mirror of
https://github.com/csukuangfj/kaldifeat.git
synced 2025-08-27 10:44:17 +00:00
Compare commits
No commits in common. "master" and "v1.2" have entirely different histories.
2
.flake8
2
.flake8
@ -3,10 +3,8 @@ max-line-length = 80
|
|||||||
|
|
||||||
exclude =
|
exclude =
|
||||||
.git,
|
.git,
|
||||||
doc,
|
|
||||||
build,
|
build,
|
||||||
build_release,
|
build_release,
|
||||||
cmake/cmake_extension.py,
|
|
||||||
kaldifeat/python/kaldifeat/__init__.py
|
kaldifeat/python/kaldifeat/__init__.py
|
||||||
|
|
||||||
ignore =
|
ignore =
|
||||||
|
|||||||
81
.github/workflows/build-doc.yml
vendored
81
.github/workflows/build-doc.yml
vendored
@ -1,81 +0,0 @@
|
|||||||
# Copyright 2022 Xiaomi Corp. (author: Fangjun Kuang)
|
|
||||||
|
|
||||||
# See ../../LICENSE for clarification regarding multiple authors
|
|
||||||
#
|
|
||||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
||||||
# you may not use this file except in compliance with the License.
|
|
||||||
# You may obtain a copy of the License at
|
|
||||||
#
|
|
||||||
# http://www.apache.org/licenses/LICENSE-2.0
|
|
||||||
#
|
|
||||||
# Unless required by applicable law or agreed to in writing, software
|
|
||||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
||||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
||||||
# See the License for the specific language governing permissions and
|
|
||||||
# limitations under the License.
|
|
||||||
|
|
||||||
# refer to https://github.com/actions/starter-workflows/pull/47/files
|
|
||||||
|
|
||||||
# You can access it at https://csukuangfj.github.io/kaldifeat
|
|
||||||
name: Generate doc
|
|
||||||
on:
|
|
||||||
push:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
- doc
|
|
||||||
workflow_dispatch:
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
build-doc:
|
|
||||||
runs-on: ${{ matrix.os }}
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
os: [ubuntu-latest]
|
|
||||||
python-version: [3.8]
|
|
||||||
steps:
|
|
||||||
# refer to https://github.com/actions/checkout
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
- name: Setup Python ${{ matrix.python-version }}
|
|
||||||
uses: actions/setup-python@v2
|
|
||||||
with:
|
|
||||||
python-version: ${{ matrix.python-version }}
|
|
||||||
|
|
||||||
- name: Display Python version
|
|
||||||
run: python -c "import sys; print(sys.version)"
|
|
||||||
|
|
||||||
- name: Update wheels
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
export KALDIFEAT_DIR=$PWD
|
|
||||||
ls -lh $KALDIFEAT_DIR
|
|
||||||
|
|
||||||
export GIT_LFS_SKIP_SMUDGE=1
|
|
||||||
export GIT_CLONE_PROTECTION_ACTIVE=false
|
|
||||||
git clone https://huggingface.co/csukuangfj/kaldifeat huggingface
|
|
||||||
cd huggingface
|
|
||||||
|
|
||||||
./run.sh
|
|
||||||
|
|
||||||
- name: Build doc
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
cd doc
|
|
||||||
git status
|
|
||||||
python3 -m pip install -r ./requirements.txt
|
|
||||||
make html
|
|
||||||
cp source/cpu.html build/html/
|
|
||||||
cp source/cuda.html build/html/
|
|
||||||
cp source/cpu-cn.html build/html/
|
|
||||||
cp source/cuda-cn.html build/html/
|
|
||||||
touch build/html/.nojekyll
|
|
||||||
|
|
||||||
- name: Deploy
|
|
||||||
uses: peaceiris/actions-gh-pages@v3
|
|
||||||
with:
|
|
||||||
github_token: ${{ secrets.GITHUB_TOKEN }}
|
|
||||||
publish_dir: ./doc/build/html
|
|
||||||
publish_branch: gh-pages
|
|
||||||
121
.github/workflows/macos-cpu-wheels.yml
vendored
121
.github/workflows/macos-cpu-wheels.yml
vendored
@ -1,121 +0,0 @@
|
|||||||
name: build-wheels-cpu-macos
|
|
||||||
|
|
||||||
on:
|
|
||||||
push:
|
|
||||||
branches:
|
|
||||||
# - wheel
|
|
||||||
- torch-2.8.0
|
|
||||||
tags:
|
|
||||||
- '*'
|
|
||||||
workflow_dispatch:
|
|
||||||
|
|
||||||
concurrency:
|
|
||||||
group: build-wheels-cpu-macos-${{ github.ref }}
|
|
||||||
cancel-in-progress: true
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
generate_build_matrix:
|
|
||||||
# see https://github.com/pytorch/pytorch/pull/50633
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
outputs:
|
|
||||||
matrix: ${{ steps.set-matrix.outputs.matrix }}
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
- name: Generating build matrix
|
|
||||||
id: set-matrix
|
|
||||||
run: |
|
|
||||||
# outputting for debugging purposes
|
|
||||||
# python ./scripts/github_actions/generate_build_matrix.py --for-macos
|
|
||||||
# MATRIX=$(python ./scripts/github_actions/generate_build_matrix.py --for-macos)
|
|
||||||
|
|
||||||
python ./scripts/github_actions/generate_build_matrix.py --for-macos --test-only-latest-torch
|
|
||||||
MATRIX=$(python ./scripts/github_actions/generate_build_matrix.py --for-macos --test-only-latest-torch)
|
|
||||||
echo "::set-output name=matrix::${MATRIX}"
|
|
||||||
|
|
||||||
build_wheels_macos_cpu:
|
|
||||||
needs: generate_build_matrix
|
|
||||||
name: ${{ matrix.torch }} ${{ matrix.python-version }}
|
|
||||||
runs-on: macos-14
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
|
|
||||||
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
- name: Setup Python
|
|
||||||
uses: actions/setup-python@v5
|
|
||||||
with:
|
|
||||||
python-version: ${{ matrix.python-version }}
|
|
||||||
|
|
||||||
- name: Install dependencies
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
pip install -q torch==${{ matrix.torch}} cmake numpy wheel>=0.40.0 twine setuptools
|
|
||||||
|
|
||||||
- name: Build wheel
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
python3 setup.py bdist_wheel
|
|
||||||
mkdir wheelhouse
|
|
||||||
cp -v dist/* wheelhouse
|
|
||||||
|
|
||||||
- name: Display wheels (before fix)
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
ls -lh ./wheelhouse/
|
|
||||||
|
|
||||||
- name: Fix wheel platform tag
|
|
||||||
run: |
|
|
||||||
# See https://github.com/glencoesoftware/zeroc-ice-py-macos-x86_64/pull/3/files
|
|
||||||
# See:
|
|
||||||
# * https://github.com/pypa/wheel/issues/406
|
|
||||||
python -m wheel tags \
|
|
||||||
--platform-tag=macosx_11_0_arm64 \
|
|
||||||
--remove wheelhouse/*.whl
|
|
||||||
|
|
||||||
- name: Display wheels (after fix)
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
ls -lh ./wheelhouse/
|
|
||||||
|
|
||||||
- name: Upload Wheel
|
|
||||||
uses: actions/upload-artifact@v4
|
|
||||||
with:
|
|
||||||
name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-macos-latest-cpu
|
|
||||||
path: wheelhouse/*.whl
|
|
||||||
|
|
||||||
# https://huggingface.co/docs/hub/spaces-github-actions
|
|
||||||
- name: Publish to huggingface
|
|
||||||
if: github.repository_owner == 'csukuangfj'
|
|
||||||
env:
|
|
||||||
HF_TOKEN: ${{ secrets.HF_TOKEN }}
|
|
||||||
uses: nick-fields/retry@v2
|
|
||||||
with:
|
|
||||||
max_attempts: 20
|
|
||||||
timeout_seconds: 200
|
|
||||||
shell: bash
|
|
||||||
command: |
|
|
||||||
git config --global user.email "csukuangfj@gmail.com"
|
|
||||||
git config --global user.name "Fangjun Kuang"
|
|
||||||
|
|
||||||
rm -rf huggingface
|
|
||||||
export GIT_LFS_SKIP_SMUDGE=1
|
|
||||||
|
|
||||||
git clone https://huggingface.co/csukuangfj/kaldifeat huggingface
|
|
||||||
cd huggingface
|
|
||||||
git pull
|
|
||||||
|
|
||||||
d=cpu/1.25.5.dev20241029/macos
|
|
||||||
mkdir -p $d
|
|
||||||
cp -v ../wheelhouse/*.whl ./$d
|
|
||||||
git status
|
|
||||||
git lfs track "*.whl"
|
|
||||||
git add .
|
|
||||||
git commit -m "upload macos wheel for torch ${{ matrix.torch }} python ${{ matrix.python-version }}"
|
|
||||||
git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/kaldifeat main
|
|
||||||
75
.github/workflows/publish_to_pypi.yml
vendored
75
.github/workflows/publish_to_pypi.yml
vendored
@ -20,37 +20,88 @@ on:
|
|||||||
push:
|
push:
|
||||||
tags:
|
tags:
|
||||||
- '*'
|
- '*'
|
||||||
workflow_dispatch:
|
|
||||||
|
|
||||||
jobs:
|
jobs:
|
||||||
pypi:
|
pypi:
|
||||||
runs-on: ubuntu-latest
|
runs-on: ${{ matrix.os }}
|
||||||
|
strategy:
|
||||||
|
fail-fast: false
|
||||||
|
matrix:
|
||||||
|
os: [ubuntu-18.04]
|
||||||
|
cuda: ["10.1"]
|
||||||
|
gcc: ["5"]
|
||||||
|
torch: ["1.8.1"]
|
||||||
|
python-version: [3.6, 3.7, 3.8]
|
||||||
steps:
|
steps:
|
||||||
- uses: actions/checkout@v4
|
- uses: actions/checkout@v2
|
||||||
with:
|
with:
|
||||||
fetch-depth: 0
|
fetch-depth: 0
|
||||||
|
|
||||||
- name: Setup Python
|
- name: Setup Python
|
||||||
uses: actions/setup-python@v2
|
uses: actions/setup-python@v2
|
||||||
with:
|
with:
|
||||||
python-version: 3.8
|
python-version: ${{ matrix.python-version }}
|
||||||
|
|
||||||
- name: Install Python dependencies
|
- name: Install CUDA Toolkit ${{ matrix.cuda }}
|
||||||
|
shell: bash
|
||||||
|
env:
|
||||||
|
cuda: ${{ matrix.cuda }}
|
||||||
|
run: |
|
||||||
|
source ./scripts/github_actions/install_cuda.sh
|
||||||
|
echo "CUDA_HOME=${CUDA_HOME}" >> $GITHUB_ENV
|
||||||
|
echo "${CUDA_HOME}/bin" >> $GITHUB_PATH
|
||||||
|
echo "LD_LIBRARY_PATH=${CUDA_HOME}/lib:${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}" >> $GITHUB_ENV
|
||||||
|
|
||||||
|
- name: Display NVCC version
|
||||||
|
run: |
|
||||||
|
which nvcc
|
||||||
|
nvcc --version
|
||||||
|
|
||||||
|
- name: Install GCC ${{ matrix.gcc }}
|
||||||
|
run: |
|
||||||
|
sudo apt-get install -y gcc-${{ matrix.gcc }} g++-${{ matrix.gcc }}
|
||||||
|
echo "CC=/usr/bin/gcc-${{ matrix.gcc }}" >> $GITHUB_ENV
|
||||||
|
echo "CXX=/usr/bin/g++-${{ matrix.gcc }}" >> $GITHUB_ENV
|
||||||
|
echo "CUDAHOSTCXX=/usr/bin/g++-${{ matrix.gcc }}" >> $GITHUB_ENV
|
||||||
|
|
||||||
|
- name: Install PyTorch ${{ matrix.torch }}
|
||||||
|
env:
|
||||||
|
cuda: ${{ matrix.cuda }}
|
||||||
|
torch: ${{ matrix.torch }}
|
||||||
shell: bash
|
shell: bash
|
||||||
run: |
|
run: |
|
||||||
python3 -m pip install --upgrade pip
|
python3 -m pip install --upgrade pip
|
||||||
python3 -m pip install wheel twine setuptools
|
python3 -m pip install wheel twine typing_extensions
|
||||||
python3 -m pip install torch==1.10.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
|
python3 -m pip install bs4 requests tqdm
|
||||||
|
|
||||||
- name: Build
|
./scripts/github_actions/install_torch.sh
|
||||||
shell: bash
|
python3 -c "import torch; print('torch version:', torch.__version__)"
|
||||||
|
|
||||||
|
- name: Download cudnn 8.0
|
||||||
|
env:
|
||||||
|
cuda: ${{ matrix.cuda }}
|
||||||
run: |
|
run: |
|
||||||
python3 setup.py sdist
|
./scripts/github_actions/install_cudnn.sh
|
||||||
ls -l dist/*
|
|
||||||
|
- name: Build pip packages
|
||||||
|
shell: bash
|
||||||
|
env:
|
||||||
|
KALDIFEAT_IS_FOR_PYPI: 1
|
||||||
|
run: |
|
||||||
|
tag=$(python3 -c "import sys; print(''.join(sys.version[:3].split('.')))")
|
||||||
|
export KALDIFEAT_MAKE_ARGS="-j2"
|
||||||
|
python3 setup.py bdist_wheel --python-tag=py${tag}
|
||||||
|
ls -lh dist/
|
||||||
|
|
||||||
- name: Publish wheels to PyPI
|
- name: Publish wheels to PyPI
|
||||||
env:
|
env:
|
||||||
TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
|
TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
|
||||||
TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
|
TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
|
||||||
run: |
|
run: |
|
||||||
twine upload dist/kaldifeat-*.tar.gz
|
twine upload dist/kaldifeat-*.whl
|
||||||
|
|
||||||
|
- name: Upload Wheel
|
||||||
|
uses: actions/upload-artifact@v2
|
||||||
|
with:
|
||||||
|
name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-${{ matrix.os }}
|
||||||
|
path: dist/*.whl
|
||||||
|
|||||||
85
.github/workflows/run-tests-macos-cpu.yml
vendored
85
.github/workflows/run-tests-macos-cpu.yml
vendored
@ -1,85 +0,0 @@
|
|||||||
# Copyright 2021 Xiaomi Corp. (author: Fangjun Kuang)
|
|
||||||
|
|
||||||
# See ../../LICENSE for clarification regarding multiple authors
|
|
||||||
#
|
|
||||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
||||||
# you may not use this file except in compliance with the License.
|
|
||||||
# You may obtain a copy of the License at
|
|
||||||
#
|
|
||||||
# http://www.apache.org/licenses/LICENSE-2.0
|
|
||||||
#
|
|
||||||
# Unless required by applicable law or agreed to in writing, software
|
|
||||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
||||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
||||||
# See the License for the specific language governing permissions and
|
|
||||||
# limitations under the License.
|
|
||||||
#
|
|
||||||
name: Run tests macos cpu
|
|
||||||
|
|
||||||
on:
|
|
||||||
push:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
|
|
||||||
pull_request:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
generate_build_matrix:
|
|
||||||
# see https://github.com/pytorch/pytorch/pull/50633
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
outputs:
|
|
||||||
matrix: ${{ steps.set-matrix.outputs.matrix }}
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
- name: Generating build matrix
|
|
||||||
id: set-matrix
|
|
||||||
run: |
|
|
||||||
# outputting for debugging purposes
|
|
||||||
python scripts/github_actions/generate_build_matrix.py --test-only-latest-torch
|
|
||||||
MATRIX=$(python scripts/github_actions/generate_build_matrix.py --test-only-latest-torch)
|
|
||||||
echo "::set-output name=matrix::${MATRIX}"
|
|
||||||
|
|
||||||
run_tests_macos_cpu:
|
|
||||||
needs: generate_build_matrix
|
|
||||||
runs-on: macos-latest
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
|
|
||||||
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
- name: Setup Python
|
|
||||||
uses: actions/setup-python@v2
|
|
||||||
with:
|
|
||||||
python-version: ${{ matrix.python-version }}
|
|
||||||
|
|
||||||
- name: Install PyTorch ${{ matrix.torch }}
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
python3 -m pip install -qq --upgrade pip
|
|
||||||
python3 -m pip install -qq wheel twine typing_extensions soundfile numpy
|
|
||||||
python3 -m pip install -qq torch==${{ matrix.torch }} -f https://download.pytorch.org/whl/torch_stable.html || python3 -m pip install -qq torch==${{ matrix.torch }} -f https://download.pytorch.org/whl/torch/
|
|
||||||
|
|
||||||
python3 -c "import torch; print('torch version:', torch.__version__)"
|
|
||||||
|
|
||||||
- name: Build
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
mkdir build_release
|
|
||||||
cd build_release
|
|
||||||
cmake -DCMAKE_CXX_STANDARD=17 ..
|
|
||||||
make VERBOSE=1 -j3
|
|
||||||
|
|
||||||
- name: Run tests
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
cd build_release
|
|
||||||
ctest --output-on-failure
|
|
||||||
88
.github/workflows/run-tests-ubuntu-cpu.yml
vendored
88
.github/workflows/run-tests-ubuntu-cpu.yml
vendored
@ -1,88 +0,0 @@
|
|||||||
# Copyright 2021 Xiaomi Corp. (author: Fangjun Kuang)
|
|
||||||
|
|
||||||
# See ../../LICENSE for clarification regarding multiple authors
|
|
||||||
#
|
|
||||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
||||||
# you may not use this file except in compliance with the License.
|
|
||||||
# You may obtain a copy of the License at
|
|
||||||
#
|
|
||||||
# http://www.apache.org/licenses/LICENSE-2.0
|
|
||||||
#
|
|
||||||
# Unless required by applicable law or agreed to in writing, software
|
|
||||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
||||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
||||||
# See the License for the specific language governing permissions and
|
|
||||||
# limitations under the License.
|
|
||||||
#
|
|
||||||
name: Run tests ubuntu cpu
|
|
||||||
|
|
||||||
on:
|
|
||||||
push:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
|
|
||||||
pull_request:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
generate_build_matrix:
|
|
||||||
# see https://github.com/pytorch/pytorch/pull/50633
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
outputs:
|
|
||||||
matrix: ${{ steps.set-matrix.outputs.matrix }}
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
- name: Generating build matrix
|
|
||||||
id: set-matrix
|
|
||||||
run: |
|
|
||||||
# outputting for debugging purposes
|
|
||||||
python scripts/github_actions/generate_build_matrix.py --test-only-latest-torch
|
|
||||||
MATRIX=$(python scripts/github_actions/generate_build_matrix.py --test-only-latest-torch)
|
|
||||||
echo "::set-output name=matrix::${MATRIX}"
|
|
||||||
|
|
||||||
run_tests_ubuntu_cpu:
|
|
||||||
needs: generate_build_matrix
|
|
||||||
runs-on: ubuntu-18.04
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
|
|
||||||
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
- name: Setup Python
|
|
||||||
uses: actions/setup-python@v2
|
|
||||||
with:
|
|
||||||
python-version: ${{ matrix.python-version }}
|
|
||||||
|
|
||||||
- name: Install PyTorch ${{ matrix.torch }}
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
sudo apt-get update
|
|
||||||
sudo apt-get install -y libsndfile1-dev libsndfile1 ffmpeg
|
|
||||||
python3 -m pip install --upgrade pip
|
|
||||||
python3 -m pip install wheel twine typing_extensions soundfile
|
|
||||||
python3 -m pip install bs4 requests tqdm numpy
|
|
||||||
python3 -m pip install -qq torch==${{ matrix.torch }}+cpu -f https://download.pytorch.org/whl/torch_stable.html || python3 -m pip install -qq torch==${{ matrix.torch }}+cpu -f https://download.pytorch.org/whl/torch/
|
|
||||||
|
|
||||||
python3 -c "import torch; print('torch version:', torch.__version__)"
|
|
||||||
|
|
||||||
- name: Build
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
mkdir build_release
|
|
||||||
cd build_release
|
|
||||||
cmake -DCMAKE_CXX_STANDARD=17 ..
|
|
||||||
make VERBOSE=1 -j3
|
|
||||||
|
|
||||||
- name: Run tests
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
cd build_release
|
|
||||||
ctest --output-on-failure
|
|
||||||
112
.github/workflows/run-tests-ubuntu-cuda.yml
vendored
112
.github/workflows/run-tests-ubuntu-cuda.yml
vendored
@ -1,112 +0,0 @@
|
|||||||
# Copyright 2021 Xiaomi Corp. (author: Fangjun Kuang)
|
|
||||||
|
|
||||||
# See ../../LICENSE for clarification regarding multiple authors
|
|
||||||
#
|
|
||||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
||||||
# you may not use this file except in compliance with the License.
|
|
||||||
# You may obtain a copy of the License at
|
|
||||||
#
|
|
||||||
# http://www.apache.org/licenses/LICENSE-2.0
|
|
||||||
#
|
|
||||||
# Unless required by applicable law or agreed to in writing, software
|
|
||||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
||||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
||||||
# See the License for the specific language governing permissions and
|
|
||||||
# limitations under the License.
|
|
||||||
#
|
|
||||||
name: Run tests ubuntu cuda
|
|
||||||
|
|
||||||
on:
|
|
||||||
push:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
|
|
||||||
pull_request:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
generate_build_matrix:
|
|
||||||
# see https://github.com/pytorch/pytorch/pull/50633
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
outputs:
|
|
||||||
matrix: ${{ steps.set-matrix.outputs.matrix }}
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
- name: Generating build matrix
|
|
||||||
id: set-matrix
|
|
||||||
run: |
|
|
||||||
# outputting for debugging purposes
|
|
||||||
python scripts/github_actions/generate_build_matrix.py --enable-cuda --test-only-latest-torch
|
|
||||||
MATRIX=$(python scripts/github_actions/generate_build_matrix.py --enable-cuda --test-only-latest-torch)
|
|
||||||
echo "::set-output name=matrix::${MATRIX}"
|
|
||||||
|
|
||||||
run_tests_ubuntu_cuda:
|
|
||||||
needs: generate_build_matrix
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
|
|
||||||
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
- name: Setup Python
|
|
||||||
uses: actions/setup-python@v2
|
|
||||||
with:
|
|
||||||
python-version: ${{ matrix.python-version }}
|
|
||||||
|
|
||||||
- name: Install CUDA Toolkit ${{ matrix.cuda }}
|
|
||||||
shell: bash
|
|
||||||
env:
|
|
||||||
cuda: ${{ matrix.cuda }}
|
|
||||||
run: |
|
|
||||||
source ./scripts/github_actions/install_cuda.sh
|
|
||||||
echo "CUDA_HOME=${CUDA_HOME}" >> $GITHUB_ENV
|
|
||||||
echo "${CUDA_HOME}/bin" >> $GITHUB_PATH
|
|
||||||
echo "LD_LIBRARY_PATH=${CUDA_HOME}/lib:${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}" >> $GITHUB_ENV
|
|
||||||
|
|
||||||
- name: Display NVCC version
|
|
||||||
run: |
|
|
||||||
which nvcc
|
|
||||||
nvcc --version
|
|
||||||
|
|
||||||
- name: Install PyTorch ${{ matrix.torch }}
|
|
||||||
env:
|
|
||||||
cuda: ${{ matrix.cuda }}
|
|
||||||
torch: ${{ matrix.torch }}
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
sudo apt-get update
|
|
||||||
sudo apt-get install -y libsndfile1-dev libsndfile1 ffmpeg
|
|
||||||
python3 -m pip install --upgrade pip
|
|
||||||
python3 -m pip install wheel twine typing_extensions soundfile
|
|
||||||
python3 -m pip install bs4 requests tqdm numpy
|
|
||||||
|
|
||||||
./scripts/github_actions/install_torch.sh
|
|
||||||
python3 -c "import torch; print('torch version:', torch.__version__)"
|
|
||||||
|
|
||||||
- name: Download cudnn 8.0
|
|
||||||
env:
|
|
||||||
cuda: ${{ matrix.cuda }}
|
|
||||||
run: |
|
|
||||||
./scripts/github_actions/install_cudnn.sh
|
|
||||||
|
|
||||||
- name: Build
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
mkdir build_release
|
|
||||||
cd build_release
|
|
||||||
cmake -DCMAKE_CXX_STANDARD=17 ..
|
|
||||||
make VERBOSE=1 -j3
|
|
||||||
|
|
||||||
- name: Run tests
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
cd build_release
|
|
||||||
ctest --output-on-failure
|
|
||||||
121
.github/workflows/run-tests-windows-cpu.yml
vendored
121
.github/workflows/run-tests-windows-cpu.yml
vendored
@ -1,121 +0,0 @@
|
|||||||
# Copyright 2021 Xiaomi Corp. (author: Fangjun Kuang)
|
|
||||||
|
|
||||||
# See ../../LICENSE for clarification regarding multiple authors
|
|
||||||
#
|
|
||||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
||||||
# you may not use this file except in compliance with the License.
|
|
||||||
# You may obtain a copy of the License at
|
|
||||||
#
|
|
||||||
# http://www.apache.org/licenses/LICENSE-2.0
|
|
||||||
#
|
|
||||||
# Unless required by applicable law or agreed to in writing, software
|
|
||||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
||||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
||||||
# See the License for the specific language governing permissions and
|
|
||||||
# limitations under the License.
|
|
||||||
#
|
|
||||||
name: Run tests windows cpu
|
|
||||||
|
|
||||||
on:
|
|
||||||
push:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
|
|
||||||
pull_request:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
generate_build_matrix:
|
|
||||||
# see https://github.com/pytorch/pytorch/pull/50633
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
outputs:
|
|
||||||
matrix: ${{ steps.set-matrix.outputs.matrix }}
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
- name: Generating build matrix
|
|
||||||
id: set-matrix
|
|
||||||
run: |
|
|
||||||
# outputting for debugging purposes
|
|
||||||
python scripts/github_actions/generate_build_matrix.py --test-only-latest-torch
|
|
||||||
MATRIX=$(python scripts/github_actions/generate_build_matrix.py --test-only-latest-torch)
|
|
||||||
echo "::set-output name=matrix::${MATRIX}"
|
|
||||||
|
|
||||||
run_tests_windows_cpu:
|
|
||||||
# see https://github.com/actions/virtual-environments/blob/win19/20210525.0/images/win/Windows2019-Readme.md
|
|
||||||
needs: generate_build_matrix
|
|
||||||
runs-on: windows-latest
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
|
|
||||||
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
# see https://github.com/microsoft/setup-msbuild
|
|
||||||
- name: Add msbuild to PATH
|
|
||||||
uses: microsoft/setup-msbuild@v1.0.2
|
|
||||||
|
|
||||||
- name: Setup Python ${{ matrix.python-version }}
|
|
||||||
uses: actions/setup-python@v2
|
|
||||||
with:
|
|
||||||
python-version: ${{ matrix.python-version }}
|
|
||||||
|
|
||||||
- name: Display Python version
|
|
||||||
run: python -c "import sys; print(sys.version)"
|
|
||||||
|
|
||||||
- name: Install PyTorch ${{ matrix.torch }}
|
|
||||||
run: |
|
|
||||||
pip3 install -qq torch==${{ matrix.torch }}+cpu -f https://download.pytorch.org/whl/torch_stable.html || pip3 install -qq torch==${{ matrix.torch }}+cpu -f https://download.pytorch.org/whl/torch/
|
|
||||||
pip3 install -qq wheel twine dataclasses numpy typing_extensions soundfile
|
|
||||||
|
|
||||||
- name: Display CMake version
|
|
||||||
run: |
|
|
||||||
cmake --version
|
|
||||||
cmake --help
|
|
||||||
|
|
||||||
- name: Configure CMake
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
mkdir build_release
|
|
||||||
cd build_release
|
|
||||||
cmake -DCMAKE_BUILD_TYPE=$BUILD_TYPE ..
|
|
||||||
ls -lh
|
|
||||||
|
|
||||||
- name: Build kaldifeat
|
|
||||||
run: |
|
|
||||||
cd build_release
|
|
||||||
cmake --build -DCMAKE_CXX_STANDARD=17 . --target _kaldifeat --config Release
|
|
||||||
|
|
||||||
- name: Display generated files
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
cd build_release
|
|
||||||
ls -lh lib/*/*
|
|
||||||
|
|
||||||
- name: Build wheel
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
python3 setup.py bdist_wheel
|
|
||||||
ls -lh dist/
|
|
||||||
pip install ./dist/*.whl
|
|
||||||
python3 -c "import kaldifeat; print(kaldifeat.__version__)"
|
|
||||||
|
|
||||||
- name: Upload Wheel
|
|
||||||
uses: actions/upload-artifact@v4
|
|
||||||
with:
|
|
||||||
name: python-${{ matrix.python-version }}-${{ matrix.os }}-cpu
|
|
||||||
path: dist/*.whl
|
|
||||||
|
|
||||||
- name: Build tests
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
cd build_release
|
|
||||||
cmake -DCMAKE_CXX_STANDARD=17 --build . --target ALL_BUILD --config Release
|
|
||||||
ls -lh bin/*/*
|
|
||||||
ctest -C Release --verbose --output-on-failure
|
|
||||||
173
.github/workflows/run-tests-windows-cuda.yml
vendored
173
.github/workflows/run-tests-windows-cuda.yml
vendored
@ -1,173 +0,0 @@
|
|||||||
# Copyright 2021 Xiaomi Corp. (author: Fangjun Kuang)
|
|
||||||
|
|
||||||
# See ../../LICENSE for clarification regarding multiple authors
|
|
||||||
#
|
|
||||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
||||||
# you may not use this file except in compliance with the License.
|
|
||||||
# You may obtain a copy of the License at
|
|
||||||
#
|
|
||||||
# http://www.apache.org/licenses/LICENSE-2.0
|
|
||||||
#
|
|
||||||
# Unless required by applicable law or agreed to in writing, software
|
|
||||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
||||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
||||||
# See the License for the specific language governing permissions and
|
|
||||||
# limitations under the License.
|
|
||||||
|
|
||||||
|
|
||||||
name: Run tests windows cuda
|
|
||||||
|
|
||||||
on:
|
|
||||||
push:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
|
|
||||||
pull_request:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
generate_build_matrix:
|
|
||||||
# see https://github.com/pytorch/pytorch/pull/50633
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
outputs:
|
|
||||||
matrix: ${{ steps.set-matrix.outputs.matrix }}
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
- name: Generating build matrix
|
|
||||||
id: set-matrix
|
|
||||||
run: |
|
|
||||||
# outputting for debugging purposes
|
|
||||||
python scripts/github_actions/generate_build_matrix.py --enable-cuda --for-windows --test-only-latest-torch
|
|
||||||
MATRIX=$(python scripts/github_actions/generate_build_matrix.py --enable-cuda --for-windows --test-only-latest-torch)
|
|
||||||
echo "::set-output name=matrix::${MATRIX}"
|
|
||||||
|
|
||||||
run_tests_windows_cuda:
|
|
||||||
needs: generate_build_matrix
|
|
||||||
runs-on: windows-latest
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
|
|
||||||
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
# see https://github.com/microsoft/setup-msbuild
|
|
||||||
- name: Add msbuild to PATH
|
|
||||||
uses: microsoft/setup-msbuild@v1.0.2
|
|
||||||
|
|
||||||
- name: Setup Python ${{ matrix.python-version }}
|
|
||||||
uses: actions/setup-python@v2
|
|
||||||
with:
|
|
||||||
python-version: ${{ matrix.python-version }}
|
|
||||||
|
|
||||||
- name: Display Python version
|
|
||||||
run: python -c "import sys; print(sys.version)"
|
|
||||||
|
|
||||||
# See https://github.com/Jimver/cuda-toolkit/blob/master/src/links/windows-links.ts
|
|
||||||
# for available CUDA versions
|
|
||||||
- uses: Jimver/cuda-toolkit@v0.2.7
|
|
||||||
id: cuda-toolkit
|
|
||||||
with:
|
|
||||||
cuda: ${{ matrix.cuda }}
|
|
||||||
|
|
||||||
- name: Display CUDA version
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
echo "Installed cuda version is: ${{ steps.cuda-toolkit.outputs.cuda }}"
|
|
||||||
echo "Cuda install location: ${{steps.cuda-toolkit.outputs.CUDA_PATH}}"
|
|
||||||
nvcc --version
|
|
||||||
|
|
||||||
- name: Remove CUDA installation package
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
rm "C:/hostedtoolcache/windows/cuda_installer-windows/${{ matrix.cuda }}/x64/cuda_installer_${{ matrix.cuda }}.exe"
|
|
||||||
|
|
||||||
- name: Download cuDNN
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/cudnn-for-windows
|
|
||||||
cd cudnn-for-windows
|
|
||||||
git lfs pull --include="cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive.zip"
|
|
||||||
unzip cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive.zip
|
|
||||||
rm cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive.zip
|
|
||||||
ls -lh *
|
|
||||||
ls -lh */*
|
|
||||||
|
|
||||||
echo "PWD: $PWD"
|
|
||||||
|
|
||||||
- name: Install PyTorch ${{ matrix.torch }}
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
version=${{ matrix.cuda }}
|
|
||||||
major=${version:0:2}
|
|
||||||
minor=${version:3:1}
|
|
||||||
v=${major}${minor}
|
|
||||||
if [ ${v} -eq 102 ]; then v=""; else v="+cu${v}"; fi
|
|
||||||
|
|
||||||
python3 -m pip install -qq --upgrade pip
|
|
||||||
python3 -m pip install -qq wheel twine numpy typing_extensions
|
|
||||||
python3 -m pip install -qq dataclasses soundfile numpy
|
|
||||||
python3 -m pip install -qq torch==${{ matrix.torch }}${v} -f https://download.pytorch.org/whl/torch_stable.html numpy || python3 -m pip install -qq torch==${{ matrix.torch }}${v} -f https://download.pytorch.org/whl/torch/ numpy
|
|
||||||
|
|
||||||
python3 -c "import torch; print('torch version:', torch.__version__)"
|
|
||||||
|
|
||||||
python3 -m torch.utils.collect_env
|
|
||||||
|
|
||||||
- name: Display CMake version
|
|
||||||
run: |
|
|
||||||
cmake --version
|
|
||||||
cmake --help
|
|
||||||
|
|
||||||
- name: Configure CMake
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
echo "PWD: $PWD"
|
|
||||||
ls -lh
|
|
||||||
|
|
||||||
mkdir build_release
|
|
||||||
cd build_release
|
|
||||||
cmake -DCMAKE_BUILD_TYPE=$BUILD_TYPE -DCUDNN_INCLUDE_PATH=d:/a/kaldifeat/kaldifeat/cudnn-for-windows/cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive/include -DCUDNN_LIBRARY_PATH=d:/a/kaldifeat/kaldifeat/cudnn-for-windows/cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive/lib/cudnn.lib ..
|
|
||||||
ls -lh
|
|
||||||
|
|
||||||
- name: Build kaldifeat
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
cd build_release
|
|
||||||
cmake --build . --target _kaldifeat --config Release
|
|
||||||
|
|
||||||
- name: Display generated files
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
cd build_release
|
|
||||||
ls -lh lib/*/*
|
|
||||||
|
|
||||||
- name: Build wheel
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
echo $PWD
|
|
||||||
ls -lh ./*
|
|
||||||
export KALDIFEAT_CMAKE_ARGS="-DCMAKE_BUILD_TYPE=$BUILD_TYPE -DCUDNN_INCLUDE_PATH=d:/a/kaldifeat/kaldifeat/cudnn-for-windows/cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive/include -DCUDNN_LIBRARY_PATH=d:/a/kaldifeat/kaldifeat/cudnn-for-windows/cudnn-windows-x86_64-8.4.1.50_cuda11.6-archive/lib/cudnn.lib"
|
|
||||||
python3 setup.py bdist_wheel
|
|
||||||
ls -lh dist/
|
|
||||||
pip install ./dist/*.whl
|
|
||||||
python3 -c "import kaldifeat; print(kaldifeat.__version__)"
|
|
||||||
|
|
||||||
- name: Upload Wheel
|
|
||||||
uses: actions/upload-artifact@v4
|
|
||||||
with:
|
|
||||||
name: python-${{ matrix.python-version }}-${{ matrix.os }}-cuda-${{ matrix.cuda }}
|
|
||||||
path: dist/*.whl
|
|
||||||
|
|
||||||
- name: Build tests
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
cd build_release
|
|
||||||
cmake -DCMAKE_CXX_STANDARD=17 --build . --target ALL_BUILD --config Release
|
|
||||||
ls -lh bin/*/*
|
|
||||||
ctest -C Release --verbose --output-on-failure
|
|
||||||
64
.github/workflows/style_check.yml
vendored
64
.github/workflows/style_check.yml
vendored
@ -1,64 +0,0 @@
|
|||||||
# Copyright 2021 Fangjun Kuang (csukuangfj@gmail.com)
|
|
||||||
|
|
||||||
# See ../../LICENSE for clarification regarding multiple authors
|
|
||||||
#
|
|
||||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
||||||
# you may not use this file except in compliance with the License.
|
|
||||||
# You may obtain a copy of the License at
|
|
||||||
#
|
|
||||||
# http://www.apache.org/licenses/LICENSE-2.0
|
|
||||||
#
|
|
||||||
# Unless required by applicable law or agreed to in writing, software
|
|
||||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
||||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
||||||
# See the License for the specific language governing permissions and
|
|
||||||
# limitations under the License.
|
|
||||||
|
|
||||||
name: style_check
|
|
||||||
|
|
||||||
on:
|
|
||||||
push:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
pull_request:
|
|
||||||
branches:
|
|
||||||
- master
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
style_check:
|
|
||||||
runs-on: ${{ matrix.os }}
|
|
||||||
strategy:
|
|
||||||
matrix:
|
|
||||||
os: [ubuntu-latest, macos-latest]
|
|
||||||
python-version: ["3.8"]
|
|
||||||
fail-fast: false
|
|
||||||
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
- name: Setup Python ${{ matrix.python-version }}
|
|
||||||
uses: actions/setup-python@v1
|
|
||||||
with:
|
|
||||||
python-version: ${{ matrix.python-version }}
|
|
||||||
|
|
||||||
- name: Install Python dependencies
|
|
||||||
run: |
|
|
||||||
python3 -m pip install --upgrade pip black==21.6b0 flake8==3.9.2 click==8.0.4
|
|
||||||
# See https://github.com/psf/black/issues/2964
|
|
||||||
# The version of click should be selected from 8.0.0, 8.0.1, 8.0.2, 8.0.3, and 8.0.4
|
|
||||||
|
|
||||||
- name: Run flake8
|
|
||||||
shell: bash
|
|
||||||
working-directory: ${{github.workspace}}
|
|
||||||
run: |
|
|
||||||
# stop the build if there are Python syntax errors or undefined names
|
|
||||||
flake8 . --count --show-source --statistics
|
|
||||||
flake8 .
|
|
||||||
|
|
||||||
- name: Run black
|
|
||||||
shell: bash
|
|
||||||
working-directory: ${{github.workspace}}
|
|
||||||
run: |
|
|
||||||
black --check --diff .
|
|
||||||
67
.github/workflows/test-wheels.yml
vendored
67
.github/workflows/test-wheels.yml
vendored
@ -1,67 +0,0 @@
|
|||||||
name: Test pre-compiled wheels
|
|
||||||
on:
|
|
||||||
workflow_dispatch:
|
|
||||||
inputs:
|
|
||||||
torch_version:
|
|
||||||
description: "torch version, e.g., 2.0.1"
|
|
||||||
required: true
|
|
||||||
kaldifeat_version:
|
|
||||||
description: "kaldifeat version, e.g., 1.25.0.dev20230726"
|
|
||||||
required: true
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
Test_pre_compiled_wheels:
|
|
||||||
name: ${{ matrix.os }} ${{ github.event.inputs.torch_version }} ${{ github.event.inputs.kaldifeat_version }} ${{ matrix.python-version }}
|
|
||||||
runs-on: ${{ matrix.os }}
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
os: [ubuntu-latest, windows-latest, macos-latest]
|
|
||||||
python-version: ["3.8", "3.9", "3.10"]
|
|
||||||
steps:
|
|
||||||
# refer to https://github.com/actions/checkout
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
- name: Setup Python ${{ matrix.python-version }}
|
|
||||||
uses: actions/setup-python@v2
|
|
||||||
with:
|
|
||||||
python-version: ${{ matrix.python-version }}
|
|
||||||
|
|
||||||
- name: Display Python version
|
|
||||||
run: python -c "import sys; print(sys.version)"
|
|
||||||
|
|
||||||
- name: Install dependencies
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
pip install numpy
|
|
||||||
|
|
||||||
- name: Install torch
|
|
||||||
if: startsWith(matrix.os, 'macos')
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
pip install torch==${{ github.event.inputs.torch_version }}
|
|
||||||
|
|
||||||
- name: Install torch
|
|
||||||
if: startsWith(matrix.os, 'ubuntu') || startsWith(matrix.os, 'windows')
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
pip install torch==${{ github.event.inputs.torch_version }}+cpu -f https://download.pytorch.org/whl/torch_stable.html || pip install torch==${{ github.event.inputs.torch_version }}+cpu -f https://download.pytorch.org/whl/torch/
|
|
||||||
|
|
||||||
- name: Install kaldifeat
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
pip install kaldifeat==${{ github.event.inputs.kaldifeat_version }}+cpu.torch${{ github.event.inputs.torch_version }} -f https://csukuangfj.github.io/kaldifeat/cpu.html
|
|
||||||
|
|
||||||
- name: Run tests
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
cd kaldifeat/python/tests
|
|
||||||
|
|
||||||
python3 -c "import kaldifeat; print(kaldifeat.__file__)"
|
|
||||||
python3 -c "import kaldifeat; print(kaldifeat.__version__)"
|
|
||||||
|
|
||||||
python3 ./test_fbank_options.py
|
|
||||||
|
|
||||||
python3 ./test_mfcc_options.py
|
|
||||||
168
.github/workflows/ubuntu-arm64-cpu-wheels.yml
vendored
168
.github/workflows/ubuntu-arm64-cpu-wheels.yml
vendored
@ -1,168 +0,0 @@
|
|||||||
name: build-wheels-cpu-arm64-ubuntu
|
|
||||||
|
|
||||||
on:
|
|
||||||
push:
|
|
||||||
branches:
|
|
||||||
# - wheel
|
|
||||||
- torch-2.8.0
|
|
||||||
tags:
|
|
||||||
- '*'
|
|
||||||
workflow_dispatch:
|
|
||||||
|
|
||||||
concurrency:
|
|
||||||
group: build-wheels-cpu-arm64-ubuntu-${{ github.ref }}
|
|
||||||
cancel-in-progress: true
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
generate_build_matrix:
|
|
||||||
# see https://github.com/pytorch/pytorch/pull/50633
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
outputs:
|
|
||||||
matrix: ${{ steps.set-matrix.outputs.matrix }}
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
- name: Generating build matrix
|
|
||||||
id: set-matrix
|
|
||||||
run: |
|
|
||||||
# outputting for debugging purposes
|
|
||||||
# python ./scripts/github_actions/generate_build_matrix.py --for-arm64
|
|
||||||
# MATRIX=$(python ./scripts/github_actions/generate_build_matrix.py --for-arm64)
|
|
||||||
|
|
||||||
python ./scripts/github_actions/generate_build_matrix.py --test-only-latest-torch --for-arm64
|
|
||||||
MATRIX=$(python ./scripts/github_actions/generate_build_matrix.py --test-only-latest-torch --for-arm64)
|
|
||||||
echo "::set-output name=matrix::${MATRIX}"
|
|
||||||
|
|
||||||
build-manylinux-wheels:
|
|
||||||
needs: generate_build_matrix
|
|
||||||
name: ${{ matrix.torch }} ${{ matrix.python-version }}
|
|
||||||
runs-on: ubuntu-22.04-arm
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
|
|
||||||
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
# see https://github.com/pytorch/test-infra/blob/9e3d392690719fac85bad0c9b67f530e48375ca1/tools/scripts/generate_binary_build_matrix.py
|
|
||||||
# https://github.com/pytorch/builder/tree/main/manywheel
|
|
||||||
# https://github.com/pytorch/builder/pull/476
|
|
||||||
# https://github.com/k2-fsa/k2/issues/733
|
|
||||||
# https://github.com/pytorch/pytorch/pull/50633 (generate build matrix)
|
|
||||||
- name: Run the build process with Docker
|
|
||||||
uses: addnab/docker-run-action@v3
|
|
||||||
with:
|
|
||||||
image: ${{ matrix.image }}
|
|
||||||
options: -v ${{ github.workspace }}:/var/www -e IS_2_28=${{ matrix.is_2_28 }} -e PYTHON_VERSION=${{ matrix.python-version }} -e TORCH_VERSION=${{ matrix.torch }}
|
|
||||||
run: |
|
|
||||||
echo "pwd: $PWD"
|
|
||||||
uname -a
|
|
||||||
id
|
|
||||||
cat /etc/*release
|
|
||||||
gcc --version
|
|
||||||
python3 --version
|
|
||||||
which python3
|
|
||||||
|
|
||||||
ls -lh /opt/python/
|
|
||||||
|
|
||||||
echo "---"
|
|
||||||
|
|
||||||
ls -lh /opt/python/cp*
|
|
||||||
|
|
||||||
ls -lh /opt/python/*/bin
|
|
||||||
|
|
||||||
echo "---"
|
|
||||||
find /opt/python/cp* -name "libpython*"
|
|
||||||
echo "-----"
|
|
||||||
find /opt/_internal/cp* -name "libpython*"
|
|
||||||
|
|
||||||
echo "-----"
|
|
||||||
find / -name "libpython*"
|
|
||||||
echo "----"
|
|
||||||
ls -lh /usr/lib64/libpython3.so
|
|
||||||
|
|
||||||
# cp36-cp36m
|
|
||||||
# cp37-cp37m
|
|
||||||
# cp38-cp38
|
|
||||||
# cp39-cp39
|
|
||||||
# cp310-cp310
|
|
||||||
# cp311-cp311
|
|
||||||
# cp312-cp312
|
|
||||||
# cp313-cp313
|
|
||||||
# cp313-cp313t (no gil)
|
|
||||||
if [[ $PYTHON_VERSION == "3.6" ]]; then
|
|
||||||
python_dir=/opt/python/cp36-cp36m
|
|
||||||
export PYTHONPATH=/opt/python/cp36-cp36m/lib/python3.6/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.7" ]]; then
|
|
||||||
python_dir=/opt/python/cp37-cp37m
|
|
||||||
export PYTHONPATH=/opt/python/cp37-cp37m/lib/python3.7/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.8" ]]; then
|
|
||||||
python_dir=/opt/python/cp38-cp38
|
|
||||||
export PYTHONPATH=/opt/python/cp38-cp38/lib/python3.8/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.9" ]]; then
|
|
||||||
python_dir=/opt/python/cp39-cp39
|
|
||||||
export PYTHONPATH=/opt/python/cp39-cp39/lib/python3.9/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.10" ]]; then
|
|
||||||
python_dir=/opt/python/cp310-cp310
|
|
||||||
export PYTHONPATH=/opt/python/cp310-cp310/lib/python3.10/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.11" ]]; then
|
|
||||||
python_dir=/opt/python/cp311-cp311
|
|
||||||
export PYTHONPATH=/opt/python/cp311-cp311/lib/python3.11/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.12" ]]; then
|
|
||||||
python_dir=/opt/python/cp312-cp312
|
|
||||||
export PYTHONPATH=/opt/python/cp312-cp312/lib/python3.12/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.13" ]]; then
|
|
||||||
python_dir=/opt/python/cp313-cp313
|
|
||||||
export PYTHONPATH=/opt/python/cp313-cp313/lib/python3.13/site-packages:$PYTHONPATH
|
|
||||||
else
|
|
||||||
echo "Unsupported Python version $PYTHON_VERSION"
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
export PYTHON_INSTALL_DIR=$python_dir
|
|
||||||
export PATH=$PYTHON_INSTALL_DIR/bin:$PATH
|
|
||||||
|
|
||||||
python3 --version
|
|
||||||
which python3
|
|
||||||
|
|
||||||
/var/www/scripts/github_actions/build-ubuntu-cpu-arm64.sh
|
|
||||||
|
|
||||||
- name: Display wheels
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
ls -lh ./wheelhouse/
|
|
||||||
|
|
||||||
# https://huggingface.co/docs/hub/spaces-github-actions
|
|
||||||
- name: Publish to huggingface
|
|
||||||
if: github.repository_owner == 'csukuangfj'
|
|
||||||
env:
|
|
||||||
HF_TOKEN: ${{ secrets.HF_TOKEN }}
|
|
||||||
uses: nick-fields/retry@v2
|
|
||||||
with:
|
|
||||||
max_attempts: 20
|
|
||||||
timeout_seconds: 200
|
|
||||||
shell: bash
|
|
||||||
command: |
|
|
||||||
git config --global user.email "csukuangfj@gmail.com"
|
|
||||||
git config --global user.name "Fangjun Kuang"
|
|
||||||
|
|
||||||
rm -rf huggingface
|
|
||||||
|
|
||||||
export GIT_LFS_SKIP_SMUDGE=1
|
|
||||||
|
|
||||||
git clone https://huggingface.co/csukuangfj/kaldifeat huggingface
|
|
||||||
cd huggingface
|
|
||||||
git pull
|
|
||||||
|
|
||||||
d=cpu/1.25.5.dev20250307/linux-arm64
|
|
||||||
mkdir -p $d
|
|
||||||
cp -v ../wheelhouse/*.whl ./$d
|
|
||||||
git status
|
|
||||||
git lfs track "*.whl"
|
|
||||||
git add .
