosa_util.normalize(win_sq, norm=norm) ** 2
|
57 |
+
win_sq = librosa_util.pad_center(win_sq, n_fft)
|
58 |
+
|
59 |
+
# Fill the envelope
|
60 |
+
for i in range(n_frames):
|
61 |
+
sample = i * hop_length
|
62 |
+
x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))]
|
63 |
+
return x
|
64 |
+
|
65 |
+
|
66 |
+
def griffin_lim(magnitudes, stft_fn, n_iters=30):
|
67 |
+
"""
|
68 |
+
PARAMS
|
69 |
+
------
|
70 |
+
magnitudes: spectrogram magnitudes
|
71 |
+
stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods
|
72 |
+
"""
|
73 |
+
|
74 |
+
angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size())))
|
75 |
+
angles = angles.astype(np.float32)
|
76 |
+
angles = torch.autograd.Variable(torch.from_numpy(angles))
|
77 |
+
signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
|
78 |
+
|
79 |
+
for i in range(n_iters):
|
80 |
+
_, angles = stft_fn.transform(signal)
|
81 |
+
signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
|
82 |
+
return signal
|
83 |
+
|
84 |
+
|
85 |
+
def dynamic_range_compression(x, C=1, clip_val=1e-5):
|
86 |
+
"""
|
87 |
+
PARAMS
|
88 |
+
------
|
89 |
+
C: compression factor
|
90 |
+
"""
|
91 |
+
return torch.log(torch.clamp(x, min=clip_val) * C)
|
92 |
+
|
93 |
+
|
94 |
+
def dynamic_range_decompression(x, C=1):
|
95 |
+
"""
|
96 |
+
PARAMS
|
97 |
+
------
|
98 |
+
C: compression factor used to compress
|
99 |
+
"""
|
100 |
+
return torch.exp(x) / C
|
ttsv/src/glow_tts/commons.py
ADDED
@@ -0,0 +1,273 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import numpy as np
|
3 |
+
import torch
|
4 |
+
from torch import nn
|
5 |
+
from torch.nn import functional as F
|
6 |
+
|
7 |
+
from librosa.filters import mel as librosa_mel_fn
|
8 |
+
from audio_processing import dynamic_range_compression
|
9 |
+
from audio_processing import dynamic_range_decompression
|
10 |
+
from stft import STFT
|
11 |
+
|
12 |
+
|
13 |
+
def intersperse(lst, item):
|
14 |
+
result = [item] * (len(lst) * 2 + 1)
|
15 |
+
result[1::2] = lst
|
16 |
+
return result
|
17 |
+
|
18 |
+
|
19 |
+
def mle_loss(z, m, logs, logdet, mask):
|
20 |
+
l = torch.sum(logs) + 0.5 * torch.sum(
|
21 |
+
torch.exp(-2 * logs) * ((z - m) ** 2)
|
22 |
+
) # neg normal likelihood w/o the constant term
|
23 |
+
l = l - torch.sum(logdet) # log jacobian determinant
|
24 |
+
l = l / torch.sum(
|
25 |
+
torch.ones_like(z) * mask
|
26 |
+
) # averaging across batch, channel and time axes
|
27 |
+
l = l + 0.5 * math.log(2 * math.pi) # add the remaining constant term
|
28 |
+
return l
|
29 |
+
|
30 |
+
|
31 |
+
def duration_loss(logw, logw_, lengths):
|
32 |
+
l = torch.sum((logw - logw_) ** 2) / torch.sum(lengths)
|
33 |
+
return l
|
34 |
+
|
35 |
+
|
36 |
+
@torch.jit.script
|
37 |
+
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
38 |
+
n_channels_int = n_channels[0]
|
39 |
+
in_act = input_a + input_b
|
40 |
+
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
41 |
+
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
42 |
+
acts = t_act * s_act
|
43 |
+
return acts
|
44 |
+
|
45 |
+
|
46 |
+
def convert_pad_shape(pad_shape):
|
47 |
+
l = pad_shape[::-1]
|
48 |
+
pad_shape = [item for sublist in l for item in sublist]
|
49 |
+
return pad_shape
|
50 |
+
|
51 |
+
|
52 |
+
def shift_1d(x):
|
53 |
+
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
54 |
+
return x
|
55 |
+
|
56 |
+
|
57 |
+
def sequence_mask(length, max_length=None):
|
58 |
+
if max_length is None:
|
59 |
+
max_length = length.max()
|
60 |
+
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
61 |
+
return x.unsqueeze(0) < length.unsqueeze(1)
|
62 |
+
|
63 |
+
|
64 |
+
def maximum_path(value, mask, max_neg_val=-np.inf):
|
65 |
+
"""Numpy-friendly version. It's about 4 times faster than torch version.
|
66 |
+
value: [b, t_x, t_y]
|
67 |
+
mask: [b, t_x, t_y]
|
68 |
+
"""
|
69 |
+
value = value * mask
|
70 |
+
|
71 |
+
device = value.device
|
72 |
+
dtype = value.dtype
|
73 |
+
value = value.cpu().detach().numpy()
|
74 |
+
mask = mask.cpu().detach().numpy().astype(np.bool)
|
75 |
+
|
76 |
+
b, t_x, t_y = value.shape
|
77 |
+
direction = np.zeros(value.shape, dtype=np.int64)
|
78 |
+
v = np.zeros((b, t_x), dtype=np.float32)
|
79 |
+
x_range = np.arange(t_x, dtype=np.float32).reshape(1, -1)
|
80 |
+
for j in range(t_y):
|
81 |
+
v0 = np.pad(v, [[0, 0], [1, 0]], mode="constant", constant_values=max_neg_val)[
|
82 |
+
:, :-1
|
83 |
+
]
|
84 |
+
v1 = v
|
85 |
+
max_mask = v1 >= v0
|
86 |
+
v_max = np.where(max_mask, v1, v0)
|
87 |
+
direction[:, :, j] = max_mask
|
88 |
+
|
89 |
+
index_mask = x_range <= j
|
90 |
+
v = np.where(index_mask, v_max + value[:, :, j], max_neg_val)
|
91 |
+
direction = np.where(mask, direction, 1)
|
92 |
+
|
93 |
+
path = np.zeros(value.shape, dtype=np.float32)
|
94 |
+
index = mask[:, :, 0].sum(1).astype(np.int64) - 1
|
95 |
+
index_range = np.arange(b)
|
96 |
+
for j in reversed(range(t_y)):
|
97 |
+
path[index_range, index, j] = 1
|
98 |
+
index = index + direction[index_range, index, j] - 1
|
99 |
+
path = path * mask.astype(np.float32)
|
100 |
+
path = torch.from_numpy(path).to(device=device, dtype=dtype)
|
101 |
+
return path
|
102 |
+
|
103 |
+
|
104 |
+
def generate_path(duration, mask):
|
105 |
+
"""
|
106 |
+
duration: [b, t_x]
|
107 |
+
mask: [b, t_x, t_y]
|
108 |
+
"""
|
109 |
+
device = duration.device
|
110 |
+
|
111 |
+
b, t_x, t_y = mask.shape
|
112 |
+
cum_duration = torch.cumsum(duration, 1)
|
113 |
+
path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device)
|
114 |
+
|
115 |
+
cum_duration_flat = cum_duration.view(b * t_x)
|
116 |
+
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
117 |
+
path = path.view(b, t_x, t_y)
|
118 |
+
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
119 |
+
path = path * mask
|
120 |
+
return path
|
121 |
+
|
122 |
+
|
123 |
+
class Adam:
|
124 |
+
def __init__(
|
125 |
+
self,
|
126 |
+
params,
|
127 |
+
scheduler,
|
128 |
+
dim_model,
|
129 |
+
warmup_steps=4000,
|
130 |
+
lr=1e0,
|
131 |
+
betas=(0.9, 0.98),
|
132 |
+
eps=1e-9,
|
133 |
+
):
|
134 |
+
self.params = params
|
135 |
+
self.scheduler = scheduler
|
136 |
+
self.dim_model = dim_model
|
137 |
+
self.warmup_steps = warmup_steps
|
138 |
+
self.lr = lr
|
139 |
+
self.betas = betas
|
140 |
+
self.eps = eps
|
141 |
+
|
142 |
+
self.step_num = 1
|
143 |
+
self.cur_lr = lr * self._get_lr_scale()
|
144 |
+
|
145 |
+
self._optim = torch.optim.Adam(params, lr=self.cur_lr, betas=betas, eps=eps)
|
146 |
+
|
147 |
+
def _get_lr_scale(self):
|
148 |
+
if self.scheduler == "noam":
|
149 |
+
return np.power(self.dim_model, -0.5) * np.min(
|
150 |
+
[
|
151 |
+
np.power(self.step_num, -0.5),
|
152 |
+
self.step_num * np.power(self.warmup_steps, -1.5),
|
153 |
+
]
|
154 |
+
)
|
155 |
+
else:
|
156 |
+
return 1
|
157 |
+
|
158 |
+
def _update_learning_rate(self):
|
159 |
+
self.step_num += 1
|
160 |
+
if self.scheduler == "noam":
|
161 |
+
self.cur_lr = self.lr * self._get_lr_scale()
|
162 |
+
for param_group in self._optim.param_groups:
|
163 |
+
param_group["lr"] = self.cur_lr
|
164 |
+
|
165 |
+
def get_lr(self):
|
166 |
+
return self.cur_lr
|
167 |
+
|
168 |
+
def step(self):
|
169 |
+
self._optim.step()
|
170 |
+
self._update_learning_rate()
|
171 |
+
|
172 |
+
def zero_grad(self):
|
173 |
+
self._optim.zero_grad()
|
174 |
+
|
175 |
+
def load_state_dict(self, d):
|
176 |
+
self._optim.load_state_dict(d)
|
177 |
+
|
178 |
+
def state_dict(self):
|
179 |
+
return self._optim.state_dict()
|
180 |
+
|
181 |
+
|
182 |
+
class TacotronSTFT(nn.Module):
|
183 |
+
def __init__(
|
184 |
+
self,
|
185 |
+
filter_length=1024,
|
186 |
+
hop_length=256,
|
187 |
+
win_length=1024,
|
188 |
+
n_mel_channels=80,
|
189 |
+
sampling_rate=22050,
|
190 |
+
mel_fmin=0.0,
|
191 |
+
mel_fmax=8000.0,
|
192 |
+
):
|
193 |
+
super(TacotronSTFT, self).__init__()
|
194 |
+
self.n_mel_channels = n_mel_channels
|
195 |
+
self.sampling_rate = sampling_rate
|
196 |
+
self.stft_fn = STFT(filter_length, hop_length, win_length)
|
197 |
+
mel_basis = librosa_mel_fn(
|
198 |
+
sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax
|
199 |
+
)
|
200 |
+
mel_basis = torch.from_numpy(mel_basis).float()
|
201 |
+
self.register_buffer("mel_basis", mel_basis)
|
202 |
+
|
203 |
+
def spectral_normalize(self, magnitudes):
|
204 |
+
output = dynamic_range_compression(magnitudes)
|
205 |
+
return output
|
206 |
+
|
207 |
+
def spectral_de_normalize(self, magnitudes):
|
208 |
+
output = dynamic_range_decompression(magnitudes)
|
209 |
+
return output
|
210 |
+
|
211 |
+
def mel_spectrogram(self, y):
|
212 |
+
"""Computes mel-spectrograms from a batch of waves
|
213 |
+
PARAMS
|
214 |
+
------
|
215 |
+
y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
|
216 |
+
|
217 |
+
RETURNS
|
218 |
+
-------
|
219 |
+
mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
|
220 |
+
"""
|
221 |
+
assert torch.min(y.data) >= -1
|
222 |
+
assert torch.max(y.data) <= 1
|
223 |
+
|
224 |
+
magnitudes, phases = self.stft_fn.transform(y)
|
225 |
+
magnitudes = magnitudes.data
|
226 |
+
mel_output = torch.matmul(self.mel_basis, magnitudes)
|
227 |
+
mel_output = self.spectral_normalize(mel_output)
|
228 |
+
return mel_output
|
229 |
+
|
230 |
+
|
231 |
+
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
232 |
+
if isinstance(parameters, torch.Tensor):
|
233 |
+
parameters = [parameters]
|
234 |
+
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
235 |
+
norm_type = float(norm_type)
|
236 |
+
clip_value = float(clip_value)
|
237 |
+
|
238 |
+
total_norm = 0
|
239 |
+
for p in parameters:
|
240 |
+
param_norm = p.grad.data.norm(norm_type)
|
241 |
+
total_norm += param_norm.item() ** norm_type
|
242 |
+
|
243 |
+
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
244 |
+
total_norm = total_norm ** (1.0 / norm_type)
|
245 |
+
return total_norm
|
246 |
+
|
247 |
+
|
248 |
+
def squeeze(x, x_mask=None, n_sqz=2):
|
249 |
+
b, c, t = x.size()
|
250 |
+
|
251 |
+
t = (t // n_sqz) * n_sqz
|
252 |
+
x = x[:, :, :t]
|
253 |
+
x_sqz = x.view(b, c, t // n_sqz, n_sqz)
|
254 |
+
x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz)
|
255 |
+
|
256 |
+
if x_mask is not None:
|
257 |
+
x_mask = x_mask[:, :, n_sqz - 1 :: n_sqz]
|
258 |
+
else:
|
259 |
+
x_mask = torch.ones(b, 1, t // n_sqz).to(device=x.device, dtype=x.dtype)
|
260 |
+
return x_sqz * x_mask, x_mask
|
261 |
+
|
262 |
+
|
263 |
+
def unsqueeze(x, x_mask=None, n_sqz=2):
|
264 |
+
b, c, t = x.size()
|
265 |
+
|
266 |
+
x_unsqz = x.view(b, n_sqz, c // n_sqz, t)
|
267 |
+
x_unsqz = x_unsqz.permute(0, 2, 3, 1).contiguous().view(b, c // n_sqz, t * n_sqz)
|
268 |
+
|
269 |
+
if x_mask is not None:
|
270 |
+
x_mask = x_mask.unsqueeze(-1).repeat(1, 1, 1, n_sqz).view(b, 1, t * n_sqz)
|
271 |
+
else:
|
272 |
+
x_mask = torch.ones(b, 1, t * n_sqz).to(device=x.device, dtype=x.dtype)
|
273 |
+
return x_unsqz * x_mask, x_mask
|
ttsv/src/glow_tts/data_utils.py
ADDED
@@ -0,0 +1,274 @@
|
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|
|
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|
|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import random
|
2 |
+
import numpy as np
|
3 |
+
import torch
|
4 |
+
import torch.utils.data
|
5 |
+
|
6 |
+
import commons
|
7 |
+
from utils import load_wav_to_torch, load_filepaths_and_text
|
8 |
+
from text import text_to_sequence
|
9 |
+
|
10 |
+
class TextMelLoader(torch.utils.data.Dataset):
|
11 |
+
"""
|
12 |
+
1) loads audio,text pairs
|
13 |
+
2) normalizes text and converts them to sequences of one-hot vectors
|
14 |
+
3) computes mel-spectrograms from audio files.
