WIP speculative

Signed-off-by: Gianluca Boiano <morf3089@gmail.com>

.bruno/LocalAI Test Requests/tts/musicgen.bru

@@ -16,7 +16,7 @@ headers {
 
 body:json {
   {
-      "backend": "transformers-musicgen",
+      "backend": "transformers",
       "model": "facebook/musicgen-small",
       "input": "80s Synths playing Jazz"
   }

.devcontainer/docker-compose-devcontainer.yml

@@ -7,7 +7,7 @@ services:
       args:
       - FFMPEG=true
       - IMAGE_TYPE=extras
-      - GO_TAGS=stablediffusion p2p tts
+      - GO_TAGS=p2p tts
     env_file:
       - ../.env
     ports:

.env

@@ -38,12 +38,12 @@
 ## Uncomment and set to true to enable rebuilding from source
 # REBUILD=true
 
-## Enable go tags, available: stablediffusion, tts
-## stablediffusion: image generation with stablediffusion
+## Enable go tags, available: p2p, tts
+## p2p: enable distributed inferencing
 ## tts: enables text-to-speech with go-piper 
 ## (requires REBUILD=true)
 #
-# GO_TAGS=stablediffusion
+# GO_TAGS=p2p
 
 ## Path where to store generated images
 # LOCALAI_IMAGE_PATH=/tmp/generated/images

.github/dependabot.yml

@@ -81,14 +81,6 @@ updates:
     directory: "/backend/python/transformers"
     schedule:
       interval: "weekly"
-  - package-ecosystem: "pip"
-    directory: "/backend/python/transformers-musicgen"
-    schedule:
-      interval: "weekly"
-  - package-ecosystem: "pip"
-    directory: "/backend/python/vall-e-x"
-    schedule:
-      interval: "weekly"
   - package-ecosystem: "pip"
     directory: "/backend/python/vllm"
     schedule:

.github/workflows/image.yml

@@ -362,43 +362,43 @@ jobs:
             base-image: "ubuntu:22.04"
             skip-drivers: 'false'
             makeflags: "--jobs=4 --output-sync=target"
-#  parallel-builds:
-#    uses: ./.github/workflows/image_build.yml
-#    with:
-#      tag-latest: ${{ matrix.tag-latest }}
-#      tag-suffix: ${{ matrix.tag-suffix }}
-#      ffmpeg: ${{ matrix.ffmpeg }}
-#      image-type: ${{ matrix.image-type }}
-#      build-type: ${{ matrix.build-type }}
-#      cuda-major-version: ${{ matrix.cuda-major-version }}
-#      cuda-minor-version: ${{ matrix.cuda-minor-version }}
-#      platforms: ${{ matrix.platforms }}
-#      runs-on: ${{ matrix.runs-on }}
-#      aio: ${{ matrix.aio }}
-#      base-image: ${{ matrix.base-image }}
-#      grpc-base-image: ${{ matrix.grpc-base-image }}
-#      makeflags: ${{ matrix.makeflags }}
-#      latest-image: ${{ matrix.latest-image }}
-#      latest-image-aio: ${{ matrix.latest-image-aio }}
-#      skip-drivers: ${{ matrix.skip-drivers }}
-#    secrets:
-#      dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
-#      dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
-#      quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
-#      quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
-#    strategy:
-#      matrix:
-#        include:
-#          - build-type: 'cublas'
-#            cuda-major-version: "12"
-#            cuda-minor-version: "0"
-#            platforms: 'linux/arm64'
-#            tag-latest: 'false'
-#            tag-suffix: '-nvidia-l4t-arm64-core'
-#            latest-image: 'latest-nvidia-l4t-arm64-core'
-#            ffmpeg: 'true'
-#            image-type: 'core'
-#            base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
-#            runs-on: 'self-hosted'
-#            makeflags: "--jobs=4 --output-sync=target"
-#            skip-drivers: 'true'
+  gh-runner:
+    uses: ./.github/workflows/image_build.yml
+    with:
+      tag-latest: ${{ matrix.tag-latest }}
+      tag-suffix: ${{ matrix.tag-suffix }}
+      ffmpeg: ${{ matrix.ffmpeg }}
+      image-type: ${{ matrix.image-type }}
+      build-type: ${{ matrix.build-type }}
+      cuda-major-version: ${{ matrix.cuda-major-version }}
+      cuda-minor-version: ${{ matrix.cuda-minor-version }}
+      platforms: ${{ matrix.platforms }}
+      runs-on: ${{ matrix.runs-on }}
+      aio: ${{ matrix.aio }}
+      base-image: ${{ matrix.base-image }}
+      grpc-base-image: ${{ matrix.grpc-base-image }}
+      makeflags: ${{ matrix.makeflags }}
+      latest-image: ${{ matrix.latest-image }}
+      latest-image-aio: ${{ matrix.latest-image-aio }}
+      skip-drivers: ${{ matrix.skip-drivers }}
+    secrets:
+      dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
+      dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
+      quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
+      quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
+    strategy:
+      matrix:
+        include:
+          - build-type: 'cublas'
+            cuda-major-version: "12"
+            cuda-minor-version: "0"
+            platforms: 'linux/arm64'
+            tag-latest: 'false'
+            tag-suffix: '-nvidia-l4t-arm64-core'
+            latest-image: 'latest-nvidia-l4t-arm64-core'
+            ffmpeg: 'true'
+            image-type: 'core'
+            base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
+            runs-on: 'ubuntu-24.04-arm'
+            makeflags: "--jobs=4 --output-sync=target"
+            skip-drivers: 'true'

.github/workflows/release.yaml

@@ -237,40 +237,7 @@ jobs:
           detached: true
           connect-timeout-seconds: 180
           limit-access-to-actor: true
-  build-stablediffusion:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Clone
-        uses: actions/checkout@v4
-        with:
-          submodules: true
-      - uses: actions/setup-go@v5
-        with:
-          go-version: '1.21.x'
-          cache: false
-      - name: Dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y --no-install-recommends libopencv-dev protobuf-compiler ccache upx-ucl
-          go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
-          go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
-      - name: Build stablediffusion
-        run: |
-          export PATH=$PATH:$GOPATH/bin
-          make backend-assets/grpc/stablediffusion
-          mkdir -p release && cp backend-assets/grpc/stablediffusion release
-        env:
-          GO_TAGS: stablediffusion
-      - uses: actions/upload-artifact@v4
-        with:
-          name: stablediffusion
-          path: release/
-      - name: Release
-        uses: softprops/action-gh-release@v2
-        if: startsWith(github.ref, 'refs/tags/')
-        with:
-          files: |
-            release/*
+
 
   build-macOS-x86_64:
     runs-on: macos-13

.github/workflows/secscan.yaml

@@ -18,7 +18,7 @@ jobs:
         if: ${{ github.actor != 'dependabot[bot]' }}
       - name: Run Gosec Security Scanner
         if: ${{ github.actor != 'dependabot[bot]' }}
-        uses: securego/gosec@v2.21.4
+        uses: securego/gosec@v2.22.0
         with:
           # we let the report trigger content trigger a failure using the GitHub Security features.
           args: '-no-fail -fmt sarif -out results.sarif ./...'

.github/workflows/test-extra.yml

@@ -35,30 +35,6 @@ jobs:
         run: |
            make --jobs=5 --output-sync=target -C backend/python/transformers
            make --jobs=5 --output-sync=target -C backend/python/transformers test
-
-  tests-sentencetransformers:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Clone
-        uses: actions/checkout@v4
-        with:
-          submodules: true
-      - name: Dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential ffmpeg
-          # Install UV
-          curl -LsSf https://astral.sh/uv/install.sh | sh
-          sudo apt-get install -y ca-certificates cmake curl patch python3-pip
-          sudo apt-get install -y libopencv-dev
-          pip install --user --no-cache-dir grpcio-tools==1.64.1
-
-      - name: Test sentencetransformers
-        run: |
-           make --jobs=5 --output-sync=target -C backend/python/sentencetransformers
-           make --jobs=5 --output-sync=target -C backend/python/sentencetransformers test
-
-
   tests-rerankers:
     runs-on: ubuntu-latest
     steps:
@@ -102,78 +78,27 @@ jobs:
           make --jobs=5 --output-sync=target -C backend/python/diffusers
           make --jobs=5 --output-sync=target -C backend/python/diffusers test
 
-  tests-parler-tts:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Clone
-        uses: actions/checkout@v4
-        with:
-          submodules: true
-      - name: Dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential ffmpeg
-          # Install UV
-          curl -LsSf https://astral.sh/uv/install.sh | sh
-          sudo apt-get install -y ca-certificates cmake curl patch python3-pip
-          sudo apt-get install -y libopencv-dev
-          pip install --user --no-cache-dir grpcio-tools==1.64.1
+  # tests-transformers-musicgen:
+  #   runs-on: ubuntu-latest
+  #   steps:
+  #     - name: Clone
+  #       uses: actions/checkout@v4
+  #       with:
+  #         submodules: true
+  #     - name: Dependencies
+  #       run: |
+  #         sudo apt-get update
+  #         sudo apt-get install build-essential ffmpeg
+  #         # Install UV
+  #         curl -LsSf https://astral.sh/uv/install.sh | sh
+  #         sudo apt-get install -y ca-certificates cmake curl patch python3-pip
+  #         sudo apt-get install -y libopencv-dev
+  #         pip install --user --no-cache-dir grpcio-tools==1.64.1
 
-      - name: Test parler-tts
-        run: |
-           make --jobs=5 --output-sync=target -C backend/python/parler-tts
-           make --jobs=5 --output-sync=target -C backend/python/parler-tts test
-      - name: Setup tmate session if tests fail
-        if: ${{ failure() }}
-        uses: mxschmitt/action-tmate@v3.19
-        with:
-          detached: true
-          connect-timeout-seconds: 180
-          limit-access-to-actor: true
-
-  tests-openvoice:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Clone
-        uses: actions/checkout@v4
-        with:
-          submodules: true
-      - name: Dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential ffmpeg
-          # Install UV
-          curl -LsSf https://astral.sh/uv/install.sh | sh
-          sudo apt-get install -y ca-certificates cmake curl patch python3-pip
-          sudo apt-get install -y libopencv-dev
-          pip install --user --no-cache-dir grpcio-tools==1.64.1
-
-      - name: Test openvoice
-        run: |
-           make --jobs=5 --output-sync=target -C backend/python/openvoice
-           make --jobs=5 --output-sync=target -C backend/python/openvoice test
-
-  tests-transformers-musicgen:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Clone
-        uses: actions/checkout@v4
-        with:
-          submodules: true
-      - name: Dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential ffmpeg
-          # Install UV
-          curl -LsSf https://astral.sh/uv/install.sh | sh
-          sudo apt-get install -y ca-certificates cmake curl patch python3-pip
-          sudo apt-get install -y libopencv-dev
-          pip install --user --no-cache-dir grpcio-tools==1.64.1
-
-      - name: Test transformers-musicgen
-        run: |
-           make --jobs=5 --output-sync=target -C backend/python/transformers-musicgen
-           make --jobs=5 --output-sync=target -C backend/python/transformers-musicgen test
+  #     - name: Test transformers-musicgen
+  #       run: |
+  #          make --jobs=5 --output-sync=target -C backend/python/transformers-musicgen
+  #          make --jobs=5 --output-sync=target -C backend/python/transformers-musicgen test
 
   # tests-bark:
   #   runs-on: ubuntu-latest
@@ -260,26 +185,6 @@ jobs:
   #       run: |
   #          make --jobs=5 --output-sync=target -C backend/python/vllm
   #          make --jobs=5 --output-sync=target -C backend/python/vllm test
-  tests-vallex:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Clone
-        uses: actions/checkout@v4
-        with:
-          submodules: true
-      - name: Dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential ffmpeg
-          # Install UV
-          curl -LsSf https://astral.sh/uv/install.sh | sh
-          sudo apt-get install -y ca-certificates cmake curl patch python3-pip
-          sudo apt-get install -y libopencv-dev
-          pip install --user --no-cache-dir grpcio-tools==1.64.1
-      - name: Test vall-e-x
-        run: |
-           make --jobs=5 --output-sync=target -C backend/python/vall-e-x
-           make --jobs=5 --output-sync=target -C backend/python/vall-e-x test
 
   tests-coqui:
     runs-on: ubuntu-latest

.github/workflows/test.yml

@@ -100,15 +100,12 @@ jobs:
           # The python3-grpc-tools package in 22.04 is too old
           pip install --user grpcio-tools
 
-          sudo rm -rfv /usr/bin/conda || true
-          PATH=$PATH:/opt/conda/bin make -C backend/python/sentencetransformers
+          make -C backend/python/transformers
 
           # Pre-build piper before we start tests in order to have shared libraries in place
           make sources/go-piper && \
           GO_TAGS="tts" make -C sources/go-piper piper.o && \
-          sudo cp -rfv sources/go-piper/piper-phonemize/pi/lib/. /usr/lib/ && \
-          # Pre-build stable diffusion before we install a newer version of abseil (not compatible with stablediffusion-ncn)
-          PATH="$PATH:/root/go/bin" GO_TAGS="stablediffusion tts" GRPC_BACKENDS=backend-assets/grpc/stablediffusion make build
+          sudo cp -rfv sources/go-piper/piper-phonemize/pi/lib/. /usr/lib/
         env:
           CUDA_VERSION: 12-4
       - name: Cache grpc
@@ -130,7 +127,7 @@ jobs:
           cd grpc && cd cmake/build && sudo make --jobs 5 install
       - name: Test
         run: |
-          PATH="$PATH:/root/go/bin" GO_TAGS="stablediffusion tts" make --jobs 5 --output-sync=target test
+          PATH="$PATH:/root/go/bin" GO_TAGS="tts" make --jobs 5 --output-sync=target test
       - name: Setup tmate session if tests fail
         if: ${{ failure() }}
         uses: mxschmitt/action-tmate@v3.19

.vscode/launch.json

@@ -26,7 +26,7 @@
                 "LOCALAI_P2P": "true",
                 "LOCALAI_FEDERATED": "true"
             },
-            "buildFlags": ["-tags", "stablediffusion p2p tts", "-v"],
+            "buildFlags": ["-tags", "p2p tts", "-v"],
             "envFile": "${workspaceFolder}/.env",
             "cwd": "${workspaceRoot}"
         }

Dockerfile

@@ -15,8 +15,7 @@ ARG TARGETARCH
 ARG TARGETVARIANT
 
 ENV DEBIAN_FRONTEND=noninteractive
-ENV EXTERNAL_GRPC_BACKENDS="coqui:/build/backend/python/coqui/run.sh,huggingface-embeddings:/build/backend/python/sentencetransformers/run.sh,transformers:/build/backend/python/transformers/run.sh,sentencetransformers:/build/backend/python/sentencetransformers/run.sh,rerankers:/build/backend/python/rerankers/run.sh,autogptq:/build/backend/python/autogptq/run.sh,bark:/build/backend/python/bark/run.sh,diffusers:/build/backend/python/diffusers/run.sh,openvoice:/build/backend/python/openvoice/run.sh,vall-e-x:/build/backend/python/vall-e-x/run.sh,vllm:/build/backend/python/vllm/run.sh,mamba:/build/backend/python/mamba/run.sh,exllama2:/build/backend/python/exllama2/run.sh,transformers-musicgen:/build/backend/python/transformers-musicgen/run.sh,parler-tts:/build/backend/python/parler-tts/run.sh"
-
+ENV EXTERNAL_GRPC_BACKENDS="coqui:/build/backend/python/coqui/run.sh,transformers:/build/backend/python/transformers/run.sh,rerankers:/build/backend/python/rerankers/run.sh,autogptq:/build/backend/python/autogptq/run.sh,bark:/build/backend/python/bark/run.sh,diffusers:/build/backend/python/diffusers/run.sh,faster-whisper:/build/backend/python/faster-whisper/run.sh,kokoro:/build/backend/python/kokoro/run.sh,vllm:/build/backend/python/vllm/run.sh,exllama2:/build/backend/python/exllama2/run.sh"
 
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
@@ -69,14 +68,10 @@ ENV PATH=/opt/rocm/bin:${PATH}
 # OpenBLAS requirements and stable diffusion
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
-        libopenblas-dev \
-        libopencv-dev && \
+        libopenblas-dev && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
-# Set up OpenCV
-RUN ln -s /usr/include/opencv4/opencv2 /usr/include/opencv2
-
 WORKDIR /build
 
 ###################################
@@ -251,7 +246,7 @@ RUN git clone --recurse-submodules --jobs 4 -b ${GRPC_VERSION} --depth 1 --shall
 
 FROM requirements-drivers AS builder-base
 
-ARG GO_TAGS="stablediffusion tts p2p"
+ARG GO_TAGS="tts p2p"
 ARG GRPC_BACKENDS
 ARG MAKEFLAGS
 ARG LD_FLAGS="-s -w"
@@ -285,35 +280,12 @@ RUN <<EOT bash
     fi
 EOT
 
-
-###################################
-###################################
-
-# This first portion of builder holds the layers specifically used to build backend-assets/grpc/stablediffusion
-# In most cases, builder is the image you should be using - however, this can save build time if one just needs to copy backend-assets/grpc/stablediffusion and nothing else.
-FROM builder-base AS builder-sd
-
-# stablediffusion does not tolerate a newer version of abseil, copy only over enough elements to build it
-COPY Makefile .
-COPY go.mod .
-COPY go.sum .
-COPY backend/backend.proto ./backend/backend.proto
-COPY backend/go/image/stablediffusion ./backend/go/image/stablediffusion
-COPY pkg/grpc ./pkg/grpc
-COPY pkg/stablediffusion ./pkg/stablediffusion
-RUN git init
-RUN make sources/go-stable-diffusion
-RUN touch prepare-sources
-
-# Actually build the backend
-RUN GRPC_BACKENDS=backend-assets/grpc/stablediffusion make backend-assets/grpc/stablediffusion
-
 ###################################
 ###################################
 
 # The builder target compiles LocalAI. This target is not the target that will be uploaded to the registry.
 # Adjustments to the build process should likely be made here.
-FROM builder-sd AS builder
+FROM builder-base AS builder
 
 # Install the pre-built GRPC
 COPY --from=grpc /opt/grpc /usr/local
@@ -331,7 +303,7 @@ RUN make prepare
 ## We only leave the most CPU-optimized variant and the fallback for the cublas/hipblas build
 ## (both will use CUDA or hipblas for the actual computation)
 RUN if [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then \
-        SKIP_GRPC_BACKEND="backend-assets/grpc/llama-cpp-avx backend-assets/grpc/llama-cpp-avx2" make build; \
+        SKIP_GRPC_BACKEND="backend-assets/grpc/llama-cpp-avx512 backend-assets/grpc/llama-cpp-avx backend-assets/grpc/llama-cpp-avx2" make build; \
     else \
         make build; \
     fi
@@ -353,8 +325,6 @@ ARG FFMPEG
 
 COPY --from=grpc /opt/grpc /usr/local
 
-COPY --from=builder-sd /build/backend-assets/grpc/stablediffusion /build/backend-assets/grpc/stablediffusion
-
 COPY .devcontainer-scripts /.devcontainer-scripts
 
 # Add FFmpeg
@@ -427,36 +397,28 @@ COPY --from=builder /build/local-ai ./
 # Copy shared libraries for piper
 COPY --from=builder /build/sources/go-piper/piper-phonemize/pi/lib/* /usr/lib/
 
-# do not let stablediffusion rebuild (requires an older version of absl)
-COPY --from=builder-sd /build/backend-assets/grpc/stablediffusion ./backend-assets/grpc/stablediffusion
-
 # Change the shell to bash so we can use [[ tests below
 SHELL ["/bin/bash", "-c"]
 # We try to strike a balance between individual layer size (as that affects total push time) and total image size
 # Splitting the backends into more groups with fewer items results in a larger image, but a smaller size for the largest layer
 # Splitting the backends into fewer groups with more items results in a smaller image, but a larger size for the largest layer
 
+RUN if [[ ( "${IMAGE_TYPE}" == "extras ")]]; then \
+        apt-get -qq -y install espeak-ng \
+    ; fi
+
 RUN if [[ ( "${EXTRA_BACKENDS}" =~ "coqui" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE_TYPE" == "extras" ]]; then \
         make -C backend/python/coqui \
     ; fi && \
-    if [[ ( "${EXTRA_BACKENDS}" =~ "parler-tts" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE_TYPE" == "extras" ]]; then \
-        make -C backend/python/parler-tts \
+    if [[ ( "${EXTRA_BACKENDS}" =~ "faster-whisper" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE_TYPE" == "extras" ]]; then \
+        make -C backend/python/faster-whisper \
     ; fi && \
     if [[ ( "${EXTRA_BACKENDS}" =~ "diffusers" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE_TYPE" == "extras" ]]; then \
         make -C backend/python/diffusers \
-    ; fi && \
-    if [[ ( "${EXTRA_BACKENDS}" =~ "transformers-musicgen" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE_TYPE" == "extras" ]]; then \
-        make -C backend/python/transformers-musicgen \
     ; fi
 
-RUN if [[ ( "${EXTRA_BACKENDS}" =~ "vall-e-x" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE_TYPE" == "extras" ]]; then \
-        make -C backend/python/vall-e-x \
-    ; fi && \
-    if [[ ( "${EXTRA_BACKENDS}" =~ "openvoice" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE_TYPE" == "extras" ]]; then \
-        make -C backend/python/openvoice \
-    ; fi && \
-    if [[ ( "${EXTRA_BACKENDS}" =~ "sentencetransformers" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE_TYPE" == "extras" ]]; then \
-        make -C backend/python/sentencetransformers \
+RUN if [[ ( "${EXTRA_BACKENDS}" =~ "kokoro" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE_TYPE" == "extras" ]]; then \
+        make -C backend/python/kokoro \
     ; fi && \
     if [[ ( "${EXTRA_BACKENDS}" =~ "exllama2" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE_TYPE" == "extras" ]]; then \
         make -C backend/python/exllama2 \
@@ -476,9 +438,6 @@ RUN if [[ ( "${EXTRA_BACKENDS}" =~ "vllm" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE
     ; fi && \
     if [[ ( "${EXTRA_BACKENDS}" =~ "rerankers" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE_TYPE" == "extras" ]]; then \
         make -C backend/python/rerankers \
-    ; fi && \
-    if [[ ( "${EXTRA_BACKENDS}" =~ "mamba" || -z "${EXTRA_BACKENDS}" ) && "$IMAGE_TYPE" == "extras" ]]; then \
-        make -C backend/python/mamba \
     ; fi
 
 # Make sure the models directory exists

Makefile

@@ -8,7 +8,7 @@ DETECT_LIBS?=true
 # llama.cpp versions
 GOLLAMA_REPO?=https://github.com/go-skynet/go-llama.cpp
 GOLLAMA_VERSION?=2b57a8ae43e4699d3dc5d1496a1ccd42922993be
-CPPLLAMA_VERSION?=ba8a1f9c5b675459c55a83e3f97f10df3a66c788
+CPPLLAMA_VERSION?=6152129d05870cb38162c422c6ba80434e021e9f
 
