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Merge pull request #120 from deezer/1.4.4

Browse Source 
1.4.4
This commit is contained in:
Félix Voituret
2019-11-21 13:02:18 +01:00
committed by GitHub
parent 3ebd23649b 0dce26c10e
commit 543f77a14c
45 changed files with 683 additions and 398 deletions
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193
.circleci/config.yml
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@@ -1,40 +1,207 @@
version: 2
jobs:
test:
# =======================================================================================
# Python 3.6 testing.
# =======================================================================================
test-3.6:
docker:
- image: python:3.6
working_directory: ~/spleeter
steps:
- checkout
- restore_cache:
key: models-{{ checksum "spleeter/model/__init__.py" }}
- run:
name: install ffmpeg
command: apt-get update && apt-get install -y ffmpeg
- run:
name: install python dependencies
command: pip install -r requirements.txt && pip install pytest pytest-xdist
- run:
name: run tests
command: make test
- save_cache:
key: models-{{ checksum "spleeter/model/__init__.py" }}
paths:
- "pretrained_models"
# =======================================================================================
# Python 3.7 testing.
# =======================================================================================
test-3.7:
docker:
- image: python:3.7
working_directory: ~/spleeter
steps:
- checkout
- restore_cache:
key: models-{{ checksum "spleeter/model/__init__.py" }}
- run:
name: install ffmpeg
command: apt-get update && apt-get install -y ffmpeg
- run:
name: install spleeter
command: pip install .
name: install python dependencies
command: pip install -r requirements.txt && pip install pytest pytest-xdist
- run:
name: test separation
command: spleeter separate -i audio_example.mp3 -o .
upload:
name: run tests
command: make test
- save_cache:
key: models-{{ checksum "spleeter/model/__init__.py" }}
paths:
- "pretrained_models"
# =======================================================================================
# Source distribution packaging.
# =======================================================================================
sdist:
docker:
- image: python:3
steps:
- checkout
- run:
name: package
command: python setup.py sdist
name: package source distribution
command: make build
- save_cache:
key: sdist-{{ .Branch }}-{{ checksum "setup.py" }}
paths:
- dist
# =======================================================================================
# PyPi deployment.
# =======================================================================================
pypi-deploy:
docker:
- image: python:3
steps:
- checkout
- restore_cache:
key: sdist-{{ .Branch }}-{{ checksum "setup.py" }}
- run:
name: upload to PyPi
command: pip install twine && twine upload dist/*
# TODO: Infer destination regarding of branch.
# - master => production PyPi
# - other => testing PyPi
command: make deploy
# =======================================================================================
# Conda distribution.
# =======================================================================================
conda-forge-deploy:
docker:
- image: python:3
steps:
- run:
name: install dependencies
command: apt-get update && apt-get install -y git openssl hub
- run:
name: checkout feedstock
command: make feedstock
# =======================================================================================
# Docker build.
# =======================================================================================
docker-conda-cpu:
docker:
- image: docker:17.05.0-ce-git
steps:
- checkout
- run: docker build -t researchdeezer/spleeter:conda -f docker/cpu/conda.dockerfile .
- run: docker build -t researchdeezer/spleeter:conda-2stems -f docker/cpu/conda-2stems.dockerfile .
- run: docker build -t researchdeezer/spleeter:conda-4stems -f docker/cpu/conda-2stems.dockerfile .
- run: docker build -t researchdeezer/spleeter:conda-5stems -f docker/cpu/conda-2stems.dockerfile .
- run: docker run -v $(pwd):/runtime researchdeezer/spleeter:conda separate -i /runtime/audio_example.mp3 -o /tmp
- run: docker run -v $(pwd):/runtime researchdeezer/spleeter:conda-2stems separate -i /runtime/audio_example.mp3 -o /tmp
- run: docker run -v $(pwd):/runtime researchdeezer/spleeter:conda-4stems separate -i /runtime/audio_example.mp3 -o /tmp
- run: docker run -v $(pwd):/runtime researchdeezer/spleeter:conda-5stems separate -i /runtime/audio_example.mp3 -o /tmp
- run: docker login -u $DOCKERHUB_USERNAME -p $DOCKERHUB_PASSWORD
- run: docker push researchdeezer/spleeter:conda
- run: docker push researchdeezer/spleeter:conda-2stems
- run: docker push researchdeezer/spleeter:conda-4stems
- run: docker push researchdeezer/spleeter:conda-5stems
docker-3.6-cpu:
docker:
- image: docker:17.05.0-ce-git
steps:
- checkout
- run: docker build -t researchdeezer/spleeter:3.6 -f docker/cpu/python-3.6.dockerfile .
- run: docker build -t researchdeezer/spleeter:3.6-2stems -f docker/cpu/python-3.6-2stems.dockerfile .
- run: docker build -t researchdeezer/spleeter:3.6-4stems -f docker/cpu/python-3.6-4stems.dockerfile .
- run: docker build -t researchdeezer/spleeter:3.6-5stems -f docker/cpu/python-3.6-5stems.dockerfile .
- run: docker run -v $(pwd):/runtime researchdeezer/spleeter:3.6 separate -i /runtime/audio_example.mp3 -o /tmp
- run: docker run -v $(pwd):/runtime researchdeezer/spleeter:3.6-2stems separate -i /runtime/audio_example.mp3 -o /tmp
- run: docker run -v $(pwd):/runtime researchdeezer/spleeter:3.6-4stems separate -i /runtime/audio_example.mp3 -o /tmp
- run: docker run -v $(pwd):/runtime researchdeezer/spleeter:3.6-5stems separate -i /runtime/audio_example.mp3 -o /tmp
- run: docker login -u $DOCKERHUB_USERNAME -p $DOCKERHUB_PASSWORD
- run: docker push researchdeezer/spleeter:3.6
- run: docker push researchdeezer/spleeter:3.6-2stems
- run: docker push researchdeezer/spleeter:3.6-4stems
- run: docker push researchdeezer/spleeter:3.6-5stems
docker-3.7-cpu:
docker:
- image: docker:17.05.0-ce-git
steps:
- checkout
- run: docker build -t spleeter:3.7 -f docker/cpu/python-3.7.dockerfile .
- run: docker build -t spleeter:3.7-2stems -f docker/cpu/python-3.7-2stems.dockerfile .
- run: docker build -t spleeter:3.7-4stems -f docker/cpu/python-3.7-4stems.dockerfile .
- run: docker build -t spleeter:3.7-5stems -f docker/cpu/python-3.7-5stems.dockerfile .
- run: docker run -v $(pwd):/runtime researchdeezer/spleeter:3.7 separate -i /runtime/audio_example.mp3 -o /tmp
- run: docker run -v $(pwd):/runtime researchdeezer/spleeter:3.7-2stems separate -i /runtime/audio_example.mp3 -o /tmp
- run: docker run -v $(pwd):/runtime researchdeezer/spleeter:3.7-4stems separate -i /runtime/audio_example.mp3 -o /tmp
- run: docker run -v $(pwd):/runtime researchdeezer/spleeter:3.7-5stems separate -i /runtime/audio_example.mp3 -o /tmp
- run: docker login -u $DOCKERHUB_USERNAME -p $DOCKERHUB_PASSWORD
- run: docker tags researchdeezer/spleeter:3.7 researchdeezer/spleeter:latest
- run: docker push researchdeezer/spleeter:latest
- run: docker push researchdeezer/spleeter:3.7
- run: docker push researchdeezer/spleeter:3.7-2stems
- run: docker push researchdeezer/spleeter:3.7-4stems
- run: docker push researchdeezer/spleeter:3.7-5stems
workflows:
version: 2
test-and-deploy:
spleeter-release-pipeline:
jobs:
- test
- upload:
- test-3.6
- test-3.7
- sdist:
requires:
- test-3.6
- test-3.7
- pypi-deploy:
filters:
branches:
only:
- master
requires:
- test
- sdist
- conda-forge-deploy:
filters:
branches:
only:
- master
requires:
- pypi-deploy
- hold:
type: approval
requires:
- pypi-deploy
- conda-forge-deploy
filters:
branches:
only:
- master
- docker-conda-cpu:
requires:
- hold
filters:
branches:
only:
- master
- docker-3.6-cpu:
requires:
- hold
filters:
branches:
only:
- master
- docker-3.7-cpu:
requires:
- hold
filters:
branches:
only:
- master