|
15 |
+
"""
|
16 |
+
|
17 |
+
def __init__(self, audiopaths_and_text, hparams):
|
18 |
+
self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
|
19 |
+
self.text_cleaners = hparams.text_cleaners
|
20 |
+
self.max_wav_value = hparams.max_wav_value
|
21 |
+
self.sampling_rate = hparams.sampling_rate
|
22 |
+
self.load_mel_from_disk = hparams.load_mel_from_disk
|
23 |
+
self.add_noise = hparams.add_noise
|
24 |
+
self.symbols = hparams.punc + hparams.chars
|
25 |
+
self.add_blank = getattr(hparams, "add_blank", False) # improved version
|
26 |
+
self.stft = commons.TacotronSTFT(
|
27 |
+
hparams.filter_length,
|
28 |
+
hparams.hop_length,
|
29 |
+
hparams.win_length,
|
30 |
+
hparams.n_mel_channels,
|
31 |
+
hparams.sampling_rate,
|
32 |
+
hparams.mel_fmin,
|
33 |
+
hparams.mel_fmax,
|
34 |
+
)
|
35 |
+
random.seed(1234)
|
36 |
+
random.shuffle(self.audiopaths_and_text)
|
37 |
+
|
38 |
+
def get_mel_text_pair(self, audiopath_and_text):
|
39 |
+
# separate filename and text
|
40 |
+
audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
|
41 |
+
text = self.get_text(text)
|
42 |
+
mel = self.get_mel(audiopath)
|
43 |
+
return (text, mel)
|
44 |
+
|
45 |
+
def get_mel(self, filename):
|
46 |
+
if not self.load_mel_from_disk:
|
47 |
+
audio, sampling_rate = load_wav_to_torch(filename)
|
48 |
+
if sampling_rate != self.stft.sampling_rate:
|
49 |
+
raise ValueError(
|
50 |
+
"{} {} SR doesn't match target {} SR".format(
|
51 |
+
sampling_rate, self.stft.sampling_rate
|
52 |
+
)
|
53 |
+
)
|
54 |
+
if self.add_noise:
|
55 |
+
audio = audio + torch.rand_like(audio)
|
56 |
+
audio_norm = audio / self.max_wav_value
|
57 |
+
audio_norm = audio_norm.unsqueeze(0)
|
58 |
+
melspec = self.stft.mel_spectrogram(audio_norm)
|
59 |
+
melspec = torch.squeeze(melspec, 0)
|
60 |
+
else:
|
61 |
+
melspec = torch.from_numpy(np.load(filename))
|
62 |
+
assert (
|
63 |
+
melspec.size(0) == self.stft.n_mel_channels
|
64 |
+
), "Mel dimension mismatch: given {}, expected {}".format(
|
65 |
+
melspec.size(0), self.stft.n_mel_channels
|
66 |
+
)
|
67 |
+
|
68 |
+
return melspec
|
69 |
+
|
70 |
+
def get_text(self, text):
|
71 |
+
text_norm = text_to_sequence(text, self.symbols, self.text_cleaners)
|
72 |
+
if self.add_blank:
|
73 |
+
text_norm = commons.intersperse(
|
74 |
+
text_norm, len(self.symbols)
|
75 |
+
) # add a blank token, whose id number is len(symbols)
|
76 |
+
text_norm = torch.IntTensor(text_norm)
|
77 |
+
return text_norm
|
78 |
+
|
79 |
+
def __getitem__(self, index):
|
80 |
+
return self.get_mel_text_pair(self.audiopaths_and_text[index])
|
81 |
+
|
82 |
+
def __len__(self):
|
83 |
+
return len(self.audiopaths_and_text)
|
84 |
+
|
85 |
+
|
86 |
+
class TextMelCollate:
|
87 |
+
"""Zero-pads model inputs and targets based on number of frames per step"""
|
88 |
+
|
89 |
+
def __init__(self, n_frames_per_step=1):
|
90 |
+
self.n_frames_per_step = n_frames_per_step
|
91 |
+
|
92 |
+
def __call__(self, batch):
|
93 |
+
"""Collate's training batch from normalized text and mel-spectrogram
|
94 |
+
PARAMS
|
95 |
+
------
|
96 |
+
batch: [text_normalized, mel_normalized]
|
97 |
+
"""
|
98 |
+
# Right zero-pad all one-hot text sequences to max input length
|
99 |
+
input_lengths, ids_sorted_decreasing = torch.sort(
|
100 |
+
torch.LongTensor([len(x[0]) for x in batch]), dim=0, descending=True
|
101 |
+
)
|
102 |
+
max_input_len = input_lengths[0]
|
103 |
+
|
104 |
+
text_padded = torch.LongTensor(len(batch), max_input_len)
|
105 |
+
text_padded.zero_()
|
106 |
+
for i in range(len(ids_sorted_decreasing)):
|
107 |
+
text = batch[ids_sorted_decreasing[i]][0]
|
108 |
+
text_padded[i, : text.size(0)] = text
|
109 |
+
|
110 |
+
# Right zero-pad mel-spec
|
111 |
+
num_mels = batch[0][1].size(0)
|
112 |
+
max_target_len = max([x[1].size(1) for x in batch])
|
113 |
+
if max_target_len % self.n_frames_per_step != 0:
|
114 |
+
max_target_len += (
|
115 |
+
self.n_frames_per_step - max_target_len % self.n_frames_per_step
|
116 |
+
)
|
117 |
+
assert max_target_len % self.n_frames_per_step == 0
|
118 |
+
|
119 |
+
# include mel padded
|
120 |
+
mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
|
121 |
+
mel_padded.zero_()
|
122 |
+
output_lengths = torch.LongTensor(len(batch))
|
123 |
+
for i in range(len(ids_sorted_decreasing)):
|
124 |
+
mel = batch[ids_sorted_decreasing[i]][1]
|
125 |
+
mel_padded[i, :, : mel.size(1)] = mel
|
126 |
+
output_lengths[i] = mel.size(1)
|
127 |
+
|
128 |
+
return text_padded, input_lengths, mel_padded, output_lengths
|
129 |
+
|
130 |
+
|
131 |
+
"""Multi speaker version"""
|
132 |
+
|
133 |
+
|
134 |
+
class TextMelSpeakerLoader(torch.utils.data.Dataset):
|
135 |
+
"""
|
136 |
+
1) loads audio, speaker_id, text pairs
|
137 |
+
2) normalizes text and converts them to sequences of one-hot vectors
|
138 |
+
3) computes mel-spectrograms from audio files.