|
|
||||||
git commit -m "upload ubuntu-arm64-cpu wheel for torch ${{ matrix.torch }} python ${{ matrix.python-version }}"
|
|
||||||
git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/kaldifeat main
|
|
||||||
168
.github/workflows/ubuntu-cpu-wheels.yml
vendored
168
.github/workflows/ubuntu-cpu-wheels.yml
vendored
@ -1,168 +0,0 @@
|
|||||||
name: build-wheels-cpu-ubuntu
|
|
||||||
|
|
||||||
on:
|
|
||||||
push:
|
|
||||||
branches:
|
|
||||||
# - wheel
|
|
||||||
- torch-2.8.0
|
|
||||||
tags:
|
|
||||||
- '*'
|
|
||||||
workflow_dispatch:
|
|
||||||
|
|
||||||
concurrency:
|
|
||||||
group: build-wheels-cpu-ubuntu-${{ github.ref }}
|
|
||||||
cancel-in-progress: true
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
generate_build_matrix:
|
|
||||||
# see https://github.com/pytorch/pytorch/pull/50633
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
outputs:
|
|
||||||
matrix: ${{ steps.set-matrix.outputs.matrix }}
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
- name: Generating build matrix
|
|
||||||
id: set-matrix
|
|
||||||
run: |
|
|
||||||
# outputting for debugging purposes
|
|
||||||
# python ./scripts/github_actions/generate_build_matrix.py
|
|
||||||
# MATRIX=$(python ./scripts/github_actions/generate_build_matrix.py)
|
|
||||||
|
|
||||||
python ./scripts/github_actions/generate_build_matrix.py --test-only-latest-torch
|
|
||||||
MATRIX=$(python ./scripts/github_actions/generate_build_matrix.py --test-only-latest-torch)
|
|
||||||
echo "::set-output name=matrix::${MATRIX}"
|
|
||||||
|
|
||||||
build-manylinux-wheels:
|
|
||||||
needs: generate_build_matrix
|
|
||||||
name: ${{ matrix.torch }} ${{ matrix.python-version }}
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
|
|
||||||
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
# see https://github.com/pytorch/test-infra/blob/9e3d392690719fac85bad0c9b67f530e48375ca1/tools/scripts/generate_binary_build_matrix.py
|
|
||||||
# https://github.com/pytorch/builder/tree/main/manywheel
|
|
||||||
# https://github.com/pytorch/builder/pull/476
|
|
||||||
# https://github.com/k2-fsa/k2/issues/733
|
|
||||||
# https://github.com/pytorch/pytorch/pull/50633 (generate build matrix)
|
|
||||||
- name: Run the build process with Docker
|
|
||||||
uses: addnab/docker-run-action@v3
|
|
||||||
with:
|
|
||||||
image: ${{ matrix.image }}
|
|
||||||
options: -v ${{ github.workspace }}:/var/www -e IS_2_28=${{ matrix.is_2_28 }} -e PYTHON_VERSION=${{ matrix.python-version }} -e TORCH_VERSION=${{ matrix.torch }}
|
|
||||||
run: |
|
|
||||||
echo "pwd: $PWD"
|
|
||||||
uname -a
|
|
||||||
id
|
|
||||||
cat /etc/*release
|
|
||||||
gcc --version
|
|
||||||
python3 --version
|
|
||||||
which python3
|
|
||||||
|
|
||||||
ls -lh /opt/python/
|
|
||||||
|
|
||||||
echo "---"
|
|
||||||
|
|
||||||
ls -lh /opt/python/cp*
|
|
||||||
|
|
||||||
ls -lh /opt/python/*/bin
|
|
||||||
|
|
||||||
echo "---"
|
|
||||||
find /opt/python/cp* -name "libpython*"
|
|
||||||
echo "-----"
|
|
||||||
find /opt/_internal/cp* -name "libpython*"
|
|
||||||
|
|
||||||
echo "-----"
|
|
||||||
find / -name "libpython*"
|
|
||||||
echo "----"
|
|
||||||
ls -lh /usr/lib64/libpython3.so || true
|
|
||||||
|
|
||||||
# cp36-cp36m
|
|
||||||
# cp37-cp37m
|
|
||||||
# cp38-cp38
|
|
||||||
# cp39-cp39
|
|
||||||
# cp310-cp310
|
|
||||||
# cp311-cp311
|
|
||||||
# cp312-cp312
|
|
||||||
# cp313-cp313
|
|
||||||
# cp313-cp313t (no gil)
|
|
||||||
if [[ $PYTHON_VERSION == "3.6" ]]; then
|
|
||||||
python_dir=/opt/python/cp36-cp36m
|
|
||||||
export PYTHONPATH=/opt/python/cp36-cp36m/lib/python3.6/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.7" ]]; then
|
|
||||||
python_dir=/opt/python/cp37-cp37m
|
|
||||||
export PYTHONPATH=/opt/python/cp37-cp37m/lib/python3.7/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.8" ]]; then
|
|
||||||
python_dir=/opt/python/cp38-cp38
|
|
||||||
export PYTHONPATH=/opt/python/cp38-cp38/lib/python3.8/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.9" ]]; then
|
|
||||||
python_dir=/opt/python/cp39-cp39
|
|
||||||
export PYTHONPATH=/opt/python/cp39-cp39/lib/python3.9/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.10" ]]; then
|
|
||||||
python_dir=/opt/python/cp310-cp310
|
|
||||||
export PYTHONPATH=/opt/python/cp310-cp310/lib/python3.10/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.11" ]]; then
|
|
||||||
python_dir=/opt/python/cp311-cp311
|
|
||||||
export PYTHONPATH=/opt/python/cp311-cp311/lib/python3.11/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.12" ]]; then
|
|
||||||
python_dir=/opt/python/cp312-cp312
|
|
||||||
export PYTHONPATH=/opt/python/cp312-cp312/lib/python3.12/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.13" ]]; then
|
|
||||||
python_dir=/opt/python/cp313-cp313
|
|
||||||
export PYTHONPATH=/opt/python/cp313-cp313/lib/python3.13/site-packages:$PYTHONPATH
|
|
||||||
else
|
|
||||||
echo "Unsupported Python version $PYTHON_VERSION"
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
export PYTHON_INSTALL_DIR=$python_dir
|
|
||||||
export PATH=$PYTHON_INSTALL_DIR/bin:$PATH
|
|
||||||
|
|
||||||
python3 --version
|
|
||||||
which python3
|
|
||||||
|
|
||||||
/var/www/scripts/github_actions/build-ubuntu-cpu.sh
|
|
||||||
|
|
||||||
- name: Display wheels
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
ls -lh ./wheelhouse/
|
|
||||||
|
|
||||||
# https://huggingface.co/docs/hub/spaces-github-actions
|
|
||||||
- name: Publish to huggingface
|
|
||||||
if: github.repository_owner == 'csukuangfj'
|
|
||||||
env:
|
|
||||||
HF_TOKEN: ${{ secrets.HF_TOKEN }}
|
|
||||||
uses: nick-fields/retry@v2
|
|
||||||
with:
|
|
||||||
max_attempts: 20
|
|
||||||
timeout_seconds: 200
|
|
||||||
shell: bash
|
|
||||||
command: |
|
|
||||||
git config --global user.email "csukuangfj@gmail.com"
|
|
||||||
git config --global user.name "Fangjun Kuang"
|
|
||||||
|
|
||||||
rm -rf huggingface
|
|
||||||
|
|
||||||
export GIT_LFS_SKIP_SMUDGE=1
|
|
||||||
|
|
||||||
git clone https://huggingface.co/csukuangfj/kaldifeat huggingface
|
|
||||||
cd huggingface
|
|
||||||
git pull
|
|
||||||
|
|
||||||
d=cpu/1.25.5.dev20250307/linux-x64
|
|
||||||
mkdir -p $d
|
|
||||||
cp -v ../wheelhouse/*.whl ./$d
|
|
||||||
git status
|
|
||||||
git lfs track "*.whl"
|
|
||||||
git add .
|
|
||||||
git commit -m "upload ubuntu-cpu wheel for torch ${{ matrix.torch }} python ${{ matrix.python-version }}"
|
|
||||||
git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/kaldifeat main
|
|
||||||
194
.github/workflows/ubuntu-cuda-wheels.yml
vendored
194
.github/workflows/ubuntu-cuda-wheels.yml
vendored
@ -1,194 +0,0 @@
|
|||||||
name: build-wheels-cuda-ubuntu
|
|
||||||
|
|
||||||
on:
|
|
||||||
push:
|
|
||||||
branches:
|
|
||||||
- wheel
|
|
||||||
# - torch-2.7.1
|
|
||||||
tags:
|
|
||||||
- '*'
|
|
||||||
workflow_dispatch:
|
|
||||||
|
|
||||||
concurrency:
|
|
||||||
group: build-wheels-cuda-ubuntu-${{ github.ref }}
|
|
||||||
cancel-in-progress: true
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
generate_build_matrix:
|
|
||||||
# see https://github.com/pytorch/pytorch/pull/50633
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
outputs:
|
|
||||||
matrix: ${{ steps.set-matrix.outputs.matrix }}
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
- name: Generating build matrix
|
|
||||||
id: set-matrix
|
|
||||||
run: |
|
|
||||||
# outputting for debugging purposes
|
|
||||||
# python ./scripts/github_actions/generate_build_matrix.py --enable-cuda
|
|
||||||
# MATRIX=$(python ./scripts/github_actions/generate_build_matrix.py --enable-cuda)
|
|
||||||
|
|
||||||
python ./scripts/github_actions/generate_build_matrix.py --enable-cuda --test-only-latest-torch
|
|
||||||
MATRIX=$(python ./scripts/github_actions/generate_build_matrix.py --enable-cuda --test-only-latest-torch)
|
|
||||||
echo "::set-output name=matrix::${MATRIX}"
|
|
||||||
|
|
||||||
build-manylinux-wheels:
|
|
||||||
needs: generate_build_matrix
|
|
||||||
name: ${{ matrix.torch }} ${{ matrix.python-version }} cuda${{ matrix.cuda }}
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
|
|
||||||
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
- name: Free space
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
df -h
|
|
||||||
rm -rf /opt/hostedtoolcache
|
|
||||||
df -h
|
|
||||||
echo "pwd: $PWD"
|
|
||||||
echo "github.workspace ${{ github.workspace }}"
|
|
||||||
|
|
||||||
# see https://github.com/pytorch/test-infra/blob/9e3d392690719fac85bad0c9b67f530e48375ca1/tools/scripts/generate_binary_build_matrix.py
|
|
||||||
# https://github.com/pytorch/builder/tree/main/manywheel
|
|
||||||
# https://github.com/pytorch/builder/pull/476
|
|
||||||
# https://github.com/k2-fsa/k2/issues/733
|
|
||||||
# https://github.com/pytorch/pytorch/pull/50633 (generate build matrix)
|
|
||||||
- name: Run the build process with Docker
|
|
||||||
uses: addnab/docker-run-action@v3
|
|
||||||
with:
|
|
||||||
image: ${{ matrix.image }}
|
|
||||||
options: -v ${{ github.workspace }}:/var/www -e IS_2_28=${{ matrix.is_2_28 }} -e PYTHON_VERSION=${{ matrix.python-version }} -e TORCH_VERSION=${{ matrix.torch }} -e CUDA_VERSION=${{ matrix.cuda }}
|
|
||||||
run: |
|
|
||||||
echo "pwd: $PWD"
|
|
||||||
uname -a
|
|
||||||
id
|
|
||||||
cat /etc/*release
|
|
||||||
gcc --version
|
|
||||||
python3 --version
|
|
||||||
which python3
|
|
||||||
|
|
||||||
ls -lh /opt/python/
|
|
||||||
|
|
||||||
echo "---"
|
|
||||||
|
|
||||||
ls -lh /opt/python/cp*
|
|
||||||
|
|
||||||
ls -lh /opt/python/*/bin
|
|
||||||
|
|
||||||
echo "---"
|
|
||||||
find /opt/python/cp* -name "libpython*"
|
|
||||||
echo "-----"
|
|
||||||
find /opt/_internal/cp* -name "libpython*"
|
|
||||||
|
|
||||||
echo "-----"
|
|
||||||
find / -name "libpython*"
|
|
||||||
|
|
||||||
# cp36-cp36m
|
|
||||||
# cp37-cp37m
|
|
||||||
# cp38-cp38
|
|
||||||
# cp39-cp39
|
|
||||||
# cp310-cp310
|
|
||||||
# cp311-cp311
|
|
||||||
# cp312-cp312
|
|
||||||
# cp313-cp313
|
|
||||||
# cp313-cp313t (no gil)
|
|
||||||
if [[ $PYTHON_VERSION == "3.6" ]]; then
|
|
||||||
python_dir=/opt/python/cp36-cp36m
|
|
||||||
export PYTHONPATH=/opt/python/cp36-cp36m/lib/python3.6/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.7" ]]; then
|
|
||||||
python_dir=/opt/python/cp37-cp37m
|
|
||||||
export PYTHONPATH=/opt/python/cp37-cp37m/lib/python3.7/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.8" ]]; then
|
|
||||||
python_dir=/opt/python/cp38-cp38
|
|
||||||
export PYTHONPATH=/opt/python/cp38-cp38/lib/python3.8/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.9" ]]; then
|
|
||||||
python_dir=/opt/python/cp39-cp39
|
|
||||||
export PYTHONPATH=/opt/python/cp39-cp39/lib/python3.9/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.10" ]]; then
|
|
||||||
python_dir=/opt/python/cp310-cp310
|
|
||||||
export PYTHONPATH=/opt/python/cp310-cp310/lib/python3.10/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.11" ]]; then
|
|
||||||
python_dir=/opt/python/cp311-cp311
|
|
||||||
export PYTHONPATH=/opt/python/cp311-cp311/lib/python3.11/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.12" ]]; then
|
|
||||||
python_dir=/opt/python/cp312-cp312
|
|
||||||
export PYTHONPATH=/opt/python/cp312-cp312/lib/python3.12/site-packages:$PYTHONPATH
|
|
||||||
elif [[ $PYTHON_VERSION == "3.13" ]]; then
|
|
||||||
python_dir=/opt/python/cp313-cp313
|
|
||||||
export PYTHONPATH=/opt/python/cp313-cp313/lib/python3.13/site-packages:$PYTHONPATH
|
|
||||||
else
|
|
||||||
echo "Unsupported Python version $PYTHON_VERSION"
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
export PYTHON_INSTALL_DIR=$python_dir
|
|
||||||
export PATH=$PYTHON_INSTALL_DIR/bin:$PATH
|
|
||||||
|
|
||||||
# There are no libpython.so inside $PYTHON_INSTALL_DIR
|
|
||||||
# since they are statically linked.
|
|
||||||
|
|
||||||
python3 --version
|
|
||||||
which python3
|
|
||||||
|
|
||||||
pushd /usr/local
|
|
||||||
rm cuda
|
|
||||||
ln -s cuda-$CUDA_VERSION cuda
|
|
||||||
popd
|
|
||||||
which nvcc
|
|
||||||
nvcc --version
|
|
||||||
|
|
||||||
cp /var/www/scripts/github_actions/install_torch.sh .
|
|
||||||
chmod +x install_torch.sh
|
|
||||||
|
|
||||||
/var/www/scripts/github_actions/build-ubuntu-cuda.sh
|
|
||||||
|
|
||||||
- name: Display wheels
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
ls -lh ./wheelhouse/
|
|
||||||
|
|
||||||
- name: Upload Wheel
|
|
||||||
if: false
|
|
||||||
uses: actions/upload-artifact@v4
|
|
||||||
with:
|
|
||||||
name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-latest-cuda-is_2_28-${{ matrix.is_2_28 }}
|
|
||||||
path: wheelhouse/*.whl
|
|
||||||
|
|
||||||
# https://huggingface.co/docs/hub/spaces-github-actions
|
|
||||||
- name: Publish to huggingface
|
|
||||||
if: github.repository_owner == 'csukuangfj'
|
|
||||||
env:
|
|
||||||
HF_TOKEN: ${{ secrets.HF_TOKEN }}
|
|
||||||
uses: nick-fields/retry@v2
|
|
||||||
with:
|
|
||||||
max_attempts: 20
|
|
||||||
timeout_seconds: 200
|
|
||||||
shell: bash
|
|
||||||
command: |
|
|
||||||
git config --global user.email "csukuangfj@gmail.com"
|
|
||||||
git config --global user.name "Fangjun Kuang"
|
|
||||||
|
|
||||||
rm -rf huggingface
|
|
||||||
export GIT_LFS_SKIP_SMUDGE=1
|
|
||||||
|
|
||||||
git clone https://huggingface.co/csukuangfj/kaldifeat huggingface
|
|
||||||
cd huggingface
|
|
||||||
git pull
|
|
||||||
|
|
||||||
d=cuda/1.25.5.dev20241029/linux
|
|
||||||
mkdir -p $d
|
|
||||||
cp -v ../wheelhouse/*.whl ./$d
|
|
||||||
git status
|
|
||||||
git lfs track "*.whl"
|
|
||||||
git add .
|
|
||||||
git commit -m "upload ubuntu-cuda wheel for torch ${{ matrix.torch }} python ${{ matrix.python-version }}"
|
|
||||||
git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/kaldifeat main
|
|
||||||
108
.github/workflows/windows-x64-cpu-wheels.yml
vendored
108
.github/workflows/windows-x64-cpu-wheels.yml
vendored
@ -1,108 +0,0 @@
|
|||||||
name: build-wheels-cpu-win64
|
|
||||||
|
|
||||||
on:
|
|
||||||
push:
|
|
||||||
branches:
|
|
||||||
# - wheel
|
|
||||||
- torch-2.8.0
|
|
||||||
tags:
|
|
||||||
- '*'
|
|
||||||
workflow_dispatch:
|
|
||||||
|
|
||||||
concurrency:
|
|
||||||
group: build-wheels-cpu-win64-${{ github.ref }}
|
|
||||||
cancel-in-progress: true
|
|
||||||
|
|
||||||
jobs:
|
|
||||||
generate_build_matrix:
|
|
||||||
# see https://github.com/pytorch/pytorch/pull/50633
|
|
||||||
runs-on: ubuntu-latest
|
|
||||||
outputs:
|
|
||||||
matrix: ${{ steps.set-matrix.outputs.matrix }}
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
- name: Generating build matrix
|
|
||||||
id: set-matrix
|
|
||||||
run: |
|
|
||||||
# outputting for debugging purposes
|
|
||||||
# python ./scripts/github_actions/generate_build_matrix.py --for-windows
|
|
||||||
# MATRIX=$(python ./scripts/github_actions/generate_build_matrix.py --for-windows)
|
|
||||||
|
|
||||||
python ./scripts/github_actions/generate_build_matrix.py --for-windows --test-only-latest-torch
|
|
||||||
MATRIX=$(python ./scripts/github_actions/generate_build_matrix.py --for-windows --test-only-latest-torch)
|
|
||||||
echo "::set-output name=matrix::${MATRIX}"
|
|
||||||
|
|
||||||
build_wheels_win64_cpu:
|
|
||||||
needs: generate_build_matrix
|
|
||||||
name: ${{ matrix.torch }} ${{ matrix.python-version }}
|
|
||||||
runs-on: windows-latest
|
|
||||||
strategy:
|
|
||||||
fail-fast: false
|
|
||||||
matrix:
|
|
||||||
${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
|
|
||||||
|
|
||||||
steps:
|
|
||||||
- uses: actions/checkout@v4
|
|
||||||
with:
|
|
||||||
fetch-depth: 0
|
|
||||||
|
|
||||||
- name: Setup Python
|
|
||||||
uses: actions/setup-python@v5
|
|
||||||
with:
|
|
||||||
python-version: ${{ matrix.python-version }}
|
|
||||||
|
|
||||||
- name: Install dependencies
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
pip install -q torch==${{ matrix.torch}} cmake numpy wheel>=0.40.0 twine setuptools
|
|
||||||
pip install torch==${{ matrix.torch}}+cpu -f https://download.pytorch.org/whl/torch_stable.html cmake numpy || pip install torch==${{ matrix.torch}}+cpu -f https://download.pytorch.org/whl/torch/ cmake numpy
|
|
||||||
|
|
||||||
- name: Build wheel
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
python3 setup.py bdist_wheel
|
|
||||||
mkdir wheelhouse
|
|
||||||
cp -v dist/* wheelhouse
|
|
||||||
|
|
||||||
- name: Display wheels
|
|
||||||
shell: bash
|
|
||||||
run: |
|
|
||||||
ls -lh ./wheelhouse/
|
|
||||||
|
|
||||||
- name: Upload Wheel
|
|
||||||
uses: actions/upload-artifact@v4
|
|
||||||
with:
|
|
||||||
name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-windows-latest-cpu
|
|
||||||
path: wheelhouse/*.whl
|
|
||||||
|
|
||||||
# https://huggingface.co/docs/hub/spaces-github-actions
|
|
||||||
- name: Publish to huggingface
|
|
||||||
if: github.repository_owner == 'csukuangfj'
|
|
||||||
env:
|
|
||||||
HF_TOKEN: ${{ secrets.HF_TOKEN }}
|
|
||||||
uses: nick-fields/retry@v2
|
|
||||||
with:
|
|
||||||
max_attempts: 20
|
|
||||||
timeout_seconds: 200
|
|
||||||
shell: bash
|
|
||||||
command: |
|
|
||||||
git config --global user.email "csukuangfj@gmail.com"
|
|
||||||
git config --global user.name "Fangjun Kuang"
|
|
||||||
|
|
||||||
rm -rf huggingface
|
|
||||||
export GIT_LFS_SKIP_SMUDGE=1
|
|
||||||
|
|
||||||
git clone https://huggingface.co/csukuangfj/kaldifeat huggingface
|
|
||||||
cd huggingface
|
|
||||||
git pull
|
|
||||||
|
|
||||||
d=cpu/1.25.5.dev20241029/windows
|
|
||||||
mkdir -p $d
|
|
||||||
cp -v ../wheelhouse/*.whl ./$d
|
|
||||||
git status
|
|
||||||
git lfs track "*.whl"
|
|
||||||
git add .
|
|
||||||
git commit -m "upload windows-cpu wheel for torch ${{ matrix.torch }} python ${{ matrix.python-version }}"
|
|
||||||
git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/kaldifeat main
|
|
||||||
3
.gitignore
vendored
3
.gitignore
vendored
@ -5,6 +5,3 @@ dist/
|
|||||||
__pycache__/
|
__pycache__/
|
||||||
test-1hour.wav
|
test-1hour.wav
|
||||||
path.sh
|
path.sh
|
||||||
torch_version.py
|
|
||||||
cpu*.html
|
|
||||||
cuda*.html
|
|
||||||
|
|||||||
@ -1,16 +1,10 @@
|
|||||||
# Copyright (c) 2021 Xiaomi Corporation (author: Fangjun Kuang)
|
# Copyright (c) 2021 Xiaomi Corporation (author: Fangjun Kuang)
|
||||||
if (CMAKE_VERSION VERSION_GREATER_EQUAL "4.0.0")
|
|
||||||
set(CMAKE_POLICY_VERSION_MINIMUM 3.5)
|
|
||||||
endif()
|
|
||||||
|
|
||||||
cmake_minimum_required(VERSION 3.8 FATAL_ERROR)
|
cmake_minimum_required(VERSION 3.8 FATAL_ERROR)
|
||||||
|
|
||||||
project(kaldifeat)
|
project(kaldifeat)
|
||||||
|
|
||||||
# remember to change the version in
|
set(kaldifeat_VERSION "1.2")
|
||||||
# scripts/conda/kaldifeat/meta.yaml
|
|
||||||
# scripts/conda-cpu/kaldifeat/meta.yaml
|
|
||||||
set(kaldifeat_VERSION "1.25.5")
|
|
||||||
|
|
||||||
set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib")
|
set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib")
|
||||||
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib")
|
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib")
|
||||||
@ -19,102 +13,32 @@ set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin")
|
|||||||
set(CMAKE_SKIP_BUILD_RPATH FALSE)
|
set(CMAKE_SKIP_BUILD_RPATH FALSE)
|
||||||
set(BUILD_RPATH_USE_ORIGIN TRUE)
|
set(BUILD_RPATH_USE_ORIGIN TRUE)
|
||||||
set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
|
set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
|
||||||
|
set(CMAKE_INSTALL_RPATH "$ORIGIN")
|
||||||
|
set(CMAKE_BUILD_RPATH "$ORIGIN")
|
||||||
if(NOT APPLE)
|
|
||||||
set(kaldifeat_rpath_origin "$ORIGIN")
|
|
||||||
else()
|
|
||||||
set(kaldifeat_rpath_origin "@loader_path")
|
|
||||||
endif()
|
|
||||||
|
|
||||||
set(CMAKE_INSTALL_RPATH ${kaldifeat_rpath_origin})
|
|
||||||
set(CMAKE_BUILD_RPATH ${kaldifeat_rpath_origin})
|
|
||||||
|
|
||||||
|
|
||||||
if(NOT CMAKE_BUILD_TYPE)
|
if(NOT CMAKE_BUILD_TYPE)
|
||||||
message(STATUS "No CMAKE_BUILD_TYPE given, default to Release")
|
message(STATUS "No CMAKE_BUILD_TYPE given, default to Release")
|
||||||
set(CMAKE_BUILD_TYPE Release)
|
set(CMAKE_BUILD_TYPE Release)
|
||||||
endif()
|
endif()
|
||||||
|
|
||||||
if (NOT CMAKE_CXX_STANDARD)
|
set(CMAKE_CXX_STANDARD 14 CACHE STRING "The C++ version to be used.")
|
||||||
set(CMAKE_CXX_STANDARD 17 CACHE STRING "The C++ version to be used.")
|
|
||||||
endif()
|
|
||||||
message(STATUS "C++ Standard version: ${CMAKE_CXX_STANDARD}")
|
|
||||||
set(CMAKE_CXX_EXTENSIONS OFF)
|
set(CMAKE_CXX_EXTENSIONS OFF)
|
||||||
|
|
||||||
list(APPEND CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/Modules)
|
message(STATUS "C++ Standard version: ${CMAKE_CXX_STANDARD}")
|
||||||
list(APPEND CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake)
|
|
||||||
|
|
||||||
option(BUILD_SHARED_LIBS "Whether to build shared libraries" ON)
|
list(APPEND CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake/Modules)
|
||||||
option(kaldifeat_BUILD_TESTS "Whether to build tests or not" OFF)
|
list(APPEND CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake)
|
||||||
option(kaldifeat_BUILD_PYMODULE "Whether to build python module or not" ON)
|
|
||||||
|
|
||||||
message(STATUS "BUILD_SHARED_LIBS ${BUILD_SHARED_LIBS}")
|
|
||||||
|
|
||||||
if(BUILD_SHARED_LIBS AND MSVC)
|
option(BUILD_TESTS "Whether to build tests or not" ON)
|
||||||
set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
|
|
||||||
endif()
|
|
||||||
|
|
||||||
if(kaldifeat_BUILD_PYMODULE)
|
include(pybind11)
|
||||||
include(pybind11)
|
|
||||||
endif()
|
|
||||||
# to prevent cmake from trying to link with system installed mkl since we not directly use it
|
|
||||||
# mkl libraries should be linked with pytorch already
|
|
||||||
# ref: https://github.com/pytorch/pytorch/blob/master/cmake/public/mkl.cmake
|
|
||||||
set(CMAKE_DISABLE_FIND_PACKAGE_MKL TRUE)
|
|
||||||
include(torch)
|
include(torch)
|
||||||
|
|
||||||
if(kaldifeat_BUILD_TESTS)
|
if(BUILD_TESTS)
|
||||||
include(googletest)
|
include(googletest)
|
||||||
enable_testing()
|
enable_testing()
|
||||||
endif()
|
endif()
|
||||||
|
|
||||||
|
include_directories(${CMAKE_SOURCE_DIR})
|
||||||
if(WIN32)
|
|
||||||
# disable various warnings for MSVC
|
|
||||||
# 4624: destructor was implicitly defined as deleted because a base class destructor is inaccessible or deleted
|
|
||||||
set(disabled_warnings
|
|
||||||
/wd4624
|
|
||||||
)
|
|
||||||
message(STATUS "Disabled warnings: ${disabled_warnings}")
|
|
||||||
foreach(w IN LISTS disabled_warnings)
|
|
||||||
string(APPEND CMAKE_CXX_FLAGS " ${w} ")
|
|
||||||
endforeach()
|
|
||||||
endif()
|
|
||||||
|
|
||||||
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
|
|
||||||
|
|
||||||
message(STATUS "CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}")
|
|
||||||
|
|
||||||
message(STATUS "CMAKE_INSTALL_PREFIX: ${CMAKE_INSTALL_PREFIX}")
|
|
||||||
|
|
||||||
add_subdirectory(kaldifeat)
|
add_subdirectory(kaldifeat)
|
||||||
|
|
||||||
# TORCH_VERSION is defined in cmake/torch.cmake
|
|
||||||
configure_file(
|
|
||||||
${PROJECT_SOURCE_DIR}/kaldifeat/python/kaldifeat/torch_version.py.in
|
|
||||||
${PROJECT_SOURCE_DIR}/kaldifeat/python/kaldifeat/torch_version.py @ONLY
|
|
||||||
)
|
|
||||||
|
|
||||||
configure_file(
|
|
||||||
${PROJECT_SOURCE_DIR}/cmake/kaldifeatConfigVersion.cmake.in
|
|
||||||
${PROJECT_BINARY_DIR}/kaldifeatConfigVersion.cmake
|
|
||||||
@ONLY
|
|
||||||
)
|
|
||||||
|
|
||||||
configure_file(
|
|
||||||
${PROJECT_SOURCE_DIR}/cmake/kaldifeatConfig.cmake.in
|
|
||||||
${PROJECT_BINARY_DIR}/kaldifeatConfig.cmake
|
|
||||||
@ONLY
|
|
||||||
)
|
|
||||||
|
|
||||||
install(FILES
|
|
||||||
${PROJECT_BINARY_DIR}/kaldifeatConfigVersion.cmake
|
|
||||||
${PROJECT_BINARY_DIR}/kaldifeatConfig.cmake
|
|
||||||
DESTINATION share/cmake/kaldifeat
|
|
||||||
)
|
|
||||||
|
|
||||||
install(FILES
|
|
||||||
${PROJECT_SOURCE_DIR}/kaldifeat/python/kaldifeat/torch_version.py
|
|
||||||
DESTINATION ./
|
|
||||||
)
|
|
||||||
|
|||||||
@ -1,6 +0,0 @@
|
|||||||
include LICENSE
|
|
||||||
include README.md
|
|
||||||
include CMakeLists.txt
|
|
||||||
exclude pyproject.toml
|
|
||||||
recursive-include kaldifeat *.*
|
|
||||||
recursive-include cmake *.*
|
|
||||||
242
README.md
242
README.md
@ -1,178 +1,8 @@
|
|||||||
# kaldifeat
|
# kaldifeat
|
||||||
|
|
||||||
<div align="center">
|
|
||||||
<img src="/doc/source/images/os-green.svg">
|
|
||||||
<img src="/doc/source/images/python_ge_3.6-blue.svg">
|
|
||||||
<img src="/doc/source/images/pytorch_ge_1.5.0-green.svg">
|
|
||||||
<img src="/doc/source/images/cuda_ge_10.1-orange.svg">
|
|
||||||
</div>
|
|
||||||
|
|
||||||
[](https://csukuangfj.github.io/kaldifeat/)
|
|
||||||
|
|
||||||
**Documentation**: <https://csukuangfj.github.io/kaldifeat>
|
|
||||||
|
|
||||||
**Note**: If you are looking for a version that does not depend on PyTorch,
|
|
||||||
please see <https://github.com/csukuangfj/kaldi-native-fbank>
|
|
||||||
|
|
||||||
# Installation
|
|
||||||
|
|
||||||
Refer to
|
|
||||||
<https://csukuangfj.github.io/kaldifeat/installation/from_wheels.html>
|
|
||||||
for installation.
|
|
||||||
|
|
||||||
> Never use `pip install kaldifeat`
|
|
||||||
|
|
||||||
> Never use `pip install kaldifeat`
|
|
||||||
|
|
||||||
> Never use `pip install kaldifeat`
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
<sub>
|
|
||||||
<table>
|
|
||||||
<tr>
|
|
||||||
<th>Comments</th>
|
|
||||||
<th>Options</th>
|
|
||||||
<th>Feature Computer</th>
|
|
||||||
<th>Usage</th>
|
|
||||||
</tr>
|
|
||||||
|
|
||||||
<tr>
|
|
||||||
<td>Fbank for <a href="https://github.com/openai/whisper">Whisper</a></td>
|
|
||||||
<td><code>kaldifeat.WhisperFbankOptions</code></td>
|
|
||||||
<td><code>kaldifeat.WhisperFbank</code></td>
|
|
||||||
<td>
|
|
||||||
<pre lang="python">
|
|
||||||
opts = kaldifeat.WhisperFbankOptions()
|
|
||||||
opts.device = torch.device('cuda', 0)
|
|
||||||
fbank = kaldifeat.WhisperFbank(opts)
|
|
||||||
features = fbank(wave)
|
|
||||||
</pre>
|
|
||||||
See <a href="https://github.com/csukuangfj/kaldifeat/pull/82">#82</a>
|
|
||||||
</td>
|
|
||||||
</tr>
|
|
||||||
|
|
||||||
<tr>
|
|
||||||
<td>Fbank for <a href="https://github.com/openai/whisper">Whisper-V3</a></td>
|
|
||||||
<td><code>kaldifeat.WhisperFbankOptions</code></td>
|
|
||||||
<td><code>kaldifeat.WhisperFbank</code></td>
|
|
||||||
<td>
|
|
||||||
<pre lang="python">
|
|
||||||
opts = kaldifeat.WhisperFbankOptions()
|
|
||||||
opts.num_mels = 128
|
|
||||||
opts.device = torch.device('cuda', 0)
|
|
||||||
fbank = kaldifeat.WhisperFbank(opts)
|
|
||||||
features = fbank(wave)
|
|
||||||
</pre>
|
|
||||||
</td>
|
|
||||||
</tr>
|
|
||||||
|
|
||||||
<tr>
|
|
||||||
<td>FBANK</td>
|
|
||||||
<td><code>kaldifeat.FbankOptions</code></td>
|
|
||||||
<td><code>kaldifeat.Fbank</code></td>
|
|
||||||
<td>
|
|
||||||
<pre lang="python">
|
|
||||||
opts = kaldifeat.FbankOptions()
|
|
||||||
opts.device = torch.device('cuda', 0)
|
|
||||||
opts.frame_opts.window_type = 'povey'
|
|
||||||
fbank = kaldifeat.Fbank(opts)
|
|
||||||
features = fbank(wave)
|
|
||||||
</pre>
|
|
||||||
</td>
|
|
||||||
</tr>
|
|
||||||
|
|
||||||
<tr>
|
|
||||||
<td>Streaming FBANK</td>
|
|
||||||
<td><code>kaldifeat.FbankOptions</code></td>
|
|
||||||
<td><code>kaldifeat.OnlineFbank</code></td>
|
|
||||||
<td>
|
|
||||||
See <a href="./kaldifeat/python/tests/test_fbank.py">
|
|
||||||
./kaldifeat/python/tests/test_fbank.py
|
|
||||||
</a>
|
|
||||||
</td>
|
|
||||||
</tr>
|
|
||||||
|
|
||||||
<tr>
|
|
||||||
<td>MFCC</td>
|
|
||||||
<td><code>kaldifeat.MfccOptions</code></td>
|
|
||||||
<td><code>kaldifeat.Mfcc</code></td>
|
|
||||||
<td>
|
|
||||||
<pre lang="python">
|
|
||||||
opts = kaldifeat.MfccOptions();
|
|
||||||
opts.num_ceps = 13
|
|
||||||
mfcc = kaldifeat.Mfcc(opts)
|
|
||||||
features = mfcc(wave)
|
|
||||||
</pre>
|
|
||||||
</td>
|
|
||||||
</tr>
|
|
||||||
|
|
||||||
<tr>
|
|
||||||
<td>Streaming MFCC</td>
|
|
||||||
<td><code>kaldifeat.MfccOptions</code></td>
|
|
||||||
<td><code>kaldifeat.OnlineMfcc</code></td>
|
|
||||||
<td>
|
|
||||||
See <a href="./kaldifeat/python/tests/test_mfcc.py">
|
|
||||||
./kaldifeat/python/tests/test_mfcc.py
|
|
||||||
</a>
|
|
||||||
</td>
|
|
||||||
</tr>
|
|
||||||
|
|
||||||
<tr>
|
|
||||||
<td>PLP</td>
|
|
||||||
<td><code>kaldifeat.PlpOptions</code></td>
|
|
||||||
<td><code>kaldifeat.Plp</code></td>
|
|
||||||
<td>
|
|
||||||
<pre lang="python">
|
|
||||||
opts = kaldifeat.PlpOptions();
|
|
||||||
opts.mel_opts.num_bins = 23
|
|
||||||
plp = kaldifeat.Plp(opts)
|
|
||||||
features = plp(wave)
|
|
||||||
</pre>
|
|
||||||
</td>
|
|
||||||
</tr>
|
|
||||||
|
|
||||||
<tr>
|
|
||||||
<td>Streaming PLP</td>
|
|
||||||
<td><code>kaldifeat.PlpOptions</code></td>
|
|
||||||
<td><code>kaldifeat.OnlinePlp</code></td>
|
|
||||||
<td>
|
|
||||||
See <a href="./kaldifeat/python/tests/test_plp.py">
|
|
||||||
./kaldifeat/python/tests/test_plp.py
|
|
||||||
</a>
|
|
||||||
</td>
|
|
||||||
</tr>
|
|
||||||
|
|
||||||
<tr>
|
|
||||||
<td>Spectorgram</td>
|
|
||||||
<td><code>kaldifeat.SpectrogramOptions</code></td>
|
|
||||||
<td><code>kaldifeat.Spectrogram</code></td>
|
|
||||||
<td>
|
|
||||||
<pre lang="python">
|
|
||||||
opts = kaldifeat.SpectrogramOptions();
|
|
||||||
print(opts)
|
|
||||||
spectrogram = kaldifeat.Spectrogram(opts)
|
|
||||||
features = spectrogram(wave)
|
|
||||||
</pre>
|
|
||||||
</td>
|
|
||||||
</tr>
|
|
||||||
</table>
|
|
||||||
</sub>
|
|
||||||
|
|
||||||
|
|
||||||
Feature extraction compatible with `Kaldi` using PyTorch, supporting
|
Feature extraction compatible with `Kaldi` using PyTorch, supporting
|
||||||
CUDA, batch processing, chunk processing, and autograd.
|
CUDA, batch processing, chunk processing, and autograd.
|
||||||
|
|
||||||
The following kaldi-compatible commandline tools are implemented:
|
|
||||||
|
|
||||||
- `compute-fbank-feats`
|
|
||||||
- `compute-mfcc-feats`
|
|
||||||
- `compute-plp-feats`
|
|
||||||
- `compute-spectrogram-feats`
|
|
||||||
|
|
||||||
(**NOTE**: We will implement other types of features, e.g., Pitch, ivector, etc, soon.)
|
|
||||||
|
|
||||||
**HINT**: It supports also streaming feature extractors for Fbank, MFCC, and Plp.
|
|
||||||
|
|
||||||
# Usage
|
# Usage
|
||||||
|
|
||||||
@ -184,7 +14,7 @@ Let us first generate a test wave using sox:
|
|||||||
sox -n -r 16000 -b 16 test.wav synth 1.2 sine 300-3300
|
sox -n -r 16000 -b 16 test.wav synth 1.2 sine 300-3300
|
||||||
```
|
```
|
||||||
|
|
||||||
**HINT**: Download [test.wav][test_wav].
|
**HINT**: Download [test_wav][test_wav].
|
||||||
|
|
||||||
[test_wav]: kaldifeat/python/tests/test_data/test.wav
|
[test_wav]: kaldifeat/python/tests/test_data/test.wav
|
||||||
|
|
||||||
@ -209,8 +39,8 @@ features = fbank(wave)
|
|||||||
```
|
```
|
||||||
|
|
||||||
To compute features that are compatible with `Kaldi`, wave samples have to be
|
To compute features that are compatible with `Kaldi`, wave samples have to be
|
||||||
scaled to the range `[-32768, 32768]`. **WARNING**: You don't have to do this if
|
scaled to the range `[-32768, 32768]`. WARNING: You don't have to do this if
|
||||||
you don't care about the compatibility with `Kaldi`.