3
.gitignore vendored
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@@ -109,4 +109,5 @@ __pycache__
pretrained_models
docs/build
.vscode
.vscode
spleeter-feedstock/

42
Makefile
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@@ -1,30 +1,40 @@
# =======================================================
# Build script for distribution packaging.
# Library lifecycle management.
#
# @author Deezer Research <research@deezer.com>
# @licence MIT Licence
# =======================================================
FFEDSTOCK = spleeter-feedstock
FEEDSTOCK_REPOSITORY = https://github.com/deezer/$(FEEDSTOCK)
FEEDSTOCK_RECIPE = $(FEEDSTOCK)/recipe/spleeter/meta.yaml
all: clean build test deploy
clean:
rm -Rf *.egg-info
rm -Rf dist
build:
@echo "=== Build CPU bdist package"
@python3 setup.py sdist
@echo "=== CPU version checksum"
@openssl sha256 dist/*.tar.gz
python3 setup.py sdist
build-gpu:
@echo "=== Build GPU bdist package"
@python3 setup.py sdist --target gpu
@echo "=== GPU version checksum"
@openssl sha256 dist/*.tar.gz
test:
pytest -W ignore::FutureWarning -W ignore::DeprecationWarning -vv --forked
upload:
twine upload dist/*
feedstock: build
$(eval VERSION = $(shell grep 'project_version = ' setup.py | cut -d' ' -f3 | sed "s/'//g"))
$(eval CHECKSUM = $(shell openssl sha256 dist/spleeter-$(VERSION).tar.gz | cut -d' ' -f2))
git clone $(FEEDSTOCK_REPOSITORY)
sed 's/{% set version = "[0-9]*\.[0-9]*\.[0-9]*" %}/{% set version = "$(VERSION)" %}/g' $(FEEDSTOCK_RECIPE)
sed 's/sha256: [0-9a-z]*/sha: $(CHECKSUM)/g' $(FEEDSTOCK_RECIPE)
git config credential.helper 'cache --timeout=120'
git config user.email "research@deezer.com"
git config user.name "spleeter-ci"
git add recipe/spleeter/meta.yaml
git commit --allow-empty -m "feat: update spleeter version from CI"
git push -q https://$$FEEDSTOCK_TOKEN@github.com/deezer/$(FEEDSTOCK)
hub pull-request -m "Update spleeter version to $(VERSION)"
test-upload:
twine upload --repository-url https://test.pypi.org/legacy/ dist/*
all: clean build build-gpu upload
deploy: pip-dependencies
pip install twine
twine upload dist/*

7
README.md
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@@ -1,6 +1,8 @@
<img src="https://github.com/deezer/spleeter/raw/master/images/spleeter_logo.png" height="80" />
[![CircleCI](https://circleci.com/gh/deezer/spleeter/tree/master.svg?style=shield)](https://circleci.com/gh/deezer/spleeter/tree/master) [![PyPI version](https://badge.fury.io/py/spleeter.svg)](https://badge.fury.io/py/spleeter) [![Conda](https://img.shields.io/conda/vn/conda-forge/spleeter)](https://anaconda.org/conda-forge/spleeter) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/spleeter) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deezer/spleeter/blob/master/spleeter.ipynb)
[![CircleCI](https://circleci.com/gh/deezer/spleeter/tree/master.svg?style=shield)](https://circleci.com/gh/deezer/spleeter/tree/master) [![PyPI version](https://badge.fury.io/py/spleeter.svg)](https://badge.fury.io/py/spleeter) [![Conda](https://img.shields.io/conda/vn/conda-forge/spleeter)](https://anaconda.org/conda-forge/spleeter) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/spleeter) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deezer/spleeter/blob/master/spleeter.ipynb) [![Gitter chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://gitter.im/spleeter/community)
## About
@@ -28,8 +30,7 @@ environment to start separating audio file as follows:
```bash
git clone https://github.com/Deezer/spleeter
conda env create -f spleeter/conda/spleeter-cpu.yaml
conda activate spleeter-cpu
conda install -c conda-forge spleeter
spleeter separate -i spleeter/audio_example.mp3 -p spleeter:2stems -o output
```
You should get two separated audio files (`vocals.wav` and `accompaniment.wav`)

24
docker/cpu.Dockerfile
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@@ -1,24 +0,0 @@
FROM continuumio/miniconda3:4.7.10
# install tensorflow
RUN conda install -y tensorflow==1.14.0
# install ffmpeg for audio loading/writing
RUN conda install -y -c conda-forge ffmpeg
# install extra python libraries
RUN conda install -y -c anaconda pandas==0.25.1
RUN conda install -y -c conda-forge libsndfile
# install ipython
RUN conda install -y ipython
WORKDIR /workspace/
COPY ./ spleeter/
RUN mkdir /cache/
WORKDIR /workspace/spleeter
RUN pip install .
ENTRYPOINT ["python", "-m", "spleeter"]

5
docker/cpu/conda-2-stems.dockerfile → docker/cpu/conda-2stems.dockerfile
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@@ -1,5 +1,6 @@
FROM deezer/spleeter:conda
FROM researchdeezer/spleeter:conda
RUN mkdir -p /model/2stems \
&& wget -O /tmp/2stems.tar.gz https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz \
&& tar -xvzf /tmp/2stems.tar.gz -C /model/2stems/
&& tar -xvzf /tmp/2stems.tar.gz -C /model/2stems/ \
&& touch /model/5stems/.probe

5
docker/cpu/conda-4-stems.dockerfile → docker/cpu/conda-4stems.dockerfile
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@@ -1,5 +1,6 @@
FROM deezer/spleeter:conda
FROM researchdeezer/spleeter:conda
RUN mkdir -p /model/4stems \
&& wget -O /tmp/4stems.tar.gz https://github.com/deezer/spleeter/releases/download/v1.4.0/4stems.tar.gz \
&& tar -xvzf /tmp/4stems.tar.gz -C /model/4stems/
&& tar -xvzf /tmp/4stems.tar.gz -C /model/4stems/ \
&& touch /model/5stems/.probe