|
139 |
+
"""
|
140 |
+
|
141 |
+
def __init__(self, audiopaths_sid_text, hparams):
|
142 |
+
self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
|
143 |
+
self.text_cleaners = hparams.text_cleaners
|
144 |
+
self.max_wav_value = hparams.max_wav_value
|
145 |
+
self.sampling_rate = hparams.sampling_rate
|
146 |
+
self.load_mel_from_disk = hparams.load_mel_from_disk
|
147 |
+
self.add_noise = hparams.add_noise
|
148 |
+
self.symbols = hparams.punc + hparams.chars
|
149 |
+
self.add_blank = getattr(hparams, "add_blank", False) # improved version
|
150 |
+
self.min_text_len = getattr(hparams, "min_text_len", 1)
|
151 |
+
self.max_text_len = getattr(hparams, "max_text_len", 190)
|
152 |
+
self.stft = commons.TacotronSTFT(
|
153 |
+
hparams.filter_length,
|
154 |
+
hparams.hop_length,
|
155 |
+
hparams.win_length,
|
156 |
+
hparams.n_mel_channels,
|
157 |
+
hparams.sampling_rate,
|
158 |
+
hparams.mel_fmin,
|
159 |
+
hparams.mel_fmax,
|
160 |
+
)
|
161 |
+
|
162 |
+
self._filter_text_len()
|
163 |
+
random.seed(1234)
|
164 |
+
random.shuffle(self.audiopaths_sid_text)
|
165 |
+
|
166 |
+
def _filter_text_len(self):
|
167 |
+
audiopaths_sid_text_new = []
|
168 |
+
for audiopath, sid, text in self.audiopaths_sid_text:
|
169 |
+
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
|
170 |
+
audiopaths_sid_text_new.append([audiopath, sid, text])
|
171 |
+
self.audiopaths_sid_text = audiopaths_sid_text_new
|
172 |
+
|
173 |
+
def get_mel_text_speaker_pair(self, audiopath_sid_text):
|
174 |
+
# separate filename, speaker_id and text
|
175 |
+
audiopath, sid, text = (
|
176 |
+
audiopath_sid_text[0],
|
177 |
+
audiopath_sid_text[1],
|
178 |
+
audiopath_sid_text[2],
|
179 |
+
)
|
180 |
+
text = self.get_text(text)
|
181 |
+
mel = self.get_mel(audiopath)
|
182 |
+
sid = self.get_sid(sid)
|
183 |
+
return (text, mel, sid)
|
184 |
+
|
185 |
+
def get_mel(self, filename):
|
186 |
+
if not self.load_mel_from_disk:
|
187 |
+
audio, sampling_rate = load_wav_to_torch(filename)
|
188 |
+
if sampling_rate != self.stft.sampling_rate:
|
189 |
+
raise ValueError(
|
190 |
+
"{} {} SR doesn't match target {} SR".format(
|
191 |
+
sampling_rate, self.stft.sampling_rate
|
192 |
+
)
|
193 |
+
)
|
194 |
+
if self.add_noise:
|
195 |
+
audio = audio + torch.rand_like(audio)
|
196 |
+
audio_norm = audio / self.max_wav_value
|
197 |
+
audio_norm = audio_norm.unsqueeze(0)
|
198 |
+
melspec = self.stft.mel_spectrogram(audio_norm)
|
199 |
+
melspec = torch.squeeze(melspec, 0)
|
200 |
+
else:
|
201 |
+
melspec = torch.from_numpy(np.load(filename))
|
202 |
+
assert (
|
203 |
+
melspec.size(0) == self.stft.n_mel_channels
|
204 |
+
), "Mel dimension mismatch: given {}, expected {}".format(
|
205 |
+
melspec.size(0), self.stft.n_mel_channels
|
206 |
+
)
|
207 |
+
|
208 |
+
return melspec
|
209 |
+
|
210 |
+
def get_text(self, text):
|
211 |
+
text_norm = text_to_sequence(text, self.symbols, self.text_cleaners)
|
212 |
+
if self.add_blank:
|
213 |
+
text_norm = commons.intersperse(
|
214 |
+
text_norm, len(self.symbols)
|
215 |
+
) # add a blank token, whose id number is len(symbols)
|
216 |
+
text_norm = torch.IntTensor(text_norm)
|
217 |
+
return text_norm
|
218 |
+
|
219 |
+
def get_sid(self, sid):
|
220 |
+
sid = torch.IntTensor([int(sid)])
|
221 |
+
return sid
|
222 |
+
|
223 |
+
def __getitem__(self, index):
|
224 |
+
return self.get_mel_text_speaker_pair(self.audiopaths_sid_text[index])
|
225 |
+
|
226 |
+
def __len__(self):
|
227 |
+
return len(self.audiopaths_sid_text)
|
228 |
+
|
229 |
+
|
230 |
+
class TextMelSpeakerCollate:
|
231 |
+
"""Zero-pads model inputs and targets based on number of frames per step"""
|
232 |
+
|
233 |
+
def __init__(self, n_frames_per_step=1):
|
234 |
+
self.n_frames_per_step = n_frames_per_step
|
235 |
+
|
236 |
+
def __call__(self, batch):
|
237 |
+
"""Collate's training batch from normalized text and mel-spectrogram
|
238 |
+
PARAMS
|
239 |
+
------
|
240 |
+
batch: [text_normalized, mel_normalized]
|
241 |
+
"""
|
242 |
+
# Right zero-pad all one-hot text sequences to max input length
|
243 |
+
input_lengths, ids_sorted_decreasing = torch.sort(
|
244 |
+
torch.LongTensor([len(x[0]) for x in batch]), dim=0, descending=True
|
245 |
+
)
|
246 |
+
max_input_len = input_lengths[0]
|
247 |
+
|
248 |
+
text_padded = torch.LongTensor(len(batch), max_input_len)
|
249 |
+
text_padded.zero_()
|
250 |
+
for i in range(len(ids_sorted_decreasing)):
|
251 |
+
text = batch[ids_sorted_decreasing[i]][0]
|
252 |
+
text_padded[i, : text.size(0)] = text
|
253 |
+
|
254 |
+
# Right zero-pad mel-spec
|
255 |
+
num_mels = batch[0][1].size(0)
|
256 |
+
max_target_len = max([x[1].size(1) for x in batch])
|
257 |
+
if max_target_len % self.n_frames_per_step != 0:
|
258 |
+
max_target_len += (
|
259 |
+
self.n_frames_per_step - max_target_len % self.n_frames_per_step
|
260 |
+
)
|
261 |
+
assert max_target_len % self.n_frames_per_step == 0
|
262 |
+
|
263 |
+
# include mel padded & sid
|
264 |
+
mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
|
265 |
+
mel_padded.zero_()
|
266 |
+
output_lengths = torch.LongTensor(len(batch))
|
267 |
+
sid = torch.LongTensor(len(batch))
|
268 |
+
for i in range(len(ids_sorted_decreasing)):
|
269 |
+
mel = batch[ids_sorted_decreasing[i]][1]
|
270 |
+
mel_padded[i, :, : mel.size(1)] = mel
|
271 |
+
output_lengths[i] = mel.size(1)
|
272 |
+
sid[i] = batch[ids_sorted_decreasing[i]][2]
|
273 |
+
|
274 |
+
return text_padded, input_lengths, mel_padded, output_lengths, sid
|
ttsv/src/glow_tts/generate_mels.py
ADDED
@@ -0,0 +1,70 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import numpy as np
|
2 |
+
import os
|
3 |
+
import torch
|
4 |
+
import commons
|
5 |
+
|
6 |
+
import models
|
7 |
+
import utils
|
8 |
+
from argparse import ArgumentParser
|
9 |
+
from tqdm import tqdm
|
10 |
+
from text import text_to_sequence
|
11 |
+
|
12 |
+
if __name__ == "__main__":
|
13 |
+
parser = ArgumentParser()
|
14 |
+
parser.add_argument("-m", "--model_dir", required=True, type=str)
|
15 |
+
parser.add_argument("-s", "--mels_dir", required=True, type=str)
|
16 |
+
args = parser.parse_args()
|
17 |
+
MODEL_DIR = args.model_dir # path to model dir
|
18 |
+
SAVE_MELS_DIR = args.mels_dir # path to save generated mels
|
19 |
+
|
20 |
+
if not os.path.exists(SAVE_MELS_DIR):
|
21 |
+
os.makedirs(SAVE_MELS_DIR)
|
22 |
+
|
23 |
+
hps = utils.get_hparams_from_dir(MODEL_DIR)
|
24 |
+
symbols = list(hps.data.punc) + list(hps.data.chars)
|
25 |
+
checkpoint_path = utils.latest_checkpoint_path(MODEL_DIR)
|
26 |
+
cleaner = hps.data.text_cleaners
|
27 |
+
|
28 |
+
model = models.FlowGenerator(
|
29 |
+
len(symbols) + getattr(hps.data, "add_blank", False),
|
30 |
+
out_channels=hps.data.n_mel_channels,
|
31 |
+
**hps.model
|
32 |
+
).to("cuda")
|
33 |
+
|
34 |
+
utils.load_checkpoint(checkpoint_path, model)
|
35 |
+
model.decoder.store_inverse() # do not calcuate jacobians for fast decoding
|
36 |
+
_ = model.eval()
|
37 |
+
|
38 |
+
def get_mel(text, fpath):
|
39 |
+
if getattr(hps.data, "add_blank", False):
|
40 |
+
text_norm = text_to_sequence(text, symbols, cleaner)
|
41 |
+
text_norm = commons.intersperse(text_norm, len(symbols))
|
42 |
+
else: # If not using "add_blank" option during training, adding spaces at the beginning and the end of utterance improves quality
|
43 |
+
text = " " + text.strip() + " "
|
44 |
+
text_norm = text_to_sequence(text, symbols, cleaner)
|
45 |
+
|
46 |
+
sequence = np.array(text_norm)[None, :]
|
47 |
+
|
48 |
+
x_tst = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()
|
49 |
+
x_tst_lengths = torch.tensor([x_tst.shape[1]]).cuda()
|
50 |
+
|
51 |
+
with torch.no_grad():
|
52 |
+
noise_scale = 0.667
|
53 |
+
length_scale = 1.0
|
54 |
+
(y_gen_tst, *_), *_, (attn_gen, *_) = model(
|
55 |
+
x_tst,
|
56 |
+
x_tst_lengths,
|
57 |
+
gen=True,
|
58 |
+
noise_scale=noise_scale,
|
59 |
+
length_scale=length_scale,
|
60 |
+
)
|
61 |
+
|
62 |
+
np.save(os.path.join(SAVE_MELS_DIR, fpath), y_gen_tst.cpu().detach().numpy())
|
63 |
+
|
64 |
+
for f in [hps.data.training_files, hps.data.validation_files]:
|
65 |
+
file_lines = open(f).read().splitlines()
|
66 |
+
|
67 |
+
for line in tqdm(file_lines):
|
68 |
+
fname, text = line.split("|")
|
69 |
+
fname = os.path.basename(fname).replace(".wav", ".npy")
|
70 |
+
get_mel(text, fname)
|
ttsv/src/glow_tts/hifi/__init__.py
ADDED
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from .env import AttrDict
|
2 |
+
from .models import Generator
|
3 |
+
|
4 |
+
if __name__ == "__main__":
|
5 |
+
pass
|
ttsv/src/glow_tts/hifi/env.py
ADDED
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import shutil
|
3 |
+
|
4 |
+
|
5 |
+
class AttrDict(dict):
|
6 |
+
def __init__(self, *args, **kwargs):
|
7 |