|
you don't care about the compatibility with `Kaldi`
|
||||||
|
|
||||||
The following is an example:
|
The following is an example:
|
||||||
|
|
||||||
@ -255,7 +85,7 @@ The output is:
|
|||||||
You can see that ``kaldifeat`` produces the same output as `Kaldi` (within some tolerance due to numerical precision).
|
You can see that ``kaldifeat`` produces the same output as `Kaldi` (within some tolerance due to numerical precision).
|
||||||
|
|
||||||
|
|
||||||
**HINT**: Download [test.scp][test_scp] and [test.txt][test_txt].
|
**HINT**: Download [test_scp][test_scp] and [test_txt][test_txt].
|
||||||
|
|
||||||
[test_scp]: kaldifeat/python/tests/test_data/test.scp
|
[test_scp]: kaldifeat/python/tests/test_data/test.scp
|
||||||
[test_txt]: kaldifeat/python/tests/test_data/test.txt
|
[test_txt]: kaldifeat/python/tests/test_data/test.txt
|
||||||
@ -273,25 +103,13 @@ fbank = kaldifeat.Fbank(opts)
|
|||||||
features = fbank(wave.to(opts.device))
|
features = fbank(wave.to(opts.device))
|
||||||
```
|
```
|
||||||
|
|
||||||
## MFCC, PLP, Spectrogram
|
## MFCC
|
||||||
|
|
||||||
To compute MFCC features, please replace `kaldifeat.FbankOptions` and `kaldifeat.Fbank`
|
To compute MFCC features, please replace `kaldifeat.FbankOptions` and `kaldifeat.Fbank`
|
||||||
with `kaldifeat.MfccOptions` and `kaldifeat.Mfcc`, respectively. The same goes
|
with `kaldifeat.MfccOptions` and `kaldifeat.Mfcc`, respectively.
|
||||||
for `PLP` and `Spectrogram`.
|
|
||||||
|
|
||||||
Please refer to
|
|
||||||
|
|
||||||
- [kaldifeat/python/tests/test_fbank.py](kaldifeat/python/tests/test_fbank.py)
|
|
||||||
- [kaldifeat/python/tests/test_mfcc.py](kaldifeat/python/tests/test_mfcc.py)
|
|
||||||
- [kaldifeat/python/tests/test_plp.py](kaldifeat/python/tests/test_plp.py)
|
|
||||||
- [kaldifeat/python/tests/test_spectrogram.py](kaldifeat/python/tests/test_spectrogram.py)
|
|
||||||
- [kaldifeat/python/tests/test_frame_extraction_options.py](kaldifeat/python/tests/test_frame_extraction_options.py)
|
|
||||||
- [kaldifeat/python/tests/test_mel_bank_options.py](kaldifeat/python/tests/test_mel_bank_options.py)
|
|
||||||
- [kaldifeat/python/tests/test_fbank_options.py](kaldifeat/python/tests/test_fbank_options.py)
|
|
||||||
- [kaldifeat/python/tests/test_mfcc_options.py](kaldifeat/python/tests/test_mfcc_options.py)
|
|
||||||
- [kaldifeat/python/tests/test_spectrogram_options.py](kaldifeat/python/tests/test_spectrogram_options.py)
|
|
||||||
- [kaldifeat/python/tests/test_plp_options.py](kaldifeat/python/tests/test_plp_options.py)
|
|
||||||
|
|
||||||
|
Please refer to [kaldifeat/python/tests/test_fbank.py](kaldifeat/python/tests/test_fbank.py)
|
||||||
|
and [kaldifeat/python/tests/test_mfcc.py](kaldifeat/python/tests/test_mfcc.py)
|
||||||
for more examples.
|
for more examples.
|
||||||
|
|
||||||
**HINT**: In the examples, you can find that
|
**HINT**: In the examples, you can find that
|
||||||
@ -299,31 +117,33 @@ for more examples.
|
|||||||
- ``kaldifeat`` supports batch processing as well as chunk processing
|
- ``kaldifeat`` supports batch processing as well as chunk processing
|
||||||
- ``kaldifeat`` uses the same options as `Kaldi`'s `compute-fbank-feats` and `compute-mfcc-feats`
|
- ``kaldifeat`` uses the same options as `Kaldi`'s `compute-fbank-feats` and `compute-mfcc-feats`
|
||||||
|
|
||||||
# Usage in other projects
|
# Installation
|
||||||
|
|
||||||
## icefall
|
## From PyPi with pip
|
||||||
|
|
||||||
[icefall](https://github.com/k2-fsa/icefall) uses kaldifeat to extract features for a pre-trained model.
|
If you install `kaldifeat` using `pip`, it will also install
|
||||||
|
PyTorch 1.8.1. If this is not what you want, please install `kaldifeat`
|
||||||
|
from source (see below).
|
||||||
|
|
||||||
See <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/conformer_ctc/pretrained.py>.
|
```bash
|
||||||
|
pip install kaldifeat
|
||||||
|
```
|
||||||
|
|
||||||
## k2
|
## From source
|
||||||
|
|
||||||
[k2](https://github.com/k2-fsa/k2) uses kaldifeat's C++ API.
|
The following are the commands to compile `kaldifeat` from source.
|
||||||
|
We assume that you have installed `cmake` and PyTorch.
|
||||||
See <https://github.com/k2-fsa/k2/blob/v2.0-pre/k2/torch/csrc/features.cu>.
|
cmake 3.11 is known to work. Other cmake versions may also work.
|
||||||
|
PyTorch 1.8.1 is known to work. Other PyTorch versions may also work.
|
||||||
## lhotse
|
|
||||||
|
|
||||||
[lhotse](https://github.com/lhotse-speech/lhotse) uses kaldifeat to extract features on GPU.
|
|
||||||
|
|
||||||
See <https://github.com/lhotse-speech/lhotse/blob/master/lhotse/features/kaldifeat.py>.
|
|
||||||
|
|
||||||
## sherpa
|
|
||||||
|
|
||||||
[sherpa](https://github.com/k2-fsa/sherpa) uses kaldifeat for streaming feature
|
|
||||||
extraction.
|
|
||||||
|
|
||||||
See <https://github.com/k2-fsa/sherpa/blob/master/sherpa/bin/pruned_stateless_emformer_rnnt2/decode.py>
|
|
||||||
|
|
||||||
|
```bash
|
||||||
|
mkdir /some/path
|
||||||
|
git clone https://github.com/csukuangfj/kaldifeat.git
|
||||||
|
cd kaldifeat
|
||||||
|
python setup.py install
|
||||||
|
```
|
||||||
|
|
||||||
|
To test whether `kaldifeat` was installed successfully, you can run:
|
||||||
|
```bash
|
||||||
|
python3 -c "import kaldifeat; print(kaldifeat.__version__)"
|
||||||
|
```
|
||||||
|
|||||||
@ -2,34 +2,19 @@
|
|||||||
|
|
||||||
import glob
|
import glob
|
||||||
import os
|
import os
|
||||||
import platform
|
|
||||||
import shutil
|
import shutil
|
||||||
import sys
|
import sys
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
|
|
||||||
import setuptools
|
import setuptools
|
||||||
import torch
|
|
||||||
from setuptools.command.build_ext import build_ext
|
from setuptools.command.build_ext import build_ext
|
||||||
|
|
||||||
|
|
||||||
def get_pytorch_version():
|
|
||||||
# if it is 1.7.1+cuda101, then strip +cuda101
|
|
||||||
return torch.__version__.split("+")[0]
|
|
||||||
|
|
||||||
|
|
||||||
def is_for_pypi():
|
def is_for_pypi():
|
||||||
ans = os.environ.get("KALDIFEAT_IS_FOR_PYPI", None)
|
ans = os.environ.get("KALDIFEAT_IS_FOR_PYPI", None)
|
||||||
return ans is not None
|
return ans is not None
|
||||||
|
|
||||||
|
|
||||||
def is_macos():
|
|
||||||
return platform.system() == "Darwin"
|
|
||||||
|
|
||||||
|
|
||||||
def is_windows():
|
|
||||||
return platform.system() == "Windows"
|
|
||||||
|
|
||||||
|
|
||||||
try:
|
try:
|
||||||
from wheel.bdist_wheel import bdist_wheel as _bdist_wheel
|
from wheel.bdist_wheel import bdist_wheel as _bdist_wheel
|
||||||
|
|
||||||
@ -37,14 +22,15 @@ try:
|
|||||||
def finalize_options(self):
|
def finalize_options(self):
|
||||||
_bdist_wheel.finalize_options(self)
|
_bdist_wheel.finalize_options(self)
|
||||||
# In this case, the generated wheel has a name in the form
|
# In this case, the generated wheel has a name in the form
|
||||||
# kaldifeat-xxx-pyxx-none-any.whl
|
# k2-xxx-pyxx-none-any.whl
|
||||||
if is_for_pypi() and not is_macos():
|
if is_for_pypi():
|
||||||
self.root_is_pure = True
|
self.root_is_pure = True
|
||||||
else:
|
else:
|
||||||
# The generated wheel has a name ending with
|
# The generated wheel has a name ending with
|
||||||
# -linux_x86_64.whl
|
# -linux_x86_64.whl
|
||||||
self.root_is_pure = False
|
self.root_is_pure = False
|
||||||
|
|
||||||
|
|
||||||
except ImportError:
|
except ImportError:
|
||||||
bdist_wheel = None
|
bdist_wheel = None
|
||||||
|
|
||||||
@ -72,67 +58,33 @@ class BuildExtension(build_ext):
|
|||||||
if cmake_args == "":
|
if cmake_args == "":
|
||||||
cmake_args = "-DCMAKE_BUILD_TYPE=Release"
|
cmake_args = "-DCMAKE_BUILD_TYPE=Release"
|
||||||
|
|
||||||
extra_cmake_args = " -Dkaldifeat_BUILD_TESTS=OFF "
|
if make_args == "" and system_make_args == "":
|
||||||
extra_cmake_args += f" -DCMAKE_INSTALL_PREFIX={Path(self.build_lib).resolve()}/kaldifeat " # noqa
|
print("For fast compilation, run:")
|
||||||
|
print('export KALDIFEAT_MAKE_ARGS="-j"; python setup.py install')
|
||||||
major, minor = get_pytorch_version().split(".")[:2]
|
|
||||||
print("major, minor", major, minor)
|
|
||||||
major = int(major)
|
|
||||||
minor = int(minor)
|
|
||||||
if major > 2 or (major == 2 and minor >= 1):
|
|
||||||
extra_cmake_args += f" -DCMAKE_CXX_STANDARD=17 "
|
|
||||||
|
|
||||||
if "PYTHON_EXECUTABLE" not in cmake_args:
|
if "PYTHON_EXECUTABLE" not in cmake_args:
|
||||||
print(f"Setting PYTHON_EXECUTABLE to {sys.executable}")
|
print(f"Setting PYTHON_EXECUTABLE to {sys.executable}")
|
||||||
cmake_args += f" -DPYTHON_EXECUTABLE={sys.executable}"
|
cmake_args += f" -DPYTHON_EXECUTABLE={sys.executable}"
|
||||||
|
|
||||||
cmake_args += extra_cmake_args
|
build_cmd = f"""
|
||||||
|
cd {self.build_temp}
|
||||||
|
|
||||||
if is_windows():
|
cmake {cmake_args} {kaldifeat_dir}
|
||||||
build_cmd = f"""
|
|
||||||
cmake {cmake_args} -B {self.build_temp} -S {kaldifeat_dir}
|
|
||||||
cmake --build {self.build_temp} --target _kaldifeat --config Release -- -m
|
make {make_args} _kaldifeat
|
||||||
cmake --build {self.build_temp} --target install --config Release -- -m
|
"""
|
||||||
"""
|
print(f"build command is:\n{build_cmd}")
|
||||||
print(f"build command is:\n{build_cmd}")
|
|
||||||
ret = os.system(
|
ret = os.system(build_cmd)
|
||||||
f"cmake {cmake_args} -B {self.build_temp} -S {kaldifeat_dir}"
|
if ret != 0:
|
||||||
|
raise Exception(
|
||||||
|
"\nBuild kaldifeat failed. Please check the error message.\n"
|
||||||
|
"You can ask for help by creating an issue on GitHub.\n"
|
||||||
|
"\nClick:\n\thttps://github.com/csukuangfj/kaldifeat/issues/new\n" # noqa
|
||||||
)
|
)
|
||||||
if ret != 0:
|
|
||||||
raise Exception("Failed to configure kaldifeat")
|
|
||||||
|
|
||||||
ret = os.system(
|
lib_so = glob.glob(f"{self.build_temp}/lib/*kaldifeat*.so")
|
||||||
f"cmake --build {self.build_temp} --target _kaldifeat --config Release -- -m"
|
for so in lib_so:
|
||||||
)
|
print(f"Copying {so} to {self.build_lib}/")
|
||||||
if ret != 0:
|
shutil.copy(f"{so}", f"{self.build_lib}/")
|
||||||
raise Exception("Failed to build kaldifeat")
|
|
||||||
|
|
||||||
ret = os.system(
|
|
||||||
f"cmake --build {self.build_temp} --target install --config Release -- -m"
|
|
||||||
)
|
|
||||||
if ret != 0:
|
|
||||||
raise Exception("Failed to install kaldifeat")
|
|
||||||
else:
|
|
||||||
if make_args == "" and system_make_args == "":
|
|
||||||
print("For fast compilation, run:")
|
|
||||||
print('export KALDIFEAT_MAKE_ARGS="-j"; python setup.py install')
|
|
||||||
make_args = " -j4 "
|
|
||||||
print("Setting make_args to '-j4'")
|
|
||||||
|
|
||||||
build_cmd = f"""
|
|
||||||
cd {self.build_temp}
|
|
||||||
|
|
||||||
cmake {cmake_args} {kaldifeat_dir}
|
|
||||||
|
|
||||||
|
|
||||||
make {make_args} _kaldifeat install
|
|
||||||
"""
|
|
||||||
print(f"build command is:\n{build_cmd}")
|
|
||||||
|
|
||||||
ret = os.system(build_cmd)
|
|
||||||
if ret != 0:
|
|
||||||
raise Exception(
|
|
||||||
"\nBuild kaldifeat failed. Please check the error message.\n"
|
|
||||||
"You can ask for help by creating an issue on GitHub.\n"
|
|
||||||
"\nClick:\n\thttps://github.com/csukuangfj/kaldifeat/issues/new\n" # noqa
|
|
||||||
)
|
|
||||||
|
|||||||
@ -18,34 +18,14 @@ function(download_googltest)
|
|||||||
# FetchContent is available since 3.11,
|
# FetchContent is available since 3.11,
|
||||||
# we've copied it to ${CMAKE_SOURCE_DIR}/cmake/Modules
|
# we've copied it to ${CMAKE_SOURCE_DIR}/cmake/Modules
|
||||||
# so that it can be used in lower CMake versions.
|
# so that it can be used in lower CMake versions.
|
||||||
message(STATUS "Use FetchContent provided by kaldifeat")
|
message(STATUS "Use FetchContent provided by k2")
|
||||||
list(APPEND CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake/Modules)
|
list(APPEND CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake/Modules)
|
||||||
endif()
|
endif()
|
||||||
|
|
||||||
include(FetchContent)
|
include(FetchContent)
|
||||||
|
|
||||||
set(googletest_URL "https://github.com/google/googletest/archive/refs/tags/v1.13.0.tar.gz")
|
set(googletest_URL "https://github.com/google/googletest/archive/release-1.10.0.tar.gz")
|
||||||
set(googletest_URL2 "https://huggingface.co/csukuangfj/k2-cmake-deps/resolve/main/googletest-1.13.0.tar.gz")
|
set(googletest_HASH "SHA256=9dc9157a9a1551ec7a7e43daea9a694a0bb5fb8bec81235d8a1e6ef64c716dcb")
|
||||||
set(googletest_HASH "SHA256=ad7fdba11ea011c1d925b3289cf4af2c66a352e18d4c7264392fead75e919363")
|
|
||||||
|
|
||||||
# If you don't have access to the Internet,
|
|
||||||
# please pre-download googletest
|
|
||||||
set(possible_file_locations
|
|
||||||
$ENV{HOME}/Downloads/googletest-1.13.0.tar.gz
|
|
||||||
${PROJECT_SOURCE_DIR}/googletest-1.13.0.tar.gz
|
|
||||||
${PROJECT_BINARY_DIR}/googletest-1.13.0.tar.gz
|
|
||||||
/tmp/googletest-1.13.0.tar.gz
|
|
||||||
/star-fj/fangjun/download/github/googletest-1.13.0.tar.gz
|
|
||||||
)
|
|
||||||
|
|
||||||
foreach(f IN LISTS possible_file_locations)
|
|
||||||
if(EXISTS ${f})
|
|
||||||
set(googletest_URL "${f}")
|
|
||||||
file(TO_CMAKE_PATH "${googletest_URL}" googletest_URL)
|
|
||||||
set(googletest_URL2)
|
|
||||||
break()
|
|
||||||
endif()
|
|
||||||
endforeach()
|
|
||||||
|
|
||||||
set(BUILD_GMOCK ON CACHE BOOL "" FORCE)
|
set(BUILD_GMOCK ON CACHE BOOL "" FORCE)
|
||||||
set(INSTALL_GTEST OFF CACHE BOOL "" FORCE)
|
set(INSTALL_GTEST OFF CACHE BOOL "" FORCE)
|
||||||
@ -53,15 +33,13 @@ function(download_googltest)
|
|||||||
set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
|
set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
|
||||||
|
|
||||||
FetchContent_Declare(googletest
|
FetchContent_Declare(googletest
|
||||||
URL
|
URL ${googletest_URL}
|
||||||
${googletest_URL}
|
|
||||||
${googletest_URL2}
|
|
||||||
URL_HASH ${googletest_HASH}
|
URL_HASH ${googletest_HASH}
|
||||||
)
|
)
|
||||||
|
|
||||||
FetchContent_GetProperties(googletest)
|
FetchContent_GetProperties(googletest)
|
||||||
if(NOT googletest_POPULATED)
|
if(NOT googletest_POPULATED)
|
||||||
message(STATUS "Downloading googletest from ${googletest_URL}")
|
message(STATUS "Downloading googletest")
|
||||||
FetchContent_Populate(googletest)
|
FetchContent_Populate(googletest)
|
||||||
endif()
|
endif()
|
||||||
message(STATUS "googletest is downloaded to ${googletest_SOURCE_DIR}")
|
message(STATUS "googletest is downloaded to ${googletest_SOURCE_DIR}")
|
||||||
|
|||||||
@ -1,65 +0,0 @@
|
|||||||
# Findkaldifeat
|
|
||||||
# -------------
|
|
||||||
#
|
|
||||||
# Finds the kaldifeat library
|
|
||||||
#
|
|
||||||
# This will define the following variables:
|
|
||||||
#
|
|
||||||
# KALDIFEAT_FOUND -- True if the system has the kaldifeat library
|
|
||||||
# KALDIFEAT_INCLUDE_DIRS -- The include directories for kaldifeat
|
|
||||||
# KALDIFEAT_LIBRARIES -- Libraries to link against
|
|
||||||
# KALDIFEAT_CXX_FLAGS -- Additional (required) compiler flags
|
|
||||||
# KALDIFEAT_TORCH_VERSION_MAJOR -- The major version of PyTorch used to compile kaldifeat
|
|
||||||
# KALDIFEAT_TORCH_VERSION_MINOR -- The minor version of PyTorch used to compile kaldifeat
|
|
||||||
# KALDIFEAT_VERSION -- The version of kaldifeat
|
|
||||||
#
|
|
||||||
# and the following imported targets:
|
|
||||||
#
|
|
||||||
# kaldifeat_core
|
|
||||||
|
|
||||||
# This file is modified from pytorch/cmake/TorchConfig.cmake.in
|
|
||||||
|
|
||||||
set(KALDIFEAT_CXX_FLAGS "@CMAKE_CXX_FLAGS@")
|
|
||||||
set(KALDIFEAT_TORCH_VERSION_MAJOR @KALDIFEAT_TORCH_VERSION_MAJOR@)
|
|
||||||
set(KALDIFEAT_TORCH_VERSION_MINOR @KALDIFEAT_TORCH_VERSION_MINOR@)
|
|
||||||
set(KALDIFEAT_VERSION @kaldifeat_VERSION@)
|
|
||||||
|
|
||||||
if(DEFINED ENV{KALDIFEAT_INSTALL_PREFIX})
|
|
||||||
set(KALDIFEAT_INSTALL_PREFIX $ENV{KALDIFEAT_INSTALL_PREFIX})
|
|
||||||
else()
|
|
||||||
# Assume we are in <install-prefix>/share/cmake/kaldifeat/kaldifeatConfig.cmake
|
|
||||||
get_filename_component(CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
|
|
||||||
get_filename_component(KALDIFEAT_INSTALL_PREFIX "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
|
|
||||||
endif()
|
|
||||||
|
|
||||||
set(KALDIFEAT_INCLUDE_DIRS ${KALDIFEAT_INSTALL_PREFIX}/include)
|
|
||||||
|
|
||||||
set(KALDIFEAT_LIBRARIES kaldifeat_core)
|
|
||||||
|
|
||||||
foreach(lib IN LISTS KALDIFEAT_LIBRARIES)
|
|
||||||
find_library(location_${lib} ${lib}
|
|
||||||
PATHS
|
|
||||||
"${KALDIFEAT_INSTALL_PREFIX}/lib"
|
|
||||||
"${KALDIFEAT_INSTALL_PREFIX}/lib64"
|
|
||||||
)
|
|
||||||
|
|
||||||
if(NOT MSVC)
|
|
||||||
add_library(${lib} SHARED IMPORTED)
|
|
||||||
else()
|
|
||||||
add_library(${lib} STATIC IMPORTED)
|
|
||||||
endif()
|
|
||||||
|
|
||||||
set_target_properties(${lib} PROPERTIES
|
|
||||||
INTERFACE_INCLUDE_DIRECTORIES "${KALDIFEAT_INCLUDE_DIRS}"
|
|
||||||
IMPORTED_LOCATION "${location_${lib}}"
|
|
||||||
CXX_STANDARD 14
|
|
||||||
)
|
|
||||||
|
|
||||||
set_property(TARGET ${lib} PROPERTY INTERFACE_COMPILE_OPTIONS @CMAKE_CXX_FLAGS@)
|
|
||||||
endforeach()
|
|
||||||
|
|
||||||
include(FindPackageHandleStandardArgs)
|
|
||||||
|
|
||||||
find_package_handle_standard_args(kaldifeat DEFAULT_MSG
|
|
||||||
location_kaldifeat_core
|
|
||||||
)
|
|
||||||
@ -1,12 +0,0 @@
|
|||||||
# This file is modified from pytorch/cmake/TorchConfigVersion.cmake.in
|
|
||||||
set(PACKAGE_VERSION "@kaldifeat_VERSION@")
|
|
||||||
|
|
||||||
# Check whether the requested PACKAGE_FIND_VERSION is compatible
|
|
||||||
if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}")
|
|
||||||
set(PACKAGE_VERSION_COMPATIBLE FALSE)
|
|
||||||
else()
|
|
||||||
set(PACKAGE_VERSION_COMPATIBLE TRUE)
|
|
||||||
if("${PACKAGE_VERSION}" VERSION_EQUAL "${PACKAGE_FIND_VERSION}")
|
|
||||||
set(PACKAGE_VERSION_EXACT TRUE)
|
|
||||||
endif()
|
|
||||||
endif()
|
|
||||||
@ -8,39 +8,23 @@ function(download_pybind11)
|
|||||||
|
|
||||||
include(FetchContent)
|
include(FetchContent)
|
||||||
|
|
||||||
set(pybind11_URL "https://github.com/pybind/pybind11/archive/refs/tags/v2.12.0.tar.gz")
|
set(pybind11_URL "https://github.com/pybind/pybind11/archive/v2.6.0.tar.gz")
|
||||||
set(pybind11_URL2 "https://hf-mirror.com/csukuangfj/sherpa-onnx-cmake-deps/resolve/main/pybind11-2.12.0.tar.gz")
|
set(pybind11_HASH "SHA256=90b705137b69ee3b5fc655eaca66d0dc9862ea1759226f7ccd3098425ae69571")
|
||||||
set(pybind11_HASH "SHA256=bf8f242abd1abcd375d516a7067490fb71abd79519a282d22b6e4d19282185a7")
|
|
||||||
|
|
||||||
# If you don't have access to the Internet,
|
|
||||||
# please pre-download pybind11
|
|
||||||
set(possible_file_locations
|
|
||||||
$ENV{HOME}/Downloads/pybind11-2.12.0.tar.gz
|
|
||||||
${CMAKE_SOURCE_DIR}/pybind11-2.12.0.tar.gz
|
|
||||||
${CMAKE_BINARY_DIR}/pybind11-2.12.0.tar.gz
|
|
||||||
/tmp/pybind11-2.12.0.tar.gz
|
|
||||||
/star-fj/fangjun/download/github/pybind11-2.12.0.tar.gz
|
|
||||||
)
|
|
||||||
|
|
||||||
foreach(f IN LISTS possible_file_locations)
|
|
||||||
if(EXISTS ${f})
|
|
||||||
set(pybind11_URL "${f}")
|
|
||||||
file(TO_CMAKE_PATH "${pybind11_URL}" pybind11_URL)
|
|
||||||
set(pybind11_URL2)
|
|
||||||
break()
|
|
||||||
endif()
|
|
||||||
endforeach()
|
|
||||||
|
|
||||||
|
set(double_quotes "\"")
|
||||||
|
set(dollar "\$")
|
||||||
|
set(semicolon "\;")
|
||||||
FetchContent_Declare(pybind11
|
FetchContent_Declare(pybind11
|
||||||
URL
|
URL ${pybind11_URL}
|
||||||
${pybind11_URL}
|
|
||||||
${pybind11_URL2}
|
|
||||||
URL_HASH ${pybind11_HASH}
|
URL_HASH ${pybind11_HASH}
|
||||||
|
PATCH_COMMAND
|
||||||
|
sed -i s/\\${double_quotes}-flto\\\\${dollar}/\\${double_quotes}-Xcompiler=-flto${dollar}/g "tools/pybind11Tools.cmake" &&
|
||||||
|
sed -i s/${seimcolon}-fno-fat-lto-objects/${seimcolon}-Xcompiler=-fno-fat-lto-objects/g "tools/pybind11Tools.cmake"
|
||||||
)
|
)
|
||||||
|
|
||||||
FetchContent_GetProperties(pybind11)
|
FetchContent_GetProperties(pybind11)
|
||||||
if(NOT pybind11_POPULATED)
|
if(NOT pybind11_POPULATED)
|
||||||
message(STATUS "Downloading pybind11 from ${pybind11_URL}")
|
message(STATUS "Downloading pybind11")
|
||||||
FetchContent_Populate(pybind11)
|
FetchContent_Populate(pybind11)
|
||||||
endif()
|
endif()
|
||||||
message(STATUS "pybind11 is downloaded to ${pybind11_SOURCE_DIR}")
|
message(STATUS "pybind11 is downloaded to ${pybind11_SOURCE_DIR}")
|
||||||
|
|||||||
@ -8,7 +8,6 @@ execute_process(
|
|||||||
OUTPUT_STRIP_TRAILING_WHITESPACE
|
OUTPUT_STRIP_TRAILING_WHITESPACE
|
||||||
OUTPUT_VARIABLE TORCH_DIR
|
OUTPUT_VARIABLE TORCH_DIR
|
||||||
)
|
)
|
||||||
message(STATUS "TORCH_DIR: ${TORCH_DIR}")
|
|
||||||
|
|
||||||
list(APPEND CMAKE_PREFIX_PATH "${TORCH_DIR}")
|
list(APPEND CMAKE_PREFIX_PATH "${TORCH_DIR}")
|
||||||
find_package(Torch REQUIRED)
|
find_package(Torch REQUIRED)
|
||||||
@ -25,14 +24,16 @@ execute_process(
|
|||||||
|
|
||||||
message(STATUS "PyTorch version: ${TORCH_VERSION}")
|
message(STATUS "PyTorch version: ${TORCH_VERSION}")
|
||||||
|
|
||||||
execute_process(
|
# Solve the following error for NVCC:
|
||||||
COMMAND "${PYTHON_EXECUTABLE}" -c "import torch; print(torch.__version__.split('.')[0])"
|
# unknown option `-Wall`
|
||||||
OUTPUT_STRIP_TRAILING_WHITESPACE
|
#
|
||||||
OUTPUT_VARIABLE KALDIFEAT_TORCH_VERSION_MAJOR
|
# It contains only some -Wno-* flags, so it is OK
|
||||||
|
# to set them to empty
|
||||||
|
set_property(TARGET torch_cuda
|
||||||
|
PROPERTY
|
||||||
|
INTERFACE_COMPILE_OPTIONS ""
|
||||||
)
|
)
|
||||||
|
set_property(TARGET torch_cpu
|
||||||
execute_process(
|
PROPERTY
|
||||||
COMMAND "${PYTHON_EXECUTABLE}" -c "import torch; print(torch.__version__.split('.')[1])"
|
INTERFACE_COMPILE_OPTIONS ""
|
||||||
OUTPUT_STRIP_TRAILING_WHITESPACE
|
|
||||||
OUTPUT_VARIABLE KALDIFEAT_TORCH_VERSION_MINOR
|
|
||||||
)
|
)
|
||||||
|
|||||||
@ -1,6 +1,6 @@
|
|||||||
dataclasses
|
dataclasses
|
||||||
recommonmark
|
recommonmark
|
||||||
sphinx<7.0
|
sphinx
|
||||||
sphinx-autodoc-typehints
|
sphinx-autodoc-typehints
|
||||||
sphinx_rtd_theme
|
sphinx_rtd_theme
|
||||||
sphinxcontrib-bibtex
|
sphinxcontrib-bibtex
|
||||||
|
|||||||
72
doc/source/code/test_fbank.py
Executable file
72
doc/source/code/test_fbank.py
Executable file
@ -0,0 +1,72 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
|
||||||
|
# Copyright 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import soundfile as sf
|
||||||
|
import torch
|
||||||
|
|
||||||
|
import kaldifeat
|
||||||
|
|
||||||
|
|
||||||
|
def read_wave(filename) -> torch.Tensor:
|
||||||
|
"""Read a wave file and return it as a 1-D tensor.
|
||||||
|
|
||||||
|
Note:
|
||||||
|
You don't need to scale it to [-32768, 32767].
|
||||||
|
We use scaling here to follow the approach in Kaldi.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
filename:
|
||||||
|
Filename of a sound file.
|
||||||
|
Returns:
|
||||||
|
Return a 1-D tensor containing audio samples.
|
||||||
|
"""
|
||||||
|
with sf.SoundFile(filename) as sf_desc:
|
||||||
|
sampling_rate = sf_desc.samplerate
|
||||||
|
assert sampling_rate == 16000
|
||||||
|
data = sf_desc.read(dtype=np.float32, always_2d=False)
|
||||||
|
data *= 32768
|
||||||
|
return torch.from_numpy(data)
|
||||||
|
|
||||||
|
|
||||||
|
def test_fbank():
|
||||||
|
device = torch.device("cpu")
|
||||||
|
if torch.cuda.is_available():
|
||||||
|
device = torch.device("cuda", 0)
|
||||||
|
|
||||||
|
wave0 = read_wave("test_data/test.wav")
|
||||||
|
wave1 = read_wave("test_data/test2.wav")
|
||||||
|
|
||||||
|
wave0 = wave0.to(device)
|
||||||
|
wave1 = wave1.to(device)
|
||||||
|
|
||||||
|
opts = kaldifeat.FbankOptions()
|
||||||
|
opts.frame_opts.dither = 0
|
||||||
|
opts.device = device
|
||||||
|
|
||||||
|
fbank = kaldifeat.Fbank(opts)
|
||||||
|
|
||||||
|
# We can compute fbank features in batches
|
||||||
|
features = fbank([wave0, wave1])
|
||||||
|
assert isinstance(features, list), f"{type(features)}"
|
||||||
|
assert len(features) == 2
|
||||||
|
|
||||||
|
# We can also compute fbank features for a single wave
|
||||||
|
features0 = fbank(wave0)
|
||||||
|
features1 = fbank(wave1)
|
||||||
|
|
||||||
|
assert torch.allclose(features[0], features0)
|
||||||
|
assert torch.allclose(features[1], features1)
|
||||||
|
|
||||||
|
# To compute fbank features for only a specified frame
|
||||||
|
audio_frames = fbank.convert_samples_to_frames(wave0)
|
||||||
|
feature_frame_1 = fbank.compute(audio_frames[1])
|
||||||
|
feature_frame_10 = fbank.compute(audio_frames[10])
|
||||||
|
|
||||||
|
assert torch.allclose(features0[1], feature_frame_1)
|
||||||
|
assert torch.allclose(features0[10], feature_frame_10)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
test_fbank()
|
||||||
@ -59,7 +59,7 @@ templates_path = ["_templates"]
|
|||||||
# List of patterns, relative to source directory, that match files and
|
# List of patterns, relative to source directory, that match files and
|
||||||
# directories to ignore when looking for source files.
|
# directories to ignore when looking for source files.
|
||||||
# This pattern also affects html_static_path and html_extra_path.
|
# This pattern also affects html_static_path and html_extra_path.
|
||||||
exclude_patterns = ["images/*.md"]
|
exclude_patterns = []
|
||||||
|
|
||||||
source_suffix = {
|
source_suffix = {
|
||||||
".rst": "restructuredtext",
|
".rst": "restructuredtext",
|
||||||
@ -102,35 +102,3 @@ html_theme_options = {
|
|||||||
"prev_next_buttons_location": "bottom",
|
"prev_next_buttons_location": "bottom",
|
||||||
"style_external_links": True,
|
"style_external_links": True,
|
||||||
}
|
}
|
||||||
|
|
||||||
rst_epilog = """
|
|
||||||
.. _kaldifeat: https://github.com/csukuangfj/kaldifeat
|
|
||||||
.. _Kaldi: https://github.com/kaldi-asr/kaldi
|
|
||||||
.. _PyTorch: https://pytorch.org/
|
|
||||||
.. _kaldifeat.Fbank: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/python/kaldifeat/fbank.py#L10
|
|
||||||
.. _kaldifeat.Mfcc: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/python/kaldifeat/mfcc.py#L10
|
|
||||||
.. _kaldifeat.Plp: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/python/kaldifeat/plp.py#L10
|
|
||||||
.. _kaldifeat.Spectrogram: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/python/kaldifeat/spectrogram.py#L9
|
|
||||||
.. _kaldifeat.OnlineFbank: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/python/kaldifeat/fbank.py#L16
|
|
||||||
.. _kaldifeat.OnlineMfcc: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/python/kaldifeat/mfcc.py#L16
|
|
||||||
.. _kaldifeat.OnlinePlp: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/python/kaldifeat/plp.py#L16
|
|
||||||
.. _compute-fbank-feats: https://github.com/kaldi-asr/kaldi/blob/master/src/featbin/compute-fbank-feats.cc
|
|
||||||
.. _compute-mfcc-feats: https://github.com/kaldi-asr/kaldi/blob/master/src/featbin/compute-mfcc-feats.cc
|
|
||||||
.. _compute-plp-feats: https://github.com/kaldi-asr/kaldi/blob/master/src/featbin/compute-plp-feats.cc
|
|
||||||
.. _compute-spectrogram-feats: https://github.com/kaldi-asr/kaldi/blob/master/src/featbin/compute-spectrogram-feats.cc
|
|
||||||
.. _kaldi::OnlineFbank: https://github.com/kaldi-asr/kaldi/blob/master/src/feat/online-feature.h#L160
|
|
||||||
.. _kaldi::OnlineMfcc: https://github.com/kaldi-asr/kaldi/blob/master/src/feat/online-feature.h#L158
|
|
||||||
.. _kaldi::OnlinePlp: https://github.com/kaldi-asr/kaldi/blob/master/src/feat/online-feature.h#L159
|
|
||||||
.. _kaldifeat.FbankOptions: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/csrc/feature-fbank.h#L19
|
|
||||||
.. _kaldi::FbankOptions: https://github.com/kaldi-asr/kaldi/blob/master/src/feat/feature-fbank.h#L41
|
|
||||||
.. _kaldifeat.MfccOptions: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/csrc/feature-mfcc.h#L22
|
|
||||||
.. _kaldi::MfccOptions: https://github.com/kaldi-asr/kaldi/blob/master/src/feat/feature-mfcc.h#L38
|
|
||||||
.. _kaldifeat.PlpOptions: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/csrc/feature-plp.h#L24
|
|
||||||
.. _kaldi::PlpOptions: https://github.com/kaldi-asr/kaldi/blob/master/src/feat/feature-plp.h#L42
|
|
||||||
.. _kaldifeat.SpectrogramOptions: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/csrc/feature-spectrogram.h#L18
|
|
||||||
.. _kaldi::SpectrogramOptions: https://github.com/kaldi-asr/kaldi/blob/master/src/feat/feature-spectrogram.h#L38
|
|
||||||
.. _kaldifeat.FrameExtractionOptions: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/csrc/feature-window.h#L30
|
|
||||||
.. _kaldi::FrameExtractionOptions: https://github.com/kaldi-asr/kaldi/blob/master/src/feat/feature-window.h#L35
|
|
||||||
.. _kaldifeat.MelBanksOptions: https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/csrc/mel-computations.h#L17
|
|
||||||
.. _kaldi::MelBanksOptions: https://github.com/kaldi-asr/kaldi/blob/master/src/feat/mel-computations.h#L43
|
|
||||||
"""
|
|
||||||
|
|||||||
@ -1,8 +0,0 @@
|
|||||||
## File description
|
|
||||||
|
|
||||||
<https://shields.io/> is used to create the following files:
|
|
||||||
|
|
||||||
- ./os.svg
|
|
||||||
- ./python_ge_3.6-blue.svg
|
|
||||||
- ./cuda_ge_10.1-orange.svg
|
|
||||||
- ./pytorch_ge_1.5.0-green.svg
|
|
||||||
@ -1 +0,0 @@
|
|||||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="94" height="20" role="img" aria-label="cuda: >= 10.1"><title>cuda: >= 10.1</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="94" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="37" height="20" fill="#555"/><rect x="37" width="57" height="20" fill="#fe7d37"/><rect width="94" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="195" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="270">cuda</text><text x="195" y="140" transform="scale(.1)" fill="#fff" textLength="270">cuda</text><text aria-hidden="true" x="645" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="470">>= 10.1</text><text x="645" y="140" transform="scale(.1)" fill="#fff" textLength="470">>= 10.1</text></g></svg>
|
|
||||||
|
Before Width: | Height: | Size: 1.1 KiB |
@ -1 +0,0 @@
|
|||||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="176" height="20" role="img" aria-label="os: Linux | macOS | Windows"><title>os: Linux | macOS | Windows</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="176" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="23" height="20" fill="#555"/><rect x="23" width="153" height="20" fill="#97ca00"/><rect width="176" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="125" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="130">os</text><text x="125" y="140" transform="scale(.1)" fill="#fff" textLength="130">os</text><text aria-hidden="true" x="985" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="1430">Linux | macOS | Windows</text><text x="985" y="140" transform="scale(.1)" fill="#fff" textLength="1430">Linux | macOS | Windows</text></g></svg>
|
|
||||||
|
Before Width: | Height: | Size: 1.2 KiB |
@ -1 +0,0 @@
|
|||||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="98" height="20" role="img" aria-label="python: >= 3.6"><title>python: >= 3.6</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="98" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="49" height="20" fill="#555"/><rect x="49" width="49" height="20" fill="#007ec6"/><rect width="98" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="255" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="390">python</text><text x="255" y="140" transform="scale(.1)" fill="#fff" textLength="390">python</text><text aria-hidden="true" x="725" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="390">>= 3.6</text><text x="725" y="140" transform="scale(.1)" fill="#fff" textLength="390">>= 3.6</text></g></svg>
|
|
||||||
|
Before Width: | Height: | Size: 1.1 KiB |
@ -1 +0,0 @@
|
|||||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="112" height="20" role="img" aria-label="pytorch: >= 1.5.0"><title>pytorch: >= 1.5.0</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="112" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="51" height="20" fill="#555"/><rect x="51" width="61" height="20" fill="#97ca00"/><rect width="112" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="265" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="410">pytorch</text><text x="265" y="140" transform="scale(.1)" fill="#fff" textLength="410">pytorch</text><text aria-hidden="true" x="805" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="510">>= 1.5.0</text><text x="805" y="140" transform="scale(.1)" fill="#fff" textLength="510">>= 1.5.0</text></g></svg>
|
|
||||||
|
Before Width: | Height: | Size: 1.1 KiB |
@ -6,11 +6,19 @@
|
|||||||
kaldifeat
|
kaldifeat
|
||||||
=========
|
=========
|
||||||
|
|
||||||
|
`kaldifeat <https://github.com/csukuangfj/kaldifeat>`_ implements
|
||||||
|
feature extraction algorithms **compatible** with kaldi using PyTorch, supporting CUDA
|
||||||
|
as well as autograd.
|
||||||
|
|
||||||
|
Currently, only fbank features are supported.
|
||||||
|
It can produce the same feature output as ``compute-fbank-feats`` (from kaldi)
|
||||||
|
when given the same options.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
.. toctree::
|
.. toctree::
|
||||||
:maxdepth: 2
|
:maxdepth: 2
|
||||||
:caption: Contents
|
:caption: Contents:
|
||||||
|
|
||||||
intro
|
installation
|
||||||
installation/index
|
usage
|
||||||
usage/index
|
|
||||||
|
|||||||
54
doc/source/installation.rst
Normal file
54
doc/source/installation.rst
Normal file
@ -0,0 +1,54 @@
|
|||||||
|
Installation
|
||||||
|
============
|
||||||
|
|
||||||
|
.. _from source:
|
||||||
|
|
||||||
|
Install kaldifeat from source
|
||||||
|
-----------------------------
|
||||||
|
|
||||||
|
You have to install ``cmake`` and ``PyTorch`` first.
|
||||||
|
|
||||||
|
- ``cmake`` 3.11 is known to work. Other CMake versions may also work.
|
||||||
|
- ``PyTorch`` 1.8.1 is known to work. Other PyTorch versions may also work.
|
||||||
|
- Python >= 3.6
|
||||||
|
|
||||||
|
|
||||||
|
The commands to install ``kaldifeat`` from source are:
|
||||||
|
|
||||||
|
.. code-block:: bash
|
||||||
|
|
||||||
|
git clone https://github.com/csukuangfj/kaldifeat
|
||||||
|
cd kaldifeat
|
||||||
|
python3 setup.py install
|
||||||
|
|
||||||
|
To test that you have installed ``kaldifeat`` successfully, please run:
|
||||||
|
|
||||||
|
.. code-block:: bash
|
||||||
|
|
||||||
|
python3 -c "import kaldifeat; print(kaldifeat.__version__)"
|
||||||
|
|
||||||
|
It should print the version, e.g., ``1.0``.
|
||||||
|
|
||||||
|
Install kaldifeat from PyPI
|
||||||
|
---------------------------
|
||||||
|
|
||||||
|
The pre-built ``kaldifeat`` hosted on PyPI uses PyTorch 1.8.1.
|
||||||
|
If you install ``kaldifeat`` using pip, it will replace your locally
|
||||||
|
installed PyTorch automatically with PyTorch 1.8.1.
|
||||||
|
|
||||||
|
If you don't want this happen, please `Install kaldifeat from source`_.
|
||||||
|
|
||||||
|
The command to install ``kaldifeat`` from PyPI is:
|
||||||
|
|
||||||
|
.. code-block:: bash
|
||||||
|
|
||||||
|
pip install kaldifeat
|
||||||
|
|
||||||
|
|
||||||
|
To test that you have installed ``kaldifeat`` successfully, please run:
|
||||||
|
|
||||||
|
.. code-block:: bash
|
||||||
|
|
||||||
|
python3 -c "import kaldifeat; print(kaldifeat.__version__)"
|
||||||
|
|
||||||
|
It should print the version, e.g., ``1.0``.
|
||||||
@ -1,48 +0,0 @@
|
|||||||
FAQs
|
|
||||||
====
|
|
||||||
|
|
||||||
How to install a CUDA version of kaldifeat from source
|
|
||||||
------------------------------------------------------
|
|
||||||
|
|
||||||
You need to first install a CUDA version of `PyTorch`_ and then install `kaldifeat`_.
|
|
||||||
|
|
||||||
.. note::
|
|
||||||
|
|
||||||
You can use a CUDA version of `kaldifeat`_ on machines with no GPUs.
|
|
||||||
|
|
||||||
How to install a CPU version of kaldifeat from source
|
|
||||||
-----------------------------------------------------
|
|
||||||
|
|
||||||
You need to first install a CPU version of `PyTorch`_ and then install `kaldifeat`_.
|
|
||||||
|
|
||||||
How to fix `Caffe2: Cannot find cuDNN library`
|
|
||||||
----------------------------------------------
|
|
||||||
|
|
||||||
.. code-block::
|
|
||||||
|
|
||||||
Your installed Caffe2 version uses cuDNN but I cannot find the cuDNN
|
|
||||||
libraries. Please set the proper cuDNN prefixes and / or install cuDNN.
|
|
||||||
|
|
||||||
You will have such an error when you want to install a CUDA version of `kaldifeat`_
|
|
||||||
by ``pip install kaldifeat`` or from source.