5
docker/cpu/conda-5-stems.dockerfile → docker/cpu/conda-5stems.dockerfile
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@@ -1,5 +1,6 @@
FROM deezer/spleeter:conda
FROM researchdeezer/spleeter:conda
RUN mkdir -p /model/5stems \
&& wget -O /tmp/5stems.tar.gz https://github.com/deezer/spleeter/releases/download/v1.4.0/5stems.tar.gz \
&& tar -xvzf /tmp/5stems.tar.gz -C /model/5stems/
&& tar -xvzf /tmp/5stems.tar.gz -C /model/5stems/ \
&& touch /model/5stems/.probe

9
docker/cpu/conda.dockerfile
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@@ -1,12 +1,9 @@
FROM continuumio/miniconda3:4.7.10
RUN conda install -y ipython \
&& conda install -y tensorflow==1.14.0 \
&& conda install -y -c conda-forge ffmpeg \
&& conda install -y -c conda-forge libsndfile \
&& conda install -y -c anaconda pandas==0.25.1 \
RUN conda install -y -c conda-forge musdb
# RUN conda install -y -c conda-forge museval
RUN conda install -y -c conda-forge spleeter=1.4.4
RUN mkdir -p /model
ENV MODEL_PATH /model
RUN pip install spleeter
ENTRYPOINT ["spleeter"]

5
docker/cpu/python-3.6-2stems.dockerfile
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@@ -1,5 +1,6 @@
FROM deezer/spleeter:3.6
FROM researchdeezer/spleeter:3.6
RUN mkdir -p /model/2stems \
&& wget -O /tmp/2stems.tar.gz https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz \
&& tar -xvzf /tmp/2stems.tar.gz -C /model/2stems/
&& tar -xvzf /tmp/2stems.tar.gz -C /model/2stems/ \
&& touch /model/5stems/.probe

5
docker/cpu/python-3.6-4stems.dockerfile
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@@ -1,5 +1,6 @@
FROM deezer/spleeter:3.6
FROM researchdeezer/spleeter:3.6
RUN mkdir -p /model/4stems \
&& wget -O /tmp/4stems.tar.gz https://github.com/deezer/spleeter/releases/download/v1.4.0/4stems.tar.gz \
&& tar -xvzf /tmp/4stems.tar.gz -C /model/4stems/
&& tar -xvzf /tmp/4stems.tar.gz -C /model/4stems/ \
&& touch /model/5stems/.probe

5
docker/cpu/python-3.6-5stems.dockerfile
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@@ -1,5 +1,6 @@
FROM deezer/spleeter:3.6
FROM researchdeezer/spleeter:3.6
RUN mkdir -p /model/5stems \
&& wget -O /tmp/5stems.tar.gz https://github.com/deezer/spleeter/releases/download/v1.4.0/5stems.tar.gz \
&& tar -xvzf /tmp/5stems.tar.gz -C /model/5stems/
&& tar -xvzf /tmp/5stems.tar.gz -C /model/5stems/ \
&& touch /model/5stems/.probe

5
docker/cpu/python-3.6.dockerfile
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@@ -1,7 +1,8 @@
FROM python:3.6
RUN apt-get update && apt-get install -y ffmpeg libsndfile
RUN pip install spleeter
RUN apt-get update && apt-get install -y ffmpeg libsndfile1
RUN pip install musdb museval
RUN pip install spleeter==1.4.4
RUN mkdir -p /model
ENV MODEL_PATH /model
ENTRYPOINT ["spleeter"]

5
docker/cpu/python-3.7-2stems.dockerfile
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@@ -1,5 +1,6 @@
FROM deezer/spleeter:3.7
FROM researchdeezer/spleeter:3.7
RUN mkdir -p /model/2stems \
&& wget -O /tmp/2stems.tar.gz https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz \
&& tar -xvzf /tmp/2stems.tar.gz -C /model/2stems/
&& tar -xvzf /tmp/2stems.tar.gz -C /model/2stems/ \
&& touch /model/5stems/.probe

5
docker/cpu/python-3.7-4stems.dockerfile
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@@ -1,5 +1,6 @@
FROM deezer/spleeter:3.7
FROM researchdeezer/spleeter:3.7
RUN mkdir -p /model/4stems \
&& wget -O /tmp/4stems.tar.gz https://github.com/deezer/spleeter/releases/download/v1.4.0/4stems.tar.gz \
&& tar -xvzf /tmp/4stems.tar.gz -C /model/4stems/
&& tar -xvzf /tmp/4stems.tar.gz -C /model/4stems/ \
&& touch /model/5stems/.probe

5
docker/cpu/python-3.7-5stems.dockerfile
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@@ -1,5 +1,6 @@
FROM deezer/spleeter:3.7
FROM researchdeezer/spleeter:3.7
RUN mkdir -p /model/5stems \
&& wget -O /tmp/5stems.tar.gz https://github.com/deezer/spleeter/releases/download/v1.4.0/5stems.tar.gz \
&& tar -xvzf /tmp/5stems.tar.gz -C /model/5stems/
&& tar -xvzf /tmp/5stems.tar.gz -C /model/5stems/ \
&& touch /model/5stems/.probe

5
docker/cpu/python-3.7.dockerfile
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@@ -1,7 +1,8 @@
FROM python:3.7
RUN apt-get update && apt-get install -y ffmpeg libsndfile
RUN pip install spleeter
RUN apt-get update && apt-get install -y ffmpeg libsndfile1
RUN pip install musdb museval
RUN pip install spleeter==1.4.4
RUN mkdir -p /model
ENV MODEL_PATH /model
ENTRYPOINT ["spleeter"]

35
docker/gpu.Dockerfile
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@@ -1,35 +0,0 @@
FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
# set work directory
WORKDIR /workspace
# install anaconda
ENV PATH /opt/conda/bin:$PATH
COPY docker/install_miniconda.sh .
RUN bash ./install_miniconda.sh && rm install_miniconda.sh
RUN conda update -n base -c defaults conda
# install tensorflow for GPU
RUN conda install -y tensorflow-gpu==1.14.0
# install ffmpeg for audio loading/writing
RUN conda install -y -c conda-forge ffmpeg
# install extra libs
RUN conda install -y -c anaconda pandas==0.25.1
RUN conda install -y -c conda-forge libsndfile
# install ipython
RUN conda install -y ipython
RUN mkdir /cache/
# clone inside image github repository
COPY ./ spleeter/
WORKDIR /workspace/spleeter
RUN pip install .
ENTRYPOINT ["python", "-m", "spleeter"]

2
docker/gpu/conda-gpu-2-stems.dockerfile
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@@ -1,4 +1,4 @@
FROM deezer/spleeter:conda-gpu
FROM researchdeezer/spleeter:conda-gpu
RUN mkdir -p /model/2stems \
&& wget -O /tmp/2stems.tar.gz https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz \

2
docker/gpu/conda-gpu-4-stems.dockerfile
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@@ -1,4 +1,4 @@
FROM deezer/spleeter:conda-gpu
FROM researchdeezer/spleeter:conda-gpu
RUN mkdir -p /model/4stems \
&& wget -O /tmp/4stems.tar.gz https://github.com/deezer/spleeter/releases/download/v1.4.0/4stems.tar.gz \