 # whisper.cpp version
 WHISPER_REPO?=https://github.com/ggerganov/whisper.cpp
@@ -18,21 +18,13 @@ WHISPER_CPP_VERSION?=6266a9f9e56a5b925e9892acf650f3eb1245814d
 PIPER_REPO?=https://github.com/mudler/go-piper
 PIPER_VERSION?=e10ca041a885d4a8f3871d52924b47792d5e5aa0
 
-# stablediffusion version
-STABLEDIFFUSION_REPO?=https://github.com/mudler/go-stable-diffusion
-STABLEDIFFUSION_VERSION?=4a3cd6aeae6f66ee57eae9a0075f8c58c3a6a38f
-
-# tinydream version
-TINYDREAM_REPO?=https://github.com/M0Rf30/go-tiny-dream
-TINYDREAM_VERSION?=c04fa463ace9d9a6464313aa5f9cd0f953b6c057
-
 # bark.cpp
 BARKCPP_REPO?=https://github.com/PABannier/bark.cpp.git
 BARKCPP_VERSION?=v1.0.0
 
 # stablediffusion.cpp (ggml)
 STABLEDIFFUSION_GGML_REPO?=https://github.com/leejet/stable-diffusion.cpp
-STABLEDIFFUSION_GGML_VERSION?=dcf91f9e0f2cbf9da472ee2a556751ed4bab2d2a
+STABLEDIFFUSION_GGML_VERSION?=5eb15ef4d022bef4a391de4f5f6556e81fbb5024
 
 ONNX_VERSION?=1.20.0
 ONNX_ARCH?=x64
@@ -183,16 +175,6 @@ ifeq ($(STATIC),true)
 	LD_FLAGS+=-linkmode external -extldflags -static
 endif
 
-ifeq ($(findstring stablediffusion,$(GO_TAGS)),stablediffusion)
-#	OPTIONAL_TARGETS+=go-stable-diffusion/libstablediffusion.a
-	OPTIONAL_GRPC+=backend-assets/grpc/stablediffusion
-endif
-
-ifeq ($(findstring tinydream,$(GO_TAGS)),tinydream)
-#	OPTIONAL_TARGETS+=go-tiny-dream/libtinydream.a
-	OPTIONAL_GRPC+=backend-assets/grpc/tinydream
-endif
-
 ifeq ($(findstring tts,$(GO_TAGS)),tts)
 #	OPTIONAL_TARGETS+=go-piper/libpiper_binding.a
 #	OPTIONAL_TARGETS+=backend-assets/espeak-ng-data
@@ -204,6 +186,7 @@ endif
 ALL_GRPC_BACKENDS=backend-assets/grpc/huggingface
 ALL_GRPC_BACKENDS+=backend-assets/grpc/llama-cpp-avx
 ALL_GRPC_BACKENDS+=backend-assets/grpc/llama-cpp-avx2
+ALL_GRPC_BACKENDS+=backend-assets/grpc/llama-cpp-avx512
 ALL_GRPC_BACKENDS+=backend-assets/grpc/llama-cpp-fallback
 ALL_GRPC_BACKENDS+=backend-assets/grpc/llama-ggml
 ALL_GRPC_BACKENDS+=backend-assets/grpc/llama-cpp-grpc
@@ -282,19 +265,6 @@ sources/go-piper:
 sources/go-piper/libpiper_binding.a: sources/go-piper
 	$(MAKE) -C sources/go-piper libpiper_binding.a example/main piper.o
 
-## stable diffusion (onnx)
-sources/go-stable-diffusion:
-	mkdir -p sources/go-stable-diffusion
-	cd sources/go-stable-diffusion && \
-	git init && \
-	git remote add origin $(STABLEDIFFUSION_REPO) && \
-	git fetch origin && \
-	git checkout $(STABLEDIFFUSION_VERSION) && \
-	git submodule update --init --recursive --depth 1 --single-branch
-
-sources/go-stable-diffusion/libstablediffusion.a: sources/go-stable-diffusion
-	CPATH="$(CPATH):/usr/include/opencv4" $(MAKE) -C sources/go-stable-diffusion libstablediffusion.a
-
 ## stablediffusion (ggml)
 sources/stablediffusion-ggml.cpp:
 	git clone --recursive $(STABLEDIFFUSION_GGML_REPO) sources/stablediffusion-ggml.cpp && \
@@ -302,14 +272,8 @@ sources/stablediffusion-ggml.cpp:
 	git checkout $(STABLEDIFFUSION_GGML_VERSION) && \
 	git submodule update --init --recursive --depth 1 --single-branch
 
-sources/stablediffusion-ggml.cpp/build/libstable-diffusion.a: sources/stablediffusion-ggml.cpp
-	cd sources/stablediffusion-ggml.cpp && \
-	mkdir -p build && \
-	cd build && \
-	cmake $(CMAKE_ARGS) .. && \
-	cmake --build . --config Release
-
-backend/go/image/stablediffusion-ggml/libsd.a: sources/stablediffusion-ggml.cpp/build/libstable-diffusion.a
+backend/go/image/stablediffusion-ggml/libsd.a: sources/stablediffusion-ggml.cpp
+	$(MAKE) -C backend/go/image/stablediffusion-ggml build/libstable-diffusion.a
 	$(MAKE) -C backend/go/image/stablediffusion-ggml libsd.a
 
 backend-assets/grpc/stablediffusion-ggml: backend/go/image/stablediffusion-ggml/libsd.a backend-assets/grpc
@@ -333,19 +297,6 @@ else
 	mv backend-assets/lib/libonnxruntime.so.$(ONNX_VERSION) backend-assets/lib/libonnxruntime.so.1
 endif
 
-## tiny-dream
-sources/go-tiny-dream:
-	mkdir -p sources/go-tiny-dream
-	cd sources/go-tiny-dream && \
-	git init && \
-	git remote add origin $(TINYDREAM_REPO) && \
-	git fetch origin && \
-	git checkout $(TINYDREAM_VERSION) && \
-	git submodule update --init --recursive --depth 1 --single-branch
-
-sources/go-tiny-dream/libtinydream.a: sources/go-tiny-dream
-	$(MAKE) -C sources/go-tiny-dream libtinydream.a
-
 ## whisper
 sources/whisper.cpp:
 	mkdir -p sources/whisper.cpp
@@ -359,22 +310,18 @@ sources/whisper.cpp:
 sources/whisper.cpp/libwhisper.a: sources/whisper.cpp
 	cd sources/whisper.cpp && $(MAKE) libwhisper.a libggml.a
 
-get-sources: sources/go-llama.cpp sources/go-piper sources/stablediffusion-ggml.cpp sources/bark.cpp sources/whisper.cpp sources/go-stable-diffusion sources/go-tiny-dream backend/cpp/llama/llama.cpp
+get-sources: sources/go-llama.cpp sources/go-piper sources/stablediffusion-ggml.cpp sources/bark.cpp sources/whisper.cpp backend/cpp/llama/llama.cpp
 
 replace:
 	$(GOCMD) mod edit -replace github.com/ggerganov/whisper.cpp=$(CURDIR)/sources/whisper.cpp
 	$(GOCMD) mod edit -replace github.com/ggerganov/whisper.cpp/bindings/go=$(CURDIR)/sources/whisper.cpp/bindings/go
-	$(GOCMD) mod edit -replace github.com/M0Rf30/go-tiny-dream=$(CURDIR)/sources/go-tiny-dream
 	$(GOCMD) mod edit -replace github.com/mudler/go-piper=$(CURDIR)/sources/go-piper
-	$(GOCMD) mod edit -replace github.com/mudler/go-stable-diffusion=$(CURDIR)/sources/go-stable-diffusion
 	$(GOCMD) mod edit -replace github.com/go-skynet/go-llama.cpp=$(CURDIR)/sources/go-llama.cpp
 
 dropreplace:
 	$(GOCMD) mod edit -dropreplace github.com/ggerganov/whisper.cpp
 	$(GOCMD) mod edit -dropreplace github.com/ggerganov/whisper.cpp/bindings/go
-	$(GOCMD) mod edit -dropreplace github.com/M0Rf30/go-tiny-dream
 	$(GOCMD) mod edit -dropreplace github.com/mudler/go-piper
-	$(GOCMD) mod edit -dropreplace github.com/mudler/go-stable-diffusion
 	$(GOCMD) mod edit -dropreplace github.com/go-skynet/go-llama.cpp
 
 prepare-sources: get-sources replace
@@ -385,9 +332,7 @@ rebuild: ## Rebuilds the project
 	$(GOCMD) clean -cache
 	$(MAKE) -C sources/go-llama.cpp clean
 	$(MAKE) -C sources/whisper.cpp clean
-	$(MAKE) -C sources/go-stable-diffusion clean
 	$(MAKE) -C sources/go-piper clean
-	$(MAKE) -C sources/go-tiny-dream clean
 	$(MAKE) build
 
 prepare: prepare-sources $(OPTIONAL_TARGETS)
@@ -501,9 +446,9 @@ prepare-test: grpcs
 
 test: prepare test-models/testmodel.ggml grpcs
 	@echo 'Running tests'
-	export GO_TAGS="tts stablediffusion debug"
+	export GO_TAGS="tts debug"
 	$(MAKE) prepare-test
-	HUGGINGFACE_GRPC=$(abspath ./)/backend/python/sentencetransformers/run.sh TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models \
+	HUGGINGFACE_GRPC=$(abspath ./)/backend/python/transformers/run.sh TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models \
 	$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="!llama && !llama-gguf"  --flake-attempts $(TEST_FLAKES) --fail-fast -v -r $(TEST_PATHS)
 	$(MAKE) test-llama
 	$(MAKE) test-llama-gguf
@@ -589,10 +534,10 @@ protogen-go-clean:
 	$(RM) bin/*
 
 .PHONY: protogen-python
-protogen-python: autogptq-protogen bark-protogen coqui-protogen diffusers-protogen exllama2-protogen mamba-protogen rerankers-protogen sentencetransformers-protogen transformers-protogen parler-tts-protogen transformers-musicgen-protogen vall-e-x-protogen vllm-protogen openvoice-protogen
+protogen-python: autogptq-protogen bark-protogen coqui-protogen diffusers-protogen exllama2-protogen rerankers-protogen transformers-protogen kokoro-protogen vllm-protogen faster-whisper-protogen
 
 .PHONY: protogen-python-clean
-protogen-python-clean: autogptq-protogen-clean bark-protogen-clean coqui-protogen-clean diffusers-protogen-clean  exllama2-protogen-clean mamba-protogen-clean sentencetransformers-protogen-clean rerankers-protogen-clean transformers-protogen-clean transformers-musicgen-protogen-clean parler-tts-protogen-clean vall-e-x-protogen-clean vllm-protogen-clean openvoice-protogen-clean
+protogen-python-clean: autogptq-protogen-clean bark-protogen-clean coqui-protogen-clean diffusers-protogen-clean  exllama2-protogen-clean rerankers-protogen-clean transformers-protogen-clean kokoro-protogen-clean vllm-protogen-clean faster-whisper-protogen-clean
 
 .PHONY: autogptq-protogen
 autogptq-protogen:
@@ -626,6 +571,14 @@ diffusers-protogen:
 diffusers-protogen-clean:
 	$(MAKE) -C backend/python/diffusers protogen-clean
 
+.PHONY: faster-whisper-protogen
+faster-whisper-protogen:
+	$(MAKE) -C backend/python/faster-whisper protogen
+
+.PHONY: faster-whisper-protogen-clean
+faster-whisper-protogen-clean:
+	$(MAKE) -C backend/python/faster-whisper protogen-clean
+
 .PHONY: exllama2-protogen
 exllama2-protogen:
 	$(MAKE) -C backend/python/exllama2 protogen
@@ -634,14 +587,6 @@ exllama2-protogen:
 exllama2-protogen-clean:
 	$(MAKE) -C backend/python/exllama2 protogen-clean
 
-.PHONY: mamba-protogen
-mamba-protogen:
-	$(MAKE) -C backend/python/mamba protogen
-
-.PHONY: mamba-protogen-clean
-mamba-protogen-clean:
-	$(MAKE) -C backend/python/mamba protogen-clean
-
 .PHONY: rerankers-protogen
 rerankers-protogen:
 	$(MAKE) -C backend/python/rerankers protogen
@@ -650,14 +595,6 @@ rerankers-protogen:
 rerankers-protogen-clean:
 	$(MAKE) -C backend/python/rerankers protogen-clean
 
-.PHONY: sentencetransformers-protogen
-sentencetransformers-protogen:
-	$(MAKE) -C backend/python/sentencetransformers protogen
-
-.PHONY: sentencetransformers-protogen-clean
-sentencetransformers-protogen-clean:
-	$(MAKE) -C backend/python/sentencetransformers protogen-clean
-
 .PHONY: transformers-protogen
 transformers-protogen:
 	$(MAKE) -C backend/python/transformers protogen
@@ -666,37 +603,13 @@ transformers-protogen:
 transformers-protogen-clean:
 	$(MAKE) -C backend/python/transformers protogen-clean
 
-.PHONY: parler-tts-protogen
-parler-tts-protogen:
-	$(MAKE) -C backend/python/parler-tts protogen
+.PHONY: kokoro-protogen
+kokoro-protogen:
+	$(MAKE) -C backend/python/kokoro protogen
 
-.PHONY: parler-tts-protogen-clean
-parler-tts-protogen-clean:
-	$(MAKE) -C backend/python/parler-tts protogen-clean
-
-.PHONY: transformers-musicgen-protogen
-transformers-musicgen-protogen:
-	$(MAKE) -C backend/python/transformers-musicgen protogen
-
-.PHONY: transformers-musicgen-protogen-clean
-transformers-musicgen-protogen-clean:
-	$(MAKE) -C backend/python/transformers-musicgen protogen-clean
-
-.PHONY: vall-e-x-protogen
-vall-e-x-protogen:
-	$(MAKE) -C backend/python/vall-e-x protogen
-
-.PHONY: vall-e-x-protogen-clean
-vall-e-x-protogen-clean:
-	$(MAKE) -C backend/python/vall-e-x protogen-clean
-
-.PHONY: openvoice-protogen
-openvoice-protogen:
-	$(MAKE) -C backend/python/openvoice protogen
-
-.PHONY: openvoice-protogen-clean
-openvoice-protogen-clean:
-	$(MAKE) -C backend/python/openvoice protogen-clean
+.PHONY: kokoro-protogen-clean
+kokoro-protogen-clean:
+	$(MAKE) -C backend/python/kokoro protogen-clean
 
 .PHONY: vllm-protogen
 vllm-protogen:
@@ -713,15 +626,11 @@ prepare-extra-conda-environments: protogen-python
 	$(MAKE) -C backend/python/bark
 	$(MAKE) -C backend/python/coqui
 	$(MAKE) -C backend/python/diffusers
+	$(MAKE) -C backend/python/faster-whisper
 	$(MAKE) -C backend/python/vllm
-	$(MAKE) -C backend/python/mamba
-	$(MAKE) -C backend/python/sentencetransformers
 	$(MAKE) -C backend/python/rerankers
 	$(MAKE) -C backend/python/transformers
-	$(MAKE) -C backend/python/transformers-musicgen
-	$(MAKE) -C backend/python/parler-tts
-	$(MAKE) -C backend/python/vall-e-x
-	$(MAKE) -C backend/python/openvoice
+	$(MAKE) -C backend/python/kokoro
 	$(MAKE) -C backend/python/exllama2
 
 prepare-test-extra: protogen-python
@@ -791,6 +700,13 @@ backend-assets/grpc/llama-cpp-avx2: backend-assets/grpc backend/cpp/llama/llama.
 	CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=off -DGGML_FMA=on -DGGML_F16C=on" $(MAKE) VARIANT="llama-avx2" build-llama-cpp-grpc-server
 	cp -rfv backend/cpp/llama-avx2/grpc-server backend-assets/grpc/llama-cpp-avx2
 
+backend-assets/grpc/llama-cpp-avx512: backend-assets/grpc backend/cpp/llama/llama.cpp
+	cp -rf backend/cpp/llama backend/cpp/llama-avx512
+	$(MAKE) -C backend/cpp/llama-avx512 purge
+	$(info ${GREEN}I llama-cpp build info:avx512${RESET})
+	CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=on -DGGML_FMA=on -DGGML_F16C=on" $(MAKE) VARIANT="llama-avx512" build-llama-cpp-grpc-server
+	cp -rfv backend/cpp/llama-avx512/grpc-server backend-assets/grpc/llama-cpp-avx512
+
 backend-assets/grpc/llama-cpp-avx: backend-assets/grpc backend/cpp/llama/llama.cpp
 	cp -rf backend/cpp/llama backend/cpp/llama-avx
 	$(MAKE) -C backend/cpp/llama-avx purge
@@ -865,13 +781,6 @@ ifneq ($(UPX),)
 	$(UPX) backend-assets/grpc/piper
 endif
 
-backend-assets/grpc/stablediffusion: sources/go-stable-diffusion sources/go-stable-diffusion/libstablediffusion.a backend-assets/grpc
-	CGO_LDFLAGS="$(CGO_LDFLAGS)" CPATH="$(CPATH):$(CURDIR)/sources/go-stable-diffusion/:/usr/include/opencv4" LIBRARY_PATH=$(CURDIR)/sources/go-stable-diffusion/ \
-	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/stablediffusion ./backend/go/image/stablediffusion
-ifneq ($(UPX),)
-	$(UPX) backend-assets/grpc/stablediffusion
-endif
-
 backend-assets/grpc/silero-vad: backend-assets/grpc backend-assets/lib/libonnxruntime.so.1
 	CGO_LDFLAGS="$(CGO_LDFLAGS)" CPATH="$(CPATH):$(CURDIR)/sources/onnxruntime/include/" LIBRARY_PATH=$(CURDIR)/backend-assets/lib \
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/silero-vad ./backend/go/vad/silero
@@ -879,13 +788,6 @@ ifneq ($(UPX),)
 	$(UPX) backend-assets/grpc/silero-vad
 endif
 
-backend-assets/grpc/tinydream: sources/go-tiny-dream sources/go-tiny-dream/libtinydream.a backend-assets/grpc
-	CGO_LDFLAGS="$(CGO_LDFLAGS)" LIBRARY_PATH=$(CURDIR)/go-tiny-dream \
-	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/tinydream ./backend/go/image/tinydream
-ifneq ($(UPX),)
-	$(UPX) backend-assets/grpc/tinydream
-endif
-
 backend-assets/grpc/whisper: sources/whisper.cpp sources/whisper.cpp/libwhisper.a backend-assets/grpc
 	CGO_LDFLAGS="$(CGO_LDFLAGS) $(CGO_LDFLAGS_WHISPER)" C_INCLUDE_PATH="$(CURDIR)/sources/whisper.cpp/include:$(CURDIR)/sources/whisper.cpp/ggml/include" LIBRARY_PATH=$(CURDIR)/sources/whisper.cpp \
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/whisper ./backend/go/transcribe/whisper

README.md

@@ -39,7 +39,7 @@
 </p>
 
 <p align="center">
-<a href="https://trendshift.io/repositories/1484" target="_blank"><img src="https://trendshift.io/api/badge/repositories/1484" alt="go-skynet%2FLocalAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+<a href="https://trendshift.io/repositories/5539" target="_blank"><img src="https://trendshift.io/api/badge/repositories/5539" alt="mudler%2FLocalAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 </p>
 
 > :bulb: Get help - [❓FAQ](https://localai.io/faq/) [💭Discussions](https://github.com/go-skynet/LocalAI/discussions) [:speech_balloon: Discord](https://discord.gg/uJAeKSAGDy) [:book: Documentation website](https://localai.io/)
@@ -92,19 +92,15 @@ local-ai run oci://localai/phi-2:latest
 
 ## 📰 Latest project news
 
+- Jan 2025: LocalAI model release: https://huggingface.co/mudler/LocalAI-functioncall-phi-4-v0.3, SANA support in diffusers: https://github.com/mudler/LocalAI/pull/4603
 - Dec 2024: stablediffusion.cpp backend (ggml) added ( https://github.com/mudler/LocalAI/pull/4289 )
 - Nov 2024: Bark.cpp backend added ( https://github.com/mudler/LocalAI/pull/4287 )
 - Nov 2024: Voice activity detection models (**VAD**) added to the API: https://github.com/mudler/LocalAI/pull/4204
 - Oct 2024: examples moved to [LocalAI-examples](https://github.com/mudler/LocalAI-examples)
 - Aug 2024:  🆕 FLUX-1, [P2P Explorer](https://explorer.localai.io)
-- July 2024: 🔥🔥 🆕 P2P Dashboard, LocalAI Federated mode and AI Swarms: https://github.com/mudler/LocalAI/pull/2723
-- June 2024: 🆕 You can browse now the model gallery without LocalAI! Check out https://models.localai.io
-- June 2024: Support for models from OCI registries: https://github.com/mudler/LocalAI/pull/2628
+- July 2024: 🔥🔥 🆕 P2P Dashboard, LocalAI Federated mode and AI Swarms: https://github.com/mudler/LocalAI/pull/2723. P2P Global community pools: https://github.com/mudler/LocalAI/issues/3113
 - May 2024: 🔥🔥 Decentralized P2P llama.cpp:  https://github.com/mudler/LocalAI/pull/2343 (peer2peer llama.cpp!) 👉 Docs  https://localai.io/features/distribute/
-- May 2024: 🔥🔥 Openvoice: https://github.com/mudler/LocalAI/pull/2334
-- May 2024: 🆕 Function calls without grammars and mixed mode: https://github.com/mudler/LocalAI/pull/2328
 - May 2024: 🔥🔥 Distributed inferencing: https://github.com/mudler/LocalAI/pull/2324
-- May 2024: Chat, TTS, and Image generation in the WebUI: https://github.com/mudler/LocalAI/pull/2222
 - April 2024: Reranker API: https://github.com/mudler/LocalAI/pull/2121
 
 Roadmap items: [List of issues](https://github.com/mudler/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3Aroadmap)
@@ -113,12 +109,10 @@ Roadmap items: [List of issues](https://github.com/mudler/LocalAI/issues?q=is%3A
 
