├── .env ├── .github ├── ISSUE_TEMPLATE │ ├── bug_report.md │ └── feature_request.md ├── PULL_REQUEST_TEMPLATE.md └── workflows │ ├── checksum.yml │ ├── close-issue.yml │ ├── docker.yml │ ├── genlocale.yml │ ├── pull_format.yml │ ├── push_format.yml │ └── unitest.yml ├── .gitignore ├── Dockerfile ├── LICENSE ├── README.md ├── assets ├── hubert │ └── .gitignore ├── indices │ └── .gitignore ├── pretrained │ └── .gitignore ├── pretrained_v2 │ └── .gitignore ├── rmvpe │ └── .gitignore ├── uvr5_weights │ └── .gitignore └── weights │ └── .gitignore ├── configs ├── __init__.py ├── config.json ├── config.py ├── inuse │ ├── .gitignore │ ├── v1 │ │ └── .gitignore │ └── v2 │ │ └── .gitignore ├── v1 │ ├── 32k.json │ ├── 40k.json │ └── 48k.json └── v2 │ ├── 32k.json │ └── 48k.json ├── docker-compose.yml ├── docs ├── cn │ ├── README.cn.md │ └── faq.md ├── en │ ├── README.en.md │ ├── faiss_tips_en.md │ ├── faq_en.md │ └── training_tips_en.md ├── fr │ ├── README.fr.md │ ├── faiss_tips_fr.md │ ├── faq_fr.md │ └── training_tips_fr.md ├── jp │ ├── README.ja.md │ ├── faiss_tips_ja.md │ ├── faq_ja.md │ └── training_tips_ja.md ├── kr │ ├── README.ko.han.md │ ├── README.ko.md │ ├── faiss_tips_ko.md │ ├── faq_ko.md │ └── training_tips_ko.md ├── pt │ ├── README.pt.md │ ├── faiss_tips_pt.md │ ├── faq_pt.md │ └── training_tips_pt.md └── tr │ ├── README.tr.md │ ├── faiss_tips_tr.md │ ├── faq_tr.md │ └── training_tips_tr.md ├── gui.py ├── i18n ├── i18n.py ├── locale │ ├── en_US.json │ ├── es_ES.json │ ├── fr_FR.json │ ├── it_IT.json │ ├── ja_JP.json │ ├── ko_KR.json │ ├── pt_BR.json │ ├── ru_RU.json │ ├── tr_TR.json │ ├── zh_CN.json │ ├── zh_HK.json │ ├── zh_SG.json │ └── zh_TW.json ├── locale_diff.py └── scan_i18n.py ├── infer ├── lib │ ├── audio.py │ ├── rtrvc.py │ ├── rvcmd.py │ ├── slicer2.py │ ├── train │ │ ├── data_utils.py │ │ ├── losses.py │ │ ├── mel_processing.py │ │ ├── process_ckpt.py │ │ └── utils.py │ └── uvr5_pack │ │ ├── lib_v5 │ │ ├── layers.py │ │ ├── layers_123821KB.py │ │ ├── layers_33966KB.py │ │ ├── layers_537238KB.py │ │ ├── model_param_init.py │ │ ├── modelparams │ │ │ ├── 1band_sr16000_hl512.json │ │ │ ├── 1band_sr32000_hl512.json │ │ │ ├── 1band_sr33075_hl384.json │ │ │ ├── 1band_sr44100_hl1024.json │ │ │ ├── 1band_sr44100_hl256.json │ │ │ ├── 1band_sr44100_hl512.json │ │ │ ├── 1band_sr44100_hl512_cut.json │ │ │ ├── 2band_32000.json │ │ │ ├── 2band_44100_lofi.json │ │ │ ├── 2band_48000.json │ │ │ ├── 3band_44100.json │ │ │ ├── 3band_44100_mid.json │ │ │ ├── 3band_44100_msb2.json │ │ │ ├── 4band_44100.json │ │ │ ├── 4band_44100_mid.json │ │ │ ├── 4band_44100_msb.json │ │ │ ├── 4band_44100_msb2.json │ │ │ ├── 4band_44100_reverse.json │ │ │ ├── 4band_44100_sw.json │ │ │ ├── 4band_v2.json │ │ │ ├── 4band_v2_sn.json │ │ │ ├── 4band_v3.json │ │ │ └── ensemble.json │ │ ├── nets.py │ │ ├── nets_123821KB.py │ │ ├── nets_33966KB.py │ │ ├── nets_537238KB.py │ │ └── spec_utils.py │ │ ├── name_params.json │ │ └── utils.py └── modules │ ├── gui │ ├── __init__.py │ ├── torchgate.py │ └── utils.py │ ├── train │ ├── extract_f0_print.py │ ├── extract_feature_print.py │ ├── preprocess.py │ └── train.py │ ├── uvr5 │ ├── mdxnet.py │ ├── modules.py │ └── vr.py │ └── vc │ ├── __init__.py │ ├── hash.py │ ├── info.py │ ├── lgdsng.npz │ ├── modules.py │ ├── pipeline.py │ └── utils.py ├── logs └── mute │ ├── 0_gt_wavs │ ├── mute32k.wav │ ├── mute40k.wav │ └── mute48k.wav │ ├── 1_16k_wavs │ └── mute.wav │ ├── 2a_f0 │ └── mute.wav.npy │ ├── 2b-f0nsf │ └── mute.wav.npy │ ├── 3_feature256 │ └── mute.npy │ └── 3_feature768 │ └── mute.npy ├── requirements ├── amd.txt ├── dml.txt ├── gui-dml.txt ├── gui.txt ├── ipex.txt ├── main.txt └── py311.txt ├── run.sh ├── rvc ├── __init__.py ├── f0 │ ├── __init__.py │ ├── crepe.py │ ├── deepunet.py │ ├── dio.py │ ├── e2e.py │ ├── f0.py │ ├── fcpe.py │ ├── gen.py │ ├── harvest.py │ ├── mel.py │ ├── models.py │ ├── pm.py │ ├── rmvpe.py │ └── stft.py ├── hubert.py ├── ipex │ ├── __init__.py │ ├── attention.py │ ├── gradscaler.py │ ├── hijacks.py │ └── init.py ├── jit │ ├── __init__.py │ └── jit.py ├── layers │ ├── __init__.py │ ├── attentions.py │ ├── discriminators.py │ ├── encoders.py │ ├── generators.py │ ├── norms.py │ ├── nsf.py │ ├── residuals.py │ ├── synthesizers.py │ ├── transforms.py │ └── utils.py ├── onnx │ ├── __init__.py │ ├── exporter.py │ ├── infer.py │ └── synthesizer.py └── synthesizer.py ├── sha256.env ├── tools ├── checksum │ ├── main.go │ └── tmpl.go ├── cmd │ ├── calc_rvc_model_similarity.py │ ├── infer-pm-index256.py │ ├── infer_batch_rvc.py │ ├── infer_cli.py │ ├── onnx │ │ ├── export.py │ │ └── infer.py │ ├── train-index-v2.py │ ├── train-index.py │ └── trans_weights.py └── web │ └── infer-only.py └── web.py /.env: -------------------------------------------------------------------------------- 1 | OPENBLAS_NUM_THREADS = 1 2 | no_proxy = localhost, 127.0.0.1, ::1 3 | 4 | # You can change the location of the model, etc. by changing here 5 | weight_root = assets/weights 6 | weight_uvr5_root = assets/uvr5_weights 7 | index_root = logs 8 | outside_index_root = assets/indices 9 | rmvpe_root = assets/rmvpe 10 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the bug** 11 | A clear and concise description of what the bug is. 12 | 13 | **To Reproduce** 14 | Steps to reproduce the behavior: 15 | 1. Go to '...' 16 | 2. Click on '....' 17 | 3. Scroll down to '....' 18 | 4. See error 19 | 20 | **Expected behavior** 21 | A clear and concise description of what you expected to happen. 22 | 23 | **Screenshots** 24 | If applicable, add screenshots to help explain your problem. 25 | 26 | **Desktop (please complete the following information):** 27 | - OS and version: [e.g. Windows, Linux] 28 | - Python version: [e.g. 3.9.7, 3.11] 29 | - Commit/Tag with the issue: [e.g. 22] 30 | 31 | **Additional context** 32 | Add any other context about the problem here. 33 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Is your feature request related to a problem? Please describe.** 11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] 12 | 13 | **Describe the solution you'd like** 14 | A clear and concise description of what you want to happen. 15 | 16 | **Describe alternatives you've considered** 17 | A clear and concise description of any alternative solutions or features you've considered. 18 | 19 | **Additional context** 20 | Add any other context or screenshots about the feature request here. 21 | -------------------------------------------------------------------------------- /.github/PULL_REQUEST_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | # Pull request checklist 2 | 3 | - [ ] The PR has a proper title. Use [Semantic Commit Messages](https://seesparkbox.com/foundry/semantic_commit_messages). (No more branch-name title please) 4 | - [ ] Make sure you are requesting the right branch: `dev`. 5 | - [ ] Make sure this is ready to be merged into the relevant branch. Please don't create a PR and let it hang for a few days. 6 | - [ ] Ensure all tests are passing. 7 | - [ ] Ensure linting is passing. 8 | 9 | # PR type 10 | 11 | - Bug fix / new feature / chore 12 | 13 | # Description 14 | 15 | - Describe what this pull request is for. 16 | - What will it affect. 17 | 18 | # Screenshot 19 | 20 | - Please include a screenshot if applicable 21 | -------------------------------------------------------------------------------- /.github/workflows/checksum.yml: -------------------------------------------------------------------------------- 1 | name: Calculate and Sync SHA256 2 | on: 3 | push: 4 | branches: 5 | - main 6 | - dev 7 | jobs: 8 | checksum: 9 | runs-on: ubuntu-latest 10 | steps: 11 | - uses: actions/checkout@master 12 | 13 | - name: Setup Go Environment 14 | uses: actions/setup-go@master 15 | 16 | - name: Run RVC-Models-Downloader 17 | run: | 18 | wget https://github.com/fumiama/RVC-Models-Downloader/releases/download/v0.2.5/rvcmd_linux_amd64.deb 19 | sudo apt -y install ./rvcmd_linux_amd64.deb 20 | rm -f ./rvcmd_linux_amd64.deb 21 | rvcmd -notrs -w 1 -notui assets/rvc 22 | 23 | - name: Calculate all Checksums 24 | run: go run tools/checksum/*.go 25 | 26 | - name: Commit back 27 | if: ${{ !github.head_ref }} 28 | id: commitback 29 | continue-on-error: true 30 | run: | 31 | git config --local user.name 'github-actions[bot]' 32 | git config --local user.email 'github-actions[bot]@users.noreply.github.com' 33 | git add --all 34 | git commit -m "chore(env): sync checksum on ${{github.ref_name}}" 35 | 36 | - name: Create Pull Request 37 | if: steps.commitback.outcome == 'success' 38 | continue-on-error: true 39 | uses: peter-evans/create-pull-request@v5 40 | with: 41 | delete-branch: true 42 | body: "Automatically sync checksum in .env" 43 | title: "chore(env): sync checksum on ${{github.ref_name}}" 44 | commit-message: "chore(env): sync checksum on ${{github.ref_name}}" 45 | branch: checksum-${{github.ref_name}} 46 | -------------------------------------------------------------------------------- /.github/workflows/close-issue.yml: -------------------------------------------------------------------------------- 1 | name: Close Inactive Issues 2 | on: 3 | schedule: 4 | - cron: "0 4 * * *" 5 | 6 | jobs: 7 | close-issues: 8 | runs-on: ubuntu-latest 9 | permissions: 10 | issues: write 11 | pull-requests: write 12 | steps: 13 | - uses: actions/stale@v5 14 | with: 15 | exempt-issue-labels: "help wanted,good first issue,documentation,following up,todo list" 16 | days-before-issue-stale: 30 17 | days-before-issue-close: 15 18 | stale-issue-label: "stale" 19 | close-issue-message: "This issue was closed because it has been inactive for 15 days since being marked as stale." 20 | days-before-pr-stale: -1 21 | days-before-pr-close: -1 22 | operations-per-run: 10000 23 | repo-token: ${{ secrets.GITHUB_TOKEN }} 24 | -------------------------------------------------------------------------------- /.github/workflows/docker.yml: -------------------------------------------------------------------------------- 1 | name: Build and Push Docker Image 2 | 3 | on: 4 | workflow_dispatch: 5 | push: 6 | # Sequence of patterns matched against refs/tags 7 | tags: 8 | - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10 9 | 10 | jobs: 11 | build: 12 | runs-on: ubuntu-latest 13 | permissions: 14 | packages: write 15 | contents: read 16 | steps: 17 | - uses: actions/checkout@v3 18 | - name: Set time zone 19 | uses: szenius/set-timezone@v1.0 20 | with: 21 | timezoneLinux: "Asia/Shanghai" 22 | timezoneMacos: "Asia/Shanghai" 23 | timezoneWindows: "China Standard Time" 24 | 25 | # # 如果有 dockerhub 账户，可以在github的secrets中配置下面两个，然后取消下面注释的这几行，并在meta步骤的images增加一行 ${{ github.repository }} 26 | # - name: Login to DockerHub 27 | # uses: docker/login-action@v1 28 | # with: 29 | # username: ${{ secrets.DOCKERHUB_USERNAME }} 30 | # password: ${{ secrets.DOCKERHUB_TOKEN }} 31 | 32 | - name: Login to GHCR 33 | uses: docker/login-action@v2 34 | with: 35 | registry: ghcr.io 36 | username: ${{ github.repository_owner }} 37 | password: ${{ secrets.GITHUB_TOKEN }} 38 | 39 | - name: Extract metadata (tags, labels) for Docker 40 | id: meta 41 | uses: docker/metadata-action@v4 42 | with: 43 | images: | 44 | ghcr.io/${{ github.repository }} 45 | # generate Docker tags based on the following events/attributes 46 | # nightly, master, pr-2, 1.2.3, 1.2, 1 47 | tags: | 48 | type=schedule,pattern=nightly 49 | type=edge 50 | type=ref,event=branch 51 | type=ref,event=pr 52 | type=semver,pattern={{version}} 53 | type=semver,pattern={{major}}.{{minor}} 54 | type=semver,pattern={{major}} 55 | 56 | - name: Set up QEMU 57 | uses: docker/setup-qemu-action@v2 58 | 59 | - name: Set up Docker Buildx 60 | uses: docker/setup-buildx-action@v2 61 | 62 | - name: Build and push 63 | id: docker_build 64 | uses: docker/build-push-action@v4 65 | with: 66 | context: . 67 | platforms: linux/amd64,linux/arm64 68 | push: true 69 | tags: ${{ steps.meta.outputs.tags }} 70 | labels: ${{ steps.meta.outputs.labels }} 71 | -------------------------------------------------------------------------------- /.github/workflows/genlocale.yml: -------------------------------------------------------------------------------- 1 | name: Generate and Sync Locale 2 | on: 3 | push: 4 | branches: 5 | - main 6 | - dev 7 | jobs: 8 | genlocale: 9 | runs-on: ubuntu-latest 10 | steps: 11 | - uses: actions/checkout@master 12 | 13 | - name: Run locale generation 14 | run: | 15 | python3 i18n/scan_i18n.py 16 | cd i18n 17 | python3 locale_diff.py 18 | 19 | - name: Commit back 20 | if: ${{ !github.head_ref }} 21 | id: commitback 22 | continue-on-error: true 23 | run: | 24 | git config --local user.name 'github-actions[bot]' 25 | git config --local user.email 'github-actions[bot]@users.noreply.github.com' 26 | git add --all 27 | git commit -m "chore(i18n): sync locale on ${{github.ref_name}}" 28 | 29 | - name: Create Pull Request 30 | if: steps.commitback.outcome == 'success' 31 | continue-on-error: true 32 | uses: peter-evans/create-pull-request@v5 33 | with: 34 | delete-branch: true 35 | body: "Automatically sync i18n translation jsons" 36 | title: "chore(i18n): sync locale on ${{github.ref_name}}" 37 | commit-message: "chore(i18n): sync locale on ${{github.ref_name}}" 38 | branch: genlocale-${{github.ref_name}} 39 | -------------------------------------------------------------------------------- /.github/workflows/pull_format.yml: -------------------------------------------------------------------------------- 1 | name: Check Pull Format 2 | 3 | on: 4 | pull_request_target: 5 | types: [opened, reopened] 6 | 7 | jobs: 8 | # This workflow closes invalid PR 9 | close_pr: 10 | # The type of runner that the job will run on 11 | runs-on: ubuntu-latest 12 | permissions: write-all 13 | 14 | # Steps represent a sequence of tasks that will be executed as part of the job 15 | steps: 16 | - name: Close PR if it is not pointed to dev branch 17 | if: github.event.pull_request.base.ref != 'dev' 18 | uses: superbrothers/close-pull-request@v3 19 | with: 20 | # Optional. Post a issue comment just before closing a pull request. 21 | comment: "Invalid PR to `non-dev` branch `${{ github.event.pull_request.base.ref }}`." 22 | 23 | pull_format: 24 | runs-on: ubuntu-latest 25 | permissions: 26 | contents: write 27 | 28 | continue-on-error: true 29 | 30 | steps: 31 | - name: Checkout 32 | continue-on-error: true 33 | uses: actions/checkout@v3 34 | with: 35 | ref: ${{ github.head_ref }} 36 | fetch-depth: 0 37 | 38 | - name: Set up Python ${{ matrix.python-version }} 39 | uses: actions/setup-python@v4 40 | with: 41 | python-version: ${{ matrix.python-version }} 42 | 43 | - name: Install Black 44 | run: pip install "black[jupyter]" 45 | 46 | - name: Run Black 47 | # run: black $(git ls-files '*.py') 48 | run: black . 49 | -------------------------------------------------------------------------------- /.github/workflows/push_format.yml: -------------------------------------------------------------------------------- 1 | name: Standardize Code Format 2 | 3 | on: 4 | push: 5 | branches: 6 | - main 7 | - dev 8 | 9 | jobs: 10 | push_format: 11 | runs-on: ubuntu-latest 12 | 13 | permissions: 14 | contents: write 15 | pull-requests: write 16 | 17 | steps: 18 | - uses: actions/checkout@v3 19 | with: 20 | ref: ${{github.ref_name}} 21 | 22 | - name: Set up Python ${{ matrix.python-version }} 23 | uses: actions/setup-python@v4 24 | with: 25 | python-version: ${{ matrix.python-version }} 26 | 27 | - name: Install Black 28 | run: pip install "black[jupyter]" 29 | 30 | - name: Run Black 31 | # run: black $(git ls-files '*.py') 32 | run: black . 33 | 34 | - name: Commit Back 35 | continue-on-error: true 36 | id: commitback 37 | run: | 38 | git config --local user.email "github-actions[bot]@users.noreply.github.com" 39 | git config --local user.name "github-actions[bot]" 40 | git add --all 41 | git commit -m "chore(format): run black on ${{github.ref_name}}" 42 | 43 | - name: Create Pull Request 44 | if: steps.commitback.outcome == 'success' 45 | continue-on-error: true 46 | uses: peter-evans/create-pull-request@v5 47 | with: 48 | delete-branch: true 49 | body: "Automatically apply code formatter change" 50 | title: "chore(format): run black on ${{github.ref_name}}" 51 | commit-message: "chore(format): run black on ${{github.ref_name}}" 52 | branch: formatter-${{github.ref_name}} 53 | -------------------------------------------------------------------------------- /.github/workflows/unitest.yml: -------------------------------------------------------------------------------- 1 | name: Unit Test 2 | on: [ push, pull_request ] 3 | jobs: 4 | build: 5 | runs-on: ${{ matrix.os }} 6 | strategy: 7 | matrix: 8 | python-version: ["3.8", "3.9", "3.10"] 9 | os: [ubuntu-latest] 10 | fail-fast: true 11 | 12 | steps: 13 | - uses: actions/checkout@master 14 | - name: Set up Python ${{ matrix.python-version }} 15 | uses: actions/setup-python@v4 16 | with: 17 | python-version: ${{ matrix.python-version }} 18 | - name: Install dependencies 19 | run: | 20 | sudo apt update 21 | wget https://github.com/fumiama/RVC-Models-Downloader/releases/download/v0.2.5/rvcmd_linux_amd64.deb 22 | sudo apt -y install ./rvcmd_linux_amd64.deb 23 | pip install --force pip==24.0 # fix fairseq installing issue https://github.com/facebookresearch/fairseq/issues/5552 24 | python -m pip install --upgrade setuptools 25 | python -m pip install --upgrade wheel 26 | pip install torch torchvision torchaudio 27 | pip install -r requirements/main.txt 28 | rvcmd -notrs -w 1 -notui assets/rvc 29 | - name: Test step 1 & 2 30 | run: | 31 | mkdir -p logs/mi-test 32 | touch logs/mi-test/preprocess.log 33 | python infer/modules/train/preprocess.py logs/mute/0_gt_wavs 48000 8 logs/mi-test True 3.7 34 | touch logs/mi-test/extract_f0_feature.log 35 | python infer/modules/train/extract_f0_print.py logs/mi-test $(nproc) pm cpu False 36 | python infer/modules/train/extract_feature_print.py cpu 1 0 0 logs/mi-test v1 True 37 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | __pycache__ 3 | /TEMP 4 | *.pyd 5 | .venv 6 | .vscode 7 | .idea 8 | xcuserdata 9 | /opt 10 | 11 | # Generated by RVC 12 | /logs 13 | 14 | /assets/weights/* 15 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # syntax=docker/dockerfile:1 2 | 3 | FROM nvidia/cuda:11.6.2-cudnn8-runtime-ubuntu20.04 4 | 5 | EXPOSE 7865 6 | 7 | WORKDIR /app 8 | 9 | # Install dependenceis to add PPAs 10 | RUN apt-get update && \ 11 | apt-get install -y -qq aria2 && apt clean && \ 12 | apt-get install -y software-properties-common && \ 13 | apt-get clean && \ 14 | rm -rf /var/lib/apt/lists/* 15 | # Add the deadsnakes PPA to get Python 3.9 16 | RUN add-apt-repository ppa:deadsnakes/ppa 17 | 18 | # Install Python 3.9 and pip 19 | RUN apt-get update && \ 20 | apt-get install -y build-essential python-dev python3-dev python3.9-distutils python3.9-dev python3.9 curl && \ 21 | apt-get clean && \ 22 | update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1 && \ 23 | curl https://bootstrap.pypa.io/get-pip.py | python3.9 24 | 25 | # Set Python 3.9 as the default 26 | RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.9 1 27 | 28 | COPY . . 29 | 30 | RUN python3 -m pip install --upgrade pip>=24.0 31 | RUN python3 -m pip install --no-cache-dir -r requirements/main.txt 32 | 33 | RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d assets/pretrained_v2/ -o D40k.pth 34 | RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d assets/pretrained_v2/ -o G40k.pth 35 | RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -d assets/pretrained_v2/ -o f0D40k.pth 36 | RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth -d assets/pretrained_v2/ -o f0G40k.pth 37 | 38 | RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d assets/uvr5_weights/ -o HP2-人声vocals+非人声instrumentals.pth 39 | RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d assets/uvr5_weights/ -o HP5-主旋律人声vocals+其他instrumentals.pth 40 | 41 | RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d assets/hubert -o hubert_base.pt 42 | 43 | RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt -d assets/rmvpe -o rmvpe.pt 44 | 45 | VOLUME [ "/app/weights", "/app/opt" ] 46 | 47 | CMD ["python3", "web.py"] 48 | -------------------------------------------------------------------------------- /assets/hubert/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore 3 | -------------------------------------------------------------------------------- /assets/indices/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore 3 | -------------------------------------------------------------------------------- /assets/pretrained/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore 3 | -------------------------------------------------------------------------------- /assets/pretrained_v2/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore 3 | -------------------------------------------------------------------------------- /assets/rmvpe/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore 3 | -------------------------------------------------------------------------------- /assets/uvr5_weights/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore 3 | -------------------------------------------------------------------------------- /assets/weights/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore 3 | -------------------------------------------------------------------------------- /configs/__init__.py: -------------------------------------------------------------------------------- 1 | from .config import singleton_variable, Config, CPUConfig 2 | -------------------------------------------------------------------------------- /configs/config.json: -------------------------------------------------------------------------------- 1 | { 2 | "pth_path": "", 3 | "index_path": "", 4 | "sg_hostapi": "MME", 5 | "sg_wasapi_exclusive": false, 6 | "sg_input_device": "", 7 | "sg_output_device": "", 8 | "sr_type": "sr_device", 9 | "threhold": -60.0, 10 | "pitch": 12.0, 11 | "formant": 0.0, 12 | "rms_mix_rate": 0.5, 13 | "index_rate": 0.0, 14 | "block_time": 0.15, 15 | "crossfade_length": 0.08, 16 | "extra_time": 2.0, 17 | "n_cpu": 4.0, 18 | "use_jit": false, 19 | "use_pv": false, 20 | "f0method": "fcpe" 21 | } -------------------------------------------------------------------------------- /configs/inuse/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore 3 | !v1 4 | !v2 5 | -------------------------------------------------------------------------------- /configs/inuse/v1/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore 3 | -------------------------------------------------------------------------------- /configs/inuse/v2/.gitignore: -------------------------------------------------------------------------------- 1 | * 2 | !.gitignore 3 | -------------------------------------------------------------------------------- /configs/v1/32k.json: -------------------------------------------------------------------------------- 1 | { 2 | "train": { 3 | "log_interval": 200, 4 | "seed": 1234, 5 | "epochs": 20000, 6 | "learning_rate": 1e-4, 7 | "betas": [0.8, 0.99], 8 | "eps": 1e-9, 9 | "batch_size": 4, 10 | "fp16_run": true, 11 | "lr_decay": 0.999875, 12 | "segment_size": 12800, 13 | "init_lr_ratio": 1, 14 | "warmup_epochs": 0, 15 | "c_mel": 45, 16 | "c_kl": 1.0 17 | }, 18 | "data": { 19 | "max_wav_value": 32768.0, 20 | "sampling_rate": 32000, 21 | "filter_length": 1024, 22 | "hop_length": 320, 23 | "win_length": 1024, 24 | "n_mel_channels": 80, 25 | "mel_fmin": 0.0, 26 | "mel_fmax": null 27 | }, 28 | "model": { 29 | "inter_channels": 192, 30 | "hidden_channels": 192, 31 | "filter_channels": 768, 32 | "n_heads": 2, 33 | "n_layers": 6, 34 | "kernel_size": 3, 35 | "p_dropout": 0, 36 | "resblock": "1", 37 | "resblock_kernel_sizes": [3,7,11], 38 | "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], 39 | "upsample_rates": [10,4,2,2,2], 40 | "upsample_initial_channel": 512, 41 | "upsample_kernel_sizes": [16,16,4,4,4], 42 | "use_spectral_norm": false, 43 | "gin_channels": 256, 44 | "spk_embed_dim": 109 45 | } 46 | } 47 | -------------------------------------------------------------------------------- /configs/v1/40k.json: -------------------------------------------------------------------------------- 1 | { 2 | "train": { 3 | "log_interval": 200, 4 | "seed": 1234, 5 | "epochs": 20000, 6 | "learning_rate": 1e-4, 7 | "betas": [0.8, 0.99], 8 | "eps": 1e-9, 9 | "batch_size": 4, 10 | "fp16_run": true, 11 | "lr_decay": 0.999875, 12 | "segment_size": 12800, 13 | "init_lr_ratio": 1, 14 | "warmup_epochs": 0, 15 | "c_mel": 45, 16 | "c_kl": 1.0 17 | }, 18 | "data": { 19 | "max_wav_value": 32768.0, 20 | "sampling_rate": 40000, 21 | "filter_length": 2048, 22 | "hop_length": 400, 23 | "win_length": 2048, 24 | "n_mel_channels": 125, 25 | "mel_fmin": 0.0, 26 | "mel_fmax": null 27 | }, 28 | "model": { 29 | "inter_channels": 192, 30 | "hidden_channels": 192, 31 | "filter_channels": 768, 32 | "n_heads": 2, 33 | "n_layers": 6, 34 | "kernel_size": 3, 35 | "p_dropout": 0, 36 | "resblock": "1", 37 | "resblock_kernel_sizes": [3,7,11], 38 | "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], 39 | "upsample_rates": [10,10,2,2], 40 | "upsample_initial_channel": 512, 41 | "upsample_kernel_sizes": [16,16,4,4], 42 | "use_spectral_norm": false, 43 | "gin_channels": 256, 44 | "spk_embed_dim": 109 45 | } 46 | } 47 | -------------------------------------------------------------------------------- /configs/v1/48k.json: -------------------------------------------------------------------------------- 1 | { 2 | "train": { 3 | "log_interval": 200, 4 | "seed": 1234, 5 | "epochs": 20000, 6 | "learning_rate": 1e-4, 7 | "betas": [0.8, 0.99], 8 | "eps": 1e-9, 9 | "batch_size": 4, 10 | "fp16_run": true, 11 | "lr_decay": 0.999875, 12 | "segment_size": 11520, 13 | "init_lr_ratio": 1, 14 | "warmup_epochs": 0, 15 | "c_mel": 45, 16 | "c_kl": 1.0 17 | }, 18 | "data": { 19 | "max_wav_value": 32768.0, 20 | "sampling_rate": 48000, 21 | "filter_length": 2048, 22 | "hop_length": 480, 23 | "win_length": 2048, 24 | "n_mel_channels": 128, 25 | "mel_fmin": 0.0, 26 | "mel_fmax": null 27 | }, 28 | "model": { 29 | "inter_channels": 192, 30 | "hidden_channels": 192, 31 | "filter_channels": 768, 32 | "n_heads": 2, 33 | "n_layers": 6, 34 | "kernel_size": 3, 35 | "p_dropout": 0, 36 | "resblock": "1", 37 | "resblock_kernel_sizes": [3,7,11], 38 | "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], 39 | "upsample_rates": [10,6,2,2,2], 40 | "upsample_initial_channel": 512, 41 | "upsample_kernel_sizes": [16,16,4,4,4], 42 | "use_spectral_norm": false, 43 | "gin_channels": 256, 44 | "spk_embed_dim": 109 45 | } 46 | } 47 | -------------------------------------------------------------------------------- /configs/v2/32k.json: -------------------------------------------------------------------------------- 1 | { 2 | "train": { 3 | "log_interval": 200, 4 | "seed": 1234, 5 | "epochs": 20000, 6 | "learning_rate": 1e-4, 7 | "betas": [0.8, 0.99], 8 | "eps": 1e-9, 9 | "batch_size": 4, 10 | "fp16_run": true, 11 | "lr_decay": 0.999875, 12 | "segment_size": 12800, 13 | "init_lr_ratio": 1, 14 | "warmup_epochs": 0, 15 | "c_mel": 45, 16 | "c_kl": 1.0 17 | }, 18 | "data": { 19 | "max_wav_value": 32768.0, 20 | "sampling_rate": 32000, 21 | "filter_length": 1024, 22 | "hop_length": 320, 23 | "win_length": 1024, 24 | "n_mel_channels": 80, 25 | "mel_fmin": 0.0, 26 | "mel_fmax": null 27 | }, 28 | "model": { 29 | "inter_channels": 192, 30 | "hidden_channels": 192, 31 | "filter_channels": 768, 32 | "n_heads": 2, 33 | "n_layers": 6, 34 | "kernel_size": 3, 35 | "p_dropout": 0, 36 | "resblock": "1", 37 | "resblock_kernel_sizes": [3,7,11], 38 | "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], 39 | "upsample_rates": [10,8,2,2], 40 | "upsample_initial_channel": 512, 41 | "upsample_kernel_sizes": [20,16,4,4], 42 | "use_spectral_norm": false, 43 | "gin_channels": 256, 44 | "spk_embed_dim": 109 45 | } 46 | } 47 | -------------------------------------------------------------------------------- /configs/v2/48k.json: -------------------------------------------------------------------------------- 1 | { 2 | "train": { 3 | "log_interval": 200, 4 | "seed": 1234, 5 | "epochs": 20000, 6 | "learning_rate": 1e-4, 7 | "betas": [0.8, 0.99], 8 | "eps": 1e-9, 9 | "batch_size": 4, 10 | "fp16_run": true, 11 | "lr_decay": 0.999875, 12 | "segment_size": 17280, 13 | "init_lr_ratio": 1, 14 | "warmup_epochs": 0, 15 | "c_mel": 45, 16 | "c_kl": 1.0 17 | }, 18 | "data": { 19 | "max_wav_value": 32768.0, 20 | "sampling_rate": 48000, 21 | "filter_length": 2048, 22 | "hop_length": 480, 23 | "win_length": 2048, 24 | "n_mel_channels": 128, 25 | "mel_fmin": 0.0, 26 | "mel_fmax": null 27 | }, 28 | "model": { 29 | "inter_channels": 192, 30 | "hidden_channels": 192, 31 | "filter_channels": 768, 32 | "n_heads": 2, 33 | "n_layers": 6, 34 | "kernel_size": 3, 35 | "p_dropout": 0, 36 | "resblock": "1", 37 | "resblock_kernel_sizes": [3,7,11], 38 | "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], 39 | "upsample_rates": [12,10,2,2], 40 | "upsample_initial_channel": 512, 41 | "upsample_kernel_sizes": [24,20,4,4], 42 | "use_spectral_norm": false, 43 | "gin_channels": 256, 44 | "spk_embed_dim": 109 45 | } 46 | } 47 | -------------------------------------------------------------------------------- /docker-compose.yml: -------------------------------------------------------------------------------- 1 | version: "3.8" 2 | services: 3 | rvc: 4 | build: 5 | context: . 6 | dockerfile: Dockerfile 7 | container_name: rvc 8 | volumes: 9 | - ./weights:/app/assets/weights 10 | - ./opt:/app/opt 11 | # - ./dataset:/app/dataset # you can use this folder in order to provide your dataset for model training 12 | ports: 13 | - 7865:7865 14 | deploy: 15 | resources: 16 | reservations: 17 | devices: 18 | - driver: nvidia 19 | count: 1 20 | capabilities: [gpu] -------------------------------------------------------------------------------- /docs/cn/faq.md: -------------------------------------------------------------------------------- 1 | ## Q1:一键训练结束没有索引 2 | 3 | 显示"Training is done. The program is closed."则模型训练成功，后续紧邻的报错是假的； 4 | 5 | 6 | 一键训练结束完成没有added开头的索引文件，可能是因为训练集太大卡住了添加索引的步骤；已通过批处理add索引解决内存add索引对内存需求过大的问题。临时可尝试再次点击"训练索引"按钮。 7 | 8 | 9 | ## Q2:训练结束推理没看到训练集的音色 10 | 点刷新音色再看看，如果还没有看看训练有没有报错，控制台和webui的截图，logs/实验名下的log，都可以发给开发者看看。 11 | 12 | 13 | ## Q3:如何分享模型 14 |   rvc_root/logs/实验名 下面存储的pth不是用来分享模型用来推理的，而是为了存储实验状态供复现，以及继续训练用的。用来分享的模型应该是weights文件夹下大小为60+MB的pth文件； 15 | 16 |   后续将把weights/exp_name.pth和logs/exp_name/added_xxx.index合并打包成weights/exp_name.zip省去填写index的步骤，那么zip文件用来分享，不要分享pth文件，除非是想换机器继续训练； 17 | 18 |   如果你把logs文件夹下的几百MB的pth文件复制/分享到weights文件夹下强行用于推理，可能会出现f0，tgt_sr等各种key不存在的报错。你需要用ckpt选项卡最下面，手工或自动（本地logs下如果能找到相关信息则会自动）选择是否携带音高、目标音频采样率的选项后进行ckpt小模型提取（输入路径填G开头的那个），提取完在weights文件夹下会出现60+MB的pth文件，刷新音色后可以选择使用。 19 | 20 | 21 | ## Q4:Connection Error. 22 | 也许你关闭了控制台（黑色窗口）。 23 | 24 | 25 | ## Q5:WebUI弹出Expecting value: line 1 column 1 (char 0). 26 | 请关闭系统局域网代理/全局代理。 27 | 28 | 29 | 这个不仅是客户端的代理，也包括服务端的代理（例如你使用autodl设置了http_proxy和https_proxy学术加速，使用时也需要unset关掉） 30 | 31 | 32 | ## Q6:不用WebUI如何通过命令训练推理 33 | 训练脚本： 34 | 35 | 可先跑通WebUI，消息窗内会显示数据集处理和训练用命令行； 36 | 37 | 38 | 推理脚本： 39 | 40 | https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/myinfer.py 41 | 42 | 43 | 例子： 44 | 45 | 46 | runtime\python.exe myinfer.py 0 "E:\codes\py39\RVC-beta\todo-songs\1111.wav" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "test.wav" "weights/mi-test.pth" 0.6 cuda:0 True 47 | 48 | 49 | f0up_key=sys.argv[1] 50 | 51 | input_path=sys.argv[2] 52 | 53 | index_path=sys.argv[3] 54 | 55 | f0method=sys.argv[4]#harvest or pm 56 | 57 | opt_path=sys.argv[5] 58 | 59 | model_path=sys.argv[6] 60 | 61 | index_rate=float(sys.argv[7]) 62 | 63 | device=sys.argv[8] 64 | 65 | is_half=bool(sys.argv[9]) 66 | 67 | 68 | ## Q7:Cuda error/Cuda out of memory. 69 | 小概率是cuda配置问题、设备不支持；大概率是显存不够（out of memory）； 70 | 71 | 72 | 训练的话缩小batch size（如果缩小到1还不够只能更换显卡训练），推理的话酌情缩小config.py结尾的x_pad，x_query，x_center，x_max。4G以下显存（例如1060（3G）和各种2G显卡）可以直接放弃，4G显存显卡还有救。 73 | 74 | 75 | ## Q8:total_epoch调多少比较好 76 | 77 | 如果训练集音质差底噪大，20~30足够了，调太高，底模音质无法带高你的低音质训练集 78 | 79 | 如果训练集音质高底噪低时长多，可以调高，200是ok的（训练速度很快，既然你有条件准备高音质训练集，显卡想必条件也不错，肯定不在乎多一些训练时间） 80 | 81 | 82 | ## Q9:需要多少训练集时长 83 |   推荐10min至50min 84 | 85 |   保证音质高底噪低的情况下，如果有个人特色的音色统一，则多多益善 86 | 87 |   高水平的训练集（精简+音色有特色），5min至10min也是ok的，仓库作者本人就经常这么玩 88 | 89 |   也有人拿1min至2min的数据来训练并且训练成功的，但是成功经验是其他人不可复现的，不太具备参考价值。这要求训练集音色特色非常明显（比如说高频气声较明显的萝莉少女音），且音质高； 90 | 91 |   1min以下时长数据目前没见有人尝试（成功）过。不建议进行这种鬼畜行为。 92 | 93 | 94 | ## Q10:index rate干嘛用的，怎么调（科普） 95 |   如果底模和推理源的音质高于训练集的音质，他们可以带高推理结果的音质，但代价可能是音色往底模/推理源的音色靠，这种现象叫做"音色泄露"； 96 | 97 |   index rate用来削减/解决音色泄露问题。调到1，则理论上不存在推理源的音色泄露问题，但音质更倾向于训练集。如果训练集音质比推理源低，则index rate调高可能降低音质。调到0，则不具备利用检索混合来保护训练集音色的效果； 98 | 99 |   如果训练集优质时长多，可调高total_epoch，此时模型本身不太会引用推理源和底模的音色，很少存在"音色泄露"问题，此时index_rate不重要，你甚至可以不建立/分享index索引文件。 100 | 101 | 102 | ## Q11:推理怎么选gpu 103 | config.py文件里device cuda:后面选择卡号； 104 | 105 | 卡号和显卡的映射关系，在训练选项卡的显卡信息栏里能看到。 106 | 107 | 108 | ## Q12:如何推理训练中间保存的pth 109 | 通过ckpt选项卡最下面提取小模型。 110 | 111 | 112 | 113 | ## Q13:如何中断和继续训练 114 | 现阶段只能关闭WebUI控制台双击go-web.bat重启程序。网页参数也要刷新重新填写； 115 | 116 | 继续训练：相同网页参数点训练模型，就会接着上次的checkpoint继续训练。 117 | 118 | 119 | ## Q14:训练时出现文件页面/内存error 120 | 进程开太多了，内存炸了。你可能可以通过如下方式解决 121 | 122 | 1、"提取音高和处理数据使用的CPU进程数" 酌情拉低； 123 | 124 | 2、训练集音频手工切一下，不要太长。 125 | 126 | 127 | 128 | ## Q15:如何中途加数据训练 129 | 1、所有数据新建一个实验名； 130 | 131 | 2、拷贝上一次的最新的那个G和D文件（或者你想基于哪个中间ckpt训练，也可以拷贝中间的）到新实验名；下 132 | 133 | 3、一键训练新实验名，他会继续上一次的最新进度训练。 134 | 135 | 136 | ## Q16: error about llvmlite.dll 137 | 138 | OSError: Could not load shared object file: llvmlite.dll 139 | 140 | FileNotFoundError: Could not find module lib\site-packages\llvmlite\binding\llvmlite.dll (or one of its dependencies). Try using the full path with constructor syntax. 141 | 142 | win平台会报这个错，装上https://aka.ms/vs/17/release/vc_redist.x64.exe这个再重启WebUI就好了。 143 | 144 | ## Q17: RuntimeError: The expanded size of the tensor (17280) must match the existing size (0) at non-singleton dimension 1. Target sizes: [1, 17280]. Tensor sizes: [0] 145 | 146 | wavs16k文件夹下，找到文件大小显著比其他都小的一些音频文件，删掉，点击训练模型，就不会报错了，不过由于一键流程中断了你训练完模型还要点训练索引。 147 | 148 | ## Q18: RuntimeError: The size of tensor a (24) must match the size of tensor b (16) at non-singleton dimension 2 149 | 150 | 不要中途变更采样率继续训练。如果一定要变更，应更换实验名从头训练。当然你也可以把上次提取的音高和特征（0/1/2/2b folders）拷贝过去加速训练流程。 151 | -------------------------------------------------------------------------------- /docs/en/training_tips_en.md: -------------------------------------------------------------------------------- 1 | Instructions and tips for RVC training 2 | ====================================== 3 | This TIPS explains how data training is done. 4 | 5 | # Training flow 6 | I will explain along the steps in the training tab of the GUI. 7 | 8 | ## step1 9 | Set the experiment name here. 10 | 11 | You can also set here whether the model should take pitch into account. 12 | If the model doesn't consider pitch, the model will be lighter, but not suitable for singing. 13 | 14 | Data for each experiment is placed in `/logs/your-experiment-name/`. 15 | 16 | ## step2a 17 | Loads and preprocesses audio. 18 | 19 | ### load audio 20 | If you specify a folder with audio, the audio files in that folder will be read automatically. 21 | For example, if you specify `C:Users\hoge\voices`, `C:Users\hoge\voices\voice.mp3` will be loaded, but `C:Users\hoge\voices\dir\voice.mp3` will Not loaded. 22 | 23 | Since ffmpeg is used internally for reading audio, if the extension is supported by ffmpeg, it will be read automatically. 24 | After converting to int16 with ffmpeg, convert to float32 and normalize between -1 to 1. 25 | 26 | ### denoising 27 | The audio is smoothed by scipy's filtfilt. 28 | 29 | ### Audio Split 30 | First, the input audio is divided by detecting parts of silence that last longer than a certain period (max_sil_kept=5 seconds?). After splitting the audio on silence, split the audio every 4 seconds with an overlap of 0.3 seconds. For audio separated within 4 seconds, after normalizing the volume, convert the wav file to `/logs/your-experiment-name/0_gt_wavs` and then convert it to 16k sampling rate to `/logs/your-experiment-name/1_16k_wavs ` as a wav file. 31 | 32 | ## step2b 33 | ### Extract pitch 34 | Extract pitch information from wav files. Extract the pitch information (=f0) using the method built into parselmouth or pyworld and save it in `/logs/your-experiment-name/2a_f0`. Then logarithmically convert the pitch information to an integer between 1 and 255 and save it in `/logs/your-experiment-name/2b-f0nsf`. 35 | 36 | ### Extract feature_print 37 | Convert the wav file to embedding in advance using HuBERT. Read the wav file saved in `/logs/your-experiment-name/1_16k_wavs`, convert the wav file to 256-dimensional features with HuBERT, and save in npy format in `/logs/your-experiment-name/3_feature256`. 38 | 39 | ## step3 40 | train the model. 41 | ### Glossary for Beginners 42 | In deep learning, the data set is divided and the learning proceeds little by little. In one model update (step), batch_size data are retrieved and predictions and error corrections are performed. Doing this once for a dataset counts as one epoch. 43 | 44 | Therefore, the learning time is the learning time per step x (the number of data in the dataset / batch size) x the number of epochs. In general, the larger the batch size, the more stable the learning becomes (learning time per step ÷ batch size) becomes smaller, but it uses more GPU memory. GPU RAM can be checked with the nvidia-smi command. Learning can be done in a short time by increasing the batch size as much as possible according to the machine of the execution environment. 45 | 46 | ### Specify pretrained model 47 | RVC starts training the model from pretrained weights instead of from 0, so it can be trained with a small dataset. 48 | 49 | By default 50 | 51 | - If you consider pitch, it loads `rvc-location/pretrained/f0G40k.pth` and `rvc-location/pretrained/f0D40k.pth`. 52 | - If you don't consider pitch, it loads `rvc-location/pretrained/G40k.pth` and `rvc-location/pretrained/D40k.pth`. 53 | 54 | When learning, model parameters are saved in `logs/your-experiment-name/G_{}.pth` and `logs/your-experiment-name/D_{}.pth` for each save_every_epoch, but by specifying this path, you can start learning. You can restart or start training from model weights learned in a different experiment. 55 | 56 | ### learning index 57 | RVC saves the HuBERT feature values used during training, and during inference, searches for feature values that are similar to the feature values used during learning to perform inference. In order to perform this search at high speed, the index is learned in advance. 58 | For index learning, we use the approximate neighborhood search library faiss. Read the feature value of `logs/your-experiment-name/3_feature256` and use it to learn the index, and save it as `logs/your-experiment-name/add_XXX.index`. 59 | 60 | (From the 20230428update version, it is read from the index, and saving / specifying is no longer necessary.) 61 | 62 | ### Button description 63 | - Train model: After executing step2b, press this button to train the model. 64 | - Train feature index: After training the model, perform index learning. 65 | - One-click training: step2b, model training and feature index training all at once. -------------------------------------------------------------------------------- /docs/jp/faiss_tips_ja.md: -------------------------------------------------------------------------------- 1 | faiss tuning TIPS 2 | ================== 3 | # about faiss 4 | faissはfacebook researchの開発する、密なベクトルに対する近傍探索をまとめたライブラリで、多くの近似近傍探索の手法を効率的に実装しています。 5 | 近似近傍探索はある程度精度を犠牲にしながら高速に類似するベクトルを探します。 6 | 7 | ## faiss in RVC 8 | RVCではHuBERTで変換した特徴量のEmbeddingに対し、学習データから生成されたEmbeddingと類似するものを検索し、混ぜることでより元の音声に近い変換を実現しています。ただ、この検索は愚直に行うと時間がかかるため、近似近傍探索を用いることで高速な変換を実現しています。 9 | 10 | # 実装のoverview 11 | モデルが配置されている '/logs/your-experiment/3_feature256'には各音声データからHuBERTで抽出された特徴量が配置されています。 12 | ここからnpyファイルをファイル名でソートした順番で読み込み、ベクトルを連結してbig_npyを作成しfaissを学習させます。(このベクトルのshapeは[N, 256]です。) 13 | 14 | 本Tipsではまずこれらのパラメータの意味を解説します。 15 | 16 | # 手法の解説 17 | ## index factory 18 | index factoryは複数の近似近傍探索の手法を繋げるパイプラインをstringで表記するfaiss独自の記法です。 19 | これにより、index factoryの文字列を変更するだけで様々な近似近傍探索の手法を試せます。 20 | RVCでは以下のように使われています。 21 | 22 | ```python 23 | index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf) 24 | ``` 25 | index_factoryの引数のうち、1つ目はベクトルの次元数、2つ目はindex factoryの文字列で、3つ目には用いる距離を指定することができます。 26 | 27 | より詳細な記法については 28 | https://github.com/facebookresearch/faiss/wiki/The-index-factory 29 | 30 | ## 距離指標 31 | embeddingの類似度として用いられる代表的な指標として以下の二つがあります。 32 | 33 | - ユークリッド距離(METRIC_L2) 34 | - 内積(METRIC_INNER_PRODUCT) 35 | 36 | ユークリッド距離では各次元において二乗の差をとり、全次元の差を足してから平方根をとります。これは日常的に用いる2次元、3次元での距離と同じです。 37 | 内積はこのままでは類似度の指標として用いず、一般的にはL2ノルムで正規化してから内積をとるコサイン類似度を用います。 38 | 39 | どちらがよいかは場合によりますが、word2vec等で得られるembeddingやArcFace等で学習した類似画像検索のモデルではコサイン類似度が用いられることが多いです。ベクトルXに対してl2正規化をnumpyで行う場合は、0 divisionを避けるために十分に小さな値をepsとして以下のコードで可能です。 40 | 41 | ```python 42 | X_normed = X / np.maximum(eps, np.linalg.norm(X, ord=2, axis=-1, keepdims=True)) 43 | ``` 44 | 45 | また、index factoryには第3引数に渡す値を選ぶことで計算に用いる距離指標を変更できます。 46 | 47 | ```python 48 | index = faiss.index_factory(dimention, text, faiss.METRIC_INNER_PRODUCT) 49 | ``` 50 | 51 | ## IVF 52 | IVF(Inverted file indexes)は全文検索における転置インデックスと似たようなアルゴリズムです。 53 | 学習時には検索対象に対してkmeansでクラスタリングを行い、クラスタ中心を用いてボロノイ分割を行います。各データ点には一つずつクラスタが割り当てられるので、クラスタからデータ点を逆引きする辞書を作成します。 54 | 55 | 例えば以下のようにクラスタが割り当てられた場合 56 | |index|クラスタ| 57 | |-----|-------| 58 | |1|A| 59 | |2|B| 60 | |3|A| 61 | |4|C| 62 | |5|B| 63 | 64 | 作成される転置インデックスは以下のようになります。 65 | 66 | |クラスタ|index| 67 | |-------|-----| 68 | |A|1, 3| 69 | |B|2, 5| 70 | |C|4| 71 | 72 | 検索時にはまずクラスタからn_probe個のクラスタを検索し、次にそれぞれのクラスタに属するデータ点について距離を計算します。 73 | 74 | # 推奨されるパラメータ 75 | indexの選び方については公式にガイドラインがあるので、それに準じて説明します。 76 | https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index 77 | 78 | 1M以下のデータセットにおいては4bit-PQが2023年4月時点ではfaissで利用できる最も効率的な手法です。 79 | これをIVFと組み合わせ、4bit-PQで候補を絞り、最後に正確な指標で距離を再計算するには以下のindex factoryを用いることで記載できます。 80 | 81 | ```python 82 | index = faiss.index_factory(256, "IVF1024,PQ128x4fs,RFlat") 83 | ``` 84 | 85 | ## IVFの推奨パラメータ 86 | IVFの数が多すぎる場合、たとえばデータ数の数だけIVFによる粗量子化を行うと、これは愚直な全探索と同じになり効率が悪いです。 87 | 1M以下の場合ではIVFの値はデータ点の数Nに対して4*sqrt(N) ~ 16*sqrt(N)に推奨しています。 88 | 89 | n_probeはn_probeの数に比例して計算時間が増えるので、精度と相談して適切に選んでください。個人的にはRVCにおいてそこまで精度は必要ないと思うのでn_probe = 1で良いと思います。 90 | 91 | ## FastScan 92 | FastScanは直積量子化で大まかに距離を近似するのを、レジスタ内で行うことにより高速に行うようにした手法です。 93 | 直積量子化は学習時にd次元ごと(通常はd=2)に独立してクラスタリングを行い、クラスタ同士の距離を事前計算してlookup tableを作成します。予測時はlookup tableを見ることで各次元の距離をO(1)で計算できます。 94 | そのため、PQの次に指定する数字は通常ベクトルの半分の次元を指定します。 95 | 96 | FastScanに関するより詳細な説明は公式のドキュメントを参照してください。 97 | https://github.com/facebookresearch/faiss/wiki/Fast-accumulation-of-PQ-and-AQ-codes-(FastScan) 98 | 99 | ## RFlat 100 | RFlatはFastScanで計算した大まかな距離を、index factoryの第三引数で指定した正確な距離で再計算する指示です。 101 | k個の近傍を取得する際は、k*k_factor個の点について再計算が行われます。 102 | -------------------------------------------------------------------------------- /docs/jp/training_tips_ja.md: -------------------------------------------------------------------------------- 1 | RVCの訓練における説明、およびTIPS 2 | =============================== 3 | 本TIPSではどのようにデータの訓練が行われているかを説明します。 4 | 5 | # 訓練の流れ 6 | GUIの訓練タブのstepに沿って説明します。 7 | 8 | ## step1 9 | 実験名の設定を行います。 10 | 11 | また、モデルに音高ガイド(ピッチ)を考慮させるかもここで設定できます。考慮させない場合はモデルは軽量になりますが、歌唱には向かなくなります。 12 | 13 | 各実験のデータは`/logs/実験名/`に配置されます。 14 | 15 | ## step2a 16 | 音声の読み込みと前処理を行います。 17 | 18 | ### load audio 19 | 音声のあるフォルダを指定すると、そのフォルダ内にある音声ファイルを自動で読み込みます。 20 | 例えば`C:Users\hoge\voices`を指定した場合、`C:Users\hoge\voices\voice.mp3`は読み込まれますが、`C:Users\hoge\voices\dir\voice.mp3`は読み込まれません。 21 | 22 | 音声の読み込みには内部でffmpegを利用しているので、ffmpegで対応している拡張子であれば自動的に読み込まれます。 23 | ffmpegでint16に変換した後、float32に変換し、-1 ~ 1の間に正規化されます。 24 | 25 | ### denoising 26 | 音声についてscipyのfiltfiltによる平滑化を行います。 27 | 28 | ### 音声の分割 29 | 入力した音声はまず、一定期間(max_sil_kept=5秒?)より長く無音が続く部分を検知して音声を分割します。無音で音声を分割した後は、0.3秒のoverlapを含む4秒ごとに音声を分割します。4秒以内に区切られた音声は、音量の正規化を行った後wavファイルを`/logs/実験名/0_gt_wavs`に、そこから16kのサンプリングレートに変換して`/logs/実験名/1_16k_wavs`にwavファイルで保存します。 30 | 31 | ## step2b 32 | ### ピッチの抽出 33 | wavファイルからピッチ(音の高低)の情報を抽出します。parselmouthやpyworldに内蔵されている手法でピッチ情報(=f0)を抽出し、`/logs/実験名/2a_f0`に保存します。その後、ピッチ情報を対数で変換して1~255の整数に変換し、`/logs/実験名/2b-f0nsf`に保存します。 34 | 35 | ### feature_printの抽出 36 | HuBERTを用いてwavファイルを事前にembeddingに変換します。`/logs/実験名/1_16k_wavs`に保存したwavファイルを読み込み、HuBERTでwavファイルを256次元の特徴量に変換し、npy形式で`/logs/実験名/3_feature256`に保存します。 37 | 38 | ## step3 39 | モデルのトレーニングを行います。 40 | ### 初心者向け用語解説 41 | 深層学習ではデータセットを分割し、少しずつ学習を進めていきます。一回のモデルの更新(step)では、batch_size個のデータを取り出し予測と誤差の修正を行います。これをデータセットに対して一通り行うと一epochと数えます。 42 | 43 | そのため、学習時間は 1step当たりの学習時間 x (データセット内のデータ数 ÷ バッチサイズ) x epoch数 かかります。一般にバッチサイズを大きくするほど学習は安定し、(1step当たりの学習時間÷バッチサイズ)は小さくなりますが、その分GPUのメモリを多く使用します。GPUのRAMはnvidia-smiコマンド等で確認できます。実行環境のマシンに合わせてバッチサイズをできるだけ大きくするとより短時間で学習が可能です。 44 | 45 | ### pretrained modelの指定 46 | RVCではモデルの訓練を0からではなく、事前学習済みの重みから開始するため、少ないデータセットで学習を行えます。 47 | 48 | デフォルトでは 49 | 50 | - 音高ガイドを考慮する場合、`RVCのある場所/pretrained/f0G40k.pth`と`RVCのある場所/pretrained/f0D40k.pth`を読み込みます。 51 | - 音高ガイドを考慮しない場合、`RVCのある場所/pretrained/G40k.pth`と`RVCのある場所/pretrained/D40k.pth`を読み込みます。 52 | 53 | 学習時はsave_every_epochごとにモデルのパラメータが`logs/実験名/G_{}.pth`と`logs/実験名/D_{}.pth`に保存されますが、このパスを指定することで学習を再開したり、もしくは違う実験で学習したモデルの重みから学習を開始できます。 54 | 55 | ### indexの学習 56 | RVCでは学習時に使われたHuBERTの特徴量を保存し、推論時は学習時の特徴量から近い特徴量を探してきて推論を行います。この検索を高速に行うために事前にindexの学習を行います。 57 | indexの学習には近似近傍探索ライブラリのfaissを用います。`/logs/実験名/3_feature256`の特徴量を読み込み、それを用いて学習したindexを`/logs/実験名/add_XXX.index`として保存します。 58 | (20230428updateよりtotal_fea.npyはindexから読み込むので不要になりました。) 59 | 60 | ### ボタンの説明 61 | - モデルのトレーニング: step2bまでを実行した後、このボタンを押すとモデルの学習を行います。 62 | - 特徴インデックスのトレーニング: モデルのトレーニング後、indexの学習を行います。 63 | - ワンクリックトレーニング: step2bまでとモデルのトレーニング、特徴インデックスのトレーニングを一括で行います。 64 | 65 | -------------------------------------------------------------------------------- /docs/kr/README.ko.han.md: -------------------------------------------------------------------------------- 1 |
2 | 3 | # Retrieval-based-Voice-Conversion-WebUI 4 | VITS基盤의 簡單하고使用하기 쉬운音聲變換틀 5 | 6 | 7 | 8 | [![madewithlove](https://img.shields.io/badge/made_with-%E2%9D%A4-red?style=for-the-badge&labelColor=orange 9 | )](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI) 10 | 11 | ![moe](https://counter.seku.su/cmoe?name=rvc&theme=r34) 12 | 13 | [![Licence](https://img.shields.io/github/license/fumiama/Retrieval-based-Voice-Conversion-WebUI?style=for-the-badge)](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/LICENSE) 14 | [![Huggingface](https://img.shields.io/badge/🤗%20-Spaces-yellow.svg?style=for-the-badge)](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/) 15 | 16 | [![Discord](https://img.shields.io/badge/RVC%20Developers-Discord-7289DA?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/HcsmBBGyVk) 17 | 18 |