|
|
||||||
|
|
||||||
You need to first install cuDNN. Assume you have installed cuDNN to the
|
|
||||||
path ``/path/to/cudnn``. You can fix the error by using ``one`` of the following
|
|
||||||
commands.
|
|
||||||
|
|
||||||
(1) Fix for installation using ``pip install``
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
export KALDIFEAT_CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Release -DCUDNN_LIBRARY_PATH=/path/to/cudnn/lib/libcudnn.so -DCUDNN_INCLUDE_PATH=/path/to/cudnn/include"
|
|
||||||
pip install --verbose kaldifeat
|
|
||||||
|
|
||||||
(2) Fix for installation from source
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
mkdir /some/path
|
|
||||||
git clone https://github.com/csukuangfj/kaldifeat.git
|
|
||||||
cd kaldifeat
|
|
||||||
export KALDIFEAT_CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Release -DCUDNN_LIBRARY_PATH=/path/to/cudnn/lib/libcudnn.so -DCUDNN_INCLUDE_PATH=/path/to/cudnn/include"
|
|
||||||
python setup.py install
|
|
||||||
@ -1,47 +0,0 @@
|
|||||||
.. _from source:
|
|
||||||
|
|
||||||
Install kaldifeat from source
|
|
||||||
=============================
|
|
||||||
|
|
||||||
You have to install ``cmake`` and `PyTorch`_ first.
|
|
||||||
|
|
||||||
- ``cmake`` 3.11 is known to work. Other CMake versions may also work.
|
|
||||||
- `PyTorch`_ >= 1.5.0 is known to work. Other PyTorch versions may also work.
|
|
||||||
- Python >= 3.6
|
|
||||||
- A compiler that supports C++ 14
|
|
||||||
|
|
||||||
|
|
||||||
The commands to install `kaldifeat`_ from source are:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
git clone https://github.com/csukuangfj/kaldifeat
|
|
||||||
cd kaldifeat
|
|
||||||
python3 setup.py install
|
|
||||||
|
|
||||||
To test that you have installed `kaldifeat`_ successfully, please run:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
python3 -c "import kaldifeat; print(kaldifeat.__version__)"
|
|
||||||
|
|
||||||
It should print the version, e.g., ``1.0``.
|
|
||||||
|
|
||||||
.. _from PyPI:
|
|
||||||
|
|
||||||
Install kaldifeat from PyPI
|
|
||||||
---------------------------
|
|
||||||
|
|
||||||
The command to install `kaldifeat`_ from PyPI is:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
pip install --verbose kaldifeat
|
|
||||||
|
|
||||||
To test that you have installed `kaldifeat`_ successfully, please run:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
python3 -c "import kaldifeat; print(kaldifeat.__version__)"
|
|
||||||
|
|
||||||
It should print the version, e.g., ``1.0``.
|
|
||||||
@ -1,139 +0,0 @@
|
|||||||
From pre-compiled wheels (Recommended)
|
|
||||||
=======================================
|
|
||||||
|
|
||||||
You can find pre-compiled wheels at
|
|
||||||
|
|
||||||
- CPU wheels: `<https://csukuangfj.github.io/kaldifeat/cpu.html>`_
|
|
||||||
- CUDA wheels: `<https://csukuangfj.github.io/kaldifeat/cuda.html>`_
|
|
||||||
|
|
||||||
We give a few examples below to show you how to install `kaldifeat`_ from
|
|
||||||
pre-compiled wheels.
|
|
||||||
|
|
||||||
.. hint::
|
|
||||||
|
|
||||||
The following lists only some examples. We suggest that you always select the
|
|
||||||
latest version of ``kaldifeat``.
|
|
||||||
|
|
||||||
Linux (CPU)
|
|
||||||
-----------
|
|
||||||
|
|
||||||
Suppose you want to install the following wheel:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
https://huggingface.co/csukuangfj/kaldifeat/resolve/main/ubuntu-cpu/kaldifeat-1.25.4.dev20240725+cpu.torch2.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
|
|
||||||
|
|
||||||
you can use one of the following methods:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
# method 1
|
|
||||||
pip install torch==2.4.0+cpu -f https://download.pytorch.org/whl/torch/
|
|
||||||
pip install kaldifeat==1.25.4.dev20240725+cpu.torch2.4.0 -f https://csukuangfj.github.io/kaldifeat/cpu.html
|
|
||||||
|
|
||||||
# For users from China
|
|
||||||
# 中国国内用户，如果访问不了 huggingface, 请使用
|
|
||||||
# pip install kaldifeat==1.25.4.dev20240725+cpu.torch2.4.0 -f https://csukuangfj.github.io/kaldifeat/cpu-cn.html
|
|
||||||
|
|
||||||
# method 2
|
|
||||||
pip install torch==2.4.0+cpu -f https://download.pytorch.org/whl/torch/
|
|
||||||
wget https://huggingface.co/csukuangfj/kaldifeat/resolve/main/ubuntu-cpu/kaldifeat-1.25.4.dev20240725+cpu.torch2.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
|
|
||||||
|
|
||||||
# For users from China
|
|
||||||
# 中国国内用户，如果访问不了 huggingface, 请使用
|
|
||||||
# wget https://hf-mirror.com/csukuangfj/kaldifeat/resolve/main/ubuntu-cpu/kaldifeat-1.25.4.dev20240725+cpu.torch2.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
|
|
||||||
|
|
||||||
pip install ./kaldifeat-1.25.4.dev20240725+cpu.torch2.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
|
|
||||||
|
|
||||||
Windows (CPU)
|
|
||||||
--------------
|
|
||||||
|
|
||||||
Suppose you want to install the following wheel:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
https://huggingface.co/csukuangfj/kaldifeat/resolve/main/windows-cpu/kaldifeat-1.25.4.dev20240725+cpu.torch2.4.0-cp312-cp312-win_amd64.whl
|
|
||||||
|
|
||||||
you can use one of the following methods:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
# method 1
|
|
||||||
pip install torch==2.4.0+cpu -f https://download.pytorch.org/whl/torch/
|
|
||||||
pip install kaldifeat==1.25.4.dev20240725+cpu.torch2.4.0 -f https://csukuangfj.github.io/kaldifeat/cpu.html
|
|
||||||
|
|
||||||
# For users from China
|
|
||||||
# 中国国内用户，如果访问不了 huggingface, 请使用
|
|
||||||
# pip install kaldifeat==1.25.4.dev20240725+cpu.torch2.4.0 -f https://csukuangfj.github.io/kaldifeat/cpu-cn.html
|
|
||||||
|
|
||||||
# method 2
|
|
||||||
pip install torch==2.4.0+cpu -f https://download.pytorch.org/whl/torch/
|
|
||||||
wget https://huggingface.co/csukuangfj/kaldifeat/resolve/main/windows-cpu/kaldifeat-1.25.4.dev20240725+cpu.torch2.4.0-cp312-cp312-win_amd64.whl
|
|
||||||
|
|
||||||
# For users from China
|
|
||||||
# 中国国内用户，如果访问不了 huggingface, 请使用
|
|
||||||
# wget https://hf-mirror.com/csukuangfj/kaldifeat/resolve/main/windows-cpu/kaldifeat-1.25.4.dev20240725+cpu.torch2.4.0-cp312-cp312-win_amd64.whl
|
|
||||||
|
|
||||||
pip install ./kaldifeat-1.25.4.dev20240725+cpu.torch2.4.0-cp312-cp312-win_amd64.whl
|
|
||||||
|
|
||||||
macOS (CPU)
|
|
||||||
-----------
|
|
||||||
|
|
||||||
Suppose you want to install the following wheel:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
https://huggingface.co/csukuangfj/kaldifeat/resolve/main/macos/kaldifeat-1.25.4.dev20240725+cpu.torch2.4.0-cp311-cp311-macosx_11_0_arm64.whl
|
|
||||||
|
|
||||||
you can use one of the following methods:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
# method 1
|
|
||||||
pip install torch==2.4.0
|
|
||||||
pip install kaldifeat==1.25.4.dev20240725+cpu.torch2.4.0 -f https://csukuangfj.github.io/kaldifeat/cpu.html
|
|
||||||
|
|
||||||
# For users from China
|
|
||||||
# 中国国内用户，如果访问不了 huggingface, 请使用
|
|
||||||
# pip install kaldifeat==1.25.4.dev20240725+cpu.torch2.4.0 -f https://csukuangfj.github.io/kaldifeat/cpu-cn.html
|
|
||||||
|
|
||||||
# method 2
|
|
||||||
pip install torch==2.4.0 -f https://download.pytorch.org/whl/torch/
|
|
||||||
wget https://huggingface.co/csukuangfj/kaldifeat/resolve/main/macos/kaldifeat-1.25.4.dev20240725+cpu.torch2.4.0-cp311-cp311-macosx_11_0_arm64.whl
|
|
||||||
|
|
||||||
# For users from China
|
|
||||||
# 中国国内用户，如果访问不了 huggingface, 请使用
|
|
||||||
# wget https://hf-mirror.com/csukuangfj/kaldifeat/resolve/main/macos/kaldifeat-1.25.4.dev20240725+cpu.torch2.4.0-cp311-cp311-macosx_11_0_arm64.whl
|
|
||||||
|
|
||||||
pip install ./kaldifeat-1.25.4.dev20240725+cpu.torch2.4.0-cp311-cp311-macosx_11_0_arm64.whl
|
|
||||||
|
|
||||||
Linux (CUDA)
|
|
||||||
------------
|
|
||||||
|
|
||||||
Suppose you want to install the following wheel:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
https://huggingface.co/csukuangfj/kaldifeat/resolve/main/ubuntu-cuda/kaldifeat-1.25.4.dev20240725+cuda12.4.torch2.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
|
|
||||||
|
|
||||||
you can use one of the following methods:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
# method 1
|
|
||||||
pip install torch==2.4.0+cu124 -f https://download.pytorch.org/whl/torch/
|
|
||||||
pip install kaldifeat==1.25.4.dev20240725+cuda12.4.torch2.4.0 -f https://csukuangfj.github.io/kaldifeat/cuda.html
|
|
||||||
|
|
||||||
# For users from China
|
|
||||||
# 中国国内用户，如果访问不了 huggingface, 请使用
|
|
||||||
# pip install kaldifeat==1.25.4.dev20240725+cuda12.4.torch2.4.0 -f https://csukuangfj.github.io/kaldifeat/cuda-cn.html
|
|
||||||
|
|
||||||
# method 2
|
|
||||||
pip install torch==2.4.0+cu124 -f https://download.pytorch.org/whl/torch/
|
|
||||||
wget https://huggingface.co/csukuangfj/kaldifeat/resolve/main/ubuntu-cuda/kaldifeat-1.25.4.dev20240725+cuda12.4.torch2.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
|
|
||||||
|
|
||||||
# For users from China
|
|
||||||
# 中国国内用户，如果访问不了 huggingface, 请使用
|
|
||||||
# wget https://hf-mirror.com/csukuangfj/kaldifeat/resolve/main/ubuntu-cuda/kaldifeat-1.25.4.dev20240725+cuda12.4.torch2.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
|
|
||||||
|
|
||||||
pip install ./kaldifeat-1.25.4.dev20240725+cuda12.4.torch2.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
|
|
||||||
@ -1,11 +0,0 @@
|
|||||||
Installation
|
|
||||||
============
|
|
||||||
|
|
||||||
.. toctree::
|
|
||||||
:maxdepth: 3
|
|
||||||
|
|
||||||
./from_wheels.rst
|
|
||||||
./from_source.rst
|
|
||||||
./faq.rst
|
|
||||||
|
|
||||||
|
|
||||||
@ -1,103 +0,0 @@
|
|||||||
Introduction
|
|
||||||
============
|
|
||||||
|
|
||||||
`kaldifeat`_ implements
|
|
||||||
speech feature extraction algorithms **compatible** with `Kaldi`_ using `PyTorch`_,
|
|
||||||
supporting CUDA as well as autograd.
|
|
||||||
|
|
||||||
`kaldifeat`_ has the following features:
|
|
||||||
|
|
||||||
- Fully compatible with `Kaldi`_
|
|
||||||
|
|
||||||
.. note::
|
|
||||||
|
|
||||||
The underlying C++ code is copied & modified from `Kaldi`_ directly.
|
|
||||||
It is rewritten with `PyTorch` C++ APIs.
|
|
||||||
|
|
||||||
- Provide not only ``C++ APIs`` but also ``Python APIs``
|
|
||||||
|
|
||||||
.. note::
|
|
||||||
|
|
||||||
You can access `kaldifeat`_ from ``Python``.
|
|
||||||
|
|
||||||
- Support autograd
|
|
||||||
- Support ``CUDA`` and ``CPU``
|
|
||||||
|
|
||||||
.. note::
|
|
||||||
|
|
||||||
You can use CUDA for feature extraction.
|
|
||||||
|
|
||||||
- Support ``online`` (i.e., ``streaming``) and ``offline`` (i.e., ``non-streaming``)
|
|
||||||
feature extraction
|
|
||||||
- Support chunk-based processing
|
|
||||||
|
|
||||||
.. note::
|
|
||||||
|
|
||||||
This is especially usefull if you want to process audios of several
|
|
||||||
hours long, which may cause OOM if you send them for computation at once.
|
|
||||||
With chunk-based processing, you can process audios of arbirtray length.
|
|
||||||
|
|
||||||
- Support batch processing
|
|
||||||
|
|
||||||
.. note::
|
|
||||||
|
|
||||||
With `kaldifeat`_ you can extract features for a batch of audios
|
|
||||||
|
|
||||||
|
|
||||||
.. see https://sublime-and-sphinx-guide.readthedocs.io/en/latest/tables.html
|
|
||||||
|
|
||||||
Currently implemented speech features and their counterparts in `Kaldi`_ are
|
|
||||||
listed in the following table.
|
|
||||||
|
|
||||||
.. list-table:: Supported speech features
|
|
||||||
:widths: 50 50
|
|
||||||
:header-rows: 1
|
|
||||||
|
|
||||||
* - Supported speech features
|
|
||||||
- Counterpart in `Kaldi`_
|
|
||||||
* - `kaldifeat.Fbank`_
|
|
||||||
- `compute-fbank-feats`_
|
|
||||||
* - `kaldifeat.Mfcc`_
|
|
||||||
- `compute-mfcc-feats`_
|
|
||||||
* - `kaldifeat.Plp`_
|
|
||||||
- `compute-plp-feats`_
|
|
||||||
* - `kaldifeat.Spectrogram`_
|
|
||||||
- `compute-spectrogram-feats`_
|
|
||||||
* - `kaldifeat.OnlineFbank`_
|
|
||||||
- `kaldi::OnlineFbank`_
|
|
||||||
* - `kaldifeat.OnlineMfcc`_
|
|
||||||
- `kaldi::OnlineMfcc`_
|
|
||||||
* - `kaldifeat.OnlinePlp`_
|
|
||||||
- `kaldi::OnlinePlp`_
|
|
||||||
|
|
||||||
Each feature computer needs an option. The following table lists the options
|
|
||||||
for each computer and the corresponding options in `Kaldi`_.
|
|
||||||
|
|
||||||
.. hint::
|
|
||||||
|
|
||||||
Note that we reuse the parameter names from `Kaldi`_.
|
|
||||||
|
|
||||||
Also, both online feature computers and offline feature computers share the
|
|
||||||
same option.
|
|
||||||
|
|
||||||
.. list-table:: Feature computer options
|
|
||||||
:widths: 50 50
|
|
||||||
:header-rows: 1
|
|
||||||
|
|
||||||
* - Options in `kaldifeat`_
|
|
||||||
- Corresponding options in `Kaldi`_
|
|
||||||
* - `kaldifeat.FbankOptions`_
|
|
||||||
- `kaldi::FbankOptions`_
|
|
||||||
* - `kaldifeat.MfccOptions`_
|
|
||||||
- `kaldi::MfccOptions`_
|
|
||||||
* - `kaldifeat.PlpOptions`_
|
|
||||||
- `kaldi::PlpOptions`_
|
|
||||||
* - `kaldifeat.SpectrogramOptions`_
|
|
||||||
- `kaldi::SpectrogramOptions`_
|
|
||||||
* - `kaldifeat.FrameExtractionOptions`_
|
|
||||||
- `kaldi::FrameExtractionOptions`_
|
|
||||||
* - `kaldifeat.MelBanksOptions`_
|
|
||||||
- `kaldi::MelBanksOptions`_
|
|
||||||
|
|
||||||
Read more to learn how to install `kaldifeat`_ and how to use each feature
|
|
||||||
computer.
|
|
||||||
212
doc/source/usage.rst
Normal file
212
doc/source/usage.rst
Normal file
@ -0,0 +1,212 @@
|
|||||||
|
Usage
|
||||||
|
=====
|
||||||
|
|
||||||
|
Let us first see the help message of kaldi's ``compute-fbank-feats``:
|
||||||
|
|
||||||
|
.. code-block:: bash
|
||||||
|
|
||||||
|
$ compute-fbank-feats
|
||||||
|
|
||||||
|
Create Mel-filter bank (FBANK) feature files.
|
||||||
|
Usage: compute-fbank-feats [options...] <wav-rspecifier> <feats-wspecifier>
|
||||||
|
|
||||||
|
Options:
|
||||||
|
--allow-downsample : If true, allow the input waveform to have a higher frequency than the specified --sample-frequency (and we'll downsample). (bool, default = false)
|
||||||
|
--allow-upsample : If true, allow the input waveform to have a lower frequency than the specified --sample-frequency (and we'll upsample). (bool, default = false)
|
||||||
|
--blackman-coeff : Constant coefficient for generalized Blackman window. (float, default = 0.42)
|
||||||
|
--channel : Channel to extract (-1 -> expect mono, 0 -> left, 1 -> right) (int, default = -1)
|
||||||
|
--debug-mel : Print out debugging information for mel bin computation (bool, default = false)
|
||||||
|
--dither : Dithering constant (0.0 means no dither). If you turn this off, you should set the --energy-floor option, e.g. to 1.0 or 0.1 (float, default = 1)
|
||||||
|
--energy-floor : Floor on energy (absolute, not relative) in FBANK computation. Only makes a difference if --use-energy=true; only necessary if --dither=0.0. Suggested values: 0.1 or 1.0 (float, default = 0)
|
||||||
|
--frame-length : Frame length in milliseconds (float, default = 25)
|
||||||
|
--frame-shift : Frame shift in milliseconds (float, default = 10)
|
||||||
|
--high-freq : High cutoff frequency for mel bins (if <= 0, offset from Nyquist) (float, default = 0)
|
||||||
|
--htk-compat : If true, put energy last. Warning: not sufficient to get HTK compatible features (need to change other parameters). (bool, default = false)
|
||||||
|
--low-freq : Low cutoff frequency for mel bins (float, default = 20)
|
||||||
|
--max-feature-vectors : Memory optimization. If larger than 0, periodically remove feature vectors so that only this number of the latest feature vectors is retained. (int, default = -1)
|
||||||
|
--min-duration : Minimum duration of segments to process (in seconds). (float, default = 0)
|
||||||
|
--num-mel-bins : Number of triangular mel-frequency bins (int, default = 23)
|
||||||
|
--output-format : Format of the output files [kaldi, htk] (string, default = "kaldi")
|
||||||
|
--preemphasis-coefficient : Coefficient for use in signal preemphasis (float, default = 0.97)
|
||||||
|
--raw-energy : If true, compute energy before preemphasis and windowing (bool, default = true)
|
||||||
|
--remove-dc-offset : Subtract mean from waveform on each frame (bool, default = true)
|
||||||
|
--round-to-power-of-two : If true, round window size to power of two by zero-padding input to FFT. (bool, default = true)
|
||||||
|
--sample-frequency : Waveform data sample frequency (must match the waveform file, if specified there) (float, default = 16000)
|
||||||
|
--snip-edges : If true, end effects will be handled by outputting only frames that completely fit in the file, and the number of frames depends on the frame-length. If false, the number of frames depends only on the frame-shift, and we reflect the data at the ends. (bool, default = true)
|
||||||
|
--subtract-mean : Subtract mean of each feature file [CMS]; not recommended to do it this way. (bool, default = false)
|
||||||
|
--use-energy : Add an extra dimension with energy to the FBANK output. (bool, default = false)
|
||||||
|
--use-log-fbank : If true, produce log-filterbank, else produce linear. (bool, default = true)
|
||||||
|
--use-power : If true, use power, else use magnitude. (bool, default = true)
|
||||||
|
--utt2spk : Utterance to speaker-id map (if doing VTLN and you have warps per speaker) (string, default = "")
|
||||||
|
--vtln-high : High inflection point in piecewise linear VTLN warping function (if negative, offset from high-mel-freq (float, default = -500)
|
||||||
|
--vtln-low : Low inflection point in piecewise linear VTLN warping function (float, default = 100)
|
||||||
|
--vtln-map : Map from utterance or speaker-id to vtln warp factor (rspecifier) (string, default = "")
|
||||||
|
--vtln-warp : Vtln warp factor (only applicable if vtln-map not specified) (float, default = 1)
|
||||||
|
--window-type : Type of window ("hamming"|"hanning"|"povey"|"rectangular"|"sine"|"blackmann") (string, default = "povey")
|
||||||
|
--write-utt2dur : Wspecifier to write duration of each utterance in seconds, e.g. 'ark,t:utt2dur'. (string, default = "")
|
||||||
|
|
||||||
|
Standard options:
|
||||||
|
--config : Configuration file to read (this option may be repeated) (string, default = "")
|
||||||
|
--help : Print out usage message (bool, default = false)
|
||||||
|
--print-args : Print the command line arguments (to stderr) (bool, default = true)
|
||||||
|
--verbose : Verbose level (higher->more logging) (int, default = 0)
|
||||||
|
|
||||||
|
FbankOptions
|
||||||
|
------------
|
||||||
|
|
||||||
|
``kaldifeat`` reuses the same options from kaldi's ``compute-fbank-feats``.
|
||||||
|
|
||||||
|
The following shows the default values of ``kaldifeat.FbankOptions``:
|
||||||
|
|
||||||
|
.. code-block:: python
|
||||||
|
|
||||||
|
>>> import kaldifeat
|
||||||
|
>>> fbank_opts = kaldifeat.FbankOptions()
|
||||||
|
>>> print(fbank_opts)
|
||||||
|
frame_opts:
|
||||||
|
samp_freq: 16000
|
||||||
|
frame_shift_ms: 10
|
||||||
|
frame_length_ms: 25
|
||||||
|
dither: 1
|
||||||
|
preemph_coeff: 0.97
|
||||||
|
remove_dc_offset: 1
|
||||||
|
window_type: povey
|
||||||
|
round_to_power_of_two: 1
|
||||||
|
blackman_coeff: 0.42
|
||||||
|
snip_edges: 1
|
||||||
|
|
||||||
|
|
||||||
|
mel_opts:
|
||||||
|
num_bins: 23
|
||||||
|
low_freq: 20
|
||||||
|
high_freq: 0
|
||||||
|
vtln_low: 100
|
||||||
|
vtln_high: -500
|
||||||
|
debug_mel: 0
|
||||||
|
htk_mode: 0
|
||||||
|
|
||||||
|
use_energy: 0
|
||||||
|
energy_floor: 0
|
||||||
|
raw_energy: 1
|
||||||
|
htk_compat: 0
|
||||||
|
use_log_fbank: 1
|
||||||
|
use_power: 1
|
||||||
|
device: cpu
|
||||||
|
|
||||||
|
It consists of three parts:
|
||||||
|
|
||||||
|
- ``frame_opts``
|
||||||
|
|
||||||
|
Options in this part are accessed by ``frame_opts.xxx``. That is, to access
|
||||||
|
the sample rate, you use:
|
||||||
|
|
||||||
|
.. code-block:: python
|
||||||
|
|
||||||
|
>>> fbank_opts = kaldifeat.FbankOptions()
|
||||||
|
>>> print(fbank_opts.frame_opts.samp_freq)
|
||||||
|
16000.0
|
||||||
|
|
||||||
|
- ``mel_opts``
|
||||||
|
|
||||||
|
Options in this part are accessed by ``mel_opts.xxx``. That is, to access
|
||||||
|
the number of mel bins, you use:
|
||||||
|
|
||||||
|
.. code-block:: python
|
||||||
|
|
||||||
|
>>> fbank_opts = kaldifeat.FbankOptions()
|
||||||
|
>>> print(fbank_opts.mel_opts.num_bins)
|
||||||
|
23
|
||||||
|
|
||||||
|
- fbank related
|
||||||
|
|
||||||
|
Options in this part are accessed directly. That is, to access the device
|
||||||
|
field, you use:
|
||||||
|
|
||||||
|
.. code-block::
|
||||||
|
|
||||||
|
>>> print(fbank_opts.device)
|
||||||
|
cpu
|
||||||
|
>>> fbank_opts.device = 'cuda:0'
|
||||||
|
>>> print(fbank_opts.device)
|
||||||
|
cuda:0
|
||||||
|
>>> import torch
|
||||||
|
>>> fbank_opts.device = torch.device('cuda', 0)
|
||||||
|
>>> print(fbank_opts.device)
|
||||||
|
cuda:0
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
To change the sample rate to 8000, you can use:
|
||||||
|
|
||||||
|
.. code-block:: python
|
||||||
|
|
||||||
|
>>> fbank_opts = kaldifeat.FbankOptions()
|
||||||
|
>>> print(fbank_opts.frame_opts.samp_freq)
|
||||||
|
16000.0
|
||||||
|
>>> fbank_opts.frame_opts.samp_freq = 8000
|
||||||
|
>>> print(fbank_opts.frame_opts.samp_freq)
|
||||||
|
8000.0
|
||||||
|
|
||||||
|
To change ``snip_edges`` to ``False``, you can use:
|
||||||
|
|
||||||
|
.. code-block:: python
|
||||||
|
|
||||||
|
>>> fbank_opts.frame_opts.snip_edges = False
|
||||||
|
>>> print(fbank_opts.frame_opts.snip_edges)
|
||||||
|
False
|
||||||
|
|
||||||
|
To change number of mel bins to 80, you can use:
|
||||||
|
|
||||||
|
.. code-block:: python
|
||||||
|
|
||||||
|
>>> print(fbank_opts.mel_opts.num_bins)
|
||||||
|
23
|
||||||
|
>>> fbank_opts.mel_opts.num_bins = 80
|
||||||
|
>>> print(fbank_opts.mel_opts.num_bins)
|
||||||
|
80
|
||||||
|
|
||||||
|
To change the device to ``cuda``, you can use:
|
||||||
|
|
||||||
|
|
||||||
|
Fbank
|
||||||
|
-----
|
||||||
|
|
||||||
|
The following shows how to use ``kaldifeat.Fbank`` to compute
|
||||||
|
the fbank features of sound files.
|
||||||
|
|
||||||
|
First, let us generate two sound files using ``sox``:
|
||||||
|
|
||||||
|
.. code-block:: bash
|
||||||
|
|
||||||
|
# generate a wav of two seconds, containing a sine-wave
|
||||||
|
# swept from 300 Hz to 3300 Hz
|
||||||
|
sox -n -r 16000 -b 16 test.wav synth 1.2 sine 300-3300
|
||||||
|
|
||||||
|
# another sound file with 0.5 seconds
|
||||||
|
sox -n -r 16000 -b 16 test2.wav synth 0.5 sine 300-3300
|
||||||
|
|
||||||
|
.. hint::
|
||||||
|
|
||||||
|
You can find the above two files by visiting the following two links:
|
||||||
|
|
||||||
|
- `test.wav <https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/python/tests/test_data/test.wav>`_
|
||||||
|
- `test2.wav <https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/python/tests/test_data/test2.wav>`_
|
||||||
|
|
||||||
|
The `following code <https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/python/tests/test_fbank.py>`_
|
||||||
|
shows the usage of ``kaldifeat.Fbank``.
|
||||||
|
|
||||||
|
It shows:
|
||||||
|
|
||||||
|
- How to read a sound file. Note that audio samples are scaled to the range [-32768, 32768].
|
||||||
|
The intention is to produce the same output as kaldi. You don't need to scale it if
|
||||||
|
you don't care about the compatibility with kaldi
|
||||||
|
|
||||||
|
- ``kaldifeat.Fbank`` supports CUDA as well as CPU
|
||||||
|
|
||||||
|
- ``kaldifeat.Fbank`` supports processing sound file in a batch as well as accepting
|
||||||
|
a single sound file
|
||||||
|
|
||||||
|
|
||||||
|
.. literalinclude:: ./code/test_fbank.py
|
||||||
|
:caption: Demo of ``kaldifeat.Fbank``
|
||||||
|
:language: python
|
||||||
@ -1,46 +0,0 @@
|
|||||||
compute-fbank-feats
|
|
||||||
|
|
||||||
Create Mel-filter bank (FBANK) feature files.
|
|
||||||
Usage: compute-fbank-feats [options...] <wav-rspecifier> <feats-wspecifier>
|
|
||||||
|
|
||||||
Options:
|
|
||||||
--allow-downsample : If true, allow the input waveform to have a higher frequency than the specified --sample-frequency (and we'll downsample). (bool, default = false)
|
|
||||||
--allow-upsample : If true, allow the input waveform to have a lower frequency than the specified --sample-frequency (and we'll upsample). (bool, default = false)
|
|
||||||
--blackman-coeff : Constant coefficient for generalized Blackman window. (float, default = 0.42)
|
|
||||||
--channel : Channel to extract (-1 -> expect mono, 0 -> left, 1 -> right) (int, default = -1)
|
|
||||||
--debug-mel : Print out debugging information for mel bin computation (bool, default = false)
|
|
||||||
--dither : Dithering constant (0.0 means no dither). If you turn this off, you should set the --energy-floor option, e.g. to 1.0 or 0.1 (float, default = 1)
|
|
||||||
--energy-floor : Floor on energy (absolute, not relative) in FBANK computation. Only makes a difference if --use-energy=true; only necessary if --dither=0.0. Suggested values: 0.1 or 1.0 (float, default = 0)
|
|
||||||
--frame-length : Frame length in milliseconds (float, default = 25)
|
|
||||||
--frame-shift : Frame shift in milliseconds (float, default = 10)
|
|
||||||
--high-freq : High cutoff frequency for mel bins (if <= 0, offset from Nyquist) (float, default = 0)
|
|
||||||
--htk-compat : If true, put energy last. Warning: not sufficient to get HTK compatible features (need to change other parameters). (bool, default = false)
|
|
||||||
--low-freq : Low cutoff frequency for mel bins (float, default = 20)
|
|
||||||
--max-feature-vectors : Memory optimization. If larger than 0, periodically remove feature vectors so that only this number of the latest feature vectors is retained. (int, default = -1)
|
|
||||||
--min-duration : Minimum duration of segments to process (in seconds). (float, default = 0)
|
|
||||||
--num-mel-bins : Number of triangular mel-frequency bins (int, default = 23)
|
|
||||||
--output-format : Format of the output files [kaldi, htk] (string, default = "kaldi")
|
|
||||||
--preemphasis-coefficient : Coefficient for use in signal preemphasis (float, default = 0.97)
|
|
||||||
--raw-energy : If true, compute energy before preemphasis and windowing (bool, default = true)
|
|
||||||
--remove-dc-offset : Subtract mean from waveform on each frame (bool, default = true)
|
|
||||||
--round-to-power-of-two : If true, round window size to power of two by zero-padding input to FFT. (bool, default = true)
|
|
||||||
--sample-frequency : Waveform data sample frequency (must match the waveform file, if specified there) (float, default = 16000)
|
|
||||||
--snip-edges : If true, end effects will be handled by outputting only frames that completely fit in the file, and the number of frames depends on the frame-length. If false, the number of frames depends only on the frame-shift, and we reflect the data at the ends. (bool, default = true)
|
|
||||||
--subtract-mean : Subtract mean of each feature file [CMS]; not recommended to do it this way. (bool, default = false)
|
|
||||||
--use-energy : Add an extra dimension with energy to the FBANK output. (bool, default = false)
|
|
||||||
--use-log-fbank : If true, produce log-filterbank, else produce linear. (bool, default = true)
|
|
||||||
--use-power : If true, use power, else use magnitude. (bool, default = true)
|
|
||||||
--utt2spk : Utterance to speaker-id map (if doing VTLN and you have warps per speaker) (string, default = "")
|
|
||||||
--vtln-high : High inflection point in piecewise linear VTLN warping function (if negative, offset from high-mel-freq (float, default = -500)
|
|
||||||
--vtln-low : Low inflection point in piecewise linear VTLN warping function (float, default = 100)
|
|
||||||
--vtln-map : Map from utterance or speaker-id to vtln warp factor (rspecifier) (string, default = "")
|
|
||||||
--vtln-warp : Vtln warp factor (only applicable if vtln-map not specified) (float, default = 1)
|
|
||||||
--window-type : Type of window ("hamming"|"hanning"|"povey"|"rectangular"|"sine"|"blackmann") (string, default = "povey")
|
|
||||||
--write-utt2dur : Wspecifier to write duration of each utterance in seconds, e.g. 'ark,t:utt2dur'. (string, default = "")
|
|
||||||
|
|
||||||
Standard options:
|
|
||||||
--config : Configuration file to read (this option may be repeated) (string, default = "")
|
|
||||||
--help : Print out usage message (bool, default = false)
|
|
||||||
--print-args : Print the command line arguments (to stderr) (bool, default = true)
|
|
||||||
--verbose : Verbose level (higher->more logging) (int, default = 0)
|
|
||||||
|
|
||||||
@ -1,65 +0,0 @@
|
|||||||
$ python3
|
|
||||||
Python 3.8.0 (default, Oct 28 2019, 16:14:01)
|
|
||||||
[GCC 8.3.0] on linux
|
|
||||||
Type "help", "copyright", "credits" or "license" for more information.
|
|
||||||
>>> import kaldifeat
|
|
||||||
>>> opts = kaldifeat.FbankOptions()
|
|
||||||
>>> print(opts)
|
|
||||||
frame_opts:
|
|
||||||
samp_freq: 16000
|
|
||||||
frame_shift_ms: 10
|
|
||||||
frame_length_ms: 25
|
|
||||||
dither: 1
|
|
||||||
preemph_coeff: 0.97
|
|
||||||
remove_dc_offset: 1
|
|
||||||
window_type: povey
|
|
||||||
round_to_power_of_two: 1
|
|
||||||
blackman_coeff: 0.42
|
|
||||||
snip_edges: 1
|
|
||||||
max_feature_vectors: -1
|
|
||||||
|
|
||||||
|
|
||||||
mel_opts:
|
|
||||||
num_bins: 23
|
|
||||||
low_freq: 20
|
|
||||||
high_freq: 0
|
|
||||||
vtln_low: 100
|
|
||||||
vtln_high: -500
|
|
||||||
debug_mel: 0
|
|
||||||
htk_mode: 0
|
|
||||||
|
|
||||||
use_energy: 0
|
|
||||||
energy_floor: 0
|
|
||||||
raw_energy: 1
|
|
||||||
htk_compat: 0
|
|
||||||
use_log_fbank: 1
|
|
||||||
use_power: 1
|
|
||||||
device: cpu
|
|
||||||
|
|
||||||
>>> print(opts.dither)
|
|
||||||
Traceback (most recent call last):
|
|
||||||
File "<stdin>", line 1, in <module>
|
|
||||||
AttributeError: '_kaldifeat.FbankOptions' object has no attribute 'dither'
|
|
||||||
>>>
|
|
||||||
>>> print(opts.frame_opts.dither)
|
|
||||||
1.0
|
|
||||||
>>> opts.frame_opts.dither = 0 # disable dither
|
|
||||||
>>> print(opts.frame_opts.dither)
|
|
||||||
0.0
|
|
||||||
>>> import torch
|
|
||||||
>>> print(opts.device)
|
|
||||||
cpu
|
|
||||||
>>> opts.device = 'cuda:0'
|
|
||||||
>>> print(opts.device)
|
|
||||||
cuda:0
|
|
||||||
>>> opts.device = torch.device('cuda', 1)
|
|
||||||
>>> print(opts.device)
|
|
||||||
cuda:1
|
|
||||||
>>> opts.device = 'cpu'
|
|
||||||
>>> print(opts.device)
|
|
||||||
cpu
|
|
||||||
>>> print(opts.mel_opts.num_bins)
|
|
||||||
23
|
|
||||||
>>> opts.mel_opts.num_bins = 80
|
|
||||||
>>> print(opts.mel_opts.num_bins)
|
|
||||||
80
|
|
||||||
@ -1 +0,0 @@
|
|||||||
../../../../kaldifeat/python/tests/test_fbank_options.py
|
|
||||||
@ -1,3 +0,0 @@
|
|||||||
kaldifeat.Fbank
|
|
||||||
===============
|
|
||||||
|
|
||||||
@ -1,51 +0,0 @@
|
|||||||
kaldifeat.FbankOptions
|
|
||||||
======================
|
|
||||||
|
|
||||||
If you want to construct an instance of `kaldifeat.Fbank`_ or
|
|
||||||
`kaldifeat.OnlineFbank`_, you have to provide an instance of
|
|
||||||
`kaldifeat.FbankOptions`_.
|
|
||||||
|
|
||||||
The following code shows how to construct an instance of `kaldifeat.FbankOptions`_.
|
|
||||||
|
|
||||||
.. literalinclude:: ./code/fbank_options-1.txt
|
|
||||||
:caption: Usage of `kaldifeat.FbankOptions`_
|
|
||||||
:emphasize-lines: 6,8,22,37
|
|
||||||
|
|
||||||
Note that we reuse the same option name with `compute-fbank-feats`_ from `Kaldi`_:
|
|
||||||
|
|
||||||
.. code-block:: bash
|
|
||||||
|
|
||||||
$ compute-fbank-feats --help
|
|
||||||
|
|
||||||
|
|
||||||
.. literalinclude:: ./code/compute-fbank-feats-help.txt
|
|
||||||
:caption: Output of ``compute-fbank-feats --help``
|
|
||||||
|
|
||||||
Please refer to the output of ``compute-fbank-feats --help`` for the meaning
|
|
||||||
of each field of `kaldifeat.FbankOptions`_.
|
|
||||||
|
|
||||||
One thing worth noting is that `kaldifeat.FbankOptions`_ has a field ``device``,
|
|
||||||
which is an instance of ``torch.device``. You can assign it either a string, e.g.,
|
|
||||||
``"cpu"`` or ``"cuda:0"``, or an instance of ``torch.device``, e.g., ``torch.device("cpu")`` or
|
|
||||||
``torch.device("cuda", 1)``.
|
|
||||||
|
|
||||||
.. hint::
|
|
||||||
|
|
||||||
You can use this field to control whether the feature computer
|
|
||||||
constructed from it performs computation on CPU or CUDA.
|
|
||||||
|
|
||||||
.. caution::
|
|
||||||
|
|
||||||
If you use a CUDA device, make sure that you have installed a CUDA version
|
|
||||||
of `PyTorch`_.
|
|
||||||
|
|
||||||
Example usage
|
|
||||||
-------------
|
|
||||||
|
|
||||||
The following code from
|
|
||||||
`<https://github.com/csukuangfj/kaldifeat/blob/master/kaldifeat/python/tests/test_fbank_options.py>`_
|
|
||||||
demonstrate the usage of `kaldifeat.FbankOptions`_:
|
|
||||||
|
|
||||||
.. literalinclude:: ./code/test_fbank_options.py
|
|
||||||
:caption: Example usage of `kaldifeat.FbankOptions`_
|
|
||||||
:language: python
|
|
||||||
@ -1,11 +0,0 @@
|
|||||||
Usage
|
|
||||||
=====
|
|
||||||
|
|
||||||
This section describes how to use feature computers in `kaldifeat`_.
|
|
||||||
|
|
||||||
.. toctree::
|
|
||||||
:maxdepth: 2
|
|
||||||
|
|
||||||
fbank_options
|
|
||||||
fbank
|
|
||||||
online_fbank
|
|
||||||
@ -1,3 +0,0 @@
|
|||||||
kaldifeat.OnlineFbank
|
|
||||||
=====================
|
|
||||||
|
|
||||||
106
get_version.py
106
get_version.py
@ -1,106 +0,0 @@
|
|||||||
#!/usr/bin/env python3
|
|
||||||
|
|
||||||
import datetime
|
|
||||||
import os
|
|
||||||
import platform
|
|
||||||
import re
|
|
||||||
import shutil
|
|
||||||
|
|
||||||
import torch
|
|
||||||
|
|
||||||
|
|
||||||
def is_macos():
|
|
||||||
return platform.system() == "Darwin"
|
|
||||||
|
|
||||||
|
|
||||||
def is_windows():
|
|
||||||
return platform.system() == "Windows"
|
|
||||||
|
|
||||||
|
|
||||||
def with_cuda():
|
|
||||||
if shutil.which("nvcc") is None:
|
|
||||||
return False
|
|
||||||
|
|
||||||
if is_macos():
|
|
||||||
return False
|
|
||||||
|
|
||||||
return True
|
|
||||||
|
|
||||||
|
|
||||||
def get_pytorch_version():
|
|
||||||
# if it is 1.7.1+cuda101, then strip +cuda101
|
|
||||||
return torch.__version__.split("+")[0]
|
|
||||||
|
|
||||||
|
|
||||||
def get_cuda_version():
|
|
||||||
from torch.utils import collect_env
|
|
||||||
|
|
||||||
running_cuda_version = collect_env.get_running_cuda_version(collect_env.run)
|
|
||||||
cuda_version = torch.version.cuda
|
|
||||||
if running_cuda_version is not None and cuda_version is not None:
|
|
||||||
assert cuda_version in running_cuda_version, (
|
|
||||||
f"PyTorch is built with CUDA version: {cuda_version}.\n"
|
|
||||||
f"The current running CUDA version is: {running_cuda_version}"
|
|
||||||
)
|
|
||||||
return cuda_version
|
|
||||||
|
|
||||||
|
|
||||||
def is_for_pypi():
|
|
||||||
ans = os.environ.get("KALDIFEAT_IS_FOR_PYPI", None)
|
|
||||||
return ans is not None
|
|
||||||
|
|
||||||
|
|
||||||
def is_stable():
|
|
||||||
ans = os.environ.get("KALDIFEAT_IS_STABLE", None)
|
|
||||||
return ans is not None
|
|
||||||
|
|
||||||
|
|
||||||
def is_for_conda():
|
|
||||||
ans = os.environ.get("KALDIFEAT_IS_FOR_CONDA", None)
|
|
||||||
return ans is not None
|
|
||||||
|
|
||||||
|
|
||||||
def get_package_version():
|
|
||||||
# Set a default CUDA version here so that `pip install kaldifeat`
|
|
||||||
# uses the default CUDA version.
|
|
||||||
#
|
|
||||||
default_cuda_version = "10.1" # CUDA 10.1
|
|
||||||
|
|
||||||
if with_cuda():
|
|
||||||
cuda_version = get_cuda_version()
|
|
||||||
if is_for_pypi() and default_cuda_version == cuda_version:
|
|
||||||
cuda_version = ""
|
|
||||||
pytorch_version = ""
|
|
||||||
local_version = ""
|
|
||||||
else:
|
|
||||||
cuda_version = f"+cuda{cuda_version}"
|
|
||||||
pytorch_version = get_pytorch_version()
|
|
||||||
local_version = f"{cuda_version}.torch{pytorch_version}"
|
|
||||||
else:
|
|
||||||
pytorch_version = get_pytorch_version()
|
|
||||||
local_version = f"+cpu.torch{pytorch_version}"
|
|
||||||
|
|
||||||
if is_for_conda():
|
|
||||||
local_version = ""
|
|
||||||
|
|
||||||
if is_for_pypi() and is_macos():
|
|
||||||
local_version = ""
|
|
||||||
|
|
||||||
with open("CMakeLists.txt") as f:
|
|
||||||
content = f.read()
|
|
||||||
|
|
||||||
latest_version = re.search(r"set\(kaldifeat_VERSION (.*)\)", content).group(
|
|
||||||
1
|
|
||||||
)
|
|
||||||
latest_version = latest_version.strip('"')
|
|
||||||
|
|
||||||
if not is_stable():
|
|
||||||
dt = datetime.datetime.utcnow()
|
|
||||||
package_version = f"{latest_version}.dev{dt.year}{dt.month:02d}{dt.day:02d}{local_version}"
|
|
||||||
else:
|
|
||||||
package_version = f"{latest_version}"
|
|
||||||
return package_version
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
print(get_package_version())
|
|
||||||
@ -1,4 +1,2 @@
|
|||||||
add_subdirectory(csrc)
|
add_subdirectory(csrc)
|
||||||
if(kaldifeat_BUILD_PYMODULE)
|
add_subdirectory(python)
|
||||||
add_subdirectory(python)
|
|
||||||
endif()
|
|
||||||
|
|||||||
@ -2,33 +2,15 @@
|
|||||||
|
|
||||||
set(kaldifeat_srcs
|
set(kaldifeat_srcs
|
||||||
feature-fbank.cc
|
feature-fbank.cc
|
||||||
feature-functions.cc
|
|
||||||
feature-mfcc.cc
|
feature-mfcc.cc
|
||||||
feature-plp.cc
|
|
||||||
feature-spectrogram.cc
|
|
||||||
feature-window.cc
|
feature-window.cc
|
||||||
matrix-functions.cc
|
matrix-functions.cc
|
||||||
mel-computations.cc
|
mel-computations.cc
|
||||||
online-feature.cc
|
|
||||||
whisper-fbank.cc
|
|
||||||
)
|
)
|
||||||
|
|
||||||
add_library(kaldifeat_core ${kaldifeat_srcs})
|
add_library(kaldifeat_core SHARED ${kaldifeat_srcs})
|
||||||
target_link_libraries(kaldifeat_core PUBLIC ${TORCH_LIBRARIES})
|
target_link_libraries(kaldifeat_core PUBLIC ${TORCH_LIBRARIES})
|
||||||
|
|
||||||
target_compile_definitions(kaldifeat_core PUBLIC KALDIFEAT_TORCH_VERSION_MAJOR=${KALDIFEAT_TORCH_VERSION_MAJOR})
|
|
||||||
target_compile_definitions(kaldifeat_core PUBLIC KALDIFEAT_TORCH_VERSION_MINOR=${KALDIFEAT_TORCH_VERSION_MINOR})
|
|
||||||
|
|
||||||
if(APPLE)
|
|
||||||
execute_process(
|
|
||||||
COMMAND "${PYTHON_EXECUTABLE}" -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())"
|
|
||||||
OUTPUT_STRIP_TRAILING_WHITESPACE
|
|
||||||
OUTPUT_VARIABLE PYTHON_SITE_PACKAGE_DIR
|
|
||||||
)
|
|
||||||
message(STATUS "PYTHON_SITE_PACKAGE_DIR: ${PYTHON_SITE_PACKAGE_DIR}")
|
|
||||||
target_link_libraries(kaldifeat_core PUBLIC "-L ${PYTHON_SITE_PACKAGE_DIR}/../..")