 - Multimodal with vLLM and Video understanding: https://github.com/mudler/LocalAI/pull/3729
 - Realtime API https://github.com/mudler/LocalAI/issues/3714
-- 🔥🔥 Distributed, P2P Global community pools: https://github.com/mudler/LocalAI/issues/3113
 - WebUI improvements: https://github.com/mudler/LocalAI/issues/2156
 - Backends v2: https://github.com/mudler/LocalAI/issues/1126
 - Improving UX v2: https://github.com/mudler/LocalAI/issues/1373
 - Assistant API: https://github.com/mudler/LocalAI/issues/1273
-- Moderation endpoint: https://github.com/mudler/LocalAI/issues/999
 - Vulkan: https://github.com/mudler/LocalAI/issues/1647
 - Anthropic API: https://github.com/mudler/LocalAI/issues/1808
 

aio/cpu/image-gen.yaml

@@ -1,56 +1,17 @@
 name: stablediffusion
-backend: stablediffusion
+backend: stablediffusion-ggml
+cfg_scale: 4.5
+
+options:
+- sampler:euler
 parameters:
-  model: stablediffusion_assets
-
-license: "BSD-3"
-urls:
-- https://github.com/EdVince/Stable-Diffusion-NCNN
-- https://github.com/EdVince/Stable-Diffusion-NCNN/blob/main/LICENSE
-
-description: |
-     Stable Diffusion in NCNN with c++, supported txt2img and img2img
+  model: stable-diffusion-v1-5-pruned-emaonly-Q4_0.gguf
+step: 25
 
 download_files:
-- filename: "stablediffusion_assets/AutoencoderKL-256-256-fp16-opt.param"
-  sha256: "18ca4b66685e21406bcf64c484b3b680b4949900415536d599cc876579c85c82"
-  uri: "https://raw.githubusercontent.com/EdVince/Stable-Diffusion-NCNN/main/x86/linux/assets/AutoencoderKL-256-256-fp16-opt.param"
-- filename: "stablediffusion_assets/AutoencoderKL-512-512-fp16-opt.param"
-  sha256: "cf45f63aacf3dbbab0f59ed92a6f2c14d9a1801314631cd3abe91e3c85639a20"
-  uri: "https://raw.githubusercontent.com/EdVince/Stable-Diffusion-NCNN/main/x86/linux/assets/AutoencoderKL-512-512-fp16-opt.param"
-- filename: "stablediffusion_assets/AutoencoderKL-base-fp16.param"
-  sha256: "0254a056dce61b0c27dc9ec1b78b53bcf55315c540f55f051eb841aa992701ba"
-  uri: "https://raw.githubusercontent.com/EdVince/Stable-Diffusion-NCNN/main/x86/linux/assets/AutoencoderKL-base-fp16.param"
-- filename: "stablediffusion_assets/AutoencoderKL-encoder-512-512-fp16.bin"
-  sha256: "ddcb79a9951b9f91e05e087739ed69da2c1c4ae30ba4168cce350b49d617c9fa"
-  uri: "https://github.com/EdVince/Stable-Diffusion-NCNN/releases/download/naifu/AutoencoderKL-encoder-512-512-fp16.bin"
-- filename: "stablediffusion_assets/AutoencoderKL-fp16.bin"
-  sha256: "f02e71f80e70252734724bbfaed5c4ddd3a8ed7e61bb2175ff5f53099f0e35dd"
-  uri: "https://github.com/EdVince/Stable-Diffusion-NCNN/releases/download/naifu/AutoencoderKL-fp16.bin"
-- filename: "stablediffusion_assets/FrozenCLIPEmbedder-fp16.bin"
-  sha256: "1c9a12f4e1dd1b295a388045f7f28a2352a4d70c3dc96a542189a3dd7051fdd6"
-  uri: "https://github.com/EdVince/Stable-Diffusion-NCNN/releases/download/naifu/FrozenCLIPEmbedder-fp16.bin"
-- filename: "stablediffusion_assets/FrozenCLIPEmbedder-fp16.param"
-  sha256: "471afbe678dd1fd3fe764ef9c6eccaccb0a7d7e601f27b462aa926b20eb368c9"
-  uri: "https://raw.githubusercontent.com/EdVince/Stable-Diffusion-NCNN/main/x86/linux/assets/FrozenCLIPEmbedder-fp16.param"
-- filename: "stablediffusion_assets/log_sigmas.bin"
-  sha256: "a2089f8aa4c61f9c200feaec541ab3f5c94233b28deb6d5e8bcd974fa79b68ac"
-  uri: "https://github.com/EdVince/Stable-Diffusion-NCNN/raw/main/x86/linux/assets/log_sigmas.bin"
-- filename: "stablediffusion_assets/UNetModel-256-256-MHA-fp16-opt.param"
-  sha256: "a58c380229f09491776df837b7aa7adffc0a87821dc4708b34535da2e36e3da1"
-  uri: "https://raw.githubusercontent.com/EdVince/Stable-Diffusion-NCNN/main/x86/linux/assets/UNetModel-256-256-MHA-fp16-opt.param"
-- filename: "stablediffusion_assets/UNetModel-512-512-MHA-fp16-opt.param"
-  sha256: "f12034067062827bd7f43d1d21888d1f03905401acf6c6eea22be23c259636fa"
-  uri: "https://raw.githubusercontent.com/EdVince/Stable-Diffusion-NCNN/main/x86/linux/assets/UNetModel-512-512-MHA-fp16-opt.param"
-- filename: "stablediffusion_assets/UNetModel-base-MHA-fp16.param"
-  sha256: "696f6975de49f4325b53ce32aff81861a6d6c07cd9ce3f0aae2cc405350af38d"
-  uri: "https://raw.githubusercontent.com/EdVince/Stable-Diffusion-NCNN/main/x86/linux/assets/UNetModel-base-MHA-fp16.param"
-- filename: "stablediffusion_assets/UNetModel-MHA-fp16.bin"
-  sha256: "d618918d011bfc1f644c0f2a33bf84931bd53b28a98492b0a8ed6f3a818852c3"
-  uri: "https://github.com/EdVince/Stable-Diffusion-NCNN/releases/download/naifu/UNetModel-MHA-fp16.bin"
-- filename: "stablediffusion_assets/vocab.txt"
-  sha256: "e30e57b6f1e47616982ef898d8922be24e535b4fa3d0110477b3a6f02ebbae7d"
-  uri: "https://raw.githubusercontent.com/EdVince/Stable-Diffusion-NCNN/main/x86/linux/assets/vocab.txt"
+- filename: "stable-diffusion-v1-5-pruned-emaonly-Q4_0.gguf"
+  sha256: "b8944e9fe0b69b36ae1b5bb0185b3a7b8ef14347fe0fa9af6c64c4829022261f"
+  uri: "huggingface://second-state/stable-diffusion-v1-5-GGUF/stable-diffusion-v1-5-pruned-emaonly-Q4_0.gguf"
 
 usage: |
         curl http://localhost:8080/v1/images/generations \

backend/backend.proto

@@ -159,6 +159,8 @@ message Reply {
   bytes message = 1;
   int32 tokens = 2;
   int32 prompt_tokens = 3;
+  double timing_prompt_processing = 4;
+  double timing_token_generation = 5;
 }
 
 message ModelOptions {
@@ -348,4 +350,4 @@ message StatusResponse {
 message Message {
   string role = 1;
   string content = 2;
-}
+}

backend/cpp/llama/grpc-server.cpp

@@ -22,6 +22,7 @@
 #include "backend.grpc.pb.h"
 #include "utils.hpp"
 #include "sampling.h"
+#include "speculative.h"
 // include std::regex
 #include <cstddef>
 #include <thread>
@@ -134,6 +135,32 @@ static std::string tokens_to_output_formatted_string(const llama_context *ctx, c
     return out;
 }
 
+// Adds an RPC server
+// https://github.com/ggerganov/llama.cpp/compare/4dbc8b9cb71876e005724f4e8f73a3544646bcf5..3edfa7d3753c29e44b964c0ff424d2ea8d5fdee6
+static void add_rpc_devices(std::string servers) {
+    auto rpc_servers = string_split<std::string>(servers, ',');
+    if (rpc_servers.empty()) {
+        throw std::invalid_argument("no RPC servers specified");
+    }
+    ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
+    if (!rpc_reg) {
+        throw std::invalid_argument("failed to find RPC backend");
+    }
+    typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint);
+    ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
+    if (!ggml_backend_rpc_add_device_fn) {
+        throw std::invalid_argument("failed to find RPC device add function");
+    }
+    for (const auto & server : rpc_servers) {
+        ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str());
+        if (dev) {
+            ggml_backend_device_register(dev);
+        } else {
+            throw std::invalid_argument("failed to register RPC device");
+        }
+    }
+}
+
 // convert a vector of completion_token_output to json
 static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> &probs)
 {
@@ -159,12 +186,45 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vector<com
     return out;
 }
 
+struct llama_slot_params {
+    uint32_t seed      = -1; // RNG seed
+    bool stream        = true;
+    bool cache_prompt  = true; // remember the prompt to avoid reprocessing all prompt
+    bool return_tokens = false;
+
+    int32_t n_keep    =  0; // number of tokens to keep from initial prompt
+    int32_t n_discard =  0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
+    int32_t n_predict = -1; // new tokens to predict
+    int32_t n_indent  =  0; // mininum line indentation for the generated text in number of whitespace characters
+
+    int64_t t_max_prompt_ms  = -1; // TODO: implement
+    int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
+
+    std::vector<common_adapter_lora_info> lora;
+
+    std::vector<std::string> antiprompt;
+    std::vector<std::string> response_fields;
+    bool timings_per_token = false;
+    bool post_sampling_probs = false;
+    bool ignore_eos = false;
+
+    json input_prefix;
+    json input_suffix;
+
+    struct common_params_sampling sampling;
+    struct common_params_speculative speculative;
+};
+
+
 struct llama_client_slot
 {
     int id;
     int task_id = -1;
 
-    struct slot_params params;
+    struct llama_slot_params params;
+    common_speculative * spec = nullptr;
+    llama_batch batch_spec = {};
+
 
     slot_state state = IDLE;
     slot_command command = NONE;
@@ -257,6 +317,7 @@ struct llama_client_slot
         images.clear();
     }
 
+
     bool has_budget(common_params &global_params) {
         if (params.n_predict == -1 && global_params.n_predict == -1)
         {
@@ -428,6 +489,11 @@ struct llama_server_context
 {
     llama_model *model = nullptr;
     llama_context *ctx = nullptr;
+    common_init_result llama_init_dft;
+    llama_context * ctx_dft = nullptr;
+    llama_model * model_dft = nullptr;
+    llama_context_params cparams_dft;
+    const llama_vocab * vocab = nullptr;
 
     clip_ctx *clp_ctx = nullptr;
 
@@ -439,6 +505,7 @@ struct llama_server_context
     bool clean_kv_cache     = true;
     bool all_slots_are_idle = false;
     bool add_bos_token      = true;
+    bool has_eos_token      = true;
 
     int32_t n_ctx;  // total context for all clients / slots
 
@@ -474,6 +541,7 @@ struct llama_server_context
         }
     }
 
+
     bool load_model(const common_params &params_)
     {
         params = params_;
@@ -502,7 +570,7 @@ struct llama_server_context
 
         if (multimodal) {
             const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
-            const int n_embd_llm  = llama_n_embd(model);
+            const int n_embd_llm  = llama_model_n_embd(model);
             if (n_embd_clip != n_embd_llm) {
                 LOG("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm);
                 llama_free(ctx);
@@ -511,23 +579,54 @@ struct llama_server_context
             }
         }
 
+        vocab = llama_model_get_vocab(model);
         n_ctx = llama_n_ctx(ctx);
 
-        add_bos_token = llama_add_bos_token(model);
+        add_bos_token = llama_vocab_get_add_bos(vocab);
+        has_eos_token = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
+
+        if (!params.speculative.model.empty()) {
+            LOG("loading draft model '%s'\n", params.speculative.model.c_str());
+
+            auto params_dft = params;
+
+            params_dft.devices      = params.speculative.devices;
+            params_dft.model        = params.speculative.model;
+            params_dft.n_ctx        = params.speculative.n_ctx == 0 ? params.n_ctx / params.n_parallel : params.speculative.n_ctx;
+            params_dft.n_gpu_layers = params.speculative.n_gpu_layers;
+            params_dft.n_parallel   = 1;
+
+            llama_init_dft = common_init_from_params(params_dft);
+
+            model_dft = llama_init_dft.model.get();
+
+            if (model_dft == nullptr) {
+                LOG("failed to load draft model, '%s'\n", params.speculative.model.c_str());
+                return false;
+            }
+
+            if (!common_speculative_are_compatible(ctx, llama_init_dft.context.get())) {
+                LOG("the draft model '%s' is not compatible with the target model '%s'\n", params.speculative.model.c_str(), params.model.c_str());
+
+                return false;
+            }
+
+            const int n_ctx_dft = llama_n_ctx(llama_init_dft.context.get());
+
+            cparams_dft = common_context_params_to_llama(params_dft);
+            cparams_dft.n_batch = n_ctx_dft;
+
+            // force F16 KV cache for the draft model for extra performance
+            cparams_dft.type_k = GGML_TYPE_F16;
+            cparams_dft.type_v = GGML_TYPE_F16;
+
+            // the context is not needed - we will create one for each slot
+            llama_init_dft.context.reset();
+        }
 
         return true;
     }
 
-    void validate_model_chat_template(server_params & sparams) {
-        llama_chat_message chat[] = {{"user", "test"}};
-        std::vector<char> buf(1);
-        int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size());
-        if (res < 0) {
-            LOG_ERR("The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", __func__);
-            sparams.chat_template = "<|im_start|>"; // llama_chat_apply_template only checks if <|im_start|> exist in the template
-        }
-    }
-
     llama_client_slot* get_active_slot() {
         for (llama_client_slot& slot : slots) {
             // Check if the slot is currently processing
@@ -553,6 +652,22 @@ struct llama_server_context
             slot.n_ctx = n_ctx_slot;
             slot.n_predict = params.n_predict;
 
+            if (model_dft) {
+                slot.batch_spec = llama_batch_init(params.speculative.n_max + 1, 0, 1);
+
+                ctx_dft = llama_init_from_model(model_dft, cparams_dft);
+                if (ctx_dft == nullptr) {
+                    LOG("%s", "failed to create draft context\n");
+                    return;
+                }
+
+                slot.spec = common_speculative_init(ctx_dft);
+                if (slot.spec == nullptr) {
+                    LOG("%s", "failed to create speculator\n");
+                    return;
+                }
+            }
+
             LOG_INFO("new slot", {
                 {"slot_id",    slot.id},
                 {"n_ctx_slot", slot.n_ctx}
@@ -661,9 +776,11 @@ struct llama_server_context
     }
 
     bool launch_slot_with_data(llama_client_slot* &slot, json data) {
-        slot_params default_params;
+        llama_slot_params default_params;
         common_params_sampling default_sparams;
- 
+        
+        default_sparams.speculative = params_base.speculative;
+
         slot->params.stream             = json_value(data, "stream",            false);
         slot->params.cache_prompt       = json_value(data, "cache_prompt",      false);
         slot->params.n_predict          = json_value(data, "n_predict",         default_params.n_predict);
@@ -687,6 +804,15 @@ struct llama_server_context
         slot->sparams.n_probs           = json_value(data, "n_probs",           default_sparams.n_probs);
         slot->sparams.min_keep          = json_value(data, "min_keep",          default_sparams.min_keep);
 
+
+        slot->sparams.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min);
+        slot->sparams.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max);
+        slot->sparams.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min);
+
+        slot->sparams.speculative.n_min = std::min(params.speculative.n_max, params.speculative.n_min);
+        slot->sparams.speculative.n_min = std::max(params.speculative.n_min, 2);
+        slot->sparams.speculative.n_max = std::max(params.speculative.n_max, 0);
+
         if (slot->n_predict > 0 && slot->params.n_predict > slot->n_predict) {
             // Might be better to reject the request with a 400 ?
             LOG_WARNING("Max tokens to predict exceeds server configuration", {
@@ -725,8 +851,8 @@ struct llama_server_context
             slot->prompt = "";
         }
 
-        if (json_value(data, "ignore_eos", false)) {
-                slot->sparams.logit_bias.push_back({llama_token_eos(model), -INFINITY});
+        if (json_value(data, "ignore_eos", false) && has_eos_token) {
+                slot->sparams.logit_bias.push_back({llama_vocab_eos(vocab), -INFINITY});
         }
         /*
         slot->sparams.penalty_prompt_tokens.clear();
@@ -765,13 +891,13 @@ struct llama_server_context
             }
         }
       */
-
         slot->sparams.logit_bias.clear();
 
         const auto &logit_bias = data.find("logit_bias");
         if (logit_bias != data.end() && logit_bias->is_array())
         {
-            const int n_vocab = llama_n_vocab(model);
+            const llama_vocab * vocab = llama_model_get_vocab(model);
+            const int n_vocab = llama_vocab_n_tokens(vocab);
             for (const auto &el : *logit_bias)
             {
                 if (el.is_array() && el.size() == 2)
@@ -800,7 +926,7 @@ struct llama_server_context
                     }
                     else if (el[0].is_string())
                     {
-                        auto toks = common_tokenize(model, el[0].get<std::string>(), false);
+                        auto toks = common_tokenize(vocab, el[0].get<std::string>(), false);
                         for (auto tok : toks)
                         {
                             slot->sparams.logit_bias.push_back({tok, bias});
@@ -1130,7 +1256,7 @@ struct llama_server_context
             slot.has_next_token = false;
         }
 
-        if (result.tok == llama_token_eos(model))
+        if (result.tok == llama_vocab_eos(vocab) || llama_vocab_is_eog(vocab, result.tok))
         {
             slot.stopped_eos = true;
             slot.has_next_token = false;
@@ -1325,7 +1451,7 @@ struct llama_server_context
         res.error = false;
         res.stop = true;
 
-        const int n_embd = llama_n_embd(model);
+        const int n_embd = llama_model_n_embd(model);
         if (!params.embedding)
         {
             LOG_WARNING("embedding disabled", {
@@ -1424,7 +1550,7 @@ struct llama_server_context
                     n_eval = n_batch;
                 }
 
-                const int n_embd = llama_n_embd(model);
+                const int n_embd = llama_model_n_embd(model);
                 float * embd = img.image_embedding + i * n_embd;
                 llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, slot.n_past, 0);
                 if (llama_decode(ctx, llava_batch.batch))
@@ -1705,11 +1831,11 @@ struct llama_server_context
                             suffix_tokens.erase(suffix_tokens.begin());
                         }
 
-                        prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
-                        prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
-                        prefix_tokens.insert(prefix_tokens.end(),   llama_token_suffix(model));
+                        prefix_tokens.insert(prefix_tokens.begin(), llama_vocab_fim_pre(vocab));
+                        prefix_tokens.insert(prefix_tokens.begin(), llama_vocab_bos(vocab)); // always add BOS
+                        prefix_tokens.insert(prefix_tokens.end(),   llama_vocab_fim_suf(vocab));
                         prefix_tokens.insert(prefix_tokens.end(),   suffix_tokens.begin(), suffix_tokens.end());
-                        prefix_tokens.push_back(llama_token_middle(model));
+                        prefix_tokens.push_back(llama_vocab_fim_mid(vocab));
                         prompt_tokens = prefix_tokens;
                     }
                     else
@@ -2004,6 +2130,97 @@ struct llama_server_context
             }
         }
 
+        // do speculative decoding
+        for (auto & slot : slots) {
+            if (!slot.is_processing() || !(ctx_dft && params.speculative.n_max > 0)) {
+                continue;
+            }
+
+            if (slot.state != PROCESSING) {
+                continue;
+            }
+
+            // determine the max draft that fits the current slot state
+            int n_draft_max = slot.params.speculative.n_max;
+
+            // note: n_past is not yet increased for the `id` token sampled above
+            //       also, need to leave space for 1 extra token to allow context shifts
+            n_draft_max = std::min(n_draft_max, slot.n_ctx - slot.n_past - 2);
+
+            if (slot.n_remaining > 0) {
+                n_draft_max = std::min(n_draft_max, slot.n_remaining - 1);
+            }
+
+            LOG("max possible draft: %d\n", n_draft_max);
+
+            if (n_draft_max < slot.params.speculative.n_min) {
+                LOG("the max possible draft is too small: %d < %d - skipping speculative decoding\n", n_draft_max, slot.params.speculative.n_min);
+
+                continue;
+            }
+
+            llama_token id = slot.sampled;
+
+            struct common_speculative_params params_spec;
+            params_spec.n_draft   = n_draft_max;
+            params_spec.n_reuse   = llama_n_ctx(ctx_dft) - slot.params.speculative.n_max;
+            params_spec.p_min     = slot.params.speculative.p_min;
+
+            llama_tokens draft = common_speculative_gen_draft(slot.spec, params_spec, slot.cache_tokens, id);
+
+            // ignore small drafts
+            if (slot.params.speculative.n_min > (int) draft.size()) {
+                LOG("ignoring small draft: %d < %d\n", (int) draft.size(), slot.params.speculative.n_min);
+
+                continue;
+            }
+
+            // construct the speculation batch
+            common_batch_clear(slot.batch_spec);
+            common_batch_add  (slot.batch_spec, id, slot.n_past, { slot.id }, true);
+
+            for (size_t i = 0; i < draft.size(); ++i) {
+                common_batch_add(slot.batch_spec, draft[i], slot.n_past + 1 + i, { slot.id }, true);
+            }
+
+            LOG("decoding speculative batch, size = %d\n", slot.batch_spec.n_tokens);
+
+            llama_decode(ctx, slot.batch_spec);
+
+            // the accepted tokens from the speculation
+            const auto ids = common_sampler_sample_and_accept_n(slot.ctx_sampling, ctx, draft);
+
+            slot.n_past    += ids.size();
+            slot.n_decoded += ids.size();
+
+            slot.cache_tokens.push_back(id);
+            slot.cache_tokens.insert(slot.cache_tokens.end(), ids.begin(), ids.end() - 1);
+
+            llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1);
+
+            for (size_t i = 0; i < ids.size(); ++i) {
+                completion_token_output result;
+
+                result.tok          = ids[i];
+                result.text_to_send = common_token_to_piece(ctx, result.tok, params.special);
+                //result.prob         = 1.0f; // set later
+
+                // TODO: set result.probs
+
+                if (!process_token(result, slot)) {
+                    // release slot because of stop condition
+                    slot.release();
+                    slot.print_timings();
+                    send_final_response(slot);
+                    metrics.on_prediction(slot);
+                    break;
+                }
+            }
+
+        LOG("accepted %d/%d draft tokens, new n_past = %d\n", (int) ids.size() - 1, (int) draft.size(), slot.n_past);
+        }
+    
+
         LOG_VERBOSE("slots updated", {});
         return true;
     }
@@ -2276,6 +2493,30 @@ static void params_parse(const backend::ModelOptions* request,
     params.cpuparams.n_threads = request->threads();
     params.n_gpu_layers = request->ngpulayers();
     params.n_batch = request->nbatch();
+    params.speculative.model = request->draftmodel();
+
+    // If options is not NULL, parse options
+    for (int i = 0; request->options()[i] != NULL; i++) {
+        char *optname = strtok(request->options()[i], ":");
+        char *optval = strtok(NULL, ":");
+        if (optval == NULL) {
+            optval = "true";
+        }
+
+        if (!strcmp(optname, "speculative.n_gpu_layers")) {
+            params.speculative.n_gpu_layers = std::stoi(optval);
+        }
+        if (!strcmp(optname, "speculative.n_ctx")) {
+            params.speculative.n_ctx = std::stoi(optval);
+        }
+    }
+
+    if params.speculative.n_gpu_layers == 0 {
+        params.speculative.n_gpu_layers = params.n_gpu_layers;
+    }
+    if params.speculative.n_ctx == 0 {
+        params.speculative.n_ctx = params.n_ctx;
+    }
     // Set params.n_parallel by environment variable (LLAMA_PARALLEL), defaults to 1
     //params.n_parallel = 1;
     const char *env_parallel = std::getenv("LLAMACPP_PARALLEL");
@@ -2288,7 +2529,7 @@ static void params_parse(const backend::ModelOptions* request,
 
     const char *llama_grpc_servers = std::getenv("LLAMACPP_GRPC_SERVERS");
     if (llama_grpc_servers != NULL) {
-        params.rpc_servers = std::string(llama_grpc_servers);
+        add_rpc_devices(std::string(llama_grpc_servers));
     }
     