19 | 20 | ------ 21 | 22 | [**English**](../en/README.en.md) | [**中文简体**](../../README.md) | [**日本語**](../jp/README.ja.md) | [**한국어**](../kr/README.ko.md) ([**韓國語**](../kr/README.ko.han.md)) | [**Français**](../fr/README.fr.md) | [**Türkçe**](../tr/README.tr.md) | [**Português**](../pt/README.pt.md) 23 | 24 | > [示範映像](https://www.bilibili.com/video/BV1pm4y1z7Gm/)을 確認해 보세요! 25 | 26 | > RVC를活用한實時間音聲變換: [w-okada/voice-changer](https://github.com/w-okada/voice-changer) 27 | 28 | > 基本모델은 50時間假量의 高品質 오픈 소스 VCTK 데이터셋을 使用하였으므로, 著作權上의 念慮가 없으니 安心하고 使用하시기 바랍니다. 29 | 30 | > 著作權問題가 없는 高品質의 노래를 以後에도 繼續해서 訓練할 豫定입니다. 31 | 32 | ## 紹介 33 | 本Repo는 다음과 같은 特徵을 가지고 있습니다: 34 | + top1檢索을利用하여 入力音色特徵을 訓練세트音色特徵으로 代替하여 音色의漏出을 防止; 35 | + 相對的으로 낮은性能의 GPU에서도 빠른訓練可能; 36 | + 적은量의 데이터로 訓練해도 좋은 結果를 얻을 수 있음 (最小10分以上의 低雜음音聲데이터를 使用하는 것을 勸獎); 37 | + 모델融合을通한 音色의 變調可能 (ckpt處理탭->ckpt混合選擇); 38 | + 使用하기 쉬운 WebUI (웹 使用者인터페이스); 39 | + UVR5 모델을 利用하여 목소리와 背景音樂의 빠른 分離; 40 | 41 | ## 環境의準備 42 | poetry를通해 依存를設置하는 것을 勸獎합니다. 43 | 44 | 다음命令은 Python 버전3.8以上의環境에서 實行되어야 합니다: 45 | ```bash 46 | # PyTorch 關聯主要依存設置, 이미設置되어 있는 境遇 건너뛰기 可能 47 | # 參照: https://pytorch.org/get-started/locally/ 48 | pip install torch torchvision torchaudio 49 | 50 | # Windows + Nvidia Ampere Architecture(RTX30xx)를 使用하고 있다面, #21 에서 명시된 것과 같이 PyTorch에 맞는 CUDA 버전을 指定해야 합니다. 51 | #pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 52 | 53 | # Poetry 設置, 이미設置되어 있는 境遇 건너뛰기 可能 54 | # Reference: https://python-poetry.org/docs/#installation 55 | curl -sSL https://install.python-poetry.org | python3 - 56 | 57 | # 依存設置 58 | poetry install 59 | ``` 60 | pip를 活用하여依存를 設置하여도 無妨합니다. 61 | 62 | ```bash 63 | pip install -r requirements/main.txt 64 | ``` 65 | 66 | ## 其他預備모델準備 67 | RVC 모델은 推論과訓練을 依하여 다른 預備모델이 必要합니다. 68 | 69 | [Huggingface space](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/)를 通해서 다운로드 할 수 있습니다. 70 | 71 | 다음은 RVC에 必要한 預備모델 및 其他 파일 目錄입니다: 72 | ```bash 73 | ./assets/hubert/hubert_base.pt 74 | 75 | ./assets/pretrained 76 | 77 | ./assets/uvr5_weights 78 | 79 | V2 버전 모델을 테스트하려면 추가 다운로드가 필요합니다. 80 | 81 | ./assets/pretrained_v2 82 | 83 | ``` 84 | 그後 以下의 命令을 使用하여 WebUI를 始作할 수 있습니다: 85 | ```bash 86 | python web.py 87 | ``` 88 | Windows를 使用하는境遇 `RVC-beta.7z`를 다운로드 및 壓縮解除하여 RVC를 直接使用하거나 `go-web.bat`을 使用하여 WebUi를 直接할 수 있습니다. 89 | 90 | ## 參考 91 | + [ContentVec](https://github.com/auspicious3000/contentvec/) 92 | + [VITS](https://github.com/jaywalnut310/vits) 93 | + [HIFIGAN](https://github.com/jik876/hifi-gan) 94 | + [Gradio](https://github.com/gradio-app/gradio) 95 | + [Ultimate Vocal Remover](https://github.com/Anjok07/ultimatevocalremovergui) 96 | + [audio-slicer](https://github.com/openvpi/audio-slicer) 97 | ## 모든寄與者분들의勞力에感謝드립니다 98 | 99 | [![contributors](https://contrib.rocks/image?repo=fumiama/Retrieval-based-Voice-Conversion-WebUI)](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/graphs/contributors) 100 | 101 | -------------------------------------------------------------------------------- /docs/kr/training_tips_ko.md: -------------------------------------------------------------------------------- 1 | RVC 훈련에 대한 설명과 팁들 2 | ====================================== 3 | 본 팁에서는 어떻게 데이터 훈련이 이루어지고 있는지 설명합니다. 4 | 5 | # 훈련의 흐름 6 | GUI의 훈련 탭의 단계를 따라 설명합니다. 7 | 8 | ## step1 9 | 실험 이름을 지정합니다. 또한, 모델이 피치(소리의 높낮이)를 고려해야 하는지 여부를 여기에서 설정할 수도 있습니다.. 10 | 각 실험을 위한 데이터는 `/logs/experiment name/`에 배치됩니다.. 11 | 12 | ## step2a 13 | 음성 파일을 불러오고 전처리합니다. 14 | 15 | ### 음성 파일 불러오기 16 | 음성 파일이 있는 폴더를 지정하면 해당 폴더에 있는 음성 파일이 자동으로 가져와집니다. 17 | 예를 들어 `C:Users\hoge\voices`를 지정하면 `C:Users\hoge\voices\voice.mp3`가 읽히지만 `C:Users\hoge\voices\dir\voice.mp3`는 읽히지 않습니다. 18 | 19 | 음성 로드에는 내부적으로 ffmpeg를 이용하고 있으므로, ffmpeg로 대응하고 있는 확장자라면 자동적으로 읽힙니다. 20 | ffmpeg에서 int16으로 변환한 후 float32로 변환하고 -1과 1 사이에 정규화됩니다. 21 | 22 | ### 잡음 제거 23 | 음성 파일에 대해 scipy의 filtfilt를 이용하여 잡음을 처리합니다. 24 | 25 | ### 음성 분할 26 | 입력한 음성 파일은 먼저 일정 기간(max_sil_kept=5초?)보다 길게 무음이 지속되는 부분을 감지하여 음성을 분할합니다.무음으로 음성을 분할한 후에는 0.3초의 overlap을 포함하여 4초마다 음성을 분할합니다.4초 이내에 구분된 음성은 음량의 정규화를 실시한 후 wav 파일을 `/logs/실험명/0_gt_wavs`로, 거기에서 16k의 샘플링 레이트로 변환해 `/logs/실험명/1_16k_wavs`에 wav 파일로 저장합니다. 27 | 28 | ## step2b 29 | ### 피치 추출 30 | wav 파일에서 피치(소리의 높낮이) 정보를 추출합니다. parselmouth나 pyworld에 내장되어 있는 메서드으로 피치 정보(=f0)를 추출해, `/logs/실험명/2a_f0`에 저장합니다. 그 후 피치 정보를 로그로 변환하여 1~255 정수로 변환하고 `/logs/실험명/2b-f0nsf`에 저장합니다. 31 | 32 | ### feature_print 추출 33 | HuBERT를 이용하여 wav 파일을 미리 embedding으로 변환합니다. `/logs/실험명/1_16k_wavs`에 저장한 wav 파일을 읽고 HuBERT에서 wav 파일을 256차원 feature들로 변환한 후 npy 형식으로 `/logs/실험명/3_feature256`에 저장합니다. 34 | 35 | ## step3 36 | 모델의 훈련을 진행합니다. 37 | 38 | ### 초보자용 용어 해설 39 | 심층학습(딥러닝)에서는 데이터셋을 분할하여 조금씩 학습을 진행합니다.한 번의 모델 업데이트(step) 단계 당 batch_size개의 데이터를 탐색하여 예측과 오차를 수정합니다. 데이터셋 전부에 대해 이 작업을 한 번 수행하는 이를 하나의 epoch라고 계산합니다. 40 | 41 | 따라서 학습 시간은 단계당 학습 시간 x (데이터셋 내 데이터의 수 / batch size) x epoch 수가 소요됩니다. 일반적으로 batch size가 클수록 학습이 안정적이게 됩니다. (step당 학습 시간 ÷ batch size)는 작아지지만 GPU 메모리를 더 많이 사용합니다. GPU RAM은 nvidia-smi 명령어를 통해 확인할 수 있습니다. 실행 환경에 따라 배치 크기를 최대한 늘리면 짧은 시간 내에 학습이 가능합니다. 42 | 43 | ### 사전 학습된 모델 지정 44 | RVC는 적은 데이터셋으로도 훈련이 가능하도록 사전 훈련된 가중치에서 모델 훈련을 시작합니다. 기본적으로 `rvc-location/pretrained/f0G40k.pth` 및 `rvc-location/pretrained/f0D40k.pth`를 불러옵니다. 학습을 할 시에, 모델 파라미터는 각 save_every_epoch별로 `logs/experiment name/G_{}.pth` 와 `logs/experiment name/D_{}.pth`로 저장이 되는데, 이 경로를 지정함으로써 학습을 재개하거나, 다른 실험에서 학습한 모델의 가중치에서 학습을 시작할 수 있습니다. 45 | 46 | ### index의 학습 47 | RVC에서는 학습시에 사용된 HuBERT의 feature값을 저장하고, 추론 시에는 학습 시 사용한 feature값과 유사한 feature 값을 탐색해 추론을 진행합니다. 이 탐색을 고속으로 수행하기 위해 사전에 index을 학습하게 됩니다. 48 | Index 학습에는 근사 근접 탐색법 라이브러리인 Faiss를 사용하게 됩니다. `/logs/실험명/3_feature256`의 feature값을 불러와, 이를 모두 결합시킨 feature값을 `/logs/실험명/total_fea.npy`로서 저장, 그것을 사용해 학습한 index를`/logs/실험명/add_XXX.index`로 저장합니다. 49 | 50 | ### 버튼 설명 51 | - モデルのトレーニング (모델 학습): step2b까지 실행한 후, 이 버튼을 눌러 모델을 학습합니다. 52 | - 特徴インデックスのトレーニング (특징 지수 훈련): 모델의 훈련 후, index를 학습합니다. 53 | - ワンクリックトレーニング (원클릭 트레이닝): step2b까지의 모델 훈련, feature index 훈련을 일괄로 실시합니다. -------------------------------------------------------------------------------- /docs/pt/training_tips_pt.md: -------------------------------------------------------------------------------- 1 | Instruções e dicas para treinamento RVC 2 | ====================================== 3 | Estas DICAS explicam como o treinamento de dados é feito. 4 | 5 | # Fluxo de treinamento 6 | Explicarei ao longo das etapas na guia de treinamento da GUI. 7 | 8 | ## Passo 1 9 | Defina o nome do experimento aqui. 10 | 11 | Você também pode definir aqui se o modelo deve levar em consideração o pitch. 12 | Se o modelo não considerar o tom, o modelo será mais leve, mas não será adequado para cantar. 13 | 14 | Os dados de cada experimento são colocados em `/logs/nome-do-seu-modelo/`. 15 | 16 | ## Passo 2a 17 | Carrega e pré-processa áudio. 18 | 19 | ### Carregar áudio 20 | Se você especificar uma pasta com áudio, os arquivos de áudio dessa pasta serão lidos automaticamente. 21 | Por exemplo, se você especificar `C:Users\hoge\voices`, `C:Users\hoge\voices\voice.mp3` será carregado, mas `C:Users\hoge\voices\dir\voice.mp3` será Não carregado. 22 | 23 | Como o ffmpeg é usado internamente para leitura de áudio, se a extensão for suportada pelo ffmpeg, ela será lida automaticamente. 24 | Após converter para int16 com ffmpeg, converta para float32 e normalize entre -1 e 1. 25 | 26 | ### Eliminar ruído 27 | O áudio é suavizado pelo filtfilt do scipy. 28 | 29 | ### Divisão de áudio 30 | Primeiro, o áudio de entrada é dividido pela detecção de partes de silêncio que duram mais que um determinado período (max_sil_kept=5 segundos?). Após dividir o áudio no silêncio, divida o áudio a cada 4 segundos com uma sobreposição de 0,3 segundos. Para áudio separado em 4 segundos, após normalizar o volume, converta o arquivo wav para `/logs/nome-do-seu-modelo/0_gt_wavs` e, em seguida, converta-o para taxa de amostragem de 16k para `/logs/nome-do-seu-modelo/1_16k_wavs ` como um arquivo wav. 31 | 32 | ## Passo 2b 33 | ### Extrair pitch 34 | Extraia informações de pitch de arquivos wav. Extraia as informações de pitch (=f0) usando o método incorporado em Parselmouth ou pyworld e salve-as em `/logs/nome-do-seu-modelo/2a_f0`. Em seguida, converta logaritmicamente as informações de pitch para um número inteiro entre 1 e 255 e salve-as em `/logs/nome-do-seu-modelo/2b-f0nsf`. 35 | 36 | ### Extrair feature_print 37 | Converta o arquivo wav para incorporação antecipadamente usando HuBERT. Leia o arquivo wav salvo em `/logs/nome-do-seu-modelo/1_16k_wavs`, converta o arquivo wav em recursos de 256 dimensões com HuBERT e salve no formato npy em `/logs/nome-do-seu-modelo/3_feature256`. 38 | 39 | ## Passo 3 40 | treinar o modelo. 41 | ### Glossário para iniciantes 42 | No aprendizado profundo, o conjunto de dados é dividido e o aprendizado avança aos poucos. Em uma atualização do modelo (etapa), os dados batch_size são recuperados e previsões e correções de erros são realizadas. Fazer isso uma vez para um conjunto de dados conta como um epoch. 43 | 44 | Portanto, o tempo de aprendizagem é o tempo de aprendizagem por etapa x (o número de dados no conjunto de dados/tamanho do lote) x o número de epoch. Em geral, quanto maior o tamanho do lote, mais estável se torna o aprendizado (tempo de aprendizado por etapa ÷ tamanho do lote) fica menor, mas usa mais memória GPU. A RAM da GPU pode ser verificada com o comando nvidia-smi. O aprendizado pode ser feito em pouco tempo aumentando o tamanho do lote tanto quanto possível de acordo com a máquina do ambiente de execução. 45 | 46 | ### Especifique o modelo pré-treinado 47 | O RVC começa a treinar o modelo a partir de pesos pré-treinados em vez de 0, para que possa ser treinado com um pequeno conjunto de dados. 48 | 49 | Por padrão 50 | 51 | - Se você considerar o pitch, ele carrega `rvc-location/pretrained/f0G40k.pth` e `rvc-location/pretrained/f0D40k.pth`. 52 | - Se você não considerar o pitch, ele carrega `rvc-location/pretrained/f0G40k.pth` e `rvc-location/pretrained/f0D40k.pth`. 53 | 54 | Ao aprender, os parâmetros do modelo são salvos em `logs/nome-do-seu-modelo/G_{}.pth` e `logs/nome-do-seu-modelo/D_{}.pth` para cada save_every_epoch, mas especificando nesse caminho, você pode começar a aprender. Você pode reiniciar ou iniciar o treinamento a partir de weights de modelo aprendidos em um experimento diferente. 55 | 56 | ### Index de aprendizado 57 | O RVC salva os valores de recursos do HuBERT usados durante o treinamento e, durante a inferência, procura valores de recursos que sejam semelhantes aos valores de recursos usados durante o aprendizado para realizar a inferência. Para realizar esta busca em alta velocidade, o index é aprendido previamente. 58 | Para aprendizagem de index, usamos a biblioteca de pesquisa de associação de áreas aproximadas faiss. Leia o valor do recurso `logs/nome-do-seu-modelo/3_feature256` e use-o para aprender o index, e salve-o como `logs/nome-do-seu-modelo/add_XXX.index`. 59 | 60 | (A partir da versão 20230428update, ele é lido do index e não é mais necessário salvar/especificar.) 61 | 62 | ### Descrição do botão 63 | - Treinar modelo: Após executar o passo 2b, pressione este botão para treinar o modelo. 64 | - Treinar índice de recursos: após treinar o modelo, execute o aprendizado do index. 65 | - Treinamento com um clique: etapa 2b, treinamento de modelo e treinamento de index de recursos, tudo de uma vez. -------------------------------------------------------------------------------- /docs/tr/training_tips_tr.md: -------------------------------------------------------------------------------- 1 | ## RVC Eğitimi için Talimatlar ve İpuçları 2 | ====================================== 3 | Bu TALİMAT, veri eğitiminin nasıl yapıldığını açıklamaktadır. 4 | 5 | # Eğitim Akışı 6 | Eğitim sekmesindeki adımları takip ederek açıklayacağım. 7 | 8 | ## Adım 1 9 | Deney adını burada belirleyin. 10 | 11 | Ayrıca burada modelin pitch'i dikkate alıp almayacağını da belirleyebilirsiniz. 12 | Eğer model pitch'i dikkate almazsa, model daha hafif olacak, ancak şarkı söyleme için uygun olmayacaktır. 13 | 14 | Her deney için veriler `/logs/your-experiment-name/` dizinine yerleştirilir. 15 | 16 | ## Adım 2a 17 | Ses yüklenir ve ön işleme yapılır. 18 | 19 | ### Ses Yükleme 20 | Ses içeren bir klasör belirtirseniz, bu klasördeki ses dosyaları otomatik olarak okunur. 21 | Örneğin, `C:Users\hoge\voices` belirtirseniz, `C:Users\hoge\voices\voice.mp3` yüklenecek, ancak `C:Users\hoge\voices\dir\voice.mp3` yüklenmeyecektir. 22 | 23 | ### Gürültü Temizleme 24 | Ses scipy'nin filtfilt işlevi ile yumuşatılır. 25 | 26 | ### Ses Ayırma 27 | İlk olarak, giriş sesi belirli bir süreden (max_sil_kept=5 saniye?) daha uzun süren sessiz kısımları tespit ederek böler. Sessizlik üzerinde ses bölündükten sonra sesi 4 saniyede bir 0.3 saniyelik bir örtüşme ile böler. 4 saniye içinde ayrılan sesler için ses normalleştirildikten sonra wav dosyası olarak `/logs/your-experiment-name/0_gt_wavs`'a, ardından 16 kHz örnekleme hızına dönüştürülerek `/logs/your-experiment-name/1_16k_wavs` olarak kaydedilir. 28 | 29 | ## Adım 2b 30 | ### Pitch Çıkarımı 31 | Wav dosyalarından pitch bilgisi çıkarılır. ParSelMouth veya PyWorld'e dahili olarak yerleştirilmiş yöntemi kullanarak pitch bilgisi (=f0) çıkarılır ve `/logs/your-experiment-name/2a_f0` dizinine kaydedilir. Ardından pitch bilgisi logaritmik olarak 1 ile 255 arasında bir tamsayıya dönüştürülüp `/logs/your-experiment-name/2b-f0nsf` dizinine kaydedilir. 32 | 33 | ### Özellik Çıkarımı 34 | HuBERT'i kullanarak önceden gömme olarak wav dosyasını çıkarır. `/logs/your-experiment-name/1_16k_wavs`'a kaydedilen wav dosyasını okuyarak, wav dosyasını 256 boyutlu HuBERT özelliklerine dönüştürür ve npy formatında `/logs/your-experiment-name/3_feature256` dizinine kaydeder. 35 | 36 | ## Adım 3 37 | Modeli eğit. 38 | ### Başlangıç Seviyesi Sözlüğü 39 | Derin öğrenmede, veri kümesi bölmeye ve öğrenmeye adım adım devam eder. Bir model güncellemesinde (adım), batch_size veri alınır ve tahminler ve hata düzeltmeleri yapılır. Bunun bir defa bir veri kümesi için yapılması bir dönem olarak sayılır. 40 | 41 | Bu nedenle, öğrenme zamanı adım başına öğrenme zamanı x (veri kümesindeki veri sayısı / batch boyutu) x dönem sayısıdır. Genel olarak, batch boyutu ne kadar büyükse, öğrenme daha istikrarlı hale gelir (adım başına öğrenme süresi ÷ batch boyutu) küçülür, ancak daha fazla GPU belleği kullanır. GPU RAM'ı nvidia-smi komutu ile kontrol edilebilir. Çalışma ortamının makinesine göre batch boyutunu mümkün olduğunca artırarak öğrenme süresini kısa sürede yapabilirsiniz. 42 | 43 | ### Önceden Eğitilmiş Modeli Belirtme 44 | RVC, modeli 0'dan değil önceden eğitilmiş ağırlıklardan başlatarak eğitir, bu nedenle küçük bir veri kümesi ile eğitilebilir. 45 | 46 | Varsayılan olarak 47 | 48 | - Eğer pitch'i dikkate alıyorsanız, `rvc-location/pretrained/f0G40k.pth` ve `rvc-location/pretrained/f0D40k.pth` yüklenir. 49 | - Eğer pitch'i dikkate almıyorsanız, yine `rvc-location/pretrained/f0G40k.pth` ve `rvc-location/pretrained/f0D40k.pth` yüklenir. 50 | 51 | Öğrenirken model parametreleri her save_every_epoch için `logs/your-experiment-name/G_{}.pth` ve `logs/your-experiment-name/D_{}.pth` olarak kaydedilir, ancak bu yolu belirterek öğrenmeye başlayabilirsiniz. Farklı bir deneyde öğrenilen model ağırlıklarından öğrenmeye yeniden başlayabilir veya eğitimi başlatabilirsiniz. 52 | 53 | ### Öğrenme İndeksi 54 | RVC, eğitim sırasında kullanılan HuBERT özellik değerlerini kaydeder ve çıkarım sırasında, öğrenme sırasında kullanılan özellik değerlerine benzer özellik değerlerini arayarak çıkarım yapar. Bu aramayı yüksek hızda gerçekleştirebilmek için indeks öğrenilir. 55 | İndeks öğrenimi için yaklaş 56 | 57 | ık komşuluk arama kütüphanesi faiss kullanılır. `/logs/your-experiment-name/3_feature256`'daki özellik değerini okur ve indeksi öğrenmek için kullanır, `logs/your-experiment-name/add_XXX.index` olarak kaydedilir. 58 | 59 | (20230428 güncelleme sürümünden itibaren indeks okunur ve kaydetmek/belirtmek artık gerekli değildir.) 60 | 61 | ### Düğme Açıklaması 62 | - Modeli Eğit: Adım 2b'yi çalıştırdıktan sonra, modeli eğitmek için bu düğmeye basın. 63 | - Özellik İndeksini Eğit: Modeli eğittikten sonra, indeks öğrenme işlemi yapın. 64 | - Tek Tıklamayla Eğitim: Adım 2b, model eğitimi ve özellik indeks eğitimini bir arada yapar. 65 | -------------------------------------------------------------------------------- /i18n/i18n.py: -------------------------------------------------------------------------------- 1 | import json 2 | import locale 3 | import os 4 | from configs import singleton_variable 5 | 6 | 7 | def load_language_list(language): 8 | with open(f"./i18n/locale/{language}.json", "r", encoding="utf-8") as f: 9 | language_list = json.load(f) 10 | return language_list 11 | 12 | 13 | @singleton_variable 14 | class I18nAuto: 15 | def __init__(self, language=None): 16 | if language in ["Auto", None]: 17 | language = locale.getdefaultlocale( 18 | envvars=("LANG", "LC_ALL", "LC_CTYPE", "LANGUAGE") 19 | )[0] 20 | if not os.path.exists(f"./i18n/locale/{language}.json"): 21 | language = "en_US" 22 | self.language = language 23 | self.language_map = load_language_list(language) 24 | 25 | def __call__(self, key): 26 | return self.language_map.get(key, key) 27 | 28 | def __repr__(self): 29 | return "Language: " + self.language 30 | -------------------------------------------------------------------------------- /i18n/locale_diff.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | from collections import OrderedDict 4 | 5 | # Define the standard file name 6 | standard_file = "locale/en_US.json" 7 | 8 | # Find all JSON files in the directory 9 | dir_path = "locale/" 10 | languages = [ 11 | os.path.join(dir_path, f) 12 | for f in os.listdir(dir_path) 13 | if f.endswith(".json") and f != standard_file 14 | ] 15 | 16 | # Load the standard file 17 | with open(standard_file, "r", encoding="utf-8") as f: 18 | standard_data = json.load(f, object_pairs_hook=OrderedDict) 19 | 20 | # Loop through each language file 21 | for lang_file in languages: 22 | # Load the language file 23 | with open(lang_file, "r", encoding="utf-8") as f: 24 | lang_data = json.load(f, object_pairs_hook=OrderedDict) 25 | 26 | # Find the difference between the language file and the standard file 27 | diff = set(standard_data.keys()) - set(lang_data.keys()) 28 | 29 | miss = set(lang_data.keys()) - set(standard_data.keys()) 30 | 31 | # Add any missing keys to the language file 32 | for key in diff: 33 | lang_data[key] = key 34 | 35 | # Del any extra keys to the language file 36 | for key in miss: 37 | del lang_data[key] 38 | 39 | # Sort the keys of the language file to match the order of the standard file 40 | lang_data = OrderedDict( 41 | sorted(lang_data.items(), key=lambda x: list(standard_data.keys()).index(x[0])) 42 | ) 43 | 44 | # Save the updated language file 45 | with open(lang_file, "w", encoding="utf-8") as f: 46 | json.dump(lang_data, f, ensure_ascii=False, indent=4, sort_keys=True) 47 | f.write("\n") 48 | -------------------------------------------------------------------------------- /i18n/scan_i18n.py: -------------------------------------------------------------------------------- 1 | import ast 2 | import glob 3 | import json 4 | from collections import OrderedDict 5 | 6 | 7 | def extract_i18n_strings(node): 8 | i18n_strings = [] 9 | 10 | if ( 11 | isinstance(node, ast.Call) 12 | and isinstance(node.func, ast.Name) 13 | and node.func.id == "i18n" 14 | ): 15 | for arg in node.args: 16 | if isinstance(arg, ast.Str): 17 | i18n_strings.append(arg.s) 18 | 19 | for child_node in ast.iter_child_nodes(node): 20 | i18n_strings.extend(extract_i18n_strings(child_node)) 21 | 22 | return i18n_strings 23 | 24 | 25 | # scan the directory for all .py files (recursively) 26 | # for each file, parse the code into an AST 27 | # for each AST, extract the i18n strings 28 | 29 | strings = [] 30 | for filename in glob.iglob("**/*.py", recursive=True): 31 | with open(filename, "r") as f: 32 | code = f.read() 33 | if "I18nAuto" in code: 34 | tree = ast.parse(code) 35 | i18n_strings = extract_i18n_strings(tree) 36 | print(filename, len(i18n_strings)) 37 | strings.extend(i18n_strings) 38 | code_keys = set(strings) 39 | print() 40 | print("Total unique:", len(code_keys)) 41 | 42 | 43 | standard_file = "i18n/locale/en_US.json" 44 | with open(standard_file, "r", encoding="utf-8") as f: 45 | standard_data = json.load(f, object_pairs_hook=OrderedDict) 46 | standard_keys = set(standard_data.keys()) 47 | 48 | # Define the standard file name 49 | unused_keys = standard_keys - code_keys 50 | print("Unused keys:", len(unused_keys)) 51 | for unused_key in unused_keys: 52 | print("\t", unused_key) 53 | 54 | missing_keys = code_keys - standard_keys 55 | print("Missing keys:", len(missing_keys)) 56 | for missing_key in missing_keys: 57 | print("\t", missing_key) 58 | 59 | code_keys_dict = OrderedDict() 60 | for s in strings: 61 | code_keys_dict[s] = s 62 | 63 | # write back 64 | with open(standard_file, "w", encoding="utf-8") as f: 65 | json.dump(code_keys_dict, f, ensure_ascii=False, indent=4, sort_keys=True) 66 | f.write("\n") 67 | -------------------------------------------------------------------------------- /infer/lib/train/losses.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | def feature_loss(fmap_r, fmap_g): 5 | loss = 0 6 | for dr, dg in zip(fmap_r, fmap_g): 7 | for rl, gl in zip(dr, dg): 8 | rl = rl.float().detach() 9 | gl = gl.float() 10 | loss += torch.mean(torch.abs(rl - gl)) 11 | 12 | return loss * 2 13 | 14 | 15 | def discriminator_loss(disc_real_outputs, disc_generated_outputs): 16 | loss = 0 17 | r_losses = [] 18 | g_losses = [] 19 | for dr, dg in zip(disc_real_outputs, disc_generated_outputs): 20 | dr = dr.float() 21 | dg = dg.float() 22 | r_loss = torch.mean((1 - dr) ** 2) 23 | g_loss = torch.mean(dg**2) 24 | loss += r_loss + g_loss 25 | r_losses.append(r_loss.item()) 26 | g_losses.append(g_loss.item()) 27 | 28 | return loss, r_losses, g_losses 29 | 30 | 31 | def generator_loss(disc_outputs): 32 | loss = 0 33 | gen_losses = [] 34 | for dg in disc_outputs: 35 | dg = dg.float() 36 | l = torch.mean((1 - dg) ** 2) 37 | gen_losses.append(l) 38 | loss += l 39 | 40 | return loss, gen_losses 41 | 42 | 43 | def kl_loss(z_p, logs_q, m_p, logs_p, z_mask): 44 | """ 45 | z_p, logs_q: [b, h, t_t] 46 | m_p, logs_p: [b, h, t_t] 47 | """ 48 | z_p = z_p.float() 49 | logs_q = logs_q.float() 50 | m_p = m_p.float() 51 | logs_p = logs_p.float() 52 | z_mask = z_mask.float() 53 | 54 | kl = logs_p - logs_q - 0.5 55 | kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p) 56 | kl = torch.sum(kl * z_mask) 57 | l = kl / torch.sum(z_mask) 58 | return l 59 | -------------------------------------------------------------------------------- /infer/lib/train/mel_processing.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.utils.data 3 | from librosa.filters import mel as librosa_mel_fn 4 | import logging 5 | 6 | logger = logging.getLogger(__name__) 7 | 8 | MAX_WAV_VALUE = 32768.0 9 | 10 | 11 | def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): 12 | """ 13 | PARAMS 14 | ------ 15 | C: compression factor 16 | """ 17 | return torch.log(torch.clamp(x, min=clip_val) * C) 18 | 19 | 20 | def dynamic_range_decompression_torch(x, C=1): 21 | """ 22 | PARAMS 23 | ------ 24 | C: compression factor used to compress 25 | """ 26 | return torch.exp(x) / C 27 | 28 | 29 | def spectral_normalize_torch(magnitudes): 30 | return dynamic_range_compression_torch(magnitudes) 31 | 32 | 33 | def spectral_de_normalize_torch(magnitudes): 34 | return dynamic_range_decompression_torch(magnitudes) 35 | 36 | 37 | # Reusable banks 38 | mel_basis = {} 39 | hann_window = {} 40 | 41 | 42 | def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): 43 | """Convert waveform into Linear-frequency Linear-amplitude spectrogram. 44 | 45 | Args: 46 | y :: (B, T) - Audio waveforms 47 | n_fft 48 | sampling_rate 49 | hop_size 50 | win_size 51 | center 52 | Returns: 53 | :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram 54 | """ 55 | 56 | # Window - Cache if needed 57 | global hann_window 58 | dtype_device = str(y.dtype) + "_" + str(y.device) 59 | wnsize_dtype_device = str(win_size) + "_" + dtype_device 60 | if wnsize_dtype_device not in hann_window: 61 | hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to( 62 | dtype=y.dtype, device=y.device 63 | ) 64 | 65 | # Padding 66 | y = torch.nn.functional.pad( 67 | y.unsqueeze(1), 68 | (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), 69 | mode="reflect", 70 | ) 71 | y = y.squeeze(1) 72 | 73 | # Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2) 74 | spec = torch.stft( 75 | y, 76 | n_fft, 77 | hop_length=hop_size, 78 | win_length=win_size, 79 | window=hann_window[wnsize_dtype_device], 80 | center=center, 81 | pad_mode="reflect", 82 | normalized=False, 83 | onesided=True, 84 | return_complex=True, 85 | ) 86 | 87 | # Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame) 88 | spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + 1e-6) 89 | return spec 90 | 91 | 92 | def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): 93 | # MelBasis - Cache if needed 94 | global mel_basis 95 | dtype_device = str(spec.dtype) + "_" + str(spec.device) 96 | fmax_dtype_device = str(fmax) + "_" + dtype_device 97 | if fmax_dtype_device not in mel_basis: 98 | mel = librosa_mel_fn( 99 | sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax 100 | ) 101 | mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to( 102 | dtype=spec.dtype, device=spec.device 103 | ) 104 | 105 | # Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame) 106 | melspec = torch.matmul(mel_basis[fmax_dtype_device], spec) 107 | melspec = spectral_normalize_torch(melspec) 108 | return melspec 109 | 110 | 111 | def mel_spectrogram_torch( 112 | y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False 113 | ): 114 | """Convert waveform into Mel-frequency Log-amplitude spectrogram. 115 | 116 | Args: 117 | y :: (B, T) - Waveforms 118 | Returns: 119 | melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram 120 | """ 121 | # Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame) 122 | spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center) 123 | 124 | # Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame) 125 | melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax) 126 | 127 | return melspec 128 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/layers.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | from torch import nn 4 | 5 | from . import spec_utils 6 | 7 | 8 | class Conv2DBNActiv(nn.Module): 9 | def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): 10 | super(Conv2DBNActiv, self).__init__() 11 | self.conv = nn.Sequential( 12 | nn.Conv2d( 13 | nin, 14 | nout, 15 | kernel_size=ksize, 16 | stride=stride, 17 | padding=pad, 18 | dilation=dilation, 19 | bias=False, 20 | ), 21 | nn.BatchNorm2d(nout), 22 | activ(), 23 | ) 24 | 25 | @torch.inference_mode() 26 | def forward(self, x): 27 | return self.conv(x) 28 | 29 | 30 | class Encoder(nn.Module): 31 | def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): 32 | super(Encoder, self).__init__() 33 | self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ) 34 | self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ) 35 | 36 | @torch.inference_mode() 37 | def forward(self, x): 38 | h = self.conv1(x) 39 | h = self.conv2(h) 40 | 41 | return h 42 | 43 | 44 | class Decoder(nn.Module): 45 | def __init__( 46 | self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False 47 | ): 48 | super(Decoder, self).__init__() 49 | self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) 50 | # self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ) 51 | self.dropout = nn.Dropout2d(0.1) if dropout else None 52 | 53 | @torch.inference_mode() 54 | def forward(self, x, skip=None): 55 | x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) 56 | 57 | if skip is not None: 58 | skip = spec_utils.crop_center(skip, x) 59 | x = torch.cat([x, skip], dim=1) 60 | 61 | h = self.conv1(x) 62 | # h = self.conv2(h) 63 | 64 | if self.dropout is not None: 65 | h = self.dropout(h) 66 | 67 | return h 68 | 69 | 70 | class ASPPModule(nn.Module): 71 | def __init__(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False): 72 | super(ASPPModule, self).__init__() 73 | self.conv1 = nn.Sequential( 74 | nn.AdaptiveAvgPool2d((1, None)), 75 | Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ), 76 | ) 77 | self.conv2 = Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ) 78 | self.conv3 = Conv2DBNActiv( 79 | nin, nout, 3, 1, dilations[0], dilations[0], activ=activ 80 | ) 81 | self.conv4 = Conv2DBNActiv( 82 | nin, nout, 3, 1, dilations[1], dilations[1], activ=activ 83 | ) 84 | self.conv5 = Conv2DBNActiv( 85 | nin, nout, 3, 1, dilations[2], dilations[2], activ=activ 86 | ) 87 | self.bottleneck = Conv2DBNActiv(nout * 5, nout, 1, 1, 0, activ=activ) 88 | self.dropout = nn.Dropout2d(0.1) if dropout else None 89 | 90 | @torch.inference_mode() 91 | def forward(self, x): 92 | _, _, h, w = x.size() 93 | feat1 = F.interpolate( 94 | self.conv1(x), size=(h, w), mode="bilinear", align_corners=True 95 | ) 96 | feat2 = self.conv2(x) 97 | feat3 = self.conv3(x) 98 | feat4 = self.conv4(x) 99 | feat5 = self.conv5(x) 100 | out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) 101 | out = self.bottleneck(out) 102 | 103 | if self.dropout is not None: 104 | out = self.dropout(out) 105 | 106 | return out 107 | 108 | 109 | class LSTMModule(nn.Module): 110 | def __init__(self, nin_conv, nin_lstm, nout_lstm): 111 | super(LSTMModule, self).