+
super(AttrDict, self).__init__(*args, **kwargs)
|
8 |
+
self.__dict__ = self
|
9 |
+
|
10 |
+
|
11 |
+
def build_env(config, config_name, path):
|
12 |
+
t_path = os.path.join(path, config_name)
|
13 |
+
if config != t_path:
|
14 |
+
os.makedirs(path, exist_ok=True)
|
15 |
+
shutil.copyfile(config, os.path.join(path, config_name))
|
ttsv/src/glow_tts/hifi/models.py
ADDED
@@ -0,0 +1,403 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn.functional as F
|
3 |
+
import torch.nn as nn
|
4 |
+
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
5 |
+
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
6 |
+
from .utils import init_weights, get_padding
|
7 |
+
|
8 |
+
LRELU_SLOPE = 0.1
|
9 |
+
|
10 |
+
|
11 |
+
class ResBlock1(torch.nn.Module):
|
12 |
+
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
|
13 |
+
super(ResBlock1, self).__init__()
|
14 |
+
self.h = h
|
15 |
+
self.convs1 = nn.ModuleList(
|
16 |
+
[
|
17 |
+
weight_norm(
|
18 |
+
Conv1d(
|
19 |
+
channels,
|
20 |
+
channels,
|
21 |
+
kernel_size,
|
22 |
+
1,
|
23 |
+
dilation=dilation[0],
|
24 |
+
padding=get_padding(kernel_size, dilation[0]),
|
25 |
+
)
|
26 |
+
),
|
27 |
+
weight_norm(
|
28 |
+
Conv1d(
|
29 |
+
channels,
|
30 |
+
channels,
|
31 |
+
kernel_size,
|
32 |
+
1,
|
33 |
+
dilation=dilation[1],
|
34 |
+
padding=get_padding(kernel_size, dilation[1]),
|
35 |
+
)
|
36 |
+
),
|
37 |
+
weight_norm(
|
38 |
+
Conv1d(
|
39 |
+
channels,
|
40 |
+
channels,
|
41 |
+
kernel_size,
|
42 |
+
1,
|
43 |
+
dilation=dilation[2],
|
44 |
+
padding=get_padding(kernel_size, dilation[2]),
|
45 |
+
)
|
46 |
+
),
|
47 |
+
]
|
48 |
+
)
|
49 |
+
self.convs1.apply(init_weights)
|
50 |
+
|
51 |
+
self.convs2 = nn.ModuleList(
|
52 |
+
[
|
53 |
+
weight_norm(
|
54 |
+
Conv1d(
|
55 |
+
channels,
|
56 |
+
channels,
|
57 |
+
kernel_size,
|
58 |
+
1,
|
59 |
+
dilation=1,
|
60 |
+
padding=get_padding(kernel_size, 1),
|
61 |
+
)
|
62 |
+
),
|
63 |
+
weight_norm(
|
64 |
+
Conv1d(
|
65 |
+
channels,
|
66 |
+
channels,
|
67 |
+
kernel_size,
|
68 |
+
1,
|
69 |
+
dilation=1,
|
70 |
+
padding=get_padding(kernel_size, 1),
|
71 |
+
)
|
72 |
+
),
|
73 |
+
weight_norm(
|
74 |
+
Conv1d(
|
75 |
+
channels,
|
76 |
+
channels,
|
77 |
+
kernel_size,
|
78 |
+
1,
|
79 |
+
dilation=1,
|
80 |
+
padding=get_padding(kernel_size, 1),
|
81 |
+
)
|
82 |
+
),
|
83 |
+
]
|
84 |
+
)
|
85 |
+
self.convs2.apply(init_weights)
|
86 |
+
|
87 |
+
def forward(self, x):
|
88 |
+
for c1, c2 in zip(self.convs1, self.convs2):
|
89 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
90 |
+
xt = c1(xt)
|
91 |
+
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
92 |
+
xt = c2(xt)
|
93 |
+
x = xt + x
|
94 |
+
return x
|
95 |
+
|
96 |
+
def remove_weight_norm(self):
|
97 |
+
for l in self.convs1:
|
98 |
+
remove_weight_norm(l)
|
99 |
+
for l in self.convs2:
|
100 |
+
remove_weight_norm(l)
|
101 |
+
|
102 |
+
|
103 |
+
class ResBlock2(torch.nn.Module):
|
104 |
+
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
|
105 |
+
super(ResBlock2, self).__init__()
|
106 |
+
self.h = h
|
107 |
+
self.convs = nn.ModuleList(
|
108 |
+
[
|
109 |
+
weight_norm(
|
110 |
+
Conv1d(
|
111 |
+
channels,
|
112 |
+
channels,
|
113 |
+
kernel_size,
|
114 |
+
1,
|
115 |
+
dilation=dilation[0],
|
116 |
+
padding=get_padding(kernel_size, dilation[0]),
|
117 |
+
)
|
118 |
+
),
|
119 |
+
weight_norm(
|
120 |
+
Conv1d(
|
121 |
+
channels,
|
122 |
+
channels,
|
123 |
+
kernel_size,
|
124 |
+
1,
|
125 |
+
dilation=dilation[1],
|
126 |
+
padding=get_padding(kernel_size, dilation[1]),
|
127 |
+
)
|
128 |
+
),
|
129 |
+
]
|
130 |
+
)
|
131 |
+
self.convs.apply(init_weights)
|
132 |
+
|
133 |
+
def forward(self, x):
|
134 |
+
for c in self.convs:
|
135 |
+
xt = F.leaky_relu(x, LRELU_SLOPE)
|
136 |
+
xt = c(xt)
|
137 |
+
x = xt + x
|
138 |
+
return x
|
139 |
+
|
140 |
+
def remove_weight_norm(self):
|
141 |
+
for l in self.convs:
|
142 |
+
remove_weight_norm(l)
|
143 |
+
|
144 |
+
|
145 |
+
class Generator(torch.nn.Module):
|
146 |
+
def __init__(self, h):
|
147 |
+
super(Generator, self).__init__()
|
148 |
+
self.h = h
|
149 |
+
self.num_kernels = len(h.resblock_kernel_sizes)
|
150 |
+
self.num_upsamples = len(h.upsample_rates)
|
151 |
+
self.conv_pre = weight_norm(
|
152 |
+
Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3)
|
153 |
+
)
|
154 |
+
resblock = ResBlock1 if h.resblock == "1" else ResBlock2
|
155 |
+
|
156 |
+
self.ups = nn.ModuleList()
|
157 |
+
for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
|
158 |
+
self.ups.append(
|
159 |
+
weight_norm(
|
160 |
+
ConvTranspose1d(
|
161 |
+
h.upsample_initial_channel // (2 ** i),
|
162 |
+
h.upsample_initial_channel // (2 ** (i + 1)),
|
163 |
+
k,
|
164 |
+
u,
|
165 |
+
padding=(k - u) // 2,
|
166 |
+
)
|
167 |
+
)
|
168 |
+
)
|
169 |
+
|
170 |
+
self.resblocks = nn.ModuleList()
|
171 |
+
for i in range(len(self.ups)):
|
172 |
+
ch = h.upsample_initial_channel // (2 ** (i + 1))
|
173 |
+
for j, (k, d) in enumerate(
|
174 |
+
zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)
|
175 |
+
):
|
176 |
+
self.resblocks.append(resblock(h, ch, k, d))
|
177 |
+
|
178 |
+
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
|
179 |
+
self.ups.apply(init_weights)
|
180 |
+
self.conv_post.apply(init_weights)
|
181 |
+
|
182 |
+
def forward(self, x):
|
183 |
+
x = self.conv_pre(x)
|
184 |
+
for i in range(self.num_upsamples):
|
185 |
+
x = F.leaky_relu(x, LRELU_SLOPE)
|
186 |
+
x = self.ups[i](x)
|
187 |
+
xs = None
|
188 |
+
for j in range(self.num_kernels):
|
189 |
+
if xs is None:
|
190 |
+
xs = self.resblocks[i * self.num_kernels + j](x)
|
191 |
+
else:
|
192 |
+
xs += self.resblocks[i * self.num_kernels + j](x)
|
193 |
+
x = xs / self.num_kernels
|
194 |
+
x = F.leaky_relu(x)
|
195 |
+
x = self.conv_post(x)
|
196 |
+
x = torch.tanh(x)
|
197 |
+
|
198 |
+
return x
|
199 |
+
|
200 |
+
def remove_weight_norm(self):
|
201 |
+
print("Removing weight norm...")
|
202 |
+
for l in self.ups:
|
203 |
+
remove_weight_norm(l)
|
204 |
+
for l in self.resblocks:
|
205 |
+
l.remove_weight_norm()
|
206 |
+
remove_weight_norm(self.conv_pre)
|
207 |
+
remove_weight_norm(self.conv_post)
|
208 |
+
|
209 |
+
|
210 |
+
class DiscriminatorP(torch.nn.Module):
|
211 |
+
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
212 |
+
super(DiscriminatorP, self).__init__()
|
213 |
+
self.period = period
|
214 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
215 |
+
self.convs = nn.ModuleList(
|
216 |
+
[
|
217 |
+
norm_f(
|
218 |
+
Conv2d(
|
219 |
+
1,
|
220 |
+
32,
|
221 |
+
(kernel_size, 1),
|
222 |
+
(stride, 1),
|
223 |
+
padding=(get_padding(5, 1), 0),
|
224 |
+
)
|
225 |
+
),
|
226 |
+
norm_f(
|
227 |
+
Conv2d(
|
228 |
+
32,
|
229 |
+
128,
|
230 |
+
(kernel_size, 1),
|
231 |
+
(stride, 1),
|
232 |
+
padding=(get_padding(5, 1), 0),
|
233 |
+
)
|
234 |
+
),
|
235 |
+
norm_f(
|
236 |
+
Conv2d(
|
237 |
+
128,
|
238 |
+
512,
|
239 |
+
(kernel_size, 1),
|
240 |
+
(stride, 1),
|
241 |
+
padding=(get_padding(5, 1), 0),
|
242 |
+
)
|
243 |
+
),
|
244 |
+
norm_f(
|
245 |
+
Conv2d(
|
246 |
+
512,
|
247 |
+
1024,
|
248 |
+
(kernel_size, 1),
|
249 |
+
(stride, 1),
|
250 |
+
padding=(get_padding(5, 1), 0),
|
251 |
+
)
|
252 |
+
),
|
253 |
+
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
|
254 |
+
]
|
255 |
+
)
|
256 |
+
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
257 |
+
|
258 |
+
def forward(self, x):
|
259 |
+
fmap = []
|
260 |
+
|
261 |
+
# 1d to 2d
|
262 |
+
b, c, t = x.shape
|
263 |
+
if t % self.period != 0: # pad first
|
264 |
+
n_pad = self.period - (t % self.period)
|
265 |
+
x = F.pad(x, (0, n_pad), "reflect")
|
266 |
+
t = t + n_pad
|
267 |
+
x = x.view(b, c, t // self.period, self.period)
|
268 |
+
|
269 |
+
for l in self.convs:
|
270 |
+
x = l(x)
|
271 |
+
x = F.leaky_relu(x, LRELU_SLOPE)
|
272 |
+
fmap.append(x)
|
273 |
+
x = self.conv_post(x)
|
274 |
+
fmap.append(x)
|
275 |
+
x = torch.flatten(x, 1, -1)
|
276 |
+
|
277 |
+
return x, fmap
|
278 |
+
|
279 |
+
|
280 |
+
class MultiPeriodDiscriminator(torch.nn.Module):
|
281 |
+
def __init__(self):
|
282 |
+
super(MultiPeriodDiscriminator, self).__init__()
|
283 |
+
self.discriminators = nn.ModuleList(
|
284 |
+
[
|
285 |
+
DiscriminatorP(2),
|
286 |
+
DiscriminatorP(3),
|
287 |
+
DiscriminatorP(5),
|
288 |
+
DiscriminatorP(7),
|
289 |
+
DiscriminatorP(11),
|
290 |
+
]
|
291 |
+
)
|
292 |
+
|
293 |
+
def forward(self, y, y_hat):
|
294 |
+
y_d_rs = []
|
295 |
+
y_d_gs = []
|
296 |
+
fmap_rs = []
|
297 |
+
fmap_gs = []
|
298 |
+
for i, d in enumerate(self.discriminators):
|
299 |
+
y_d_r, fmap_r = d(y)
|
300 |
+
y_d_g, fmap_g = d(y_hat)
|
301 |
+
y_d_rs.append(y_d_r)
|
302 |
+
fmap_rs.append(fmap_r)
|
303 |
+
y_d_gs.append(y_d_g)
|
304 |
+
fmap_gs.append(fmap_g)
|
305 |