2
docker/gpu/conda-gpu-5-stems.dockerfile
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@@ -1,4 +1,4 @@
FROM deezer/spleeter:conda-gpu
FROM researchdeezer/spleeter:conda-gpu
RUN mkdir -p /model/5stems \
&& wget -O /tmp/5stems.tar.gz https://github.com/deezer/spleeter/releases/download/v1.4.0/5stems.tar.gz \

26
docker/gpu/conda-gpu.dockerfile
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@@ -1,12 +1,22 @@
FROM continuumio/miniconda3:4.7.10
FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
RUN conda install -y ipython \
RUN apt-get update --fix-missing \
&& apt-get install -y wget bzip2 ca-certificates curl git \
&& apt-get clean && \
&& rm -rf /var/lib/apt/lists/* \
&& wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.6.14-Linux-x86_64.sh -O ~/miniconda.sh \
&& /bin/bash ~/miniconda.sh -b -p /opt/conda \
&& rm ~/miniconda.sh \
&& /opt/conda/bin/conda clean -tipsy \
&& ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh \
&& echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc \
&& echo "conda activate base" >> ~/.bashrc
RUN conda install -y cudatoolkit=9.0 \
&& conda install -y tensorflow-gpu==1.14.0 \
&& conda install -y -c conda-forge ffmpeg \
&& conda install -y -c conda-forge libsndfile \
&& conda install -y -c anaconda pandas==0.25.1 \
RUN mkdir -p /model
ENV MODEL_PATH /model
RUN pip install spleeter
&& conda install -y -c conda-forge musdb
# RUN conda install -y -c conda-forge museval
# Note: switch to spleeter GPU once published.
RUN conda install -y -c conda-forge spleeter=1.4.4
ENTRYPOINT ["spleeter"]

13
docker/install_miniconda.sh
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@@ -1,13 +0,0 @@
#!/bin/bash
apt-get update --fix-missing && \
apt-get install -y wget bzip2 ca-certificates curl git && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.6.14-Linux-x86_64.sh -O ~/miniconda.sh && \
/bin/bash ~/miniconda.sh -b -p /opt/conda && \
rm ~/miniconda.sh && \
/opt/conda/bin/conda clean -tipsy && \
ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \
echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
echo "conda activate base" >> ~/.bashrc

7
requirements.txt Normal file
View File

@@ -0,0 +1,7 @@
importlib_resources; python_version<'3.7'
requests
setuptools>=41.0.0
pandas==0.25.1
tensorflow==1.14.0
ffmpeg-python
norbert==0.2.1

4
setup.py
View File

@@ -14,7 +14,7 @@ __license__ = 'MIT License'
# Default project values.
project_name = 'spleeter'
project_version = '1.4.3'
project_version = '1.4.4'
device_target = 'cpu'
tensorflow_dependency = 'tensorflow'
tensorflow_version = '1.14.0'
@@ -51,13 +51,13 @@ setup(
license='MIT License',
packages=[
'spleeter',
'spleeter.audio',
'spleeter.commands',
'spleeter.model',
'spleeter.model.functions',
'spleeter.model.provider',
'spleeter.resources',
'spleeter.utils',
'spleeter.utils.audio',
],
package_data={'spleeter.resources': ['*.json']},
python_requires='>=3.6, <3.8',

6
spleeter/__init__.py
View File

@@ -16,3 +16,9 @@
__email__ = 'research@deezer.com'
__author__ = 'Deezer Research'
__license__ = 'MIT License'
class SpleeterError(Exception):
""" Custom exception for Spleeter related error. """
pass

35
spleeter/__main__.py
View File

@@ -10,9 +10,13 @@
import sys
import warnings
from . import SpleeterError
from .commands import create_argument_parser
from .utils.configuration import load_configuration
from .utils.logging import enable_logging, enable_tensorflow_logging
from .utils.logging import (
enable_logging,
enable_tensorflow_logging,
get_logger)
__email__ = 'research@deezer.com'
__author__ = 'Deezer Research'
@@ -26,19 +30,22 @@ def main(argv):
:param argv: Provided command line arguments.
"""
parser = create_argument_parser()
arguments = parser.parse_args(argv[1:])
enable_logging()
if arguments.verbose:
enable_tensorflow_logging()
if arguments.command == 'separate':
from .commands.separate import entrypoint
elif arguments.command == 'train':
from .commands.train import entrypoint
elif arguments.command == 'evaluate':
from .commands.evaluate import entrypoint
params = load_configuration(arguments.params_filename)
entrypoint(arguments, params)
try:
parser = create_argument_parser()
arguments = parser.parse_args(argv[1:])
enable_logging()
if arguments.verbose:
enable_tensorflow_logging()
if arguments.command == 'separate':
from .commands.separate import entrypoint
elif arguments.command == 'train':
from .commands.train import entrypoint
elif arguments.command == 'evaluate':
from .commands.evaluate import entrypoint
params = load_configuration(arguments.configuration)
entrypoint(arguments, params)
except SpleeterError as e:
get_logger().error(e)
def entrypoint():

0
spleeter/utils/audio/__init__.py → spleeter/audio/__init__.py
View File

14
spleeter/utils/audio/adapter.py → spleeter/audio/adapter.py
View File

@@ -16,7 +16,8 @@ import tensorflow as tf
from tensorflow.contrib.signal import stft, hann_window
# pylint: enable=import-error
from ..logging import get_logger
from .. import SpleeterError
from ..utils.logging import get_logger
__email__ = 'research@deezer.com'
__author__ = 'Deezer Research'
@@ -73,7 +74,8 @@ class AudioAdapter(ABC):
# Defined safe loading function.
def safe_load(path, offset, duration, sample_rate, dtype):
get_logger().info(
logger = get_logger()
logger.info(
f'Loading audio {path} from {offset} to {offset + duration}')
try:
(data, _) = self.load(
@@ -82,10 +84,12 @@ class AudioAdapter(ABC):
duration.numpy(),
sample_rate.numpy(),
dtype=dtype.numpy())
get_logger().info('Audio data loaded successfully')
logger.info('Audio data loaded successfully')
return (data, False)
except Exception as e:
get_logger().warning(e)
logger.exception(
'An error occurs while loading audio',
exc_info=e)
return (np.float32(-1.0), True)
# Execute function and format results.
@@ -140,6 +144,6 @@ def get_audio_adapter(descriptor):
adapter_module = import_module(module_path)
adapter_class = getattr(adapter_module, adapter_class_name)
if not isinstance(adapter_class, AudioAdapter):
raise ValueError(
raise SpleeterError(
f'{adapter_class_name} is not a valid AudioAdapter class')
return adapter_class()

2
spleeter/utils/audio/convertor.py → spleeter/audio/convertor.py
View File

@@ -8,7 +8,7 @@ import numpy as np
import tensorflow as tf
# pylint: enable=import-error
from ..tensor import from_float32_to_uint8, from_uint8_to_float32
from ..utils.tensor import from_float32_to_uint8, from_uint8_to_float32
__email__ = 'research@deezer.com'
__author__ = 'Deezer Research'