__init__() 112 | self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0) 113 | self.lstm = nn.LSTM( 114 | input_size=nin_lstm, hidden_size=nout_lstm // 2, bidirectional=True 115 | ) 116 | self.dense = nn.Sequential( 117 | nn.Linear(nout_lstm, nin_lstm), nn.BatchNorm1d(nin_lstm), nn.ReLU() 118 | ) 119 | 120 | @torch.inference_mode() 121 | def forward(self, x): 122 | N, _, nbins, nframes = x.size() 123 | h = self.conv(x)[:, 0] # N, nbins, nframes 124 | h = h.permute(2, 0, 1) # nframes, N, nbins 125 | h, _ = self.lstm(h) 126 | h = self.dense(h.reshape(-1, h.size()[-1])) # nframes * N, nbins 127 | h = h.reshape(nframes, N, 1, nbins) 128 | h = h.permute(1, 2, 3, 0) 129 | 130 | return h 131 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/layers_123821KB.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | from torch import nn 4 | 5 | from . import spec_utils 6 | 7 | 8 | class Conv2DBNActiv(nn.Module): 9 | def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): 10 | super(Conv2DBNActiv, self).__init__() 11 | self.conv = nn.Sequential( 12 | nn.Conv2d( 13 | nin, 14 | nout, 15 | kernel_size=ksize, 16 | stride=stride, 17 | padding=pad, 18 | dilation=dilation, 19 | bias=False, 20 | ), 21 | nn.BatchNorm2d(nout), 22 | activ(), 23 | ) 24 | 25 | def __call__(self, x): 26 | return self.conv(x) 27 | 28 | 29 | class SeperableConv2DBNActiv(nn.Module): 30 | def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): 31 | super(SeperableConv2DBNActiv, self).__init__() 32 | self.conv = nn.Sequential( 33 | nn.Conv2d( 34 | nin, 35 | nin, 36 | kernel_size=ksize, 37 | stride=stride, 38 | padding=pad, 39 | dilation=dilation, 40 | groups=nin, 41 | bias=False, 42 | ), 43 | nn.Conv2d(nin, nout, kernel_size=1, bias=False), 44 | nn.BatchNorm2d(nout), 45 | activ(), 46 | ) 47 | 48 | def __call__(self, x): 49 | return self.conv(x) 50 | 51 | 52 | class Encoder(nn.Module): 53 | def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): 54 | super(Encoder, self).__init__() 55 | self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) 56 | self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) 57 | 58 | def __call__(self, x): 59 | skip = self.conv1(x) 60 | h = self.conv2(skip) 61 | 62 | return h, skip 63 | 64 | 65 | class Decoder(nn.Module): 66 | def __init__( 67 | self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False 68 | ): 69 | super(Decoder, self).__init__() 70 | self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) 71 | self.dropout = nn.Dropout2d(0.1) if dropout else None 72 | 73 | def __call__(self, x, skip=None): 74 | x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) 75 | if skip is not None: 76 | skip = spec_utils.crop_center(skip, x) 77 | x = torch.cat([x, skip], dim=1) 78 | h = self.conv(x) 79 | 80 | if self.dropout is not None: 81 | h = self.dropout(h) 82 | 83 | return h 84 | 85 | 86 | class ASPPModule(nn.Module): 87 | def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU): 88 | super(ASPPModule, self).__init__() 89 | self.conv1 = nn.Sequential( 90 | nn.AdaptiveAvgPool2d((1, None)), 91 | Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), 92 | ) 93 | self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) 94 | self.conv3 = SeperableConv2DBNActiv( 95 | nin, nin, 3, 1, dilations[0], dilations[0], activ=activ 96 | ) 97 | self.conv4 = SeperableConv2DBNActiv( 98 | nin, nin, 3, 1, dilations[1], dilations[1], activ=activ 99 | ) 100 | self.conv5 = SeperableConv2DBNActiv( 101 | nin, nin, 3, 1, dilations[2], dilations[2], activ=activ 102 | ) 103 | self.bottleneck = nn.Sequential( 104 | Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) 105 | ) 106 | 107 | def forward(self, x): 108 | _, _, h, w = x.size() 109 | feat1 = F.interpolate( 110 | self.conv1(x), size=(h, w), mode="bilinear", align_corners=True 111 | ) 112 | feat2 = self.conv2(x) 113 | feat3 = self.conv3(x) 114 | feat4 = self.conv4(x) 115 | feat5 = self.conv5(x) 116 | out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) 117 | bottle = self.bottleneck(out) 118 | return bottle 119 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/layers_33966KB.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | from torch import nn 4 | 5 | from . import spec_utils 6 | 7 | 8 | class Conv2DBNActiv(nn.Module): 9 | def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): 10 | super(Conv2DBNActiv, self).__init__() 11 | self.conv = nn.Sequential( 12 | nn.Conv2d( 13 | nin, 14 | nout, 15 | kernel_size=ksize, 16 | stride=stride, 17 | padding=pad, 18 | dilation=dilation, 19 | bias=False, 20 | ), 21 | nn.BatchNorm2d(nout), 22 | activ(), 23 | ) 24 | 25 | def __call__(self, x): 26 | return self.conv(x) 27 | 28 | 29 | class SeperableConv2DBNActiv(nn.Module): 30 | def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): 31 | super(SeperableConv2DBNActiv, self).__init__() 32 | self.conv = nn.Sequential( 33 | nn.Conv2d( 34 | nin, 35 | nin, 36 | kernel_size=ksize, 37 | stride=stride, 38 | padding=pad, 39 | dilation=dilation, 40 | groups=nin, 41 | bias=False, 42 | ), 43 | nn.Conv2d(nin, nout, kernel_size=1, bias=False), 44 | nn.BatchNorm2d(nout), 45 | activ(), 46 | ) 47 | 48 | def __call__(self, x): 49 | return self.conv(x) 50 | 51 | 52 | class Encoder(nn.Module): 53 | def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): 54 | super(Encoder, self).__init__() 55 | self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) 56 | self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) 57 | 58 | def __call__(self, x): 59 | skip = self.conv1(x) 60 | h = self.conv2(skip) 61 | 62 | return h, skip 63 | 64 | 65 | class Decoder(nn.Module): 66 | def __init__( 67 | self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False 68 | ): 69 | super(Decoder, self).__init__() 70 | self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) 71 | self.dropout = nn.Dropout2d(0.1) if dropout else None 72 | 73 | def __call__(self, x, skip=None): 74 | x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) 75 | if skip is not None: 76 | skip = spec_utils.crop_center(skip, x) 77 | x = torch.cat([x, skip], dim=1) 78 | h = self.conv(x) 79 | 80 | if self.dropout is not None: 81 | h = self.dropout(h) 82 | 83 | return h 84 | 85 | 86 | class ASPPModule(nn.Module): 87 | def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU): 88 | super(ASPPModule, self).__init__() 89 | self.conv1 = nn.Sequential( 90 | nn.AdaptiveAvgPool2d((1, None)), 91 | Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), 92 | ) 93 | self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) 94 | self.conv3 = SeperableConv2DBNActiv( 95 | nin, nin, 3, 1, dilations[0], dilations[0], activ=activ 96 | ) 97 | self.conv4 = SeperableConv2DBNActiv( 98 | nin, nin, 3, 1, dilations[1], dilations[1], activ=activ 99 | ) 100 | self.conv5 = SeperableConv2DBNActiv( 101 | nin, nin, 3, 1, dilations[2], dilations[2], activ=activ 102 | ) 103 | self.conv6 = SeperableConv2DBNActiv( 104 | nin, nin, 3, 1, dilations[2], dilations[2], activ=activ 105 | ) 106 | self.conv7 = SeperableConv2DBNActiv( 107 | nin, nin, 3, 1, dilations[2], dilations[2], activ=activ 108 | ) 109 | self.bottleneck = nn.Sequential( 110 | Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) 111 | ) 112 | 113 | def forward(self, x): 114 | _, _, h, w = x.size() 115 | feat1 = F.interpolate( 116 | self.conv1(x), size=(h, w), mode="bilinear", align_corners=True 117 | ) 118 | feat2 = self.conv2(x) 119 | feat3 = self.conv3(x) 120 | feat4 = self.conv4(x) 121 | feat5 = self.conv5(x) 122 | feat6 = self.conv6(x) 123 | feat7 = self.conv7(x) 124 | out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1) 125 | bottle = self.bottleneck(out) 126 | return bottle 127 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/layers_537238KB.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | from torch import nn 4 | 5 | from . import spec_utils 6 | 7 | 8 | class Conv2DBNActiv(nn.Module): 9 | def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): 10 | super(Conv2DBNActiv, self).__init__() 11 | self.conv = nn.Sequential( 12 | nn.Conv2d( 13 | nin, 14 | nout, 15 | kernel_size=ksize, 16 | stride=stride, 17 | padding=pad, 18 | dilation=dilation, 19 | bias=False, 20 | ), 21 | nn.BatchNorm2d(nout), 22 | activ(), 23 | ) 24 | 25 | def __call__(self, x): 26 | return self.conv(x) 27 | 28 | 29 | class SeperableConv2DBNActiv(nn.Module): 30 | def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): 31 | super(SeperableConv2DBNActiv, self).__init__() 32 | self.conv = nn.Sequential( 33 | nn.Conv2d( 34 | nin, 35 | nin, 36 | kernel_size=ksize, 37 | stride=stride, 38 | padding=pad, 39 | dilation=dilation, 40 | groups=nin, 41 | bias=False, 42 | ), 43 | nn.Conv2d(nin, nout, kernel_size=1, bias=False), 44 | nn.BatchNorm2d(nout), 45 | activ(), 46 | ) 47 | 48 | def __call__(self, x): 49 | return self.conv(x) 50 | 51 | 52 | class Encoder(nn.Module): 53 | def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): 54 | super(Encoder, self).__init__() 55 | self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) 56 | self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) 57 | 58 | def __call__(self, x): 59 | skip = self.conv1(x) 60 | h = self.conv2(skip) 61 | 62 | return h, skip 63 | 64 | 65 | class Decoder(nn.Module): 66 | def __init__( 67 | self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False 68 | ): 69 | super(Decoder, self).__init__() 70 | self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) 71 | self.dropout = nn.Dropout2d(0.1) if dropout else None 72 | 73 | def __call__(self, x, skip=None): 74 | x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) 75 | if skip is not None: 76 | skip = spec_utils.crop_center(skip, x) 77 | x = torch.cat([x, skip], dim=1) 78 | h = self.conv(x) 79 | 80 | if self.dropout is not None: 81 | h = self.dropout(h) 82 | 83 | return h 84 | 85 | 86 | class ASPPModule(nn.Module): 87 | def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU): 88 | super(ASPPModule, self).__init__() 89 | self.conv1 = nn.Sequential( 90 | nn.AdaptiveAvgPool2d((1, None)), 91 | Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), 92 | ) 93 | self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) 94 | self.conv3 = SeperableConv2DBNActiv( 95 | nin, nin, 3, 1, dilations[0], dilations[0], activ=activ 96 | ) 97 | self.conv4 = SeperableConv2DBNActiv( 98 | nin, nin, 3, 1, dilations[1], dilations[1], activ=activ 99 | ) 100 | self.conv5 = SeperableConv2DBNActiv( 101 | nin, nin, 3, 1, dilations[2], dilations[2], activ=activ 102 | ) 103 | self.conv6 = SeperableConv2DBNActiv( 104 | nin, nin, 3, 1, dilations[2], dilations[2], activ=activ 105 | ) 106 | self.conv7 = SeperableConv2DBNActiv( 107 | nin, nin, 3, 1, dilations[2], dilations[2], activ=activ 108 | ) 109 | self.bottleneck = nn.Sequential( 110 | Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) 111 | ) 112 | 113 | def forward(self, x): 114 | _, _, h, w = x.size() 115 | feat1 = F.interpolate( 116 | self.conv1(x), size=(h, w), mode="bilinear", align_corners=True 117 | ) 118 | feat2 = self.conv2(x) 119 | feat3 = self.conv3(x) 120 | feat4 = self.conv4(x) 121 | feat5 = self.conv5(x) 122 | feat6 = self.conv6(x) 123 | feat7 = self.conv7(x) 124 | out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1) 125 | bottle = self.bottleneck(out) 126 | return bottle 127 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/model_param_init.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import pathlib 4 | 5 | default_param = {} 6 | default_param["bins"] = 768 7 | default_param["unstable_bins"] = 9 # training only 8 | default_param["reduction_bins"] = 762 # training only 9 | default_param["sr"] = 44100 10 | default_param["pre_filter_start"] = 757 11 | default_param["pre_filter_stop"] = 768 12 | default_param["band"] = {} 13 | 14 | 15 | default_param["band"][1] = { 16 | "sr": 11025, 17 | "hl": 128, 18 | "n_fft": 960, 19 | "crop_start": 0, 20 | "crop_stop": 245, 21 | "lpf_start": 61, # inference only 22 | "res_type": "polyphase", 23 | } 24 | 25 | default_param["band"][2] = { 26 | "sr": 44100, 27 | "hl": 512, 28 | "n_fft": 1536, 29 | "crop_start": 24, 30 | "crop_stop": 547, 31 | "hpf_start": 81, # inference only 32 | "res_type": "sinc_best", 33 | } 34 | 35 | 36 | def int_keys(d): 37 | r = {} 38 | for k, v in d: 39 | if k.isdigit(): 40 | k = int(k) 41 | r[k] = v 42 | return r 43 | 44 | 45 | class ModelParameters(object): 46 | def __init__(self, config_path=""): 47 | if ".pth" == pathlib.Path(config_path).suffix: 48 | import zipfile 49 | 50 | with zipfile.ZipFile(config_path, "r") as zip: 51 | self.param = json.loads( 52 | zip.read("param.json"), object_pairs_hook=int_keys 53 | ) 54 | elif ".json" == pathlib.Path(config_path).suffix: 55 | with open(config_path, "r") as f: 56 | self.param = json.loads(f.read(), object_pairs_hook=int_keys) 57 | else: 58 | self.param = default_param 59 | 60 | for k in [ 61 | "mid_side", 62 | "mid_side_b", 63 | "mid_side_b2", 64 | "stereo_w", 65 | "stereo_n", 66 | "reverse", 67 | ]: 68 | if not k in self.param: 69 | self.param[k] = False 70 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/1band_sr16000_hl512.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 1024, 3 | "unstable_bins": 0, 4 | "reduction_bins": 0, 5 | "band": { 6 | "1": { 7 | "sr": 16000, 8 | "hl": 512, 9 | "n_fft": 2048, 10 | "crop_start": 0, 11 | "crop_stop": 1024, 12 | "hpf_start": -1, 13 | "res_type": "sinc_best" 14 | } 15 | }, 16 | "sr": 16000, 17 | "pre_filter_start": 1023, 18 | "pre_filter_stop": 1024 19 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/1band_sr32000_hl512.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 1024, 3 | "unstable_bins": 0, 4 | "reduction_bins": 0, 5 | "band": { 6 | "1": { 7 | "sr": 32000, 8 | "hl": 512, 9 | "n_fft": 2048, 10 | "crop_start": 0, 11 | "crop_stop": 1024, 12 | "hpf_start": -1, 13 | "res_type": "kaiser_fast" 14 | } 15 | }, 16 | "sr": 32000, 17 | "pre_filter_start": 1000, 18 | "pre_filter_stop": 1021 19 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/1band_sr33075_hl384.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 1024, 3 | "unstable_bins": 0, 4 | "reduction_bins": 0, 5 | "band": { 6 | "1": { 7 | "sr": 33075, 8 | "hl": 384, 9 | "n_fft": 2048, 10 | "crop_start": 0, 11 | "crop_stop": 1024, 12 | "hpf_start": -1, 13 | "res_type": "sinc_best" 14 | } 15 | }, 16 | "sr": 33075, 17 | "pre_filter_start": 1000, 18 | "pre_filter_stop": 1021 19 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl1024.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 1024, 3 | "unstable_bins": 0, 4 | "reduction_bins": 0, 5 | "band": { 6 | "1": { 7 | "sr": 44100, 8 | "hl": 1024, 9 | "n_fft": 2048, 10 | "crop_start": 0, 11 | "crop_stop": 1024, 12 | "hpf_start": -1, 13 | "res_type": "sinc_best" 14 | } 15 | }, 16 | "sr": 44100, 17 | "pre_filter_start": 1023, 18 | "pre_filter_stop": 1024 19 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl256.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 256, 3 | "unstable_bins": 0, 4 | "reduction_bins": 0, 5 | "band": { 6 | "1": { 7 | "sr": 44100, 8 | "hl": 256, 9 | "n_fft": 512, 10 | "crop_start": 0, 11 | "crop_stop": 256, 12 | "hpf_start": -1, 13 | "res_type": "sinc_best" 14 | } 15 | }, 16 | "sr": 44100, 17 | "pre_filter_start": 256, 18 | "pre_filter_stop": 256 19 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 1024, 3 | "unstable_bins": 0, 4 | "reduction_bins": 0, 5 | "band": { 6 | "1": { 7 | "sr": 44100, 8 | "hl": 512, 9 | "n_fft": 2048, 10 | "crop_start": 0, 11 | "crop_stop": 1024, 12 | "hpf_start": -1, 13 | "res_type": "sinc_best" 14 | } 15 | }, 16 | "sr": 44100, 17 | "pre_filter_start": 1023, 18 | "pre_filter_stop": 1024 19 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/1band_sr44100_hl512_cut.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 1024, 3 | "unstable_bins": 0, 4 | "reduction_bins": 0, 5 | "band": { 6 | "1": { 7 | "sr": 44100, 8 | "hl": 512, 9 | "n_fft": 2048, 10 | "crop_start": 0, 11 | "crop_stop": 700, 12 | "hpf_start": -1, 13 | "res_type": "sinc_best" 14 | } 15 | }, 16 | "sr": 44100, 17 | "pre_filter_start": 1023, 18 | "pre_filter_stop": 700 19 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/2band_32000.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 768, 3 | "unstable_bins": 7, 4 | "reduction_bins": 705, 5 | "band": { 6 | "1": { 7 | "sr": 6000, 8 | "hl": 66, 9 | "n_fft": 512, 10 | "crop_start": 0, 11 | "crop_stop": 240, 12 | "lpf_start": 60, 13 | "lpf_stop": 118, 14 | "res_type": "sinc_fastest" 15 | }, 16 | "2": { 17 | "sr": 32000, 18 | "hl": 352, 19 | "n_fft": 1024, 20 | "crop_start": 22, 21 | "crop_stop": 505, 22 | "hpf_start": 44, 23 | "hpf_stop": 23, 24 | "res_type": "sinc_medium" 25 | } 26 | }, 27 | "sr": 32000, 28 | "pre_filter_start": 710, 29 | "pre_filter_stop": 731 30 | } 31 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/2band_44100_lofi.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 512, 3 | "unstable_bins": 7, 4 | "reduction_bins": 510, 5 | "band": { 6 | "1": { 7 | "sr": 11025, 8 | "hl": 160, 9 | "n_fft": 768, 10 | "crop_start": 0, 11 | "crop_stop": 192, 12 | "lpf_start": 41, 13 | "lpf_stop": 139, 14 | "res_type": "sinc_fastest" 15 | }, 16 | "2": { 17 | "sr": 44100, 18 | "hl": 640, 19 | "n_fft": 1024, 20 | "crop_start": 10, 21 | "crop_stop": 320, 22 | "hpf_start": 47, 23 | "hpf_stop": 15, 24 | "res_type": "sinc_medium" 25 | } 26 | }, 27 | "sr": 44100, 28 | "pre_filter_start": 510, 29 | "pre_filter_stop": 512 30 | } 31 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/2band_48000.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 768, 3 | "unstable_bins": 7, 4 | "reduction_bins": 705, 5 | "band": { 6 | "1": { 7 | "sr": 6000, 8 | "hl": 66, 9 | "n_fft": 512, 10 | "crop_start": 0, 11 | "crop_stop": 240, 12 | "lpf_start": 60, 13 | "lpf_stop": 240, 14 | "res_type": "sinc_fastest" 15 | }, 16 | "2": { 17 | "sr": 48000, 18 | "hl": 528, 19 | "n_fft": 1536, 20 | "crop_start": 22, 21 | "crop_stop": 505, 22 | "hpf_start": 82, 23 | "hpf_stop": 22, 24 | "res_type": "sinc_medium" 25 | } 26 | }, 27 | "sr": 48000, 28 | "pre_filter_start": 710, 29 | "pre_filter_stop": 731 30 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/3band_44100.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 768, 3 | "unstable_bins": 5, 4 | "reduction_bins": 733, 5 | "band": { 6 | "1": { 7 | "sr": 11025, 8 | "hl": 128, 9 | "n_fft": 768, 10 | "crop_start": 0, 11 | "crop_stop": 278, 12 | "lpf_start": 28, 13 | "lpf_stop": 140, 14 | "res_type": "polyphase" 15 | }, 16 | "2": { 17 | "sr": 22050, 18 | "hl": 256, 19 | "n_fft": 768, 20 | "crop_start": 14, 21 | "crop_stop": 322, 22 | "hpf_start": 70, 23 | "hpf_stop": 14, 24 | "lpf_start": 283, 25 | "lpf_stop": 314, 26 | "res_type": "polyphase" 27 | }, 28 | "3": { 29 | "sr": 44100, 30 | "hl": 512, 31 | "n_fft": 768, 32 | "crop_start": 131, 33 | "crop_stop": 313, 34 | "hpf_start": 154, 35 | "hpf_stop": 141, 36 | "res_type": "sinc_medium" 37 | } 38 | }, 39 | "sr": 44100, 40 | "pre_filter_start": 757, 41 | "pre_filter_stop": 768 42 | } 43 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/3band_44100_mid.json: -------------------------------------------------------------------------------- 1 | { 2 | "mid_side": true, 3 | "bins": 768, 4 | "unstable_bins": 5, 5 | "reduction_bins": 733, 6 | "band": { 7 | "1": { 8 | "sr": 11025, 9 | "hl": 128, 10 | "n_fft": 768, 11 | "crop_start": 0, 12 | "crop_stop": 278, 13 | "lpf_start": 28, 14 | "lpf_stop": 140, 15 | "res_type": "polyphase" 16 | }, 17 | "2": { 18 | "sr": 22050, 19 | "hl": 256, 20 | "n_fft": 768, 21 | "crop_start": 14, 22 | "crop_stop": 322, 23 | "hpf_start": 70, 24 | "hpf_stop": 14, 25 | "lpf_start": 283, 26 | "lpf_stop": 314, 27 | "res_type": "polyphase" 28 | }, 29 | "3": { 30 | "sr": 44100, 31 | "hl": 512, 32 | "n_fft": 768, 33 | "crop_start": 131, 34 | "crop_stop": 313, 35 | "hpf_start": 154, 36 | "hpf_stop": 141, 37 | "res_type": "sinc_medium" 38 | } 39 | }, 40 | "sr": 44100, 41 | "pre_filter_start": 757, 42 | "pre_filter_stop": 768 43 | } 44 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/3band_44100_msb2.json: -------------------------------------------------------------------------------- 1 | { 2 | "mid_side_b2": true, 3 | "bins": 640, 4 | "unstable_bins": 7, 5 | "reduction_bins": 565, 6 | "band": { 7 | "1": { 8 | "sr": 11025, 9 | "hl": 108, 10 | "n_fft": 1024, 11 | "crop_start": 0, 12 | "crop_stop": 187, 13 | "lpf_start": 92, 14 | "lpf_stop": 186, 15 | "res_type": "polyphase" 16 | }, 17 | "2": { 18 | "sr": 22050, 19 | "hl": 216, 20 | "n_fft": 768, 21 | "crop_start": 0, 22 | "crop_stop": 212, 23 | "hpf_start": 68, 24 | "hpf_stop": 34, 25 | "lpf_start": 174, 26 | "lpf_stop": 209, 27 | "res_type": "polyphase" 28 | }, 29 | "3": { 30 | "sr": 44100, 31 | "hl": 432, 32 | "n_fft": 640, 33 | "crop_start": 66, 34 | "crop_stop": 307, 35 | "hpf_start": 86, 36 | "hpf_stop": 72, 37 | "res_type": "kaiser_fast" 38 | } 39 | }, 40 | "sr": 44100, 41 | "pre_filter_start": 639, 42 | "pre_filter_stop": 640 43 | } 44 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/4band_44100.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 768, 3 | "unstable_bins": 7, 4 | "reduction_bins": 668, 5 | "band": { 6 | "1": { 7 | "sr": 11025, 8 | "hl": 128, 9 | "n_fft": 1024, 10 | "crop_start": 0, 11 | "crop_stop": 186, 12 | "lpf_start": 37, 13 | "lpf_stop": 73, 14 | "res_type": "polyphase" 15 | }, 16 | "2": { 17 | "sr": 11025, 18 | "hl": 128, 19 | "n_fft": 512, 20 | "crop_start": 4, 21 | "crop_stop": 185, 22 | "hpf_start": 36, 23 | "hpf_stop": 18, 24 | "lpf_start": 93, 25 | "lpf_stop": 185, 26 | "res_type": "polyphase" 27 | }, 28 | "3": { 29 | "sr": 22050, 30 | "hl": 256, 31 | "n_fft": 512, 32 | "crop_start": 46, 33 | "crop_stop": 186, 34 | "hpf_start": 93, 35 | "hpf_stop": 46, 36 | "lpf_start": 164, 37 | "lpf_stop": 186, 38 | "res_type": "polyphase" 39 | }, 40 | "4": { 41 | "sr": 44100, 42 | "hl": 512, 43 | "n_fft": 768, 44 | "crop_start": 121, 45 | "crop_stop": 382, 46 | "hpf_start": 138, 47 | "hpf_stop": 123, 48 | "res_type": "sinc_medium" 49 | } 50 | }, 51 | "sr": 44100, 52 | "pre_filter_start": 740, 53 | "pre_filter_stop": 768 54 | } 55 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/4band_44100_mid.