|
|
||||||
endif()
|
|
||||||
|
|
||||||
add_executable(test_kaldifeat test_kaldifeat.cc)
|
add_executable(test_kaldifeat test_kaldifeat.cc)
|
||||||
target_link_libraries(test_kaldifeat PRIVATE kaldifeat_core)
|
target_link_libraries(test_kaldifeat PRIVATE kaldifeat_core)
|
||||||
|
|
||||||
@ -42,52 +24,19 @@ function(kaldifeat_add_test source)
|
|||||||
gtest_main
|
gtest_main
|
||||||
)
|
)
|
||||||
|
|
||||||
# NOTE: We set the working directory here so that
|
|
||||||
# it works also on windows. The reason is that
|
|
||||||
# the required DLLs are inside ${TORCH_DIR}/lib
|
|
||||||
# and they can be found by the exe if the current
|
|
||||||
# working directory is ${TORCH_DIR}\lib
|
|
||||||
add_test(NAME "Test.${name}"
|
add_test(NAME "Test.${name}"
|
||||||
COMMAND
|
COMMAND
|
||||||
$<TARGET_FILE:${name}>
|
$<TARGET_FILE:${name}>
|
||||||
WORKING_DIRECTORY ${TORCH_DIR}/lib
|
|
||||||
)
|
)
|
||||||
endfunction()
|
endfunction()
|
||||||
|
|
||||||
if(kaldifeat_BUILD_TESTS)
|
if(BUILD_TESTS)
|
||||||
# please sort the source files alphabetically
|
# please sort the source files alphabetically
|
||||||
set(test_srcs
|
set(test_srcs
|
||||||
feature-window-test.cc
|
feature-window-test.cc
|
||||||
online-feature-test.cc
|
|
||||||
)
|
)
|
||||||
|
|
||||||
foreach(source IN LISTS test_srcs)
|
foreach(source IN LISTS test_srcs)
|
||||||
kaldifeat_add_test(${source})
|
kaldifeat_add_test(${source})
|
||||||
endforeach()
|
endforeach()
|
||||||
endif()
|
endif()
|
||||||
|
|
||||||
file(MAKE_DIRECTORY
|
|
||||||
DESTINATION
|
|
||||||
${PROJECT_BINARY_DIR}/include/kaldifeat/csrc
|
|
||||||
)
|
|
||||||
|
|
||||||
file(GLOB_RECURSE all_headers *.h)
|
|
||||||
message(STATUS "All headers: ${all_headers}")
|
|
||||||
|
|
||||||
file(COPY
|
|
||||||
${all_headers}
|
|
||||||
DESTINATION
|
|
||||||
${PROJECT_BINARY_DIR}/include/kaldifeat/csrc
|
|
||||||
)
|
|
||||||
if(BUILD_SHARED_LIBS AND WIN32)
|
|
||||||
install(TARGETS kaldifeat_core
|
|
||||||
DESTINATION ../
|
|
||||||
)
|
|
||||||
endif()
|
|
||||||
install(TARGETS kaldifeat_core
|
|
||||||
DESTINATION ${CMAKE_INSTALL_LIBDIR}
|
|
||||||
)
|
|
||||||
|
|
||||||
install(FILES ${all_headers}
|
|
||||||
DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}/kaldifeat/csrc
|
|
||||||
)
|
|
||||||
|
|||||||
@ -1 +0,0 @@
|
|||||||
exclude_files=whisper-mel-bank.h,whisper-v3-mel-bank.h
|
|
||||||
@ -55,17 +55,10 @@ torch::Tensor OfflineFeatureTpl<F>::ComputeFeatures(const torch::Tensor &wave,
|
|||||||
int32_t padding = frame_opts.PaddedWindowSize() - strided_input.size(1);
|
int32_t padding = frame_opts.PaddedWindowSize() - strided_input.size(1);
|
||||||
|
|
||||||
if (padding > 0) {
|
if (padding > 0) {
|
||||||
#ifdef __ANDROID__
|
|
||||||
auto padding_value = torch::zeros(
|
|
||||||
{strided_input.size(0), padding},
|
|
||||||
torch::dtype(torch::kFloat).device(strided_input.device()));
|
|
||||||
strided_input = torch::cat({strided_input, padding_value}, 1);
|
|
||||||
#else
|
|
||||||
strided_input = torch::nn::functional::pad(
|
strided_input = torch::nn::functional::pad(
|
||||||
strided_input, torch::nn::functional::PadFuncOptions({0, padding})
|
strided_input, torch::nn::functional::PadFuncOptions({0, padding})
|
||||||
.mode(torch::kConstant)
|
.mode(torch::kConstant)
|
||||||
.value(0));
|
.value(0));
|
||||||
#endif
|
|
||||||
}
|
}
|
||||||
|
|
||||||
return computer_.Compute(log_energy_pre_window, vtln_warp, strided_input);
|
return computer_.Compute(log_energy_pre_window, vtln_warp, strided_input);
|
||||||
|
|||||||
@ -7,23 +7,7 @@
|
|||||||
#ifndef KALDIFEAT_CSRC_FEATURE_COMMON_H_
|
#ifndef KALDIFEAT_CSRC_FEATURE_COMMON_H_
|
||||||
#define KALDIFEAT_CSRC_FEATURE_COMMON_H_
|
#define KALDIFEAT_CSRC_FEATURE_COMMON_H_
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-functions.h"
|
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
#include "kaldifeat/csrc/feature-window.h"
|
||||||
// See "The torch.fft module in PyTorch 1.7"
|
|
||||||
// https://github.com/pytorch/pytorch/wiki/The-torch.fft-module-in-PyTorch-1.7
|
|
||||||
#if KALDIFEAT_TORCH_VERSION_MAJOR > 1 || \
|
|
||||||
(KALDIFEAT_TORCH_VERSION_MAJOR == 1 && KALDIFEAT_TORCH_VERSION_MINOR > 6)
|
|
||||||
#include "torch/fft.h"
|
|
||||||
#define KALDIFEAT_HAS_FFT_NAMESPACE
|
|
||||||
// It uses torch::fft::rfft
|
|
||||||
// Its input shape is [x, N], output shape is [x, N/2]
|
|
||||||
// which is a complex tensor
|
|
||||||
#else
|
|
||||||
#include "ATen/Functions.h"
|
|
||||||
// It uses torch::fft
|
|
||||||
// Its input shape is [x, N], output shape is [x, N/2, 2]
|
|
||||||
// which contains the real part [..., ], and imaginary part [..., 1]
|
|
||||||
#endif
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
namespace kaldifeat {
|
||||||
|
|
||||||
@ -34,7 +18,7 @@ class OfflineFeatureTpl {
|
|||||||
|
|
||||||
// Note: feature_window_function_ is the windowing function, which initialized
|
// Note: feature_window_function_ is the windowing function, which initialized
|
||||||
// using the options class, that we cache at this level.
|
// using the options class, that we cache at this level.
|
||||||
explicit OfflineFeatureTpl(const Options &opts)
|
OfflineFeatureTpl(const Options &opts)
|
||||||
: computer_(opts),
|
: computer_(opts),
|
||||||
feature_window_function_(computer_.GetFrameOptions(), opts.device) {}
|
feature_window_function_(computer_.GetFrameOptions(), opts.device) {}
|
||||||
|
|
||||||
@ -62,10 +46,6 @@ class OfflineFeatureTpl {
|
|||||||
int32_t Dim() const { return computer_.Dim(); }
|
int32_t Dim() const { return computer_.Dim(); }
|
||||||
const Options &GetOptions() const { return computer_.GetOptions(); }
|
const Options &GetOptions() const { return computer_.GetOptions(); }
|
||||||
|
|
||||||
const FrameExtractionOptions &GetFrameOptions() const {
|
|
||||||
return GetOptions().frame_opts;
|
|
||||||
}
|
|
||||||
|
|
||||||
// Copy constructor.
|
// Copy constructor.
|
||||||
OfflineFeatureTpl(const OfflineFeatureTpl<F> &) = delete;
|
OfflineFeatureTpl(const OfflineFeatureTpl<F> &) = delete;
|
||||||
OfflineFeatureTpl<F> &operator=(const OfflineFeatureTpl<F> &) = delete;
|
OfflineFeatureTpl<F> &operator=(const OfflineFeatureTpl<F> &) = delete;
|
||||||
|
|||||||
@ -8,6 +8,9 @@
|
|||||||
|
|
||||||
#include <cmath>
|
#include <cmath>
|
||||||
|
|
||||||
|
#include "torch/fft.h"
|
||||||
|
#include "torch/torch.h"
|
||||||
|
|
||||||
namespace kaldifeat {
|
namespace kaldifeat {
|
||||||
|
|
||||||
std::ostream &operator<<(std::ostream &os, const FbankOptions &opts) {
|
std::ostream &operator<<(std::ostream &os, const FbankOptions &opts) {
|
||||||
@ -63,29 +66,14 @@ torch::Tensor FbankComputer::Compute(torch::Tensor signal_raw_log_energy,
|
|||||||
}
|
}
|
||||||
|
|
||||||
// note spectrum is in magnitude, not power, because of `abs()`
|
// note spectrum is in magnitude, not power, because of `abs()`
|
||||||
#if defined(KALDIFEAT_HAS_FFT_NAMESPACE)
|
|
||||||
// signal_frame shape: [x, 512]
|
|
||||||
// spectrum shape [x, 257]
|
|
||||||
torch::Tensor spectrum = torch::fft::rfft(signal_frame).abs();
|
torch::Tensor spectrum = torch::fft::rfft(signal_frame).abs();
|
||||||
#else
|
|
||||||
// signal_frame shape [x, 512]
|
|
||||||
// real_imag shape [x, 257, 2],
|
|
||||||
// where [..., 0] is the real part
|
|
||||||
// [..., 1] is the imaginary part
|
|
||||||
torch::Tensor real_imag = torch::rfft(signal_frame, 1);
|
|
||||||
torch::Tensor real = real_imag.index({"...", 0});
|
|
||||||
torch::Tensor imag = real_imag.index({"...", 1});
|
|
||||||
torch::Tensor spectrum = (real.square() + imag.square()).sqrt();
|
|
||||||
#endif
|
|
||||||
|
|
||||||
// remove the last column, i.e., the highest fft bin
|
// remove the last column, i.e., the highest fft bin
|
||||||
spectrum = spectrum.index(
|
spectrum = spectrum.index(
|
||||||
{"...", torch::indexing::Slice(0, -1, torch::indexing::None)});
|
{"...", torch::indexing::Slice(0, -1, torch::indexing::None)});
|
||||||
|
|
||||||
// Use power instead of magnitude if requested.
|
// Use power instead of magnitude if requested.
|
||||||
if (opts_.use_power) {
|
if (opts_.use_power) spectrum.pow_(2);
|
||||||
spectrum = spectrum.pow(2);
|
|
||||||
}
|
|
||||||
|
|
||||||
torch::Tensor mel_energies = mel_banks.Compute(spectrum);
|
torch::Tensor mel_energies = mel_banks.Compute(spectrum);
|
||||||
if (opts_.use_log_fbank) {
|
if (opts_.use_log_fbank) {
|
||||||
|
|||||||
@ -13,6 +13,7 @@
|
|||||||
#include "kaldifeat/csrc/feature-common.h"
|
#include "kaldifeat/csrc/feature-common.h"
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
#include "kaldifeat/csrc/feature-window.h"
|
||||||
#include "kaldifeat/csrc/mel-computations.h"
|
#include "kaldifeat/csrc/mel-computations.h"
|
||||||
|
#include "torch/torch.h"
|
||||||
|
|
||||||
namespace kaldifeat {
|
namespace kaldifeat {
|
||||||
|
|
||||||
@ -44,18 +45,20 @@ struct FbankOptions {
|
|||||||
|
|
||||||
std::string ToString() const {
|
std::string ToString() const {
|
||||||
std::ostringstream os;
|
std::ostringstream os;
|
||||||
os << "FbankOptions(";
|
os << "frame_opts: \n";
|
||||||
|
os << frame_opts << "\n";
|
||||||
|
os << "\n";
|
||||||
|
|
||||||
os << "frame_opts=" << frame_opts.ToString() << ", ";
|
os << "mel_opts: \n";
|
||||||
os << "mel_opts=" << mel_opts.ToString() << ", ";
|
os << mel_opts << "\n";
|
||||||
|
|
||||||
os << "use_energy=" << (use_energy ? "True" : "False") << ", ";
|
os << "use_energy: " << use_energy << "\n";
|
||||||
os << "energy_floor=" << energy_floor << ", ";
|
os << "energy_floor: " << energy_floor << "\n";
|
||||||
os << "raw_energy=" << (raw_energy ? "True" : "False") << ", ";
|
os << "raw_energy: " << raw_energy << "\n";
|
||||||
os << "htk_compat=" << (htk_compat ? "True" : "False") << ", ";
|
os << "htk_compat: " << htk_compat << "\n";
|
||||||
os << "use_log_fbank=" << (use_log_fbank ? "True" : "False") << ", ";
|
os << "use_log_fbank: " << use_log_fbank << "\n";
|
||||||
os << "use_power=" << (use_power ? "True" : "False") << ", ";
|
os << "use_power: " << use_power << "\n";
|
||||||
os << "device=\"" << device << "\")";
|
os << "device: " << device << "\n";
|
||||||
return os.str();
|
return os.str();
|
||||||
}
|
}
|
||||||
};
|
};
|
||||||
|
|||||||
@ -1,33 +0,0 @@
|
|||||||
// kaldifeat/csrc/feature-functions.cc
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
// This file is copied/modified from kaldi/src/feat/feature-functions.cc
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-functions.h"
|
|
||||||
|
|
||||||
#include <cmath>
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
void InitIdftBases(int32_t n_bases, int32_t dimension, torch::Tensor *mat_out) {
|
|
||||||
float angle = M_PI / (dimension - 1);
|
|
||||||
float scale = 1.0f / (2 * (dimension - 1));
|
|
||||||
|
|
||||||
*mat_out = torch::empty({n_bases, dimension}, torch::kFloat);
|
|
||||||
float *data = mat_out->data_ptr<float>();
|
|
||||||
|
|
||||||
int32_t stride = mat_out->stride(0);
|
|
||||||
|
|
||||||
for (int32_t i = 0; i < n_bases; ++i) {
|
|
||||||
float *this_row = data + i * stride;
|
|
||||||
this_row[0] = scale;
|
|
||||||
for (int32_t j = 1; j < dimension - 1; ++j) {
|
|
||||||
this_row[j] = 2 * scale * std::cos(angle * i * j);
|
|
||||||
}
|
|
||||||
|
|
||||||
this_row[dimension - 1] = scale * std::cos(angle * i * (dimension - 1));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
@ -1,18 +0,0 @@
|
|||||||
// kaldifeat/csrc/feature-functions.h
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
// This file is copied/modified from kaldi/src/feat/feature-functions.h
|
|
||||||
|
|
||||||
#ifndef KALDIFEAT_CSRC_FEATURE_FUNCTIONS_H_
|
|
||||||
#define KALDIFEAT_CSRC_FEATURE_FUNCTIONS_H_
|
|
||||||
|
|
||||||
#include "torch/script.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
void InitIdftBases(int32_t n_bases, int32_t dimension, torch::Tensor *mat_out);
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
#endif // KALDIFEAT_CSRC_FEATURE_FUNCTIONS_H_
|
|
||||||
@ -31,7 +31,7 @@ MfccComputer::MfccComputer(const MfccOptions &opts) : opts_(opts) {
|
|||||||
// energy we replace this with the energy. This means a different
|
// energy we replace this with the energy. This means a different
|
||||||
// ordering of features than HTK.
|
// ordering of features than HTK.
|
||||||
|
|
||||||
using namespace torch::indexing; // It imports: Slice, None // NOLINT
|
using namespace torch::indexing; // It imports: Slice, None
|
||||||
|
|
||||||
// dct_matrix[:opts.num_cepts, :]
|
// dct_matrix[:opts.num_cepts, :]
|
||||||
torch::Tensor dct_rows =
|
torch::Tensor dct_rows =
|
||||||
@ -91,27 +91,14 @@ torch::Tensor MfccComputer::Compute(torch::Tensor signal_raw_log_energy,
|
|||||||
}
|
}
|
||||||
|
|
||||||
// note spectrum is in magnitude, not power, because of `abs()`
|
// note spectrum is in magnitude, not power, because of `abs()`
|
||||||
#if defined(KALDIFEAT_HAS_FFT_NAMESPACE)
|
|
||||||
// signal_frame shape: [x, 512]
|
|
||||||
// spectrum shape [x, 257
|
|
||||||
torch::Tensor spectrum = torch::fft::rfft(signal_frame).abs();
|
torch::Tensor spectrum = torch::fft::rfft(signal_frame).abs();
|
||||||
#else
|
|
||||||
// signal_frame shape [x, 512]
|
|
||||||
// real_imag shape [x, 257, 2],
|
|
||||||
// where [..., 0] is the real part
|
|
||||||
// [..., 1] is the imaginary part
|
|
||||||
torch::Tensor real_imag = torch::rfft(signal_frame, 1);
|
|
||||||
torch::Tensor real = real_imag.index({"...", 0});
|
|
||||||
torch::Tensor imag = real_imag.index({"...", 1});
|
|
||||||
torch::Tensor spectrum = (real.square() + imag.square()).sqrt();
|
|
||||||
#endif
|
|
||||||
|
|
||||||
// remove the last column, i.e., the highest fft bin
|
// remove the last column, i.e., the highest fft bin
|
||||||
spectrum = spectrum.index(
|
spectrum = spectrum.index(
|
||||||
{"...", torch::indexing::Slice(0, -1, torch::indexing::None)});
|
{"...", torch::indexing::Slice(0, -1, torch::indexing::None)});
|
||||||
|
|
||||||
// Use power instead of magnitude
|
// Use power instead of magnitude
|
||||||
spectrum = spectrum.pow(2);
|
spectrum.pow_(2);
|
||||||
|
|
||||||
torch::Tensor mel_energies = mel_banks.Compute(spectrum);
|
torch::Tensor mel_energies = mel_banks.Compute(spectrum);
|
||||||
|
|
||||||
|
|||||||
@ -7,13 +7,10 @@
|
|||||||
#ifndef KALDIFEAT_CSRC_FEATURE_MFCC_H_
|
#ifndef KALDIFEAT_CSRC_FEATURE_MFCC_H_
|
||||||
#define KALDIFEAT_CSRC_FEATURE_MFCC_H_
|
#define KALDIFEAT_CSRC_FEATURE_MFCC_H_
|
||||||
|
|
||||||
#include <map>
|
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-common.h"
|
#include "kaldifeat/csrc/feature-common.h"
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
#include "kaldifeat/csrc/feature-window.h"
|
||||||
#include "kaldifeat/csrc/mel-computations.h"
|
#include "kaldifeat/csrc/mel-computations.h"
|
||||||
#include "torch/script.h"
|
#include "torch/torch.h"
|
||||||
|
|
||||||
namespace kaldifeat {
|
namespace kaldifeat {
|
||||||
|
|
||||||
@ -53,18 +50,20 @@ struct MfccOptions {
|
|||||||
|
|
||||||
std::string ToString() const {
|
std::string ToString() const {
|
||||||
std::ostringstream os;
|
std::ostringstream os;
|
||||||
os << "MfccOptions(";
|
os << "frame_opts: \n";
|
||||||
os << "frame_opts=" << frame_opts.ToString() << ", ";
|
os << frame_opts << "\n";
|
||||||
os << "mel_opts=" << mel_opts.ToString() << ", ";
|
os << "\n";
|
||||||
|
|
||||||
os << "num_ceps=" << num_ceps << ", ";
|
os << "mel_opts: \n";
|
||||||
os << "use_energy=" << (use_energy ? "True" : "False") << ", ";
|
os << mel_opts << "\n";
|
||||||
os << "energy_floor=" << energy_floor << ", ";
|
|
||||||
os << "raw_energy=" << (raw_energy ? "True" : "False") << ", ";
|
|
||||||
os << "cepstral_lifter=" << cepstral_lifter << ", ";
|
|
||||||
os << "htk_compat=" << (htk_compat ? "True" : "False") << ", ";
|
|
||||||
os << "device=\"" << device << "\")";
|
|
||||||
|
|
||||||
|
os << "num_ceps: " << num_ceps << "\n";
|
||||||
|
os << "use_energy: " << use_energy << "\n";
|
||||||
|
os << "energy_floor: " << energy_floor << "\n";
|
||||||
|
os << "raw_energy: " << raw_energy << "\n";
|
||||||
|
os << "cepstral_lifter: " << cepstral_lifter << "\n";
|
||||||
|
os << "htk_compat: " << htk_compat << "\n";
|
||||||
|
os << "device: " << device << "\n";
|
||||||
return os.str();
|
return os.str();
|
||||||
}
|
}
|
||||||
};
|
};
|
||||||
|
|||||||
@ -1,185 +0,0 @@
|
|||||||
// kaldifeat/csrc/feature-plp.cc
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
// This file is copied/modified from kaldi/src/feat/feature-plp.cc
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-plp.h"
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-functions.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
std::ostream &operator<<(std::ostream &os, const PlpOptions &opts) {
|
|
||||||
os << opts.ToString();
|
|
||||||
return os;
|
|
||||||
}
|
|
||||||
|
|
||||||
PlpComputer::PlpComputer(const PlpOptions &opts) : opts_(opts) {
|
|
||||||
// our num-ceps includes C0.
|
|
||||||
KALDIFEAT_ASSERT(opts_.num_ceps <= opts_.lpc_order + 1);
|
|
||||||
|
|
||||||
if (opts.cepstral_lifter != 0.0) {
|
|
||||||
lifter_coeffs_ = torch::empty({1, opts.num_ceps}, torch::kFloat32);
|
|
||||||
ComputeLifterCoeffs(opts.cepstral_lifter, &lifter_coeffs_);
|
|
||||||
lifter_coeffs_ = lifter_coeffs_.to(opts.device);
|
|
||||||
}
|
|
||||||
|
|
||||||
InitIdftBases(opts_.lpc_order + 1, opts_.mel_opts.num_bins + 2, &idft_bases_);
|
|
||||||
|
|
||||||
// CAUTION: we save a transposed version of idft_bases_
|
|
||||||
idft_bases_ = idft_bases_.to(opts.device).t();
|
|
||||||
|
|
||||||
if (opts.energy_floor > 0.0) log_energy_floor_ = logf(opts.energy_floor);
|
|
||||||
|
|
||||||
// We'll definitely need the filterbanks info for VTLN warping factor 1.0.
|
|
||||||
// [note: this call caches it.]
|
|
||||||
GetMelBanks(1.0);
|
|
||||||
}
|
|
||||||
|
|
||||||
PlpComputer::~PlpComputer() {
|
|
||||||
for (auto iter = mel_banks_.begin(); iter != mel_banks_.end(); ++iter)
|
|
||||||
delete iter->second;
|
|
||||||
|
|
||||||
for (auto iter = equal_loudness_.begin(); iter != equal_loudness_.end();
|
|
||||||
++iter)
|
|
||||||
delete iter->second;
|
|
||||||
}
|
|
||||||
|
|
||||||
const MelBanks *PlpComputer::GetMelBanks(float vtln_warp) {
|
|
||||||
MelBanks *this_mel_banks = nullptr;
|
|
||||||
|
|
||||||
// std::map<float, MelBanks *>::iterator iter = mel_banks_.find(vtln_warp);
|
|
||||||
auto iter = mel_banks_.find(vtln_warp);
|
|
||||||
if (iter == mel_banks_.end()) {
|
|
||||||
this_mel_banks =
|
|
||||||
new MelBanks(opts_.mel_opts, opts_.frame_opts, vtln_warp, opts_.device);
|
|
||||||
mel_banks_[vtln_warp] = this_mel_banks;
|
|
||||||
} else {
|
|
||||||
this_mel_banks = iter->second;
|
|
||||||
}
|
|
||||||
return this_mel_banks;
|
|
||||||
}
|
|
||||||
|
|
||||||
const torch::Tensor *PlpComputer::GetEqualLoudness(float vtln_warp) {
|
|
||||||
const MelBanks *this_mel_banks = GetMelBanks(vtln_warp);
|
|
||||||
torch::Tensor *ans = NULL;
|
|
||||||
auto iter = equal_loudness_.find(vtln_warp);
|
|
||||||
if (iter == equal_loudness_.end()) {
|
|
||||||
ans = new torch::Tensor;
|
|
||||||
GetEqualLoudnessVector(*this_mel_banks, ans);
|
|
||||||
*ans = ans->to(opts_.device);
|
|
||||||
equal_loudness_[vtln_warp] = ans;
|
|
||||||
} else {
|
|
||||||
ans = iter->second;
|
|
||||||
}
|
|
||||||
return ans;
|
|
||||||
}
|
|
||||||
|
|
||||||
// ans.shape [signal_frame.size(0), this->Dim()]
|
|
||||||
torch::Tensor PlpComputer::Compute(torch::Tensor signal_raw_log_energy,
|
|
||||||
float vtln_warp,
|
|
||||||
const torch::Tensor &signal_frame) {
|
|
||||||
KALDIFEAT_ASSERT(signal_frame.dim() == 2);
|
|
||||||
KALDIFEAT_ASSERT(signal_frame.size(1) == opts_.frame_opts.PaddedWindowSize());
|
|
||||||
|
|
||||||
const MelBanks &mel_banks = *GetMelBanks(vtln_warp);
|
|
||||||
const torch::Tensor &equal_loudness = *GetEqualLoudness(vtln_warp);
|
|
||||||
|
|
||||||
// torch.finfo(torch.float32).eps
|
|
||||||
constexpr float kEps = 1.1920928955078125e-07f;
|
|
||||||
|
|
||||||
// Compute energy after window function (not the raw one).
|
|
||||||
if (opts_.use_energy && !opts_.raw_energy) {
|
|
||||||
signal_raw_log_energy =
|
|
||||||
torch::clamp_min(signal_frame.pow(2).sum(1), kEps).log();
|
|
||||||
}
|
|
||||||
|
|
||||||
// note spectrum is in magnitude, not power, because of `abs()`
|
|
||||||
#if defined(KALDIFEAT_HAS_FFT_NAMESPACE)
|
|
||||||
// signal_frame shape: [x, 512]
|
|
||||||
// spectrum shape [x, 257
|
|
||||||
torch::Tensor spectrum = torch::fft::rfft(signal_frame).abs();
|
|
||||||
#else
|
|
||||||
// signal_frame shape [x, 512]
|
|
||||||
// real_imag shape [x, 257, 2],
|
|
||||||
// where [..., 0] is the real part
|
|
||||||
// [..., 1] is the imaginary part
|
|
||||||
torch::Tensor real_imag = torch::rfft(signal_frame, 1);
|
|
||||||
torch::Tensor real = real_imag.index({"...", 0});
|
|
||||||
torch::Tensor imag = real_imag.index({"...", 1});
|
|
||||||
torch::Tensor spectrum = (real.square() + imag.square()).sqrt();
|
|
||||||
#endif
|
|
||||||
|
|
||||||
// remove the last column, i.e., the highest fft bin
|
|
||||||
spectrum = spectrum.index(
|
|
||||||
{"...", torch::indexing::Slice(0, -1, torch::indexing::None)});
|
|
||||||
|
|
||||||
// Use power instead of magnitude
|
|
||||||
spectrum = spectrum.pow(2);
|
|
||||||
|
|
||||||
torch::Tensor mel_energies = mel_banks.Compute(spectrum);
|
|
||||||
|
|
||||||
mel_energies = torch::mul(mel_energies, equal_loudness);
|
|
||||||
mel_energies = mel_energies.pow(opts_.compress_factor);
|
|
||||||
|
|
||||||
// duplicate first and last elements
|
|
||||||
//
|
|
||||||
// left_padding = wave[:num_left_padding].flip(dims=(0,))
|
|
||||||
// first = mel_energies[:, 0]
|
|
||||||
// first.shape [num_frames, 1]
|
|
||||||
torch::Tensor first = mel_energies.index({"...", 0}).unsqueeze(-1);
|
|
||||||
// last = mel_energies[:, -1]
|
|
||||||
// last.shape [num_frames, 1]
|
|
||||||
torch::Tensor last = mel_energies.index({"...", -1}).unsqueeze(-1);
|
|
||||||
|
|
||||||
mel_energies = torch::cat({first, mel_energies, last}, 1);
|
|
||||||
|
|
||||||
torch::Tensor autocorr_coeffs = torch::mm(mel_energies, idft_bases_);
|
|
||||||
|
|
||||||
torch::Tensor lpc_coeffs;
|
|
||||||
torch::Tensor residual_log_energy = ComputeLpc(autocorr_coeffs, &lpc_coeffs);
|
|
||||||
|
|
||||||
residual_log_energy = torch::clamp_min(residual_log_energy, kEps);
|
|
||||||
|
|
||||||
torch::Tensor raw_cepstrum = Lpc2Cepstrum(lpc_coeffs);
|
|
||||||
|
|
||||||
// torch.cat((residual_log_energy.unsqueeze(-1),
|
|
||||||
// raw_cepstrum[:opts.num_ceps-1]), 1)
|
|
||||||
//
|
|
||||||
using namespace torch::indexing; // It imports: Slice, None // NOLINT
|
|
||||||
torch::Tensor features = torch::cat(
|
|
||||||
{residual_log_energy.unsqueeze(-1),
|
|
||||||
raw_cepstrum.index({"...", Slice(0, opts_.num_ceps - 1, None)})},
|
|
||||||
1);
|
|
||||||
|
|
||||||
if (opts_.cepstral_lifter != 0.0) {
|
|
||||||
features = torch::mul(features, lifter_coeffs_);
|
|
||||||
}
|
|
||||||
|
|
||||||
if (opts_.cepstral_scale != 1.0) {
|
|
||||||
features = features * opts_.cepstral_scale;
|
|
||||||
}
|
|
||||||
|
|
||||||
if (opts_.use_energy) {
|
|
||||||
if (opts_.energy_floor > 0.0f) {
|
|
||||||
signal_raw_log_energy =
|
|
||||||
torch::clamp_min(signal_raw_log_energy, log_energy_floor_);
|
|
||||||
}
|
|
||||||
// column 0 is replaced by signal_raw_log_energy
|
|
||||||
//
|
|
||||||
// features[:, 0] = signal_raw_log_energy
|
|
||||||
//
|
|
||||||
features.index({"...", 0}) = signal_raw_log_energy;
|
|
||||||
}
|
|
||||||
|
|
||||||
if (opts_.htk_compat) { // reorder the features.
|
|
||||||
// shift left, so the original 0th column
|
|
||||||
// becomes the last column;
|
|
||||||
// the original first column becomes the 0th column
|
|
||||||
features = torch::roll(features, -1, 1);
|
|
||||||
}
|
|
||||||
return features;
|
|
||||||
}
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
@ -1,129 +0,0 @@
|
|||||||
// kaldifeat/csrc/feature-plp.h
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
// This file is copied/modified from kaldi/src/feat/feature-plp.h
|
|
||||||
|
|
||||||
#ifndef KALDIFEAT_CSRC_FEATURE_PLP_H_
|
|
||||||
#define KALDIFEAT_CSRC_FEATURE_PLP_H_
|
|
||||||
|
|
||||||
#include <map>
|
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-common.h"
|
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
|
||||||
#include "kaldifeat/csrc/mel-computations.h"
|
|
||||||
#include "torch/script.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
/// PlpOptions contains basic options for computing PLP features.
|
|
||||||
/// It only includes things that can be done in a "stateless" way, i.e.
|
|
||||||
/// it does not include energy max-normalization.
|
|
||||||
/// It does not include delta computation.
|
|
||||||
struct PlpOptions {
|
|
||||||
FrameExtractionOptions frame_opts;
|
|
||||||
MelBanksOptions mel_opts;
|
|
||||||
|
|
||||||
// Order of LPC analysis in PLP computation
|
|
||||||
//
|
|
||||||
// 12 seems to be common for 16kHz-sampled data. For 8kHz-sampled
|
|
||||||
// data, 15 may be better.
|
|
||||||
int32_t lpc_order = 12;
|
|
||||||
|
|
||||||
// Number of cepstra in PLP computation (including C0)
|
|
||||||
int32_t num_ceps = 13;
|
|
||||||
bool use_energy = true; // use energy; else C0
|
|
||||||
|
|
||||||
// Floor on energy (absolute, not relative) in PLP computation.
|
|
||||||
// Only makes a difference if --use-energy=true; only necessary if
|
|
||||||
// dither is 0.0. Suggested values: 0.1 or 1.0
|
|
||||||
float energy_floor = 0.0;
|
|
||||||
|
|
||||||
// If true, compute energy before preemphasis and windowing
|
|
||||||
bool raw_energy = true;
|
|
||||||
|
|
||||||
// Compression factor in PLP computation
|
|
||||||
float compress_factor = 0.33333;
|
|
||||||
|
|
||||||
// Constant that controls scaling of PLPs
|
|
||||||
int32_t cepstral_lifter = 22;
|
|
||||||
|
|
||||||
// Scaling constant in PLP computation
|
|
||||||
float cepstral_scale = 1.0;
|
|
||||||
|
|
||||||
bool htk_compat = false; // if true, put energy/C0 last and introduce a
|
|
||||||
// factor of sqrt(2) on C0 to be the same as HTK.
|
|
||||||
//
|
|
||||||
torch::Device device{"cpu"};
|
|
||||||
|
|
||||||
PlpOptions() { mel_opts.num_bins = 23; }
|
|
||||||
|
|
||||||
std::string ToString() const {
|
|
||||||
std::ostringstream os;
|
|
||||||
os << "PlpOptions(";
|
|
||||||
|
|
||||||
os << "frame_opts=" << frame_opts.ToString() << ", ";
|
|
||||||
os << "mel_opts=" << mel_opts.ToString() << ", ";
|
|
||||||
|
|
||||||
os << "lpc_order=" << lpc_order << ", ";
|
|
||||||
os << "num_ceps=" << num_ceps << ", ";
|
|
||||||
os << "use_energy=" << (use_energy ? "True" : "False") << ", ";
|
|
||||||
os << "energy_floor=" << energy_floor << ", ";
|
|
||||||
os << "raw_energy=" << (raw_energy ? "True" : "False") << ", ";
|
|
||||||
os << "compress_factor=" << compress_factor << ", ";
|
|
||||||
os << "cepstral_lifter=" << cepstral_lifter << ", ";
|
|
||||||
os << "cepstral_scale=" << cepstral_scale << ", ";
|
|
||||||
os << "htk_compat=" << (htk_compat ? "True" : "False") << ", ";
|
|
||||||
os << "device=\"" << device << "\")";
|
|
||||||
return os.str();
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
std::ostream &operator<<(std::ostream &os, const PlpOptions &opts);
|
|
||||||
|
|
||||||
class PlpComputer {
|
|
||||||
public:
|
|
||||||
using Options = PlpOptions;
|
|
||||||
|
|
||||||
explicit PlpComputer(const PlpOptions &opts);
|
|
||||||
~PlpComputer();
|
|
||||||
|
|
||||||
PlpComputer &operator=(const PlpComputer &) = delete;
|
|
||||||
PlpComputer(const PlpComputer &) = delete;
|
|
||||||
|
|
||||||
int32_t Dim() const { return opts_.num_ceps; }
|
|
||||||
|
|
||||||
bool NeedRawLogEnergy() const { return opts_.use_energy && opts_.raw_energy; }
|
|
||||||
|
|
||||||
const FrameExtractionOptions &GetFrameOptions() const {
|
|
||||||
return opts_.frame_opts;
|
|
||||||
}
|
|
||||||
|
|
||||||
const PlpOptions &GetOptions() const { return opts_; }
|
|
||||||
|
|
||||||
// signal_raw_log_energy is log_energy_pre_window, which is not empty
|
|
||||||
// iff NeedRawLogEnergy() returns true.
|
|
||||||
torch::Tensor Compute(torch::Tensor signal_raw_log_energy, float vtln_warp,
|
|
||||||
const torch::Tensor &signal_frame);
|
|
||||||
|
|
||||||
private:
|
|
||||||
const MelBanks *GetMelBanks(float vtln_warp);
|
|
||||||
|
|
||||||
const torch::Tensor *GetEqualLoudness(float vtln_warp);
|
|
||||||
|
|
||||||
PlpOptions opts_;
|
|
||||||
torch::Tensor lifter_coeffs_;
|
|
||||||
torch::Tensor idft_bases_; // 2-D tensor, kFloat. Caution: it is transposed
|
|
||||||
float log_energy_floor_;
|
|
||||||
std::map<float, MelBanks *> mel_banks_; // float is VTLN coefficient.
|
|
||||||
|
|
||||||
// value is a 1-D torch.Tensor
|
|
||||||
std::map<float, torch::Tensor *> equal_loudness_;
|
|
||||||
};
|
|
||||||
|
|
||||||
using Plp = OfflineFeatureTpl<PlpComputer>;
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
|
|
||||||
#endif // KALDIFEAT_CSRC_FEATURE_PLP_H_
|
|
||||||
@ -1,78 +0,0 @@
|
|||||||
// kaldifeat/csrc/feature-spectrogram.cc
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
// This file is copied/modified from kaldi/src/feat/feature-spectrogram.cc
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-spectrogram.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
std::ostream &operator<<(std::ostream &os, const SpectrogramOptions &opts) {
|
|
||||||
os << opts.ToString();
|
|
||||||
return os;
|
|
||||||
}
|
|
||||||
|
|
||||||
SpectrogramComputer::SpectrogramComputer(const SpectrogramOptions &opts)
|
|
||||||
: opts_(opts) {
|
|
||||||
if (opts.energy_floor > 0.0) log_energy_floor_ = logf(opts.energy_floor);
|
|
||||||
}
|
|
||||||
|
|
||||||
// ans.shape [signal_frame.size(0), this->Dim()]
|
|
||||||
torch::Tensor SpectrogramComputer::Compute(torch::Tensor signal_raw_log_energy,
|
|
||||||
float vtln_warp,
|
|
||||||
const torch::Tensor &signal_frame) {
|
|
||||||
KALDIFEAT_ASSERT(signal_frame.dim() == 2);
|
|
||||||
|
|
||||||
KALDIFEAT_ASSERT(signal_frame.size(1) == opts_.frame_opts.PaddedWindowSize());
|
|
||||||
|
|
||||||
// torch.finfo(torch.float32).eps
|
|
||||||
constexpr float kEps = 1.1920928955078125e-07f;
|
|
||||||
|
|
||||||
// Compute energy after window function (not the raw one).
|
|
||||||
if (!opts_.raw_energy) {
|
|
||||||
signal_raw_log_energy =
|
|
||||||
torch::clamp_min(signal_frame.pow(2).sum(1), kEps).log();
|
|
||||||
}
|
|
||||||
|
|
||||||
// note spectrum is in magnitude, not power, because of `abs()`
|
|
||||||
#if defined(KALDIFEAT_HAS_FFT_NAMESPACE)
|
|
||||||
// signal_frame shape: [x, 512]
|
|
||||||
// spectrum shape [x, 257
|
|
||||||
torch::Tensor spectrum = torch::fft::rfft(signal_frame).abs();
|
|
||||||
#else
|
|
||||||
// signal_frame shape [x, 512]
|
|
||||||
// real_imag shape [x, 257, 2],
|
|
||||||
// where [..., 0] is the real part
|
|
||||||
// [..., 1] is the imaginary part
|
|
||||||
torch::Tensor real_imag = torch::rfft(signal_frame, 1);
|
|
||||||
torch::Tensor real = real_imag.index({"...", 0});
|
|
||||||
torch::Tensor imag = real_imag.index({"...", 1});
|
|
||||||
torch::Tensor spectrum = (real.square() + imag.square()).sqrt();
|
|
||||||
#endif
|
|
||||||
|
|
||||||
if (opts_.return_raw_fft) {
|
|
||||||
KALDIFEAT_ERR << "return raw fft is not supported yet";
|
|
||||||
}
|
|
||||||
|
|
||||||
// compute power spectrum
|
|
||||||
spectrum = spectrum.pow(2);
|
|
||||||
|
|
||||||
// NOTE: take the log
|
|
||||||
spectrum = torch::clamp_min(spectrum, kEps).log();
|
|
||||||
|
|
||||||
if (opts_.energy_floor > 0.0f) {
|
|
||||||
signal_raw_log_energy =
|
|
||||||
torch::clamp_min(signal_raw_log_energy, log_energy_floor_);
|
|
||||||
}
|
|
||||||
|
|
||||||
// The zeroth spectrogram component is always set to the signal energy,
|
|
||||||
// instead of the square of the constant component of the signal.
|
|
||||||
//
|
|
||||||
// spectrum[:,0] = signal_raw_log_energy
|
|
||||||
spectrum.index({"...", 0}) = signal_raw_log_energy;
|
|
||||||
|
|
||||||
return spectrum;
|
|
||||||
}
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
@ -1,92 +0,0 @@
|
|||||||
// kaldifeat/csrc/feature-spectrogram.h
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
// This file is copied/modified from kaldi/src/feat/feature-spectrogram.h
|
|
||||||
|
|
||||||
#ifndef KALDIFEAT_CSRC_FEATURE_SPECTROGRAM_H_
|
|
||||||
#define KALDIFEAT_CSRC_FEATURE_SPECTROGRAM_H_
|
|
||||||
|
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-common.h"
|
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
|
||||||
#include "torch/script.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
struct SpectrogramOptions {
|
|
||||||
FrameExtractionOptions frame_opts;
|
|
||||||
|
|
||||||
// Floor on energy (absolute, not relative) in Spectrogram
|
|
||||||
// computation. Caution: this floor is applied to the
|
|
||||||
// zeroth component, representing the total signal energy.
|
|
||||||
// The floor on the individual spectrogram elements is fixed at
|
|
||||||
// std::numeric_limits<float>::epsilon()
|
|
||||||
float energy_floor = 0.0;
|
|
||||||
|
|
||||||
// If true, compute energy before preemphasis and windowing
|
|
||||||
bool raw_energy = true;
|
|
||||||
|
|
||||||
// If true, return raw FFT complex numbers instead of log magnitudes
|
|
||||||
// Not implemented yet
|
|
||||||
bool return_raw_fft = false;
|
|
||||||
|
|
||||||
torch::Device device{"cpu"};
|
|
||||||
|
|
||||||
std::string ToString() const {
|
|
||||||
std::ostringstream os;
|
|
||||||
os << "SpectrogramOptions(";
|
|
||||||
os << "frame_opts=" << frame_opts.ToString() << ", ";
|
|
||||||
os << "energy_floor=" << energy_floor << ", ";
|
|
||||||
os << "raw_energy=" << (raw_energy ? "True" : "False") << ", ";
|
|
||||||
os << "return_raw_fft=" << (return_raw_fft ? "True" : "False") << ", ";
|
|
||||||
os << "device=\"" << device << "\")";
|
|
||||||
return os.str();
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
std::ostream &operator<<(std::ostream &os, const SpectrogramOptions &opts);
|
|
||||||
|
|
||||||
class SpectrogramComputer {
|
|
||||||
public:
|
|
||||||
using Options = SpectrogramOptions;
|
|
||||||
|
|
||||||
explicit SpectrogramComputer(const SpectrogramOptions &opts);
|
|
||||||
|
|
||||||
~SpectrogramComputer() = default;
|
|
||||||
|
|
||||||
const FrameExtractionOptions &GetFrameOptions() const {
|
|
||||||
return opts_.frame_opts;
|
|
||||||
}
|
|
||||||
|
|
||||||
const SpectrogramOptions &GetOptions() const { return opts_; }
|
|
||||||
|
|
||||||
int32_t Dim() const {
|
|
||||||
if (opts_.return_raw_fft) {
|
|
||||||
return opts_.frame_opts.PaddedWindowSize();
|
|
||||||
} else {
|
|
||||||
return opts_.frame_opts.PaddedWindowSize() / 2 + 1;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
bool NeedRawLogEnergy() const { return opts_.raw_energy; }
|
|
||||||
|
|
||||||
// signal_raw_log_energy is log_energy_pre_window, which is not empty
|
|
||||||
// iff NeedRawLogEnergy() returns true.