+
|
306 |
+
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
307 |
+
|
308 |
+
|
309 |
+
class DiscriminatorS(torch.nn.Module):
|
310 |
+
def __init__(self, use_spectral_norm=False):
|
311 |
+
super(DiscriminatorS, self).__init__()
|
312 |
+
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
313 |
+
self.convs = nn.ModuleList(
|
314 |
+
[
|
315 |
+
norm_f(Conv1d(1, 128, 15, 1, padding=7)),
|
316 |
+
norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
|
317 |
+
norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
|
318 |
+
norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
|
319 |
+
norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
|
320 |
+
norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
|
321 |
+
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
322 |
+
]
|
323 |
+
)
|
324 |
+
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
325 |
+
|
326 |
+
def forward(self, x):
|
327 |
+
fmap = []
|
328 |
+
for l in self.convs:
|
329 |
+
x = l(x)
|
330 |
+
x = F.leaky_relu(x, LRELU_SLOPE)
|
331 |
+
fmap.append(x)
|
332 |
+
x = self.conv_post(x)
|
333 |
+
fmap.append(x)
|
334 |
+
x = torch.flatten(x, 1, -1)
|
335 |
+
|
336 |
+
return x, fmap
|
337 |
+
|
338 |
+
|
339 |
+
class MultiScaleDiscriminator(torch.nn.Module):
|
340 |
+
def __init__(self):
|
341 |
+
super(MultiScaleDiscriminator, self).__init__()
|
342 |
+
self.discriminators = nn.ModuleList(
|
343 |
+
[
|
344 |
+
DiscriminatorS(use_spectral_norm=True),
|
345 |
+
DiscriminatorS(),
|
346 |
+
DiscriminatorS(),
|
347 |
+
]
|
348 |
+
)
|
349 |
+
self.meanpools = nn.ModuleList(
|
350 |
+
[AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)]
|
351 |
+
)
|
352 |
+
|
353 |
+
def forward(self, y, y_hat):
|
354 |
+
y_d_rs = []
|
355 |
+
y_d_gs = []
|
356 |
+
fmap_rs = []
|
357 |
+
fmap_gs = []
|
358 |
+
for i, d in enumerate(self.discriminators):
|
359 |
+
if i != 0:
|
360 |
+
y = self.meanpools[i - 1](y)
|
361 |
+
y_hat = self.meanpools[i - 1](y_hat)
|
362 |
+
y_d_r, fmap_r = d(y)
|
363 |
+
y_d_g, fmap_g = d(y_hat)
|
364 |
+
y_d_rs.append(y_d_r)
|
365 |
+
fmap_rs.append(fmap_r)
|
366 |
+
y_d_gs.append(y_d_g)
|
367 |
+
fmap_gs.append(fmap_g)
|
368 |
+
|
369 |
+
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
370 |
+
|
371 |
+
|
372 |
+
def feature_loss(fmap_r, fmap_g):
|
373 |
+
loss = 0
|
374 |
+
for dr, dg in zip(fmap_r, fmap_g):
|
375 |
+
for rl, gl in zip(dr, dg):
|
376 |
+
loss += torch.mean(torch.abs(rl - gl))
|
377 |
+
|
378 |
+
return loss * 2
|
379 |
+
|
380 |
+
|
381 |
+
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
382 |
+
loss = 0
|
383 |
+
r_losses = []
|
384 |
+
g_losses = []
|
385 |
+
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
386 |
+
r_loss = torch.mean((1 - dr) ** 2)
|
387 |
+
g_loss = torch.mean(dg ** 2)
|
388 |
+
loss += r_loss + g_loss
|
389 |
+
r_losses.append(r_loss.item())
|
390 |
+
g_losses.append(g_loss.item())
|
391 |
+
|
392 |
+
return loss, r_losses, g_losses
|
393 |
+
|
394 |
+
|
395 |
+
def generator_loss(disc_outputs):
|
396 |
+
loss = 0
|
397 |
+
gen_losses = []
|
398 |
+
for dg in disc_outputs:
|
399 |
+
l = torch.mean((1 - dg) ** 2)
|
400 |
+
gen_losses.append(l)
|
401 |
+
loss += l
|
402 |
+
|
403 |
+
return loss, gen_losses
|
ttsv/src/glow_tts/hifi/utils.py
ADDED
@@ -0,0 +1,57 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import glob
|
2 |
+
import os
|
3 |
+
import matplotlib
|
4 |
+
import torch
|
5 |
+
from torch.nn.utils import weight_norm
|
6 |
+
|
7 |
+
matplotlib.use("Agg")
|
8 |
+
import matplotlib.pylab as plt
|
9 |
+
|
10 |
+
|
11 |
+
def plot_spectrogram(spectrogram):
|
12 |
+
fig, ax = plt.subplots(figsize=(10, 2))
|
13 |
+
im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
|
14 |
+
plt.colorbar(im, ax=ax)
|
15 |
+
|
16 |
+
fig.canvas.draw()
|
17 |
+
plt.close()
|
18 |
+
|
19 |
+
return fig
|
20 |
+
|
21 |
+
|
22 |
+
def init_weights(m, mean=0.0, std=0.01):
|
23 |
+
classname = m.__class__.__name__
|
24 |
+
if classname.find("Conv") != -1:
|
25 |
+
m.weight.data.normal_(mean, std)
|
26 |
+
|
27 |
+
|
28 |
+
def apply_weight_norm(m):
|
29 |
+
classname = m.__class__.__name__
|
30 |
+
if classname.find("Conv") != -1:
|
31 |
+
weight_norm(m)
|
32 |
+
|
33 |
+
|
34 |
+
def get_padding(kernel_size, dilation=1):
|
35 |
+
return int((kernel_size * dilation - dilation) / 2)
|
36 |
+
|
37 |
+
|
38 |
+
def load_checkpoint(filepath, device):
|
39 |
+
assert os.path.isfile(filepath)
|
40 |
+
print("Loading '{}'".format(filepath))
|
41 |
+
checkpoint_dict = torch.load(filepath, map_location=device)
|
42 |
+
print("Complete.")
|
43 |
+
return checkpoint_dict
|
44 |
+
|
45 |
+
|
46 |
+
def save_checkpoint(filepath, obj):
|
47 |
+
print("Saving checkpoint to {}".format(filepath))
|
48 |
+
torch.save(obj, filepath)
|
49 |
+
print("Complete.")
|
50 |
+
|
51 |
+
|
52 |
+
def scan_checkpoint(cp_dir, prefix):
|
53 |
+
pattern = os.path.join(cp_dir, prefix + "????????")
|
54 |
+
cp_list = glob.glob(pattern)
|
55 |
+
if len(cp_list) == 0:
|
56 |
+
return None
|
57 |
+
return sorted(cp_list)[-1]
|
ttsv/src/glow_tts/init.py
ADDED
@@ -0,0 +1,79 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import json
|
3 |
+
import argparse
|
4 |
+
import math
|
5 |
+
import torch
|
6 |
+
from torch import nn, optim
|
7 |
+
from torch.nn import functional as F
|
8 |
+
from torch.utils.data import DataLoader
|
9 |
+
|
10 |
+
from data_utils import TextMelLoader, TextMelCollate
|
11 |
+
import models
|
12 |
+
import commons
|
13 |
+
import utils
|
14 |
+
|
15 |
+
|
16 |
+
class FlowGenerator_DDI(models.FlowGenerator):
|
17 |
+
"""A helper for Data-dependent Initialization"""
|
18 |
+
|
19 |
+
def __init__(self, *args, **kwargs):
|
20 |
+
super().__init__(*args, **kwargs)
|
21 |
+
for f in self.decoder.flows:
|
22 |
+
if getattr(f, "set_ddi", False):
|
23 |
+
f.set_ddi(True)
|
24 |
+
|
25 |
+
|
26 |
+
def main():
|
27 |
+
hps = utils.get_hparams()
|
28 |
+
logger = utils.get_logger(hps.log_dir)
|
29 |
+
logger.info(hps)
|
30 |
+
utils.check_git_hash(hps.log_dir)
|
31 |
+
|
32 |
+
torch.manual_seed(hps.train.seed)
|
33 |
+
|
34 |
+
train_dataset = TextMelLoader(hps.data.training_files, hps.data)
|
35 |
+
collate_fn = TextMelCollate(1)
|
36 |
+
train_loader = DataLoader(
|
37 |
+
train_dataset,
|
38 |
+
num_workers=8,
|
39 |
+
shuffle=True,
|
40 |
+
batch_size=hps.train.batch_size,
|
41 |
+
pin_memory=True,
|
42 |
+
drop_last=True,
|
43 |
+
collate_fn=collate_fn,
|
44 |
+
)
|
45 |
+
symbols = hps.data.punc + hps.data.chars
|
46 |
+
generator = FlowGenerator_DDI(
|
47 |
+
len(symbols) + getattr(hps.data, "add_blank", False),
|
48 |
+
out_channels=hps.data.n_mel_channels,
|
49 |
+
**hps.model
|
50 |
+
).cuda()
|
51 |
+
optimizer_g = commons.Adam(
|
52 |
+
generator.parameters(),
|
53 |
+
scheduler=hps.train.scheduler,
|
54 |
+
dim_model=hps.model.hidden_channels,
|
55 |
+
warmup_steps=hps.train.warmup_steps,
|
56 |
+
lr=hps.train.learning_rate,
|
57 |
+
betas=hps.train.betas,
|
58 |
+
eps=hps.train.eps,
|
59 |
+
)
|
60 |
+
|
61 |
+
generator.train()
|
62 |
+
for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(train_loader):
|
63 |
+
x, x_lengths = x.cuda(), x_lengths.cuda()
|
64 |
+
y, y_lengths = y.cuda(), y_lengths.cuda()
|
65 |
+
|
66 |
+
_ = generator(x, x_lengths, y, y_lengths, gen=False)
|
67 |
+
break
|
68 |
+
|
69 |
+
utils.save_checkpoint(
|
70 |
+
generator,
|
71 |
+
optimizer_g,
|
72 |
+
hps.train.learning_rate,
|
73 |
+
0,
|
74 |
+
os.path.join(hps.model_dir, "ddi_G.pth"),
|
75 |
+
)
|
76 |
+
|
77 |
+
|
78 |
+
if __name__ == "__main__":
|
79 |
+
main()
|
ttsv/src/glow_tts/models.py
ADDED
@@ -0,0 +1,403 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
from torch.nn import functional as F
|
5 |
+
|
6 |
+
import modules
|
7 |
+
import commons
|
8 |
+
import attentions
|
9 |
+
import monotonic_align
|
10 |
+
|
11 |
+
|
12 |
+
class DurationPredictor(nn.Module):
|
13 |
+
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout):
|
14 |
+
super().__init__()
|
15 |
+
|
16 |
+
self.in_channels = in_channels
|
17 |
+
self.filter_channels = filter_channels
|
18 |
+
self.kernel_size = kernel_size
|
19 |
+
self.p_dropout = p_dropout
|
20 |
+
|
21 |
+
self.drop = nn.Dropout(p_dropout)
|
22 |
+
self.conv_1 = nn.Conv1d(
|
23 |
+
in_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
24 |
+
)
|
25 |
+
self.norm_1 = attentions.LayerNorm(filter_channels)
|
26 |
+
self.conv_2 = nn.Conv1d(
|
27 |
+
filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
|
28 |
+
)
|
29 |
+
self.norm_2 = attentions.LayerNorm(filter_channels)
|
30 |
+
self.proj = nn.Conv1d(filter_channels, 1, 1)
|
31 |
+
|
32 |
+
def forward(self, x, x_mask):
|
33 |
+
x = self.conv_1(x * x_mask)
|
34 |
+
x = torch.relu(x)
|
35 |
+
x = self.norm_1(x)
|