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 768, 3 | "unstable_bins": 7, 4 | "mid_side": true, 5 | "reduction_bins": 668, 6 | "band": { 7 | "1": { 8 | "sr": 11025, 9 | "hl": 128, 10 | "n_fft": 1024, 11 | "crop_start": 0, 12 | "crop_stop": 186, 13 | "lpf_start": 37, 14 | "lpf_stop": 73, 15 | "res_type": "polyphase" 16 | }, 17 | "2": { 18 | "sr": 11025, 19 | "hl": 128, 20 | "n_fft": 512, 21 | "crop_start": 4, 22 | "crop_stop": 185, 23 | "hpf_start": 36, 24 | "hpf_stop": 18, 25 | "lpf_start": 93, 26 | "lpf_stop": 185, 27 | "res_type": "polyphase" 28 | }, 29 | "3": { 30 | "sr": 22050, 31 | "hl": 256, 32 | "n_fft": 512, 33 | "crop_start": 46, 34 | "crop_stop": 186, 35 | "hpf_start": 93, 36 | "hpf_stop": 46, 37 | "lpf_start": 164, 38 | "lpf_stop": 186, 39 | "res_type": "polyphase" 40 | }, 41 | "4": { 42 | "sr": 44100, 43 | "hl": 512, 44 | "n_fft": 768, 45 | "crop_start": 121, 46 | "crop_stop": 382, 47 | "hpf_start": 138, 48 | "hpf_stop": 123, 49 | "res_type": "sinc_medium" 50 | } 51 | }, 52 | "sr": 44100, 53 | "pre_filter_start": 740, 54 | "pre_filter_stop": 768 55 | } 56 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/4band_44100_msb.json: -------------------------------------------------------------------------------- 1 | { 2 | "mid_side_b": true, 3 | "bins": 768, 4 | "unstable_bins": 7, 5 | "reduction_bins": 668, 6 | "band": { 7 | "1": { 8 | "sr": 11025, 9 | "hl": 128, 10 | "n_fft": 1024, 11 | "crop_start": 0, 12 | "crop_stop": 186, 13 | "lpf_start": 37, 14 | "lpf_stop": 73, 15 | "res_type": "polyphase" 16 | }, 17 | "2": { 18 | "sr": 11025, 19 | "hl": 128, 20 | "n_fft": 512, 21 | "crop_start": 4, 22 | "crop_stop": 185, 23 | "hpf_start": 36, 24 | "hpf_stop": 18, 25 | "lpf_start": 93, 26 | "lpf_stop": 185, 27 | "res_type": "polyphase" 28 | }, 29 | "3": { 30 | "sr": 22050, 31 | "hl": 256, 32 | "n_fft": 512, 33 | "crop_start": 46, 34 | "crop_stop": 186, 35 | "hpf_start": 93, 36 | "hpf_stop": 46, 37 | "lpf_start": 164, 38 | "lpf_stop": 186, 39 | "res_type": "polyphase" 40 | }, 41 | "4": { 42 | "sr": 44100, 43 | "hl": 512, 44 | "n_fft": 768, 45 | "crop_start": 121, 46 | "crop_stop": 382, 47 | "hpf_start": 138, 48 | "hpf_stop": 123, 49 | "res_type": "sinc_medium" 50 | } 51 | }, 52 | "sr": 44100, 53 | "pre_filter_start": 740, 54 | "pre_filter_stop": 768 55 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/4band_44100_msb2.json: -------------------------------------------------------------------------------- 1 | { 2 | "mid_side_b": true, 3 | "bins": 768, 4 | "unstable_bins": 7, 5 | "reduction_bins": 668, 6 | "band": { 7 | "1": { 8 | "sr": 11025, 9 | "hl": 128, 10 | "n_fft": 1024, 11 | "crop_start": 0, 12 | "crop_stop": 186, 13 | "lpf_start": 37, 14 | "lpf_stop": 73, 15 | "res_type": "polyphase" 16 | }, 17 | "2": { 18 | "sr": 11025, 19 | "hl": 128, 20 | "n_fft": 512, 21 | "crop_start": 4, 22 | "crop_stop": 185, 23 | "hpf_start": 36, 24 | "hpf_stop": 18, 25 | "lpf_start": 93, 26 | "lpf_stop": 185, 27 | "res_type": "polyphase" 28 | }, 29 | "3": { 30 | "sr": 22050, 31 | "hl": 256, 32 | "n_fft": 512, 33 | "crop_start": 46, 34 | "crop_stop": 186, 35 | "hpf_start": 93, 36 | "hpf_stop": 46, 37 | "lpf_start": 164, 38 | "lpf_stop": 186, 39 | "res_type": "polyphase" 40 | }, 41 | "4": { 42 | "sr": 44100, 43 | "hl": 512, 44 | "n_fft": 768, 45 | "crop_start": 121, 46 | "crop_stop": 382, 47 | "hpf_start": 138, 48 | "hpf_stop": 123, 49 | "res_type": "sinc_medium" 50 | } 51 | }, 52 | "sr": 44100, 53 | "pre_filter_start": 740, 54 | "pre_filter_stop": 768 55 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/4band_44100_reverse.json: -------------------------------------------------------------------------------- 1 | { 2 | "reverse": true, 3 | "bins": 768, 4 | "unstable_bins": 7, 5 | "reduction_bins": 668, 6 | "band": { 7 | "1": { 8 | "sr": 11025, 9 | "hl": 128, 10 | "n_fft": 1024, 11 | "crop_start": 0, 12 | "crop_stop": 186, 13 | "lpf_start": 37, 14 | "lpf_stop": 73, 15 | "res_type": "polyphase" 16 | }, 17 | "2": { 18 | "sr": 11025, 19 | "hl": 128, 20 | "n_fft": 512, 21 | "crop_start": 4, 22 | "crop_stop": 185, 23 | "hpf_start": 36, 24 | "hpf_stop": 18, 25 | "lpf_start": 93, 26 | "lpf_stop": 185, 27 | "res_type": "polyphase" 28 | }, 29 | "3": { 30 | "sr": 22050, 31 | "hl": 256, 32 | "n_fft": 512, 33 | "crop_start": 46, 34 | "crop_stop": 186, 35 | "hpf_start": 93, 36 | "hpf_stop": 46, 37 | "lpf_start": 164, 38 | "lpf_stop": 186, 39 | "res_type": "polyphase" 40 | }, 41 | "4": { 42 | "sr": 44100, 43 | "hl": 512, 44 | "n_fft": 768, 45 | "crop_start": 121, 46 | "crop_stop": 382, 47 | "hpf_start": 138, 48 | "hpf_stop": 123, 49 | "res_type": "sinc_medium" 50 | } 51 | }, 52 | "sr": 44100, 53 | "pre_filter_start": 740, 54 | "pre_filter_stop": 768 55 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/4band_44100_sw.json: -------------------------------------------------------------------------------- 1 | { 2 | "stereo_w": true, 3 | "bins": 768, 4 | "unstable_bins": 7, 5 | "reduction_bins": 668, 6 | "band": { 7 | "1": { 8 | "sr": 11025, 9 | "hl": 128, 10 | "n_fft": 1024, 11 | "crop_start": 0, 12 | "crop_stop": 186, 13 | "lpf_start": 37, 14 | "lpf_stop": 73, 15 | "res_type": "polyphase" 16 | }, 17 | "2": { 18 | "sr": 11025, 19 | "hl": 128, 20 | "n_fft": 512, 21 | "crop_start": 4, 22 | "crop_stop": 185, 23 | "hpf_start": 36, 24 | "hpf_stop": 18, 25 | "lpf_start": 93, 26 | "lpf_stop": 185, 27 | "res_type": "polyphase" 28 | }, 29 | "3": { 30 | "sr": 22050, 31 | "hl": 256, 32 | "n_fft": 512, 33 | "crop_start": 46, 34 | "crop_stop": 186, 35 | "hpf_start": 93, 36 | "hpf_stop": 46, 37 | "lpf_start": 164, 38 | "lpf_stop": 186, 39 | "res_type": "polyphase" 40 | }, 41 | "4": { 42 | "sr": 44100, 43 | "hl": 512, 44 | "n_fft": 768, 45 | "crop_start": 121, 46 | "crop_stop": 382, 47 | "hpf_start": 138, 48 | "hpf_stop": 123, 49 | "res_type": "sinc_medium" 50 | } 51 | }, 52 | "sr": 44100, 53 | "pre_filter_start": 740, 54 | "pre_filter_stop": 768 55 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/4band_v2.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 672, 3 | "unstable_bins": 8, 4 | "reduction_bins": 637, 5 | "band": { 6 | "1": { 7 | "sr": 7350, 8 | "hl": 80, 9 | "n_fft": 640, 10 | "crop_start": 0, 11 | "crop_stop": 85, 12 | "lpf_start": 25, 13 | "lpf_stop": 53, 14 | "res_type": "polyphase" 15 | }, 16 | "2": { 17 | "sr": 7350, 18 | "hl": 80, 19 | "n_fft": 320, 20 | "crop_start": 4, 21 | "crop_stop": 87, 22 | "hpf_start": 25, 23 | "hpf_stop": 12, 24 | "lpf_start": 31, 25 | "lpf_stop": 62, 26 | "res_type": "polyphase" 27 | }, 28 | "3": { 29 | "sr": 14700, 30 | "hl": 160, 31 | "n_fft": 512, 32 | "crop_start": 17, 33 | "crop_stop": 216, 34 | "hpf_start": 48, 35 | "hpf_stop": 24, 36 | "lpf_start": 139, 37 | "lpf_stop": 210, 38 | "res_type": "polyphase" 39 | }, 40 | "4": { 41 | "sr": 44100, 42 | "hl": 480, 43 | "n_fft": 960, 44 | "crop_start": 78, 45 | "crop_stop": 383, 46 | "hpf_start": 130, 47 | "hpf_stop": 86, 48 | "res_type": "kaiser_fast" 49 | } 50 | }, 51 | "sr": 44100, 52 | "pre_filter_start": 668, 53 | "pre_filter_stop": 672 54 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/4band_v2_sn.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 672, 3 | "unstable_bins": 8, 4 | "reduction_bins": 637, 5 | "band": { 6 | "1": { 7 | "sr": 7350, 8 | "hl": 80, 9 | "n_fft": 640, 10 | "crop_start": 0, 11 | "crop_stop": 85, 12 | "lpf_start": 25, 13 | "lpf_stop": 53, 14 | "res_type": "polyphase" 15 | }, 16 | "2": { 17 | "sr": 7350, 18 | "hl": 80, 19 | "n_fft": 320, 20 | "crop_start": 4, 21 | "crop_stop": 87, 22 | "hpf_start": 25, 23 | "hpf_stop": 12, 24 | "lpf_start": 31, 25 | "lpf_stop": 62, 26 | "res_type": "polyphase" 27 | }, 28 | "3": { 29 | "sr": 14700, 30 | "hl": 160, 31 | "n_fft": 512, 32 | "crop_start": 17, 33 | "crop_stop": 216, 34 | "hpf_start": 48, 35 | "hpf_stop": 24, 36 | "lpf_start": 139, 37 | "lpf_stop": 210, 38 | "res_type": "polyphase" 39 | }, 40 | "4": { 41 | "sr": 44100, 42 | "hl": 480, 43 | "n_fft": 960, 44 | "crop_start": 78, 45 | "crop_stop": 383, 46 | "hpf_start": 130, 47 | "hpf_stop": 86, 48 | "convert_channels": "stereo_n", 49 | "res_type": "kaiser_fast" 50 | } 51 | }, 52 | "sr": 44100, 53 | "pre_filter_start": 668, 54 | "pre_filter_stop": 672 55 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/4band_v3.json: -------------------------------------------------------------------------------- 1 | { 2 | "bins": 672, 3 | "unstable_bins": 8, 4 | "reduction_bins": 530, 5 | "band": { 6 | "1": { 7 | "sr": 7350, 8 | "hl": 80, 9 | "n_fft": 640, 10 | "crop_start": 0, 11 | "crop_stop": 85, 12 | "lpf_start": 25, 13 | "lpf_stop": 53, 14 | "res_type": "polyphase" 15 | }, 16 | "2": { 17 | "sr": 7350, 18 | "hl": 80, 19 | "n_fft": 320, 20 | "crop_start": 4, 21 | "crop_stop": 87, 22 | "hpf_start": 25, 23 | "hpf_stop": 12, 24 | "lpf_start": 31, 25 | "lpf_stop": 62, 26 | "res_type": "polyphase" 27 | }, 28 | "3": { 29 | "sr": 14700, 30 | "hl": 160, 31 | "n_fft": 512, 32 | "crop_start": 17, 33 | "crop_stop": 216, 34 | "hpf_start": 48, 35 | "hpf_stop": 24, 36 | "lpf_start": 139, 37 | "lpf_stop": 210, 38 | "res_type": "polyphase" 39 | }, 40 | "4": { 41 | "sr": 44100, 42 | "hl": 480, 43 | "n_fft": 960, 44 | "crop_start": 78, 45 | "crop_stop": 383, 46 | "hpf_start": 130, 47 | "hpf_stop": 86, 48 | "res_type": "kaiser_fast" 49 | } 50 | }, 51 | "sr": 44100, 52 | "pre_filter_start": 668, 53 | "pre_filter_stop": 672 54 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/modelparams/ensemble.json: -------------------------------------------------------------------------------- 1 | { 2 | "mid_side_b2": true, 3 | "bins": 1280, 4 | "unstable_bins": 7, 5 | "reduction_bins": 565, 6 | "band": { 7 | "1": { 8 | "sr": 11025, 9 | "hl": 108, 10 | "n_fft": 2048, 11 | "crop_start": 0, 12 | "crop_stop": 374, 13 | "lpf_start": 92, 14 | "lpf_stop": 186, 15 | "res_type": "polyphase" 16 | }, 17 | "2": { 18 | "sr": 22050, 19 | "hl": 216, 20 | "n_fft": 1536, 21 | "crop_start": 0, 22 | "crop_stop": 424, 23 | "hpf_start": 68, 24 | "hpf_stop": 34, 25 | "lpf_start": 348, 26 | "lpf_stop": 418, 27 | "res_type": "polyphase" 28 | }, 29 | "3": { 30 | "sr": 44100, 31 | "hl": 432, 32 | "n_fft": 1280, 33 | "crop_start": 132, 34 | "crop_stop": 614, 35 | "hpf_start": 172, 36 | "hpf_stop": 144, 37 | "res_type": "polyphase" 38 | } 39 | }, 40 | "sr": 44100, 41 | "pre_filter_start": 1280, 42 | "pre_filter_stop": 1280 43 | } -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/nets.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | from torch import nn 4 | 5 | from . import layers 6 | 7 | 8 | class BaseNet(nn.Module): 9 | def __init__( 10 | self, nin, nout, nin_lstm, nout_lstm, dilations=((4, 2), (8, 4), (12, 6)) 11 | ): 12 | super(BaseNet, self).__init__() 13 | self.enc1 = layers.Conv2DBNActiv(nin, nout, 3, 1, 1) 14 | self.enc2 = layers.Encoder(nout, nout * 2, 3, 2, 1) 15 | self.enc3 = layers.Encoder(nout * 2, nout * 4, 3, 2, 1) 16 | self.enc4 = layers.Encoder(nout * 4, nout * 6, 3, 2, 1) 17 | self.enc5 = layers.Encoder(nout * 6, nout * 8, 3, 2, 1) 18 | 19 | self.aspp = layers.ASPPModule(nout * 8, nout * 8, dilations, dropout=True) 20 | 21 | self.dec4 = layers.Decoder(nout * (6 + 8), nout * 6, 3, 1, 1) 22 | self.dec3 = layers.Decoder(nout * (4 + 6), nout * 4, 3, 1, 1) 23 | self.dec2 = layers.Decoder(nout * (2 + 4), nout * 2, 3, 1, 1) 24 | self.lstm_dec2 = layers.LSTMModule(nout * 2, nin_lstm, nout_lstm) 25 | self.dec1 = layers.Decoder(nout * (1 + 2) + 1, nout * 1, 3, 1, 1) 26 | 27 | @torch.inference_mode() 28 | def forward(self, x): 29 | e1 = self.enc1(x) 30 | e2 = self.enc2(e1) 31 | e3 = self.enc3(e2) 32 | e4 = self.enc4(e3) 33 | e5 = self.enc5(e4) 34 | 35 | h = self.aspp(e5) 36 | 37 | h = self.dec4(h, e4) 38 | h = self.dec3(h, e3) 39 | h = self.dec2(h, e2) 40 | h = torch.cat([h, self.lstm_dec2(h)], dim=1) 41 | h = self.dec1(h, e1) 42 | 43 | return h 44 | 45 | 46 | class CascadedNet(nn.Module): 47 | def __init__(self, n_fft, nout=32, nout_lstm=128): 48 | super(CascadedNet, self).__init__() 49 | 50 | self.max_bin = n_fft // 2 51 | self.output_bin = n_fft // 2 + 1 52 | self.nin_lstm = self.max_bin // 2 53 | self.offset = 64 54 | 55 | self.stg1_low_band_net = nn.Sequential( 56 | BaseNet(2, nout // 2, self.nin_lstm // 2, nout_lstm), 57 | layers.Conv2DBNActiv(nout // 2, nout // 4, 1, 1, 0), 58 | ) 59 | 60 | self.stg1_high_band_net = BaseNet( 61 | 2, nout // 4, self.nin_lstm // 2, nout_lstm // 2 62 | ) 63 | 64 | self.stg2_low_band_net = nn.Sequential( 65 | BaseNet(nout // 4 + 2, nout, self.nin_lstm // 2, nout_lstm), 66 | layers.Conv2DBNActiv(nout, nout // 2, 1, 1, 0), 67 | ) 68 | self.stg2_high_band_net = BaseNet( 69 | nout // 4 + 2, nout // 2, self.nin_lstm // 2, nout_lstm // 2 70 | ) 71 | 72 | self.stg3_full_band_net = BaseNet( 73 | 3 * nout // 4 + 2, nout, self.nin_lstm, nout_lstm 74 | ) 75 | 76 | self.out = nn.Conv2d(nout, 2, 1, bias=False) 77 | self.aux_out = nn.Conv2d(3 * nout // 4, 2, 1, bias=False) 78 | 79 | @torch.inference_mode() 80 | def forward(self, x): 81 | x = x[:, :, : self.max_bin] 82 | 83 | bandw = x.size()[2] // 2 84 | l1_in = x[:, :, :bandw] 85 | h1_in = x[:, :, bandw:] 86 | l1 = self.stg1_low_band_net(l1_in) 87 | h1 = self.stg1_high_band_net(h1_in) 88 | aux1 = torch.cat([l1, h1], dim=2) 89 | 90 | l2_in = torch.cat([l1_in, l1], dim=1) 91 | h2_in = torch.cat([h1_in, h1], dim=1) 92 | l2 = self.stg2_low_band_net(l2_in) 93 | h2 = self.stg2_high_band_net(h2_in) 94 | aux2 = torch.cat([l2, h2], dim=2) 95 | 96 | f3_in = torch.cat([x, aux1, aux2], dim=1) 97 | f3 = self.stg3_full_band_net(f3_in) 98 | 99 | mask = torch.sigmoid(self.out(f3)) 100 | mask = F.pad( 101 | input=mask, 102 | pad=(0, 0, 0, self.output_bin - mask.size()[2]), 103 | mode="replicate", 104 | ) 105 | 106 | if self.training: 107 | aux = torch.cat([aux1, aux2], dim=1) 108 | aux = torch.sigmoid(self.aux_out(aux)) 109 | aux = F.pad( 110 | input=aux, 111 | pad=(0, 0, 0, self.output_bin - aux.size()[2]), 112 | mode="replicate", 113 | ) 114 | return mask, aux 115 | else: 116 | return mask 117 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/nets_123821KB.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | from torch import nn 4 | 5 | from . import layers_123821KB as layers 6 | 7 | 8 | class BaseASPPNet(nn.Module): 9 | def __init__(self, nin, ch, dilations=(4, 8, 16)): 10 | super(BaseASPPNet, self).__init__() 11 | self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) 12 | self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) 13 | self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) 14 | self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) 15 | 16 | self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) 17 | 18 | self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) 19 | self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) 20 | self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) 21 | self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) 22 | 23 | def __call__(self, x): 24 | h, e1 = self.enc1(x) 25 | h, e2 = self.enc2(h) 26 | h, e3 = self.enc3(h) 27 | h, e4 = self.enc4(h) 28 | 29 | h = self.aspp(h) 30 | 31 | h = self.dec4(h, e4) 32 | h = self.dec3(h, e3) 33 | h = self.dec2(h, e2) 34 | h = self.dec1(h, e1) 35 | 36 | return h 37 | 38 | 39 | class CascadedASPPNet(nn.Module): 40 | def __init__(self, n_fft): 41 | super(CascadedASPPNet, self).__init__() 42 | self.stg1_low_band_net = BaseASPPNet(2, 32) 43 | self.stg1_high_band_net = BaseASPPNet(2, 32) 44 | 45 | self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) 46 | self.stg2_full_band_net = BaseASPPNet(16, 32) 47 | 48 | self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) 49 | self.stg3_full_band_net = BaseASPPNet(32, 64) 50 | 51 | self.out = nn.Conv2d(64, 2, 1, bias=False) 52 | self.aux1_out = nn.Conv2d(32, 2, 1, bias=False) 53 | self.aux2_out = nn.Conv2d(32, 2, 1, bias=False) 54 | 55 | self.max_bin = n_fft // 2 56 | self.output_bin = n_fft // 2 + 1 57 | 58 | self.offset = 128 59 | 60 | def forward(self, x, aggressiveness=None): 61 | mix = x.detach() 62 | x = x.clone() 63 | 64 | x = x[:, :, : self.max_bin] 65 | 66 | bandw = x.size()[2] // 2 67 | aux1 = torch.cat( 68 | [ 69 | self.stg1_low_band_net(x[:, :, :bandw]), 70 | self.stg1_high_band_net(x[:, :, bandw:]), 71 | ], 72 | dim=2, 73 | ) 74 | 75 | h = torch.cat([x, aux1], dim=1) 76 | aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) 77 | 78 | h = torch.cat([x, aux1, aux2], dim=1) 79 | h = self.stg3_full_band_net(self.stg3_bridge(h)) 80 | 81 | mask = torch.sigmoid(self.out(h)) 82 | mask = F.pad( 83 | input=mask, 84 | pad=(0, 0, 0, self.output_bin - mask.size()[2]), 85 | mode="replicate", 86 | ) 87 | 88 | if self.training: 89 | aux1 = torch.sigmoid(self.aux1_out(aux1)) 90 | aux1 = F.pad( 91 | input=aux1, 92 | pad=(0, 0, 0, self.output_bin - aux1.size()[2]), 93 | mode="replicate", 94 | ) 95 | aux2 = torch.sigmoid(self.aux2_out(aux2)) 96 | aux2 = F.pad( 97 | input=aux2, 98 | pad=(0, 0, 0, self.output_bin - aux2.size()[2]), 99 | mode="replicate", 100 | ) 101 | return mask * mix, aux1 * mix, aux2 * mix 102 | else: 103 | if aggressiveness: 104 | mask[:, :, : aggressiveness["split_bin"]] = torch.pow( 105 | mask[:, :, : aggressiveness["split_bin"]], 106 | 1 + aggressiveness["value"] / 3, 107 | ) 108 | mask[:, :, aggressiveness["split_bin"] :] = torch.pow( 109 | mask[:, :, aggressiveness["split_bin"] :], 110 | 1 + aggressiveness["value"], 111 | ) 112 | 113 | return mask * mix 114 | 115 | def predict(self, x_mag, aggressiveness=None): 116 | h = self.forward(x_mag, aggressiveness) 117 | 118 | if self.offset > 0: 119 | h = h[:, :, :, self.offset : -self.offset] 120 | assert h.size()[3] > 0 121 | 122 | return h 123 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/nets_33966KB.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | from torch import nn 4 | 5 | from . import layers_33966KB as layers 6 | 7 | 8 | class BaseASPPNet(nn.Module): 9 | def __init__(self, nin, ch, dilations=(4, 8, 16, 32)): 10 | super(BaseASPPNet, self).__init__() 11 | self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) 12 | self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) 13 | self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) 14 | self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) 15 | 16 | self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) 17 | 18 | self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) 19 | self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) 20 | self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) 21 | self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) 22 | 23 | def __call__(self, x): 24 | h, e1 = self.enc1(x) 25 | h, e2 = self.enc2(h) 26 | h, e3 = self.enc3(h) 27 | h, e4 = self.enc4(h) 28 | 29 | h = self.aspp(h) 30 | 31 | h = self.dec4(h, e4) 32 | h = self.dec3(h, e3) 33 | h = self.dec2(h, e2) 34 | h = self.dec1(h, e1) 35 | 36 | return h 37 | 38 | 39 | class CascadedASPPNet(nn.Module): 40 | def __init__(self, n_fft): 41 | super(CascadedASPPNet, self).__init__() 42 | self.stg1_low_band_net = BaseASPPNet(2, 16) 43 | self.stg1_high_band_net = BaseASPPNet(2, 16) 44 | 45 | self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0) 46 | self.stg2_full_band_net = BaseASPPNet(8, 16) 47 | 48 | self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) 49 | self.stg3_full_band_net = BaseASPPNet(16, 32) 50 | 51 | self.out = nn.Conv2d(32, 2, 1, bias=False) 52 | self.aux1_out = nn.Conv2d(16, 2, 1, bias=False) 53 | self.aux2_out = nn.Conv2d(16, 2, 1, bias=False) 54 | 55 | self.max_bin = n_fft // 2 56 | self.output_bin = n_fft // 2 + 1 57 | 58 | self.offset = 128 59 | 60 | def forward(self, x, aggressiveness=None): 61 | mix = x.detach() 62 | x = x.clone() 63 | 64 | x = x[:, :, : self.max_bin] 65 | 66 | bandw = x.size()[2] // 2 67 | aux1 = torch.cat( 68 | [ 69 | self.stg1_low_band_net(x[:, :, :bandw]), 70 | self.stg1_high_band_net(x[:, :, bandw:]), 71 | ], 72 | dim=2, 73 | ) 74 | 75 | h = torch.cat([x, aux1], dim=1) 76 | aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) 77 | 78 | h = torch.cat([x, aux1, aux2], dim=1) 79 | h = self.stg3_full_band_net(self.stg3_bridge(h)) 80 | 81 | mask = torch.sigmoid(self.out(h)) 82 | mask = F.pad( 83 | input=mask, 84 | pad=(0, 0, 0, self.output_bin - mask.size()[2]), 85 | mode="replicate", 86 | ) 87 | 88 | if self.training: 89 | aux1 = torch.sigmoid(self.aux1_out(aux1)) 90 | aux1 = F.pad( 91 | input=aux1, 92 | pad=(0, 0, 0, self.output_bin - aux1.size()[2]), 93 | mode="replicate", 94 | ) 95 | aux2 = torch.sigmoid(self.aux2_out(aux2)) 96 | aux2 = F.pad( 97 | input=aux2, 98 | pad=(0, 0, 0, self.output_bin - aux2.size()[2]), 99 | mode="replicate", 100 | ) 101 | return mask * mix, aux1 * mix, aux2 * mix 102 | else: 103 | if aggressiveness: 104 | mask[:, :, : aggressiveness["split_bin"]] = torch.pow( 105 | mask[:, :, : aggressiveness["split_bin"]], 106 | 1 + aggressiveness["value"] / 3, 107 | ) 108 | mask[:, :, aggressiveness["split_bin"] :] = torch.pow( 109 | mask[:, :, aggressiveness["split_bin"] :], 110 | 1 + aggressiveness["value"], 111 | ) 112 | 113 | return mask * mix 114 | 115 | def predict(self, x_mag, aggressiveness=None): 116 | h = self.forward(x_mag, aggressiveness) 117 | 118 | if self.offset > 0: 119 | h = h[:, :, :, self.offset : -self.offset] 120 | assert h.size()[3] > 0 121 | 122 | return h 123 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/lib_v5/nets_537238KB.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | import torch.nn.functional as F 4 | from torch import nn 5 | 6 | from . import layers_537238KB as layers 7 | 8 | 9 | class BaseASPPNet(nn.Module): 10 | def __init__(self, nin, ch, dilations=(4, 8, 16)): 11 | super(BaseASPPNet, self).__init__() 12 | self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) 13 | self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) 14 | self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) 15 | self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) 16 | 17 | self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) 18 | 19 | self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) 20 | self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) 21 | self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) 22 | self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) 23 | 24 | def __call__(self, x): 25 | h, e1 = self.enc1(x) 26 | h, e2 = self.enc2(h) 27 | h, e3 = self.enc3(h) 28 | h, e4 = self.enc4(h) 29 | 30 | h = self.aspp(h) 31 | 32 | h = self.dec4(h, e4) 33 | h = self.dec3(h, e3) 34 | h = self.dec2(h, e2) 35 | h = self.dec1(h, e1) 36 | 37 | return h 38 | 39 | 40 | class CascadedASPPNet(nn.Module): 41 | def __init__(self, n_fft): 42 | super(CascadedASPPNet, self).__init__() 43 | self.stg1_low_band_net = BaseASPPNet(2, 64) 44 | self.stg1_high_band_net = BaseASPPNet(2, 64) 45 | 46 | self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) 47 | self.stg2_full_band_net = BaseASPPNet(32, 64) 48 | 49 | self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0) 50 | self.stg3_full_band_net = BaseASPPNet(64, 128) 51 | 52 | self.out = nn.Conv2d(128, 2, 1, bias=False) 53 | self.aux1_out = nn.Conv2d(64, 2, 1, bias=False) 54 | self.aux2_out = nn.Conv2d(64, 2, 1, bias=False) 55 | 56 | self.max_bin = n_fft // 2 57 | self.output_bin = n_fft // 2 + 1 58 | 59 | self.offset = 128 60 | 61 | def forward(self, x, aggressiveness=None): 62 | mix = x.detach() 63 | x = x.clone() 64 | 65 | x = x[:, :, : self.max_bin] 66 | 67 | bandw = x.size()[2] // 2 68 | aux1 = torch.cat( 69 | [ 70 | self.stg1_low_band_net(x[:, :, :bandw]), 71 | self.stg1_high_band_net(x[:, :, bandw:]), 72 | ], 73 | dim=2, 74 | ) 75 | 76 | h = torch.cat([x, aux1], dim=1) 77 | aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) 78 | 79 | h = torch.cat([x, aux1, aux2], dim=1) 80 | h = self.stg3_full_band_net(self.stg3_bridge(h)) 81 | 82 | mask = torch.sigmoid(self.out(h)) 83 | mask = F.pad( 84 | input=mask, 85 | pad=(0, 0, 0, self.output_bin - mask.size()[2]), 86 | mode="replicate", 87 | ) 88 | 89 | if self.training: 90 | aux1 = torch.sigmoid(self.aux1_out(aux1)) 91 | aux1 = F.pad( 92 | input=aux1, 93 | pad=(0, 0, 0, self.output_bin - aux1.size()[2]), 94 | mode="replicate", 95 | ) 96 | aux2 = torch.sigmoid(self.aux2_out(aux2)) 97 | aux2 = F.pad( 98 | input=aux2, 99 | pad=(0, 0, 0, self.output_bin - aux2.size()[2]), 100 | mode="replicate", 101 | ) 102 | return mask * mix, aux1 * mix, aux2 * mix 103 | else: 104 | if aggressiveness: 105 | mask[:, :, : aggressiveness["split_bin"]] = torch.pow( 106 | mask[:, :, : aggressiveness["split_bin"]], 107 | 1 + aggressiveness["value"] / 3, 108 | ) 109 | mask[:, :, aggressiveness["split_bin"] :] = torch.pow( 110 | mask[:, :, aggressiveness["split_bin"] :], 111 | 1 + aggressiveness["value"], 112 | ) 113 | 114 | return mask * mix 115 | 116 | def predict(self, x_mag, aggressiveness=None): 117 | h = self.forward(x_mag, aggressiveness) 118 | 119 | if self.offset > 0: 120 | h = h[:, :, :, self.offset : -self.offset] 121 | assert h.size()[3] > 0 122 | 123 | return h 124 | -------------------------------------------------------------------------------- /infer/lib/uvr5_pack/utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | from tqdm import tqdm 4 | 5 | 6 | def make_padding(width, cropsize, offset): 7 | left = offset 8 | roi_size = cropsize - left * 2 9 | if roi_size == 0: 10 | roi_size = cropsize 11 | right = roi_size - (width % roi_size) + left 12 | 13 | return left, right, roi_size 14 | 15 | 16 | def inference(X_spec, device, model, aggressiveness, data): 17 | """ 18 | data ： dic configs 19 | """ 20 | 21 | def _execute( 22 | X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True 23 | ): 24 | model.eval() 25 | with torch.no_grad(): 26 | preds = [] 27 | 28 | iterations = [n_window] 29 | 30 | total_iterations = sum(iterations) 31 | for i in tqdm(range(n_window)): 32 | start = i * roi_size 33 | X_mag_window = X_mag_pad[ 34 | None, :, :, start : start + data["window_size"] 35 | ] 36 | X_mag_window = torch.from_numpy(X_mag_window) 37 | if is_half: 38 | X_mag_window = X_mag_window.half() 39 | X_mag_window = X_mag_window.to(device) 40 | 41 | pred = model.predict(X_mag_window, aggressiveness) 42 | 43 | pred = pred.detach().cpu().numpy() 44 | preds.append(pred[0]) 45 | 46 | pred = np.concatenate(preds, axis=2) 47 | return pred 48 | 49 | def preprocess(X_spec): 50 | X_mag = np.abs(X_spec) 51 | X_phase = np.angle(X_spec) 52 | 53 | return X_mag, X_phase 54 | 55 | X_mag, X_phase = preprocess(X_spec) 56 | 57 | coef = X_mag.max() 58 | X_mag_pre = X_mag / coef 59 | 60 | n_frame = X_mag_pre.shape[2] 61 | pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset) 62 | n_window = int(np.ceil(n_frame / roi_size)) 63 | 64 | X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant") 65 | 66 | if list(model.state_dict().values())[0].dtype == torch.float16: 67 | is_half = True 68 | else: 69 | is_half = False 70 | pred = _execute( 71 | X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half 72 | ) 73 | pred = pred[:, :, :n_frame] 74 | 75 | if data["tta"]: 76 | pad_l += roi_size // 2 77 | pad_r += roi_size // 2 78 | n_window += 1 79 | 80 | X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant") 81 | 82 | pred_tta = _execute( 83 | X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half 84 | ) 85 | pred_tta = pred_tta[:, :, roi_size // 2 :] 86 | pred_tta = pred_tta[:, :, :n_frame] 87 | 88 | return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase) 89 | else: 90 | return pred * coef, X_mag, np.exp(1.0j * X_phase) 91 | -------------------------------------------------------------------------------- /infer/modules/gui/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | TorchGating is a PyTorch-based implementation of Spectral Gating 3 | ================================================ 4 | Author: Asaf Zorea 5 | 6 | Contents 7 | -------- 8 | torchgate imports all the functions from PyTorch, and in addition provides: 9 | TorchGating --- A PyTorch module that applies a spectral gate to an input signal 10 | 11 | """ 12 | 13 | from .torchgate import TorchGate 14 | -------------------------------------------------------------------------------- /infer/modules/gui/utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.types import Number 3 | 4 | 5 | @torch.no_grad() 6 | def amp_to_db( 7 | x: torch.Tensor, eps=torch.finfo(torch.float64).eps, top_db=40 8 | ) -> torch.Tensor: 9 | """ 10 | Convert the input tensor from amplitude to decibel scale. 