15
spleeter/utils/audio/ffmpeg.py → spleeter/audio/ffmpeg.py
View File

@@ -16,7 +16,8 @@ import numpy as np
# pylint: enable=import-error
from .adapter import AudioAdapter
from ..logging import get_logger
from .. import SpleeterError
from ..utils.logging import get_logger
__email__ = 'research@deezer.com'
__author__ = 'Deezer Research'
@@ -54,12 +55,18 @@ class FFMPEGProcessAudioAdapter(AudioAdapter):
:param sample_rate: (Optional) Sample rate to load audio with.
:param dtype: (Optional) Numpy data type to use, default to float32.
:returns: Loaded data a (waveform, sample_rate) tuple.
:raise SpleeterError: If any error occurs while loading audio.
"""
if not isinstance(path, str):
path = path.decode()
probe = ffmpeg.probe(path)
try:
probe = ffmpeg.probe(path)
except ffmpeg._run.Error as e:
raise SpleeterError(
'An error occurs with ffprobe (see ffprobe output below)\n\n{}'
.format(e.stderr.decode()))
if 'streams' not in probe or len(probe['streams']) == 0:
raise IOError('No stream was found with ffprobe')
raise SpleeterError('No stream was found with ffprobe')
metadata = next(
stream
for stream in probe['streams']
@@ -117,5 +124,5 @@ class FFMPEGProcessAudioAdapter(AudioAdapter):
process.stdin.close()
process.wait()
except IOError:
raise IOError(f'FFMPEG error: {process.stderr.read()}')
raise SpleeterError(f'FFMPEG error: {process.stderr.read()}')
get_logger().info('File %s written', path)

0
spleeter/utils/audio/spectrogram.py → spleeter/audio/spectrogram.py
View File

76
spleeter/commands/__init__.py
View File

@@ -13,67 +13,77 @@ __email__ = 'research@deezer.com'
__author__ = 'Deezer Research'
__license__ = 'MIT License'
# -i opt specification.
# -i opt specification (separate).
OPT_INPUT = {
'dest': 'audio_filenames',
'dest': 'inputs',
'nargs': '+',
'help': 'List of input audio filenames',
'required': True
}
# -o opt specification.
# -o opt specification (evaluate and separate).
OPT_OUTPUT = {
'dest': 'output_path',
'default': join(gettempdir(), 'separated_audio'),
'help': 'Path of the output directory to write audio files in'
}
# -p opt specification.
# -f opt specification (separate).
OPT_FORMAT = {
'dest': 'filename_format',
'default': '{filename}/{instrument}.{codec}',
'help': (
'Template string that will be formatted to generated'
'output filename. Such template should be Python formattable'
'string, and could use {filename}, {instrument}, and {codec}'
'variables.'
)
}
# -p opt specification (train, evaluate and separate).
OPT_PARAMS = {
'dest': 'params_filename',
'dest': 'configuration',
'default': 'spleeter:2stems',
'type': str,
'action': 'store',
'help': 'JSON filename that contains params'
}
# -n opt specification.
OPT_OUTPUT_NAMING = {
'dest': 'output_naming',
'default': 'filename',
'choices': ('directory', 'filename'),
'help': (
'Choice for naming the output base path: '
'"filename" (use the input filename, i.e '
'/path/to/audio/mix.wav will be separated to '
'<output_path>/mix/<instument1>.wav, '
'<output_path>/mix/<instument2>.wav...) or '
'"directory" (use the name of the input last level'
' directory, for instance /path/to/audio/mix.wav '
'will be separated to <output_path>/audio/<instument1>.wav'
', <output_path>/audio/<instument2>.wav)')
# -s opt specification (separate).
OPT_OFFSET = {
'dest': 'offset',
'type': float,
'default': 0.,
'help': 'Set the starting offset to separate audio from.'
}
# -d opt specification (separate).
OPT_DURATION = {
'dest': 'max_duration',
'dest': 'duration',
'type': float,
'default': 600.,
'help': (
'Set a maximum duration for processing audio '
'(only separate max_duration first seconds of '
'(only separate offset + duration first seconds of '
'the input file)')
}
# -c opt specification.
# -c opt specification (separate).
OPT_CODEC = {
'dest': 'audio_codec',
'dest': 'codec',
'choices': ('wav', 'mp3', 'ogg', 'm4a', 'wma', 'flac'),
'default': 'wav',
'help': 'Audio codec to be used for the separated output'
}
# -m opt specification.
# -b opt specification (separate).
OPT_BITRATE = {
'dest': 'bitrate',
'default': '128k',
'help': 'Audio bitrate to be used for the separated output'
}
# -m opt specification (evaluate and separate).
OPT_MWF = {
'dest': 'MWF',
'action': 'store_const',
@@ -82,7 +92,7 @@ OPT_MWF = {
'help': 'Whether to use multichannel Wiener filtering for separation',
}
# --mus_dir opt specification.
# --mus_dir opt specification (evaluate).
OPT_MUSDB = {
'dest': 'mus_dir',
'type': str,
@@ -98,14 +108,14 @@ OPT_DATA = {
'help': 'Path of the folder containing audio data for training'
}
# -a opt specification.
# -a opt specification (train, evaluate and separate).
OPT_ADAPTER = {
'dest': 'audio_adapter',
'type': str,
'help': 'Name of the audio adapter to use for audio I/O'
}
# -a opt specification.
# -a opt specification (train, evaluate and separate).
OPT_VERBOSE = {
'action': 'store_true',
'help': 'Shows verbose logs'
@@ -158,11 +168,13 @@ def _create_separate_parser(parser_factory):
"""
parser = parser_factory('separate', help='Separate audio files')
_add_common_options(parser)
parser.add_argument('-i', '--audio_filenames', **OPT_INPUT)
parser.add_argument('-i', '--inputs', **OPT_INPUT)
parser.add_argument('-o', '--output_path', **OPT_OUTPUT)
parser.add_argument('-n', '--output_naming', **OPT_OUTPUT_NAMING)
parser.add_argument('-d', '--max_duration', **OPT_DURATION)
parser.add_argument('-c', '--audio_codec', **OPT_CODEC)
parser.add_argument('-f', '--filename_format', **OPT_FORMAT)
parser.add_argument('-d', '--duration', **OPT_DURATION)
parser.add_argument('-s', '--offset', **OPT_OFFSET)
parser.add_argument('-c', '--codec', **OPT_CODEC)
parser.add_argument('-b', '--birate', **OPT_BITRATE)
parser.add_argument('-m', '--mwf', **OPT_MWF)
return parser

2
spleeter/commands/evaluate.py
View File

@@ -44,7 +44,6 @@ __license__ = 'MIT License'
_SPLIT = 'test'
_MIXTURE = 'mixture.wav'
_NAMING = 'directory'
_AUDIO_DIRECTORY = 'audio'
_METRICS_DIRECTORY = 'metrics'
_INSTRUMENTS = ('vocals', 'drums', 'bass', 'other')
@@ -71,7 +70,6 @@ def _separate_evaluation_dataset(arguments, musdb_root_directory, params):
audio_filenames=mixtures,
audio_codec='wav',
output_path=join(audio_output_directory, _SPLIT),
output_naming=_NAMING,
max_duration=600.,
MWF=arguments.MWF,
verbose=arguments.verbose),