36 |
+
x = self.drop(x)
|
37 |
+
x = self.conv_2(x * x_mask)
|
38 |
+
x = torch.relu(x)
|
39 |
+
x = self.norm_2(x)
|
40 |
+
x = self.drop(x)
|
41 |
+
x = self.proj(x * x_mask)
|
42 |
+
return x * x_mask
|
43 |
+
|
44 |
+
|
45 |
+
class TextEncoder(nn.Module):
|
46 |
+
def __init__(
|
47 |
+
self,
|
48 |
+
n_vocab,
|
49 |
+
out_channels,
|
50 |
+
hidden_channels,
|
51 |
+
filter_channels,
|
52 |
+
filter_channels_dp,
|
53 |
+
n_heads,
|
54 |
+
n_layers,
|
55 |
+
kernel_size,
|
56 |
+
p_dropout,
|
57 |
+
window_size=None,
|
58 |
+
block_length=None,
|
59 |
+
mean_only=False,
|
60 |
+
prenet=False,
|
61 |
+
gin_channels=0,
|
62 |
+
):
|
63 |
+
|
64 |
+
super().__init__()
|
65 |
+
|
66 |
+
self.n_vocab = n_vocab
|
67 |
+
self.out_channels = out_channels
|
68 |
+
self.hidden_channels = hidden_channels
|
69 |
+
self.filter_channels = filter_channels
|
70 |
+
self.filter_channels_dp = filter_channels_dp
|
71 |
+
self.n_heads = n_heads
|
72 |
+
self.n_layers = n_layers
|
73 |
+
self.kernel_size = kernel_size
|
74 |
+
self.p_dropout = p_dropout
|
75 |
+
self.window_size = window_size
|
76 |
+
self.block_length = block_length
|
77 |
+
self.mean_only = mean_only
|
78 |
+
self.prenet = prenet
|
79 |
+
self.gin_channels = gin_channels
|
80 |
+
|
81 |
+
self.emb = nn.Embedding(n_vocab, hidden_channels)
|
82 |
+
nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
|
83 |
+
|
84 |
+
if prenet:
|
85 |
+
self.pre = modules.ConvReluNorm(
|
86 |
+
hidden_channels,
|
87 |
+
hidden_channels,
|
88 |
+
hidden_channels,
|
89 |
+
kernel_size=5,
|
90 |
+
n_layers=3,
|
91 |
+
p_dropout=0.5,
|
92 |
+
)
|
93 |
+
self.encoder = attentions.Encoder(
|
94 |
+
hidden_channels,
|
95 |
+
filter_channels,
|
96 |
+
n_heads,
|
97 |
+
n_layers,
|
98 |
+
kernel_size,
|
99 |
+
p_dropout,
|
100 |
+
window_size=window_size,
|
101 |
+
block_length=block_length,
|
102 |
+
)
|
103 |
+
|
104 |
+
self.proj_m = nn.Conv1d(hidden_channels, out_channels, 1)
|
105 |
+
if not mean_only:
|
106 |
+
self.proj_s = nn.Conv1d(hidden_channels, out_channels, 1)
|
107 |
+
self.proj_w = DurationPredictor(
|
108 |
+
hidden_channels + gin_channels, filter_channels_dp, kernel_size, p_dropout
|
109 |
+
)
|
110 |
+
|
111 |
+
def forward(self, x, x_lengths, g=None):
|
112 |
+
x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
|
113 |
+
x = torch.transpose(x, 1, -1) # [b, h, t]
|
114 |
+
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
|
115 |
+
x.dtype
|
116 |
+
)
|
117 |
+
|
118 |
+
if self.prenet:
|
119 |
+
x = self.pre(x, x_mask)
|
120 |
+
x = self.encoder(x, x_mask)
|
121 |
+
|
122 |
+
if g is not None:
|
123 |
+
g_exp = g.expand(-1, -1, x.size(-1))
|
124 |
+
x_dp = torch.cat([torch.detach(x), g_exp], 1)
|
125 |
+
else:
|
126 |
+
x_dp = torch.detach(x)
|
127 |
+
|
128 |
+
x_m = self.proj_m(x) * x_mask
|
129 |
+
if not self.mean_only:
|
130 |
+
x_logs = self.proj_s(x) * x_mask
|
131 |
+
else:
|
132 |
+
x_logs = torch.zeros_like(x_m)
|
133 |
+
|
134 |
+
logw = self.proj_w(x_dp, x_mask)
|
135 |
+
return x_m, x_logs, logw, x_mask
|
136 |
+
|
137 |
+
|
138 |
+
class FlowSpecDecoder(nn.Module):
|
139 |
+
def __init__(
|
140 |
+
self,
|
141 |
+
in_channels,
|
142 |
+
hidden_channels,
|
143 |
+
kernel_size,
|
144 |
+
dilation_rate,
|
145 |
+
n_blocks,
|
146 |
+
n_layers,
|
147 |
+
p_dropout=0.0,
|
148 |
+
n_split=4,
|
149 |
+
n_sqz=2,
|
150 |
+
sigmoid_scale=False,
|
151 |
+
gin_channels=0,
|
152 |
+
):
|
153 |
+
super().__init__()
|
154 |
+
|
155 |
+
self.in_channels = in_channels
|
156 |
+
self.hidden_channels = hidden_channels
|
157 |
+
self.kernel_size = kernel_size
|
158 |
+
self.dilation_rate = dilation_rate
|
159 |
+
self.n_blocks = n_blocks
|
160 |
+
self.n_layers = n_layers
|
161 |
+
self.p_dropout = p_dropout
|
162 |
+
self.n_split = n_split
|
163 |
+
self.n_sqz = n_sqz
|
164 |
+
self.sigmoid_scale = sigmoid_scale
|
165 |
+
self.gin_channels = gin_channels
|
166 |
+
|
167 |
+
self.flows = nn.ModuleList()
|
168 |
+
for b in range(n_blocks):
|
169 |
+
self.flows.append(modules.ActNorm(channels=in_channels * n_sqz))
|
170 |
+
self.flows.append(
|
171 |
+
modules.InvConvNear(channels=in_channels * n_sqz, n_split=n_split)
|
172 |
+
)
|
173 |
+
self.flows.append(
|
174 |
+
attentions.CouplingBlock(
|
175 |
+
in_channels * n_sqz,
|
176 |
+
hidden_channels,
|
177 |
+
kernel_size=kernel_size,
|
178 |
+
dilation_rate=dilation_rate,
|
179 |
+
n_layers=n_layers,
|
180 |
+
gin_channels=gin_channels,
|
181 |
+
p_dropout=p_dropout,
|
182 |
+
sigmoid_scale=sigmoid_scale,
|
183 |
+
)
|
184 |
+
)
|
185 |
+
|
186 |
+
def forward(self, x, x_mask, g=None, reverse=False):
|
187 |
+
if not reverse:
|
188 |
+
flows = self.flows
|
189 |
+
logdet_tot = 0
|
190 |
+
else:
|
191 |
+
flows = reversed(self.flows)
|
192 |
+
logdet_tot = None
|
193 |
+
|
194 |
+
if self.n_sqz > 1:
|
195 |
+
x, x_mask = commons.squeeze(x, x_mask, self.n_sqz)
|
196 |
+
for f in flows:
|
197 |
+
if not reverse:
|
198 |
+
x, logdet = f(x, x_mask, g=g, reverse=reverse)
|
199 |
+
logdet_tot += logdet
|
200 |
+
else:
|
201 |
+
x, logdet = f(x, x_mask, g=g, reverse=reverse)
|
202 |
+
if self.n_sqz > 1:
|
203 |
+
x, x_mask = commons.unsqueeze(x, x_mask, self.n_sqz)
|
204 |
+
return x, logdet_tot
|
205 |
+
|
206 |
+
def store_inverse(self):
|
207 |
+
for f in self.flows:
|
208 |
+
f.store_inverse()
|
209 |
+
|
210 |
+
|
211 |
+
class FlowGenerator(nn.Module):
|
212 |
+
def __init__(
|
213 |
+
self,
|
214 |
+
n_vocab,
|
215 |
+
hidden_channels,
|
216 |
+
filter_channels,
|
217 |
+
filter_channels_dp,
|
218 |
+
out_channels,
|
219 |
+
kernel_size=3,
|
220 |
+
n_heads=2,
|
221 |
+
n_layers_enc=6,
|
222 |
+
p_dropout=0.0,
|
223 |
+
n_blocks_dec=12,
|
224 |
+
kernel_size_dec=5,
|
225 |
+
dilation_rate=5,
|
226 |
+
n_block_layers=4,
|
227 |
+
p_dropout_dec=0.0,
|
228 |
+
n_speakers=0,
|
229 |
+
gin_channels=0,
|
230 |
+
n_split=4,
|
231 |
+
n_sqz=1,
|
232 |
+
sigmoid_scale=False,
|
233 |
+
window_size=None,
|
234 |
+
block_length=None,
|
235 |
+
mean_only=False,
|
236 |
+
hidden_channels_enc=None,
|
237 |
+
hidden_channels_dec=None,
|
238 |
+
prenet=False,
|
239 |
+
**kwargs
|
240 |
+
):
|
241 |
+
|
242 |
+
super().__init__()
|
243 |
+
self.n_vocab = n_vocab
|
244 |
+
self.hidden_channels = hidden_channels
|
245 |
+
self.filter_channels = filter_channels
|
246 |
+
self.filter_channels_dp = filter_channels_dp
|
247 |
+
self.out_channels = out_channels
|
248 |
+
self.kernel_size = kernel_size
|
249 |
+
self.n_heads = n_heads
|
250 |
+
self.n_layers_enc = n_layers_enc
|
251 |
+
self.p_dropout = p_dropout
|
252 |
+
self.n_blocks_dec = n_blocks_dec
|
253 |
+
self.kernel_size_dec = kernel_size_dec
|
254 |
+
self.dilation_rate = dilation_rate
|
255 |
+
self.n_block_layers = n_block_layers
|
256 |
+
self.p_dropout_dec = p_dropout_dec
|
257 |
+
self.n_speakers = n_speakers
|
258 |
+
self.gin_channels = gin_channels
|
259 |
+
self.n_split = n_split
|
260 |
+
self.n_sqz = n_sqz
|
261 |
+
self.sigmoid_scale = sigmoid_scale
|
262 |
+
self.window_size = window_size
|
263 |
+
self.block_length = block_length
|
264 |
+
self.mean_only = mean_only
|
265 |
+
self.hidden_channels_enc = hidden_channels_enc
|
266 |
+
self.hidden_channels_dec = hidden_channels_dec
|
267 |
+
self.prenet = prenet
|
268 |
+
|
269 |
+
self.encoder = TextEncoder(
|
270 |
+
n_vocab,
|
271 |
+
out_channels,
|
272 |
+
hidden_channels_enc or hidden_channels,
|
273 |
+
filter_channels,
|
274 |
+
filter_channels_dp,
|
275 |
+
n_heads,
|
276 |
+
n_layers_enc,
|
277 |
+
kernel_size,
|
278 |
+
p_dropout,
|
279 |
+
window_size=window_size,
|
280 |
+
block_length=block_length,
|
281 |
+
mean_only=mean_only,
|
282 |
+
prenet=prenet,
|
283 |
+
gin_channels=gin_channels,
|
284 |
+
)
|
285 |
+
|
286 |
+
self.decoder = FlowSpecDecoder(
|
287 |
+
out_channels,
|
288 |
+
hidden_channels_dec or hidden_channels,
|
289 |
+
kernel_size_dec,
|
290 |
+
dilation_rate,
|
291 |
+
n_blocks_dec,
|
292 |
+
n_block_layers,
|
293 |
+
p_dropout=p_dropout_dec,
|
294 |
+
n_split=n_split,
|
295 |
+
n_sqz=n_sqz,
|
296 |
+
sigmoid_scale=sigmoid_scale,
|
297 |
+
gin_channels=gin_channels,
|
298 |
+
)
|
299 |
+
|
300 |
+
if n_speakers > 1:
|
301 |
+
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
302 |
+
nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
|
303 |
+
|
304 |
+
def forward(
|
305 |
+
self,
|
306 |
+
x,
|
307 |
+
x_lengths,
|
308 |
+
y=None,
|
309 |
+
y_lengths=None,
|
310 |
+
g=None,
|
311 |
+
gen=False,
|
312 |
+
noise_scale=1.0,
|
313 |
+
length_scale=1.0,
|
314 |
+
):
|
315 |
+
if g is not None:
|
316 |
+
g = F.normalize(self.emb_g(g)).unsqueeze(-1) # [b, h]
|
317 |
+
x_m, x_logs, logw, x_mask = self.encoder(x, x_lengths, g=g)
|
318 |
+
|
319 |
+
if gen:
|
320 |
+
w = torch.exp(logw) * x_mask * length_scale
|
321 |
+
w_ceil = torch.ceil(w)