     // TODO: Add yarn
@@ -2414,6 +2655,13 @@ public:
                 int32_t tokens_evaluated = result.result_json.value("tokens_evaluated", 0);
                 reply.set_prompt_tokens(tokens_evaluated);
 
+                if (result.result_json.contains("timings")) {
+                    double timing_prompt_processing = result.result_json.at("timings").value("prompt_ms", 0.0);
+                    reply.set_timing_prompt_processing(timing_prompt_processing);
+                    double timing_token_generation = result.result_json.at("timings").value("predicted_ms", 0.0);
+                    reply.set_timing_token_generation(timing_token_generation);
+                }
+                
                 // Log Request Correlation Id
                 LOG_VERBOSE("correlation:", {
                     { "id", data["correlation_id"] }
@@ -2454,6 +2702,13 @@ public:
             reply->set_prompt_tokens(tokens_evaluated);
             reply->set_tokens(tokens_predicted);
             reply->set_message(completion_text);
+
+            if (result.result_json.contains("timings")) {
+                double timing_prompt_processing = result.result_json.at("timings").value("prompt_ms", 0.0);
+                reply->set_timing_prompt_processing(timing_prompt_processing);
+                double timing_token_generation = result.result_json.at("timings").value("predicted_ms", 0.0);
+                reply->set_timing_token_generation(timing_token_generation);
+            }
         }
         else
         {

backend/go/image/stablediffusion-ggml/Makefile

@@ -2,20 +2,95 @@ INCLUDE_PATH := $(abspath ./)
 LIBRARY_PATH := $(abspath ./)
 
 AR?=ar
-
+CMAKE_ARGS?=
 BUILD_TYPE?=
+ONEAPI_VARS?=/opt/intel/oneapi/setvars.sh
 # keep standard at C11 and C++11
 CXXFLAGS = -I. -I$(INCLUDE_PATH)/../../../../sources/stablediffusion-ggml.cpp/thirdparty -I$(INCLUDE_PATH)/../../../../sources/stablediffusion-ggml.cpp/ggml/include -I$(INCLUDE_PATH)/../../../../sources/stablediffusion-ggml.cpp -O3 -DNDEBUG -std=c++17 -fPIC
 
+# Disable Shared libs as we are linking on static gRPC and we can't mix shared and static
+CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF
+
+# If build type is cublas, then we set -DGGML_CUDA=ON to CMAKE_ARGS automatically
+ifeq ($(BUILD_TYPE),cublas)
+	CMAKE_ARGS+=-DGGML_CUDA=ON
+# If build type is openblas then we set -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
+# to CMAKE_ARGS automatically
+else ifeq ($(BUILD_TYPE),openblas)
+	CMAKE_ARGS+=-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
+# If build type is clblas (openCL) we set -DGGML_CLBLAST=ON -DCLBlast_DIR=/some/path
+else ifeq ($(BUILD_TYPE),clblas)
+	CMAKE_ARGS+=-DGGML_CLBLAST=ON -DCLBlast_DIR=/some/path
+# If it's hipblas we do have also to set CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ 
+else ifeq ($(BUILD_TYPE),hipblas)
+	CMAKE_ARGS+=-DGGML_HIP=ON
+# If it's OSX, DO NOT embed the metal library - -DGGML_METAL_EMBED_LIBRARY=ON requires further investigation
+# But if it's OSX without metal, disable it here
+else ifeq ($(OS),Darwin)
+	ifneq ($(BUILD_TYPE),metal)
+		CMAKE_ARGS+=-DGGML_METAL=OFF
+	else
+		CMAKE_ARGS+=-DGGML_METAL=ON
+		CMAKE_ARGS+=-DGGML_METAL_EMBED_LIBRARY=ON
+		TARGET+=--target ggml-metal
+	endif
+endif
+
+# ifeq ($(BUILD_TYPE),sycl_f16)
+# 	CMAKE_ARGS+=-DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON -DSD_SYCL=ON -DGGML_SYCL_F16=ON
+# endif
+
+# ifeq ($(BUILD_TYPE),sycl_f32)
+# 	CMAKE_ARGS+=-DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DSD_SYCL=ON
+# endif
+
 # warnings
 CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function
 
+# Find all .a archives in ARCHIVE_DIR
+# (ggml can have different backends cpu, cuda, etc., each backend generates a .a archive)
+GGML_ARCHIVE_DIR := build/ggml/src/
+ALL_ARCHIVES := $(shell find $(GGML_ARCHIVE_DIR) -type f -name '*.a')
+
+# Name of the single merged library
+COMBINED_LIB := libggmlall.a
+
+# Rule to merge all the .a files into one
+$(COMBINED_LIB): $(ALL_ARCHIVES)
+	@echo "Merging all .a into $(COMBINED_LIB)"
+	rm -f $@
+	mkdir -p merge-tmp
+	for a in $(ALL_ARCHIVES); do \
+		( cd merge-tmp && ar x ../$$a ); \
+	done
+	( cd merge-tmp && ar rcs ../$@ *.o )
+	# Ensure we have a proper index
+	ranlib $@
+	# Clean up
+	rm -rf merge-tmp
+
+build/libstable-diffusion.a:
+	@echo "Building SD with $(BUILD_TYPE) build type and $(CMAKE_ARGS)"
+ifneq (,$(findstring sycl,$(BUILD_TYPE)))
+	+bash -c "source $(ONEAPI_VARS); \
+	mkdir -p build && \
+	cd build && \
+	cmake $(CMAKE_ARGS) ../../../../../sources/stablediffusion-ggml.cpp && \
+	cmake --build . --config Release"
+else
+	mkdir -p build && \
+	cd build && \
+	cmake $(CMAKE_ARGS) ../../../../../sources/stablediffusion-ggml.cpp && \
+	cmake --build . --config Release
+endif
+	$(MAKE) $(COMBINED_LIB)
+
 gosd.o:
 	$(CXX) $(CXXFLAGS) gosd.cpp -o gosd.o -c
 
 libsd.a: gosd.o
-	cp $(INCLUDE_PATH)/../../../../sources/stablediffusion-ggml.cpp/build/libstable-diffusion.a ./libsd.a
+	cp $(INCLUDE_PATH)/build/libstable-diffusion.a ./libsd.a
 	$(AR) rcs libsd.a gosd.o
 
 clean:
-	rm -f gosd.o libsd.a
+	rm -rf gosd.o libsd.a build $(COMBINED_LIB)

backend/go/image/stablediffusion-ggml/gosd.go

@@ -1,7 +1,7 @@
 package main
 
 // #cgo CXXFLAGS: -I${SRCDIR}/../../../../sources/stablediffusion-ggml.cpp/thirdparty -I${SRCDIR}/../../../../sources/stablediffusion-ggml.cpp -I${SRCDIR}/../../../../sources/stablediffusion-ggml.cpp/ggml/include
-// #cgo LDFLAGS: -L${SRCDIR}/ -L${SRCDIR}/../../../../sources/stablediffusion-ggml.cpp/build/ggml/src/ggml-cpu -L${SRCDIR}/../../../../sources/stablediffusion-ggml.cpp/build/ggml/src -lsd -lstdc++ -lm -lggml -lggml-base -lggml-cpu -lgomp
+// #cgo LDFLAGS: -L${SRCDIR}/ -lsd -lstdc++ -lm -lggmlall -lgomp
 // #include <gosd.h>
 // #include <stdlib.h>
 import "C"

backend/go/image/stablediffusion/main.go

@@ -1,21 +0,0 @@
-package main
-
-// Note: this is started internally by LocalAI and a server is allocated for each model
-
-import (
-	"flag"
-
-	grpc "github.com/mudler/LocalAI/pkg/grpc"
-)
-
-var (
-	addr = flag.String("addr", "localhost:50051", "the address to connect to")
-)
-
-func main() {
-	flag.Parse()
-
-	if err := grpc.StartServer(*addr, &Image{}); err != nil {
-		panic(err)
-	}
-}

backend/go/image/stablediffusion/stablediffusion.go

@@ -1,33 +0,0 @@
-package main
-
-// This is a wrapper to statisfy the GRPC service interface
-// It is meant to be used by the main executable that is the server for the specific backend type (falcon, gpt3, etc)
-import (
-	"github.com/mudler/LocalAI/pkg/grpc/base"
-	pb "github.com/mudler/LocalAI/pkg/grpc/proto"
-	"github.com/mudler/LocalAI/pkg/stablediffusion"
-)
-
-type Image struct {
-	base.SingleThread
-	stablediffusion *stablediffusion.StableDiffusion
-}
-
-func (image *Image) Load(opts *pb.ModelOptions) error {
-	var err error
-	// Note: the Model here is a path to a directory containing the model files
-	image.stablediffusion, err = stablediffusion.New(opts.ModelFile)
-	return err
-}
-
-func (image *Image) GenerateImage(opts *pb.GenerateImageRequest) error {
-	return image.stablediffusion.GenerateImage(
-		int(opts.Height),
-		int(opts.Width),
-		int(opts.Mode),
-		int(opts.Step),
-		int(opts.Seed),
-		opts.PositivePrompt,
-		opts.NegativePrompt,
-		opts.Dst)
-}

backend/go/image/tinydream/main.go

@@ -1,21 +0,0 @@
-package main
-
-// Note: this is started internally by LocalAI and a server is allocated for each model
-
-import (
-	"flag"
-
-	grpc "github.com/mudler/LocalAI/pkg/grpc"
-)
-
-var (
-	addr = flag.String("addr", "localhost:50051", "the address to connect to")
-)
-
-func main() {
-	flag.Parse()
-
-	if err := grpc.StartServer(*addr, &Image{}); err != nil {
-		panic(err)
-	}
-}

backend/go/image/tinydream/tinydream.go

@@ -1,32 +0,0 @@
-package main
-
-// This is a wrapper to statisfy the GRPC service interface
-// It is meant to be used by the main executable that is the server for the specific backend type (falcon, gpt3, etc)
-import (
-	"github.com/mudler/LocalAI/pkg/grpc/base"
-	pb "github.com/mudler/LocalAI/pkg/grpc/proto"
-	"github.com/mudler/LocalAI/pkg/tinydream"
-)
-
-type Image struct {
-	base.SingleThread
-	tinydream *tinydream.TinyDream
-}
-
-func (image *Image) Load(opts *pb.ModelOptions) error {
-	var err error
-	// Note: the Model here is a path to a directory containing the model files
-	image.tinydream, err = tinydream.New(opts.ModelFile)
-	return err
-}
-
-func (image *Image) GenerateImage(opts *pb.GenerateImageRequest) error {
-	return image.tinydream.GenerateImage(
-		int(opts.Height),
-		int(opts.Width),
-		int(opts.Step),
-		int(opts.Seed),
-		opts.PositivePrompt,
-		opts.NegativePrompt,
-		opts.Dst)
-}

backend/go/stores/store.go

@@ -311,12 +311,16 @@ func (s *Store) StoresGet(opts *pb.StoresGetOptions) (pb.StoresGetResult, error)
 }
 
 func isNormalized(k []float32) bool {
-	var sum float32
+	var sum float64
+
 	for _, v := range k {
-		sum += v
+		v64 := float64(v)
+		sum += v64*v64
 	}
 
-	return sum == 1.0
+	s := math.Sqrt(sum)
+
+	return s >= 0.99 && s <= 1.01
 }
 
 // TODO: This we could replace with handwritten SIMD code
@@ -328,7 +332,7 @@ func normalizedCosineSimilarity(k1, k2 []float32) float32 {
 		dot += k1[i] * k2[i]
 	}
 
-	assert(dot >= -1 && dot <= 1, fmt.Sprintf("dot = %f", dot))
+	assert(dot >= -1.01 && dot <= 1.01, fmt.Sprintf("dot = %f", dot))
 
 	// 2.0 * (1.0 - dot) would be the Euclidean distance
 	return dot
@@ -418,7 +422,7 @@ func cosineSimilarity(k1, k2 []float32, mag1 float64) float32 {
 
 	sim := float32(dot / (mag1 * math.Sqrt(mag2)))
 
-	assert(sim >= -1 && sim <= 1, fmt.Sprintf("sim = %f", sim))
+	assert(sim >= -1.01 && sim <= 1.01, fmt.Sprintf("sim = %f", sim))
 
 	return sim
 }

backend/python/diffusers/backend.py

@@ -17,7 +17,7 @@ import backend_pb2_grpc
 
 import grpc
 
-from diffusers import StableDiffusion3Pipeline, StableDiffusionXLPipeline, StableDiffusionDepth2ImgPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline, DiffusionPipeline, \
+from diffusers import SanaPipeline, StableDiffusion3Pipeline, StableDiffusionXLPipeline, StableDiffusionDepth2ImgPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline, DiffusionPipeline, \
     EulerAncestralDiscreteScheduler, FluxPipeline, FluxTransformer2DModel
 from diffusers import StableDiffusionImg2ImgPipeline, AutoPipelineForText2Image, ControlNetModel, StableVideoDiffusionPipeline
 from diffusers.pipelines.stable_diffusion import safety_checker
@@ -275,6 +275,13 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
 
                     if request.LowVRAM:
                         self.pipe.enable_model_cpu_offload()
+            elif request.PipelineType == "SanaPipeline":
+                self.pipe = SanaPipeline.from_pretrained(
+                    request.Model,
+                    variant="bf16",
+                    torch_dtype=torch.bfloat16)
+                self.pipe.vae.to(torch.bfloat16)
+                self.pipe.text_encoder.to(torch.bfloat16)
 
             if CLIPSKIP and request.CLIPSkip != 0:
                 self.clip_skip = request.CLIPSkip

backend/python/faster-whisper/Makefile

@@ -1,8 +1,9 @@
 .DEFAULT_GOAL := install
 
 .PHONY: install
-install: protogen
+install:
 	bash install.sh
+	$(MAKE) protogen
 
 .PHONY: protogen
 protogen: backend_pb2_grpc.py backend_pb2.py
@@ -12,14 +13,8 @@ protogen-clean:
 	$(RM) backend_pb2_grpc.py backend_pb2.py
 
 backend_pb2_grpc.py backend_pb2.py:
-	python3 -m grpc_tools.protoc -I../.. --python_out=. --grpc_python_out=. backend.proto
+	bash protogen.sh
 
 .PHONY: clean
 clean: protogen-clean
-	rm -rf venv __pycache__
-
-.PHONY: test
-test: protogen
-	@echo "Testing openvoice..."
-	bash test.sh
-	@echo "openvoice tested."
+	rm -rf venv __pycache__

backend/python/faster-whisper/backend.py

@@ -1,85 +1,65 @@
 #!/usr/bin/env python3
 """
-Extra gRPC server for HuggingFace SentenceTransformer models.
+This is an extra gRPC server of LocalAI for Bark TTS
 """
 from concurrent import futures
-
+import time
 import argparse
 import signal
 import sys
 import os
-
-import time
 import backend_pb2
 import backend_pb2_grpc
 
+from faster_whisper import WhisperModel
+
 import grpc
 
-from sentence_transformers import SentenceTransformer
 
 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
 
 # If MAX_WORKERS are specified in the environment use it, otherwise default to 1
 MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
+COQUI_LANGUAGE = os.environ.get('COQUI_LANGUAGE', None)
 
 # Implement the BackendServicer class with the service methods
 class BackendServicer(backend_pb2_grpc.BackendServicer):
     """
-    A gRPC servicer for the backend service.
-
-    This class implements the gRPC methods for the backend service, including Health, LoadModel, and Embedding.
+    BackendServicer is the class that implements the gRPC service
     """
     def Health(self, request, context):
-        """
-        A gRPC method that returns the health status of the backend service.
-
-        Args:
-            request: A HealthRequest object that contains the request parameters.
-            context: A grpc.ServicerContext object that provides information about the RPC.
-
-        Returns:
-            A Reply object that contains the health status of the backend service.
-        """
         return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
-
     def LoadModel(self, request, context):
-        """
-        A gRPC method that loads a model into memory.
+        device = "cpu"
+        # Get device
+        # device = "cuda" if request.CUDA else "cpu"
+        if request.CUDA:
+            device = "cuda"
 
-        Args:
-            request: A LoadModelRequest object that contains the request parameters.
-            context: A grpc.ServicerContext object that provides information about the RPC.
-
-        Returns:
-            A Result object that contains the result of the LoadModel operation.
-        """
-        model_name = request.Model
         try:
-            self.model = SentenceTransformer(model_name, trust_remote_code=request.TrustRemoteCode)
+            print("Preparing models, please wait", file=sys.stderr)
+            self.model = WhisperModel(request.Model, device=device, compute_type="float16")
         except Exception as err:
             return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
-
         # Implement your logic here for the LoadModel service
         # Replace this with your desired response
         return backend_pb2.Result(message="Model loaded successfully", success=True)
 
-    def Embedding(self, request, context):
-        """
-        A gRPC method that calculates embeddings for a given sentence.
-
-        Args:
-            request: An EmbeddingRequest object that contains the request parameters.
-            context: A grpc.ServicerContext object that provides information about the RPC.
-
-        Returns:
-            An EmbeddingResult object that contains the calculated embeddings.
-        """
-        # Implement your logic here for the Embedding service
-        # Replace this with your desired response
-        print("Calculated embeddings for: " + request.Embeddings, file=sys.stderr)
-        sentence_embeddings = self.model.encode(request.Embeddings)
-        return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings)
+    def AudioTranscription(self, request, context):
+        resultSegments = []
+        text = ""
+        try:
+            segments, info = self.model.transcribe(request.dst, beam_size=5, condition_on_previous_text=False)
+            id = 0
+            for segment in segments:
+                print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
+                resultSegments.append(backend_pb2.TranscriptSegment(id=id, start=segment.start, end=segment.end, text=segment.text))
+                text += segment.text
+                id += 1            
+        except Exception as err:
+            print(f"Unexpected {err=}, {type(err)=}", file=sys.stderr)
 
+        return backend_pb2.TranscriptResult(segments=resultSegments, text=text)
 
 def serve(address):
     server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))

backend/python/faster-whisper/requirements-cpu.txt

@@ -0,0 +1,8 @@
+faster-whisper
+opencv-python
+accelerate
+compel
+peft
+sentencepiece
+torch==2.4.1
+optimum-quanto

backend/python/faster-whisper/requirements-cublas11.txt

@@ -1,4 +1,9 @@
 --extra-index-url https://download.pytorch.org/whl/cu118
-accelerate
 torch==2.4.1+cu118
-torchaudio==2.4.1+cu118
+faster-whisper
+opencv-python
+accelerate
+compel
+peft
+sentencepiece
+optimum-quanto

backend/python/faster-whisper/requirements-cublas12.txt

@@ -0,0 +1,8 @@
+torch==2.4.1
+faster-whisper
+opencv-python
+accelerate
+compel
+peft
+sentencepiece
+optimum-quanto

backend/python/faster-whisper/requirements-hipblas.txt

@@ -1,4 +1,3 @@
 --extra-index-url https://download.pytorch.org/whl/rocm6.0
-transformers
-accelerate
-torch==2.4.1+rocm6.0
+torch
+faster-whisper

backend/python/faster-whisper/requirements-intel.txt

@@ -1,8 +1,6 @@
 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 intel-extension-for-pytorch==2.3.110+xpu
-transformers
-oneccl_bind_pt==2.3.100+xpu
-accelerate
 torch==2.3.1+cxx11.abi
+oneccl_bind_pt==2.3.100+xpu
 optimum[openvino]
-setuptools
+faster-whisper

backend/python/faster-whisper/requirements.txt

@@ -1,3 +1,3 @@
 grpcio==1.69.0
 protobuf
-certifi
+grpcio-tools

backend/python/kokoro/Makefile

@@ -0,0 +1,20 @@
+.DEFAULT_GOAL := install
+
+.PHONY: install
+install:
+	bash install.sh
+	$(MAKE) protogen
+
+.PHONY: protogen
+protogen: backend_pb2_grpc.py backend_pb2.py
+
+.PHONY: protogen-clean
+protogen-clean:
+	$(RM) backend_pb2_grpc.py backend_pb2.py
+
+backend_pb2_grpc.py backend_pb2.py:
+	bash protogen.sh
+
+.PHONY: clean
+clean: protogen-clean
+	rm -rf venv __pycache__

backend/python/kokoro/backend.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Extra gRPC server for MusicgenForConditionalGeneration models.
+Extra gRPC server for Kokoro models.
 """
 from concurrent import futures
 
@@ -8,20 +8,17 @@ import argparse
 import signal
 import sys
 import os
-
 import time
 import backend_pb2
 import backend_pb2_grpc
-
+import soundfile as sf
 import grpc
 
-from scipy.io.wavfile import write as write_wav
-
-from parler_tts import ParlerTTSForConditionalGeneration
-from transformers import AutoTokenizer
-import soundfile as sf  
+from models import build_model
+from kokoro import generate
 import torch
 
+SAMPLE_RATE = 22050
 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
 
 # If MAX_WORKERS are specified in the environment use it, otherwise default to 1
@@ -59,10 +56,31 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             A Result object that contains the result of the LoadModel operation.
         """
         model_name = request.Model
-        device = "cuda:0" if torch.cuda.is_available() else "cpu"
         try:
-            self.model = ParlerTTSForConditionalGeneration.from_pretrained(model_name).to(device)
-            self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+            device = "cuda:0" if torch.cuda.is_available() else "cpu"
+            self.MODEL = build_model(request.ModelFile, device)
+            options = request.Options
+            # Find the voice from the options, options are a list of strings in this form optname:optvalue:
+            VOICE_NAME = None
+            for opt in options:
+                if opt.startswith("voice:"):
+                    VOICE_NAME = opt.split(":")[1]
+                    break
+            if VOICE_NAME is None:
+                return backend_pb2.Result(success=False, message=f"No voice specified in options")
+            MODELPATH = request.ModelPath
+            # If voice name contains a plus, split it and load the two models and combine them
+            if "+" in VOICE_NAME:
+                voice1, voice2 = VOICE_NAME.split("+")
+                voice1 = torch.load(f'{MODELPATH}/{voice1}.pt', weights_only=True).to(device)
+                voice2 = torch.load(f'{MODELPATH}/{voice2}.pt', weights_only=True).to(device)
+                self.VOICEPACK = torch.mean(torch.stack([voice1, voice2]), dim=0)
+            else:
+                self.VOICEPACK = torch.load(f'{MODELPATH}/{VOICE_NAME}.pt', weights_only=True).to(device)
+
+            self.VOICE_NAME = VOICE_NAME
+
+            print(f'Loaded voice: {VOICE_NAME}')
         except Exception as err:
             return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
 