|
|
||||||
//
|
|
||||||
// vtln_warp is ignored by this function, it's only
|
|
||||||
// needed for interface compatibility.
|
|
||||||
torch::Tensor Compute(torch::Tensor signal_raw_log_energy, float vtln_warp,
|
|
||||||
const torch::Tensor &signal_frame);
|
|
||||||
|
|
||||||
private:
|
|
||||||
SpectrogramOptions opts_;
|
|
||||||
float log_energy_floor_;
|
|
||||||
};
|
|
||||||
|
|
||||||
using Spectrogram = OfflineFeatureTpl<SpectrogramComputer>;
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
|
|
||||||
#endif // KALDIFEAT_CSRC_FEATURE_SPECTROGRAM_H_
|
|
||||||
@ -7,7 +7,8 @@
|
|||||||
#include "kaldifeat/csrc/feature-window.h"
|
#include "kaldifeat/csrc/feature-window.h"
|
||||||
|
|
||||||
#include <cmath>
|
#include <cmath>
|
||||||
#include <vector>
|
|
||||||
|
#include "torch/torch.h"
|
||||||
|
|
||||||
#ifndef M_2PI
|
#ifndef M_2PI
|
||||||
#define M_2PI 6.283185307179586476925286766559005
|
#define M_2PI 6.283185307179586476925286766559005
|
||||||
@ -29,13 +30,6 @@ FeatureWindowFunction::FeatureWindowFunction(const FrameExtractionOptions &opts,
|
|||||||
float *window_data = window.data_ptr<float>();
|
float *window_data = window.data_ptr<float>();
|
||||||
|
|
||||||
double a = M_2PI / (frame_length - 1);
|
double a = M_2PI / (frame_length - 1);
|
||||||
|
|
||||||
if (opts.window_type == "hann") {
|
|
||||||
// see https://pytorch.org/docs/stable/generated/torch.hann_window.html
|
|
||||||
// We assume periodic is true
|
|
||||||
a = M_2PI / frame_length;
|
|
||||||
}
|
|
||||||
|
|
||||||
for (int32_t i = 0; i < frame_length; i++) {
|
for (int32_t i = 0; i < frame_length; i++) {
|
||||||
double i_fl = static_cast<double>(i);
|
double i_fl = static_cast<double>(i);
|
||||||
if (opts.window_type == "hanning") {
|
if (opts.window_type == "hanning") {
|
||||||
@ -46,8 +40,6 @@ FeatureWindowFunction::FeatureWindowFunction(const FrameExtractionOptions &opts,
|
|||||||
window_data[i] = sin(0.5 * a * i_fl);
|
window_data[i] = sin(0.5 * a * i_fl);
|
||||||
} else if (opts.window_type == "hamming") {
|
} else if (opts.window_type == "hamming") {
|
||||||
window_data[i] = 0.54 - 0.46 * cos(a * i_fl);
|
window_data[i] = 0.54 - 0.46 * cos(a * i_fl);
|
||||||
} else if (opts.window_type == "hann") {
|
|
||||||
window_data[i] = 0.50 - 0.50 * cos(a * i_fl);
|
|
||||||
} else if (opts.window_type ==
|
} else if (opts.window_type ==
|
||||||
"povey") { // like hamming but goes to zero at edges.
|
"povey") { // like hamming but goes to zero at edges.
|
||||||
window_data[i] = pow(0.5 - 0.5 * cos(a * i_fl), 0.85);
|
window_data[i] = pow(0.5 - 0.5 * cos(a * i_fl), 0.85);
|
||||||
@ -164,26 +156,25 @@ torch::Tensor GetStrided(const torch::Tensor &wave,
|
|||||||
}
|
}
|
||||||
|
|
||||||
torch::Tensor Dither(const torch::Tensor &wave, float dither_value) {
|
torch::Tensor Dither(const torch::Tensor &wave, float dither_value) {
|
||||||
if (dither_value == 0.0f) return wave;
|
if (dither_value == 0.0f) wave;
|
||||||
|
|
||||||
torch::Tensor rand_gauss = torch::randn_like(wave);
|
torch::Tensor rand_gauss = torch::randn_like(wave);
|
||||||
#if 1
|
#if 1
|
||||||
return wave + rand_gauss * dither_value;
|
return wave + rand_gauss * dither_value;
|
||||||
#else
|
#else
|
||||||
// use in-place version of wave and change it to pointer type
|
// use in-place version of wave and change its to pointer type
|
||||||
wave_->add_(rand_gauss, dither_value);
|
wave_->add_(rand_gauss, dither_value);
|
||||||
#endif
|
#endif
|
||||||
}
|
}
|
||||||
|
|
||||||
torch::Tensor Preemphasize(float preemph_coeff, const torch::Tensor &wave) {
|
torch::Tensor Preemphasize(float preemph_coeff, const torch::Tensor &wave) {
|
||||||
|
using namespace torch::indexing; // It imports: Slice, None
|
||||||
if (preemph_coeff == 0.0f) return wave;
|
if (preemph_coeff == 0.0f) return wave;
|
||||||
|
|
||||||
KALDIFEAT_ASSERT(preemph_coeff >= 0.0f && preemph_coeff <= 1.0f);
|
KALDIFEAT_ASSERT(preemph_coeff >= 0.0f && preemph_coeff <= 1.0f);
|
||||||
|
|
||||||
torch::Tensor ans = torch::empty_like(wave);
|
torch::Tensor ans = torch::empty_like(wave);
|
||||||
|
|
||||||
using torch::indexing::None;
|
|
||||||
using torch::indexing::Slice;
|
|
||||||
// right = wave[:, 1:]
|
// right = wave[:, 1:]
|
||||||
torch::Tensor right = wave.index({"...", Slice(1, None, None)});
|
torch::Tensor right = wave.index({"...", Slice(1, None, None)});
|
||||||
|
|
||||||
@ -198,59 +189,4 @@ torch::Tensor Preemphasize(float preemph_coeff, const torch::Tensor &wave) {
|
|||||||
return ans;
|
return ans;
|
||||||
}
|
}
|
||||||
|
|
||||||
torch::Tensor ExtractWindow(int64_t sample_offset, const torch::Tensor &wave,
|
|
||||||
int32_t f, const FrameExtractionOptions &opts) {
|
|
||||||
KALDIFEAT_ASSERT(sample_offset >= 0 && wave.numel() != 0);
|
|
||||||
|
|
||||||
int32_t frame_length = opts.WindowSize();
|
|
||||||
int64_t num_samples = sample_offset + wave.numel();
|
|
||||||
int64_t start_sample = FirstSampleOfFrame(f, opts);
|
|
||||||
int64_t end_sample = start_sample + frame_length;
|
|
||||||
|
|
||||||
if (opts.snip_edges) {
|
|
||||||
KALDIFEAT_ASSERT(start_sample >= sample_offset &&
|
|
||||||
end_sample <= num_samples);
|
|
||||||
} else {
|
|
||||||
KALDIFEAT_ASSERT(sample_offset == 0 || start_sample >= sample_offset);
|
|
||||||
}
|
|
||||||
|
|
||||||
// wave_start and wave_end are start and end indexes into 'wave', for the
|
|
||||||
// piece of wave that we're trying to extract.
|
|
||||||
int32_t wave_start = static_cast<int32_t>(start_sample - sample_offset);
|
|
||||||
int32_t wave_end = wave_start + frame_length;
|
|
||||||
|
|
||||||
if (wave_start >= 0 && wave_end <= wave.numel()) {
|
|
||||||
// the normal case -- no edge effects to consider.
|
|
||||||
// return wave[wave_start:wave_end]
|
|
||||||
return wave.index({torch::indexing::Slice(wave_start, wave_end)});
|
|
||||||
} else {
|
|
||||||
torch::Tensor window = torch::empty({frame_length}, torch::kFloat);
|
|
||||||
auto p_window = window.accessor<float, 1>();
|
|
||||||
auto p_wave = wave.accessor<float, 1>();
|
|
||||||
|
|
||||||
// Deal with any end effects by reflection, if needed. This code will only
|
|
||||||
// be reached for about two frames per utterance, so we don't concern
|
|
||||||
// ourselves excessively with efficiency.
|
|
||||||
int32_t wave_dim = wave.numel();
|
|
||||||
for (int32_t s = 0; s != frame_length; ++s) {
|
|
||||||
int32_t s_in_wave = s + wave_start;
|
|
||||||
while (s_in_wave < 0 || s_in_wave >= wave_dim) {
|
|
||||||
// reflect around the beginning or end of the wave.
|
|
||||||
// e.g. -1 -> 0, -2 -> 1.
|
|
||||||
// dim -> dim - 1, dim + 1 -> dim - 2.
|
|
||||||
// the code supports repeated reflections, although this
|
|
||||||
// would only be needed in pathological cases.
|
|
||||||
if (s_in_wave < 0) {
|
|
||||||
s_in_wave = -s_in_wave - 1;
|
|
||||||
} else {
|
|
||||||
s_in_wave = 2 * wave_dim - 1 - s_in_wave;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
p_window[s] = p_wave[s_in_wave];
|
|
||||||
}
|
|
||||||
return window;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
} // namespace kaldifeat
|
||||||
|
|||||||
@ -4,11 +4,8 @@
|
|||||||
|
|
||||||
// This file is copied/modified from kaldi/src/feat/feature-window.h
|
// This file is copied/modified from kaldi/src/feat/feature-window.h
|
||||||
|
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/log.h"
|
#include "kaldifeat/csrc/log.h"
|
||||||
#include "torch/all.h"
|
#include "torch/torch.h"
|
||||||
#include "torch/script.h"
|
|
||||||
|
|
||||||
#ifndef KALDIFEAT_CSRC_FEATURE_WINDOW_H_
|
#ifndef KALDIFEAT_CSRC_FEATURE_WINDOW_H_
|
||||||
#define KALDIFEAT_CSRC_FEATURE_WINDOW_H_
|
#define KALDIFEAT_CSRC_FEATURE_WINDOW_H_
|
||||||
@ -44,11 +41,7 @@ struct FrameExtractionOptions {
|
|||||||
bool snip_edges = true;
|
bool snip_edges = true;
|
||||||
// bool allow_downsample = false;
|
// bool allow_downsample = false;
|
||||||
// bool allow_upsample = false;
|
// bool allow_upsample = false;
|
||||||
|
// int32_t max_feature_vectors = -1;
|
||||||
// Used for streaming feature extraction. It indicates the number
|
|
||||||
// of feature frames to keep in the recycling vector. -1 means to
|
|
||||||
// keep all feature frames.
|
|
||||||
int32_t max_feature_vectors = -1;
|
|
||||||
|
|
||||||
int32_t WindowShift() const {
|
int32_t WindowShift() const {
|
||||||
return static_cast<int32_t>(samp_freq * 0.001f * frame_shift_ms);
|
return static_cast<int32_t>(samp_freq * 0.001f * frame_shift_ms);
|
||||||
@ -62,20 +55,21 @@ struct FrameExtractionOptions {
|
|||||||
}
|
}
|
||||||
std::string ToString() const {
|
std::string ToString() const {
|
||||||
std::ostringstream os;
|
std::ostringstream os;
|
||||||
os << "FrameExtractionOptions(";
|
#define KALDIFEAT_PRINT(x) os << #x << ": " << x << "\n"
|
||||||
os << "samp_freq=" << samp_freq << ", ";
|
KALDIFEAT_PRINT(samp_freq);
|
||||||
os << "frame_shift_ms=" << frame_shift_ms << ", ";
|
KALDIFEAT_PRINT(frame_shift_ms);
|
||||||
os << "frame_length_ms=" << frame_length_ms << ", ";
|
KALDIFEAT_PRINT(frame_length_ms);
|
||||||
os << "dither=" << dither << ", ";
|
KALDIFEAT_PRINT(dither);
|
||||||
os << "preemph_coeff=" << preemph_coeff << ", ";
|
KALDIFEAT_PRINT(preemph_coeff);
|
||||||
os << "remove_dc_offset=" << (remove_dc_offset ? "True" : "False") << ", ";
|
KALDIFEAT_PRINT(remove_dc_offset);
|
||||||
os << "window_type=" << '"' << window_type << '"' << ", ";
|
KALDIFEAT_PRINT(window_type);
|
||||||
os << "round_to_power_of_two=" << (round_to_power_of_two ? "True" : "False")
|
KALDIFEAT_PRINT(round_to_power_of_two);
|
||||||
<< ", ";
|
KALDIFEAT_PRINT(blackman_coeff);
|
||||||
os << "blackman_coeff=" << blackman_coeff << ", ";
|
KALDIFEAT_PRINT(snip_edges);
|
||||||
os << "snip_edges=" << (snip_edges ? "True" : "False") << ", ";
|
// KALDIFEAT_PRINT(allow_downsample);
|
||||||
os << "max_feature_vectors=" << max_feature_vectors << ")";
|
// KALDIFEAT_PRINT(allow_upsample);
|
||||||
|
// KALDIFEAT_PRINT(max_feature_vectors);
|
||||||
|
#undef KALDIFEAT_PRINT
|
||||||
return os.str();
|
return os.str();
|
||||||
}
|
}
|
||||||
};
|
};
|
||||||
@ -103,11 +97,11 @@ class FeatureWindowFunction {
|
|||||||
|
|
||||||
@param [in] flush True if we are asserting that this number of samples
|
@param [in] flush True if we are asserting that this number of samples
|
||||||
is 'all there is', false if we expecting more data to possibly come in. This
|
is 'all there is', false if we expecting more data to possibly come in. This
|
||||||
only makes a difference to the answer
|
only makes a difference to the answer if opts.snips_edges
|
||||||
if opts.snips_edges== false. For offline feature extraction you always want
|
== false. For offline feature extraction you always want flush ==
|
||||||
flush == true. In an online-decoding context, once you know (or decide) that
|
true. In an online-decoding context, once you know (or decide)
|
||||||
no more data is coming in, you'd call it with flush == true at the end to
|
that no more data is coming in, you'd call it with flush == true at the end
|
||||||
flush out any remaining data.
|
to flush out any remaining data.
|
||||||
*/
|
*/
|
||||||
int32_t NumFrames(int64_t num_samples, const FrameExtractionOptions &opts,
|
int32_t NumFrames(int64_t num_samples, const FrameExtractionOptions &opts,
|
||||||
bool flush = true);
|
bool flush = true);
|
||||||
@ -136,29 +130,6 @@ torch::Tensor Dither(const torch::Tensor &wave, float dither_value);
|
|||||||
|
|
||||||
torch::Tensor Preemphasize(float preemph_coeff, const torch::Tensor &wave);
|
torch::Tensor Preemphasize(float preemph_coeff, const torch::Tensor &wave);
|
||||||
|
|
||||||
/*
|
|
||||||
ExtractWindow() extracts "frame_length" samples from the given waveform.
|
|
||||||
Note: This function only extracts "frame_length" samples
|
|
||||||
from the input waveform, without any further processing.
|
|
||||||
|
|
||||||
@param [in] sample_offset If 'wave' is not the entire waveform, but
|
|
||||||
part of it to the left has been discarded, then the
|
|
||||||
number of samples prior to 'wave' that we have
|
|
||||||
already discarded. Set this to zero if you are
|
|
||||||
processing the entire waveform in one piece, or
|
|
||||||
if you get 'no matching function' compilation
|
|
||||||
errors when updating the code.
|
|
||||||
@param [in] wave The waveform
|
|
||||||
@param [in] f The frame index to be extracted, with
|
|
||||||
0 <= f < NumFrames(sample_offset + wave.numel(), opts, true)
|
|
||||||
@param [in] opts The options class to be used
|
|
||||||
@return Return a tensor containing "frame_length" samples extracted from
|
|
||||||
`wave`, without any further processing. Its shape is
|
|
||||||
(1, frame_length).
|
|
||||||
*/
|
|
||||||
torch::Tensor ExtractWindow(int64_t sample_offset, const torch::Tensor &wave,
|
|
||||||
int32_t f, const FrameExtractionOptions &opts);
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
} // namespace kaldifeat
|
||||||
|
|
||||||
#endif // KALDIFEAT_CSRC_FEATURE_WINDOW_H_
|
#endif // KALDIFEAT_CSRC_FEATURE_WINDOW_H_
|
||||||
|
|||||||
@ -1,39 +0,0 @@
|
|||||||
#!/usr/bin/env python3
|
|
||||||
|
|
||||||
# Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
import librosa
|
|
||||||
import numpy as np
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
|
||||||
m = librosa.filters.mel(sr=16000, n_fft=400, n_mels=128)
|
|
||||||
assert m.shape == (128, 201)
|
|
||||||
s = "// Auto-generated. Do NOT edit!\n\n"
|
|
||||||
s += "// Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)\n\n"
|
|
||||||
s += "\n"
|
|
||||||
s += "#ifndef KALDIFEAT_CSRC_WHISPER_V3_MEL_BANK_H_\n"
|
|
||||||
s += "#define KALDIFEAT_CSRC_WHISPER_V3_MEL_BANK_H_\n"
|
|
||||||
s += "namespace kaldifeat {\n\n"
|
|
||||||
s += f"constexpr int32_t kWhisperV3MelRows = {m.shape[0]};\n"
|
|
||||||
s += f"constexpr int32_t kWhisperV3MelCols = {m.shape[1]};\n"
|
|
||||||
s += "\n"
|
|
||||||
s += "constexpr float kWhisperV3MelArray[] = {\n"
|
|
||||||
sep = ""
|
|
||||||
for i, f in enumerate(m.reshape(-1).tolist()):
|
|
||||||
s += f"{sep}{f:.8f}"
|
|
||||||
sep = ", "
|
|
||||||
if i and i % 7 == 0:
|
|
||||||
s += ",\n"
|
|
||||||
sep = ""
|
|
||||||
|
|
||||||
s += "};\n\n"
|
|
||||||
s += "} // namespace kaldifeat\n\n"
|
|
||||||
s += "#endif // KALDIFEAT_CSRC_WHISPER_V3_MEL_BANK_H_\n"
|
|
||||||
|
|
||||||
with open("whisper-v3-mel-bank.h", "w") as f:
|
|
||||||
f.write(s)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
main()
|
|
||||||
@ -1,39 +0,0 @@
|
|||||||
#!/usr/bin/env python3
|
|
||||||
|
|
||||||
# Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
import librosa
|
|
||||||
import numpy as np
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
|
||||||
m = librosa.filters.mel(sr=16000, n_fft=400, n_mels=80)
|
|
||||||
assert m.shape == (80, 201)
|
|
||||||
s = "// Auto-generated. Do NOT edit!\n\n"
|
|
||||||
s += "// Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)\n\n"
|
|
||||||
s += "\n"
|
|
||||||
s += "#ifndef KALDIFEAT_CSRC_WHISPER_MEL_BANK_H_\n"
|
|
||||||
s += "#define KALDIFEAT_CSRC_WHISPER_MEL_BANK_H_\n"
|
|
||||||
s += "namespace kaldifeat {\n\n"
|
|
||||||
s += f"constexpr int32_t kWhisperMelRows = {m.shape[0]};\n"
|
|
||||||
s += f"constexpr int32_t kWhisperMelCols = {m.shape[1]};\n"
|
|
||||||
s += "\n"
|
|
||||||
s += "constexpr float kWhisperMelArray[] = {\n"
|
|
||||||
sep = ""
|
|
||||||
for i, f in enumerate(m.reshape(-1).tolist()):
|
|
||||||
s += f"{sep}{f:.8f}"
|
|
||||||
sep = ", "
|
|
||||||
if i and i % 7 == 0:
|
|
||||||
s += ",\n"
|
|
||||||
sep = ""
|
|
||||||
|
|
||||||
s += "};\n\n"
|
|
||||||
s += "} // namespace kaldifeat\n\n"
|
|
||||||
s += "#endif // KALDIFEAT_CSRC_WHISPER_MEL_BANK_H_\n"
|
|
||||||
|
|
||||||
with open("whisper-mel-bank.h", "w") as f:
|
|
||||||
f.write(s)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
main()
|
|
||||||
@ -5,7 +5,6 @@
|
|||||||
#ifndef KALDIFEAT_CSRC_LOG_H_
|
#ifndef KALDIFEAT_CSRC_LOG_H_
|
||||||
#define KALDIFEAT_CSRC_LOG_H_
|
#define KALDIFEAT_CSRC_LOG_H_
|
||||||
|
|
||||||
#include <cstdint>
|
|
||||||
#include <cstdlib>
|
#include <cstdlib>
|
||||||
#include <iostream>
|
#include <iostream>
|
||||||
#include <sstream>
|
#include <sstream>
|
||||||
|
|||||||
@ -7,7 +7,7 @@
|
|||||||
#ifndef KALDIFEAT_CSRC_MATRIX_FUNCTIONS_H_
|
#ifndef KALDIFEAT_CSRC_MATRIX_FUNCTIONS_H_
|
||||||
#define KALDIFEAT_CSRC_MATRIX_FUNCTIONS_H_
|
#define KALDIFEAT_CSRC_MATRIX_FUNCTIONS_H_
|
||||||
|
|
||||||
#include "torch/script.h"
|
#include "torch/torch.h"
|
||||||
|
|
||||||
namespace kaldifeat {
|
namespace kaldifeat {
|
||||||
|
|
||||||
|
|||||||
@ -3,11 +3,9 @@
|
|||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
||||||
//
|
//
|
||||||
// This file is copied/modified from kaldi/src/feat/mel-computations.cc
|
// This file is copied/modified from kaldi/src/feat/mel-computations.cc
|
||||||
|
//
|
||||||
#include "kaldifeat/csrc/mel-computations.h"
|
#include "kaldifeat/csrc/mel-computations.h"
|
||||||
|
|
||||||
#include <algorithm>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
#include "kaldifeat/csrc/feature-window.h"
|
||||||
|
|
||||||
namespace kaldifeat {
|
namespace kaldifeat {
|
||||||
@ -138,14 +136,9 @@ MelBanks::MelBanks(const MelBanksOptions &opts,
|
|||||||
<< " and vtln-high " << vtln_high << ", versus "
|
<< " and vtln-high " << vtln_high << ", versus "
|
||||||
<< "low-freq " << low_freq << " and high-freq " << high_freq;
|
<< "low-freq " << low_freq << " and high-freq " << high_freq;
|
||||||
|
|
||||||
// we will transpose bins_mat_ at the end of this function
|
|
||||||
bins_mat_ = torch::zeros({num_bins, num_fft_bins}, torch::kFloat);
|
bins_mat_ = torch::zeros({num_bins, num_fft_bins}, torch::kFloat);
|
||||||
|
|
||||||
int32_t stride = bins_mat_.strides()[0];
|
int32_t stride = bins_mat_.strides()[0];
|
||||||
|
|
||||||
center_freqs_ = torch::empty({num_bins}, torch::kFloat);
|
|
||||||
float *center_freqs_data = center_freqs_.data_ptr<float>();
|
|
||||||
|
|
||||||
for (int32_t bin = 0; bin < num_bins; ++bin) {
|
for (int32_t bin = 0; bin < num_bins; ++bin) {
|
||||||
float left_mel = mel_low_freq + bin * mel_freq_delta,
|
float left_mel = mel_low_freq + bin * mel_freq_delta,
|
||||||
center_mel = mel_low_freq + (bin + 1) * mel_freq_delta,
|
center_mel = mel_low_freq + (bin + 1) * mel_freq_delta,
|
||||||
@ -159,7 +152,6 @@ MelBanks::MelBanks(const MelBanksOptions &opts,
|
|||||||
right_mel = VtlnWarpMelFreq(vtln_low, vtln_high, low_freq, high_freq,
|
right_mel = VtlnWarpMelFreq(vtln_low, vtln_high, low_freq, high_freq,
|
||||||
vtln_warp_factor, right_mel);
|
vtln_warp_factor, right_mel);
|
||||||
}
|
}
|
||||||
center_freqs_data[bin] = InverseMelScale(center_mel);
|
|
||||||
// this_bin will be a vector of coefficients that is only
|
// this_bin will be a vector of coefficients that is only
|
||||||
// nonzero where this mel bin is active.
|
// nonzero where this mel bin is active.
|
||||||
float *this_bin = bins_mat_.data_ptr<float>() + bin * stride;
|
float *this_bin = bins_mat_.data_ptr<float>() + bin * stride;
|
||||||
@ -179,14 +171,12 @@ MelBanks::MelBanks(const MelBanksOptions &opts,
|
|||||||
last_index = i;
|
last_index = i;
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
KALDIFEAT_ASSERT(first_index != -1 && last_index >= first_index &&
|
||||||
// Note: It is possible that first_index == last_index == -1 at this line.
|
"You may have set num_mel_bins too large.");
|
||||||
|
|
||||||
// Replicate a bug in HTK, for testing purposes.
|
// Replicate a bug in HTK, for testing purposes.
|
||||||
if (opts.htk_mode && bin == 0 && mel_low_freq != 0.0f &&
|
if (opts.htk_mode && bin == 0 && mel_low_freq != 0.0f)
|
||||||
first_index != -1) {
|
|
||||||
this_bin[first_index] = 0.0f;
|
this_bin[first_index] = 0.0f;
|
||||||
}
|
|
||||||
}
|
}
|
||||||
|
|
||||||
if (debug_) KALDIFEAT_LOG << bins_mat_;
|
if (debug_) KALDIFEAT_LOG << bins_mat_;
|
||||||
@ -198,15 +188,6 @@ MelBanks::MelBanks(const MelBanksOptions &opts,
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
MelBanks::MelBanks(const float *weights, int32_t num_rows, int32_t num_cols,
|
|
||||||
torch::Device device)
|
|
||||||
: debug_(false), htk_mode_(false) {
|
|
||||||
bins_mat_ = torch::from_blob(const_cast<float *>(weights),
|
|
||||||
{num_rows, num_cols}, torch::kFloat)
|
|
||||||
.t()
|
|
||||||
.to(device);
|
|
||||||
}
|
|
||||||
|
|
||||||
torch::Tensor MelBanks::Compute(const torch::Tensor &spectrum) const {
|
torch::Tensor MelBanks::Compute(const torch::Tensor &spectrum) const {
|
||||||
return torch::mm(spectrum, bins_mat_);
|
return torch::mm(spectrum, bins_mat_);
|
||||||
}
|
}
|
||||||
@ -222,141 +203,4 @@ void ComputeLifterCoeffs(float Q, torch::Tensor *coeffs) {
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
void GetEqualLoudnessVector(const MelBanks &mel_banks, torch::Tensor *ans) {
|
|
||||||
int32_t n = mel_banks.NumBins();
|
|
||||||
// Central frequency of each mel bin.
|
|
||||||
const torch::Tensor &f0 = mel_banks.GetCenterFreqs();
|
|
||||||
const float *f0_data = f0.data_ptr<float>();
|
|
||||||
|
|
||||||
*ans = torch::empty({1, n}, torch::kFloat);
|
|
||||||
float *ans_data = ans->data_ptr<float>();
|
|
||||||
for (int32_t i = 0; i < n; ++i) {
|
|
||||||
float fsq = f0_data[i] * f0_data[i];
|
|
||||||
float fsub = fsq / (fsq + 1.6e5);
|
|
||||||
ans_data[i] = fsub * fsub * ((fsq + 1.44e6) / (fsq + 9.61e6));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// Durbin's recursion - converts autocorrelation coefficients to the LPC
|
|
||||||
// pTmp - temporal place [n]
|
|
||||||
// pAC - autocorrelation coefficients [n + 1]
|
|
||||||
// pLP - linear prediction coefficients [n]
|
|
||||||
// (predicted_sn = sum_1^P{a[i-1] * s[n-i]}})
|
|
||||||
// F(z) = 1 / (1 - A(z)), 1 is not stored in the denominator
|
|
||||||
static float Durbin(int n, const float *pAC, float *pLP, float *pTmp) {
|
|
||||||
float ki; // reflection coefficient
|
|
||||||
int i;
|
|
||||||
int j;
|
|
||||||
|
|
||||||
float E = pAC[0];
|
|
||||||
|
|
||||||
for (i = 0; i < n; ++i) {
|
|
||||||
// next reflection coefficient
|
|
||||||
ki = pAC[i + 1];
|
|
||||||
|
|
||||||
for (j = 0; j < i; ++j) ki += pLP[j] * pAC[i - j];
|
|
||||||
|
|
||||||
ki = ki / E;
|
|
||||||
|
|
||||||
// new error
|
|
||||||
float c = 1 - ki * ki;
|
|
||||||
if (c < 1.0e-5) // remove NaNs for constant signal
|
|
||||||
c = 1.0e-5;
|
|
||||||
|
|
||||||
E *= c;
|
|
||||||
|
|
||||||
// new LP coefficients
|
|
||||||
pTmp[i] = -ki;
|
|
||||||
|
|
||||||
for (j = 0; j < i; ++j) pTmp[j] = pLP[j] - ki * pLP[i - j - 1];
|
|
||||||
|
|
||||||
for (j = 0; j <= i; ++j) pLP[j] = pTmp[j];
|
|
||||||
}
|
|
||||||
|
|
||||||
return E;
|
|
||||||
}
|
|
||||||
|
|
||||||
// Compute LP coefficients from autocorrelation coefficients.
|
|
||||||
torch::Tensor ComputeLpc(const torch::Tensor &autocorr_in,
|
|
||||||
torch::Tensor *lpc_out) {
|
|
||||||
KALDIFEAT_ASSERT(autocorr_in.dim() == 2);
|
|
||||||
|
|
||||||
int32_t num_frames = autocorr_in.size(0);
|
|
||||||
int32_t lpc_order = autocorr_in.size(1) - 1;
|
|
||||||
|
|
||||||
*lpc_out = torch::empty({num_frames, lpc_order}, torch::kFloat);
|
|
||||||
torch::Tensor ans = torch::empty({num_frames}, torch::kFloat);
|
|
||||||
|
|
||||||
// TODO(fangjun): Durbin runs only on CPU. Implement a CUDA version
|
|
||||||
torch::Device saved_device = autocorr_in.device();
|
|
||||||
torch::Device cpu("cpu");
|
|
||||||
torch::Tensor in_cpu = autocorr_in.to(cpu);
|
|
||||||
|
|
||||||
torch::Tensor tmp = torch::empty_like(*lpc_out);
|
|
||||||
|
|
||||||
int32_t in_stride = in_cpu.stride(0);
|
|
||||||
int32_t ans_stride = ans.stride(0);
|
|
||||||
int32_t tmp_stride = tmp.stride(0);
|
|
||||||
int32_t lpc_stride = lpc_out->stride(0);
|
|
||||||
|
|
||||||
const float *in_data = in_cpu.data_ptr<float>();
|
|
||||||
float *ans_data = ans.data_ptr<float>();
|
|
||||||
float *tmp_data = tmp.data_ptr<float>();
|
|
||||||
float *lpc_data = lpc_out->data_ptr<float>();
|
|
||||||
|
|
||||||
// see
|
|
||||||
// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Parallel.h#L58
|
|
||||||
at::parallel_for(0, num_frames, 1, [&](int32_t begin, int32_t end) -> void {
|
|
||||||
for (int32_t i = begin; i != end; ++i) {
|
|
||||||
float ret = Durbin(lpc_order, in_data + i * in_stride,
|
|
||||||
lpc_data + i * lpc_stride, tmp_data + i * tmp_stride);
|
|
||||||
|
|
||||||
if (ret <= 0.0) KALDIFEAT_WARN << "Zero energy in LPC computation";
|
|
||||||
|
|
||||||
ans_data[i] = -logf(1.0 / ret); // forms the C0 value
|
|
||||||
}
|
|
||||||
});
|
|
||||||
|
|
||||||
*lpc_out = lpc_out->to(saved_device);
|
|
||||||
return ans.to(saved_device);
|
|
||||||
}
|
|
||||||
|
|
||||||
static void Lpc2CepstrumInternal(int n, const float *pLPC, float *pCepst) {
|
|
||||||
for (int32_t i = 0; i < n; ++i) {
|
|
||||||
double sum = 0.0;
|
|
||||||
for (int32_t j = 0; j < i; ++j) {
|
|
||||||
sum += (i - j) * pLPC[j] * pCepst[i - j - 1];
|
|
||||||
}
|
|
||||||
pCepst[i] = -pLPC[i] - sum / (i + 1);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
torch::Tensor Lpc2Cepstrum(const torch::Tensor &lpc) {
|
|
||||||
KALDIFEAT_ASSERT(lpc.dim() == 2);
|
|
||||||
torch::Device cpu("cpu");
|
|
||||||
torch::Device saved_device = lpc.device();
|
|
||||||
|
|
||||||
// TODO(fangjun): support cuda
|
|
||||||
torch::Tensor in_cpu = lpc.to(cpu);
|
|
||||||
|
|
||||||
int32_t num_frames = in_cpu.size(0);
|
|
||||||
int32_t lpc_order = in_cpu.size(1);
|
|
||||||
|
|
||||||
const float *in_data = in_cpu.data_ptr<float>();
|
|
||||||
int32_t in_stride = in_cpu.stride(0);
|
|
||||||
|
|
||||||
torch::Tensor ans = torch::zeros({num_frames, lpc_order}, torch::kFloat);
|
|
||||||
int32_t ans_stride = ans.stride(0);
|
|
||||||
float *ans_data = ans.data_ptr<float>();
|
|
||||||
|
|
||||||
at::parallel_for(0, num_frames, 1, [&](int32_t begin, int32_t end) -> void {
|
|
||||||
for (int32_t i = begin; i != end; ++i) {
|
|
||||||
Lpc2CepstrumInternal(lpc_order, in_data + i * in_stride,
|
|
||||||
ans_data + i * ans_stride);
|
|
||||||
}
|
|
||||||
});
|
|
||||||
|
|
||||||
return ans.to(saved_device);
|
|
||||||
}
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
} // namespace kaldifeat
|
||||||
|
|||||||
@ -5,7 +5,6 @@
|
|||||||
// This file is copied/modified from kaldi/src/feat/mel-computations.h
|
// This file is copied/modified from kaldi/src/feat/mel-computations.h
|
||||||
|
|
||||||
#include <cmath>
|
#include <cmath>
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
#include "kaldifeat/csrc/feature-window.h"
|
||||||
|
|
||||||
@ -36,14 +35,13 @@ struct MelBanksOptions {
|
|||||||
|
|
||||||
std::string ToString() const {
|
std::string ToString() const {
|
||||||
std::ostringstream os;
|
std::ostringstream os;
|
||||||
os << "MelBanksOptions(";
|
os << "num_bins: " << num_bins << "\n";
|
||||||
os << "num_bins=" << num_bins << ", ";
|
os << "low_freq: " << low_freq << "\n";
|
||||||
os << "low_freq=" << low_freq << ", ";
|
os << "high_freq: " << high_freq << "\n";
|
||||||
os << "high_freq=" << high_freq << ", ";
|
os << "vtln_low: " << vtln_low << "\n";
|
||||||
os << "vtln_low=" << vtln_low << ", ";
|
os << "vtln_high: " << vtln_high << "\n";
|
||||||
os << "vtln_high=" << vtln_high << ", ";
|
os << "debug_mel: " << debug_mel << "\n";
|
||||||
os << "debug_mel=" << (debug_mel ? "True" : "False") << ", ";
|
os << "htk_mode: " << htk_mode << "\n";
|
||||||
os << "htk_mode=" << (htk_mode ? "True" : "False") << ")";
|
|
||||||
return os.str();
|
return os.str();
|
||||||
}
|
}
|
||||||
};
|
};
|
||||||
@ -76,22 +74,7 @@ class MelBanks {
|
|||||||
const FrameExtractionOptions &frame_opts, float vtln_warp_factor,
|
const FrameExtractionOptions &frame_opts, float vtln_warp_factor,
|
||||||
torch::Device device);
|
torch::Device device);
|
||||||
|
|
||||||
// Initialize with a 2-d weights matrix
|
int32_t NumBins() const { return static_cast<int32_t>(bins_mat_.size(0)); }
|
||||||
//
|
|
||||||
// Note: This constructor is for Whisper. It does not initialize
|
|
||||||
// center_freqs_.
|
|
||||||
//
|
|
||||||
// @param weights Pointer to the start address of the matrix
|
|
||||||
// @param num_rows It equals to number of mel bins
|
|
||||||
// @param num_cols It equals to (number of fft bins)/2+1
|
|
||||||
MelBanks(const float *weights, int32_t num_rows, int32_t num_cols,
|
|
||||||
torch::Device device);
|
|
||||||
|
|
||||||
// CAUTION: we save a transposed version of bins_mat_, so return size(1) here
|
|
||||||
int32_t NumBins() const { return static_cast<int32_t>(bins_mat_.size(1)); }
|
|
||||||
|
|
||||||
// returns vector of central freq of each bin; needed by plp code.
|
|
||||||
const torch::Tensor &GetCenterFreqs() const { return center_freqs_; }
|
|
||||||
|
|
||||||
torch::Tensor Compute(const torch::Tensor &spectrum) const;
|
torch::Tensor Compute(const torch::Tensor &spectrum) const;
|
||||||
|
|
||||||
@ -99,15 +82,9 @@ class MelBanks {
|
|||||||
const torch::Tensor &GetBinsMat() const { return bins_mat_; }
|
const torch::Tensor &GetBinsMat() const { return bins_mat_; }
|
||||||
|
|
||||||
private:
|
private:
|
||||||
// A 2-D matrix. Its shape is NOT [num_bins, num_fft_bins]
|
// A 2-D matrix of shape [num_bins, num_fft_bins]
|
||||||
// Its shape is [num_fft_bins, num_bins] for non-whisper.
|
|
||||||
// For whisper, its shape is [num_fft_bins/2+1, num_bins]
|
|
||||||
torch::Tensor bins_mat_;
|
torch::Tensor bins_mat_;
|
||||||
|
|
||||||
// center frequencies of bins, numbered from 0 ... num_bins-1.
|
|
||||||
// Needed by GetCenterFreqs().
|
|
||||||
torch::Tensor center_freqs_; // It's always on CPU
|
|
||||||
|
|
||||||
bool debug_;
|
bool debug_;
|
||||||
bool htk_mode_;
|
bool htk_mode_;
|
||||||
};
|
};
|
||||||
@ -119,26 +96,6 @@ class MelBanks {
|
|||||||
// coeffs is a 1-D float tensor
|
// coeffs is a 1-D float tensor
|
||||||
void ComputeLifterCoeffs(float Q, torch::Tensor *coeffs);
|
void ComputeLifterCoeffs(float Q, torch::Tensor *coeffs);
|
||||||
|
|
||||||
void GetEqualLoudnessVector(const MelBanks &mel_banks, torch::Tensor *ans);
|
|
||||||
|
|
||||||
/* Compute LP coefficients from autocorrelation coefficients.
|
|
||||||
*
|
|
||||||
* @param [in] autocorr_in A 2-D tensor. Each row is a frame. Its number of
|
|
||||||
* columns is lpc_order + 1
|
|
||||||
* @param [out] lpc_coeffs A 2-D tensor. On return, it has as many rows as the
|
|
||||||
* input tensor. Its number of columns is lpc_order.
|
|
||||||
*
|
|
||||||
* @return Returns log energy of residual in a 1-D tensor. It has as many
|
|
||||||
* elements as the number of rows in `autocorr_in`.
|
|
||||||
*/
|
|
||||||
torch::Tensor ComputeLpc(const torch::Tensor &autocorr_in,
|
|
||||||
torch::Tensor *lpc_coeffs);
|
|
||||||
|
|
||||||
/*
|
|
||||||
* @param [in] lpc It is the output argument `lpc_coeffs` in ComputeLpc().
|
|
||||||
*/
|
|
||||||
torch::Tensor Lpc2Cepstrum(const torch::Tensor &lpc);
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
} // namespace kaldifeat
|
||||||
|
|
||||||
#endif // KALDIFEAT_CSRC_MEL_COMPUTATIONS_H_
|
#endif // KALDIFEAT_CSRC_MEL_COMPUTATIONS_H_
|
||||||
|
|||||||
@ -1,89 +0,0 @@
|
|||||||
// kaldifeat/csrc/online-feature-itf.h
|
|
||||||
//
|
|
||||||
// Copyright (c) 2022 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
// This file is copied/modified from kaldi/src/itf/online-feature-itf.h
|
|
||||||
|
|
||||||
#ifndef KALDIFEAT_CSRC_ONLINE_FEATURE_ITF_H_
|
|
||||||
#define KALDIFEAT_CSRC_ONLINE_FEATURE_ITF_H_
|
|
||||||
|
|
||||||
#include <utility>
|
|
||||||
#include <vector>
|
|
||||||
|
|
||||||
#include "torch/script.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
class OnlineFeatureInterface {
|
|
||||||
public:
|
|
||||||
virtual ~OnlineFeatureInterface() = default;
|
|
||||||
|
|
||||||
virtual int32_t Dim() const = 0; /// returns the feature dimension.
|
|
||||||
//
|
|
||||||
// Returns frame shift in seconds. Helps to estimate duration from frame
|
|
||||||
// counts.
|
|
||||||
virtual float FrameShiftInSeconds() const = 0;
|
|
||||||
|
|
||||||
/// Returns the total number of frames, since the start of the utterance, that
|
|
||||||
/// are now available. In an online-decoding context, this will likely
|
|
||||||
/// increase with time as more data becomes available.
|
|
||||||
virtual int32_t NumFramesReady() const = 0;
|
|
||||||
|
|
||||||
/// Returns true if this is the last frame. Frame indices are zero-based, so
|
|
||||||
/// the first frame is zero. IsLastFrame(-1) will return false, unless the
|
|
||||||
/// file is empty (which is a case that I'm not sure all the code will handle,
|
|
||||||
/// so be careful). This function may return false for some frame if we
|
|
||||||
/// haven't yet decided to terminate decoding, but later true if we decide to
|
|
||||||
/// terminate decoding. This function exists mainly to correctly handle end
|
|
||||||
/// effects in feature extraction, and is not a mechanism to determine how
|
|
||||||
/// many frames are in the decodable object (as it used to be, and for
|
|
||||||
/// backward compatibility, still is, in the Decodable interface).
|
|
||||||
virtual bool IsLastFrame(int32_t frame) const = 0;
|
|
||||||
|
|
||||||
/// Gets the feature vector for this frame. Before calling this for a given
|
|
||||||
/// frame, it is assumed that you called NumFramesReady() and it returned a
|
|
||||||
/// number greater than "frame". Otherwise this call will likely crash with
|
|
||||||
/// an assert failure. This function is not declared const, in case there is
|
|
||||||
/// some kind of caching going on, but most of the time it shouldn't modify
|
|
||||||
/// the class.
|
|
||||||
///
|
|
||||||
/// The returned tensor has shape (1, Dim()).
|
|
||||||
virtual torch::Tensor GetFrame(int32_t frame) = 0;
|
|
||||||
|
|
||||||
/// This is like GetFrame() but for a collection of frames. There is a
|
|
||||||
/// default implementation that just gets the frames one by one, but it
|
|
||||||
/// may be overridden for efficiency by child classes (since sometimes
|
|
||||||
/// it's more efficient to do things in a batch).
|
|
||||||
///
|
|
||||||
/// The returned tensor has shape (frames.size(), Dim()).
|
|
||||||
virtual std::vector<torch::Tensor> GetFrames(
|
|
||||||
const std::vector<int32_t> &frames) {
|
|
||||||
std::vector<torch::Tensor> features;
|
|
||||||
features.reserve(frames.size());
|
|
||||||
|
|
||||||
for (auto i : frames) {
|
|
||||||
torch::Tensor f = GetFrame(i);
|
|
||||||
features.push_back(std::move(f));
|
|
||||||
}
|
|
||||||
return features;
|
|
||||||
#if 0
|
|
||||||
return torch::cat(features, /*dim*/ 0);
|
|
||||||
#endif
|
|
||||||
}
|
|
||||||
|
|
||||||
/// This would be called from the application, when you get more wave data.
|
|
||||||
/// Note: the sampling_rate is typically only provided so the code can assert
|
|
||||||
/// that it matches the sampling rate expected in the options.
|
|
||||||
virtual void AcceptWaveform(float sampling_rate,
|
|
||||||
const torch::Tensor &waveform) = 0;
|
|
||||||
|
|
||||||
/// InputFinished() tells the class you won't be providing any
|
|
||||||
/// more waveform. This will help flush out the last few frames
|
|
||||||
/// of delta or LDA features (it will typically affect the return value
|
|
||||||
/// of IsLastFrame.