|
322 |
+
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
323 |
+
y_max_length = None
|
324 |
+
else:
|
325 |
+
y_max_length = y.size(2)
|
326 |
+
y, y_lengths, y_max_length = self.preprocess(y, y_lengths, y_max_length)
|
327 |
+
z_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, y_max_length), 1).to(
|
328 |
+
x_mask.dtype
|
329 |
+
)
|
330 |
+
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(z_mask, 2)
|
331 |
+
|
332 |
+
if gen:
|
333 |
+
attn = commons.generate_path(
|
334 |
+
w_ceil.squeeze(1), attn_mask.squeeze(1)
|
335 |
+
).unsqueeze(1)
|
336 |
+
z_m = torch.matmul(
|
337 |
+
attn.squeeze(1).transpose(1, 2), x_m.transpose(1, 2)
|
338 |
+
).transpose(
|
339 |
+
1, 2
|
340 |
+
) # [b, t', t], [b, t, d] -> [b, d, t']
|
341 |
+
z_logs = torch.matmul(
|
342 |
+
attn.squeeze(1).transpose(1, 2), x_logs.transpose(1, 2)
|
343 |
+
).transpose(
|
344 |
+
1, 2
|
345 |
+
) # [b, t', t], [b, t, d] -> [b, d, t']
|
346 |
+
logw_ = torch.log(1e-8 + torch.sum(attn, -1)) * x_mask
|
347 |
+
|
348 |
+
z = (z_m + torch.exp(z_logs) * torch.randn_like(z_m) * noise_scale) * z_mask
|
349 |
+
y, logdet = self.decoder(z, z_mask, g=g, reverse=True)
|
350 |
+
return (
|
351 |
+
(y, z_m, z_logs, logdet, z_mask),
|
352 |
+
(x_m, x_logs, x_mask),
|
353 |
+
(attn, logw, logw_),
|
354 |
+
)
|
355 |
+
else:
|
356 |
+
z, logdet = self.decoder(y, z_mask, g=g, reverse=False)
|
357 |
+
with torch.no_grad():
|
358 |
+
x_s_sq_r = torch.exp(-2 * x_logs)
|
359 |
+
logp1 = torch.sum(-0.5 * math.log(2 * math.pi) - x_logs, [1]).unsqueeze(
|
360 |
+
-1
|
361 |
+
) # [b, t, 1]
|
362 |
+
logp2 = torch.matmul(
|
363 |
+
x_s_sq_r.transpose(1, 2), -0.5 * (z ** 2)
|
364 |
+
) # [b, t, d] x [b, d, t'] = [b, t, t']
|
365 |
+
logp3 = torch.matmul(
|
366 |
+
(x_m * x_s_sq_r).transpose(1, 2), z
|
367 |
+
) # [b, t, d] x [b, d, t'] = [b, t, t']
|
368 |
+
logp4 = torch.sum(-0.5 * (x_m ** 2) * x_s_sq_r, [1]).unsqueeze(
|
369 |
+
-1
|
370 |
+
) # [b, t, 1]
|
371 |
+
logp = logp1 + logp2 + logp3 + logp4 # [b, t, t']
|
372 |
+
|
373 |
+
attn = (
|
374 |
+
monotonic_align.maximum_path(logp, attn_mask.squeeze(1))
|
375 |
+
.unsqueeze(1)
|
376 |
+
.detach()
|
377 |
+
)
|
378 |
+
z_m = torch.matmul(
|
379 |
+
attn.squeeze(1).transpose(1, 2), x_m.transpose(1, 2)
|
380 |
+
).transpose(
|
381 |
+
1, 2
|
382 |
+
) # [b, t', t], [b, t, d] -> [b, d, t']
|
383 |
+
z_logs = torch.matmul(
|
384 |
+
attn.squeeze(1).transpose(1, 2), x_logs.transpose(1, 2)
|
385 |
+
).transpose(
|
386 |
+
1, 2
|
387 |
+
) # [b, t', t], [b, t, d] -> [b, d, t']
|
388 |
+
logw_ = torch.log(1e-8 + torch.sum(attn, -1)) * x_mask
|
389 |
+
return (
|
390 |
+
(z, z_m, z_logs, logdet, z_mask),
|
391 |
+
(x_m, x_logs, x_mask),
|
392 |
+
(attn, logw, logw_),
|
393 |
+
)
|
394 |
+
|
395 |
+
def preprocess(self, y, y_lengths, y_max_length):
|
396 |
+
if y_max_length is not None:
|
397 |
+
y_max_length = (y_max_length // self.n_sqz) * self.n_sqz
|
398 |
+
y = y[:, :, :y_max_length]
|
399 |
+
y_lengths = (y_lengths // self.n_sqz) * self.n_sqz
|
400 |
+
return y, y_lengths, y_max_length
|
401 |
+
|
402 |
+
def store_inverse(self):
|
403 |
+
self.decoder.store_inverse()
|
ttsv/src/glow_tts/modules.py
ADDED
@@ -0,0 +1,276 @@
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import copy
|
2 |
+
import math
|
3 |
+
import numpy as np
|
4 |
+
import scipy
|
5 |
+
import torch
|
6 |
+
from torch import nn
|
7 |
+
from torch.nn import functional as F
|
8 |
+
|
9 |
+
import commons
|
10 |
+
|
11 |
+
|
12 |
+
class LayerNorm(nn.Module):
|
13 |
+
def __init__(self, channels, eps=1e-4):
|
14 |
+
super().__init__()
|
15 |
+
self.channels = channels
|
16 |
+
self.eps = eps
|
17 |
+
|
18 |
+
self.gamma = nn.Parameter(torch.ones(channels))
|
19 |
+
self.beta = nn.Parameter(torch.zeros(channels))
|
20 |
+
|
21 |
+
def forward(self, x):
|
22 |
+
n_dims = len(x.shape)
|
23 |
+
mean = torch.mean(x, 1, keepdim=True)
|
24 |
+
variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
|
25 |
+
|
26 |
+
x = (x - mean) * torch.rsqrt(variance + self.eps)
|
27 |
+
|
28 |
+
shape = [1, -1] + [1] * (n_dims - 2)
|
29 |
+
x = x * self.gamma.view(*shape) + self.beta.view(*shape)
|
30 |
+
return x
|
31 |
+
|
32 |
+
|
33 |
+
class ConvReluNorm(nn.Module):
|
34 |
+
def __init__(
|
35 |
+
self,
|
36 |
+
in_channels,
|
37 |
+
hidden_channels,
|
38 |
+
out_channels,
|
39 |
+
kernel_size,
|
40 |
+
n_layers,
|
41 |
+
p_dropout,
|
42 |
+
):
|
43 |
+
super().__init__()
|
44 |
+
self.in_channels = in_channels
|
45 |
+
self.hidden_channels = hidden_channels
|
46 |
+
self.out_channels = out_channels
|
47 |
+
self.kernel_size = kernel_size
|
48 |
+
self.n_layers = n_layers
|
49 |
+
self.p_dropout = p_dropout
|
50 |
+
assert n_layers > 1, "Number of layers should be larger than 0."
|
51 |
+
|
52 |
+
self.conv_layers = nn.ModuleList()
|
53 |
+
self.norm_layers = nn.ModuleList()
|
54 |
+
self.conv_layers.append(
|
55 |
+
nn.Conv1d(
|
56 |
+
in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
|
57 |
+
)
|
58 |
+
)
|
59 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
60 |
+
self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
|
61 |
+
for _ in range(n_layers - 1):
|
62 |
+
self.conv_layers.append(
|
63 |
+
nn.Conv1d(
|
64 |
+
hidden_channels,
|
65 |
+
hidden_channels,
|
66 |
+
kernel_size,
|
67 |
+
padding=kernel_size // 2,
|
68 |
+
)
|
69 |
+
)
|
70 |
+
self.norm_layers.append(LayerNorm(hidden_channels))
|
71 |
+
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
72 |
+
self.proj.weight.data.zero_()
|
73 |
+
self.proj.bias.data.zero_()
|
74 |
+
|
75 |
+
def forward(self, x, x_mask):
|
76 |
+
x_org = x
|
77 |
+
for i in range(self.n_layers):
|
78 |
+
x = self.conv_layers[i](x * x_mask)
|
79 |
+
x = self.norm_layers[i](x)
|
80 |
+
x = self.relu_drop(x)
|
81 |
+
x = x_org + self.proj(x)
|
82 |
+
return x * x_mask
|
83 |
+
|
84 |
+
|
85 |
+
class WN(torch.nn.Module):
|
86 |
+
def __init__(
|
87 |
+
self,
|
88 |
+
in_channels,
|
89 |
+
hidden_channels,
|
90 |
+
kernel_size,
|
91 |
+
dilation_rate,
|
92 |
+
n_layers,
|
93 |
+
gin_channels=0,
|
94 |
+
p_dropout=0,
|
95 |
+
):
|
96 |
+
super(WN, self).__init__()
|
97 |
+
assert kernel_size % 2 == 1
|
98 |
+
assert hidden_channels % 2 == 0
|
99 |
+
self.in_channels = in_channels
|
100 |
+
self.hidden_channels = hidden_channels
|
101 |
+
self.kernel_size = (kernel_size,)
|
102 |
+
self.dilation_rate = dilation_rate
|
103 |
+
self.n_layers = n_layers
|
104 |
+
self.gin_channels = gin_channels
|
105 |
+
self.p_dropout = p_dropout
|
106 |
+
|
107 |
+
self.in_layers = torch.nn.ModuleList()
|
108 |
+
self.res_skip_layers = torch.nn.ModuleList()
|
109 |
+
self.drop = nn.Dropout(p_dropout)
|
110 |
+
|
111 |
+
if gin_channels != 0:
|
112 |
+
cond_layer = torch.nn.Conv1d(
|
113 |
+
gin_channels, 2 * hidden_channels * n_layers, 1
|
114 |
+
)
|
115 |
+
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
|
116 |
+
|
117 |
+
for i in range(n_layers):
|
118 |
+
dilation = dilation_rate ** i
|
119 |
+
padding = int((kernel_size * dilation - dilation) / 2)
|
120 |
+
in_layer = torch.nn.Conv1d(
|
121 |
+
hidden_channels,
|
122 |
+
2 * hidden_channels,
|
123 |
+
kernel_size,
|
124 |
+
dilation=dilation,
|
125 |
+
padding=padding,
|
126 |
+
)
|
127 |
+
in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
|
128 |
+
self.in_layers.append(in_layer)
|
129 |
+
|
130 |
+
# last one is not necessary
|
131 |
+
if i < n_layers - 1:
|
132 |
+
res_skip_channels = 2 * hidden_channels
|
133 |
+
else:
|
134 |
+
res_skip_channels = hidden_channels
|
135 |
+
|
136 |
+
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
137 |
+
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
|
138 |
+
self.res_skip_layers.append(res_skip_layer)
|
139 |
+
|
140 |
+
def forward(self, x, x_mask=None, g=None, **kwargs):
|
141 |
+
output = torch.zeros_like(x)
|
142 |
+
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
143 |
+
|
144 |
+
if g is not None:
|
145 |
+
g = self.cond_layer(g)
|
146 |
+
|
147 |
+
for i in range(self.n_layers):
|
148 |
+
x_in = self.in_layers[i](x)
|
149 |
+
x_in = self.drop(x_in)
|
150 |
+
if g is not None:
|
151 |
+
cond_offset = i * 2 * self.hidden_channels
|
152 |
+
g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
|
153 |
+
else:
|
154 |
+
g_l = torch.zeros_like(x_in)
|
155 |
+
|
156 |
+
acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
|
157 |
+
|
158 |
+
res_skip_acts = self.res_skip_layers[i](acts)
|
159 |
+
if i < self.n_layers - 1:
|
160 |
+
x = (x + res_skip_acts[:, : self.hidden_channels, :]) * x_mask
|
161 |
+
output = output + res_skip_acts[:, self.hidden_channels :, :]
|
162 |
+
else:
|
163 |
+
output = output + res_skip_acts
|
164 |
+
return output * x_mask
|
165 |
+
|
166 |
+
def remove_weight_norm(self):