11 | 12 | Arguments: 13 | x {[torch.Tensor]} -- [Input tensor.] 14 | 15 | Keyword Arguments: 16 | eps {[float]} -- [Small value to avoid numerical instability.] 17 | (default: {torch.finfo(torch.float64).eps}) 18 | top_db {[float]} -- [threshold the output at ``top_db`` below the peak] 19 | ` (default: {40}) 20 | 21 | Returns: 22 | [torch.Tensor] -- [Output tensor in decibel scale.] 23 | """ 24 | x_db = 20 * torch.log10(x.abs() + eps) 25 | return torch.max(x_db, (x_db.max(-1).values - top_db).unsqueeze(-1)) 26 | 27 | 28 | @torch.no_grad() 29 | def temperature_sigmoid(x: torch.Tensor, x0: float, temp_coeff: float) -> torch.Tensor: 30 | """ 31 | Apply a sigmoid function with temperature scaling. 32 | 33 | Arguments: 34 | x {[torch.Tensor]} -- [Input tensor.] 35 | x0 {[float]} -- [Parameter that controls the threshold of the sigmoid.] 36 | temp_coeff {[float]} -- [Parameter that controls the slope of the sigmoid.] 37 | 38 | Returns: 39 | [torch.Tensor] -- [Output tensor after applying the sigmoid with temperature scaling.] 40 | """ 41 | return torch.sigmoid((x - x0) / temp_coeff) 42 | 43 | 44 | @torch.no_grad() 45 | def linspace( 46 | start: Number, stop: Number, num: int = 50, endpoint: bool = True, **kwargs 47 | ) -> torch.Tensor: 48 | """ 49 | Generate a linearly spaced 1-D tensor. 50 | 51 | Arguments: 52 | start {[Number]} -- [The starting value of the sequence.] 53 | stop {[Number]} -- [The end value of the sequence, unless `endpoint` is set to False. 54 | In that case, the sequence consists of all but the last of ``num + 1`` 55 | evenly spaced samples, so that `stop` is excluded. Note that the step 56 | size changes when `endpoint` is False.] 57 | 58 | Keyword Arguments: 59 | num {[int]} -- [Number of samples to generate. Default is 50. Must be non-negative.] 60 | endpoint {[bool]} -- [If True, `stop` is the last sample. Otherwise, it is not included. 61 | Default is True.] 62 | **kwargs -- [Additional arguments to be passed to the underlying PyTorch `linspace` function.] 63 | 64 | Returns: 65 | [torch.Tensor] -- [1-D tensor of `num` equally spaced samples from `start` to `stop`.] 66 | """ 67 | if endpoint: 68 | return torch.linspace(start, stop, num, **kwargs) 69 | else: 70 | return torch.linspace(start, stop, num + 1, **kwargs)[:-1] 71 | -------------------------------------------------------------------------------- /infer/modules/train/extract_f0_print.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import traceback 4 | from pathlib import Path 5 | 6 | from dotenv import load_dotenv 7 | 8 | now_dir = os.getcwd() 9 | sys.path.append(now_dir) 10 | load_dotenv() 11 | load_dotenv("sha256.env") 12 | 13 | now_dir = os.getcwd() 14 | sys.path.append(now_dir) 15 | import logging 16 | 17 | import numpy as np 18 | 19 | from infer.lib.audio import load_audio 20 | 21 | from rvc.f0 import Generator 22 | 23 | logging.getLogger("numba").setLevel(logging.WARNING) 24 | from multiprocessing import Process 25 | 26 | exp_dir = sys.argv[1] 27 | f = open("%s/extract_f0_feature.log" % exp_dir, "a+") 28 | 29 | 30 | def printt(strr): 31 | print(strr) 32 | f.write("%s\n" % strr) 33 | f.flush() 34 | 35 | 36 | n_p = int(sys.argv[2]) 37 | f0method = sys.argv[3] 38 | device = sys.argv[4] 39 | is_half = sys.argv[5] == "True" 40 | 41 | 42 | class FeatureInput(object): 43 | def __init__(self, is_half: bool, device="cpu", samplerate=16000, hop_size=160): 44 | self.fs = samplerate 45 | self.hop = hop_size 46 | 47 | self.f0_bin = 256 48 | self.f0_max = 1100.0 49 | self.f0_min = 50.0 50 | self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700) 51 | self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700) 52 | 53 | self.f0_gen = Generator( 54 | Path(os.environ["rmvpe_root"]), 55 | is_half, 56 | 0, 57 | device, 58 | hop_size, 59 | samplerate, 60 | ) 61 | 62 | def go(self, paths, f0_method): 63 | if len(paths) == 0: 64 | printt("no-f0-todo") 65 | else: 66 | printt("todo-f0-%s" % len(paths)) 67 | n = max(len(paths) // 5, 1) # 每个进程最多打印5条 68 | for idx, (inp_path, opt_path1, opt_path2) in enumerate(paths): 69 | try: 70 | if idx % n == 0: 71 | printt("f0ing,now-%s,all-%s,-%s" % (idx, len(paths), inp_path)) 72 | if ( 73 | os.path.exists(opt_path1 + ".npy") == True 74 | and os.path.exists(opt_path2 + ".npy") == True 75 | ): 76 | continue 77 | x = load_audio(inp_path, self.fs) 78 | coarse_pit, feature_pit = self.f0_gen.calculate( 79 | x, x.shape[0] // self.hop, 0, f0_method, None 80 | ) 81 | np.save( 82 | opt_path2, 83 | feature_pit, 84 | allow_pickle=False, 85 | ) # nsf 86 | np.save( 87 | opt_path1, 88 | coarse_pit, 89 | allow_pickle=False, 90 | ) # ori 91 | except: 92 | printt("f0fail-%s-%s-%s" % (idx, inp_path, traceback.format_exc())) 93 | 94 | 95 | if __name__ == "__main__": 96 | # exp_dir=r"E:\codes\py39\dataset\mi-test" 97 | # n_p=16 98 | # f = open("%s/log_extract_f0.log"%exp_dir, "w") 99 | printt(" ".join(sys.argv)) 100 | featureInput = FeatureInput(is_half, device) 101 | paths = [] 102 | inp_root = "%s/1_16k_wavs" % (exp_dir) 103 | opt_root1 = "%s/2a_f0" % (exp_dir) 104 | opt_root2 = "%s/2b-f0nsf" % (exp_dir) 105 | 106 | os.makedirs(opt_root1, exist_ok=True) 107 | os.makedirs(opt_root2, exist_ok=True) 108 | for name in sorted(list(os.listdir(inp_root))): 109 | inp_path = "%s/%s" % (inp_root, name) 110 | if "spec" in inp_path: 111 | continue 112 | opt_path1 = "%s/%s" % (opt_root1, name) 113 | opt_path2 = "%s/%s" % (opt_root2, name) 114 | paths.append([inp_path, opt_path1, opt_path2]) 115 | 116 | ps = [] 117 | for i in range(n_p): 118 | p = Process( 119 | target=featureInput.go, 120 | args=( 121 | paths[i::n_p], 122 | f0method, 123 | ), 124 | ) 125 | ps.append(p) 126 | p.start() 127 | for i in range(n_p): 128 | ps[i].join() 129 | -------------------------------------------------------------------------------- /infer/modules/train/extract_feature_print.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import traceback 4 | 5 | now_dir = os.getcwd() 6 | sys.path.append(now_dir) 7 | 8 | from infer.lib.audio import load_audio 9 | 10 | os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" 11 | os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0" 12 | 13 | device = sys.argv[1] 14 | n_part = int(sys.argv[2]) 15 | i_part = int(sys.argv[3]) 16 | if len(sys.argv) == 7: 17 | exp_dir = sys.argv[4] 18 | version = sys.argv[5] 19 | is_half = sys.argv[6].lower() == "true" 20 | else: 21 | i_gpu = sys.argv[4] 22 | exp_dir = sys.argv[5] 23 | os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu) 24 | version = sys.argv[6] 25 | is_half = sys.argv[7].lower() == "true" 26 | import fairseq 27 | import numpy as np 28 | import torch 29 | import torch.nn.functional as F 30 | 31 | if "privateuseone" not in device: 32 | device = "cpu" 33 | if torch.cuda.is_available(): 34 | device = "cuda" 35 | elif torch.backends.mps.is_available(): 36 | device = "mps" 37 | else: 38 | import torch_directml 39 | 40 | device = torch_directml.device(torch_directml.default_device()) 41 | 42 | def forward_dml(ctx, x, scale): 43 | ctx.scale = scale 44 | res = x.clone().detach() 45 | return res 46 | 47 | fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml 48 | 49 | f = open("%s/extract_f0_feature.log" % exp_dir, "a+") 50 | 51 | 52 | def printt(strr): 53 | print(strr) 54 | f.write("%s\n" % strr) 55 | f.flush() 56 | 57 | 58 | printt(" ".join(sys.argv)) 59 | model_path = "assets/hubert/hubert_base.pt" 60 | 61 | printt("exp_dir: " + exp_dir) 62 | wavPath = "%s/1_16k_wavs" % exp_dir 63 | outPath = ( 64 | "%s/3_feature256" % exp_dir if version == "v1" else "%s/3_feature768" % exp_dir 65 | ) 66 | os.makedirs(outPath, exist_ok=True) 67 | 68 | 69 | # wave must be 16k, hop_size=320 70 | def readwave(wav_path, normalize=False): 71 | wav, sr = load_audio(wav_path) 72 | assert sr == 16000 73 | feats = torch.from_numpy(wav).float() 74 | assert feats.dim() == 1, feats.dim() 75 | if normalize: 76 | with torch.no_grad(): 77 | feats = F.layer_norm(feats, feats.shape) 78 | feats = feats.view(1, -1) 79 | return feats 80 | 81 | 82 | # HuBERT model 83 | printt("load model(s) from {}".format(model_path)) 84 | # if hubert model is exist 85 | if os.access(model_path, os.F_OK) == False: 86 | printt( 87 | "Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main" 88 | % model_path 89 | ) 90 | exit(0) 91 | models, saved_cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task( 92 | [model_path], 93 | suffix="", 94 | ) 95 | model = models[0] 96 | model = model.to(device) 97 | printt("move model to %s" % device) 98 | if is_half: 99 | if device not in ["mps", "cpu"]: 100 | model = model.half() 101 | model.eval() 102 | 103 | todo = sorted(list(os.listdir(wavPath)))[i_part::n_part] 104 | n = max(1, len(todo) // 10) # 最多打印十条 105 | if len(todo) == 0: 106 | printt("no-feature-todo") 107 | else: 108 | printt("all-feature-%s" % len(todo)) 109 | for idx, file in enumerate(todo): 110 | try: 111 | if file.endswith(".wav"): 112 | wav_path = "%s/%s" % (wavPath, file) 113 | out_path = "%s/%s" % (outPath, file.replace("wav", "npy")) 114 | 115 | if os.path.exists(out_path): 116 | continue 117 | 118 | feats = readwave(wav_path, normalize=saved_cfg.task.normalize) 119 | padding_mask = torch.BoolTensor(feats.shape).fill_(False) 120 | inputs = { 121 | "source": ( 122 | feats.half().to(device) 123 | if is_half and device not in ["mps", "cpu"] 124 | else feats.to(device) 125 | ), 126 | "padding_mask": padding_mask.to(device), 127 | "output_layer": 9 if version == "v1" else 12, # layer 9 128 | } 129 | with torch.no_grad(): 130 | logits = model.extract_features(**inputs) 131 | feats = ( 132 | model.final_proj(logits[0]) if version == "v1" else logits[0] 133 | ) 134 | 135 | feats = feats.squeeze(0).float().cpu().numpy() 136 | if np.isnan(feats).sum() == 0: 137 | np.save(out_path, feats, allow_pickle=False) 138 | else: 139 | printt("%s-contains nan" % file) 140 | if idx % n == 0: 141 | printt("now-%s,all-%s,%s,%s" % (len(todo), idx, file, feats.shape)) 142 | except: 143 | printt(traceback.format_exc()) 144 | printt("all-feature-done") 145 | -------------------------------------------------------------------------------- /infer/modules/train/preprocess.py: -------------------------------------------------------------------------------- 1 | import multiprocessing 2 | import os 3 | import sys 4 | 5 | from scipy import signal 6 | 7 | now_dir = os.getcwd() 8 | sys.path.append(now_dir) 9 | print(*sys.argv[1:]) 10 | inp_root = sys.argv[1] 11 | sr = int(sys.argv[2]) 12 | n_p = int(sys.argv[3]) 13 | exp_dir = sys.argv[4] 14 | noparallel = sys.argv[5] == "True" 15 | per = float(sys.argv[6]) 16 | import os 17 | import traceback 18 | 19 | import numpy as np 20 | 21 | from infer.lib.audio import load_audio, float_np_array_to_wav_buf, save_audio 22 | from infer.lib.slicer2 import Slicer 23 | 24 | f = open("%s/preprocess.log" % exp_dir, "a+") 25 | 26 | 27 | def println(strr): 28 | print(strr) 29 | f.write("%s\n" % strr) 30 | f.flush() 31 | 32 | 33 | class PreProcess: 34 | def __init__(self, sr, exp_dir, per=3.7): 35 | self.slicer = Slicer( 36 | sr=sr, 37 | threshold=-42, 38 | min_length=1500, 39 | min_interval=400, 40 | hop_size=15, 41 | max_sil_kept=500, 42 | ) 43 | self.sr = sr 44 | self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr) 45 | self.per = per 46 | self.overlap = 0.3 47 | self.tail = self.per + self.overlap 48 | self.max = 0.9 49 | self.alpha = 0.75 50 | self.exp_dir = exp_dir 51 | self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir 52 | self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir 53 | os.makedirs(self.exp_dir, exist_ok=True) 54 | os.makedirs(self.gt_wavs_dir, exist_ok=True) 55 | os.makedirs(self.wavs16k_dir, exist_ok=True) 56 | 57 | def norm_write(self, tmp_audio, idx0, idx1): 58 | tmp_max = np.abs(tmp_audio).max() 59 | if tmp_max > 2.5: 60 | print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max)) 61 | return 62 | tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + ( 63 | 1 - self.alpha 64 | ) * tmp_audio 65 | save_audio( 66 | "%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1), 67 | tmp_audio, 68 | self.sr, 69 | f32=True, 70 | ) 71 | with open("%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1), "wb") as f: 72 | f.write( 73 | float_np_array_to_wav_buf( 74 | load_audio( 75 | float_np_array_to_wav_buf(tmp_audio, self.sr, f32=True), 76 | sr=16000, 77 | format="wav", 78 | ), 79 | 16000, 80 | True, 81 | ).getbuffer() 82 | ) 83 | 84 | def pipeline(self, path, idx0): 85 | try: 86 | audio = load_audio(path, self.sr) 87 | # zero phased digital filter cause pre-ringing noise... 88 | # audio = signal.filtfilt(self.bh, self.ah, audio) 89 | audio = signal.lfilter(self.bh, self.ah, audio) 90 | 91 | idx1 = 0 92 | for audio in self.slicer.slice(audio): 93 | i = 0 94 | while 1: 95 | start = int(self.sr * (self.per - self.overlap) * i) 96 | i += 1 97 | if len(audio[start:]) > self.tail * self.sr: 98 | tmp_audio = audio[start : start + int(self.per * self.sr)] 99 | self.norm_write(tmp_audio, idx0, idx1) 100 | idx1 += 1 101 | else: 102 | tmp_audio = audio[start:] 103 | idx1 += 1 104 | break 105 | self.norm_write(tmp_audio, idx0, idx1) 106 | println("%s\t-> Success" % path) 107 | except: 108 | println("%s\t-> %s" % (path, traceback.format_exc())) 109 | 110 | def pipeline_mp(self, infos): 111 | for path, idx0 in infos: 112 | self.pipeline(path, idx0) 113 | 114 | def pipeline_mp_inp_dir(self, inp_root, n_p): 115 | try: 116 | infos = [ 117 | ("%s/%s" % (inp_root, name), idx) 118 | for idx, name in enumerate(sorted(list(os.listdir(inp_root)))) 119 | ] 120 | if noparallel: 121 | for i in range(n_p): 122 | self.pipeline_mp(infos[i::n_p]) 123 | else: 124 | ps = [] 125 | for i in range(n_p): 126 | p = multiprocessing.Process( 127 | target=self.pipeline_mp, args=(infos[i::n_p],) 128 | ) 129 | ps.append(p) 130 | p.start() 131 | for i in range(n_p): 132 | ps[i].join() 133 | except: 134 | println("Fail. %s" % traceback.format_exc()) 135 | 136 | 137 | def preprocess_trainset(inp_root, sr, n_p, exp_dir, per): 138 | pp = PreProcess(sr, exp_dir, per) 139 | println("start preprocess") 140 | pp.pipeline_mp_inp_dir(inp_root, n_p) 141 | println("end preprocess") 142 | 143 | 144 | if __name__ == "__main__": 145 | preprocess_trainset(inp_root, sr, n_p, exp_dir, per) 146 | -------------------------------------------------------------------------------- /infer/modules/uvr5/modules.py: -------------------------------------------------------------------------------- 1 | import os 2 | import traceback 3 | import logging 4 | 5 | logger = logging.getLogger(__name__) 6 | 7 | from infer.lib.audio import resample_audio, get_audio_properties 8 | import torch 9 | 10 | from configs import Config 11 | from infer.modules.uvr5.mdxnet import MDXNetDereverb 12 | from infer.modules.uvr5.vr import AudioPre 13 | 14 | config = Config() 15 | 16 | 17 | def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0): 18 | infos = [] 19 | try: 20 | inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") 21 | save_root_vocal = ( 22 | save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ") 23 | ) 24 | save_root_ins = ( 25 | save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ") 26 | ) 27 | if model_name == "onnx_dereverb_By_FoxJoy": 28 | pre_fun = MDXNetDereverb(15, config.device) 29 | else: 30 | pre_fun = AudioPre( 31 | agg=int(agg), 32 | model_path=os.path.join( 33 | os.getenv("weight_uvr5_root"), model_name + ".pth" 34 | ), 35 | device=config.device, 36 | is_half=config.is_half, 37 | ) 38 | if inp_root != "": 39 | paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)] 40 | else: 41 | paths = [path.name for path in paths] 42 | for path in paths: 43 | inp_path = os.path.join(inp_root, path) 44 | need_reformat = 1 45 | done = 0 46 | try: 47 | channels, rate = get_audio_properties(inp_path) 48 | 49 | # Check the audio stream's properties 50 | if channels == 2 and rate == 44100: 51 | pre_fun._path_audio_( 52 | inp_path, save_root_ins, save_root_vocal, format0 53 | ) 54 | need_reformat = 0 55 | done = 1 56 | except Exception as e: 57 | need_reformat = 1 58 | logger.warning(f"Exception {e} occured. Will reformat") 59 | if need_reformat == 1: 60 | tmp_path = "%s/%s.reformatted.wav" % ( 61 | os.path.join(os.environ["TEMP"]), 62 | os.path.basename(inp_path), 63 | ) 64 | resample_audio(inp_path, tmp_path, "pcm_s16le", "s16", 44100, "stereo") 65 | try: # Remove the original file 66 | os.remove(inp_path) 67 | except Exception as e: 68 | print(f"Failed to remove the original file: {e}") 69 | inp_path = tmp_path 70 | try: 71 | if done == 0: 72 | pre_fun._path_audio_( 73 | inp_path, save_root_ins, save_root_vocal, format0 74 | ) 75 | infos.append("%s->Success" % (os.path.basename(inp_path))) 76 | yield "\n".join(infos) 77 | except: 78 | infos.append( 79 | "%s->%s" % (os.path.basename(inp_path), traceback.format_exc()) 80 | ) 81 | yield "\n".join(infos) 82 | except: 83 | infos.append(traceback.format_exc()) 84 | yield "\n".join(infos) 85 | finally: 86 | try: 87 | if model_name == "onnx_dereverb_By_FoxJoy": 88 | del pre_fun.pred.model 89 | del pre_fun.pred.model_ 90 | else: 91 | del pre_fun.model 92 | del pre_fun 93 | except: 94 | traceback.print_exc() 95 | if torch.cuda.is_available(): 96 | torch.cuda.empty_cache() 97 | logger.info("Executed torch.cuda.empty_cache()") 98 | elif torch.backends.mps.is_available(): 99 | torch.mps.empty_cache() 100 | logger.info("Executed torch.mps.empty_cache()") 101 | yield "\n".join(infos) 102 | -------------------------------------------------------------------------------- /infer/modules/vc/__init__.py: -------------------------------------------------------------------------------- 1 | from .pipeline import Pipeline 2 | from .modules import VC 3 | from .utils import get_index_path_from_model, load_hubert 4 | from .info import show_info 5 | from .hash import model_hash_ckpt, hash_id, hash_similarity 6 | -------------------------------------------------------------------------------- /infer/modules/vc/info.py: -------------------------------------------------------------------------------- 1 | import traceback 2 | from i18n.i18n import I18nAuto 3 | from datetime import datetime 4 | import torch 5 | 6 | from .hash import model_hash_ckpt, hash_id, hash_similarity 7 | 8 | i18n = I18nAuto() 9 | 10 | 11 | def show_model_info(cpt, show_long_id=False): 12 | try: 13 | h = model_hash_ckpt(cpt) 14 | id = hash_id(h) 15 | idread = cpt.get("id", "None") 16 | hread = cpt.get("hash", "None") 17 | if id != idread: 18 | id += ( 19 | "(" 20 | + i18n("Actually calculated") 21 | + "), " 22 | + idread 23 | + "(" 24 | + i18n("Read from model") 25 | + ")" 26 | ) 27 | sim = hash_similarity(h, hread) 28 | if not isinstance(sim, str): 29 | sim = "%.2f%%" % (sim * 100) 30 | if not show_long_id: 31 | h = i18n("Hidden") 32 | if h != hread: 33 | h = i18n("Similarity") + " " + sim + " -> " + h 34 | elif h != hread: 35 | h = ( 36 | i18n("Similarity") 37 | + " " 38 | + sim 39 | + " -> " 40 | + h 41 | + "(" 42 | + i18n("Actually calculated") 43 | + "), " 44 | + hread 45 | + "(" 46 | + i18n("Read from model") 47 | + ")" 48 | ) 49 | txt = f"""{i18n("Model name")}: %s 50 | {i18n("Sealing date")}: %s 51 | {i18n("Model Author")}: %s 52 | {i18n("Information")}: %s 53 | {i18n("Sampling rate")}: %s 54 | {i18n("Pitch guidance (f0)")}: %s 55 | {i18n("Version")}: %s 56 | {i18n("ID(short)")}: %s 57 | {i18n("ID(long)")}: %s""" % ( 58 | cpt.get("name", i18n("Unknown")), 59 | datetime.fromtimestamp(float(cpt.get("timestamp", 0))), 60 | cpt.get("author", i18n("Unknown")), 61 | cpt.get("info", i18n("None")), 62 | cpt.get("sr", i18n("Unknown")), 63 | i18n("Exist") if cpt.get("f0", 0) == 1 else i18n("Not exist"), 64 | cpt.get("version", i18n("None")), 65 | id, 66 | h, 67 | ) 68 | except: 69 | txt = traceback.format_exc() 70 | 71 | return txt 72 | 73 | 74 | def show_info(path): 75 | try: 76 | if hasattr(path, "name"): 77 | path = path.name 78 | a = torch.load(path, map_location="cpu") 79 | txt = show_model_info(a, show_long_id=True) 80 | del a 81 | except: 82 | txt = traceback.format_exc() 83 | 84 | return txt 85 | -------------------------------------------------------------------------------- /infer/modules/vc/lgdsng.npz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/e1aeb16630158cd6b0b9db8d6d27fe1ad869e42d/infer/modules/vc/lgdsng.npz -------------------------------------------------------------------------------- /infer/modules/vc/utils.py: -------------------------------------------------------------------------------- 1 | import os, pathlib 2 | 3 | from fairseq import checkpoint_utils 4 | 5 | 6 | def get_index_path_from_model(sid): 7 | return next( 8 | ( 9 | f 10 | for f in [ 11 | str(pathlib.Path(root, name)) 12 | for path in [os.getenv("outside_index_root"), os.getenv("index_root")] 13 | for root, _, files in os.walk(path, topdown=False) 14 | for name in files 15 | if name.endswith(".index") and "trained" not in name 16 | ] 17 | if sid.split(".")[0] in f 18 | ), 19 | "", 20 | ) 21 | 22 | 23 | def load_hubert(device, is_half): 24 | models, _, _ = checkpoint_utils.load_model_ensemble_and_task( 25 | ["assets/hubert/hubert_base.pt"], 26 | suffix="", 27 | ) 28 | hubert_model = models[0] 29 | hubert_model = hubert_model.to(device) 30 | if is_half: 31 | hubert_model = hubert_model.half() 32 | else: 33 | hubert_model = hubert_model.float() 34 | return hubert_model.eval() 35 | -------------------------------------------------------------------------------- /logs/mute/0_gt_wavs/mute32k.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/e1aeb16630158cd6b0b9db8d6d27fe1ad869e42d/logs/mute/0_gt_wavs/mute32k.wav -------------------------------------------------------------------------------- /logs/mute/0_gt_wavs/mute40k.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/e1aeb16630158cd6b0b9db8d6d27fe1ad869e42d/logs/mute/0_gt_wavs/mute40k.wav -------------------------------------------------------------------------------- /logs/mute/0_gt_wavs/mute48k.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/e1aeb16630158cd6b0b9db8d6d27fe1ad869e42d/logs/mute/0_gt_wavs/mute48k.wav -------------------------------------------------------------------------------- /logs/mute/1_16k_wavs/mute.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/e1aeb16630158cd6b0b9db8d6d27fe1ad869e42d/logs/mute/1_16k_wavs/mute.wav -------------------------------------------------------------------------------- /logs/mute/2a_f0/mute.wav.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/e1aeb16630158cd6b0b9db8d6d27fe1ad869e42d/logs/mute/2a_f0/mute.wav.npy -------------------------------------------------------------------------------- /logs/mute/2b-f0nsf/mute.wav.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/e1aeb16630158cd6b0b9db8d6d27fe1ad869e42d/logs/mute/2b-f0nsf/mute.wav.npy -------------------------------------------------------------------------------- /logs/mute/3_feature256/mute.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/e1aeb16630158cd6b0b9db8d6d27fe1ad869e42d/logs/mute/3_feature256/mute.npy -------------------------------------------------------------------------------- /logs/mute/3_feature768/mute.