@@ -70,38 +88,26 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
 
     def TTS(self, request, context):
         model_name = request.model
-        voice = request.voice
-        if voice == "":
-            voice = "A female speaker with a slightly low-pitched voice delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks very fast."
         if model_name == "":
             return backend_pb2.Result(success=False, message="request.model is required")
         try:
-            device = "cuda:0" if torch.cuda.is_available() else "cpu"
-            input_ids = self.tokenizer(voice, return_tensors="pt").input_ids.to(device)
-            prompt_input_ids = self.tokenizer(request.text, return_tensors="pt").input_ids.to(device)
-           
-            generation = self.model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
-            audio_arr = generation.cpu().numpy().squeeze()
-            print("[parler-tts] TTS generated!", file=sys.stderr)
-            sf.write(request.dst, audio_arr, self.model.config.sampling_rate)
-            print("[parler-tts] TTS saved to", request.dst, file=sys.stderr)
-            print("[parler-tts] TTS for", file=sys.stderr)
-            print(request, file=sys.stderr)
+            audio, out_ps = generate(self.MODEL, request.text, self.VOICEPACK, lang=self.VOICE_NAME)
+            print(out_ps)
+            sf.write(request.dst, audio, SAMPLE_RATE)
         except Exception as err:
             return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
         return backend_pb2.Result(success=True)
 
-
 def serve(address):
     server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
     backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
     server.add_insecure_port(address)
     server.start()
-    print("[parler-tts] Server started. Listening on: " + address, file=sys.stderr)
+    print("[Kokoro] Server started. Listening on: " + address, file=sys.stderr)
 
     # Define the signal handler function
     def signal_handler(sig, frame):
-        print("[parler-tts] Received termination signal. Shutting down...")
+        print("[Kokoro] Received termination signal. Shutting down...")
         server.stop(0)
         sys.exit(0)
 
@@ -121,5 +127,5 @@ if __name__ == "__main__":
         "--addr", default="localhost:50051", help="The address to bind the server to."
     )
     args = parser.parse_args()
-    print(f"[parler-tts] startup: {args}", file=sys.stderr)
+    print(f"[Kokoro] startup: {args}", file=sys.stderr)
     serve(args.addr)

backend/python/kokoro/istftnet.py

@@ -0,0 +1,524 @@
+# https://huggingface.co/hexgrad/Kokoro-82M/blob/main/istftnet.py
+# https://github.com/yl4579/StyleTTS2/blob/main/Modules/istftnet.py
+from scipy.signal import get_window
+from torch.nn import Conv1d, ConvTranspose1d
+from torch.nn.utils import weight_norm, remove_weight_norm
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# https://github.com/yl4579/StyleTTS2/blob/main/Modules/utils.py
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size*dilation - dilation)/2)
+
+LRELU_SLOPE = 0.1
+
+class AdaIN1d(nn.Module):
+    def __init__(self, style_dim, num_features):
+        super().__init__()
+        self.norm = nn.InstanceNorm1d(num_features, affine=False)
+        self.fc = nn.Linear(style_dim, num_features*2)
+
+    def forward(self, x, s):
+        h = self.fc(s)
+        h = h.view(h.size(0), h.size(1), 1)
+        gamma, beta = torch.chunk(h, chunks=2, dim=1)
+        return (1 + gamma) * self.norm(x) + beta
+
+class AdaINResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), style_dim=64):
+        super(AdaINResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+                               padding=get_padding(kernel_size, dilation[2])))
+        ])
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1)))
+        ])
+        self.convs2.apply(init_weights)
+        
+        self.adain1 = nn.ModuleList([
+            AdaIN1d(style_dim, channels),
+            AdaIN1d(style_dim, channels),
+            AdaIN1d(style_dim, channels),
+        ])
+        
+        self.adain2 = nn.ModuleList([
+            AdaIN1d(style_dim, channels),
+            AdaIN1d(style_dim, channels),
+            AdaIN1d(style_dim, channels),
+        ])
+        
+        self.alpha1 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs1))])
+        self.alpha2 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs2))])
+
+
+    def forward(self, x, s):
+        for c1, c2, n1, n2, a1, a2 in zip(self.convs1, self.convs2, self.adain1, self.adain2, self.alpha1, self.alpha2):
+            xt = n1(x, s)
+            xt = xt + (1 / a1) * (torch.sin(a1 * xt) ** 2)  # Snake1D
+            xt = c1(xt)
+            xt = n2(xt, s)
+            xt = xt + (1 / a2) * (torch.sin(a2 * xt) ** 2)  # Snake1D
+            xt = c2(xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+            
+class TorchSTFT(torch.nn.Module):
+    def __init__(self, filter_length=800, hop_length=200, win_length=800, window='hann'):
+        super().__init__()
+        self.filter_length = filter_length
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.window = torch.from_numpy(get_window(window, win_length, fftbins=True).astype(np.float32))
+
+    def transform(self, input_data):
+        forward_transform = torch.stft(
+            input_data,
+            self.filter_length, self.hop_length, self.win_length, window=self.window.to(input_data.device),
+            return_complex=True)
+
+        return torch.abs(forward_transform), torch.angle(forward_transform)
+
+    def inverse(self, magnitude, phase):
+        inverse_transform = torch.istft(
+            magnitude * torch.exp(phase * 1j),
+            self.filter_length, self.hop_length, self.win_length, window=self.window.to(magnitude.device))
+
+        return inverse_transform.unsqueeze(-2)  # unsqueeze to stay consistent with conv_transpose1d implementation
+
+    def forward(self, input_data):
+        self.magnitude, self.phase = self.transform(input_data)
+        reconstruction = self.inverse(self.magnitude, self.phase)
+        return reconstruction
+    
+class SineGen(torch.nn.Module):
+    """ Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(self, samp_rate, upsample_scale, harmonic_num=0,
+                 sine_amp=0.1, noise_std=0.003,
+                 voiced_threshold=0,
+                 flag_for_pulse=False):
+        super(SineGen, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.dim = self.harmonic_num + 1
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+        self.flag_for_pulse = flag_for_pulse
+        self.upsample_scale = upsample_scale
+
+    def _f02uv(self, f0):
+        # generate uv signal
+        uv = (f0 > self.voiced_threshold).type(torch.float32)
+        return uv
+
+    def _f02sine(self, f0_values):
+        """ f0_values: (batchsize, length, dim)
+            where dim indicates fundamental tone and overtones
+        """
+        # convert to F0 in rad. The interger part n can be ignored
+        # because 2 * np.pi * n doesn't affect phase
+        rad_values = (f0_values / self.sampling_rate) % 1
+
+        # initial phase noise (no noise for fundamental component)
+        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
+                              device=f0_values.device)
+        rand_ini[:, 0] = 0
+        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+
+        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
+        if not self.flag_for_pulse:
+#             # for normal case
+
+#             # To prevent torch.cumsum numerical overflow,
+#             # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
+#             # Buffer tmp_over_one_idx indicates the time step to add -1.
+#             # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
+#             tmp_over_one = torch.cumsum(rad_values, 1) % 1
+#             tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
+#             cumsum_shift = torch.zeros_like(rad_values)
+#             cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+
+#             phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
+            rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2), 
+                                                         scale_factor=1/self.upsample_scale, 
+                                                         mode="linear").transpose(1, 2)
+    
+#             tmp_over_one = torch.cumsum(rad_values, 1) % 1
+#             tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
+#             cumsum_shift = torch.zeros_like(rad_values)
+#             cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+    
+            phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
+            phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale, 
+                                                    scale_factor=self.upsample_scale, mode="linear").transpose(1, 2)
+            sines = torch.sin(phase)
+            
+        else:
+            # If necessary, make sure that the first time step of every
+            # voiced segments is sin(pi) or cos(0)
+            # This is used for pulse-train generation
+
+            # identify the last time step in unvoiced segments
+            uv = self._f02uv(f0_values)
+            uv_1 = torch.roll(uv, shifts=-1, dims=1)
+            uv_1[:, -1, :] = 1
+            u_loc = (uv < 1) * (uv_1 > 0)
+
+            # get the instantanouse phase
+            tmp_cumsum = torch.cumsum(rad_values, dim=1)
+            # different batch needs to be processed differently
+            for idx in range(f0_values.shape[0]):
+                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
+                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
+                # stores the accumulation of i.phase within
+                # each voiced segments
+                tmp_cumsum[idx, :, :] = 0
+                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
+
+            # rad_values - tmp_cumsum: remove the accumulation of i.phase
+            # within the previous voiced segment.
+            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
+
+            # get the sines
+            sines = torch.cos(i_phase * 2 * np.pi)
+        return sines
+
+    def forward(self, f0):
+        """ sine_tensor, uv = forward(f0)
+        input F0: tensor(batchsize=1, length, dim=1)
+                  f0 for unvoiced steps should be 0
+        output sine_tensor: tensor(batchsize=1, length, dim)
+        output uv: tensor(batchsize=1, length, 1)
+        """
+        f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
+                             device=f0.device)
+        # fundamental component
+        fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
+
+        # generate sine waveforms
+        sine_waves = self._f02sine(fn) * self.sine_amp
+
+        # generate uv signal
+        # uv = torch.ones(f0.shape)
+        # uv = uv * (f0 > self.voiced_threshold)
+        uv = self._f02uv(f0)
+
+        # noise: for unvoiced should be similar to sine_amp
+        #        std = self.sine_amp/3 -> max value ~ self.sine_amp
+        # .       for voiced regions is self.noise_std
+        noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+        noise = noise_amp * torch.randn_like(sine_waves)
+
+        # first: set the unvoiced part to 0 by uv
+        # then: additive noise
+        sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
+
+
+class SourceModuleHnNSF(torch.nn.Module):
+    """ SourceModule for hn-nsf
+    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+    harmonic_num: number of harmonic above F0 (default: 0)
+    sine_amp: amplitude of sine source signal (default: 0.1)
+    add_noise_std: std of additive Gaussian noise (default: 0.003)
+        note that amplitude of noise in unvoiced is decided
+        by sine_amp
+    voiced_threshold: threhold to set U/V given F0 (default: 0)
+    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+    F0_sampled (batchsize, length, 1)
+    Sine_source (batchsize, length, 1)
+    noise_source (batchsize, length 1)
+    uv (batchsize, length, 1)
+    """
+
+    def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0):
+        super(SourceModuleHnNSF, self).__init__()
+
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+
+        # to produce sine waveforms
+        self.l_sin_gen = SineGen(sampling_rate, upsample_scale, harmonic_num,
+                                 sine_amp, add_noise_std, voiced_threshod)
+
+        # to merge source harmonics into a single excitation
+        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+        self.l_tanh = torch.nn.Tanh()
+
+    def forward(self, x):
+        """
+        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+        F0_sampled (batchsize, length, 1)
+        Sine_source (batchsize, length, 1)
+        noise_source (batchsize, length 1)
+        """
+        # source for harmonic branch
+        with torch.no_grad():
+            sine_wavs, uv, _ = self.l_sin_gen(x)
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+
+        # source for noise branch, in the same shape as uv
+        noise = torch.randn_like(uv) * self.sine_amp / 3
+        return sine_merge, noise, uv
+def padDiff(x):
+    return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0)
+
+    
+class Generator(torch.nn.Module):
+    def __init__(self, style_dim, resblock_kernel_sizes, upsample_rates, upsample_initial_channel, resblock_dilation_sizes, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size):
+        super(Generator, self).__init__()
+
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        resblock = AdaINResBlock1
+
+        self.m_source = SourceModuleHnNSF(
+                    sampling_rate=24000,
+                    upsample_scale=np.prod(upsample_rates) * gen_istft_hop_size,
+                    harmonic_num=8, voiced_threshod=10)
+        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * gen_istft_hop_size)
+        self.noise_convs = nn.ModuleList()
+        self.noise_res = nn.ModuleList()
+        
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes,resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d, style_dim))
+                
+            c_cur = upsample_initial_channel // (2 ** (i + 1))
+            
+            if i + 1 < len(upsample_rates):  #
+                stride_f0 = np.prod(upsample_rates[i + 1:])
+                self.noise_convs.append(Conv1d(
+                    gen_istft_n_fft + 2, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+1) // 2))
+                self.noise_res.append(resblock(c_cur, 7, [1,3,5], style_dim))
+            else:
+                self.noise_convs.append(Conv1d(gen_istft_n_fft + 2, c_cur, kernel_size=1))
+                self.noise_res.append(resblock(c_cur, 11, [1,3,5], style_dim))
+                
+                
+        self.post_n_fft = gen_istft_n_fft
+        self.conv_post = weight_norm(Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+        self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
+        self.stft = TorchSTFT(filter_length=gen_istft_n_fft, hop_length=gen_istft_hop_size, win_length=gen_istft_n_fft)
+        
+        
+    def forward(self, x, s, f0):
+        with torch.no_grad():
+            f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)  # bs,n,t
+
+            har_source, noi_source, uv = self.m_source(f0)
+            har_source = har_source.transpose(1, 2).squeeze(1)
+            har_spec, har_phase = self.stft.transform(har_source)
+            har = torch.cat([har_spec, har_phase], dim=1)
+        
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            x_source = self.noise_convs[i](har)
+            x_source = self.noise_res[i](x_source, s)
+
+            x = self.ups[i](x)
+            if i == self.num_upsamples - 1:
+                x = self.reflection_pad(x)
+
+            x = x + x_source
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i*self.num_kernels+j](x, s)
+                else:
+                    xs += self.resblocks[i*self.num_kernels+j](x, s)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :])
+        phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :])
+        return self.stft.inverse(spec, phase)
+    
+    def fw_phase(self, x, s):
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i*self.num_kernels+j](x, s)
+                else:
+                    xs += self.resblocks[i*self.num_kernels+j](x, s)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.reflection_pad(x)
+        x = self.conv_post(x)
+        spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :])
+        phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :])
+        return spec, phase
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)
+
+        
+class AdainResBlk1d(nn.Module):
+    def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2),
+                 upsample='none', dropout_p=0.0):
+        super().__init__()
+        self.actv = actv
+        self.upsample_type = upsample
+        self.upsample = UpSample1d(upsample)
+        self.learned_sc = dim_in != dim_out
+        self._build_weights(dim_in, dim_out, style_dim)
+        self.dropout = nn.Dropout(dropout_p)
+        
+        if upsample == 'none':
+            self.pool = nn.Identity()
+        else:
+            self.pool = weight_norm(nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1))
+        
+        
+    def _build_weights(self, dim_in, dim_out, style_dim):
+        self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
+        self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1))
+        self.norm1 = AdaIN1d(style_dim, dim_in)
+        self.norm2 = AdaIN1d(style_dim, dim_out)
+        if self.learned_sc:
+            self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))
+
+    def _shortcut(self, x):
+        x = self.upsample(x)
+        if self.learned_sc:
+            x = self.conv1x1(x)
+        return x
+
+    def _residual(self, x, s):
+        x = self.norm1(x, s)
+        x = self.actv(x)
+        x = self.pool(x)
+        x = self.conv1(self.dropout(x))
+        x = self.norm2(x, s)
+        x = self.actv(x)
+        x = self.conv2(self.dropout(x))
+        return x
+
+    def forward(self, x, s):
+        out = self._residual(x, s)
+        out = (out + self._shortcut(x)) / np.sqrt(2)
+        return out
+    
+class UpSample1d(nn.Module):
+    def __init__(self, layer_type):
+        super().__init__()
+        self.layer_type = layer_type
+
+    def forward(self, x):
+        if self.layer_type == 'none':
+            return x
+        else:
+            return F.interpolate(x, scale_factor=2, mode='nearest')
+
+class Decoder(nn.Module):
+    def __init__(self, dim_in=512, F0_channel=512, style_dim=64, dim_out=80, 
+                resblock_kernel_sizes = [3,7,11],
+                upsample_rates = [10, 6],
+                upsample_initial_channel=512,
+                resblock_dilation_sizes=[[1,3,5], [1,3,5], [1,3,5]],
+                upsample_kernel_sizes=[20, 12], 
+                gen_istft_n_fft=20, gen_istft_hop_size=5):
+        super().__init__()
+        
+        self.decode = nn.ModuleList()
+        
+        self.encode = AdainResBlk1d(dim_in + 2, 1024, style_dim)
+        
+        self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
+        self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
+        self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
+        self.decode.append(AdainResBlk1d(1024 + 2 + 64, 512, style_dim, upsample=True))
+
+        self.F0_conv = weight_norm(nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1))
+        
+        self.N_conv = weight_norm(nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1))
+        
+        self.asr_res = nn.Sequential(
+            weight_norm(nn.Conv1d(512, 64, kernel_size=1)),
+        )
+        
+        
+        self.generator = Generator(style_dim, resblock_kernel_sizes, upsample_rates, 
+                                   upsample_initial_channel, resblock_dilation_sizes, 
+                                   upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size)
+        
+    def forward(self, asr, F0_curve, N, s):
+        F0 = self.F0_conv(F0_curve.unsqueeze(1))
+        N = self.N_conv(N.unsqueeze(1))
+        
+        x = torch.cat([asr, F0, N], axis=1)
+        x = self.encode(x, s)
+        
+        asr_res = self.asr_res(asr)
+        
+        res = True
+        for block in self.decode:
+            if res:
+                x = torch.cat([x, asr_res, F0, N], axis=1)
+            x = block(x, s)
+            if block.upsample_type != "none":
+                res = False
+                
+        x = self.generator(x, s, F0_curve)
+        return x

backend/python/kokoro/kokoro.py

@@ -0,0 +1,166 @@
+# https://huggingface.co/hexgrad/Kokoro-82M/blob/main/kokoro.py
+import phonemizer
+import re
+import torch
+import numpy as np
+
+def split_num(num):
+    num = num.group()
+    if '.' in num:
+        return num
+    elif ':' in num:
+        h, m = [int(n) for n in num.split(':')]
+        if m == 0:
+            return f"{h} o'clock"
+        elif m < 10:
+            return f'{h} oh {m}'
+        return f'{h} {m}'
+    year = int(num[:4])
+    if year < 1100 or year % 1000 < 10:
+        return num
+    left, right = num[:2], int(num[2:4])
+    s = 's' if num.endswith('s') else ''
+    if 100 <= year % 1000 <= 999:
+        if right == 0:
+            return f'{left} hundred{s}'
+        elif right < 10:
+            return f'{left} oh {right}{s}'
+    return f'{left} {right}{s}'
+
+def flip_money(m):
+    m = m.group()
+    bill = 'dollar' if m[0] == '$' else 'pound'
+    if m[-1].isalpha():
+        return f'{m[1:]} {bill}s'
+    elif '.' not in m:
+        s = '' if m[1:] == '1' else 's'
+        return f'{m[1:]} {bill}{s}'
+    b, c = m[1:].split('.')
+    s = '' if b == '1' else 's'
+    c = int(c.ljust(2, '0'))
+    coins = f"cent{'' if c == 1 else 's'}" if m[0] == '$' else ('penny' if c == 1 else 'pence')
+    return f'{b} {bill}{s} and {c} {coins}'
+
+def point_num(num):
+    a, b = num.group().split('.')
+    return ' point '.join([a, ' '.join(b)])
+
+def normalize_text(text):
+    text = text.replace(chr(8216), "'").replace(chr(8217), "'")
+    text = text.replace('«', chr(8220)).replace('»', chr(8221))
+    text = text.replace(chr(8220), '"').replace(chr(8221), '"')
+    text = text.replace('(', '«').replace(')', '»')
+    for a, b in zip('、。！，：；？', ',.!,:;?'):
+        text = text.replace(a, b+' ')
+    text = re.sub(r'[^\S \n]', ' ', text)
+    text = re.sub(r'  +', ' ', text)
+    text = re.sub(r'(?<=\n) +(?=\n)', '', text)
+    text = re.sub(r'\bD[Rr]\.(?= [A-Z])', 'Doctor', text)
+    text = re.sub(r'\b(?:Mr\.|MR\.(?= [A-Z]))', 'Mister', text)
+    text = re.sub(r'\b(?:Ms\.|MS\.(?= [A-Z]))', 'Miss', text)
+    text = re.sub(r'\b(?:Mrs\.|MRS\.(?= [A-Z]))', 'Mrs', text)
+    text = re.sub(r'\betc\.(?! [A-Z])', 'etc', text)
+    text = re.sub(r'(?i)\b(y)eah?\b', r"\1e'a", text)
+    text = re.sub(r'\d*\.\d+|\b\d{4}s?\b|(?<!:)\b(?:[1-9]|1[0-2]):[0-5]\d\b(?!:)', split_num, text)
+    text = re.sub(r'(?<=\d),(?=\d)', '', text)
+    text = re.sub(r'(?i)[$£]\d+(?:\.\d+)?(?: hundred| thousand| (?:[bm]|tr)illion)*\b|[$£]\d+\.\d\d?\b', flip_money, text)
+    text = re.sub(r'\d*\.\d+', point_num, text)
+    text = re.sub(r'(?<=\d)-(?=\d)', ' to ', text)
+    text = re.sub(r'(?<=\d)S', ' S', text)
+    text = re.sub(r"(?<=[BCDFGHJ-NP-TV-Z])'?s\b", "'S", text)
+    text = re.sub(r"(?<=X')S\b", 's', text)
+    text = re.sub(r'(?:[A-Za-z]\.){2,} [a-z]', lambda m: m.group().replace('.', '-'), text)
+    text = re.sub(r'(?i)(?<=[A-Z])\.(?=[A-Z])', '-', text)
+    return text.strip()
+
+def get_vocab():
+    _pad = "$"
+    _punctuation = ';:,.!?¡¿—…"«»“” '
+    _letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
+    _letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
+    symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
+    dicts = {}
+    for i in range(len((symbols))):
+        dicts[symbols[i]] = i
+    return dicts
+
+VOCAB = get_vocab()
+def tokenize(ps):
+    return [i for i in map(VOCAB.get, ps) if i is not None]
+
+phonemizers = dict(
+    a=phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True),
+    b=phonemizer.backend.EspeakBackend(language='en-gb', preserve_punctuation=True, with_stress=True),
+)
+def phonemize(text, lang, norm=True):
+    if norm:
+        text = normalize_text(text)
+    ps = phonemizers[lang].phonemize([text])
+    ps = ps[0] if ps else ''
+    # https://en.wiktionary.org/wiki/kokoro#English
+    ps = ps.replace('kəkˈoːɹoʊ', 'kˈoʊkəɹoʊ').replace('kəkˈɔːɹəʊ', 'kˈəʊkəɹəʊ')
+    ps = ps.replace('ʲ', 'j').replace('r', 'ɹ').replace('x', 'k').replace('ɬ', 'l')
+    ps = re.sub(r'(?<=[a-zɹː])(?=hˈʌndɹɪd)', ' ', ps)
+    ps = re.sub(r' z(?=[;:,.!?¡¿—…"«»“” ]|$)', 'z', ps)
+    if lang == 'a':
+        ps = re.sub(r'(?<=nˈaɪn)ti(?!ː)', 'di', ps)
+    ps = ''.join(filter(lambda p: p in VOCAB, ps))
+    return ps.strip()
+
+def length_to_mask(lengths):
+    mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
+    mask = torch.gt(mask+1, lengths.unsqueeze(1))
+    return mask
+
+@torch.no_grad()
+def forward(model, tokens, ref_s, speed):
+    device = ref_s.device
+    tokens = torch.LongTensor([[0, *tokens, 0]]).to(device)
+    input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
+    text_mask = length_to_mask(input_lengths).to(device)
+    bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
+    d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
+    s = ref_s[:, 128:]
+    d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
+    x, _ = model.predictor.lstm(d)
+    duration = model.predictor.duration_proj(x)
+    duration = torch.sigmoid(duration).sum(axis=-1) / speed
+    pred_dur = torch.round(duration).clamp(min=1).long()
+    pred_aln_trg = torch.zeros(input_lengths, pred_dur.sum().item())
+    c_frame = 0
+    for i in range(pred_aln_trg.size(0)):
+        pred_aln_trg[i, c_frame:c_frame + pred_dur[0,i].item()] = 1
+        c_frame += pred_dur[0,i].item()
+    en = d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device)
+    F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
+    t_en = model.text_encoder(tokens, input_lengths, text_mask)
+    asr = t_en @ pred_aln_trg.unsqueeze(0).to(device)
+    return model.decoder(asr, F0_pred, N_pred, ref_s[:, :128]).squeeze().cpu().numpy()
+
+def generate(model, text, voicepack, lang='a', speed=1, ps=None):
+    ps = ps or phonemize(text, lang)
+    tokens = tokenize(ps)
+    if not tokens:
+        return None
+    elif len(tokens) > 510:
+        tokens = tokens[:510]
+        print('Truncated to 510 tokens')
+    ref_s = voicepack[len(tokens)]
+    out = forward(model, tokens, ref_s, speed)
+    ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens)
+    return out, ps
+
+def generate_full(model, text, voicepack, lang='a', speed=1, ps=None):
+    ps = ps or phonemize(text, lang)
+    tokens = tokenize(ps)
+    if not tokens:
+        return None
+    outs = []
+    loop_count = len(tokens)//510 + (1 if len(tokens) % 510 != 0 else 0)
+    for i in range(loop_count):
+        ref_s = voicepack[len(tokens[i*510:(i+1)*510])]
+        out = forward(model, tokens[i*510:(i+1)*510], ref_s, speed)
+        outs.append(out)
+    outs = np.concatenate(outs)
+    ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens)
+    return outs, ps