|
167 |
+
if self.gin_channels != 0:
|
168 |
+
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
169 |
+
for l in self.in_layers:
|
170 |
+
torch.nn.utils.remove_weight_norm(l)
|
171 |
+
for l in self.res_skip_layers:
|
172 |
+
torch.nn.utils.remove_weight_norm(l)
|
173 |
+
|
174 |
+
|
175 |
+
class ActNorm(nn.Module):
|
176 |
+
def __init__(self, channels, ddi=False, **kwargs):
|
177 |
+
super().__init__()
|
178 |
+
self.channels = channels
|
179 |
+
self.initialized = not ddi
|
180 |
+
|
181 |
+
self.logs = nn.Parameter(torch.zeros(1, channels, 1))
|
182 |
+
self.bias = nn.Parameter(torch.zeros(1, channels, 1))
|
183 |
+
|
184 |
+
def forward(self, x, x_mask=None, reverse=False, **kwargs):
|
185 |
+
if x_mask is None:
|
186 |
+
x_mask = torch.ones(x.size(0), 1, x.size(2)).to(
|
187 |
+
device=x.device, dtype=x.dtype
|
188 |
+
)
|
189 |
+
x_len = torch.sum(x_mask, [1, 2])
|
190 |
+
if not self.initialized:
|
191 |
+
self.initialize(x, x_mask)
|
192 |
+
self.initialized = True
|
193 |
+
|
194 |
+
if reverse:
|
195 |
+
z = (x - self.bias) * torch.exp(-self.logs) * x_mask
|
196 |
+
logdet = None
|
197 |
+
else:
|
198 |
+
z = (self.bias + torch.exp(self.logs) * x) * x_mask
|
199 |
+
logdet = torch.sum(self.logs) * x_len # [b]
|
200 |
+
|
201 |
+
return z, logdet
|
202 |
+
|
203 |
+
def store_inverse(self):
|
204 |
+
pass
|
205 |
+
|
206 |
+
def set_ddi(self, ddi):
|
207 |
+
self.initialized = not ddi
|
208 |
+
|
209 |
+
def initialize(self, x, x_mask):
|
210 |
+
with torch.no_grad():
|
211 |
+
denom = torch.sum(x_mask, [0, 2])
|
212 |
+
m = torch.sum(x * x_mask, [0, 2]) / denom
|
213 |
+
m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom
|
214 |
+
v = m_sq - (m ** 2)
|
215 |
+
logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6))
|
216 |
+
|
217 |
+
bias_init = (
|
218 |
+
(-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype)
|
219 |
+
)
|
220 |
+
logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype)
|
221 |
+
|
222 |
+
self.bias.data.copy_(bias_init)
|
223 |
+
self.logs.data.copy_(logs_init)
|
224 |
+
|
225 |
+
|
226 |
+
class InvConvNear(nn.Module):
|
227 |
+
def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs):
|
228 |
+
super().__init__()
|
229 |
+
assert n_split % 2 == 0
|
230 |
+
self.channels = channels
|
231 |
+
self.n_split = n_split
|
232 |
+
self.no_jacobian = no_jacobian
|
233 |
+
|
234 |
+
w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0]
|
235 |
+
if torch.det(w_init) < 0:
|
236 |
+
w_init[:, 0] = -1 * w_init[:, 0]
|
237 |
+
self.weight = nn.Parameter(w_init)
|
238 |
+
|
239 |
+
def forward(self, x, x_mask=None, reverse=False, **kwargs):
|
240 |
+
b, c, t = x.size()
|
241 |
+
assert c % self.n_split == 0
|
242 |
+
if x_mask is None:
|
243 |
+
x_mask = 1
|
244 |
+
x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t
|
245 |
+
else:
|
246 |
+
x_len = torch.sum(x_mask, [1, 2])
|
247 |
+
|
248 |
+
x = x.view(b, 2, c // self.n_split, self.n_split // 2, t)
|
249 |
+
x = (
|
250 |
+
x.permute(0, 1, 3, 2, 4)
|
251 |
+
.contiguous()
|
252 |
+
.view(b, self.n_split, c // self.n_split, t)
|
253 |
+
)
|
254 |
+
|
255 |
+
if reverse:
|
256 |
+
if hasattr(self, "weight_inv"):
|
257 |
+
weight = self.weight_inv
|
258 |
+
else:
|
259 |
+
weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
|
260 |
+
logdet = None
|
261 |
+
else:
|
262 |
+
weight = self.weight
|
263 |
+
if self.no_jacobian:
|
264 |
+
logdet = 0
|
265 |
+
else:
|
266 |
+
logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b]
|
267 |
+
|
268 |
+
weight = weight.view(self.n_split, self.n_split, 1, 1)
|
269 |
+
z = F.conv2d(x, weight)
|
270 |
+
|
271 |
+
z = z.view(b, 2, self.n_split // 2, c // self.n_split, t)
|
272 |
+
z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask
|
273 |
+
return z, logdet
|
274 |
+
|
275 |
+
def store_inverse(self):
|
276 |
+
self.weight_inv = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
|
ttsv/src/glow_tts/monotonic_align/monotonic_align/__init__.py
ADDED
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import pkg_resources
|
2 |
+
|
3 |
+
__version__ = pkg_resources.get_distribution("monotonic_align").version
|
4 |
+
|
5 |
+
from monotonic_align.mas import *
|
ttsv/src/glow_tts/monotonic_align/monotonic_align/core.pyx
ADDED
@@ -0,0 +1,45 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import numpy as np
|
2 |
+
cimport numpy as np
|
3 |
+
cimport cython
|
4 |
+
from cython.parallel import prange
|
5 |
+
|
6 |
+
|
7 |
+
@cython.boundscheck(False)
|
8 |
+
@cython.wraparound(False)
|
9 |
+
cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_x, int t_y, float max_neg_val) nogil:
|
10 |
+
cdef int x
|
11 |
+
cdef int y
|
12 |
+
cdef float v_prev
|
13 |
+
cdef float v_cur
|
14 |
+
cdef float tmp
|
15 |
+
cdef int index = t_x - 1
|
16 |
+
|
17 |
+
for y in range(t_y):
|
18 |
+
for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
|
19 |
+
if x == y:
|
20 |
+
v_cur = max_neg_val
|
21 |
+
else:
|
22 |
+
v_cur = value[x, y-1]
|
23 |
+
if x == 0:
|
24 |
+
if y == 0:
|
25 |
+
v_prev = 0.
|
26 |
+
else:
|
27 |
+
v_prev = max_neg_val
|
28 |
+
else:
|
29 |
+
v_prev = value[x-1, y-1]
|
30 |
+
value[x, y] = max(v_cur, v_prev) + value[x, y]
|
31 |
+
|
32 |
+
for y in range(t_y - 1, -1, -1):
|
33 |
+
path[index, y] = 1
|
34 |
+
if index != 0 and (index == y or value[index, y-1] < value[index-1, y-1]):
|
35 |
+
index = index - 1
|
36 |
+
|
37 |
+
|
38 |
+
@cython.boundscheck(False)
|
39 |
+
@cython.wraparound(False)
|
40 |
+
cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_xs, int[::1] t_ys, float max_neg_val=-1e9) nogil:
|
41 |
+
cdef int b = values.shape[0]
|
42 |
+
|
43 |
+
cdef int i
|
44 |
+
for i in prange(b, nogil=True):
|
45 |
+
maximum_path_each(paths[i], values[i], t_xs[i], t_ys[i], max_neg_val)
|
ttsv/src/glow_tts/monotonic_align/monotonic_align/mas.py
ADDED
@@ -0,0 +1,57 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from typing import overload
|
2 |
+
import numpy as np
|
3 |
+
import torch
|
4 |
+
from monotonic_align.core import maximum_path_c
|
5 |
+
|
6 |
+
|
7 |
+
def mask_from_len(lens: torch.Tensor, max_len=None):
|
8 |
+
"""
|
9 |
+
Make a `mask` from lens.
|
10 |
+
|
11 |
+
:param inputs: (B, T, D)
|
12 |
+
:param lens: (B)
|
13 |
+
|
14 |
+
:return:
|
15 |
+
`mask`: (B, T)
|
16 |
+
"""
|
17 |
+
if max_len is None:
|
18 |
+
max_len = lens.max()
|
19 |
+
index = torch.arange(max_len).to(lens).view(1, -1)
|
20 |
+
return index < lens.unsqueeze(1) # (B, T)
|
21 |
+
|
22 |
+
|
23 |
+
def mask_from_lens(
|
24 |
+
similarity: torch.Tensor,
|
25 |
+
symbol_lens: torch.Tensor,
|
26 |
+
mel_lens: torch.Tensor,
|
27 |
+
):
|
28 |
+
"""
|
29 |
+
:param similarity: (B, S, T)
|
30 |
+
:param symbol_lens: (B,)
|
31 |
+
:param mel_lens: (B,)
|
32 |
+
"""
|
33 |
+
_, S, T = similarity.size()
|
34 |
+
mask_S = mask_from_len(symbol_lens, S)
|
35 |
+
mask_T = mask_from_len(mel_lens, T)
|
36 |
+
mask_ST = mask_S.unsqueeze(2) * mask_T.unsqueeze(1)
|
37 |
+
return mask_ST.to(similarity)
|
38 |
+
|
39 |
+
|
40 |
+
def maximum_path(value, mask=None):
|
41 |
+
"""Cython optimised version.
|
42 |
+
value: [b, t_x, t_y]
|
43 |
+
mask: [b, t_x, t_y]
|
44 |
+
"""
|
45 |
+
if mask is None:
|
46 |
+
mask = torch.zeros_like(value)
|
47 |
+
|
48 |
+
value = value * mask
|
49 |
+
device = value.device
|
50 |
+
dtype = value.dtype
|
51 |
+
value = value.data.cpu().numpy().astype(np.float32)
|
52 |
+
path = np.zeros_like(value).astype(np.int32)
|
53 |
+
mask = mask.data.cpu().numpy()
|
54 |
+
t_x_max = mask.sum(1)[:, 0].astype(np.int32)
|
55 |
+
t_y_max = mask.sum(2)[:, 0].astype(np.int32)
|
56 |
+
maximum_path_c(path, value, t_x_max, t_y_max)
|
57 |
+
return torch.from_numpy(path).to(device=device, dtype=dtype)
|
ttsv/src/glow_tts/monotonic_align/pyproject.toml
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[build-system]
|
2 |
+
requires = [
|
3 |
+
"wheel",
|
4 |
+
"setuptools",
|
5 |
+
"cython>=0.24.0",
|
6 |
+
"numpy<v1.20.0",
|
7 |
+
]
|
ttsv/src/glow_tts/monotonic_align/setup.py
ADDED
@@ -0,0 +1,23 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import numpy
|
2 |
+
from setuptools import Extension, find_packages
|
3 |
+
from distutils.core import setup
|
4 |
+
from Cython.Build import cythonize
|
5 |
+
|
6 |
+
|
7 |
+
_VERSION = "1.1"
|
8 |
+
|
9 |
+
|
10 |
+
ext_modules = cythonize(
|
11 |
+
"monotonic_align/core.pyx",
|
12 |
+
compiler_directives={"language_level": "3"},
|
13 |
+
)
|
14 |
+
|
15 |
+
setup(
|
16 |
+
name="monotonic_align",
|
17 |
+
ext_modules=ext_modules,
|
18 |
+
include_dirs=[numpy.get_include(), "monotonic_align"],
|
19 |
+
packages=find_packages(),
|
20 |
+
setup_requires=["numpy", "cython"],
|
21 |
+
install_requires=["numpy"],
|
22 |
+
version=_VERSION,
|
23 |
+
)
|