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/e1aeb16630158cd6b0b9db8d6d27fe1ad869e42d/logs/mute/3_feature768/mute.npy -------------------------------------------------------------------------------- /requirements/amd.txt: -------------------------------------------------------------------------------- 1 | tensorflow-rocm 2 | joblib>=1.1.0 3 | numba==0.56.4 4 | numpy==1.23.5 5 | scipy 6 | librosa>=0.10.2 7 | llvmlite==0.39.0 8 | fairseq==0.12.2 9 | faiss-cpu==1.7.3 10 | gradio 11 | Cython 12 | pydub>=0.25.1 13 | tensorboardX 14 | Jinja2>=3.1.2 15 | json5 16 | Markdown 17 | matplotlib>=3.7.0 18 | matplotlib-inline>=0.1.3 19 | praat-parselmouth>=0.4.2 20 | Pillow>=9.1.1 21 | resampy>=0.4.2 22 | scikit-learn 23 | tensorboard 24 | tqdm>=4.63.1 25 | tornado>=6.1 26 | Werkzeug>=2.2.3 27 | uc-micro-py>=1.0.1 28 | sympy>=1.11.1 29 | tabulate>=0.8.10 30 | PyYAML>=6.0 31 | pyasn1>=0.4.8 32 | pyasn1-modules>=0.2.8 33 | fsspec>=2022.11.0 34 | absl-py>=1.2.0 35 | audioread 36 | uvicorn>=0.21.1 37 | colorama>=0.4.5 38 | pyworld==0.3.2 39 | httpx 40 | onnxruntime 41 | onnxruntime-gpu 42 | torchcrepe>=0.0.23 43 | fastapi 44 | python-dotenv>=1.0.0 45 | av 46 | torchfcpe 47 | pybase16384 48 | -------------------------------------------------------------------------------- /requirements/dml.txt: -------------------------------------------------------------------------------- 1 | joblib>=1.1.0 2 | numba==0.56.4 3 | numpy==1.23.5 4 | scipy 5 | librosa>=0.10.2 6 | llvmlite==0.39.0 7 | fairseq==0.12.2 8 | faiss-cpu==1.7.3 9 | gradio 10 | Cython 11 | pydub>=0.25.1 12 | tensorboardX 13 | Jinja2>=3.1.2 14 | json5 15 | Markdown 16 | matplotlib>=3.7.0 17 | matplotlib-inline>=0.1.3 18 | praat-parselmouth>=0.4.2 19 | Pillow>=9.1.1 20 | resampy>=0.4.2 21 | scikit-learn 22 | tensorboard 23 | tqdm>=4.63.1 24 | tornado>=6.1 25 | Werkzeug>=2.2.3 26 | uc-micro-py>=1.0.1 27 | sympy>=1.11.1 28 | tabulate>=0.8.10 29 | PyYAML>=6.0 30 | pyasn1>=0.4.8 31 | pyasn1-modules>=0.2.8 32 | fsspec>=2022.11.0 33 | absl-py>=1.2.0 34 | audioread 35 | uvicorn>=0.21.1 36 | colorama>=0.4.5 37 | pyworld==0.3.2 38 | httpx 39 | onnxruntime-directml 40 | torchcrepe>=0.0.23 41 | fastapi 42 | python-dotenv>=1.0.0 43 | av 44 | torchfcpe 45 | pybase16384 46 | -------------------------------------------------------------------------------- /requirements/gui-dml.txt: -------------------------------------------------------------------------------- 1 | #1.Install torch from pytorch.org: 2 | #torch 2.0 with cuda 11.8 3 | #pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 4 | #torch 1.11.0 with cuda 11.3 5 | #pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113 6 | einops 7 | fairseq 8 | flask 9 | flask_cors 10 | gin 11 | gin_config 12 | librosa 13 | local_attention 14 | matplotlib 15 | praat-parselmouth 16 | pyworld 17 | PyYAML 18 | resampy 19 | scikit_learn 20 | scipy 21 | tensorboard 22 | tqdm 23 | wave 24 | FreeSimpleGUI 25 | sounddevice 26 | gradio 27 | noisereduce 28 | onnxruntime-directml 29 | torchfcpe -------------------------------------------------------------------------------- /requirements/gui.txt: -------------------------------------------------------------------------------- 1 | #1.Install torch from pytorch.org: 2 | #torch 2.0 with cuda 11.8 3 | #pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 4 | #torch 1.11.0 with cuda 11.3 5 | #pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113 6 | einops 7 | fairseq 8 | flask 9 | flask_cors 10 | gin 11 | gin_config 12 | librosa 13 | local_attention 14 | matplotlib 15 | praat-parselmouth 16 | pyworld 17 | PyYAML 18 | resampy 19 | scikit_learn 20 | scipy 21 | tensorboard 22 | tqdm 23 | wave 24 | FreeSimpleGUI 25 | sounddevice 26 | gradio 27 | noisereduce 28 | torchfcpe 29 | -------------------------------------------------------------------------------- /requirements/ipex.txt: -------------------------------------------------------------------------------- 1 | torch==2.0.1a0 2 | intel_extension_for_pytorch==2.0.110+xpu 3 | torchvision==0.15.2a0 4 | https://github.com/Disty0/Retrieval-based-Voice-Conversion-WebUI/releases/download/torchaudio_wheels_for_ipex/torchaudio-2.0.2+31de77d-cp310-cp310-linux_x86_64.whl 5 | --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ 6 | joblib>=1.1.0 7 | numba==0.56.4 8 | numpy==1.23.5 9 | scipy 10 | librosa>=0.10.2 11 | llvmlite==0.39.0 12 | fairseq==0.12.2 13 | faiss-cpu==1.7.3 14 | gradio 15 | Cython 16 | pydub>=0.25.1 17 | tensorboardX 18 | Jinja2>=3.1.2 19 | json5 20 | Markdown 21 | matplotlib>=3.7.0 22 | matplotlib-inline>=0.1.3 23 | praat-parselmouth>=0.4.2 24 | Pillow>=9.1.1 25 | resampy>=0.4.2 26 | scikit-learn 27 | tensorboard 28 | tqdm>=4.63.1 29 | tornado>=6.1 30 | Werkzeug>=2.2.3 31 | uc-micro-py>=1.0.1 32 | sympy>=1.11.1 33 | tabulate>=0.8.10 34 | PyYAML>=6.0 35 | pyasn1>=0.4.8 36 | pyasn1-modules>=0.2.8 37 | fsspec>=2022.11.0 38 | absl-py>=1.2.0 39 | audioread 40 | uvicorn>=0.21.1 41 | colorama>=0.4.5 42 | pyworld==0.3.2 43 | httpx 44 | onnxruntime; sys_platform == 'darwin' 45 | onnxruntime-gpu; sys_platform != 'darwin' 46 | torchcrepe>=0.0.23 47 | fastapi 48 | python-dotenv>=1.0.0 49 | av 50 | FreeSimpleGUI 51 | sounddevice 52 | torchfcpe 53 | pybase16384 54 | -------------------------------------------------------------------------------- /requirements/main.txt: -------------------------------------------------------------------------------- 1 | joblib>=1.1.0 2 | numba 3 | numpy==1.23.5 4 | scipy 5 | librosa>=0.10.2 6 | llvmlite 7 | fairseq 8 | faiss-cpu 9 | gradio 10 | Cython 11 | pydub>=0.25.1 12 | tensorboardX 13 | Jinja2>=3.1.2 14 | json5 15 | Markdown 16 | matplotlib>=3.7.0 17 | matplotlib-inline>=0.1.3 18 | praat-parselmouth>=0.4.2 19 | Pillow>=9.1.1 20 | resampy>=0.4.2 21 | scikit-learn 22 | tensorboard 23 | tqdm>=4.63.1 24 | tornado>=6.1 25 | Werkzeug>=2.2.3 26 | uc-micro-py>=1.0.1 27 | sympy>=1.11.1 28 | tabulate>=0.8.10 29 | PyYAML>=6.0 30 | pyasn1>=0.4.8 31 | pyasn1-modules>=0.2.8 32 | fsspec>=2022.11.0 33 | absl-py>=1.2.0 34 | audioread 35 | uvicorn>=0.21.1 36 | colorama>=0.4.5 37 | pyworld==0.3.2 38 | httpx 39 | onnxruntime; sys_platform == 'darwin' 40 | onnxruntime-gpu; sys_platform != 'darwin' 41 | torchcrepe>=0.0.23 42 | fastapi 43 | torchfcpe 44 | python-dotenv>=1.0.0 45 | av 46 | pybase16384 47 | -------------------------------------------------------------------------------- /requirements/py311.txt: -------------------------------------------------------------------------------- 1 | joblib>=1.1.0 2 | numba 3 | numpy 4 | scipy 5 | librosa>=0.10.2 6 | llvmlite 7 | fairseq @ git+https://github.com/One-sixth/fairseq.git 8 | faiss-cpu 9 | gradio 10 | Cython 11 | pydub>=0.25.1 12 | tensorboardX 13 | Jinja2>=3.1.2 14 | json5 15 | Markdown 16 | matplotlib>=3.7.0 17 | matplotlib-inline>=0.1.3 18 | praat-parselmouth>=0.4.2 19 | Pillow>=9.1.1 20 | resampy>=0.4.2 21 | scikit-learn 22 | tensorboard 23 | tqdm>=4.63.1 24 | tornado>=6.1 25 | Werkzeug>=2.2.3 26 | uc-micro-py>=1.0.1 27 | sympy>=1.11.1 28 | tabulate>=0.8.10 29 | PyYAML>=6.0 30 | pyasn1>=0.4.8 31 | pyasn1-modules>=0.2.8 32 | fsspec>=2022.11.0 33 | absl-py>=1.2.0 34 | audioread 35 | uvicorn>=0.21.1 36 | colorama>=0.4.5 37 | pyworld==0.3.2 38 | httpx 39 | onnxruntime; sys_platform == 'darwin' 40 | onnxruntime-gpu; sys_platform != 'darwin' 41 | torchcrepe>=0.0.23 42 | fastapi 43 | torchfcpe 44 | python-dotenv>=1.0.0 45 | av 46 | pybase16384 47 | -------------------------------------------------------------------------------- /run.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | set -fa 4 | 5 | # Check if Python is installed 6 | if ! command -v python; then 7 | echo "Python not found. Please install Python using your package manager or via PyEnv." 8 | exit 1 9 | fi 10 | 11 | requirements_file="requirements/main.txt" 12 | venv_path=".venv" 13 | 14 | if [[ ! -d "${venv_path}" ]]; then 15 | echo "Creating venv..." 16 | 17 | python -m venv "${venv_path}" 18 | source "${venv_path}/bin/activate" 19 | 20 | # Check if required packages are up-to-date 21 | pip install --upgrade -r "${requirements_file}" 22 | fi 23 | echo "Activating venv..." 24 | source "${venv_path}/bin/activate" 25 | 26 | # Run the main script 27 | python web.py --pycmd python 28 | -------------------------------------------------------------------------------- /rvc/__init__.py: -------------------------------------------------------------------------------- 1 | from . import ipex 2 | import sys 3 | 4 | del sys.modules["rvc.ipex"] 5 | -------------------------------------------------------------------------------- /rvc/f0/__init__.py: -------------------------------------------------------------------------------- 1 | from .gen import Generator 2 | -------------------------------------------------------------------------------- /rvc/f0/crepe.py: -------------------------------------------------------------------------------- 1 | from typing import Any, Optional, Union 2 | 3 | import numpy as np 4 | import torch 5 | import torchcrepe 6 | 7 | from .f0 import F0Predictor 8 | 9 | 10 | class CRePE(F0Predictor): 11 | def __init__( 12 | self, 13 | hop_length=512, 14 | f0_min=50, 15 | f0_max=1100, 16 | sampling_rate=44100, 17 | device="cpu", 18 | ): 19 | if "privateuseone" in str(device): 20 | device = "cpu" 21 | super().__init__( 22 | hop_length, 23 | f0_min, 24 | f0_max, 25 | sampling_rate, 26 | device, 27 | ) 28 | 29 | def compute_f0( 30 | self, 31 | wav: np.ndarray, 32 | p_len: Optional[int] = None, 33 | filter_radius: Optional[Union[int, float]] = None, 34 | ): 35 | if p_len is None: 36 | p_len = wav.shape[0] // self.hop_length 37 | if not torch.is_tensor(wav): 38 | wav = torch.from_numpy(wav) 39 | # Pick a batch size that doesn't cause memory errors on your gpu 40 | batch_size = 512 41 | # Compute pitch using device 'device' 42 | f0, pd = torchcrepe.predict( 43 | wav.float().to(self.device).unsqueeze(dim=0), 44 | self.sampling_rate, 45 | self.hop_length, 46 | self.f0_min, 47 | self.f0_max, 48 | batch_size=batch_size, 49 | device=self.device, 50 | return_periodicity=True, 51 | ) 52 | pd = torchcrepe.filter.median(pd, 3) 53 | f0 = torchcrepe.filter.mean(f0, 3) 54 | f0[pd < 0.1] = 0 55 | f0 = f0[0].cpu().numpy() 56 | return self._interpolate_f0(self._resize_f0(f0, p_len))[0] 57 | -------------------------------------------------------------------------------- /rvc/f0/dio.py: -------------------------------------------------------------------------------- 1 | from typing import Any, Optional, Union 2 | 3 | import numpy as np 4 | import pyworld 5 | 6 | from .f0 import F0Predictor 7 | 8 | 9 | class Dio(F0Predictor): 10 | def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100): 11 | super().__init__(hop_length, f0_min, f0_max, sampling_rate) 12 | 13 | def compute_f0( 14 | self, 15 | wav: np.ndarray, 16 | p_len: Optional[int] = None, 17 | filter_radius: Optional[Union[int, float]] = None, 18 | ): 19 | if p_len is None: 20 | p_len = wav.shape[0] // self.hop_length 21 | f0, t = pyworld.dio( 22 | wav.astype(np.double), 23 | fs=self.sampling_rate, 24 | f0_floor=self.f0_min, 25 | f0_ceil=self.f0_max, 26 | frame_period=1000 * self.hop_length / self.sampling_rate, 27 | ) 28 | f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate) 29 | for index, pitch in enumerate(f0): 30 | f0[index] = round(pitch, 1) 31 | return self._interpolate_f0(self._resize_f0(f0, p_len))[0] 32 | -------------------------------------------------------------------------------- /rvc/f0/e2e.py: -------------------------------------------------------------------------------- 1 | from typing import Tuple 2 | 3 | import torch.nn as nn 4 | 5 | from .deepunet import DeepUnet 6 | 7 | 8 | class E2E(nn.Module): 9 | def __init__( 10 | self, 11 | n_blocks: int, 12 | n_gru: int, 13 | kernel_size: Tuple[int, int], 14 | en_de_layers=5, 15 | inter_layers=4, 16 | in_channels=1, 17 | en_out_channels=16, 18 | ): 19 | super(E2E, self).__init__() 20 | 21 | self.unet = DeepUnet( 22 | kernel_size, 23 | n_blocks, 24 | en_de_layers, 25 | inter_layers, 26 | in_channels, 27 | en_out_channels, 28 | ) 29 | self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1)) 30 | if n_gru: 31 | self.fc = nn.Sequential( 32 | self.BiGRU(3 * 128, 256, n_gru), 33 | nn.Linear(512, 360), 34 | nn.Dropout(0.25), 35 | nn.Sigmoid(), 36 | ) 37 | else: 38 | self.fc = nn.Sequential( 39 | nn.Linear(3 * nn.N_MELS, nn.N_CLASS), 40 | nn.Dropout(0.25), 41 | nn.Sigmoid(), 42 | ) 43 | 44 | def forward(self, mel): 45 | mel = mel.transpose(-1, -2).unsqueeze(1) 46 | x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2) 47 | x = self.fc(x) 48 | return x 49 | 50 | class BiGRU(nn.Module): 51 | def __init__( 52 | self, 53 | input_features: int, 54 | hidden_features: int, 55 | num_layers: int, 56 | ): 57 | super().__init__() 58 | self.gru = nn.GRU( 59 | input_features, 60 | hidden_features, 61 | num_layers=num_layers, 62 | batch_first=True, 63 | bidirectional=True, 64 | ) 65 | 66 | def forward(self, x): 67 | return self.gru(x)[0] 68 | -------------------------------------------------------------------------------- /rvc/f0/f0.py: -------------------------------------------------------------------------------- 1 | from typing import Optional, Union 2 | 3 | import torch 4 | import numpy as np 5 | 6 | 7 | class F0Predictor(object): 8 | def __init__( 9 | self, 10 | hop_length=512, 11 | f0_min=50, 12 | f0_max=1100, 13 | sampling_rate=44100, 14 | device: Optional[str] = None, 15 | ): 16 | self.hop_length = hop_length 17 | self.f0_min = f0_min 18 | self.f0_max = f0_max 19 | self.sampling_rate = sampling_rate 20 | if device is None: 21 | device = "cuda:0" if torch.cuda.is_available() else "cpu" 22 | self.device = device 23 | 24 | def compute_f0( 25 | self, 26 | wav: np.ndarray, 27 | p_len: Optional[int] = None, 28 | filter_radius: Optional[Union[int, float]] = None, 29 | ): ... 30 | 31 | def _interpolate_f0(self, f0: np.ndarray): 32 | """ 33 | 对F0进行插值处理 34 | """ 35 | 36 | data = np.reshape(f0, (f0.size, 1)) 37 | 38 | vuv_vector = np.zeros((data.size, 1), dtype=np.float32) 39 | vuv_vector[data > 0.0] = 1.0 40 | vuv_vector[data <= 0.0] = 0.0 41 | 42 | ip_data = data 43 | 44 | frame_number = data.size 45 | last_value = 0.0 46 | for i in range(frame_number): 47 | if data[i] <= 0.0: 48 | j = i + 1 49 | for j in range(i + 1, frame_number): 50 | if data[j] > 0.0: 51 | break 52 | if j < frame_number - 1: 53 | if last_value > 0.0: 54 | step = (data[j] - data[i - 1]) / float(j - i) 55 | for k in range(i, j): 56 | ip_data[k] = data[i - 1] + step * (k - i + 1) 57 | else: 58 | for k in range(i, j): 59 | ip_data[k] = data[j] 60 | else: 61 | for k in range(i, frame_number): 62 | ip_data[k] = last_value 63 | else: 64 | ip_data[i] = data[i] # 这里可能存在一个没有必要的拷贝 65 | last_value = data[i] 66 | 67 | return ip_data[:, 0], vuv_vector[:, 0] 68 | 69 | def _resize_f0(self, x: np.ndarray, target_len: int): 70 | source = np.array(x) 71 | source[source < 0.001] = np.nan 72 | target = np.interp( 73 | np.arange(0, len(source) * target_len, len(source)) / target_len, 74 | np.arange(0, len(source)), 75 | source, 76 | ) 77 | res = np.nan_to_num(target) 78 | return res 79 | -------------------------------------------------------------------------------- /rvc/f0/fcpe.py: -------------------------------------------------------------------------------- 1 | from typing import Optional, Union 2 | 3 | import numpy as np 4 | import torch 5 | 6 | from .f0 import F0Predictor 7 | 8 | 9 | class FCPE(F0Predictor): 10 | def __init__( 11 | self, 12 | hop_length=512, 13 | f0_min=50, 14 | f0_max=1100, 15 | sampling_rate=44100, 16 | device="cpu", 17 | ): 18 | super().__init__( 19 | hop_length, 20 | f0_min, 21 | f0_max, 22 | sampling_rate, 23 | device, 24 | ) 25 | 26 | from torchfcpe import ( 27 | spawn_bundled_infer_model, 28 | ) # must be imported at here, or it will cause fairseq crash on training 29 | 30 | self.model = spawn_bundled_infer_model(self.device) 31 | 32 | def compute_f0( 33 | self, 34 | wav: np.ndarray, 35 | p_len: Optional[int] = None, 36 | filter_radius: Optional[Union[int, float]] = 0.006, 37 | ): 38 | if p_len is None: 39 | p_len = wav.shape[0] // self.hop_length 40 | if not torch.is_tensor(wav): 41 | wav = torch.from_numpy(wav) 42 | f0 = ( 43 | self.model.infer( 44 | wav.float().to(self.device).unsqueeze(0), 45 | sr=self.sampling_rate, 46 | decoder_mode="local_argmax", 47 | threshold=filter_radius, 48 | ) 49 | .squeeze() 50 | .cpu() 51 | .numpy() 52 | ) 53 | return self._interpolate_f0(self._resize_f0(f0, p_len))[0] 54 | -------------------------------------------------------------------------------- /rvc/f0/harvest.py: -------------------------------------------------------------------------------- 1 | from typing import Any, Optional, Union 2 | 3 | import numpy as np 4 | import pyworld 5 | from scipy import signal 6 | 7 | from .f0 import F0Predictor 8 | 9 | 10 | class Harvest(F0Predictor): 11 | def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100): 12 | super().__init__(hop_length, f0_min, f0_max, sampling_rate) 13 | 14 | def compute_f0( 15 | self, 16 | wav: np.ndarray, 17 | p_len: Optional[int] = None, 18 | filter_radius: Optional[Union[int, float]] = None, 19 | ): 20 | if p_len is None: 21 | p_len = wav.shape[0] // self.hop_length 22 | f0, t = pyworld.harvest( 23 | wav.astype(np.double), 24 | fs=self.sampling_rate, 25 | f0_ceil=self.f0_max, 26 | f0_floor=self.f0_min, 27 | frame_period=1000 * self.hop_length / self.sampling_rate, 28 | ) 29 | f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate) 30 | if filter_radius is not None and filter_radius > 2: 31 | f0 = signal.medfilt(f0, filter_radius) 32 | return self._interpolate_f0(self._resize_f0(f0, p_len))[0] 33 | -------------------------------------------------------------------------------- /rvc/f0/mel.py: -------------------------------------------------------------------------------- 1 | from typing import Optional 2 | 3 | import torch 4 | import numpy as np 5 | from librosa.filters import mel 6 | 7 | from .stft import STFT 8 | 9 | 10 | class MelSpectrogram(torch.nn.Module): 11 | def __init__( 12 | self, 13 | is_half: bool, 14 | n_mel_channels: int, 15 | sampling_rate: int, 16 | win_length: int, 17 | hop_length: int, 18 | n_fft: Optional[int] = None, 19 | mel_fmin: int = 0, 20 | mel_fmax: int = None, 21 | clamp: float = 1e-5, 22 | device=torch.device("cpu"), 23 | ): 24 | super().__init__() 25 | if n_fft is None: 26 | n_fft = win_length 27 | mel_basis = mel( 28 | sr=sampling_rate, 29 | n_fft=n_fft, 30 | n_mels=n_mel_channels, 31 | fmin=mel_fmin, 32 | fmax=mel_fmax, 33 | htk=True, 34 | ) 35 | mel_basis = torch.from_numpy(mel_basis).float() 36 | self.register_buffer("mel_basis", mel_basis) 37 | self.n_fft = n_fft 38 | self.hop_length = hop_length 39 | self.win_length = win_length 40 | self.clamp = clamp 41 | self.is_half = is_half 42 | 43 | self.stft = STFT( 44 | filter_length=n_fft, 45 | hop_length=hop_length, 46 | win_length=win_length, 47 | window="hann", 48 | use_torch_stft="privateuseone" not in str(device), 49 | ).to(device) 50 | 51 | def forward( 52 | self, 53 | audio: torch.Tensor, 54 | keyshift=0, 55 | speed=1, 56 | center=True, 57 | ): 58 | factor = 2 ** (keyshift / 12) 59 | win_length_new = int(np.round(self.win_length * factor)) 60 | magnitude = self.stft(audio, keyshift, speed, center) 61 | if keyshift != 0: 62 | size = self.n_fft // 2 + 1 63 | resize = magnitude.size(1) 64 | if resize < size: 65 | magnitude = torch.nn.functional.pad(magnitude, (0, 0, 0, size - resize)) 66 | magnitude = magnitude[:, :size, :] * self.win_length / win_length_new 67 | mel_output = torch.matmul(self.mel_basis, magnitude) 68 | if self.is_half: 69 | mel_output = mel_output.half() 70 | log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp)) 71 | return log_mel_spec 72 | -------------------------------------------------------------------------------- /rvc/f0/models.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | def get_rmvpe( 5 | model_path="assets/rmvpe/rmvpe.pt", device=torch.device("cpu"), is_half=False 6 | ): 7 | from rvc.f0.e2e import E2E 8 | 9 | model = E2E(4, 1, (2, 2)) 10 | ckpt = torch.load(model_path, map_location=device, weights_only=True) 11 | model.load_state_dict(ckpt) 12 | del ckpt 13 | model.eval() 14 | if is_half: 15 | model = model.half() 16 | model = model.to(device) 17 | return model 18 | -------------------------------------------------------------------------------- /rvc/f0/pm.py: -------------------------------------------------------------------------------- 1 | from typing import Optional 2 | 3 | import numpy as np 4 | import parselmouth 5 | 6 | from .f0 import F0Predictor 7 | 8 | 9 | class PM(F0Predictor): 10 | def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100): 11 | super().__init__(hop_length, f0_min, f0_max, sampling_rate) 12 | 13 | def compute_f0( 14 | self, 15 | wav: np.ndarray, 16 | p_len: Optional[int] = None, 17 | filter_radius: Optional[int] = None, 18 | ): 19 | x = wav 20 | if p_len is None: 21 | p_len = x.shape[0] // self.hop_length 22 | else: 23 | assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error" 24 | time_step = self.hop_length / self.sampling_rate * 1000 25 | f0 = ( 26 | parselmouth.Sound(x, self.sampling_rate) 27 | .to_pitch_ac( 28 | time_step=time_step / 1000, 29 | voicing_threshold=0.6, 30 | pitch_floor=self.f0_min, 31 | pitch_ceiling=self.f0_max, 32 | ) 33 | .selected_array["frequency"] 34 | ) 35 | 36 | pad_size = (p_len - len(f0) + 1) // 2 37 | if pad_size > 0 or p_len - len(f0) - pad_size > 0: 38 | f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant") 39 | return self._interpolate_f0(f0)[0] 40 | -------------------------------------------------------------------------------- /rvc/ipex/__init__.py: -------------------------------------------------------------------------------- 1 | try: 2 | import torch 3 | 4 | if torch.xpu.is_available(): 5 | from .init import ipex_init 6 | 7 | ipex_init() 8 | from .gradscaler import gradscaler_init 9 | except Exception: # pylint: disable=broad-exception-caught 10 | pass 11 | -------------------------------------------------------------------------------- /rvc/jit/__init__.py: -------------------------------------------------------------------------------- 1 | from .jit import load_inputs, get_jit_model, export_jit_model, save_pickle 2 | -------------------------------------------------------------------------------- /rvc/jit/jit.py: -------------------------------------------------------------------------------- 1 | import pickle 2 | from io import BytesIO 3 | from collections import OrderedDict 4 | import os 5 | 6 | import torch 7 | 8 | 9 | def load_pickle(path: str): 10 | with open(path, "rb") as f: 11 | return pickle.load(f) 12 | 13 | 14 | def save_pickle(ckpt: dict, save_path: str): 15 | with open(save_path, "wb") as f: 16 | pickle.dump(ckpt, f) 17 | 18 | 19 | def load_inputs(path: torch.serialization.FILE_LIKE, device: str, is_half=False): 20 | parm = torch.load(path, map_location=torch.device("cpu")) 21 | for key in parm.keys(): 22 | parm[key] = parm[key].to(device) 23 | if is_half and parm[key].dtype == torch.float32: 24 | parm[key] = parm[key].half() 25 | elif not is_half and parm[key].dtype == torch.float16: 26 | parm[key] = parm[key].float() 27 | return parm 28 | 29 | 30 | def export_jit_model( 31 | model: torch.nn.Module, 32 | mode: str = "trace", 33 | inputs: dict = None, 34 | device=torch.device("cpu"), 35 | is_half: bool = False, 36 | ) -> dict: 37 | model = model.half() if is_half else model.float() 38 | model.eval() 39 | if mode == "trace": 40 | assert inputs is not None 41 | model_jit = torch.jit.trace(model, example_kwarg_inputs=inputs) 42 | elif mode == "script": 43 | model_jit = torch.jit.script(model) 44 | model_jit.to(device) 45 | model_jit = model_jit.half() if is_half else model_jit.float() 46 | buffer = BytesIO() 47 | # model_jit=model_jit.cpu() 48 | torch.jit.save(model_jit, buffer) 49 | del model_jit 50 | cpt = OrderedDict() 51 | cpt["model"] = buffer.getvalue() 52 | cpt["is_half"] = is_half 53 | return cpt 54 | 55 | 56 | def get_jit_model(model_path: str, is_half: bool, device: str, exporter): 57 | jit_model_path = model_path.rstrip(".pth") 58 | jit_model_path += ".half.jit" if is_half else ".jit" 59 | ckpt = None 60 | 61 | if os.path.exists(jit_model_path): 62 | ckpt = load_pickle(jit_model_path) 63 | model_device = ckpt["device"] 64 | if model_device != str(device): 65 | del ckpt 66 | ckpt = None 67 | 68 | if ckpt is None: 69 | ckpt = exporter( 70 | model_path=model_path, 71 | mode="script", 72 | inputs_path=None, 73 | save_path=jit_model_path, 74 | device=device, 75 | is_half=is_half, 76 | ) 77 | 78 | return ckpt 79 | -------------------------------------------------------------------------------- /rvc/layers/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/e1aeb16630158cd6b0b9db8d6d27fe1ad869e42d/rvc/layers/__init__.py -------------------------------------------------------------------------------- /rvc/layers/utils.py: -------------------------------------------------------------------------------- 1 | from typing import List, Optional, Tuple, Iterator 2 | 3 | import torch 4 | 5 | 6 | def call_weight_data_normal_if_Conv(m: torch.nn.Module): 7 | classname = m.__class__.__name__ 8 | if classname.find("Conv") != -1: 9 | mean = 0.0 10 | std = 0.01 11 | m.weight.data.normal_(mean, std) 12 | 13 | 14 | def get_padding(kernel_size: int, dilation=1) -> int: 15 | return int((kernel_size * dilation - dilation) / 2) 16 | 17 | 18 | def slice_on_last_dim( 19 | x: torch.Tensor, 20 | start_indices: List[int], 21 | segment_size=4, 22 | ) -> torch.Tensor: 23 | new_shape = [*x.shape] 24 | new_shape[-1] = segment_size 25 | ret = torch.empty(new_shape, device=x.device) 26 | for i in range(x.size(0)): 27 | idx_str = start_indices[i] 28 | idx_end = idx_str + segment_size 29 | ret[i, ..., :] = x[i, ..., idx_str:idx_end] 30 | return ret 31 | 32 | 33 | def rand_slice_segments_on_last_dim( 34 | x: torch.Tensor, 35 | x_lengths: int = None, 36 | segment_size=4, 37 | ) -> Tuple[torch.Tensor, List[int]]: 38 | b, _, t = x.size() 39 | if x_lengths is None: 40 | x_lengths = t 41 | ids_str_max = x_lengths - segment_size + 1 42 | ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) 43 | ret = slice_on_last_dim(x, ids_str, segment_size) 44 | return ret, ids_str 45 | 46 | 47 | @torch.jit.script 48 | def activate_add_tanh_sigmoid_multiply( 49 | input_a: torch.Tensor, input_b: torch.Tensor, n_channels: int 50 | ) -> torch.Tensor: 51 | in_act = input_a + input_b 52 | t_act = torch.tanh(in_act[:, :n_channels, :]) 53 | s_act = torch.sigmoid(in_act[:, n_channels:, :]) 54 | acts = t_act * s_act 55 | return acts 56 | 57 | 58 | def sequence_mask( 59 | length: torch.Tensor, 60 | max_length: Optional[int] = None, 61 | ) -> torch.BoolTensor: 62 | if max_length is None: 63 | max_length = int(length.max()) 64 | x = torch.arange(max_length, dtype=length.dtype, device=length.device) 65 | return x.unsqueeze(0) < length.unsqueeze(1) 66 | 67 | 68 | def total_grad_norm( 69 | parameters: Iterator[torch.nn.Parameter], 70 | norm_type: float = 2.0, 71 | ) -> float: 72 | norm_type = float(norm_type) 73 | total_norm = 0.0 74 | 75 | for p in parameters: 76 | if p.grad is None: 77 | continue 78 | param_norm = p.grad.data.norm(norm_type) 79 | total_norm += float(param_norm.item()) ** norm_type 80 | total_norm = total_norm ** (1.0 / norm_type) 81 | 82 | return total_norm 83 | -------------------------------------------------------------------------------- /rvc/onnx/__init__.py: -------------------------------------------------------------------------------- 1 | from .infer import RVC 2 | from .exporter import export_onnx 3 | -------------------------------------------------------------------------------- /rvc/onnx/exporter.