|
|
||||||
virtual void InputFinished() = 0;
|
|
||||||
};
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
|
|
||||||
#endif // KALDIFEAT_CSRC_ONLINE_FEATURE_ITF_H_
|
|
||||||
@ -1,49 +0,0 @@
|
|||||||
// kaldifeat/csrc/online-feature-test.h
|
|
||||||
//
|
|
||||||
// Copyright (c) 2022 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/online-feature.h"
|
|
||||||
|
|
||||||
#include "gtest/gtest.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
TEST(RecyclingVector, TestUnlimited) {
|
|
||||||
RecyclingVector v(-1);
|
|
||||||
constexpr int32_t N = 100;
|
|
||||||
for (int32_t i = 0; i != N; ++i) {
|
|
||||||
torch::Tensor t = torch::tensor({i, i + 1, i + 2});
|
|
||||||
v.PushBack(t);
|
|
||||||
}
|
|
||||||
ASSERT_EQ(v.Size(), N);
|
|
||||||
|
|
||||||
for (int32_t i = 0; i != N; ++i) {
|
|
||||||
torch::Tensor t = v.At(i);
|
|
||||||
torch::Tensor expected = torch::tensor({i, i + 1, i + 2});
|
|
||||||
EXPECT_TRUE(t.equal(expected));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
TEST(RecyclingVector, Testlimited) {
|
|
||||||
constexpr int32_t K = 3;
|
|
||||||
constexpr int32_t N = 10;
|
|
||||||
RecyclingVector v(K);
|
|
||||||
for (int32_t i = 0; i != N; ++i) {
|
|
||||||
torch::Tensor t = torch::tensor({i, i + 1, i + 2});
|
|
||||||
v.PushBack(t);
|
|
||||||
}
|
|
||||||
|
|
||||||
ASSERT_EQ(v.Size(), N);
|
|
||||||
|
|
||||||
for (int32_t i = 0; i < N - K; ++i) {
|
|
||||||
ASSERT_DEATH(v.At(i), "");
|
|
||||||
}
|
|
||||||
|
|
||||||
for (int32_t i = N - K; i != N; ++i) {
|
|
||||||
torch::Tensor t = v.At(i);
|
|
||||||
torch::Tensor expected = torch::tensor({i, i + 1, i + 2});
|
|
||||||
EXPECT_TRUE(t.equal(expected));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
@ -1,133 +0,0 @@
|
|||||||
// kaldifeat/csrc/online-feature.cc
|
|
||||||
//
|
|
||||||
// Copyright (c) 2022 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
// This file is copied/modified from kaldi/src/feat/online-feature.cc
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/online-feature.h"
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
|
||||||
#include "kaldifeat/csrc/log.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
RecyclingVector::RecyclingVector(int32_t items_to_hold)
|
|
||||||
: items_to_hold_(items_to_hold == 0 ? -1 : items_to_hold),
|
|
||||||
first_available_index_(0) {}
|
|
||||||
|
|
||||||
torch::Tensor RecyclingVector::At(int32_t index) const {
|
|
||||||
if (index < first_available_index_) {
|
|
||||||
KALDIFEAT_ERR << "Attempted to retrieve feature vector that was "
|
|
||||||
"already removed by the RecyclingVector (index = "
|
|
||||||
<< index << "; "
|
|
||||||
<< "first_available_index = " << first_available_index_
|
|
||||||
<< "; "
|
|
||||||
<< "size = " << Size() << ")";
|
|
||||||
}
|
|
||||||
// 'at' does size checking.
|
|
||||||
return items_.at(index - first_available_index_);
|
|
||||||
}
|
|
||||||
|
|
||||||
void RecyclingVector::PushBack(torch::Tensor item) {
|
|
||||||
// Note: -1 is a larger number when treated as unsigned
|
|
||||||
if (items_.size() == static_cast<size_t>(items_to_hold_)) {
|
|
||||||
items_.pop_front();
|
|
||||||
++first_available_index_;
|
|
||||||
}
|
|
||||||
items_.push_back(item);
|
|
||||||
}
|
|
||||||
|
|
||||||
int32_t RecyclingVector::Size() const {
|
|
||||||
return first_available_index_ + static_cast<int32_t>(items_.size());
|
|
||||||
}
|
|
||||||
|
|
||||||
template <class C>
|
|
||||||
OnlineGenericBaseFeature<C>::OnlineGenericBaseFeature(
|
|
||||||
const typename C::Options &opts)
|
|
||||||
: computer_(opts),
|
|
||||||
window_function_(opts.frame_opts, opts.device),
|
|
||||||
features_(opts.frame_opts.max_feature_vectors),
|
|
||||||
input_finished_(false),
|
|
||||||
waveform_offset_(0) {}
|
|
||||||
|
|
||||||
template <class C>
|
|
||||||
void OnlineGenericBaseFeature<C>::AcceptWaveform(
|
|
||||||
float sampling_rate, const torch::Tensor &original_waveform) {
|
|
||||||
if (original_waveform.numel() == 0) return; // Nothing to do.
|
|
||||||
|
|
||||||
KALDIFEAT_ASSERT(original_waveform.dim() == 1);
|
|
||||||
KALDIFEAT_ASSERT(sampling_rate == computer_.GetFrameOptions().samp_freq);
|
|
||||||
|
|
||||||
if (input_finished_)
|
|
||||||
KALDIFEAT_ERR << "AcceptWaveform called after InputFinished() was called.";
|
|
||||||
|
|
||||||
if (waveform_remainder_.numel() == 0) {
|
|
||||||
waveform_remainder_ = original_waveform;
|
|
||||||
} else {
|
|
||||||
waveform_remainder_ =
|
|
||||||
torch::cat({waveform_remainder_, original_waveform}, /*dim*/ 0);
|
|
||||||
}
|
|
||||||
|
|
||||||
ComputeFeatures();
|
|
||||||
}
|
|
||||||
|
|
||||||
template <class C>
|
|
||||||
void OnlineGenericBaseFeature<C>::InputFinished() {
|
|
||||||
input_finished_ = true;
|
|
||||||
ComputeFeatures();
|
|
||||||
}
|
|
||||||
|
|
||||||
template <class C>
|
|
||||||
void OnlineGenericBaseFeature<C>::ComputeFeatures() {
|
|
||||||
const FrameExtractionOptions &frame_opts = computer_.GetFrameOptions();
|
|
||||||
|
|
||||||
int64_t num_samples_total = waveform_offset_ + waveform_remainder_.numel();
|
|
||||||
int32_t num_frames_old = features_.Size();
|
|
||||||
int32_t num_frames_new =
|
|
||||||
NumFrames(num_samples_total, frame_opts, input_finished_);
|
|
||||||
|
|
||||||
KALDIFEAT_ASSERT(num_frames_new >= num_frames_old);
|
|
||||||
|
|
||||||
// note: this online feature-extraction code does not support VTLN.
|
|
||||||
float vtln_warp = 1.0;
|
|
||||||
|
|
||||||
for (int32_t frame = num_frames_old; frame < num_frames_new; ++frame) {
|
|
||||||
torch::Tensor window =
|
|
||||||
ExtractWindow(waveform_offset_, waveform_remainder_, frame, frame_opts);
|
|
||||||
|
|
||||||
// TODO(fangjun): We can compute all feature frames at once
|
|
||||||
torch::Tensor this_feature =
|
|
||||||
computer_.ComputeFeatures(window.unsqueeze(0), vtln_warp);
|
|
||||||
features_.PushBack(this_feature);
|
|
||||||
}
|
|
||||||
|
|
||||||
// OK, we will now discard any portion of the signal that will not be
|
|
||||||
// necessary to compute frames in the future.
|
|
||||||
int64_t first_sample_of_next_frame =
|
|
||||||
FirstSampleOfFrame(num_frames_new, frame_opts);
|
|
||||||
int32_t samples_to_discard = first_sample_of_next_frame - waveform_offset_;
|
|
||||||
if (samples_to_discard > 0) {
|
|
||||||
// discard the leftmost part of the waveform that we no longer need.
|
|
||||||
int32_t new_num_samples = waveform_remainder_.numel() - samples_to_discard;
|
|
||||||
if (new_num_samples <= 0) {
|
|
||||||
// odd, but we'll try to handle it.
|
|
||||||
waveform_offset_ += waveform_remainder_.numel();
|
|
||||||
waveform_remainder_.resize_({0});
|
|
||||||
} else {
|
|
||||||
using torch::indexing::None;
|
|
||||||
using torch::indexing::Slice;
|
|
||||||
|
|
||||||
waveform_remainder_ =
|
|
||||||
waveform_remainder_.index({Slice(samples_to_discard, None)});
|
|
||||||
|
|
||||||
waveform_offset_ += samples_to_discard;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
// instantiate the templates defined here for MFCC, PLP and filterbank classes.
|
|
||||||
template class OnlineGenericBaseFeature<Mfcc>;
|
|
||||||
template class OnlineGenericBaseFeature<Plp>;
|
|
||||||
template class OnlineGenericBaseFeature<Fbank>;
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
@ -1,127 +0,0 @@
|
|||||||
// kaldifeat/csrc/online-feature.h
|
|
||||||
//
|
|
||||||
// Copyright (c) 2022 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
// This file is copied/modified from kaldi/src/feat/online-feature.h
|
|
||||||
|
|
||||||
#ifndef KALDIFEAT_CSRC_ONLINE_FEATURE_H_
|
|
||||||
#define KALDIFEAT_CSRC_ONLINE_FEATURE_H_
|
|
||||||
|
|
||||||
#include <deque>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-fbank.h"
|
|
||||||
#include "kaldifeat/csrc/feature-mfcc.h"
|
|
||||||
#include "kaldifeat/csrc/feature-plp.h"
|
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
|
||||||
#include "kaldifeat/csrc/online-feature-itf.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
/// This class serves as a storage for feature vectors with an option to limit
|
|
||||||
/// the memory usage by removing old elements. The deleted frames indices are
|
|
||||||
/// "remembered" so that regardless of the MAX_ITEMS setting, the user always
|
|
||||||
/// provides the indices as if no deletion was being performed.
|
|
||||||
/// This is useful when processing very long recordings which would otherwise
|
|
||||||
/// cause the memory to eventually blow up when the features are not being
|
|
||||||
/// removed.
|
|
||||||
class RecyclingVector {
|
|
||||||
public:
|
|
||||||
/// By default it does not remove any elements.
|
|
||||||
explicit RecyclingVector(int32_t items_to_hold = -1);
|
|
||||||
|
|
||||||
~RecyclingVector() = default;
|
|
||||||
RecyclingVector(const RecyclingVector &) = delete;
|
|
||||||
RecyclingVector &operator=(const RecyclingVector &) = delete;
|
|
||||||
|
|
||||||
torch::Tensor At(int32_t index) const;
|
|
||||||
|
|
||||||
void PushBack(torch::Tensor item);
|
|
||||||
|
|
||||||
/// This method returns the size as if no "recycling" had happened,
|
|
||||||
/// i.e. equivalent to the number of times the PushBack method has been
|
|
||||||
/// called.
|
|
||||||
int32_t Size() const;
|
|
||||||
|
|
||||||
private:
|
|
||||||
std::deque<torch::Tensor> items_;
|
|
||||||
int32_t items_to_hold_;
|
|
||||||
int32_t first_available_index_;
|
|
||||||
};
|
|
||||||
|
|
||||||
/// This is a templated class for online feature extraction;
|
|
||||||
/// it's templated on a class like MfccComputer or PlpComputer
|
|
||||||
/// that does the basic feature extraction.
|
|
||||||
template <class C>
|
|
||||||
class OnlineGenericBaseFeature : public OnlineFeatureInterface {
|
|
||||||
public:
|
|
||||||
// Constructor from options class
|
|
||||||
explicit OnlineGenericBaseFeature(const typename C::Options &opts);
|
|
||||||
|
|
||||||
int32_t Dim() const override { return computer_.Dim(); }
|
|
||||||
|
|
||||||
float FrameShiftInSeconds() const override {
|
|
||||||
return computer_.GetFrameOptions().frame_shift_ms / 1000.0f;
|
|
||||||
}
|
|
||||||
|
|
||||||
int32_t NumFramesReady() const override { return features_.Size(); }
|
|
||||||
|
|
||||||
// Note: IsLastFrame() will only ever return true if you have called
|
|
||||||
// InputFinished() (and this frame is the last frame).
|
|
||||||
bool IsLastFrame(int32_t frame) const override {
|
|
||||||
return input_finished_ && frame == NumFramesReady() - 1;
|
|
||||||
}
|
|
||||||
|
|
||||||
torch::Tensor GetFrame(int32_t frame) override { return features_.At(frame); }
|
|
||||||
|
|
||||||
// This would be called from the application, when you get
|
|
||||||
// more wave data. Note: the sampling_rate is only provided so
|
|
||||||
// the code can assert that it matches the sampling rate
|
|
||||||
// expected in the options.
|
|
||||||
void AcceptWaveform(float sampling_rate,
|
|
||||||
const torch::Tensor &waveform) override;
|
|
||||||
|
|
||||||
// InputFinished() tells the class you won't be providing any
|
|
||||||
// more waveform. This will help flush out the last frame or two
|
|
||||||
// of features, in the case where snip-edges == false; it also
|
|
||||||
// affects the return value of IsLastFrame().
|
|
||||||
void InputFinished() override;
|
|
||||||
|
|
||||||
private:
|
|
||||||
// This function computes any additional feature frames that it is possible to
|
|
||||||
// compute from 'waveform_remainder_', which at this point may contain more
|
|
||||||
// than just a remainder-sized quantity (because AcceptWaveform() appends to
|
|
||||||
// waveform_remainder_ before calling this function). It adds these feature
|
|
||||||
// frames to features_, and shifts off any now-unneeded samples of input from
|
|
||||||
// waveform_remainder_ while incrementing waveform_offset_ by the same amount.
|
|
||||||
void ComputeFeatures();
|
|
||||||
|
|
||||||
C computer_; // class that does the MFCC or PLP or filterbank computation
|
|
||||||
|
|
||||||
FeatureWindowFunction window_function_;
|
|
||||||
|
|
||||||
// features_ is the Mfcc or Plp or Fbank features that we have already
|
|
||||||
// computed.
|
|
||||||
|
|
||||||
RecyclingVector features_;
|
|
||||||
|
|
||||||
// True if the user has called "InputFinished()"
|
|
||||||
bool input_finished_;
|
|
||||||
|
|
||||||
// waveform_offset_ is the number of samples of waveform that we have
|
|
||||||
// already discarded, i.e. that were prior to 'waveform_remainder_'.
|
|
||||||
int64_t waveform_offset_;
|
|
||||||
|
|
||||||
// waveform_remainder_ is a short piece of waveform that we may need to keep
|
|
||||||
// after extracting all the whole frames we can (whatever length of feature
|
|
||||||
// will be required for the next phase of computation).
|
|
||||||
// It is a 1-D tensor
|
|
||||||
torch::Tensor waveform_remainder_;
|
|
||||||
};
|
|
||||||
|
|
||||||
using OnlineMfcc = OnlineGenericBaseFeature<Mfcc>;
|
|
||||||
using OnlinePlp = OnlineGenericBaseFeature<Plp>;
|
|
||||||
using OnlineFbank = OnlineGenericBaseFeature<Fbank>;
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
|
|
||||||
#endif // KALDIFEAT_CSRC_ONLINE_FEATURE_H_
|
|
||||||
@ -1,154 +0,0 @@
|
|||||||
// kaldifeat/csrc/pitch-functions.h
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
// This file is copied/modified from kaldi/src/feat/pitch-functions.h
|
|
||||||
|
|
||||||
#ifndef KALDIFEAT_CSRC_PITCH_FUNCTIONS_H_
|
|
||||||
#define KALDIFEAT_CSRC_PITCH_FUNCTIONS_H_
|
|
||||||
|
|
||||||
// References
|
|
||||||
//
|
|
||||||
// Talkin, David, and W. Bastiaan Kleijn. "A robust algorithm for pitch
|
|
||||||
// tracking (RAPT)." coding and synthesis 495 (1995): 518.
|
|
||||||
// (https://www.ee.columbia.edu/~dpwe/papers/Talkin95-rapt.pdf)
|
|
||||||
//
|
|
||||||
// Ghahremani, Pegah, et al. "A pitch extraction algorithm tuned for
|
|
||||||
// automatic speech recognition." 2014 IEEE international conference on
|
|
||||||
// acoustics, speech and signal processing (ICASSP). IEEE, 2014.
|
|
||||||
// (http://danielpovey.com/files/2014_icassp_pitch.pdf)
|
|
||||||
|
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "torch/script.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
struct PitchExtractionOptions {
|
|
||||||
// sample frequency in hertz
|
|
||||||
// must match the waveform file
|
|
||||||
float samp_freq = 16000;
|
|
||||||
float frame_shift_ms = 10.0; // in milliseconds.
|
|
||||||
float frame_length_ms = 25.0; // in milliseconds.
|
|
||||||
|
|
||||||
// Preemphasis coefficient. [use is deprecated.]
|
|
||||||
float preemph_coeff = 0.0;
|
|
||||||
|
|
||||||
float min_f0 = 50; // min f0 to search (Hz)
|
|
||||||
float max_f0 = 400; // max f0 to search (Hz)
|
|
||||||
float soft_min_f0 = 10.0; // Minimum f0, applied in soft way, must not
|
|
||||||
// exceed min-f0
|
|
||||||
float penalty_factor = 0.1; // cost factor for FO change
|
|
||||||
float lowpass_cutoff = 1000; // cutoff frequency for Low pass filter (Hz)
|
|
||||||
|
|
||||||
// Integer that determines filter width when
|
|
||||||
// upsampling NCCF
|
|
||||||
// Frequency that we down-sample the signal to. Must be
|
|
||||||
// more than twice lowpass-cutoff
|
|
||||||
float resample_freq = 4000;
|
|
||||||
|
|
||||||
float delta_pitch = 0.005; // the pitch tolerance in pruning lags
|
|
||||||
float nccf_ballast = 7000; // Increasing this factor reduces NCCF for
|
|
||||||
// quiet frames, helping ensure pitch
|
|
||||||
// continuity in unvoiced region
|
|
||||||
int32_t lowpass_filter_width = 1; // Integer that determines filter width of
|
|
||||||
// lowpass filter
|
|
||||||
int32_t upsample_filter_width = 5; // Integer that determines filter width
|
|
||||||
// when upsampling NCCF
|
|
||||||
|
|
||||||
// Below are newer config variables, not present in the original paper,
|
|
||||||
// that relate to the online pitch extraction algorithm.
|
|
||||||
|
|
||||||
// The maximum number of frames of latency that we allow the pitch-processing
|
|
||||||
// to introduce, for online operation. If you set this to a large value,
|
|
||||||
// there would be no inaccuracy from the Viterbi traceback (but it might make
|
|
||||||
// you wait to see the pitch). This is not very relevant for the online
|
|
||||||
// operation: normalization-right-context is more relevant, you
|
|
||||||
// can just leave this value at zero.
|
|
||||||
int32_t max_frames_latency = 0;
|
|
||||||
|
|
||||||
// Only relevant for the function ComputeKaldiPitch which is called by
|
|
||||||
// compute-kaldi-pitch-feats. If nonzero, we provide the input as chunks of
|
|
||||||
// this size. This affects the energy normalization which has a small effect
|
|
||||||
// on the resulting features, especially at the beginning of a file. For best
|
|
||||||
// compatibility with online operation (e.g. if you plan to train models for
|
|
||||||
// the online-deocding setup), you might want to set this to a small value,
|
|
||||||
// like one frame.
|
|
||||||
int32_t frames_per_chunk = 0;
|
|
||||||
|
|
||||||
// Only relevant for the function ComputeKaldiPitch which is called by
|
|
||||||
// compute-kaldi-pitch-feats, and only relevant if frames_per_chunk is
|
|
||||||
// nonzero. If true, it will query the features as soon as they are
|
|
||||||
// available, which simulates the first-pass features you would get in online
|
|
||||||
// decoding. If false, the features you will get will be the same as those
|
|
||||||
// available at the end of the utterance, after InputFinished() has been
|
|
||||||
// called: e.g. during lattice rescoring.
|
|
||||||
bool simulate_first_pass_online = false;
|
|
||||||
|
|
||||||
// Only relevant for online operation or when emulating online operation
|
|
||||||
// (e.g. when setting frames_per_chunk). This is the frame-index on which we
|
|
||||||
// recompute the NCCF (e.g. frame-index 500 = after 5 seconds); if the
|
|
||||||
// segment ends before this we do it when the segment ends. We do this by
|
|
||||||
// re-computing the signal average energy, which affects the NCCF via the
|
|
||||||
// "ballast term", scaling the resampled NCCF by a factor derived from the
|
|
||||||
// average change in the "ballast term", and re-doing the backtrace
|
|
||||||
// computation. Making this infinity would be the most exact, but would
|
|
||||||
// introduce unwanted latency at the end of long utterances, for little
|
|
||||||
// benefit.
|
|
||||||
int32_t recompute_frame = 500;
|
|
||||||
|
|
||||||
// This is a "hidden config" used only for testing the online pitch
|
|
||||||
// extraction. If true, we compute the signal root-mean-squared for the
|
|
||||||
// ballast term, only up to the current frame, rather than the end of the
|
|
||||||
// current chunk of signal. This makes the output insensitive to the
|
|
||||||
// chunking, which is useful for testing purposes.
|
|
||||||
bool nccf_ballast_online = false;
|
|
||||||
bool snip_edges = true;
|
|
||||||
|
|
||||||
torch::Device device{"cpu"};
|
|
||||||
|
|
||||||
PitchExtractionOptions() = default;
|
|
||||||
|
|
||||||
/// Returns the window-size in samples, after resampling. This is the
|
|
||||||
/// "basic window size", not the full window size after extending by max-lag.
|
|
||||||
// Because of floating point representation, it is more reliable to divide
|
|
||||||
// by 1000 instead of multiplying by 0.001, but it is a bit slower.
|
|
||||||
int32_t NccfWindowSize() const {
|
|
||||||
return static_cast<int32_t>(resample_freq * frame_length_ms / 1000.0);
|
|
||||||
}
|
|
||||||
/// Returns the window-shift in samples, after resampling.
|
|
||||||
int32_t NccfWindowShift() const {
|
|
||||||
return static_cast<int32_t>(resample_freq * frame_shift_ms / 1000.0);
|
|
||||||
}
|
|
||||||
|
|
||||||
std::string ToString() const {
|
|
||||||
std::ostringstream os;
|
|
||||||
os << "samp_freq: " << samp_freq << "\n";
|
|
||||||
os << "frame_shift_ms: " << frame_shift_ms << "\n";
|
|
||||||
os << "frame_length_ms: " << frame_length_ms << "\n";
|
|
||||||
os << "preemph_coeff: " << preemph_coeff << "\n";
|
|
||||||
os << "min_f0: " << min_f0 << "\n";
|
|
||||||
os << "max_f0: " << max_f0 << "\n";
|
|
||||||
os << "soft_min_f0: " << soft_min_f0 << "\n";
|
|
||||||
os << "penalty_factor: " << penalty_factor << "\n";
|
|
||||||
os << "lowpass_cutoff: " << lowpass_cutoff << "\n";
|
|
||||||
os << "resample_freq: " << resample_freq << "\n";
|
|
||||||
os << "delta_pitch: " << delta_pitch << "\n";
|
|
||||||
os << "nccf_ballast: " << nccf_ballast << "\n";
|
|
||||||
os << "lowpass_filter_width: " << lowpass_filter_width << "\n";
|
|
||||||
os << "upsample_filter_width: " << upsample_filter_width << "\n";
|
|
||||||
os << "max_frames_latency: " << max_frames_latency << "\n";
|
|
||||||
os << "frames_per_chunk: " << frames_per_chunk << "\n";
|
|
||||||
os << "simulate_first_pass_online: " << simulate_first_pass_online << "\n";
|
|
||||||
os << "recompute_frame: " << recompute_frame << "\n";
|
|
||||||
os << "nccf_ballast_online: " << nccf_ballast_online << "\n";
|
|
||||||
os << "snip_edges: " << snip_edges << "\n";
|
|
||||||
os << "device: " << device << "\n";
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
// TODO(fangjun): Implement it
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
|
|
||||||
#endif // KALDIFEAT_CSRC_PITCH_FUNCTIONS_H_
|
|
||||||
@ -2,8 +2,7 @@
|
|||||||
//
|
//
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
||||||
|
|
||||||
#include "torch/all.h"
|
#include "torch/torch.h"
|
||||||
#include "torch/script.h"
|
|
||||||
|
|
||||||
static void TestPreemph() {
|
static void TestPreemph() {
|
||||||
torch::Tensor a = torch::arange(0, 12).reshape({3, 4}).to(torch::kFloat);
|
torch::Tensor a = torch::arange(0, 12).reshape({3, 4}).to(torch::kFloat);
|
||||||
|
|||||||
@ -1,88 +0,0 @@
|
|||||||
/**
|
|
||||||
* Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
*
|
|
||||||
* See LICENSE for clarification regarding multiple authors
|
|
||||||
*
|
|
||||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
||||||
* you may not use this file except in compliance with the License.
|
|
||||||
* You may obtain a copy of the License at
|
|
||||||
*
|
|
||||||
* http://www.apache.org/licenses/LICENSE-2.0
|
|
||||||
*
|
|
||||||
* Unless required by applicable law or agreed to in writing, software
|
|
||||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
||||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
||||||
* See the License for the specific language governing permissions and
|
|
||||||
* limitations under the License.
|
|
||||||
*/
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/whisper-fbank.h"
|
|
||||||
|
|
||||||
#include <cmath>
|
|
||||||
#include <vector>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/mel-computations.h"
|
|
||||||
#include "kaldifeat/csrc/whisper-mel-bank.h"
|
|
||||||
#include "kaldifeat/csrc/whisper-v3-mel-bank.h"
|
|
||||||
|
|
||||||
#ifndef M_2PI
|
|
||||||
#define M_2PI 6.283185307179586476925286766559005
|
|
||||||
#endif
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
WhisperFbankComputer::WhisperFbankComputer(const WhisperFbankOptions &opts)
|
|
||||||
: opts_(opts) {
|
|
||||||
if (opts.num_mels == 80) {
|
|
||||||
mel_banks_ = std::make_unique<MelBanks>(kWhisperMelArray, kWhisperMelRows,
|
|
||||||
kWhisperMelCols, opts.device);
|
|
||||||
} else if (opts.num_mels == 128) {
|
|
||||||
mel_banks_ = std::make_unique<MelBanks>(
|
|
||||||
kWhisperV3MelArray, kWhisperV3MelRows, kWhisperV3MelCols, opts.device);
|
|
||||||
} else {
|
|
||||||
KALDIFEAT_ERR << "Unsupported num_mels: " << opts.num_mels
|
|
||||||
<< ". Support only 80 and 128";
|
|
||||||
}
|
|
||||||
|
|
||||||
opts_.frame_opts.samp_freq = 16000;
|
|
||||||
opts_.frame_opts.frame_shift_ms = 10;
|
|
||||||
opts_.frame_opts.frame_length_ms = 25;
|
|
||||||
opts_.frame_opts.dither = 0;
|
|
||||||
opts_.frame_opts.preemph_coeff = 0;
|
|
||||||
opts_.frame_opts.remove_dc_offset = false;
|
|
||||||
opts_.frame_opts.window_type = "hann";
|
|
||||||
opts_.frame_opts.round_to_power_of_two = false;
|
|
||||||
opts_.frame_opts.snip_edges = false;
|
|
||||||
}
|
|
||||||
|
|
||||||
torch::Tensor WhisperFbankComputer::Compute(
|
|
||||||
torch::Tensor /*signal_raw_log_energy*/, float /*vtln_warp*/,
|
|
||||||
const torch::Tensor &signal_frame) {
|
|
||||||
KALDIFEAT_ASSERT(signal_frame.dim() == 2);
|
|
||||||
KALDIFEAT_ASSERT(signal_frame.size(1) == opts_.frame_opts.PaddedWindowSize());
|
|
||||||
|
|
||||||
// note spectrum is in magnitude, not power, because of `abs()`
|
|
||||||
#if defined(KALDIFEAT_HAS_FFT_NAMESPACE)
|
|
||||||
// signal_frame shape: [x, 512]
|
|
||||||
// power shape [x, 257]
|
|
||||||
torch::Tensor power = torch::fft::rfft(signal_frame).abs().pow(2);
|
|
||||||
#else
|
|
||||||
// signal_frame shape [x, 512]
|
|
||||||
// real_imag shape [x, 257, 2],
|
|
||||||
// where [..., 0] is the real part
|
|
||||||
// [..., 1] is the imaginary part
|
|
||||||
torch::Tensor real_imag = torch::rfft(signal_frame, 1);
|
|
||||||
torch::Tensor real = real_imag.index({"...", 0});
|
|
||||||
torch::Tensor imag = real_imag.index({"...", 1});
|
|
||||||
torch::Tensor power = (real.square() + imag.square());
|
|
||||||
#endif
|
|
||||||
|
|
||||||
torch::Tensor mel_energies = mel_banks_->Compute(power);
|
|
||||||
torch::Tensor log_spec = torch::clamp_min(mel_energies, 1e-10).log10();
|
|
||||||
log_spec = torch::maximum(log_spec, log_spec.max() - 8.0);
|
|
||||||
torch::Tensor mel = (log_spec + 4.0) / 4.0;
|
|
||||||
|
|
||||||
return mel;
|
|
||||||
}
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
@ -1,78 +0,0 @@
|
|||||||
/**
|
|
||||||
* Copyright (c) 2023 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
*
|
|
||||||
* See LICENSE for clarification regarding multiple authors
|
|
||||||
*
|
|
||||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
||||||
* you may not use this file except in compliance with the License.
|
|
||||||
* You may obtain a copy of the License at
|
|
||||||
*
|
|
||||||
* http://www.apache.org/licenses/LICENSE-2.0
|
|
||||||
*
|
|
||||||
* Unless required by applicable law or agreed to in writing, software
|
|
||||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
||||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
||||||
* See the License for the specific language governing permissions and
|
|
||||||
* limitations under the License.
|
|
||||||
*/
|
|
||||||
|
|
||||||
#ifndef KALDIFEAT_CSRC_WHISPER_FBANK_H_
|
|
||||||
#define KALDIFEAT_CSRC_WHISPER_FBANK_H_
|
|
||||||
|
|
||||||
#include <memory>
|
|
||||||
#include <string>
|
|
||||||
#include <vector>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-common.h"
|
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
|
||||||
#include "kaldifeat/csrc/mel-computations.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
struct WhisperFbankOptions {
|
|
||||||
FrameExtractionOptions frame_opts;
|
|
||||||
// for large v3, please use 128
|
|
||||||
int32_t num_mels = 80;
|
|
||||||
|
|
||||||
torch::Device device{"cpu"};
|
|
||||||
std::string ToString() const {
|
|
||||||
std::ostringstream os;
|
|
||||||
os << "WhisperFbankOptions(";
|
|
||||||
os << "frame_opts=" << frame_opts.ToString() << ", ";
|
|
||||||
os << "num_mels=" << num_mels << ", ";
|
|
||||||
os << "device=\"" << device << "\")";
|
|
||||||
return os.str();
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
class WhisperFbankComputer {
|
|
||||||
public:
|
|
||||||
// note: Only frame_opts.device is used. All other fields from frame_opts
|
|
||||||
// are ignored
|
|
||||||
explicit WhisperFbankComputer(const WhisperFbankOptions &opts = {});
|
|
||||||
|
|
||||||
int32_t Dim() const { return opts_.num_mels; }
|
|
||||||
|
|
||||||
const FrameExtractionOptions &GetFrameOptions() const {
|
|
||||||
return opts_.frame_opts;
|
|
||||||
}
|
|
||||||
|
|
||||||
const WhisperFbankOptions &GetOptions() const { return opts_; }
|
|
||||||
|
|
||||||
torch::Tensor Compute(torch::Tensor /*signal_raw_log_energy*/,
|
|
||||||
float /*vtln_warp*/, const torch::Tensor &signal_frame);
|
|
||||||
|
|
||||||
// if true, compute log_energy_pre_window but after dithering and dc removal
|
|
||||||
bool NeedRawLogEnergy() const { return false; }
|
|
||||||
using Options = WhisperFbankOptions;
|
|
||||||
|
|
||||||
private:
|
|
||||||
WhisperFbankOptions opts_;
|
|
||||||
std::unique_ptr<MelBanks> mel_banks_;
|
|
||||||
};
|
|
||||||
|
|
||||||
using WhisperFbank = OfflineFeatureTpl<WhisperFbankComputer>;
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
|
|
||||||
#endif // KALDIFEAT_CSRC_WHISPER_FBANK_H_
|
|
||||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,5 +1 @@
|
|||||||
add_subdirectory(csrc)
|
add_subdirectory(csrc)
|
||||||
|
|
||||||
if(kaldifeat_BUILD_TESTS)
|
|
||||||
add_subdirectory(tests)
|
|
||||||
endif()
|
|
||||||
|
|||||||
@ -2,39 +2,9 @@ add_definitions(-DTORCH_API_INCLUDE_EXTENSION_H)
|
|||||||
pybind11_add_module(_kaldifeat
|
pybind11_add_module(_kaldifeat
|
||||||
feature-fbank.cc
|
feature-fbank.cc
|
||||||
feature-mfcc.cc
|
feature-mfcc.cc
|
||||||
feature-plp.cc
|
|
||||||
feature-spectrogram.cc
|
|
||||||
feature-window.cc
|
feature-window.cc
|
||||||
kaldifeat.cc
|
kaldifeat.cc
|
||||||
mel-computations.cc
|
mel-computations.cc
|
||||||
online-feature.cc
|
|
||||||
utils.cc
|
|
||||||
whisper-fbank.cc
|
|
||||||
)
|
)
|
||||||
|
|
||||||
if(APPLE)
|
|
||||||
execute_process(
|
|
||||||
COMMAND "${PYTHON_EXECUTABLE}" -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())"
|
|
||||||
OUTPUT_STRIP_TRAILING_WHITESPACE
|
|
||||||
OUTPUT_VARIABLE PYTHON_SITE_PACKAGE_DIR
|
|
||||||
)
|
|
||||||
message(STATUS "PYTHON_SITE_PACKAGE_DIR: ${PYTHON_SITE_PACKAGE_DIR}")
|
|
||||||
target_link_libraries(_kaldifeat PRIVATE "-Wl,-rpath,${PYTHON_SITE_PACKAGE_DIR}")
|
|
||||||
endif()
|
|
||||||
|
|
||||||
if(NOT WIN32)
|
|
||||||
target_link_libraries(_kaldifeat PRIVATE "-Wl,-rpath,${kaldifeat_rpath_origin}/kaldifeat/${CMAKE_INSTALL_LIBDIR}")
|
|
||||||
endif()
|
|
||||||
|
|
||||||
target_link_libraries(_kaldifeat PRIVATE kaldifeat_core)
|
target_link_libraries(_kaldifeat PRIVATE kaldifeat_core)
|
||||||
if(UNIX AND NOT APPLE)
|
target_link_libraries(_kaldifeat PRIVATE ${TORCH_DIR}/lib/libtorch_python.so)
|
||||||
target_link_libraries(_kaldifeat PUBLIC ${TORCH_DIR}/lib/libtorch_python.so)
|
|
||||||
# target_link_libraries(_kaldifeat PUBLIC ${PYTHON_LIBRARY})
|
|
||||||
elseif(WIN32)
|
|
||||||
target_link_libraries(_kaldifeat PUBLIC ${TORCH_DIR}/lib/torch_python.lib)
|
|
||||||
# target_link_libraries(_kaldifeat PUBLIC ${PYTHON_LIBRARIES})
|
|
||||||
endif()
|
|
||||||
|
|
||||||
install(TARGETS _kaldifeat
|
|
||||||
DESTINATION ../
|
|
||||||
)
|
|
||||||
|
|||||||
@ -1 +0,0 @@
|
|||||||
filter=-runtime/references
|
|
||||||
@ -4,11 +4,9 @@
|
|||||||
|
|
||||||
#include "kaldifeat/python/csrc/feature-fbank.h"
|
#include "kaldifeat/python/csrc/feature-fbank.h"
|
||||||
|
|
||||||
#include <memory>
|
|
||||||
#include <string>
|
#include <string>
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-fbank.h"
|
#include "kaldifeat/csrc/feature-fbank.h"
|
||||||
#include "kaldifeat/python/csrc/utils.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
namespace kaldifeat {
|
||||||
|
|
||||||
@ -16,35 +14,6 @@ static void PybindFbankOptions(py::module &m) {
|
|||||||
using PyClass = FbankOptions;
|
using PyClass = FbankOptions;
|
||||||
py::class_<PyClass>(m, "FbankOptions")
|
py::class_<PyClass>(m, "FbankOptions")
|
||||||
.def(py::init<>())
|
.def(py::init<>())
|
||||||
.def(py::init([](const MelBanksOptions &mel_opts,
|
|
||||||
const FrameExtractionOptions &frame_opts =
|
|
||||||
FrameExtractionOptions(),
|
|
||||||
bool use_energy = false, float energy_floor = 0.0f,
|
|
||||||
bool raw_energy = true, bool htk_compat = false,
|
|
||||||
bool use_log_fbank = true, bool use_power = true,
|
|
||||||
py::object device =
|
|
||||||
py::str("cpu")) -> std::unique_ptr<FbankOptions> {
|
|
||||||
auto opts = std::make_unique<FbankOptions>();
|
|
||||||
opts->frame_opts = frame_opts;
|
|
||||||
opts->mel_opts = mel_opts;
|
|
||||||
opts->use_energy = use_energy;
|
|
||||||
opts->energy_floor = energy_floor;
|
|
||||||
opts->raw_energy = raw_energy;
|
|
||||||
opts->htk_compat = htk_compat;
|
|
||||||
opts->use_log_fbank = use_log_fbank;
|
|
||||||
opts->use_power = use_power;
|
|
||||||
|
|
||||||
std::string s = static_cast<py::str>(device);
|
|
||||||
opts->device = torch::Device(s);
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}),
|
|
||||||
py::arg("mel_opts"),
|
|
||||||
py::arg("frame_opts") = FrameExtractionOptions(),
|
|
||||||
py::arg("use_energy") = false, py::arg("energy_floor") = 0.0f,
|
|
||||||
py::arg("raw_energy") = true, py::arg("htk_compat") = false,
|
|
||||||
py::arg("use_log_fbank") = true, py::arg("use_power") = true,
|
|
||||||
py::arg("device") = py::str("cpu"))
|
|
||||||
.def_readwrite("frame_opts", &PyClass::frame_opts)
|
.def_readwrite("frame_opts", &PyClass::frame_opts)
|
||||||
.def_readwrite("mel_opts", &PyClass::mel_opts)
|
.def_readwrite("mel_opts", &PyClass::mel_opts)
|
||||||
.def_readwrite("use_energy", &PyClass::use_energy)
|
.def_readwrite("use_energy", &PyClass::use_energy)
|
||||||
@ -64,15 +33,7 @@ static void PybindFbankOptions(py::module &m) {
|
|||||||
self.device = torch::Device(s);
|
self.device = torch::Device(s);
|
||||||
})
|
})
|
||||||
.def("__str__",
|
.def("__str__",
|
||||||
[](const PyClass &self) -> std::string { return self.ToString(); })
|
[](const PyClass &self) -> std::string { return self.ToString(); });
|
||||||
.def("as_dict",
|
|
||||||
[](const PyClass &self) -> py::dict { return AsDict(self); })
|
|
||||||
.def_static(
|
|
||||||
"from_dict",
|
|
||||||
[](py::dict dict) -> PyClass { return FbankOptionsFromDict(dict); })
|
|
||||||
.def(py::pickle(
|
|
||||||
[](const PyClass &self) -> py::dict { return AsDict(self); },
|
|
||||||
[](py::dict dict) -> PyClass { return FbankOptionsFromDict(dict); }));
|
|
||||||
}
|
}
|
||||||
|
|
||||||
static void PybindFbank(py::module &m) {
|
static void PybindFbank(py::module &m) {
|
||||||
@ -82,14 +43,7 @@ static void PybindFbank(py::module &m) {
|
|||||||
.def("dim", &PyClass::Dim)
|
.def("dim", &PyClass::Dim)
|
||||||
.def_property_readonly("options", &PyClass::GetOptions)
|
.def_property_readonly("options", &PyClass::GetOptions)
|
||||||
.def("compute_features", &PyClass::ComputeFeatures, py::arg("wave"),
|
.def("compute_features", &PyClass::ComputeFeatures, py::arg("wave"),
|
||||||
py::arg("vtln_warp"), py::call_guard<py::gil_scoped_release>())
|
py::arg("vtln_warp"));
|
||||||
.def(py::pickle(
|
|
||||||
[](const PyClass &self) -> py::dict {
|
|
||||||
return AsDict(self.GetOptions());
|
|
||||||
},
|
|
||||||
[](py::dict dict) -> std::unique_ptr<PyClass> {
|
|
||||||
return std::make_unique<PyClass>(FbankOptionsFromDict(dict));
|
|
||||||
}));
|
|
||||||
}
|
}
|
||||||
|
|
||||||
void PybindFeatureFbank(py::module &m) {
|
void PybindFeatureFbank(py::module &m) {
|
||||||
|
|||||||
@ -4,11 +4,7 @@
|
|||||||
|
|
||||||
#include "kaldifeat/python/csrc/feature-mfcc.h"
|
#include "kaldifeat/python/csrc/feature-mfcc.h"
|
||||||
|
|
||||||
#include <memory>
|
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-mfcc.h"
|
#include "kaldifeat/csrc/feature-mfcc.h"
|
||||||
#include "kaldifeat/python/csrc/utils.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
namespace kaldifeat {
|
||||||
|
|
||||||
@ -16,35 +12,6 @@ void PybindMfccOptions(py::module &m) {
|
|||||||
using PyClass = MfccOptions;
|
using PyClass = MfccOptions;
|
||||||
py::class_<PyClass>(m, "MfccOptions")
|
py::class_<PyClass>(m, "MfccOptions")
|
||||||
.def(py::init<>())
|
.def(py::init<>())
|
||||||
.def(py::init([](const MelBanksOptions &mel_opts,
|
|
||||||
const FrameExtractionOptions &frame_opts =
|
|
||||||
FrameExtractionOptions(),
|
|
||||||
int32_t num_ceps = 13, bool use_energy = true,
|
|
||||||
float energy_floor = 0.0, bool raw_energy = true,
|
|
||||||
float cepstral_lifter = 22.0, bool htk_compat = false,
|
|
||||||
py::object device =
|
|
||||||
py::str("cpu")) -> std::unique_ptr<MfccOptions> {
|
|
||||||
auto opts = std::make_unique<MfccOptions>();
|
|
||||||
opts->frame_opts = frame_opts;
|
|
||||||
opts->mel_opts = mel_opts;
|
|
||||||
opts->num_ceps = num_ceps;
|
|
||||||
opts->use_energy = use_energy;
|
|
||||||
opts->energy_floor = energy_floor;
|
|
||||||
opts->raw_energy = raw_energy;
|
|