171
spleeter/commands/separate.py
View File

@@ -11,168 +11,35 @@
-i /path/to/audio1.wav /path/to/audio2.mp3
"""
from multiprocessing import Pool
from os.path import isabs, join, split, splitext
from tempfile import gettempdir
# pylint: disable=import-error
import tensorflow as tf
import numpy as np
# pylint: enable=import-error
from ..utils.audio.adapter import get_audio_adapter
from ..utils.audio.convertor import to_n_channels
from ..utils.estimator import create_estimator
from ..utils.tensor import set_tensor_shape
from ..audio.adapter import get_audio_adapter
from ..separator import Separator
__email__ = 'research@deezer.com'
__author__ = 'Deezer Research'
__license__ = 'MIT License'
def get_dataset(audio_adapter, filenames_and_crops, sample_rate, n_channels):
""""
Build a tensorflow dataset of waveform from a filename list wit crop
information.
Params:
- audio_adapter: An AudioAdapter instance to load audio from.
- filenames_and_crops: list of (audio_filename, start, duration)
tuples separation is performed on each filaneme
from start (in seconds) to start + duration
(in seconds).
- sample_rate: audio sample_rate of the input and output audio
signals
- n_channels: int, number of channels of the input and output
audio signals
Returns
A tensorflow dataset of waveform to feed a tensorflow estimator in
predict mode.
"""
filenames, starts, ends = list(zip(*filenames_and_crops))
dataset = tf.data.Dataset.from_tensor_slices({
'audio_id': list(filenames),
'start': list(starts),
'end': list(ends)
})
# Load waveform.
dataset = dataset.map(
lambda sample: dict(
sample,
**audio_adapter.load_tf_waveform(
sample['audio_id'],
sample_rate=sample_rate,
offset=sample['start'],
duration=sample['end'] - sample['start'])),
num_parallel_calls=2)
# Filter out error.
dataset = dataset.filter(
lambda sample: tf.logical_not(sample['waveform_error']))
# Convert waveform to the right number of channels.
dataset = dataset.map(
lambda sample: dict(
sample,
waveform=to_n_channels(sample['waveform'], n_channels)))
# Set number of channels (required for the model).
dataset = dataset.map(
lambda sample: dict(
sample,
waveform=set_tensor_shape(sample['waveform'], (None, n_channels))))
return dataset
def process_audio(
audio_adapter,
filenames_and_crops, estimator, output_path,
sample_rate, n_channels, codec, output_naming):
"""
Perform separation on a list of audio ids.
Params:
- audio_adapter: Audio adapter to use for audio I/O.
- filenames_and_crops: list of (audio_filename, start, duration)
tuples separation is performed on each filaneme
from start (in seconds) to start + duration
(in seconds).
- estimator: the tensorflow estimator that performs the
source separation.
- output_path: output_path where to export separated files.
- sample_rate: audio sample_rate of the input and output audio
signals
- n_channels: int, number of channels of the input and output
audio signals
- codec: string codec to be used for export (could be
"wav", "mp3", "ogg", "m4a") could be anything
supported by ffmpeg.
- output_naming: string (= "filename" of "directory")
naming convention for output.
for an input file /path/to/audio/input_file.wav:
* if output_naming is equal to "filename":
output files will be put in the directory <output_path>/input_file
(<output_path>/input_file/<instrument1>.<codec>,
<output_path>/input_file/<instrument2>.<codec>...).
* if output_naming is equal to "directory":
output files will be put in the directory <output_path>/audio/
(<output_path>/audio/<instrument1>.<codec>,
<output_path>/audio/<instrument2>.<codec>...)
Use "directory" when separating the MusDB dataset.
"""
# Get estimator
prediction = estimator.predict(
lambda: get_dataset(
audio_adapter,
filenames_and_crops,
sample_rate,
n_channels),
yield_single_examples=False)
# initialize pool for audio export
pool = Pool(16)
for sample in prediction:
sample_filename = sample.pop('audio_id', 'unknown_filename').decode()
input_directory, input_filename = split(sample_filename)
if output_naming == 'directory':
output_dirname = split(input_directory)[1]
elif output_naming == 'filename':
output_dirname = splitext(input_filename)[0]
else:
raise ValueError(f'Unknown output naming {output_naming}')
for instrument, waveform in sample.items():
filename = join(
output_path,
output_dirname,
f'{instrument}.{codec}')
pool.apply_async(
audio_adapter.save,
(filename, waveform, sample_rate, codec))
# Wait for everything to be written
pool.close()
pool.join()
def entrypoint(arguments, params):
""" Command entrypoint.
:param arguments: Command line parsed argument as argparse.Namespace.
:param params: Deserialized JSON configuration file provided in CLI args.
"""
# TODO: check with output naming.
audio_adapter = get_audio_adapter(arguments.audio_adapter)
filenames = arguments.audio_filenames
output_path = arguments.output_path
max_duration = arguments.max_duration
audio_codec = arguments.audio_codec
output_naming = arguments.output_naming
estimator = create_estimator(params, arguments.MWF)
filenames_and_crops = [
(filename, 0., max_duration)
for filename in filenames]
process_audio(
audio_adapter,
filenames_and_crops,
estimator,
output_path,
params['sample_rate'],
params['n_channels'],
codec=audio_codec,
output_naming=output_naming)
separator = Separator(
arguments.configuration,
arguments.MWF)
for filename in arguments.inputs:
separator.separate_to_file(
filename,
arguments.output_path,
audio_adapter=audio_adapter,
offset=arguments.offset,
duration=arguments.duration,
codec=arguments.codec,
bitrate=arguments.bitrate,
filename_format=arguments.filename_format,
synchronous=False
)
separator.join()

4
spleeter/commands/train.py
View File

@@ -13,9 +13,10 @@ from functools import partial
import tensorflow as tf
# pylint: enable=import-error
from ..audio.adapter import get_audio_adapter
from ..dataset import get_training_dataset, get_validation_dataset
from ..model import model_fn
from ..utils.audio.adapter import get_audio_adapter
from ..model.provider import ModelProvider
from ..utils.logging import get_logger
__email__ = 'research@deezer.com'
@@ -95,4 +96,5 @@ def entrypoint(arguments, params):
estimator,
train_spec,
evaluation_spec)
ModelProvider.writeProbe(params['model_dir'])
get_logger().info('Model training done')

30
spleeter/dataset.py
View File

@@ -2,15 +2,16 @@
# coding: utf8
"""
Module for building data preprocessing pipeline using the tensorflow data
API.
Data preprocessing such as audio loading, spectrogram computation, cropping,
feature caching or data augmentation is done using a tensorflow dataset object
that output a tuple (input_, output) where:
- input_ is a dictionary with a single key that contains the (batched) mix
spectrogram of audio samples
- output is a dictionary of spectrogram of the isolated tracks (ground truth)
Module for building data preprocessing pipeline using the tensorflow
data API. Data preprocessing such as audio loading, spectrogram
computation, cropping, feature caching or data augmentation is done
using a tensorflow dataset object that output a tuple (input_, output)
where:
- input is a dictionary with a single key that contains the (batched)
mix spectrogram of audio samples
- output is a dictionary of spectrogram of the isolated tracks
(ground truth)
"""
import time
@@ -23,10 +24,10 @@ import numpy as np
import tensorflow as tf
# pylint: enable=import-error
from .utils.audio.convertor import (
from .audio.convertor import (
db_uint_spectrogram_to_gain,
spectrogram_to_db_uint)
from .utils.audio.spectrogram import (
from .audio.spectrogram import (
compute_spectrogram_tf,
random_pitch_shift,
random_time_stretch)
@@ -41,15 +42,6 @@ __email__ = 'research@deezer.com'
__author__ = 'Deezer Research'
__license__ = 'MIT License'
# Default datasets path parameter to use.
DEFAULT_DATASETS_PATH = join(
'audio_database',
'separated_sources',
'experiments',
'karaoke_vocal_extraction',
'tensorflow_experiment'
)
# Default audio parameters to use.
DEFAULT_AUDIO_PARAMS = {
'instrument_list': ('vocals', 'accompaniment'),