backend/python/kokoro/models.py

@@ -0,0 +1,373 @@
+# https://github.com/yl4579/StyleTTS2/blob/main/models.py
+# https://huggingface.co/hexgrad/Kokoro-82M/blob/main/models.py
+from istftnet import AdaIN1d, Decoder
+from munch import Munch
+from pathlib import Path
+from plbert import load_plbert
+from torch.nn.utils import weight_norm, spectral_norm
+import json
+import numpy as np
+import os
+import os.path as osp
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class LinearNorm(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
+        super(LinearNorm, self).__init__()
+        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.linear_layer.weight,
+            gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, x):
+        return self.linear_layer(x)
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+    
+class TextEncoder(nn.Module):
+    def __init__(self, channels, kernel_size, depth, n_symbols, actv=nn.LeakyReLU(0.2)):
+        super().__init__()
+        self.embedding = nn.Embedding(n_symbols, channels)
+
+        padding = (kernel_size - 1) // 2
+        self.cnn = nn.ModuleList()
+        for _ in range(depth):
+            self.cnn.append(nn.Sequential(
+                weight_norm(nn.Conv1d(channels, channels, kernel_size=kernel_size, padding=padding)),
+                LayerNorm(channels),
+                actv,
+                nn.Dropout(0.2),
+            ))
+        # self.cnn = nn.Sequential(*self.cnn)
+
+        self.lstm = nn.LSTM(channels, channels//2, 1, batch_first=True, bidirectional=True)
+
+    def forward(self, x, input_lengths, m):
+        x = self.embedding(x)  # [B, T, emb]
+        x = x.transpose(1, 2)  # [B, emb, T]
+        m = m.to(input_lengths.device).unsqueeze(1)
+        x.masked_fill_(m, 0.0)
+        
+        for c in self.cnn:
+            x = c(x)
+            x.masked_fill_(m, 0.0)
+            
+        x = x.transpose(1, 2)  # [B, T, chn]
+
+        input_lengths = input_lengths.cpu().numpy()
+        x = nn.utils.rnn.pack_padded_sequence(
+            x, input_lengths, batch_first=True, enforce_sorted=False)
+
+        self.lstm.flatten_parameters()
+        x, _ = self.lstm(x)
+        x, _ = nn.utils.rnn.pad_packed_sequence(
+            x, batch_first=True)
+                
+        x = x.transpose(-1, -2)
+        x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]])
+
+        x_pad[:, :, :x.shape[-1]] = x
+        x = x_pad.to(x.device)
+        
+        x.masked_fill_(m, 0.0)
+        
+        return x
+
+    def inference(self, x):
+        x = self.embedding(x)
+        x = x.transpose(1, 2)
+        x = self.cnn(x)
+        x = x.transpose(1, 2)
+        self.lstm.flatten_parameters()
+        x, _ = self.lstm(x)
+        return x
+    
+    def length_to_mask(self, lengths):
+        mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
+        mask = torch.gt(mask+1, lengths.unsqueeze(1))
+        return mask
+
+
+class UpSample1d(nn.Module):
+    def __init__(self, layer_type):
+        super().__init__()
+        self.layer_type = layer_type
+
+    def forward(self, x):
+        if self.layer_type == 'none':
+            return x
+        else:
+            return F.interpolate(x, scale_factor=2, mode='nearest')
+
+class AdainResBlk1d(nn.Module):
+    def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2),
+                 upsample='none', dropout_p=0.0):
+        super().__init__()
+        self.actv = actv
+        self.upsample_type = upsample
+        self.upsample = UpSample1d(upsample)
+        self.learned_sc = dim_in != dim_out
+        self._build_weights(dim_in, dim_out, style_dim)
+        self.dropout = nn.Dropout(dropout_p)
+        
+        if upsample == 'none':
+            self.pool = nn.Identity()
+        else:
+            self.pool = weight_norm(nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1))
+        
+        
+    def _build_weights(self, dim_in, dim_out, style_dim):
+        self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
+        self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1))
+        self.norm1 = AdaIN1d(style_dim, dim_in)
+        self.norm2 = AdaIN1d(style_dim, dim_out)
+        if self.learned_sc:
+            self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))
+
+    def _shortcut(self, x):
+        x = self.upsample(x)
+        if self.learned_sc:
+            x = self.conv1x1(x)
+        return x
+
+    def _residual(self, x, s):
+        x = self.norm1(x, s)
+        x = self.actv(x)
+        x = self.pool(x)
+        x = self.conv1(self.dropout(x))
+        x = self.norm2(x, s)
+        x = self.actv(x)
+        x = self.conv2(self.dropout(x))
+        return x
+
+    def forward(self, x, s):
+        out = self._residual(x, s)
+        out = (out + self._shortcut(x)) / np.sqrt(2)
+        return out
+    
+class AdaLayerNorm(nn.Module):
+    def __init__(self, style_dim, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.fc = nn.Linear(style_dim, channels*2)
+
+    def forward(self, x, s):
+        x = x.transpose(-1, -2)
+        x = x.transpose(1, -1)
+                
+        h = self.fc(s)
+        h = h.view(h.size(0), h.size(1), 1)
+        gamma, beta = torch.chunk(h, chunks=2, dim=1)
+        gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1)
+        
+        
+        x = F.layer_norm(x, (self.channels,), eps=self.eps)
+        x = (1 + gamma) * x + beta
+        return x.transpose(1, -1).transpose(-1, -2)
+
+class ProsodyPredictor(nn.Module):
+
+    def __init__(self, style_dim, d_hid, nlayers, max_dur=50, dropout=0.1):
+        super().__init__() 
+        
+        self.text_encoder = DurationEncoder(sty_dim=style_dim, 
+                                            d_model=d_hid,
+                                            nlayers=nlayers, 
+                                            dropout=dropout)
+
+        self.lstm = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
+        self.duration_proj = LinearNorm(d_hid, max_dur)
+        
+        self.shared = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
+        self.F0 = nn.ModuleList()
+        self.F0.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
+        self.F0.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout))
+        self.F0.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout))
+
+        self.N = nn.ModuleList()
+        self.N.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
+        self.N.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout))
+        self.N.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout))
+        
+        self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
+        self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
+
+
+    def forward(self, texts, style, text_lengths, alignment, m):
+        d = self.text_encoder(texts, style, text_lengths, m)
+        
+        batch_size = d.shape[0]
+        text_size = d.shape[1]
+        
+        # predict duration
+        input_lengths = text_lengths.cpu().numpy()
+        x = nn.utils.rnn.pack_padded_sequence(
+            d, input_lengths, batch_first=True, enforce_sorted=False)
+        
+        m = m.to(text_lengths.device).unsqueeze(1)
+        
+        self.lstm.flatten_parameters()
+        x, _ = self.lstm(x)
+        x, _ = nn.utils.rnn.pad_packed_sequence(
+            x, batch_first=True)
+        
+        x_pad = torch.zeros([x.shape[0], m.shape[-1], x.shape[-1]])
+
+        x_pad[:, :x.shape[1], :] = x
+        x = x_pad.to(x.device)
+                
+        duration = self.duration_proj(nn.functional.dropout(x, 0.5, training=self.training))
+        
+        en = (d.transpose(-1, -2) @ alignment)
+
+        return duration.squeeze(-1), en
+    
+    def F0Ntrain(self, x, s):
+        x, _ = self.shared(x.transpose(-1, -2))
+        
+        F0 = x.transpose(-1, -2)
+        for block in self.F0:
+            F0 = block(F0, s)
+        F0 = self.F0_proj(F0)
+
+        N = x.transpose(-1, -2)
+        for block in self.N:
+            N = block(N, s)
+        N = self.N_proj(N)
+        
+        return F0.squeeze(1), N.squeeze(1)
+    
+    def length_to_mask(self, lengths):
+        mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
+        mask = torch.gt(mask+1, lengths.unsqueeze(1))
+        return mask
+
+class DurationEncoder(nn.Module):
+
+    def __init__(self, sty_dim, d_model, nlayers, dropout=0.1):
+        super().__init__()
+        self.lstms = nn.ModuleList()
+        for _ in range(nlayers):
+            self.lstms.append(nn.LSTM(d_model + sty_dim, 
+                                 d_model // 2, 
+                                 num_layers=1, 
+                                 batch_first=True, 
+                                 bidirectional=True, 
+                                 dropout=dropout))
+            self.lstms.append(AdaLayerNorm(sty_dim, d_model))
+        
+        
+        self.dropout = dropout
+        self.d_model = d_model
+        self.sty_dim = sty_dim
+
+    def forward(self, x, style, text_lengths, m):
+        masks = m.to(text_lengths.device)
+        
+        x = x.permute(2, 0, 1)
+        s = style.expand(x.shape[0], x.shape[1], -1)
+        x = torch.cat([x, s], axis=-1)
+        x.masked_fill_(masks.unsqueeze(-1).transpose(0, 1), 0.0)
+                
+        x = x.transpose(0, 1)
+        input_lengths = text_lengths.cpu().numpy()
+        x = x.transpose(-1, -2)
+        
+        for block in self.lstms:
+            if isinstance(block, AdaLayerNorm):
+                x = block(x.transpose(-1, -2), style).transpose(-1, -2)
+                x = torch.cat([x, s.permute(1, -1, 0)], axis=1)
+                x.masked_fill_(masks.unsqueeze(-1).transpose(-1, -2), 0.0)
+            else:
+                x = x.transpose(-1, -2)
+                x = nn.utils.rnn.pack_padded_sequence(
+                    x, input_lengths, batch_first=True, enforce_sorted=False)
+                block.flatten_parameters()
+                x, _ = block(x)
+                x, _ = nn.utils.rnn.pad_packed_sequence(
+                    x, batch_first=True)
+                x = F.dropout(x, p=self.dropout, training=self.training)
+                x = x.transpose(-1, -2)
+                
+                x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]])
+
+                x_pad[:, :, :x.shape[-1]] = x
+                x = x_pad.to(x.device)
+        
+        return x.transpose(-1, -2)
+    
+    def inference(self, x, style):
+        x = self.embedding(x.transpose(-1, -2)) * np.sqrt(self.d_model)
+        style = style.expand(x.shape[0], x.shape[1], -1)
+        x = torch.cat([x, style], axis=-1)
+        src = self.pos_encoder(x)
+        output = self.transformer_encoder(src).transpose(0, 1)
+        return output
+    
+    def length_to_mask(self, lengths):
+        mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
+        mask = torch.gt(mask+1, lengths.unsqueeze(1))
+        return mask
+
+# https://github.com/yl4579/StyleTTS2/blob/main/utils.py
+def recursive_munch(d):
+    if isinstance(d, dict):
+        return Munch((k, recursive_munch(v)) for k, v in d.items())
+    elif isinstance(d, list):
+        return [recursive_munch(v) for v in d]
+    else:
+        return d
+
+def build_model(path, device):
+    config = Path(__file__).parent / 'config.json'
+    assert config.exists(), f'Config path incorrect: config.json not found at {config}'
+    with open(config, 'r') as r:
+        args = recursive_munch(json.load(r))
+    assert args.decoder.type == 'istftnet', f'Unknown decoder type: {args.decoder.type}'
+    decoder = Decoder(dim_in=args.hidden_dim, style_dim=args.style_dim, dim_out=args.n_mels,
+            resblock_kernel_sizes = args.decoder.resblock_kernel_sizes,
+            upsample_rates = args.decoder.upsample_rates,
+            upsample_initial_channel=args.decoder.upsample_initial_channel,
+            resblock_dilation_sizes=args.decoder.resblock_dilation_sizes,
+            upsample_kernel_sizes=args.decoder.upsample_kernel_sizes,
+            gen_istft_n_fft=args.decoder.gen_istft_n_fft, gen_istft_hop_size=args.decoder.gen_istft_hop_size)
+    text_encoder = TextEncoder(channels=args.hidden_dim, kernel_size=5, depth=args.n_layer, n_symbols=args.n_token)
+    predictor = ProsodyPredictor(style_dim=args.style_dim, d_hid=args.hidden_dim, nlayers=args.n_layer, max_dur=args.max_dur, dropout=args.dropout)
+    bert = load_plbert()
+    bert_encoder = nn.Linear(bert.config.hidden_size, args.hidden_dim)
+    for parent in [bert, bert_encoder, predictor, decoder, text_encoder]:
+        for child in parent.children():
+            if isinstance(child, nn.RNNBase):
+                child.flatten_parameters()
+    model = Munch(
+        bert=bert.to(device).eval(),
+        bert_encoder=bert_encoder.to(device).eval(),
+        predictor=predictor.to(device).eval(),
+        decoder=decoder.to(device).eval(),
+        text_encoder=text_encoder.to(device).eval(),
+    )
+    for key, state_dict in torch.load(path, map_location='cpu', weights_only=True)['net'].items():
+        assert key in model, key
+        try:
+            model[key].load_state_dict(state_dict)
+        except:
+            state_dict = {k[7:]: v for k, v in state_dict.items()}
+            model[key].load_state_dict(state_dict, strict=False)
+    return model

backend/python/kokoro/plbert.py

@@ -0,0 +1,16 @@
+# https://huggingface.co/hexgrad/Kokoro-82M/blob/main/plbert.py
+# https://github.com/yl4579/StyleTTS2/blob/main/Utils/PLBERT/util.py
+from transformers import AlbertConfig, AlbertModel
+
+class CustomAlbert(AlbertModel):
+    def forward(self, *args, **kwargs):
+        # Call the original forward method
+        outputs = super().forward(*args, **kwargs)
+        # Only return the last_hidden_state
+        return outputs.last_hidden_state
+
+def load_plbert():
+    plbert_config = {'vocab_size': 178, 'hidden_size': 768, 'num_attention_heads': 12, 'intermediate_size': 2048, 'max_position_embeddings': 512, 'num_hidden_layers': 12, 'dropout': 0.1}
+    albert_base_configuration = AlbertConfig(**plbert_config)
+    bert = CustomAlbert(albert_base_configuration)
+    return bert

backend/python/kokoro/protogen.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+set -e
+
+source $(dirname $0)/../common/libbackend.sh
+
+python3 -m grpc_tools.protoc -I../.. --python_out=. --grpc_python_out=. backend.proto

backend/python/kokoro/requirements-hipblas.txt

@@ -1,5 +1,3 @@
 --extra-index-url https://download.pytorch.org/whl/rocm6.0
 torch==2.4.1+rocm6.0
-accelerate
-sentence-transformers==3.3.1
 transformers

backend/python/kokoro/requirements-intel.txt

@@ -1,8 +1,5 @@
 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
 intel-extension-for-pytorch==2.3.110+xpu
 torch==2.3.1+cxx11.abi
-torchaudio==2.3.1+cxx11.abi
 oneccl_bind_pt==2.3.100+xpu
-optimum[openvino]
-transformers
-accelerate
+transformers

backend/python/kokoro/requirements.txt

@@ -0,0 +1,7 @@
+grpcio==1.69.0
+protobuf
+phonemizer
+scipy
+munch
+setuptools
+soundfile

backend/python/mamba/Makefile

@@ -1,29 +0,0 @@
-.PHONY: mamba
-mamba: protogen
-	bash install.sh 
-
-.PHONY: run
-run: protogen
-	@echo "Running mamba..."
-	bash run.sh
-	@echo "mamba run."
-
-.PHONY: test
-test: protogen
-	@echo "Testing mamba..."
-	bash test.sh
-	@echo "mamba tested."
-
-.PHONY: protogen
-protogen: backend_pb2_grpc.py backend_pb2.py
-
-.PHONY: protogen-clean
-protogen-clean:
-	$(RM) backend_pb2_grpc.py backend_pb2.py
-
-backend_pb2_grpc.py backend_pb2.py:
-	python3 -m grpc_tools.protoc -I../.. --python_out=. --grpc_python_out=. backend.proto
-
-.PHONY: clean
-clean: protogen-clean
-	$(RM) -r venv __pycache__

backend/python/mamba/README.md

@@ -1,5 +0,0 @@
-# Creating a separate environment for the mamba project
-
-```
-make mamba
-```

backend/python/mamba/backend.py

@@ -1,179 +0,0 @@
-#!/usr/bin/env python3
-from concurrent import futures
-import time
-import argparse
-import signal
-import sys
-import os
-
-import backend_pb2
-import backend_pb2_grpc
-
-import grpc
-
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
-
-_ONE_DAY_IN_SECONDS = 60 * 60 * 24
-
-# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
-MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
-MAMBA_CHAT= os.environ.get('MAMBA_CHAT', '1') == '1'
-
-# Implement the BackendServicer class with the service methods
-class BackendServicer(backend_pb2_grpc.BackendServicer):
-    """
-    A gRPC servicer that implements the Backend service defined in backend.proto.
-    """
-    def generate(self,prompt, max_new_tokens):
-        """
-        Generates text based on the given prompt and maximum number of new tokens.
-
-        Args:
-            prompt (str): The prompt to generate text from.
-            max_new_tokens (int): The maximum number of new tokens to generate.
-
-        Returns:
-            str: The generated text.
-        """
-        self.generator.end_beam_search()
-
-        # Tokenizing the input
-        ids = self.generator.tokenizer.encode(prompt)
-
-        self.generator.gen_begin_reuse(ids)
-        initial_len = self.generator.sequence[0].shape[0]
-        has_leading_space = False
-        decoded_text = ''
-        for i in range(max_new_tokens):
-            token = self.generator.gen_single_token()
-            if i == 0 and self.generator.tokenizer.tokenizer.IdToPiece(int(token)).startswith('▁'):
-                has_leading_space = True
-
-            decoded_text = self.generator.tokenizer.decode(self.generator.sequence[0][initial_len:])
-            if has_leading_space:
-                decoded_text = ' ' + decoded_text
-
-            if token.item() == self.generator.tokenizer.eos_token_id:
-                break
-        return decoded_text
-
-    def Health(self, request, context):
-        """
-        Returns a health check message.
-
-        Args:
-            request: The health check request.
-            context: The gRPC context.
-
-        Returns:
-            backend_pb2.Reply: The health check reply.
-        """
-        return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
-
-    def LoadModel(self, request, context):
-        """
-        Loads a language model.
-
-        Args:
-            request: The load model request.
-            context: The gRPC context.
-
-        Returns:
-            backend_pb2.Result: The load model result.
-        """
-        try:
-            tokenizerModel = request.Tokenizer
-            if tokenizerModel == "":
-                tokenizerModel = request.Model
-
-            tokenizer = AutoTokenizer.from_pretrained(tokenizerModel)
-            if MAMBA_CHAT:
-                tokenizer.eos_token = "<|endoftext|>"
-                tokenizer.pad_token = tokenizer.eos_token
-            self.tokenizer = tokenizer
-            self.model = MambaLMHeadModel.from_pretrained(request.Model, device="cuda", dtype=torch.float16)
-        except Exception as err:
-            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
-        return backend_pb2.Result(message="Model loaded successfully", success=True)
-
-    def Predict(self, request, context):
-        """
-        Generates text based on the given prompt and sampling parameters.
-
-        Args:
-            request: The predict request.
-            context: The gRPC context.
-
-        Returns:
-            backend_pb2.Result: The predict result.
-        """
-        if request.TopP == 0:
-            request.TopP = 0.9
-
-        max_tokens = request.Tokens
-
-        if request.Tokens == 0:
-            max_tokens = 2000
-
-        # encoded_input = self.tokenizer(request.Prompt)
-        tokens = self.tokenizer(request.Prompt, return_tensors="pt")
-        input_ids = tokens.input_ids.to(device="cuda")
-        out = self.model.generate(input_ids=input_ids, max_length=max_tokens, temperature=request.Temperature,
-                                     top_p=request.TopP, eos_token_id=self.tokenizer.eos_token_id)
-
-        decoded = self.tokenizer.batch_decode(out)
-       
-        generated_text = decoded[0]
-
-        # Remove prompt from response if present
-        if request.Prompt in generated_text:
-            generated_text = generated_text.replace(request.Prompt, "")
-
-        return backend_pb2.Reply(message=bytes(generated_text, encoding='utf-8'))
-
-    def PredictStream(self, request, context):
-        """
-        Generates text based on the given prompt and sampling parameters, and streams the results.
-
-        Args:
-            request: The predict stream request.
-            context: The gRPC context.
-
-        Returns:
-            backend_pb2.Result: The predict stream result.
-        """
-        yield self.Predict(request, context)
-
-def serve(address):
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
-    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
-    server.add_insecure_port(address)
-    server.start()
-    print("Server started. Listening on: " + address, file=sys.stderr)
-
-    # Define the signal handler function
-    def signal_handler(sig, frame):
-        print("Received termination signal. Shutting down...")
-        server.stop(0)
-        sys.exit(0)
-
-    # Set the signal handlers for SIGINT and SIGTERM
-    signal.signal(signal.SIGINT, signal_handler)
-    signal.signal(signal.SIGTERM, signal_handler)
-
-    try:
-        while True:
-            time.sleep(_ONE_DAY_IN_SECONDS)
-    except KeyboardInterrupt:
-        server.stop(0)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run the gRPC server.")
-    parser.add_argument(
-        "--addr", default="localhost:50051", help="The address to bind the server to."
-    )
-    args = parser.parse_args()
-
-    serve(args.addr)