||||||
opts->cepstral_lifter = cepstral_lifter;
|
|
||||||
opts->htk_compat = htk_compat;
|
|
||||||
|
|
||||||
std::string s = static_cast<py::str>(device);
|
|
||||||
opts->device = torch::Device(s);
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}),
|
|
||||||
py::arg("mel_opts"),
|
|
||||||
py::arg("frame_opts") = FrameExtractionOptions(),
|
|
||||||
py::arg("num_ceps") = 13, py::arg("use_energy") = true,
|
|
||||||
py::arg("energy_floor") = 0.0f, py::arg("raw_energy") = true,
|
|
||||||
py::arg("cepstral_lifter") = 22.0, py::arg("htk_compat") = false,
|
|
||||||
py::arg("device") = py::str("cpu"))
|
|
||||||
.def_readwrite("frame_opts", &PyClass::frame_opts)
|
.def_readwrite("frame_opts", &PyClass::frame_opts)
|
||||||
.def_readwrite("mel_opts", &PyClass::mel_opts)
|
.def_readwrite("mel_opts", &PyClass::mel_opts)
|
||||||
.def_readwrite("num_ceps", &PyClass::num_ceps)
|
.def_readwrite("num_ceps", &PyClass::num_ceps)
|
||||||
@ -64,15 +31,7 @@ void PybindMfccOptions(py::module &m) {
|
|||||||
self.device = torch::Device(s);
|
self.device = torch::Device(s);
|
||||||
})
|
})
|
||||||
.def("__str__",
|
.def("__str__",
|
||||||
[](const PyClass &self) -> std::string { return self.ToString(); })
|
[](const PyClass &self) -> std::string { return self.ToString(); });
|
||||||
.def("as_dict",
|
|
||||||
[](const PyClass &self) -> py::dict { return AsDict(self); })
|
|
||||||
.def_static(
|
|
||||||
"from_dict",
|
|
||||||
[](py::dict dict) -> PyClass { return MfccOptionsFromDict(dict); })
|
|
||||||
.def(py::pickle(
|
|
||||||
[](const PyClass &self) -> py::dict { return AsDict(self); },
|
|
||||||
[](py::dict dict) -> PyClass { return MfccOptionsFromDict(dict); }));
|
|
||||||
}
|
}
|
||||||
|
|
||||||
static void PybindMfcc(py::module &m) {
|
static void PybindMfcc(py::module &m) {
|
||||||
@ -82,14 +41,7 @@ static void PybindMfcc(py::module &m) {
|
|||||||
.def("dim", &PyClass::Dim)
|
.def("dim", &PyClass::Dim)
|
||||||
.def_property_readonly("options", &PyClass::GetOptions)
|
.def_property_readonly("options", &PyClass::GetOptions)
|
||||||
.def("compute_features", &PyClass::ComputeFeatures, py::arg("wave"),
|
.def("compute_features", &PyClass::ComputeFeatures, py::arg("wave"),
|
||||||
py::arg("vtln_warp"), py::call_guard<py::gil_scoped_release>())
|
py::arg("vtln_warp"));
|
||||||
.def(py::pickle(
|
|
||||||
[](const PyClass &self) -> py::dict {
|
|
||||||
return AsDict(self.GetOptions());
|
|
||||||
},
|
|
||||||
[](py::dict dict) -> std::unique_ptr<PyClass> {
|
|
||||||
return std::make_unique<PyClass>(MfccOptionsFromDict(dict));
|
|
||||||
}));
|
|
||||||
}
|
}
|
||||||
|
|
||||||
void PybindFeatureMfcc(py::module &m) {
|
void PybindFeatureMfcc(py::module &m) {
|
||||||
|
|||||||
@ -1,109 +0,0 @@
|
|||||||
// kaldifeat/python/csrc/feature-plp.cc
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
#include "kaldifeat/python/csrc/feature-plp.h"
|
|
||||||
|
|
||||||
#include <memory>
|
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-plp.h"
|
|
||||||
#include "kaldifeat/python/csrc/utils.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
void PybindPlpOptions(py::module &m) {
|
|
||||||
using PyClass = PlpOptions;
|
|
||||||
py::class_<PyClass>(m, "PlpOptions")
|
|
||||||
.def(py::init<>())
|
|
||||||
.def(py::init([](const MelBanksOptions &mel_opts,
|
|
||||||
const FrameExtractionOptions &frame_opts =
|
|
||||||
FrameExtractionOptions(),
|
|
||||||
int32_t lpc_order = 12, int32_t num_ceps = 13,
|
|
||||||
bool use_energy = true, float energy_floor = 0.0,
|
|
||||||
bool raw_energy = true, float compress_factor = 0.33333,
|
|
||||||
int32_t cepstral_lifter = 22, float cepstral_scale = 1.0,
|
|
||||||
bool htk_compat = false,
|
|
||||||
py::object device =
|
|
||||||
py::str("cpu")) -> std::unique_ptr<PlpOptions> {
|
|
||||||
auto opts = std::make_unique<PlpOptions>();
|
|
||||||
opts->frame_opts = frame_opts;
|
|
||||||
opts->mel_opts = mel_opts;
|
|
||||||
opts->lpc_order = lpc_order;
|
|
||||||
opts->num_ceps = num_ceps;
|
|
||||||
opts->use_energy = use_energy;
|
|
||||||
opts->energy_floor = energy_floor;
|
|
||||||
opts->raw_energy = raw_energy;
|
|
||||||
opts->compress_factor = compress_factor;
|
|
||||||
opts->cepstral_lifter = cepstral_lifter;
|
|
||||||
opts->cepstral_scale = cepstral_scale;
|
|
||||||
opts->htk_compat = htk_compat;
|
|
||||||
|
|
||||||
std::string s = static_cast<py::str>(device);
|
|
||||||
opts->device = torch::Device(s);
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}),
|
|
||||||
py::arg("mel_opts"),
|
|
||||||
py::arg("frame_opts") = FrameExtractionOptions(),
|
|
||||||
py::arg("lpc_order") = 12, py::arg("num_ceps") = 13,
|
|
||||||
py::arg("use_energy") = true, py::arg("energy_floor") = 0.0,
|
|
||||||
py::arg("raw_energy") = true, py::arg("compress_factor") = 0.33333,
|
|
||||||
py::arg("cepstral_lifter") = 22, py::arg("cepstral_scale") = 1.0,
|
|
||||||
py::arg("htk_compat") = false, py::arg("device") = py::str("cpu"))
|
|
||||||
.def_readwrite("frame_opts", &PyClass::frame_opts)
|
|
||||||
.def_readwrite("mel_opts", &PyClass::mel_opts)
|
|
||||||
.def_readwrite("lpc_order", &PyClass::lpc_order)
|
|
||||||
.def_readwrite("num_ceps", &PyClass::num_ceps)
|
|
||||||
.def_readwrite("use_energy", &PyClass::use_energy)
|
|
||||||
.def_readwrite("energy_floor", &PyClass::energy_floor)
|
|
||||||
.def_readwrite("raw_energy", &PyClass::raw_energy)
|
|
||||||
.def_readwrite("compress_factor", &PyClass::compress_factor)
|
|
||||||
.def_readwrite("cepstral_lifter", &PyClass::cepstral_lifter)
|
|
||||||
.def_readwrite("cepstral_scale", &PyClass::cepstral_scale)
|
|
||||||
.def_readwrite("htk_compat", &PyClass::htk_compat)
|
|
||||||
.def_property(
|
|
||||||
"device",
|
|
||||||
[](const PyClass &self) -> py::object {
|
|
||||||
py::object ans = py::module_::import("torch").attr("device");
|
|
||||||
return ans(self.device.str());
|
|
||||||
},
|
|
||||||
[](PyClass &self, py::object obj) -> void {
|
|
||||||
std::string s = static_cast<py::str>(obj);
|
|
||||||
self.device = torch::Device(s);
|
|
||||||
})
|
|
||||||
.def("__str__",
|
|
||||||
[](const PyClass &self) -> std::string { return self.ToString(); })
|
|
||||||
.def("as_dict",
|
|
||||||
[](const PyClass &self) -> py::dict { return AsDict(self); })
|
|
||||||
.def_static(
|
|
||||||
"from_dict",
|
|
||||||
[](py::dict dict) -> PyClass { return PlpOptionsFromDict(dict); })
|
|
||||||
.def(py::pickle(
|
|
||||||
[](const PyClass &self) -> py::dict { return AsDict(self); },
|
|
||||||
[](py::dict dict) -> PyClass { return PlpOptionsFromDict(dict); }));
|
|
||||||
}
|
|
||||||
|
|
||||||
static void PybindPlp(py::module &m) {
|
|
||||||
using PyClass = Plp;
|
|
||||||
py::class_<PyClass>(m, "Plp")
|
|
||||||
.def(py::init<const PlpOptions &>(), py::arg("opts"))
|
|
||||||
.def("dim", &PyClass::Dim)
|
|
||||||
.def_property_readonly("options", &PyClass::GetOptions)
|
|
||||||
.def("compute_features", &PyClass::ComputeFeatures, py::arg("wave"),
|
|
||||||
py::arg("vtln_warp"), py::call_guard<py::gil_scoped_release>())
|
|
||||||
.def(py::pickle(
|
|
||||||
[](const PyClass &self) -> py::dict {
|
|
||||||
return AsDict(self.GetOptions());
|
|
||||||
},
|
|
||||||
[](py::dict dict) -> std::unique_ptr<PyClass> {
|
|
||||||
return std::make_unique<PyClass>(PlpOptionsFromDict(dict));
|
|
||||||
}));
|
|
||||||
}
|
|
||||||
|
|
||||||
void PybindFeaturePlp(py::module &m) {
|
|
||||||
PybindPlpOptions(m);
|
|
||||||
PybindPlp(m);
|
|
||||||
}
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
@ -1,16 +0,0 @@
|
|||||||
// kaldifeat/python/csrc/feature-plp.h
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
#ifndef KALDIFEAT_PYTHON_CSRC_FEATURE_PLP_H_
|
|
||||||
#define KALDIFEAT_PYTHON_CSRC_FEATURE_PLP_H_
|
|
||||||
|
|
||||||
#include "kaldifeat/python/csrc/kaldifeat.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
void PybindFeaturePlp(py::module &m);
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
|
|
||||||
#endif // KALDIFEAT_PYTHON_CSRC_FEATURE_PLP_H_
|
|
||||||
@ -1,91 +0,0 @@
|
|||||||
// kaldifeat/python/csrc/feature-spectrogram.cc
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
#include "kaldifeat/python/csrc/feature-spectrogram.h"
|
|
||||||
|
|
||||||
#include <memory>
|
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-spectrogram.h"
|
|
||||||
#include "kaldifeat/python/csrc/utils.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
static void PybindSpectrogramOptions(py::module &m) {
|
|
||||||
using PyClass = SpectrogramOptions;
|
|
||||||
py::class_<PyClass>(m, "SpectrogramOptions")
|
|
||||||
.def(py::init([](const FrameExtractionOptions &frame_opts =
|
|
||||||
FrameExtractionOptions(),
|
|
||||||
float energy_floor = 0.0, bool raw_energy = true,
|
|
||||||
bool return_raw_fft = false,
|
|
||||||
py::object device = py::str(
|
|
||||||
"cpu")) -> std::unique_ptr<SpectrogramOptions> {
|
|
||||||
auto opts = std::make_unique<SpectrogramOptions>();
|
|
||||||
opts->frame_opts = frame_opts;
|
|
||||||
opts->energy_floor = energy_floor;
|
|
||||||
opts->raw_energy = raw_energy;
|
|
||||||
opts->return_raw_fft = return_raw_fft;
|
|
||||||
|
|
||||||
std::string s = static_cast<py::str>(device);
|
|
||||||
opts->device = torch::Device(s);
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}),
|
|
||||||
py::arg("frame_opts") = FrameExtractionOptions(),
|
|
||||||
py::arg("energy_floor") = 0.0, py::arg("raw_energy") = true,
|
|
||||||
py::arg("return_raw_fft") = false,
|
|
||||||
py::arg("device") = py::str("cpu"))
|
|
||||||
.def_readwrite("frame_opts", &PyClass::frame_opts)
|
|
||||||
.def_readwrite("energy_floor", &PyClass::energy_floor)
|
|
||||||
.def_readwrite("raw_energy", &PyClass::raw_energy)
|
|
||||||
// .def_readwrite("return_raw_fft", &PyClass::return_raw_fft) // not
|
|
||||||
// implemented yet
|
|
||||||
.def_property(
|
|
||||||
"device",
|
|
||||||
[](const PyClass &self) -> py::object {
|
|
||||||
py::object ans = py::module_::import("torch").attr("device");
|
|
||||||
return ans(self.device.str());
|
|
||||||
},
|
|
||||||
[](PyClass &self, py::object obj) -> void {
|
|
||||||
std::string s = static_cast<py::str>(obj);
|
|
||||||
self.device = torch::Device(s);
|
|
||||||
})
|
|
||||||
.def("__str__",
|
|
||||||
[](const PyClass &self) -> std::string { return self.ToString(); })
|
|
||||||
.def("as_dict",
|
|
||||||
[](const PyClass &self) -> py::dict { return AsDict(self); })
|
|
||||||
.def_static("from_dict",
|
|
||||||
[](py::dict dict) -> PyClass {
|
|
||||||
return SpectrogramOptionsFromDict(dict);
|
|
||||||
})
|
|
||||||
.def(py::pickle(
|
|
||||||
[](const PyClass &self) -> py::dict { return AsDict(self); },
|
|
||||||
[](py::dict dict) -> PyClass {
|
|
||||||
return SpectrogramOptionsFromDict(dict);
|
|
||||||
}));
|
|
||||||
}
|
|
||||||
|
|
||||||
static void PybindSpectrogram(py::module &m) {
|
|
||||||
using PyClass = Spectrogram;
|
|
||||||
py::class_<PyClass>(m, "Spectrogram")
|
|
||||||
.def(py::init<const SpectrogramOptions &>(), py::arg("opts"))
|
|
||||||
.def("dim", &PyClass::Dim)
|
|
||||||
.def_property_readonly("options", &PyClass::GetOptions)
|
|
||||||
.def("compute_features", &PyClass::ComputeFeatures, py::arg("wave"),
|
|
||||||
py::arg("vtln_warp"), py::call_guard<py::gil_scoped_release>())
|
|
||||||
.def(py::pickle(
|
|
||||||
[](const PyClass &self) -> py::dict {
|
|
||||||
return AsDict(self.GetOptions());
|
|
||||||
},
|
|
||||||
[](py::dict dict) -> std::unique_ptr<PyClass> {
|
|
||||||
return std::make_unique<PyClass>(SpectrogramOptionsFromDict(dict));
|
|
||||||
}));
|
|
||||||
}
|
|
||||||
|
|
||||||
void PybindFeatureSpectrogram(py::module &m) {
|
|
||||||
PybindSpectrogramOptions(m);
|
|
||||||
PybindSpectrogram(m);
|
|
||||||
}
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
@ -1,16 +0,0 @@
|
|||||||
// kaldifeat/python/csrc/feature-spectrogram.h
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
#ifndef KALDIFEAT_PYTHON_CSRC_FEATURE_SPECTROGRAM_H_
|
|
||||||
#define KALDIFEAT_PYTHON_CSRC_FEATURE_SPECTROGRAM_H_
|
|
||||||
|
|
||||||
#include "kaldifeat/python/csrc/kaldifeat.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
void PybindFeatureSpectrogram(py::module &m);
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
|
|
||||||
#endif // KALDIFEAT_PYTHON_CSRC_FEATURE_SPECTROGRAM_H_
|
|
||||||
@ -4,78 +4,36 @@
|
|||||||
|
|
||||||
#include "kaldifeat/python/csrc/feature-window.h"
|
#include "kaldifeat/python/csrc/feature-window.h"
|
||||||
|
|
||||||
#include <memory>
|
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
#include "kaldifeat/csrc/feature-window.h"
|
||||||
#include "kaldifeat/python/csrc/utils.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
namespace kaldifeat {
|
||||||
|
|
||||||
static void PybindFrameExtractionOptions(py::module &m) {
|
static void PybindFrameExtractionOptions(py::module &m) {
|
||||||
using PyClass = FrameExtractionOptions;
|
py::class_<FrameExtractionOptions>(m, "FrameExtractionOptions")
|
||||||
py::class_<PyClass>(m, "FrameExtractionOptions")
|
.def(py::init<>())
|
||||||
.def(
|
.def_readwrite("samp_freq", &FrameExtractionOptions::samp_freq)
|
||||||
py::init([](float samp_freq = 16000, float frame_shift_ms = 10.0f,
|
.def_readwrite("frame_shift_ms", &FrameExtractionOptions::frame_shift_ms)
|
||||||
float frame_length_ms = 25.0f, float dither = 1.0f,
|
.def_readwrite("frame_length_ms",
|
||||||
float preemph_coeff = 0.97f, bool remove_dc_offset = true,
|
&FrameExtractionOptions::frame_length_ms)
|
||||||
const std::string &window_type = "povey",
|
.def_readwrite("dither", &FrameExtractionOptions::dither)
|
||||||
bool round_to_power_of_two = true,
|
.def_readwrite("preemph_coeff", &FrameExtractionOptions::preemph_coeff)
|
||||||
float blackman_coeff = 0.42f, bool snip_edges = true,
|
.def_readwrite("remove_dc_offset",
|
||||||
int32_t max_feature_vectors =
|
&FrameExtractionOptions::remove_dc_offset)
|
||||||
-1) -> std::unique_ptr<FrameExtractionOptions> {
|
.def_readwrite("window_type", &FrameExtractionOptions::window_type)
|
||||||
auto opts = std::make_unique<FrameExtractionOptions>();
|
.def_readwrite("round_to_power_of_two",
|
||||||
|
&FrameExtractionOptions::round_to_power_of_two)
|
||||||
opts->samp_freq = samp_freq;
|
.def_readwrite("blackman_coeff", &FrameExtractionOptions::blackman_coeff)
|
||||||
opts->frame_shift_ms = frame_shift_ms;
|
.def_readwrite("snip_edges", &FrameExtractionOptions::snip_edges)
|
||||||
opts->frame_length_ms = frame_length_ms;
|
|
||||||
opts->dither = dither;
|
|
||||||
opts->preemph_coeff = preemph_coeff;
|
|
||||||
opts->remove_dc_offset = remove_dc_offset;
|
|
||||||
opts->window_type = window_type;
|
|
||||||
opts->round_to_power_of_two = round_to_power_of_two;
|
|
||||||
opts->blackman_coeff = blackman_coeff;
|
|
||||||
opts->snip_edges = snip_edges;
|
|
||||||
opts->max_feature_vectors = max_feature_vectors;
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}),
|
|
||||||
py::arg("samp_freq") = 16000, py::arg("frame_shift_ms") = 10.0f,
|
|
||||||
py::arg("frame_length_ms") = 25.0f, py::arg("dither") = 1.0f,
|
|
||||||
py::arg("preemph_coeff") = 0.97f, py::arg("remove_dc_offset") = true,
|
|
||||||
py::arg("window_type") = "povey",
|
|
||||||
py::arg("round_to_power_of_two") = true,
|
|
||||||
py::arg("blackman_coeff") = 0.42f, py::arg("snip_edges") = true,
|
|
||||||
py::arg("max_feature_vectors") = -1)
|
|
||||||
.def_readwrite("samp_freq", &PyClass::samp_freq)
|
|
||||||
.def_readwrite("frame_shift_ms", &PyClass::frame_shift_ms)
|
|
||||||
.def_readwrite("frame_length_ms", &PyClass::frame_length_ms)
|
|
||||||
.def_readwrite("dither", &PyClass::dither)
|
|
||||||
.def_readwrite("preemph_coeff", &PyClass::preemph_coeff)
|
|
||||||
.def_readwrite("remove_dc_offset", &PyClass::remove_dc_offset)
|
|
||||||
.def_readwrite("window_type", &PyClass::window_type)
|
|
||||||
.def_readwrite("round_to_power_of_two", &PyClass::round_to_power_of_two)
|
|
||||||
.def_readwrite("blackman_coeff", &PyClass::blackman_coeff)
|
|
||||||
.def_readwrite("snip_edges", &PyClass::snip_edges)
|
|
||||||
.def_readwrite("max_feature_vectors", &PyClass::max_feature_vectors)
|
|
||||||
.def("as_dict",
|
|
||||||
[](const PyClass &self) -> py::dict { return AsDict(self); })
|
|
||||||
.def_static("from_dict",
|
|
||||||
[](py::dict dict) -> PyClass {
|
|
||||||
return FrameExtractionOptionsFromDict(dict);
|
|
||||||
})
|
|
||||||
#if 0
|
#if 0
|
||||||
.def_readwrite("allow_downsample",
|
.def_readwrite("allow_downsample",
|
||||||
&PyClass::allow_downsample)
|
&FrameExtractionOptions::allow_downsample)
|
||||||
.def_readwrite("allow_upsample", &PyClass::allow_upsample)
|
.def_readwrite("allow_upsample", &FrameExtractionOptions::allow_upsample)
|
||||||
|
.def_readwrite("max_feature_vectors",
|
||||||
|
&FrameExtractionOptions::max_feature_vectors)
|
||||||
#endif
|
#endif
|
||||||
.def("__str__",
|
.def("__str__", [](const FrameExtractionOptions &self) -> std::string {
|
||||||
[](const PyClass &self) -> std::string { return self.ToString(); })
|
return self.ToString();
|
||||||
.def(py::pickle(
|
});
|
||||||
[](const PyClass &self) -> py::dict { return AsDict(self); },
|
|
||||||
[](py::dict dict) -> PyClass {
|
|
||||||
return FrameExtractionOptionsFromDict(dict);
|
|
||||||
}));
|
|
||||||
|
|
||||||
m.def("num_frames", &NumFrames, py::arg("num_samples"), py::arg("opts"),
|
m.def("num_frames", &NumFrames, py::arg("num_samples"), py::arg("opts"),
|
||||||
py::arg("flush") = true);
|
py::arg("flush") = true);
|
||||||
|
|||||||
@ -7,12 +7,8 @@
|
|||||||
#include "kaldifeat/csrc/feature-fbank.h"
|
#include "kaldifeat/csrc/feature-fbank.h"
|
||||||
#include "kaldifeat/python/csrc/feature-fbank.h"
|
#include "kaldifeat/python/csrc/feature-fbank.h"
|
||||||
#include "kaldifeat/python/csrc/feature-mfcc.h"
|
#include "kaldifeat/python/csrc/feature-mfcc.h"
|
||||||
#include "kaldifeat/python/csrc/feature-plp.h"
|
|
||||||
#include "kaldifeat/python/csrc/feature-spectrogram.h"
|
|
||||||
#include "kaldifeat/python/csrc/feature-window.h"
|
#include "kaldifeat/python/csrc/feature-window.h"
|
||||||
#include "kaldifeat/python/csrc/mel-computations.h"
|
#include "kaldifeat/python/csrc/mel-computations.h"
|
||||||
#include "kaldifeat/python/csrc/online-feature.h"
|
|
||||||
#include "kaldifeat/python/csrc/whisper-fbank.h"
|
|
||||||
#include "torch/torch.h"
|
#include "torch/torch.h"
|
||||||
|
|
||||||
namespace kaldifeat {
|
namespace kaldifeat {
|
||||||
@ -23,11 +19,7 @@ PYBIND11_MODULE(_kaldifeat, m) {
|
|||||||
PybindFeatureWindow(m);
|
PybindFeatureWindow(m);
|
||||||
PybindMelComputations(m);
|
PybindMelComputations(m);
|
||||||
PybindFeatureFbank(m);
|
PybindFeatureFbank(m);
|
||||||
PybindWhisperFbank(&m);
|
|
||||||
PybindFeatureMfcc(m);
|
PybindFeatureMfcc(m);
|
||||||
PybindFeaturePlp(m);
|
|
||||||
PybindFeatureSpectrogram(m);
|
|
||||||
PybindOnlineFeature(m);
|
|
||||||
}
|
}
|
||||||
|
|
||||||
} // namespace kaldifeat
|
} // namespace kaldifeat
|
||||||
|
|||||||
@ -6,7 +6,6 @@
|
|||||||
#define KALDIFEAT_PYTHON_CSRC_KALDIFEAT_H_
|
#define KALDIFEAT_PYTHON_CSRC_KALDIFEAT_H_
|
||||||
|
|
||||||
#include "pybind11/pybind11.h"
|
#include "pybind11/pybind11.h"
|
||||||
#include "torch/torch.h"
|
|
||||||
namespace py = pybind11;
|
namespace py = pybind11;
|
||||||
|
|
||||||
#endif // KALDIFEAT_PYTHON_CSRC_KALDIFEAT_H_
|
#endif // KALDIFEAT_PYTHON_CSRC_KALDIFEAT_H_
|
||||||
|
|||||||
@ -2,37 +2,16 @@
|
|||||||
//
|
//
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
||||||
|
|
||||||
#include "kaldifeat/python/csrc/mel-computations.h"
|
|
||||||
|
|
||||||
#include <memory>
|
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/mel-computations.h"
|
#include "kaldifeat/csrc/mel-computations.h"
|
||||||
#include "kaldifeat/python/csrc/utils.h"
|
|
||||||
|
#include "kaldifeat/python/csrc/feature-window.h"
|
||||||
|
|
||||||
namespace kaldifeat {
|
namespace kaldifeat {
|
||||||
|
|
||||||
static void PybindMelBanksOptions(py::module &m) {
|
static void PybindMelBanksOptions(py::module &m) {
|
||||||
using PyClass = MelBanksOptions;
|
using PyClass = MelBanksOptions;
|
||||||
py::class_<PyClass>(m, "MelBanksOptions")
|
py::class_<PyClass>(m, "MelBanksOptions")
|
||||||
.def(py::init(
|
.def(py::init<>())
|
||||||
[](int32_t num_bins = 25, float low_freq = 20,
|
|
||||||
float high_freq = 0, float vtln_low = 100,
|
|
||||||
float vtln_high = -500,
|
|
||||||
bool debug_mel = false) -> std::unique_ptr<MelBanksOptions> {
|
|
||||||
auto opts = std::make_unique<MelBanksOptions>();
|
|
||||||
|
|
||||||
opts->num_bins = num_bins;
|
|
||||||
opts->low_freq = low_freq;
|
|
||||||
opts->high_freq = high_freq;
|
|
||||||
opts->vtln_low = vtln_low;
|
|
||||||
opts->vtln_high = vtln_high;
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}),
|
|
||||||
py::arg("num_bins") = 25, py::arg("low_freq") = 20,
|
|
||||||
py::arg("high_freq") = 0, py::arg("vtln_low") = 100,
|
|
||||||
py::arg("vtln_high") = -500, py::arg("debug_mel") = false)
|
|
||||||
.def_readwrite("num_bins", &PyClass::num_bins)
|
.def_readwrite("num_bins", &PyClass::num_bins)
|
||||||
.def_readwrite("low_freq", &PyClass::low_freq)
|
.def_readwrite("low_freq", &PyClass::low_freq)
|
||||||
.def_readwrite("high_freq", &PyClass::high_freq)
|
.def_readwrite("high_freq", &PyClass::high_freq)
|
||||||
@ -41,18 +20,8 @@ static void PybindMelBanksOptions(py::module &m) {
|
|||||||
.def_readwrite("debug_mel", &PyClass::debug_mel)
|
.def_readwrite("debug_mel", &PyClass::debug_mel)
|
||||||
.def_readwrite("htk_mode", &PyClass::htk_mode)
|
.def_readwrite("htk_mode", &PyClass::htk_mode)
|
||||||
.def("__str__",
|
.def("__str__",
|
||||||
[](const PyClass &self) -> std::string { return self.ToString(); })
|
[](const PyClass &self) -> std::string { return self.ToString(); });
|
||||||
.def("as_dict",
|
;
|
||||||
[](const PyClass &self) -> py::dict { return AsDict(self); })
|
|
||||||
.def_static("from_dict",
|
|
||||||
[](py::dict dict) -> PyClass {
|
|
||||||
return MelBanksOptionsFromDict(dict);
|
|
||||||
})
|
|
||||||
.def(py::pickle(
|
|
||||||
[](const PyClass &self) -> py::dict { return AsDict(self); },
|
|
||||||
[](py::dict dict) -> PyClass {
|
|
||||||
return MelBanksOptionsFromDict(dict);
|
|
||||||
}));
|
|
||||||
}
|
}
|
||||||
|
|
||||||
void PybindMelComputations(py::module &m) { PybindMelBanksOptions(m); }
|
void PybindMelComputations(py::module &m) { PybindMelBanksOptions(m); }
|
||||||
|
|||||||
@ -1,39 +0,0 @@
|
|||||||
// kaldifeat/python/csrc/online-feature.cc
|
|
||||||
//
|
|
||||||
// Copyright (c) 2022 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
#include "kaldifeat/python/csrc/online-feature.h"
|
|
||||||
|
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/online-feature.h"
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
template <typename C>
|
|
||||||
void PybindOnlineFeatureTpl(py::module &m, const std::string &class_name,
|
|
||||||
const std::string &class_help_doc = "") {
|
|
||||||
using PyClass = OnlineGenericBaseFeature<C>;
|
|
||||||
using Options = typename C::Options;
|
|
||||||
py::class_<PyClass>(m, class_name.c_str(), class_help_doc.c_str())
|
|
||||||
.def(py::init<const Options &>(), py::arg("opts"))
|
|
||||||
.def_property_readonly("dim", &PyClass::Dim)
|
|
||||||
.def_property_readonly("frame_shift_in_seconds",
|
|
||||||
&PyClass::FrameShiftInSeconds)
|
|
||||||
.def_property_readonly("num_frames_ready", &PyClass::NumFramesReady)
|
|
||||||
.def("is_last_frame", &PyClass::IsLastFrame, py::arg("frame"))
|
|
||||||
.def("get_frame", &PyClass::GetFrame, py::arg("frame"))
|
|
||||||
.def("get_frames", &PyClass::GetFrames, py::arg("frames"),
|
|
||||||
py::call_guard<py::gil_scoped_release>())
|
|
||||||
.def("accept_waveform", &PyClass::AcceptWaveform,
|
|
||||||
py::arg("sampling_rate"), py::arg("waveform"),
|
|
||||||
py::call_guard<py::gil_scoped_release>())
|
|
||||||
.def("input_finished", &PyClass::InputFinished);
|
|
||||||
}
|
|
||||||
|
|
||||||
void PybindOnlineFeature(py::module &m) {
|
|
||||||
PybindOnlineFeatureTpl<Mfcc>(m, "OnlineMfcc");
|
|
||||||
PybindOnlineFeatureTpl<Fbank>(m, "OnlineFbank");
|
|
||||||
PybindOnlineFeatureTpl<Plp>(m, "OnlinePlp");
|
|
||||||
}
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
@ -1,16 +0,0 @@
|
|||||||
// kaldifeat/python/csrc/online-feature.h
|
|
||||||
//
|
|
||||||
// Copyright (c) 2022 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
#ifndef KALDIFEAT_PYTHON_CSRC_ONLINE_FEATURE_H_
|
|
||||||
#define KALDIFEAT_PYTHON_CSRC_ONLINE_FEATURE_H_
|
|
||||||
|
|
||||||
#include "kaldifeat/python/csrc/kaldifeat.h"
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
void PybindOnlineFeature(py::module &m);
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
|
|
||||||
#endif // KALDIFEAT_PYTHON_CSRC_ONLINE_FEATURE_H_
|
|
||||||
@ -1,284 +0,0 @@
|
|||||||
// kaldifeat/python/csrc/utils.cc
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
#include "kaldifeat/python/csrc/utils.h"
|
|
||||||
|
|
||||||
#include <string>
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
|
||||||
|
|
||||||
#define FROM_DICT(type, key) \
|
|
||||||
if (dict.contains(#key)) { \
|
|
||||||
opts.key = py::type(dict[#key]); \
|
|
||||||
}
|
|
||||||
|
|
||||||
#define AS_DICT(key) dict[#key] = opts.key
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
FrameExtractionOptions FrameExtractionOptionsFromDict(py::dict dict) {
|
|
||||||
FrameExtractionOptions opts;
|
|
||||||
|
|
||||||
FROM_DICT(float_, samp_freq);
|
|
||||||
FROM_DICT(float_, frame_shift_ms);
|
|
||||||
FROM_DICT(float_, frame_length_ms);
|
|
||||||
FROM_DICT(float_, dither);
|
|
||||||
FROM_DICT(float_, preemph_coeff);
|
|
||||||
FROM_DICT(bool_, remove_dc_offset);
|
|
||||||
FROM_DICT(str, window_type);
|
|
||||||
FROM_DICT(bool_, round_to_power_of_two);
|
|
||||||
FROM_DICT(float_, blackman_coeff);
|
|
||||||
FROM_DICT(bool_, snip_edges);
|
|
||||||
FROM_DICT(int_, max_feature_vectors);
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}
|
|
||||||
|
|
||||||
py::dict AsDict(const FrameExtractionOptions &opts) {
|
|
||||||
py::dict dict;
|
|
||||||
|
|
||||||
AS_DICT(samp_freq);
|
|
||||||
AS_DICT(frame_shift_ms);
|
|
||||||
AS_DICT(frame_length_ms);
|
|
||||||
AS_DICT(dither);
|
|
||||||
AS_DICT(preemph_coeff);
|
|
||||||
AS_DICT(remove_dc_offset);
|
|
||||||
AS_DICT(window_type);
|
|
||||||
AS_DICT(round_to_power_of_two);
|
|
||||||
AS_DICT(blackman_coeff);
|
|
||||||
AS_DICT(snip_edges);
|
|
||||||
AS_DICT(max_feature_vectors);
|
|
||||||
|
|
||||||
return dict;
|
|
||||||
}
|
|
||||||
|
|
||||||
MelBanksOptions MelBanksOptionsFromDict(py::dict dict) {
|
|
||||||
MelBanksOptions opts;
|
|
||||||
|
|
||||||
FROM_DICT(int_, num_bins);
|
|
||||||
FROM_DICT(float_, low_freq);
|
|
||||||
FROM_DICT(float_, high_freq);
|
|
||||||
FROM_DICT(float_, vtln_low);
|
|
||||||
FROM_DICT(float_, vtln_high);
|
|
||||||
FROM_DICT(bool_, debug_mel);
|
|
||||||
FROM_DICT(bool_, htk_mode);
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}
|
|
||||||
py::dict AsDict(const MelBanksOptions &opts) {
|
|
||||||
py::dict dict;
|
|
||||||
|
|
||||||
AS_DICT(num_bins);
|
|
||||||
AS_DICT(low_freq);
|
|
||||||
AS_DICT(high_freq);
|
|
||||||
AS_DICT(vtln_low);
|
|
||||||
AS_DICT(vtln_high);
|
|
||||||
AS_DICT(debug_mel);
|
|
||||||
AS_DICT(htk_mode);
|
|
||||||
|
|
||||||
return dict;
|
|
||||||
}
|
|
||||||
|
|
||||||
FbankOptions FbankOptionsFromDict(py::dict dict) {
|
|
||||||
FbankOptions opts;
|
|
||||||
|
|
||||||
if (dict.contains("frame_opts")) {
|
|
||||||
opts.frame_opts = FrameExtractionOptionsFromDict(dict["frame_opts"]);
|
|
||||||
}
|
|
||||||
|
|
||||||
if (dict.contains("mel_opts")) {
|
|
||||||
opts.mel_opts = MelBanksOptionsFromDict(dict["mel_opts"]);
|
|
||||||
}
|
|
||||||
|
|
||||||
FROM_DICT(bool_, use_energy);
|
|
||||||
FROM_DICT(float_, energy_floor);
|
|
||||||
FROM_DICT(bool_, raw_energy);
|
|
||||||
FROM_DICT(bool_, htk_compat);
|
|
||||||
FROM_DICT(bool_, use_log_fbank);
|
|
||||||
FROM_DICT(bool_, use_power);
|
|
||||||
|
|
||||||
if (dict.contains("device")) {
|
|
||||||
opts.device = torch::Device(std::string(py::str(dict["device"])));
|
|
||||||
}
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}
|
|
||||||
|
|
||||||
py::dict AsDict(const FbankOptions &opts) {
|
|
||||||
py::dict dict;
|
|
||||||
|
|
||||||
dict["frame_opts"] = AsDict(opts.frame_opts);
|
|
||||||
dict["mel_opts"] = AsDict(opts.mel_opts);
|
|
||||||
AS_DICT(use_energy);
|
|
||||||
AS_DICT(energy_floor);
|
|
||||||
AS_DICT(raw_energy);
|
|
||||||
AS_DICT(htk_compat);
|
|
||||||
AS_DICT(use_log_fbank);
|
|
||||||
AS_DICT(use_power);
|
|
||||||
|
|
||||||
auto torch_device = py::module_::import("torch").attr("device");
|
|
||||||
dict["device"] = torch_device(opts.device.str());
|
|
||||||
|
|
||||||
return dict;
|
|
||||||
}
|
|
||||||
|
|
||||||
WhisperFbankOptions WhisperFbankOptionsFromDict(py::dict dict) {
|
|
||||||
WhisperFbankOptions opts;
|
|
||||||
|
|
||||||
if (dict.contains("frame_opts")) {
|
|
||||||
opts.frame_opts = FrameExtractionOptionsFromDict(dict["frame_opts"]);
|
|
||||||
}
|
|
||||||
|
|
||||||
FROM_DICT(int_, num_mels);
|
|
||||||
|
|
||||||
if (dict.contains("device")) {
|
|
||||||
opts.device = torch::Device(std::string(py::str(dict["device"])));
|
|
||||||
}
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}
|
|
||||||
|
|
||||||
py::dict AsDict(const WhisperFbankOptions &opts) {
|
|
||||||
py::dict dict;
|
|
||||||
|
|
||||||
dict["frame_opts"] = AsDict(opts.frame_opts);
|
|
||||||
|
|
||||||
AS_DICT(num_mels);
|
|
||||||
|
|
||||||
auto torch_device = py::module_::import("torch").attr("device");
|
|
||||||
dict["device"] = torch_device(opts.device.str());
|
|
||||||
|
|
||||||
return dict;
|
|
||||||
}
|
|
||||||
|
|
||||||
MfccOptions MfccOptionsFromDict(py::dict dict) {
|
|
||||||
MfccOptions opts;
|
|
||||||
|
|
||||||
if (dict.contains("frame_opts")) {
|
|
||||||
opts.frame_opts = FrameExtractionOptionsFromDict(dict["frame_opts"]);
|
|
||||||
}
|
|
||||||
|
|
||||||
if (dict.contains("mel_opts")) {
|
|
||||||
opts.mel_opts = MelBanksOptionsFromDict(dict["mel_opts"]);
|
|
||||||
}
|
|
||||||
|
|
||||||
FROM_DICT(int_, num_ceps);
|
|
||||||
FROM_DICT(bool_, use_energy);
|
|
||||||
FROM_DICT(float_, energy_floor);
|
|
||||||
FROM_DICT(bool_, raw_energy);
|
|
||||||
FROM_DICT(float_, cepstral_lifter);
|
|
||||||
FROM_DICT(bool_, htk_compat);
|
|
||||||
|
|
||||||
if (dict.contains("device")) {
|
|
||||||
opts.device = torch::Device(std::string(py::str(dict["device"])));
|
|
||||||
}
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}
|
|
||||||
|
|
||||||
py::dict AsDict(const MfccOptions &opts) {
|
|
||||||
py::dict dict;
|
|
||||||
|
|
||||||
dict["frame_opts"] = AsDict(opts.frame_opts);
|
|
||||||
dict["mel_opts"] = AsDict(opts.mel_opts);
|
|
||||||
|
|
||||||
AS_DICT(num_ceps);
|
|
||||||
AS_DICT(use_energy);
|
|
||||||
AS_DICT(energy_floor);
|
|
||||||
AS_DICT(raw_energy);
|
|
||||||
AS_DICT(cepstral_lifter);
|
|
||||||
AS_DICT(htk_compat);
|
|
||||||
|
|
||||||
auto torch_device = py::module_::import("torch").attr("device");
|
|
||||||
dict["device"] = torch_device(opts.device.str());
|
|
||||||
|
|
||||||
return dict;
|
|
||||||
}
|
|
||||||
|
|
||||||
SpectrogramOptions SpectrogramOptionsFromDict(py::dict dict) {
|
|
||||||
SpectrogramOptions opts;
|
|
||||||
|
|
||||||
if (dict.contains("frame_opts")) {
|
|
||||||
opts.frame_opts = FrameExtractionOptionsFromDict(dict["frame_opts"]);
|
|
||||||
}
|
|
||||||
|
|
||||||
FROM_DICT(float_, energy_floor);
|
|
||||||
FROM_DICT(bool_, raw_energy);
|
|
||||||
// FROM_DICT(bool_, return_raw_fft);
|
|
||||||
|
|
||||||
if (dict.contains("device")) {
|
|
||||||
opts.device = torch::Device(std::string(py::str(dict["device"])));
|
|
||||||
}
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}
|
|
||||||
|
|
||||||
py::dict AsDict(const SpectrogramOptions &opts) {
|
|
||||||
py::dict dict;
|
|
||||||
|
|
||||||
dict["frame_opts"] = AsDict(opts.frame_opts);
|
|
||||||
|
|
||||||
AS_DICT(energy_floor);
|
|
||||||
AS_DICT(raw_energy);
|
|
||||||
|
|
||||||
auto torch_device = py::module_::import("torch").attr("device");
|
|
||||||
dict["device"] = torch_device(opts.device.str());
|
|
||||||
|
|
||||||
return dict;
|
|
||||||
}
|
|
||||||
|
|
||||||
PlpOptions PlpOptionsFromDict(py::dict dict) {
|
|
||||||
PlpOptions opts;
|
|
||||||
|
|
||||||
if (dict.contains("frame_opts")) {
|
|
||||||
opts.frame_opts = FrameExtractionOptionsFromDict(dict["frame_opts"]);
|
|
||||||
}
|
|
||||||
|
|
||||||
if (dict.contains("mel_opts")) {
|
|
||||||
opts.mel_opts = MelBanksOptionsFromDict(dict["mel_opts"]);
|
|
||||||
}
|
|
||||||
|
|
||||||
FROM_DICT(int_, lpc_order);
|
|
||||||
FROM_DICT(int_, num_ceps);
|
|
||||||
FROM_DICT(bool_, use_energy);
|
|
||||||
FROM_DICT(float_, energy_floor);
|
|
||||||
FROM_DICT(bool_, raw_energy);
|
|
||||||
FROM_DICT(float_, compress_factor);
|
|
||||||
FROM_DICT(int_, cepstral_lifter);
|
|
||||||
FROM_DICT(float_, cepstral_scale);
|
|
||||||
FROM_DICT(bool_, htk_compat);
|
|
||||||
|
|
||||||
if (dict.contains("device")) {
|
|
||||||
opts.device = torch::Device(std::string(py::str(dict["device"])));
|
|
||||||
}
|
|
||||||
|
|
||||||
return opts;
|
|
||||||
}
|
|
||||||
|
|
||||||
py::dict AsDict(const PlpOptions &opts) {
|
|
||||||
py::dict dict;
|
|
||||||
|
|
||||||
dict["frame_opts"] = AsDict(opts.frame_opts);
|
|
||||||
dict["mel_opts"] = AsDict(opts.mel_opts);
|
|
||||||
|
|
||||||
AS_DICT(lpc_order);
|
|
||||||
AS_DICT(num_ceps);
|
|
||||||
AS_DICT(use_energy);
|
|
||||||
AS_DICT(energy_floor);
|
|
||||||
AS_DICT(raw_energy);
|
|
||||||
AS_DICT(compress_factor);
|
|
||||||
AS_DICT(cepstral_lifter);
|
|
||||||
AS_DICT(cepstral_scale);
|
|
||||||
AS_DICT(htk_compat);
|
|
||||||
|
|
||||||
auto torch_device = py::module_::import("torch").attr("device");
|
|
||||||
dict["device"] = torch_device(opts.device.str());
|
|
||||||
|
|
||||||
return dict;
|
|
||||||
}
|
|
||||||
|
|
||||||
#undef FROM_DICT
|
|
||||||
#undef AS_DICT
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
@ -1,54 +0,0 @@
|
|||||||
// kaldifeat/python/csrc/utils.h
|
|
||||||
//
|
|
||||||
// Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang)
|
|
||||||
|
|
||||||
#ifndef KALDIFEAT_PYTHON_CSRC_UTILS_H_
|
|
||||||
#define KALDIFEAT_PYTHON_CSRC_UTILS_H_
|
|
||||||
|
|
||||||
#include "kaldifeat/csrc/feature-fbank.h"
|
|
||||||
#include "kaldifeat/csrc/feature-mfcc.h"
|
|
||||||
#include "kaldifeat/csrc/feature-plp.h"
|
|
||||||
#include "kaldifeat/csrc/feature-spectrogram.h"
|
|
||||||
#include "kaldifeat/csrc/feature-window.h"
|
|
||||||
#include "kaldifeat/csrc/mel-computations.h"
|
|
||||||
#include "kaldifeat/csrc/whisper-fbank.h"
|
|
||||||
#include "kaldifeat/python/csrc/kaldifeat.h"
|
|
||||||
|
|
||||||
/*
|
|
||||||
* This file contains code about `from_dict` and
|
|
||||||
* `as_dict` for various options in kaldifeat.
|
|
||||||
*
|
|
||||||
* Regarding `from_dict`, users don't need to provide
|
|
||||||
* all the fields in the options. If some fields
|
|
||||||
* are not provided, it just uses the default one.
|
|
||||||
*
|
|
||||||
* If the provided dict in `from_dict` is empty,
|
|
||||||
* all fields use their default values.
|
|
||||||
*/
|
|
||||||
|
|
||||||
namespace kaldifeat {
|
|
||||||
|
|
||||||
FrameExtractionOptions FrameExtractionOptionsFromDict(py::dict dict);
|
|
||||||
py::dict AsDict(const FrameExtractionOptions &opts);
|
|
||||||
|
|
||||||
MelBanksOptions MelBanksOptionsFromDict(py::dict dict);
|
|
||||||
py::dict AsDict(const MelBanksOptions &opts);
|
|
||||||
|
|
||||||
FbankOptions FbankOptionsFromDict(py::dict dict);
|
|
||||||
py::dict AsDict(const FbankOptions &opts);
|
|
||||||
|
|
||||||
WhisperFbankOptions WhisperFbankOptionsFromDict(py::dict dict);
|
|
||||||
py::dict AsDict(const WhisperFbankOptions &opts);
|
|
||||||
|
|
||||||
MfccOptions MfccOptionsFromDict(py::dict dict);
|
|
||||||
py::dict AsDict(const MfccOptions &opts);
|
|
||||||
|
|
||||||
SpectrogramOptions SpectrogramOptionsFromDict(py::dict dict);
|
|
||||||
py::dict AsDict(const SpectrogramOptions &opts);
|
|
||||||
|
|
||||||
PlpOptions PlpOptionsFromDict(py::dict dict);
|
|
||||||
py::dict AsDict(const PlpOptions &opts);
|
|
||||||
|
|
||||||
} // namespace kaldifeat
|
|
||||||
|
|
||||||
#endif // KALDIFEAT_PYTHON_CSRC_UTILS_H_
|
|
||||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
x
Reference in New Issue
Block a user