6
spleeter/model/provider/__init__.py
View File

@@ -38,12 +38,14 @@ class ModelProvider(ABC):
"""
pass
def writeProbe(self, directory):
@staticmethod
def writeProbe(directory):
""" Write a model probe file into the given directory.
:param directory: Directory to write probe into.
"""
with open(join(directory, self.MODEL_PROBE_PATH), 'w') as stream:
probe = join(directory, ModelProvider.MODEL_PROBE_PATH)
with open(probe, 'w') as stream:
stream.write('OK')
def get(self, model_directory):

58
spleeter/model/provider/github.py
View File

@@ -14,11 +14,10 @@
>>> provider.download('2stems', '/path/to/local/storage')
"""
import hashlib
import tarfile
from os import environ
from tempfile import TemporaryFile
from shutil import copyfileobj
from tempfile import NamedTemporaryFile
import requests
@@ -30,11 +29,25 @@ __author__ = 'Deezer Research'
__license__ = 'MIT License'
def compute_file_checksum(path):
""" Computes given path file sha256.
:param path: Path of the file to compute checksum for.
:returns: File checksum.
"""
sha256 = hashlib.sha256()
with open(path, 'rb') as stream:
for chunk in iter(lambda: stream.read(4096), b''):
sha256.update(chunk)
return sha256.hexdigest()
class GithubModelProvider(ModelProvider):
""" A ModelProvider implementation backed on Github for remote storage. """
LATEST_RELEASE = 'v1.4.0'
RELEASE_PATH = 'releases/download'
CHECKSUM_INDEX = 'checksum.json'
def __init__(self, host, repository, release):
""" Default constructor.
@@ -47,6 +60,26 @@ class GithubModelProvider(ModelProvider):
self._repository = repository
self._release = release
def checksum(self, name):
""" Downloads and returns reference checksum for the given model name.
:param name: Name of the model to get checksum for.
:returns: Checksum of the required model.
:raise ValueError: If the given model name is not indexed.
"""
url = '{}/{}/{}/{}/{}'.format(
self._host,
self._repository,
self.RELEASE_PATH,
self._release,
self.CHECKSUM_INDEX)
response = requests.get(url)
response.raise_for_status()
index = response.json()
if name not in index:
raise ValueError('No checksum for model {}'.format(name))
return index[name]
def download(self, name, path):
""" Download model denoted by the given name to disk.
@@ -60,14 +93,19 @@ class GithubModelProvider(ModelProvider):
self._release,
name)
get_logger().info('Downloading model archive %s', url)
response = requests.get(url, stream=True)
if response.status_code != 200:
raise IOError(f'Resource {url} not found')
with TemporaryFile() as stream:
copyfileobj(response.raw, stream)
with requests.get(url, stream=True) as response:
response.raise_for_status()
archive = NamedTemporaryFile(delete=False)
with archive as stream:
# Note: check for chunk size parameters ?
for chunk in response.iter_content(chunk_size=8192):
if chunk:
stream.write(chunk)
get_logger().info('Validating archive checksum')
if compute_file_checksum(archive.name) != self.checksum(name):
raise IOError('Downloaded file is corrupted, please retry')
get_logger().info('Extracting downloaded %s archive', name)
stream.seek(0)
tar = tarfile.open(fileobj=stream)
tar = tarfile.open(name=archive.name)
tar.extractall(path=path)
tar.close()
get_logger().info('%s model file(s) extracted', name)

28
spleeter/separator.py
View File

@@ -18,11 +18,12 @@ import json
from functools import partial
from multiprocessing import Pool
from pathlib import Path
from os.path import join
from os.path import basename, join
from . import SpleeterError
from .audio.adapter import get_default_audio_adapter
from .audio.convertor import to_stereo
from .model import model_fn
from .utils.audio.adapter import get_default_audio_adapter
from .utils.audio.convertor import to_stereo
from .utils.configuration import load_configuration
from .utils.estimator import create_estimator, to_predictor
@@ -57,7 +58,7 @@ class Separator(object):
self._predictor = to_predictor(estimator)
return self._predictor
def join(self, timeout=20):
def join(self, timeout=200):
""" Wait for all pending tasks to be finished.
:param timeout: (Optional) task waiting timeout.
@@ -93,10 +94,13 @@ class Separator(object):
self, audio_descriptor, destination,
audio_adapter=get_default_audio_adapter(),
offset=0, duration=600., codec='wav', bitrate='128k',
synchronous=True):
filename_format='{filename}/{instrument}.{codec}', synchronous=True):
""" Performs source separation and export result to file using
given audio adapter.
Filename format should be a Python formattable string that could use
following parameters : {instrument}, {filename} and {codec}.
:param audio_descriptor: Describe song to separate, used by audio
adapter to retrieve and load audio data,
in case of file based audio adapter, such
@@ -107,6 +111,7 @@ class Separator(object):
:param duration: (Optional) Duration of loaded song.
:param codec: (Optional) Export codec.
:param bitrate: (Optional) Export bitrate.
:param filename_format: (Optional) Filename format.
:param synchronous: (Optional) True is should by synchronous.
"""
waveform, _ = audio_adapter.load(
@@ -115,9 +120,20 @@ class Separator(object):
duration=duration,
sample_rate=self._sample_rate)
sources = self.separate(waveform)
filename = basename(audio_descriptor)
generated = []
for instrument, data in sources.items():
path = join(destination, filename_format.format(
filename=filename,
instrument=instrument,
codec=codec))
if path in generated:
raise SpleeterError((
f'Separated source path conflict : {path},'
'please check your filename format'))
generated.append(path)
task = self._pool.apply_async(audio_adapter.save, (
join(destination, f'{instrument}.{codec}'),
path,
data,
self._sample_rate,
codec,

8
spleeter/utils/configuration.py
View File

@@ -13,7 +13,7 @@ except ImportError:
from os.path import exists
from .. import resources
from .. import resources, SpleeterError
__email__ = 'research@deezer.com'
@@ -31,17 +31,17 @@ def load_configuration(descriptor):
:param descriptor: Configuration descriptor to use for lookup.
:returns: Loaded description as dict.
:raise ValueError: If required embedded configuration does not exists.
:raise IOError: If required configuration file does not exists.
:raise SpleeterError: If required configuration file does not exists.
"""
# Embedded configuration reading.
if descriptor.startswith(_EMBEDDED_CONFIGURATION_PREFIX):
name = descriptor[len(_EMBEDDED_CONFIGURATION_PREFIX):]
if not loader.is_resource(resources, f'{name}.json'):
raise ValueError(f'No embedded configuration {name} found')
raise SpleeterError(f'No embedded configuration {name} found')
with loader.open_text(resources, f'{name}.json') as stream:
return json.load(stream)
# Standard file reading.
if not exists(descriptor):
raise IOError(f'Configuration file {descriptor} not found')
raise SpleeterError(f'Configuration file {descriptor} not found')
with open(descriptor, 'r') as stream:
return json.load(stream)