backend/python/mamba/install.sh

@@ -1,9 +0,0 @@
-#!/bin/bash
-set -e
-
-LIMIT_TARGETS="cublas"
-EXTRA_PIP_INSTALL_FLAGS="--no-build-isolation"
-
-source $(dirname $0)/../common/libbackend.sh
-
-installRequirements

backend/python/mamba/requirements-after.txt

@@ -1,2 +0,0 @@
-causal-conv1d==1.4.0
-mamba-ssm==2.2.2

backend/python/mamba/requirements-install.txt

@@ -1,6 +0,0 @@
-# mabma does not specify it's build dependencies per PEP517, so we need to disable build isolation
-# this also means that we need to install the basic build dependencies into the venv ourselves
-# https://github.com/Dao-AILab/causal-conv1d/issues/24
-packaging
-setuptools
-wheel

backend/python/mamba/run.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-LIMIT_TARGETS="cublas"
-
-source $(dirname $0)/../common/libbackend.sh
-
-startBackend $@

backend/python/mamba/test.py

@@ -1,76 +0,0 @@
-import unittest
-import subprocess
-import time
-import backend_pb2
-import backend_pb2_grpc
-
-import grpc
-
-import unittest
-import subprocess
-import time
-import grpc
-import backend_pb2_grpc
-import backend_pb2
-
-class TestBackendServicer(unittest.TestCase):
-    """
-    TestBackendServicer is the class that tests the gRPC service.
-
-    This class contains methods to test the startup and shutdown of the gRPC service.
-    """
-    def setUp(self):
-        self.service = subprocess.Popen(["python", "backend.py", "--addr", "localhost:50051"])
-        time.sleep(10)
-
-    def tearDown(self) -> None:
-        self.service.terminate()
-        self.service.wait()
-
-    def test_server_startup(self):
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.Health(backend_pb2.HealthMessage())
-                self.assertEqual(response.message, b'OK')
-        except Exception as err:
-            print(err)
-            self.fail("Server failed to start")
-        finally:
-            self.tearDown()
-    def test_load_model(self):
-        """
-        This method tests if the model is loaded successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.LoadModel(backend_pb2.ModelOptions(Model="facebook/opt-125m"))
-                self.assertTrue(response.success)
-                self.assertEqual(response.message, "Model loaded successfully")
-        except Exception as err:
-            print(err)
-            self.fail("LoadModel service failed")
-        finally:
-            self.tearDown()
-
-    def test_text(self):
-        """
-        This method tests if the embeddings are generated successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.LoadModel(backend_pb2.ModelOptions(Model="facebook/opt-125m"))
-                self.assertTrue(response.success)
-                req = backend_pb2.PredictOptions(Prompt="The capital of France is")
-                resp = stub.Predict(req)
-                self.assertIsNotNone(resp.message)
-        except Exception as err:
-            print(err)
-            self.fail("text service failed")
-        finally:
-            self.tearDown()

backend/python/openvoice/backend.py

@@ -1,158 +0,0 @@
-#!/usr/bin/env python3
-"""
-Extra gRPC server for OpenVoice models.
-"""
-from concurrent import futures
-
-import argparse
-import signal
-import sys
-import os
-import torch
-from openvoice import se_extractor
-from openvoice.api import ToneColorConverter
-from melo.api import TTS
-
-import time
-import backend_pb2
-import backend_pb2_grpc
-
-import grpc
-
-
-_ONE_DAY_IN_SECONDS = 60 * 60 * 24
-
-# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
-MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
-
-# Implement the BackendServicer class with the service methods
-class BackendServicer(backend_pb2_grpc.BackendServicer):
-    """
-    A gRPC servicer for the backend service.
-
-    This class implements the gRPC methods for the backend service, including Health, LoadModel, and Embedding.
-    """
-    def Health(self, request, context):
-        """
-        A gRPC method that returns the health status of the backend service.
-
-        Args:
-            request: A HealthRequest object that contains the request parameters.
-            context: A grpc.ServicerContext object that provides information about the RPC.
-
-        Returns:
-            A Reply object that contains the health status of the backend service.
-        """
-        return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
-
-    def LoadModel(self, request, context):
-        """
-        A gRPC method that loads a model into memory.
-
-        Args:
-            request: A LoadModelRequest object that contains the request parameters.
-            context: A grpc.ServicerContext object that provides information about the RPC.
-
-        Returns:
-            A Result object that contains the result of the LoadModel operation.
-        """
-        model_name = request.Model
-        try:
-
-            self.clonedVoice = False
-            # Assume directory from request.ModelFile.
-            # Only if request.LoraAdapter it's not an absolute path
-            if request.AudioPath and request.ModelFile != "" and not os.path.isabs(request.AudioPath):
-                # get base path of modelFile
-                modelFileBase = os.path.dirname(request.ModelFile)
-                request.AudioPath = os.path.join(modelFileBase, request.AudioPath)
-            if request.AudioPath != "":
-                self.clonedVoice = True
-
-            self.modelpath = request.ModelFile
-            self.speaker = request.Type
-            self.ClonedVoicePath = request.AudioPath
-            
-            ckpt_converter = request.Model+'/converter'
-            device = "cuda:0" if torch.cuda.is_available() else "cpu"
-            self.device = device
-            self.tone_color_converter = None
-            if self.clonedVoice:
-                self.tone_color_converter = ToneColorConverter(f'{ckpt_converter}/config.json', device=device)
-                self.tone_color_converter.load_ckpt(f'{ckpt_converter}/checkpoint.pth')
-       
-        except Exception as err:
-            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
-
-        return backend_pb2.Result(message="Model loaded successfully", success=True)
-
-    def TTS(self, request, context):
-        model_name = request.model
-        if model_name == "":
-            return backend_pb2.Result(success=False, message="request.model is required")
-        try:
-            # Speed is adjustable
-            speed = 1.0
-            voice = "EN"
-            if request.voice:
-                voice = request.voice
-            model = TTS(language=voice, device=self.device)
-            speaker_ids = model.hps.data.spk2id
-            speaker_key = self.speaker
-            modelpath = self.modelpath
-            for s in speaker_ids.keys():
-                print(f"Speaker: {s} - ID: {speaker_ids[s]}")
-            speaker_id = speaker_ids[speaker_key]
-            speaker_key = speaker_key.lower().replace('_', '-')
-            source_se = torch.load(f'{modelpath}/base_speakers/ses/{speaker_key}.pth', map_location=self.device)
-            model.tts_to_file(request.text, speaker_id, request.dst, speed=speed)
-            if self.clonedVoice:
-                reference_speaker = self.ClonedVoicePath
-                target_se, audio_name = se_extractor.get_se(reference_speaker, self.tone_color_converter, vad=False)
-                # Run the tone color converter
-                encode_message = "@MyShell"
-                self.tone_color_converter.convert(
-                    audio_src_path=request.dst, 
-                    src_se=source_se, 
-                    tgt_se=target_se, 
-                    output_path=request.dst,
-                    message=encode_message)
-           
-            print("[OpenVoice] TTS generated!", file=sys.stderr)
-            print("[OpenVoice] TTS saved to", request.dst, file=sys.stderr)
-            print(request, file=sys.stderr)
-        except Exception as err:
-            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
-        return backend_pb2.Result(success=True)
-
-def serve(address):
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
-    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
-    server.add_insecure_port(address)
-    server.start()
-    print("[OpenVoice] Server started. Listening on: " + address, file=sys.stderr)
-
-    # Define the signal handler function
-    def signal_handler(sig, frame):
-        print("[OpenVoice] Received termination signal. Shutting down...")
-        server.stop(0)
-        sys.exit(0)
-
-    # Set the signal handlers for SIGINT and SIGTERM
-    signal.signal(signal.SIGINT, signal_handler)
-    signal.signal(signal.SIGTERM, signal_handler)
-
-    try:
-        while True:
-            time.sleep(_ONE_DAY_IN_SECONDS)
-    except KeyboardInterrupt:
-        server.stop(0)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run the gRPC server.")
-    parser.add_argument(
-        "--addr", default="localhost:50051", help="The address to bind the server to."
-    )
-    args = parser.parse_args()
-    print(f"[OpenVoice] startup: {args}", file=sys.stderr)
-    serve(args.addr)

backend/python/openvoice/install.sh

@@ -1,16 +0,0 @@
-#!/bin/bash
-set -e
-
-source $(dirname $0)/../common/libbackend.sh
-
-# This is here because the Intel pip index is broken and returns 200 status codes for every package name, it just doesn't return any package links.
-# This makes uv think that the package exists in the Intel pip index, and by default it stops looking at other pip indexes once it finds a match.
-# We need uv to continue falling through to the pypi default index to find optimum[openvino] in the pypi index
-# the --upgrade actually allows us to *downgrade* torch to the version provided in the Intel pip index
-if [ "x${BUILD_PROFILE}" == "xintel" ]; then
-    EXTRA_PIP_INSTALL_FLAGS+=" --upgrade --index-strategy=unsafe-first-match"
-fi
-
-installRequirements
-
-python -m unidic download

backend/python/openvoice/requirements-cpu.txt

@@ -1,7 +0,0 @@
-torch==2.4.1
-git+https://github.com/myshell-ai/MeloTTS.git
-git+https://github.com/myshell-ai/OpenVoice.git
-whisper-timestamped
-pydub==0.25.1
-wavmark==0.0.3
-eng_to_ipa==0.0.2

backend/python/openvoice/requirements-cublas11.txt

@@ -1,8 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/cu118
-torch==2.4.1+cu118
-git+https://github.com/myshell-ai/MeloTTS.git
-git+https://github.com/myshell-ai/OpenVoice.git
-whisper-timestamped
-pydub==0.25.1
-wavmark==0.0.3
-eng_to_ipa==0.0.2

backend/python/openvoice/requirements-cublas12.txt

@@ -1,7 +0,0 @@
-torch==2.4.1
-git+https://github.com/myshell-ai/MeloTTS.git
-git+https://github.com/myshell-ai/OpenVoice.git
-whisper-timestamped
-pydub==0.25.1
-wavmark==0.0.3
-eng_to_ipa==0.0.2

backend/python/openvoice/requirements-hipblas.txt

@@ -1,8 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/rocm6.0
-torch==2.4.1+rocm6.0
-git+https://github.com/myshell-ai/MeloTTS.git
-git+https://github.com/myshell-ai/OpenVoice.git
-whisper-timestamped
-pydub==0.25.1
-wavmark==0.0.3
-eng_to_ipa==0.0.2

backend/python/openvoice/requirements-intel.txt

@@ -1,24 +0,0 @@
---extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-intel-extension-for-pytorch==2.3.110+xpu
-torch==2.3.1+cxx11.abi
-torchaudio==2.3.1+cxx11.abi
-oneccl_bind_pt==2.3.100+xpu
-optimum[openvino]
-grpcio==1.69.0
-protobuf
-librosa==0.9.1
-faster-whisper==0.9.0
-pydub==0.25.1
-wavmark==0.0.3
-eng_to_ipa==0.0.2
-inflect==7.0.0
-unidecode==1.3.7
-whisper-timestamped==1.14.2
-openai
-python-dotenv
-pypinyin==0.50.0
-cn2an==0.5.22
-jieba==0.42.1
-langid==1.1.6
-git+https://github.com/myshell-ai/MeloTTS.git
-git+https://github.com/myshell-ai/OpenVoice.git

backend/python/openvoice/requirements.txt

@@ -1,17 +0,0 @@
-grpcio==1.69.0
-protobuf
-librosa
-faster-whisper
-inflect
-unidecode
-openai
-python-dotenv
-pypinyin
-cn2an==0.5.22
-numpy==1.22.0
-networkx==2.8.8
-jieba==0.42.1
-gradio==5.9.1
-langid==1.1.6
-llvmlite==0.43.0
-setuptools

backend/python/openvoice/test.py

@@ -1,82 +0,0 @@
-"""
-A test script to test the gRPC service
-"""
-import unittest
-import subprocess
-import time
-import backend_pb2
-import backend_pb2_grpc
-
-import grpc
-
-
-class TestBackendServicer(unittest.TestCase):
-    """
-    TestBackendServicer is the class that tests the gRPC service
-    """
-    def setUp(self):
-        """
-        This method sets up the gRPC service by starting the server
-        """
-        self.service = subprocess.Popen(["python3", "backend.py", "--addr", "localhost:50051"])
-        time.sleep(30)
-
-    def tearDown(self) -> None:
-        """
-        This method tears down the gRPC service by terminating the server
-        """
-        self.service.terminate()
-        self.service.wait()
-
-    def test_server_startup(self):
-        """
-        This method tests if the server starts up successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.Health(backend_pb2.HealthMessage())
-                self.assertEqual(response.message, b'OK')
-        except Exception as err:
-            print(err)
-            self.fail("Server failed to start")
-        finally:
-            self.tearDown()
-
-    def test_load_model(self):
-        """
-        This method tests if the model is loaded successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.LoadModel(backend_pb2.ModelOptions(Model="checkpoints_v2", 
-                                                                    Type="en-us"))
-                self.assertTrue(response.success)
-                self.assertEqual(response.message, "Model loaded successfully")
-        except Exception as err:
-            print(err)
-            self.fail("LoadModel service failed")
-        finally:
-            self.tearDown()
-
-    def test_tts(self):
-        """
-        This method tests if the embeddings are generated successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.LoadModel(backend_pb2.ModelOptions(Model="dingzhen"))
-                self.assertTrue(response.success)
-                tts_request = backend_pb2.TTSRequest(text="80s TV news production music hit for tonight's biggest story", voice="EN")
-                tts_response = stub.TTS(tts_request)
-                self.assertIsNotNone(tts_response)
-        except Exception as err:
-            print(err)
-            self.fail("TTS service failed")
-        finally:
-            self.tearDown()

backend/python/openvoice/test.sh

@@ -1,12 +0,0 @@
-#!/bin/bash
-set -e
-
-source $(dirname $0)/../common/libbackend.sh
-
-# Download checkpoints if not present
-if [ ! -d "checkpoints_v2" ]; then
-    wget https://myshell-public-repo-host.s3.amazonaws.com/openvoice/checkpoints_v2_0417.zip -O checkpoints_v2.zip
-    unzip checkpoints_v2.zip
-fi
-
-runUnittests

backend/python/parler-tts/Makefile

@@ -1,44 +0,0 @@
-export CONDA_ENV_PATH = "parler.yml"
-SKIP_CONDA?=0
-ifeq ($(BUILD_TYPE), cublas)
-export CONDA_ENV_PATH = "parler-nvidia.yml"
-endif
-
-# Intel GPU are supposed to have dependencies installed in the main python
-# environment, so we skip conda installation for SYCL builds.
-# https://github.com/intel/intel-extension-for-pytorch/issues/538
-ifneq (,$(findstring sycl,$(BUILD_TYPE)))
-export SKIP_CONDA=1
-endif
-
-.PHONY: parler-tts
-parler-tts:
-	@echo "Installing $(CONDA_ENV_PATH)..."
-	bash install.sh $(CONDA_ENV_PATH)
-	$(MAKE) protogen
-
-.PHONY: run
-run: protogen
-	@echo "Running transformers..."
-	bash run.sh
-	@echo "transformers run."
-
-.PHONY: test
-test: protogen
-	@echo "Testing transformers..."
-	bash test.sh
-	@echo "transformers tested."
-
-.PHONY: protogen
-protogen: backend_pb2_grpc.py backend_pb2.py
-
-.PHONY: protogen-clean
-protogen-clean:
-	$(RM) backend_pb2_grpc.py backend_pb2.py
-
-backend_pb2_grpc.py backend_pb2.py:
-	bash protogen.sh
-
-.PHONY: clean
-clean: protogen-clean
-	$(RM) -r venv __pycache__

backend/python/parler-tts/install.sh

@@ -1,28 +0,0 @@
-#!/bin/bash
-set -e
-
-source $(dirname $0)/../common/libbackend.sh
-
-# This is here because the Intel pip index is broken and returns 200 status codes for every package name, it just doesn't return any package links.
-# This makes uv think that the package exists in the Intel pip index, and by default it stops looking at other pip indexes once it finds a match.
-# We need uv to continue falling through to the pypi default index to find optimum[openvino] in the pypi index
-# the --upgrade actually allows us to *downgrade* torch to the version provided in the Intel pip index
-if [ "x${BUILD_PROFILE}" == "xintel" ]; then
-    EXTRA_PIP_INSTALL_FLAGS+=" --upgrade --index-strategy=unsafe-first-match"
-fi
-
-
-installRequirements
-
-
-# https://github.com/descriptinc/audiotools/issues/101
-# incompatible protobuf versions.
-PYDIR=python3.10
-pyenv="${MY_DIR}/venv/lib/${PYDIR}/site-packages/google/protobuf/internal/"
-
-if [ ! -d ${pyenv} ]; then
-    echo "(parler-tts/install.sh): Error: ${pyenv} does not exist"
-    exit 1
-fi
-
-curl -L https://raw.githubusercontent.com/protocolbuffers/protobuf/main/python/google/protobuf/internal/builder.py -o ${pyenv}/builder.py

backend/python/parler-tts/requirements-after.txt

@@ -1,4 +0,0 @@
-git+https://github.com/huggingface/parler-tts.git@8e465f1b5fcd223478e07175cb40494d19ffbe17
-llvmlite==0.43.0
-numba==0.60.0
-grpcio-tools==1.42.0

backend/python/parler-tts/requirements-cpu.txt

@@ -1,3 +0,0 @@
-transformers
-accelerate
-torch==2.4.1

backend/python/parler-tts/requirements-cublas11.txt

@@ -1,5 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/cu118
-torch==2.4.1+cu118
-torchaudio==2.4.1+cu118
-transformers
-accelerate

backend/python/parler-tts/requirements-cublas12.txt

@@ -1,4 +0,0 @@
-torch==2.4.1
-torchaudio==2.4.1
-transformers
-accelerate

backend/python/parler-tts/requirements-hipblas.txt

@@ -1,5 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/rocm6.0
-torch==2.3.0+rocm6.0
-torchaudio==2.3.0+rocm6.0
-transformers
-accelerate

backend/python/parler-tts/requirements.txt

@@ -1,4 +0,0 @@
-grpcio==1.69.0
-certifi
-llvmlite==0.43.0
-setuptools

backend/python/parler-tts/test.py

@@ -1,81 +0,0 @@
-"""
-A test script to test the gRPC service
-"""
-import unittest
-import subprocess
-import time
-import backend_pb2
-import backend_pb2_grpc
-
-import grpc
-
-
-class TestBackendServicer(unittest.TestCase):
-    """
-    TestBackendServicer is the class that tests the gRPC service
-    """
-    def setUp(self):
-        """
-        This method sets up the gRPC service by starting the server
-        """
-        self.service = subprocess.Popen(["python3", "backend.py", "--addr", "localhost:50051"])
-        time.sleep(10)
-
-    def tearDown(self) -> None:
-        """
-        This method tears down the gRPC service by terminating the server
-        """
-        self.service.terminate()
-        self.service.wait()
-
-    def test_server_startup(self):
-        """
-        This method tests if the server starts up successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.Health(backend_pb2.HealthMessage())
-                self.assertEqual(response.message, b'OK')
-        except Exception as err:
-            print(err)
-            self.fail("Server failed to start")
-        finally:
-            self.tearDown()
-
-    def test_load_model(self):
-        """
-        This method tests if the model is loaded successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.LoadModel(backend_pb2.ModelOptions(Model="parler-tts/parler_tts_mini_v0.1"))
-                self.assertTrue(response.success)
-                self.assertEqual(response.message, "Model loaded successfully")
-        except Exception as err:
-            print(err)
-            self.fail("LoadModel service failed")
-        finally:
-            self.tearDown()
-
-    def test_tts(self):
-        """
-        This method tests if the embeddings are generated successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.LoadModel(backend_pb2.ModelOptions(Model="parler-tts/parler_tts_mini_v0.1"))
-                self.assertTrue(response.success)
-                tts_request = backend_pb2.TTSRequest(text="Hey, how are you doing today?")
-                tts_response = stub.TTS(tts_request)
-                self.assertIsNotNone(tts_response)
-        except Exception as err:
-            print(err)
-            self.fail("TTS service failed")
-        finally:
-            self.tearDown()

backend/python/sentencetransformers/Makefile

@@ -1,31 +0,0 @@
-.PHONY: sentencetransformers
-sentencetransformers: protogen
-	bash ./install.sh
-
-
-.PHONY: run
-run: protogen
-	@echo "Running sentencetransformers..."
-	bash run.sh
-	@echo "sentencetransformers run."
-
-# It is not working well by using command line. It only6 works with IDE like VSCode.
-.PHONY: test
-test: protogen
-	@echo "Testing sentencetransformers..."
-	bash test.sh
-	@echo "sentencetransformers tested."
-
-.PHONY: protogen
-protogen: backend_pb2_grpc.py backend_pb2.py
-
-.PHONY: protogen-clean
-protogen-clean:
-	$(RM) backend_pb2_grpc.py backend_pb2.py
-
-backend_pb2_grpc.py backend_pb2.py:
-	python3 -m grpc_tools.protoc -I../.. --python_out=. --grpc_python_out=. backend.proto
-
-.PHONY: clean
-clean: protogen-clean
-	rm -rf venv __pycache__

backend/python/sentencetransformers/README.md

@@ -1,5 +0,0 @@
-# Creating a separate environment for the sentencetransformers project
-
-```
-make sentencetransformers
-```

backend/python/sentencetransformers/requirements-cpu.txt

@@ -1,6 +0,0 @@
-torch==2.4.1
-accelerate
-transformers
-bitsandbytes
-sentence-transformers==3.3.1
-transformers

backend/python/sentencetransformers/requirements-cublas11.txt

@@ -1,5 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/cu118
-torch==2.4.1+cu118
-accelerate
-sentence-transformers==3.3.1
-transformers

backend/python/sentencetransformers/requirements-cublas12.txt

@@ -1,4 +0,0 @@
-torch==2.4.1
-accelerate
-sentence-transformers==3.3.1
-transformers

backend/python/sentencetransformers/requirements-intel.txt

@@ -1,9 +0,0 @@
---extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-intel-extension-for-pytorch==2.3.110+xpu
-torch==2.3.1+cxx11.abi
-oneccl_bind_pt==2.3.100+xpu
-optimum[openvino]
-setuptools
-accelerate
-sentence-transformers==3.3.1
-transformers

backend/python/sentencetransformers/requirements.txt

@@ -1,5 +0,0 @@
-grpcio==1.69.0
-protobuf
-certifi
-datasets
-einops

backend/python/sentencetransformers/run.sh

@@ -1,4 +0,0 @@
-#!/bin/bash
-source $(dirname $0)/../common/libbackend.sh
-
-startBackend $@

