Fixed GHA workflows to only release from main branch, changed runpod …

…docker image to install from conda, bumped version to trigger re-release to conda

.github/workflows/publish-to-conda.yml

@@ -2,12 +2,13 @@ name: publish-to-conda
 
 on:
   push:
+    branches:
+      - main
     paths:
       - pyproject.toml
       - conda/meta.yaml
   workflow_dispatch:
 
-
 jobs:
   conda_deployment_with_new_tag:
     name: Conda deployment of package with Python ${{ matrix.python-version }}

.github/workflows/publish-to-docker.yml

@@ -2,8 +2,13 @@ name: publish-to-docker
 
 on:
   push:
+    branches:
+      - main
     paths:
       - pyproject.toml
+      - Dockerfile.cpu
+      - Dockerfile.gpu
+      - Dockerfile.runpod
   workflow_dispatch:
 
 jobs:
@@ -16,7 +21,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: '3.x'
+          python-version: "3.x"
 
       - name: Install TOML
         run: pip install toml
@@ -63,6 +68,8 @@ jobs:
             beveradb/audio-separator:gpu-${{ env.VERSION }}
             beveradb/audio-separator:gpu
 
+      # Deliberately commented out because Github CI can't build this runpod image due to disk space limits
+      # Instead, I build this (17GB) image locally and push it to Docker Hub manually.
       # - name: Build and push Docker image for Runpod
       #   if: ${{ github.ref == 'refs/heads/main' }}
       #   uses: docker/build-push-action@v5

.github/workflows/publish-to-pypi.yml

@@ -2,6 +2,8 @@ name: publish-to-pypi
 
 on:
   push:
+    branches:
+      - main
     paths:
       - pyproject.toml
   workflow_dispatch:

Dockerfile.runpod

@@ -2,9 +2,6 @@
 ARG WORKER_CUDA_VERSION=11.8
 FROM runpod/base:0.4.4-cuda${WORKER_CUDA_VERSION}.0
 
-# Install audio-separator dependency ffmpeg
-RUN apt-get update && apt-get install -y ffmpeg wget && rm -rf /var/lib/apt/lists/*
-
 # Install miniconda
 RUN wget \
     https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
@@ -14,5 +11,9 @@ RUN wget \
 ENV PATH="/root/miniconda3/bin:$PATH"
 
 # Install the GPU version of audio-separator
-RUN --mount=type=cache,target=/root/.cache \
-    pip3 install "audio-separator[gpu]"
+RUN --mount=type=cache,target=/root/miniconda3/pkgs \
+    conda install -y -c beveradb -c pytorch -c conda-forge \
+    pytorch=*=*cuda* \
+    onnxruntime=*=*cuda* \
+    cudatoolkit \
+    audio-separator

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "audio-separator"
-version = "0.12.0"
+version = "0.12.1"
 description = "Easy to use vocal separation, using MDX-Net models from UVR trained by @Anjok07"
 authors = ["Andrew Beveridge <[email protected]>"]
 license = "MIT"