8
tests/__init__.py Normal file
View File

@@ -0,0 +1,8 @@
#!/usr/bin/env python
# coding: utf8
""" Unit testing package. """
__email__ = 'research@deezer.com'
__author__ = 'Deezer Research'
__license__ = 'MIT License'

88
tests/test_ffmpeg_adapter.py Normal file
View File

@@ -0,0 +1,88 @@
#!/usr/bin/env python
# coding: utf8
""" Unit testing for audio adapter. """
__email__ = 'research@deezer.com'
__author__ = 'Deezer Research'
__license__ = 'MIT License'
from os.path import join
from tempfile import TemporaryDirectory
# pylint: disable=import-error
from pytest import fixture, raises
import numpy as np
import ffmpeg
# pylint: enable=import-error
from spleeter import SpleeterError
from spleeter.audio.adapter import AudioAdapter
from spleeter.audio.adapter import get_default_audio_adapter
from spleeter.audio.adapter import get_audio_adapter
from spleeter.audio.ffmpeg import FFMPEGProcessAudioAdapter
TEST_AUDIO_DESCRIPTOR = 'audio_example.mp3'
TEST_OFFSET = 0
TEST_DURATION = 600.
TEST_SAMPLE_RATE = 44100
@fixture(scope='session')
def adapter():
""" Target test audio adapter fixture. """
return get_default_audio_adapter()
@fixture(scope='session')
def audio_data(adapter):
""" Audio data fixture based on sample loading from adapter. """
return adapter.load(
TEST_AUDIO_DESCRIPTOR,
TEST_OFFSET,
TEST_DURATION,
TEST_SAMPLE_RATE)
def test_default_adapter(adapter):
""" Test adapter as default adapter. """
assert isinstance(adapter, FFMPEGProcessAudioAdapter)
assert adapter is AudioAdapter.DEFAULT
def test_load(audio_data):
""" Test audio loading. """
waveform, sample_rate = audio_data
assert sample_rate == TEST_SAMPLE_RATE
assert waveform is not None
assert waveform.dtype == np.dtype('float32')
assert len(waveform.shape) == 2
assert waveform.shape[0] == 479832
assert waveform.shape[1] == 2
def test_load_error(adapter):
""" Test load ffprobe exception """
with raises(SpleeterError):
adapter.load(
'Paris City Jazz',
TEST_OFFSET,
TEST_DURATION,
TEST_SAMPLE_RATE)
def test_save(adapter, audio_data):
""" Test audio saving. """
with TemporaryDirectory() as directory:
path = join(directory, 'ffmpeg-save.mp3')
adapter.save(
path,
audio_data[0],
audio_data[1])
probe = ffmpeg.probe(TEST_AUDIO_DESCRIPTOR)
assert len(probe['streams']) == 1
stream = probe['streams'][0]
assert stream['codec_type'] == 'audio'
assert stream['channels'] == 2
assert stream['duration'] == '10.919184'

21
tests/test_github_model_provider.py Normal file
View File

@@ -0,0 +1,21 @@
#!/usr/bin/env python
# coding: utf8
""" TO DOCUMENT """
from pytest import raises
from spleeter.model.provider import get_default_model_provider
def test_checksum():
""" Test archive checksum index retrieval. """
provider = get_default_model_provider()
assert provider.checksum('2stems') == \
'f3a90b39dd2874269e8b05a48a86745df897b848c61f3958efc80a39152bd692'
assert provider.checksum('4stems') == \
'3adb4a50ad4eb18c7c4d65fcf4cf2367a07d48408a5eb7d03cd20067429dfaa8'
assert provider.checksum('5stems') == \
'25a1e87eb5f75cc72a4d2d5467a0a50ac75f05611f877c278793742513cc7218'
with raises(ValueError):
provider.checksum('laisse moi stems stems stems')

85
tests/test_separator.py Normal file
View File

@@ -0,0 +1,85 @@
#!/usr/bin/env python
# coding: utf8
""" Unit testing for Separator class. """
__email__ = 'research@deezer.com'
__author__ = 'Deezer Research'
__license__ = 'MIT License'
import filecmp
from os.path import basename, exists, join
from tempfile import TemporaryDirectory
import pytest
from spleeter import SpleeterError
from spleeter.audio.adapter import get_default_audio_adapter
from spleeter.separator import Separator
TEST_AUDIO_DESCRIPTOR = 'audio_example.mp3'
TEST_AUDIO_BASENAME = basename(TEST_AUDIO_DESCRIPTOR)
TEST_CONFIGURATIONS = [
('spleeter:2stems', ('vocals', 'accompaniment')),
('spleeter:4stems', ('vocals', 'drums', 'bass', 'other')),
('spleeter:5stems', ('vocals', 'drums', 'bass', 'piano', 'other'))
]
@pytest.mark.parametrize('configuration, instruments', TEST_CONFIGURATIONS)
def test_separate(configuration, instruments):
""" Test separation from raw data. """
adapter = get_default_audio_adapter()
waveform, _ = adapter.load(TEST_AUDIO_DESCRIPTOR)
separator = Separator(configuration)
prediction = separator.separate(waveform)
assert len(prediction) == len(instruments)
for instrument in instruments:
assert instrument in prediction
for instrument in instruments:
track = prediction[instrument]
assert not (waveform == track).all()
for compared in instruments:
if instrument != compared:
assert not (track == prediction[compared]).all()
@pytest.mark.parametrize('configuration, instruments', TEST_CONFIGURATIONS)
def test_separate_to_file(configuration, instruments):
""" Test file based separation. """
separator = Separator(configuration)
with TemporaryDirectory() as directory:
separator.separate_to_file(
TEST_AUDIO_DESCRIPTOR,
directory)
for instrument in instruments:
assert exists(join(
directory,
'{}/{}.wav'.format(TEST_AUDIO_BASENAME, instrument)))
@pytest.mark.parametrize('configuration, instruments', TEST_CONFIGURATIONS)
def test_filename_format(configuration, instruments):
""" Test custom filename format. """
separator = Separator(configuration)
with TemporaryDirectory() as directory:
separator.separate_to_file(
TEST_AUDIO_DESCRIPTOR,
directory,
filename_format='export/{filename}/{instrument}.{codec}')
for instrument in instruments:
assert exists(join(
directory,
'export/{}/{}.wav'.format(TEST_AUDIO_BASENAME, instrument)))
def test_filename_confilct():
""" Test error handling with static pattern. """
separator = Separator(TEST_CONFIGURATIONS[0][0])
with TemporaryDirectory() as directory:
with pytest.raises(SpleeterError):
separator.separate_to_file(
TEST_AUDIO_DESCRIPTOR,
directory,
filename_format='I wanna be your lover')