backend/python/sentencetransformers/test.py

@@ -1,81 +0,0 @@
-"""
-A test script to test the gRPC service
-"""
-import unittest
-import subprocess
-import time
-import backend_pb2
-import backend_pb2_grpc
-
-import grpc
-
-
-class TestBackendServicer(unittest.TestCase):
-    """
-    TestBackendServicer is the class that tests the gRPC service
-    """
-    def setUp(self):
-        """
-        This method sets up the gRPC service by starting the server
-        """
-        self.service = subprocess.Popen(["python3", "backend.py", "--addr", "localhost:50051"])
-        time.sleep(10)
-
-    def tearDown(self) -> None:
-        """
-        This method tears down the gRPC service by terminating the server
-        """
-        self.service.kill()
-        self.service.wait()
-
-    def test_server_startup(self):
-        """
-        This method tests if the server starts up successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.Health(backend_pb2.HealthMessage())
-                self.assertEqual(response.message, b'OK')
-        except Exception as err:
-            print(err)
-            self.fail("Server failed to start")
-        finally:
-            self.tearDown()
-
-    def test_load_model(self):
-        """
-        This method tests if the model is loaded successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.LoadModel(backend_pb2.ModelOptions(Model="bert-base-nli-mean-tokens"))
-                self.assertTrue(response.success)
-                self.assertEqual(response.message, "Model loaded successfully")
-        except Exception as err:
-            print(err)
-            self.fail("LoadModel service failed")
-        finally:
-            self.tearDown()
-
-    def test_embedding(self):
-        """
-        This method tests if the embeddings are generated successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.LoadModel(backend_pb2.ModelOptions(Model="bert-base-nli-mean-tokens"))
-                self.assertTrue(response.success)
-                embedding_request = backend_pb2.PredictOptions(Embeddings="This is a test sentence.")
-                embedding_response = stub.Embedding(embedding_request)
-                self.assertIsNotNone(embedding_response.embeddings)
-        except Exception as err:
-            print(err)
-            self.fail("Embedding service failed")
-        finally:
-            self.tearDown()

backend/python/sentencetransformers/test.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-set -e
-
-source $(dirname $0)/../common/libbackend.sh
-
-runUnittests

backend/python/transformers-musicgen/Makefile

@@ -1,29 +0,0 @@
-.PHONY: transformers-musicgen
-transformers-musicgen: protogen
-	bash install.sh
-
-.PHONY: run
-run: protogen
-	@echo "Running transformers..."
-	bash run.sh
-	@echo "transformers run."
-
-.PHONY: test
-test: protogen
-	@echo "Testing transformers..."
-	bash test.sh
-	@echo "transformers tested."
-
-.PHONY: protogen
-protogen: backend_pb2_grpc.py backend_pb2.py
-
-.PHONY: protogen-clean
-protogen-clean:
-	$(RM) backend_pb2_grpc.py backend_pb2.py
-
-backend_pb2_grpc.py backend_pb2.py:
-	python3 -m grpc_tools.protoc -I../.. --python_out=. --grpc_python_out=. backend.proto
-
-.PHONY: clean
-clean: protogen-clean
-	rm -rf venv __pycache__

backend/python/transformers-musicgen/README.md

@@ -1,5 +0,0 @@
-# Creating a separate environment for the transformers project
-
-```
-make transformers-musicgen
-```

backend/python/transformers-musicgen/backend.py

@@ -1,176 +0,0 @@
-#!/usr/bin/env python3
-"""
-Extra gRPC server for MusicgenForConditionalGeneration models.
-"""
-from concurrent import futures
-
-import argparse
-import signal
-import sys
-import os
-
-import time
-import backend_pb2
-import backend_pb2_grpc
-
-import grpc
-
-from scipy.io import wavfile
-from transformers import AutoProcessor, MusicgenForConditionalGeneration
-
-_ONE_DAY_IN_SECONDS = 60 * 60 * 24
-
-# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
-MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
-
-# Implement the BackendServicer class with the service methods
-class BackendServicer(backend_pb2_grpc.BackendServicer):
-    """
-    A gRPC servicer for the backend service.
-
-    This class implements the gRPC methods for the backend service, including Health, LoadModel, and Embedding.
-    """
-    def Health(self, request, context):
-        """
-        A gRPC method that returns the health status of the backend service.
-
-        Args:
-            request: A HealthRequest object that contains the request parameters.
-            context: A grpc.ServicerContext object that provides information about the RPC.
-
-        Returns:
-            A Reply object that contains the health status of the backend service.
-        """
-        return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
-
-    def LoadModel(self, request, context):
-        """
-        A gRPC method that loads a model into memory.
-
-        Args:
-            request: A LoadModelRequest object that contains the request parameters.
-            context: A grpc.ServicerContext object that provides information about the RPC.
-
-        Returns:
-            A Result object that contains the result of the LoadModel operation.
-        """
-        model_name = request.Model
-        try:
-            self.processor = AutoProcessor.from_pretrained(model_name)
-            self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
-        except Exception as err:
-            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
-
-        return backend_pb2.Result(message="Model loaded successfully", success=True)
-
-    def SoundGeneration(self, request, context):
-        model_name = request.model
-        if model_name == "":
-            return backend_pb2.Result(success=False, message="request.model is required")
-        try:
-            self.processor = AutoProcessor.from_pretrained(model_name)
-            self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
-            inputs = None
-            if request.text == "":
-                inputs = self.model.get_unconditional_inputs(num_samples=1)
-            elif request.HasField('src'):
-                # TODO SECURITY CODE GOES HERE LOL
-                # WHO KNOWS IF THIS WORKS???
-                sample_rate, wsamples = wavfile.read('path_to_your_file.wav')
-                
-                if request.HasField('src_divisor'):
-                    wsamples = wsamples[: len(wsamples) // request.src_divisor]
-                
-                inputs = self.processor(
-                    audio=wsamples,
-                    sampling_rate=sample_rate,
-                    text=[request.text],
-                    padding=True,
-                    return_tensors="pt",
-                )
-            else:
-                inputs = self.processor(
-                    text=[request.text],
-                    padding=True,
-                    return_tensors="pt",
-                )
-            
-            tokens = 256
-            if request.HasField('duration'):
-                tokens = int(request.duration * 51.2) # 256 tokens = 5 seconds, therefore 51.2 tokens is one second
-            guidance = 3.0
-            if request.HasField('temperature'):
-                guidance = request.temperature
-            dosample = True
-            if request.HasField('sample'):
-                dosample = request.sample
-            audio_values = self.model.generate(**inputs, do_sample=dosample, guidance_scale=guidance, max_new_tokens=tokens)
-            print("[transformers-musicgen] SoundGeneration generated!", file=sys.stderr)
-            sampling_rate = self.model.config.audio_encoder.sampling_rate
-            wavfile.write(request.dst, rate=sampling_rate, data=audio_values[0, 0].numpy())
-            print("[transformers-musicgen] SoundGeneration saved to", request.dst, file=sys.stderr)
-            print("[transformers-musicgen] SoundGeneration for", file=sys.stderr)
-            print("[transformers-musicgen] SoundGeneration requested tokens", tokens, file=sys.stderr)
-            print(request, file=sys.stderr)
-        except Exception as err:
-            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
-        return backend_pb2.Result(success=True)
-
-
-# The TTS endpoint is older, and provides fewer features, but exists for compatibility reasons
-    def TTS(self, request, context):
-        model_name = request.model
-        if model_name == "":
-            return backend_pb2.Result(success=False, message="request.model is required")
-        try:
-            self.processor = AutoProcessor.from_pretrained(model_name)
-            self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
-            inputs = self.processor(
-                text=[request.text],
-                padding=True,
-                return_tensors="pt",
-            )
-            tokens = 512 # No good place to set the "length" in TTS, so use 10s as a sane default
-            audio_values = self.model.generate(**inputs, max_new_tokens=tokens)
-            print("[transformers-musicgen] TTS generated!", file=sys.stderr)
-            sampling_rate = self.model.config.audio_encoder.sampling_rate
-            write_wav(request.dst, rate=sampling_rate, data=audio_values[0, 0].numpy())
-            print("[transformers-musicgen] TTS saved to", request.dst, file=sys.stderr)
-            print("[transformers-musicgen] TTS for", file=sys.stderr)
-            print(request, file=sys.stderr)
-        except Exception as err:
-            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
-        return backend_pb2.Result(success=True)
-
-
-def serve(address):
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
-    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
-    server.add_insecure_port(address)
-    server.start()
-    print("[transformers-musicgen] Server started. Listening on: " + address, file=sys.stderr)
-
-    # Define the signal handler function
-    def signal_handler(sig, frame):
-        print("[transformers-musicgen] Received termination signal. Shutting down...")
-        server.stop(0)
-        sys.exit(0)
-
-    # Set the signal handlers for SIGINT and SIGTERM
-    signal.signal(signal.SIGINT, signal_handler)
-    signal.signal(signal.SIGTERM, signal_handler)
-
-    try:
-        while True:
-            time.sleep(_ONE_DAY_IN_SECONDS)
-    except KeyboardInterrupt:
-        server.stop(0)
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run the gRPC server.")
-    parser.add_argument(
-        "--addr", default="localhost:50051", help="The address to bind the server to."
-    )
-    args = parser.parse_args()
-    print(f"[transformers-musicgen] startup: {args}", file=sys.stderr)
-    serve(args.addr)

backend/python/transformers-musicgen/requirements-cpu.txt

@@ -1,3 +0,0 @@
-transformers
-accelerate
-torch==2.4.1

backend/python/transformers-musicgen/requirements-cublas11.txt

@@ -1,4 +0,0 @@
---extra-index-url https://download.pytorch.org/whl/cu118
-transformers
-accelerate
-torch==2.4.1+cu118

backend/python/transformers-musicgen/requirements-cublas12.txt

@@ -1,3 +0,0 @@
-transformers
-accelerate
-torch==2.4.1

backend/python/transformers-musicgen/requirements.txt

@@ -1,4 +0,0 @@
-grpcio==1.69.0
-protobuf
-scipy==1.14.0
-certifi

backend/python/transformers-musicgen/run.sh

@@ -1,4 +0,0 @@
-#!/bin/bash
-source $(dirname $0)/../common/libbackend.sh
-
-startBackend $@

backend/python/transformers-musicgen/test.py

@@ -1,100 +0,0 @@
-"""
-A test script to test the gRPC service
-"""
-import unittest
-import subprocess
-import time
-import backend_pb2
-import backend_pb2_grpc
-
-import grpc
-
-
-class TestBackendServicer(unittest.TestCase):
-    """
-    TestBackendServicer is the class that tests the gRPC service
-    """
-    def setUp(self):
-        """
-        This method sets up the gRPC service by starting the server
-        """
-        self.service = subprocess.Popen(["python3", "backend.py", "--addr", "localhost:50051"])
-        time.sleep(10)
-
-    def tearDown(self) -> None:
-        """
-        This method tears down the gRPC service by terminating the server
-        """
-        self.service.terminate()
-        self.service.wait()
-
-    def test_server_startup(self):
-        """
-        This method tests if the server starts up successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.Health(backend_pb2.HealthMessage())
-                self.assertEqual(response.message, b'OK')
-        except Exception as err:
-            print(err)
-            self.fail("Server failed to start")
-        finally:
-            self.tearDown()
-
-    def test_load_model(self):
-        """
-        This method tests if the model is loaded successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.LoadModel(backend_pb2.ModelOptions(Model="facebook/musicgen-small"))
-                self.assertTrue(response.success)
-                self.assertEqual(response.message, "Model loaded successfully")
-        except Exception as err:
-            print(err)
-            self.fail("LoadModel service failed")
-        finally:
-            self.tearDown()
-
-    def test_tts(self):
-        """
-        This method tests if TTS is generated successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.LoadModel(backend_pb2.ModelOptions(Model="facebook/musicgen-small"))
-                self.assertTrue(response.success)
-                tts_request = backend_pb2.TTSRequest(text="80s TV news production music hit for tonight's biggest story")
-                tts_response = stub.TTS(tts_request)
-                self.assertIsNotNone(tts_response)
-        except Exception as err:
-            print(err)
-            self.fail("TTS service failed")
-        finally:
-            self.tearDown()
-
-    def test_sound_generation(self):
-        """
-        This method tests if SoundGeneration is generated successfully
-        """
-        try:
-            self.setUp()
-            with grpc.insecure_channel("localhost:50051") as channel:
-                stub = backend_pb2_grpc.BackendStub(channel)
-                response = stub.LoadModel(backend_pb2.ModelOptions(Model="facebook/musicgen-small"))
-                self.assertTrue(response.success)
-                sg_request = backend_pb2.SoundGenerationRequest(text="80s TV news production music hit for tonight's biggest story")
-                sg_response = stub.SoundGeneration(sg_request)
-                self.assertIsNotNone(sg_response)
-        except Exception as err:
-            print(err)
-            self.fail("SoundGeneration service failed")
-        finally:
-            self.tearDown()

backend/python/transformers-musicgen/test.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-set -e
-
-source $(dirname $0)/../common/libbackend.sh
-
-runUnittests

backend/python/transformers/backend.py

@@ -21,7 +21,11 @@ import torch.cuda
 
 
 XPU=os.environ.get("XPU", "0") == "1"
-from transformers import AutoTokenizer, AutoModel, set_seed, TextIteratorStreamer, StoppingCriteriaList, StopStringCriteria
+from transformers import AutoTokenizer, AutoModel, set_seed, TextIteratorStreamer, StoppingCriteriaList, StopStringCriteria, MambaConfig, MambaForCausalLM
+from transformers import AutoProcessor, MusicgenForConditionalGeneration
+from scipy.io import wavfile
+import outetts
+from sentence_transformers import SentenceTransformer
 
 
 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
@@ -85,10 +89,13 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
 
         self.CUDA = torch.cuda.is_available()
         self.OV=False
+        self.OuteTTS=False
+        self.SentenceTransformer = False
 
         device_map="cpu"
 
         quantization = None
+        autoTokenizer = True
 
         if self.CUDA:
             from transformers import BitsAndBytesConfig, AutoModelForCausalLM
@@ -191,6 +198,57 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                                                                 export=True,
                                                                 device=device_map)
                 self.OV = True
+            elif request.Type == "MusicgenForConditionalGeneration":
+                autoTokenizer = False
+                self.processor = AutoProcessor.from_pretrained(model_name)
+                self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
+            elif request.Type == "OuteTTS":
+                autoTokenizer = False
+                options = request.Options
+                MODELNAME = "OuteAI/OuteTTS-0.3-1B"
+                TOKENIZER = "OuteAI/OuteTTS-0.3-1B"
+                VERSION = "0.3"
+                SPEAKER = "en_male_1"
+                for opt in options:
+                    if opt.startswith("tokenizer:"):
+                        TOKENIZER = opt.split(":")[1]
+                        break
+                    if opt.startswith("version:"):
+                        VERSION = opt.split(":")[1]
+                        break
+                    if opt.startswith("speaker:"):
+                        SPEAKER = opt.split(":")[1]
+                        break
+                
+                if model_name != "":
+                    MODELNAME = model_name
+
+                # Configure the model
+                model_config = outetts.HFModelConfig_v2(
+                    model_path=MODELNAME,
+                    tokenizer_path=TOKENIZER
+                )
+                # Initialize the interface
+                self.interface = outetts.InterfaceHF(model_version=VERSION, cfg=model_config)
+                self.OuteTTS = True
+
+                self.interface.print_default_speakers()
+                if request.AudioPath:
+                    if os.path.isabs(request.AudioPath):
+                        self.AudioPath = request.AudioPath
+                    else:
+                        self.AudioPath = os.path.join(request.ModelPath, request.AudioPath)
+                    self.speaker = self.interface.create_speaker(audio_path=self.AudioPath)
+                else:
+                    self.speaker = self.interface.load_default_speaker(name=SPEAKER)               
+            elif request.Type == "SentenceTransformer":
+                autoTokenizer = False
+                self.model = SentenceTransformer(model_name, trust_remote_code=request.TrustRemoteCode)
+                self.SentenceTransformer = True
+            elif request.Type == "Mamba":
+                autoTokenizer = False
+                self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+                self.model = MambaForCausalLM.from_pretrained(model_name)
             else:
                 print("Automodel", file=sys.stderr)
                 self.model = AutoModel.from_pretrained(model_name, 
@@ -201,19 +259,22 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                                                        torch_dtype=compute)
             if request.ContextSize > 0:
                 self.max_tokens = request.ContextSize
-            else:
+            elif hasattr(self.model, 'config') and hasattr(self.model.config, 'max_position_embeddings'):
                 self.max_tokens = self.model.config.max_position_embeddings
+            else:
+                self.max_tokens = 512
  
-            self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_safetensors=True)
-            self.XPU = False
+            if autoTokenizer:
+                self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_safetensors=True)
+                self.XPU = False
 
-            if XPU and self.OV == False:
-                self.XPU = True
-                try:
-                    print("Optimizing model", model_name, "to XPU.", file=sys.stderr)
-                    self.model = ipex.optimize_transformers(self.model, inplace=True, dtype=torch.float16, device="xpu")
-                except Exception as err:
-                    print("Not using XPU:", err, file=sys.stderr)
+                if XPU and self.OV == False:
+                    self.XPU = True
+                    try:
+                        print("Optimizing model", model_name, "to XPU.", file=sys.stderr)
+                        self.model = ipex.optimize_transformers(self.model, inplace=True, dtype=torch.float16, device="xpu")
+                    except Exception as err:
+                        print("Not using XPU:", err, file=sys.stderr)
 
         except Exception as err:
             print("Error:", err, file=sys.stderr)
@@ -239,18 +300,26 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         max_length = 512
         if request.Tokens != 0:
             max_length = request.Tokens
-        encoded_input = self.tokenizer(request.Embeddings, padding=True, truncation=True, max_length=max_length, return_tensors="pt")    
 
-        # Create word embeddings
-        if self.CUDA:
-            encoded_input = encoded_input.to("cuda")
+        embeds = None
 
-        with torch.no_grad():    
-            model_output = self.model(**encoded_input)
+        if self.SentenceTransformer:
+            print("Calculated embeddings for: " + request.Embeddings, file=sys.stderr)
+            embeds = self.model.encode(request.Embeddings)
+        else:
+            encoded_input = self.tokenizer(request.Embeddings, padding=True, truncation=True, max_length=max_length, return_tensors="pt")    
 
-        # Pool to get sentence embeddings; i.e. generate one 1024 vector for the entire sentence
-        sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-        return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings[0])
+            # Create word embeddings
+            if self.CUDA:
+                encoded_input = encoded_input.to("cuda")
+
+            with torch.no_grad():    
+                model_output = self.model(**encoded_input)
+
+            # Pool to get sentence embeddings; i.e. generate one 1024 vector for the entire sentence
+            sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+            embeds = sentence_embeddings[0]
+        return backend_pb2.EmbeddingResult(embeddings=embeds)
 
     async def _predict(self, request, context, streaming=False): 
         set_seed(request.Seed)
@@ -380,6 +449,114 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         finally:
             await iterations.aclose()
 
+    def SoundGeneration(self, request, context):
+        model_name = request.model
+        try:
+            if self.processor is None:
+                if model_name == "":
+                    return backend_pb2.Result(success=False, message="request.model is required")
+                self.processor = AutoProcessor.from_pretrained(model_name)
+            if self.model is None:
+                if model_name == "":
+                    return backend_pb2.Result(success=False, message="request.model is required")
+                self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
+            inputs = None
+            if request.text == "":
+                inputs = self.model.get_unconditional_inputs(num_samples=1)
+            elif request.HasField('src'):
+                # TODO SECURITY CODE GOES HERE LOL
+                # WHO KNOWS IF THIS WORKS???
+                sample_rate, wsamples = wavfile.read('path_to_your_file.wav')
+                
+                if request.HasField('src_divisor'):
+                    wsamples = wsamples[: len(wsamples) // request.src_divisor]
+                
+                inputs = self.processor(
+                    audio=wsamples,
+                    sampling_rate=sample_rate,
+                    text=[request.text],
+                    padding=True,
+                    return_tensors="pt",
+                )
+            else:
+                inputs = self.processor(
+                    text=[request.text],
+                    padding=True,
+                    return_tensors="pt",
+                )
+            
+            tokens = 256
+            if request.HasField('duration'):
+                tokens = int(request.duration * 51.2) # 256 tokens = 5 seconds, therefore 51.2 tokens is one second
+            guidance = 3.0
+            if request.HasField('temperature'):
+                guidance = request.temperature
+            dosample = True
+            if request.HasField('sample'):
+                dosample = request.sample
+            audio_values = self.model.generate(**inputs, do_sample=dosample, guidance_scale=guidance, max_new_tokens=tokens)
+            print("[transformers-musicgen] SoundGeneration generated!", file=sys.stderr)
+            sampling_rate = self.model.config.audio_encoder.sampling_rate
+            wavfile.write(request.dst, rate=sampling_rate, data=audio_values[0, 0].numpy())
+            print("[transformers-musicgen] SoundGeneration saved to", request.dst, file=sys.stderr)
+            print("[transformers-musicgen] SoundGeneration for", file=sys.stderr)
+            print("[transformers-musicgen] SoundGeneration requested tokens", tokens, file=sys.stderr)
+            print(request, file=sys.stderr)
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        return backend_pb2.Result(success=True)
+
+    def OuteTTS(self, request, context):
+        try:
+            print("[OuteTTS] generating TTS", file=sys.stderr)
+            gen_cfg = outetts.GenerationConfig(
+                text="Speech synthesis is the artificial production of human speech.",
+                temperature=0.1,
+                repetition_penalty=1.1,
+                max_length=self.max_tokens,
+                speaker=self.speaker,
+                # voice_characteristics="upbeat enthusiasm, friendliness, clarity, professionalism, and trustworthiness"
+            )
+            output = self.interface.generate(config=gen_cfg)
+            print("[OuteTTS] Generated TTS", file=sys.stderr)
+            output.save(request.dst)
+            print("[OuteTTS] TTS done", file=sys.stderr)
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        return backend_pb2.Result(success=True)
+
+# The TTS endpoint is older, and provides fewer features, but exists for compatibility reasons
+    def TTS(self, request, context):
+        if self.OuteTTS:
+            return self.OuteTTS(request, context)
+
+        model_name = request.model
+        try:
+            if self.processor is None:
+                if model_name == "":
+                    return backend_pb2.Result(success=False, message="request.model is required")
+                self.processor = AutoProcessor.from_pretrained(model_name)
+            if self.model is None:
+                if model_name == "":
+                    return backend_pb2.Result(success=False, message="request.model is required")
+                self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
+            inputs = self.processor(
+                text=[request.text],
+                padding=True,
+                return_tensors="pt",
+            )
+            tokens = self.max_tokens # No good place to set the "length" in TTS, so use 10s as a sane default
+            audio_values = self.model.generate(**inputs, max_new_tokens=tokens)
+            print("[transformers-musicgen] TTS generated!", file=sys.stderr)
+            sampling_rate = self.model.config.audio_encoder.sampling_rate
+            wavfile.write(request.dst, rate=sampling_rate, data=audio_values[0, 0].numpy())
+            print("[transformers-musicgen] TTS saved to", request.dst, file=sys.stderr)
+            print("[transformers-musicgen] TTS for", file=sys.stderr)
+            print(request, file=sys.stderr)
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        return backend_pb2.Result(success=True)
+
 async def serve(address):
     # Start asyncio gRPC server
     server = grpc.aio.server(migration_thread_pool=futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))