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from .synthesizer import SynthesizerTrnMsNSFsid 4 | 5 | 6 | def export_onnx(from_cpkt_pth: str, to_onnx_pth: str) -> str: 7 | cpt = torch.load(from_cpkt_pth, map_location="cpu") 8 | cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] 9 | vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768 10 | 11 | test_phone = torch.rand(1, 200, vec_channels) # hidden unit 12 | test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用） 13 | test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹） 14 | test_pitchf = torch.rand(1, 200) # nsf基频 15 | test_ds = torch.LongTensor([0]) # 说话人ID 16 | test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子） 17 | 18 | device = "cpu" # 导出时设备（不影响使用模型） 19 | 20 | net_g = SynthesizerTrnMsNSFsid( 21 | *cpt["config"], encoder_dim=vec_channels 22 | ) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16） 23 | net_g.load_state_dict(cpt["weight"], strict=False) 24 | input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"] 25 | output_names = [ 26 | "audio", 27 | ] 28 | # net_g.construct_spkmixmap() #多角色混合轨道导出 29 | torch.onnx.export( 30 | net_g, 31 | ( 32 | test_phone.to(device), 33 | test_phone_lengths.to(device), 34 | test_pitch.to(device), 35 | test_pitchf.to(device), 36 | test_ds.to(device), 37 | test_rnd.to(device), 38 | ), 39 | to_onnx_pth, 40 | dynamic_axes={ 41 | "phone": [1], 42 | "pitch": [1], 43 | "pitchf": [1], 44 | "rnd": [2], 45 | }, 46 | do_constant_folding=False, 47 | opset_version=17, 48 | verbose=False, 49 | input_names=input_names, 50 | output_names=output_names, 51 | ) 52 | return "Finished" 53 | -------------------------------------------------------------------------------- /rvc/onnx/infer.py: -------------------------------------------------------------------------------- 1 | import typing 2 | import os 3 | 4 | import librosa 5 | import numpy as np 6 | import onnxruntime 7 | 8 | from rvc.f0 import Generator 9 | 10 | 11 | class Model: 12 | def __init__( 13 | self, 14 | path: typing.Union[str, bytes, os.PathLike], 15 | device: typing.Literal["cpu", "cuda", "dml"] = "cpu", 16 | ): 17 | if device == "cpu": 18 | providers = ["CPUExecutionProvider"] 19 | elif device == "cuda": 20 | providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] 21 | elif device == "dml": 22 | providers = ["DmlExecutionProvider"] 23 | else: 24 | raise RuntimeError("Unsportted Device") 25 | self.model = onnxruntime.InferenceSession(path, providers=providers) 26 | 27 | 28 | class ContentVec(Model): 29 | def __init__( 30 | self, 31 | vec_path: typing.Union[str, bytes, os.PathLike], 32 | device: typing.Literal["cpu", "cuda", "dml"] = "cpu", 33 | ): 34 | super().__init__(vec_path, device) 35 | 36 | def __call__(self, wav: np.ndarray[typing.Any, np.dtype]): 37 | return self.forward(wav) 38 | 39 | def forward(self, wav: np.ndarray[typing.Any, np.dtype]): 40 | if wav.ndim == 2: # double channels 41 | wav = wav.mean(-1) 42 | assert wav.ndim == 1, wav.ndim 43 | wav = np.expand_dims(np.expand_dims(wav, 0), 0) 44 | onnx_input = {self.model.get_inputs()[0].name: wav} 45 | logits = self.model.run(None, onnx_input)[0] 46 | return logits.transpose(0, 2, 1) 47 | 48 | 49 | class RVC(Model): 50 | def __init__( 51 | self, 52 | model_path: typing.Union[str, bytes, os.PathLike], 53 | hop_len=512, 54 | model_sr=40000, 55 | vec_path: typing.Union[str, bytes, os.PathLike] = "vec-768-layer-12.onnx", 56 | device: typing.Literal["cpu", "cuda", "dml"] = "cpu", 57 | ): 58 | super().__init__(model_path, device) 59 | self.vec_model = ContentVec(vec_path, device) 60 | self.hop_len = hop_len 61 | self.f0_gen = Generator(None, False, 0, window=hop_len, sr=model_sr) 62 | 63 | def infer( 64 | self, 65 | wav: np.ndarray[typing.Any, np.dtype], 66 | wav_sr: int, 67 | sid: int = 0, 68 | f0_method="dio", 69 | f0_up_key=0, 70 | ) -> np.ndarray[typing.Any, np.dtype[np.int16]]: 71 | org_length = len(wav) 72 | if org_length / wav_sr > 50.0: 73 | raise RuntimeError("wav max length exceeded") 74 | 75 | hubert = self.vec_model(librosa.resample(wav, orig_sr=wav_sr, target_sr=16000)) 76 | hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32) 77 | hubert_length = hubert.shape[1] 78 | 79 | pitch, pitchf = self.f0_gen.calculate( 80 | wav, hubert_length, f0_up_key, f0_method, None 81 | ) 82 | pitch = pitch.astype(np.int64) 83 | 84 | pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32) 85 | pitch = pitch.reshape(1, len(pitch)) 86 | ds = np.array([sid]).astype(np.int64) 87 | 88 | rnd = np.random.randn(1, 192, hubert_length).astype(np.float32) 89 | hubert_length = np.array([hubert_length]).astype(np.int64) 90 | 91 | out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze() 92 | 93 | out_wav = np.pad(out_wav, (0, 2 * self.hop_len), "constant") 94 | 95 | return out_wav[0:org_length] 96 | 97 | def forward( 98 | self, 99 | hubert: np.ndarray[typing.Any, np.dtype[np.float32]], 100 | hubert_length: int, 101 | pitch: np.ndarray[typing.Any, np.dtype[np.int64]], 102 | pitchf: np.ndarray[typing.Any, np.dtype[np.float32]], 103 | ds: np.ndarray[typing.Any, np.dtype[np.int64]], 104 | rnd: np.ndarray[typing.Any, np.dtype[np.float32]], 105 | ) -> np.ndarray[typing.Any, np.dtype[np.int16]]: 106 | onnx_input = { 107 | self.model.get_inputs()[0].name: hubert, 108 | self.model.get_inputs()[1].name: hubert_length, 109 | self.model.get_inputs()[2].name: pitch, 110 | self.model.get_inputs()[3].name: pitchf, 111 | self.model.get_inputs()[4].name: ds, 112 | self.model.get_inputs()[5].name: rnd, 113 | } 114 | return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16) 115 | -------------------------------------------------------------------------------- /rvc/onnx/synthesizer.py: -------------------------------------------------------------------------------- 1 | from typing import List, Optional, Union 2 | 3 | import torch 4 | 5 | from rvc.layers.synthesizers import SynthesizerTrnMsNSFsid as SynthesizerBase 6 | 7 | 8 | class SynthesizerTrnMsNSFsid(SynthesizerBase): 9 | def __init__( 10 | self, 11 | spec_channels: int, 12 | segment_size: int, 13 | inter_channels: int, 14 | hidden_channels: int, 15 | filter_channels: int, 16 | n_heads: int, 17 | n_layers: int, 18 | kernel_size: int, 19 | p_dropout: int, 20 | resblock: str, 21 | resblock_kernel_sizes: List[int], 22 | resblock_dilation_sizes: List[List[int]], 23 | upsample_rates: List[int], 24 | upsample_initial_channel: int, 25 | upsample_kernel_sizes: List[int], 26 | spk_embed_dim: int, 27 | gin_channels: int, 28 | sr: Optional[Union[str, int]], 29 | encoder_dim: int, 30 | ): 31 | super().__init__( 32 | spec_channels, 33 | segment_size, 34 | inter_channels, 35 | hidden_channels, 36 | filter_channels, 37 | n_heads, 38 | n_layers, 39 | kernel_size, 40 | p_dropout, 41 | resblock, 42 | resblock_kernel_sizes, 43 | resblock_dilation_sizes, 44 | upsample_rates, 45 | upsample_initial_channel, 46 | upsample_kernel_sizes, 47 | spk_embed_dim, 48 | gin_channels, 49 | sr, 50 | encoder_dim, 51 | True, 52 | ) 53 | self.speaker_map = None 54 | 55 | def remove_weight_norm(self): 56 | self.dec.remove_weight_norm() 57 | self.flow.remove_weight_norm() 58 | self.enc_q.remove_weight_norm() 59 | 60 | def construct_spkmixmap(self): 61 | self.speaker_map = torch.zeros((self.n_speaker, 1, 1, self.gin_channels)) 62 | for i in range(self.n_speaker): 63 | self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]])) 64 | self.speaker_map = self.speaker_map.unsqueeze(0) 65 | 66 | def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None): 67 | if self.speaker_map is not None: # [N, S] * [S, B, 1, H] 68 | g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1] 69 | g = g * self.speaker_map # [N, S, B, 1, H] 70 | g = torch.sum(g, dim=1) # [N, 1, B, 1, H] 71 | g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N] 72 | else: 73 | g = g.unsqueeze(0) 74 | g = self.emb_g(g).transpose(1, 2) 75 | 76 | m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) 77 | z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask 78 | z = self.flow(z_p, x_mask, g=g, reverse=True) 79 | o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g) 80 | return o 81 | -------------------------------------------------------------------------------- /rvc/synthesizer.py: -------------------------------------------------------------------------------- 1 | from collections import OrderedDict 2 | 3 | import torch 4 | 5 | from .layers.synthesizers import SynthesizerTrnMsNSFsid 6 | from .jit import load_inputs, export_jit_model, save_pickle 7 | 8 | 9 | def get_synthesizer(cpt: OrderedDict, device=torch.device("cpu")): 10 | cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] 11 | if_f0 = cpt.get("f0", 1) 12 | version = cpt.get("version", "v1") 13 | if version == "v1": 14 | encoder_dim = 256 15 | elif version == "v2": 16 | encoder_dim = 768 17 | net_g = SynthesizerTrnMsNSFsid( 18 | *cpt["config"], 19 | encoder_dim=encoder_dim, 20 | use_f0=if_f0 == 1, 21 | ) 22 | del net_g.enc_q 23 | net_g.load_state_dict(cpt["weight"], strict=False) 24 | net_g = net_g.float() 25 | net_g.eval().to(device) 26 | net_g.remove_weight_norm() 27 | return net_g, cpt 28 | 29 | 30 | def load_synthesizer( 31 | pth_path: torch.serialization.FILE_LIKE, device=torch.device("cpu") 32 | ): 33 | return get_synthesizer( 34 | torch.load(pth_path, map_location=torch.device("cpu"), weights_only=True), 35 | device, 36 | ) 37 | 38 | 39 | def synthesizer_jit_export( 40 | model_path: str, 41 | mode: str = "script", 42 | inputs_path: str = None, 43 | save_path: str = None, 44 | device=torch.device("cpu"), 45 | is_half=False, 46 | ): 47 | if not save_path: 48 | save_path = model_path.rstrip(".pth") 49 | save_path += ".half.jit" if is_half else ".jit" 50 | if "cuda" in str(device) and ":" not in str(device): 51 | device = torch.device("cuda:0") 52 | from rvc.synthesizer import load_synthesizer 53 | 54 | model, cpt = load_synthesizer(model_path, device) 55 | assert isinstance(cpt, dict) 56 | model.forward = model.infer 57 | inputs = None 58 | if mode == "trace": 59 | inputs = load_inputs(inputs_path, device, is_half) 60 | ckpt = export_jit_model(model, mode, inputs, device, is_half) 61 | cpt.pop("weight") 62 | cpt["model"] = ckpt["model"] 63 | cpt["device"] = device 64 | save_pickle(cpt, save_path) 65 | return cpt 66 | -------------------------------------------------------------------------------- /sha256.env: -------------------------------------------------------------------------------- 1 | sha256_hubert_base_pt = f54b40fd2802423a5643779c4861af1e9ee9c1564dc9d32f54f20b5ffba7db96 2 | sha256_rmvpe_pt = 6d62215f4306e3ca278246188607209f09af3dc77ed4232efdd069798c4ec193 3 | sha256_rmvpe_onnx = 5370e71ac80af8b4b7c793d27efd51fd8bf962de3a7ede0766dac0befa3660fd 4 | 5 | sha256_v1_D32k_pth = 2ab20645829460fdad0d3c44254f1ab53c32cae50c22a66c926ae5aa30abda6f 6 | sha256_v1_D40k_pth = 547f66dbbcd9023b9051ed244d12ab043ba8a4e854b154cc28761ac7c002909b 7 | sha256_v1_D48k_pth = 8cc013fa60ed9c3f902f5bd99f48c7e3b9352d763d4d3cd6bc241c37b0bfd9ad 8 | sha256_v1_G32k_pth = 81817645cde7ed2e2d83f23ef883f33dda564924b497e84d792743912eca4c23 9 | sha256_v1_G40k_pth = e428573bda1124b0ae0ae843fd8dcded6027d3993444790b3e9b0100938b2113 10 | sha256_v1_G48k_pth = 3862a67ea6313e8ffefc05cee6bee656ef3e089442e9ecf4a6618d60721f3e95 11 | sha256_v1_f0D32k_pth = 294db3087236e2c75260d6179056791c9231245daf5d0485545d9e54c4057c77 12 | sha256_v1_f0D40k_pth = 7d4f5a441594b470d67579958b2fd4c6b992852ded28ff9e72eda67abcebe423 13 | sha256_v1_f0D48k_pth = 1b84c8bf347ad1e539c842e8f2a4c36ecd9e7fb23c16041189e4877e9b07925c 14 | sha256_v1_f0G32k_pth = 285f524bf48bb692c76ad7bd0bc654c12bd9e5edeb784dddf7f61a789a608574 15 | sha256_v1_f0G40k_pth = 9115654aeef1995f7dd3c6fc4140bebbef0ca9760bed798105a2380a34299831 16 | sha256_v1_f0G48k_pth = 78bc9cab27e34bcfc194f93029374d871d8b3e663ddedea32a9709e894cc8fe8 17 | 18 | sha256_v2_D32k_pth = d8043378cc6619083d385f5a045de09b83fb3bf8de45c433ca863b71723ac3ca 19 | sha256_v2_D40k_pth = 471378e894e7191f89a94eda8288c5947b16bbe0b10c3f1f17efdb7a1d998242 20 | sha256_v2_D48k_pth = db01094a93c09868a278e03dafe8bb781bfcc1a5ba8df168c948bf9168c84d82 21 | sha256_v2_G32k_pth = 869b26a47f75168d6126f64ac39e6de5247017a8658cfd68aca600f7323efb9f 22 | sha256_v2_G40k_pth = a3843da7fde33db1dab176146c70d6c2df06eafe9457f4e3aa10024e9c6a4b69 23 | sha256_v2_G48k_pth = 2e2b1581a436d07a76b10b9d38765f64aa02836dc65c7dee1ce4140c11ea158b 24 | sha256_v2_f0D32k_pth = bd7134e7793674c85474d5145d2d982e3c5d8124fc7bb6c20f710ed65808fa8a 25 | sha256_v2_f0D40k_pth = 6b6ab091e70801b28e3f41f335f2fc5f3f35c75b39ae2628d419644ec2b0fa09 26 | sha256_v2_f0D48k_pth = 2269b73c7a4cf34da09aea99274dabf99b2ddb8a42cbfb065fb3c0aa9a2fc748 27 | sha256_v2_f0G32k_pth = 2332611297b8d88c7436de8f17ef5f07a2119353e962cd93cda5806d59a1133d 28 | sha256_v2_f0G40k_pth = 3b2c44035e782c4b14ddc0bede9e2f4a724d025cd073f736d4f43708453adfcb 29 | sha256_v2_f0G48k_pth = b5d51f589cc3632d4eae36a315b4179397695042edc01d15312e1bddc2b764a4 30 | 31 | sha256_uvr5_HP2-人声vocals+非人声instrumentals_pth = 39796caa5db18d7f9382d8ac997ac967bfd85f7761014bb807d2543cc844ef05 32 | sha256_uvr5_HP2_all_vocals_pth = 39796caa5db18d7f9382d8ac997ac967bfd85f7761014bb807d2543cc844ef05 33 | sha256_uvr5_HP3_all_vocals_pth = 45e6b65199e781b4a6542002699be9f19cd3d1cb7d1558bc2bfbcd84674dfe28 34 | sha256_uvr5_HP5-主旋律人声vocals+其他instrumentals_pth = 5908891829634926119720241e8573d97cbeb8277110a7512bdb0bd7563258ee 35 | sha256_uvr5_HP5_only_main_vocal_pth = 5908891829634926119720241e8573d97cbeb8277110a7512bdb0bd7563258ee 36 | sha256_uvr5_VR-DeEchoAggressive_pth = 8c8fd1582f9aabc363e47af62ddb88df6cae7e064cae75bbf041a067a5e0aee2 37 | sha256_uvr5_VR-DeEchoDeReverb_pth = 01376dd2a571bf3cb9cced680732726d2d732609d09216a610b0d110f133febe 38 | sha256_uvr5_VR-DeEchoNormal_pth = 56aba59db3bcdd14a14464e62f3129698ecdea62eee0f003b9360923eb3ac79e 39 | sha256_uvr5_vocals_onnx = 233bb5c6aaa365e568659a0a81211746fa881f8f47f82d9e864fce1f7692db80 40 | -------------------------------------------------------------------------------- /tools/checksum/main.go: -------------------------------------------------------------------------------- 1 | package main 2 | 3 | import ( 4 | "crypto/sha256" 5 | "encoding/hex" 6 | "fmt" 7 | "io" 8 | "os" 9 | ) 10 | 11 | func main() { 12 | var buf [32]byte 13 | h := sha256.New() 14 | lst := make([]any, 0, 64) 15 | for _, fname := range files { 16 | f, err := os.Open("assets/" + fname) 17 | if err != nil { 18 | panic(err) 19 | } 20 | _, err = io.Copy(h, f) 21 | if err != nil { 22 | panic(err) 23 | } 24 | s := hex.EncodeToString(h.Sum(buf[:0])) 25 | fmt.Println("sha256 of", fname, "=", s) 26 | lst = append(lst, s) 27 | h.Reset() 28 | f.Close() 29 | } 30 | f, err := os.Create("sha256.env") 31 | if err != nil { 32 | panic(err) 33 | } 34 | _, err = fmt.Fprintf(f, envtmpl, lst...) 35 | if err != nil { 36 | panic(err) 37 | } 38 | } 39 | -------------------------------------------------------------------------------- /tools/checksum/tmpl.go: -------------------------------------------------------------------------------- 1 | package main 2 | 3 | var files = [...]string{ 4 | "hubert/hubert_base.pt", 5 | "rmvpe/rmvpe.pt", 6 | "rmvpe/rmvpe.onnx", 7 | 8 | "pretrained/D32k.pth", 9 | "pretrained/D40k.pth", 10 | "pretrained/D48k.pth", 11 | "pretrained/G32k.pth", 12 | "pretrained/G40k.pth", 13 | "pretrained/G48k.pth", 14 | "pretrained/f0D32k.pth", 15 | "pretrained/f0D40k.pth", 16 | "pretrained/f0D48k.pth", 17 | "pretrained/f0G32k.pth", 18 | "pretrained/f0G40k.pth", 19 | "pretrained/f0G48k.pth", 20 | 21 | "pretrained_v2/D32k.pth", 22 | "pretrained_v2/D40k.pth", 23 | "pretrained_v2/D48k.pth", 24 | "pretrained_v2/G32k.pth", 25 | "pretrained_v2/G40k.pth", 26 | "pretrained_v2/G48k.pth", 27 | "pretrained_v2/f0D32k.pth", 28 | "pretrained_v2/f0D40k.pth", 29 | "pretrained_v2/f0D48k.pth", 30 | "pretrained_v2/f0G32k.pth", 31 | "pretrained_v2/f0G40k.pth", 32 | "pretrained_v2/f0G48k.pth", 33 | 34 | "uvr5_weights/HP2-人声vocals+非人声instrumentals.pth", 35 | "uvr5_weights/HP2_all_vocals.pth", 36 | "uvr5_weights/HP3_all_vocals.pth", 37 | "uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth", 38 | "uvr5_weights/HP5_only_main_vocal.pth", 39 | "uvr5_weights/VR-DeEchoAggressive.pth", 40 | "uvr5_weights/VR-DeEchoDeReverb.pth", 41 | "uvr5_weights/VR-DeEchoNormal.pth", 42 | "uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx", 43 | } 44 | 45 | const envtmpl = `sha256_hubert_base_pt = %s 46 | sha256_rmvpe_pt = %s 47 | sha256_rmvpe_onnx = %s 48 | 49 | sha256_v1_D32k_pth = %s 50 | sha256_v1_D40k_pth = %s 51 | sha256_v1_D48k_pth = %s 52 | sha256_v1_G32k_pth = %s 53 | sha256_v1_G40k_pth = %s 54 | sha256_v1_G48k_pth = %s 55 | sha256_v1_f0D32k_pth = %s 56 | sha256_v1_f0D40k_pth = %s 57 | sha256_v1_f0D48k_pth = %s 58 | sha256_v1_f0G32k_pth = %s 59 | sha256_v1_f0G40k_pth = %s 60 | sha256_v1_f0G48k_pth = %s 61 | 62 | sha256_v2_D32k_pth = %s 63 | sha256_v2_D40k_pth = %s 64 | sha256_v2_D48k_pth = %s 65 | sha256_v2_G32k_pth = %s 66 | sha256_v2_G40k_pth = %s 67 | sha256_v2_G48k_pth = %s 68 | sha256_v2_f0D32k_pth = %s 69 | sha256_v2_f0D40k_pth = %s 70 | sha256_v2_f0D48k_pth = %s 71 | sha256_v2_f0G32k_pth = %s 72 | sha256_v2_f0G40k_pth = %s 73 | sha256_v2_f0G48k_pth = %s 74 | 75 | sha256_uvr5_HP2-人声vocals+非人声instrumentals_pth = %s 76 | sha256_uvr5_HP2_all_vocals_pth = %s 77 | sha256_uvr5_HP3_all_vocals_pth = %s 78 | sha256_uvr5_HP5-主旋律人声vocals+其他instrumentals_pth = %s 79 | sha256_uvr5_HP5_only_main_vocal_pth = %s 80 | sha256_uvr5_VR-DeEchoAggressive_pth = %s 81 | sha256_uvr5_VR-DeEchoDeReverb_pth = %s 82 | sha256_uvr5_VR-DeEchoNormal_pth = %s 83 | sha256_uvr5_vocals_onnx = %s 84 | ` 85 | -------------------------------------------------------------------------------- /tools/cmd/calc_rvc_model_similarity.py: -------------------------------------------------------------------------------- 1 | # This code references https://huggingface.co/JosephusCheung/ASimilarityCalculatior/blob/main/qwerty.py 2 | # Fill in the path of the model to be queried and the root directory of the reference models, and this script will return the similarity between the model to be queried and all reference models. 3 | import os 4 | import logging 5 | 6 | logger = logging.getLogger(__name__) 7 | 8 | import torch 9 | import torch.nn as nn 10 | import torch.nn.functional as F 11 | 12 | 13 | def cal_cross_attn(to_q, to_k, to_v, rand_input): 14 | hidden_dim, embed_dim = to_q.shape 15 | attn_to_q = nn.Linear(hidden_dim, embed_dim, bias=False) 16 | attn_to_k = nn.Linear(hidden_dim, embed_dim, bias=False) 17 | attn_to_v = nn.Linear(hidden_dim, embed_dim, bias=False) 18 | attn_to_q.load_state_dict({"weight": to_q}) 19 | attn_to_k.load_state_dict({"weight": to_k}) 20 | attn_to_v.load_state_dict({"weight": to_v}) 21 | 22 | return torch.einsum( 23 | "ik, jk -> ik", 24 | F.softmax( 25 | torch.einsum("ij, kj -> ik", attn_to_q(rand_input), attn_to_k(rand_input)), 26 | dim=-1, 27 | ), 28 | attn_to_v(rand_input), 29 | ) 30 | 31 | 32 | def model_hash(filename): 33 | try: 34 | with open(filename, "rb") as file: 35 | import hashlib 36 | 37 | m = hashlib.sha256() 38 | 39 | file.seek(0x100000) 40 | m.update(file.read(0x10000)) 41 | return m.hexdigest()[0:8] 42 | except FileNotFoundError: 43 | return "NOFILE" 44 | 45 | 46 | def eval(model, n, input): 47 | qk = f"enc_p.encoder.attn_layers.{n}.conv_q.weight" 48 | uk = f"enc_p.encoder.attn_layers.{n}.conv_k.weight" 49 | vk = f"enc_p.encoder.attn_layers.{n}.conv_v.weight" 50 | atoq, atok, atov = model[qk][:, :, 0], model[uk][:, :, 0], model[vk][:, :, 0] 51 | 52 | attn = cal_cross_attn(atoq, atok, atov, input) 53 | return attn 54 | 55 | 56 | def main(path, root): 57 | torch.manual_seed(114514) 58 | model_a = torch.load(path, map_location="cpu")["weight"] 59 | 60 | logger.info("Query:\t\t%s\t%s" % (path, model_hash(path))) 61 | 62 | map_attn_a = {} 63 | map_rand_input = {} 64 | for n in range(6): 65 | hidden_dim, embed_dim, _ = model_a[ 66 | f"enc_p.encoder.attn_layers.{n}.conv_v.weight" 67 | ].shape 68 | rand_input = torch.randn([embed_dim, hidden_dim]) 69 | 70 | map_attn_a[n] = eval(model_a, n, rand_input) 71 | map_rand_input[n] = rand_input 72 | 73 | del model_a 74 | 75 | for name in sorted(list(os.listdir(root))): 76 | path = "%s/%s" % (root, name) 77 | model_b = torch.load(path, map_location="cpu")["weight"] 78 | 79 | sims = [] 80 | for n in range(6): 81 | attn_a = map_attn_a[n] 82 | attn_b = eval(model_b, n, map_rand_input[n]) 83 | 84 | sim = torch.mean(torch.cosine_similarity(attn_a, attn_b)) 85 | sims.append(sim) 86 | 87 | logger.info( 88 | "Reference:\t%s\t%s\t%s" 89 | % (path, model_hash(path), f"{torch.mean(torch.stack(sims)) * 1e2:.2f}%") 90 | ) 91 | 92 | 93 | if __name__ == "__main__": 94 | query_path = r"assets\weights\mi v3.pth" 95 | reference_root = r"assets\weights" 96 | main(query_path, reference_root) 97 | -------------------------------------------------------------------------------- /tools/cmd/infer_batch_rvc.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import sys 4 | 5 | print("Command-line arguments:", sys.argv) 6 | 7 | now_dir = os.getcwd() 8 | sys.path.append(now_dir) 9 | import sys 10 | 11 | import tqdm as tq 12 | from dotenv import load_dotenv 13 | from scipy.io import wavfile 14 | 15 | from configs import Config 16 | from infer.modules.vc import VC 17 | 18 | 19 | def arg_parse() -> tuple: 20 | parser = argparse.ArgumentParser() 21 | parser.add_argument("--f0up_key", type=int, default=0) 22 | parser.add_argument("--input_path", type=str, help="input path") 23 | parser.add_argument("--index_path", type=str, help="index path") 24 | parser.add_argument("--f0method", type=str, default="harvest", help="harvest or pm") 25 | parser.add_argument("--opt_path", type=str, help="opt path") 26 | parser.add_argument("--model_name", type=str, help="store in assets/weight_root") 27 | parser.add_argument("--index_rate", type=float, default=0.66, help="index rate") 28 | parser.add_argument("--device", type=str, help="device") 29 | parser.add_argument("--is_half", type=bool, help="use half -> True") 30 | parser.add_argument("--filter_radius", type=int, default=3, help="filter radius") 31 | parser.add_argument("--resample_sr", type=int, default=0, help="resample sr") 32 | parser.add_argument("--rms_mix_rate", type=float, default=1, help="rms mix rate") 33 | parser.add_argument("--protect", type=float, default=0.33, help="protect") 34 | 35 | args = parser.parse_args() 36 | sys.argv = sys.argv[:1] 37 | 38 | return args 39 | 40 | 41 | def main(): 42 | load_dotenv() 43 | args = arg_parse() 44 | config = Config() 45 | config.device = args.device if args.device else config.device 46 | config.is_half = args.is_half if args.is_half else config.is_half 47 | vc = VC(config) 48 | vc.get_vc(args.model_name) 49 | audios = os.listdir(args.input_path) 50 | for file in tq.tqdm(audios): 51 | if file.endswith(".wav"): 52 | file_path = os.path.join(args.input_path, file) 53 | _, wav_opt = vc.vc_single( 54 | 0, 55 | file_path, 56 | args.f0up_key, 57 | None, 58 | args.f0method, 59 | args.index_path, 60 | None, 61 | args.index_rate, 62 | args.filter_radius, 63 | args.resample_sr, 64 | args.rms_mix_rate, 65 | args.protect, 66 | ) 67 | out_path = os.path.join(args.opt_path, file) 68 | wavfile.write(out_path, wav_opt[0], wav_opt[1]) 69 | 70 | 71 | if __name__ == "__main__": 72 | main() 73 | -------------------------------------------------------------------------------- /tools/cmd/infer_cli.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import sys 4 | 5 | now_dir = os.getcwd() 6 | sys.path.append(now_dir) 7 | from dotenv import load_dotenv 8 | from scipy.io import wavfile 9 | 10 | from configs import Config 11 | from infer.modules.vc import VC 12 | 13 | #### 14 | # USAGE 15 | # 16 | # In your Terminal or CMD or whatever 17 | 18 | 19 | def arg_parse() -> tuple: 20 | parser = argparse.ArgumentParser() 21 | parser.add_argument("--f0up_key", type=int, default=0) 22 | parser.add_argument("--input_path", type=str, help="input path") 23 | parser.add_argument("--index_path", type=str, help="index path") 24 | parser.add_argument("--f0method", type=str, default="harvest", help="harvest or pm") 25 | parser.add_argument("--opt_path", type=str, help="opt path") 26 | parser.add_argument("--model_name", type=str, help="store in assets/weight_root") 27 | parser.add_argument("--index_rate", type=float, default=0.66, help="index rate") 28 | parser.add_argument("--device", type=str, help="device") 29 | parser.add_argument("--is_half", type=bool, help="use half -> True") 30 | parser.add_argument("--filter_radius", type=int, default=3, help="filter radius") 31 | parser.add_argument("--resample_sr", type=int, default=0, help="resample sr") 32 | parser.add_argument("--rms_mix_rate", type=float, default=1, help="rms mix rate") 33 | parser.add_argument("--protect", type=float, default=0.33, help="protect") 34 | 35 | args = parser.parse_args() 36 | sys.argv = sys.argv[:1] 37 | 38 | return args 39 | 40 | 41 | def main(): 42 | load_dotenv() 43 | args = arg_parse() 44 | config = Config() 45 | config.device = args.device if args.device else config.device 46 | config.is_half = args.is_half if args.is_half else config.is_half 47 | vc = VC(config) 48 | vc.get_vc(args.model_name) 49 | _, wav_opt = vc.vc_single( 50 | 0, 51 | args.input_path, 52 | args.f0up_key, 53 | None, 54 | args.f0method, 55 | args.index_path, 56 | None, 57 | args.index_rate, 58 | args.filter_radius, 59 | args.resample_sr, 60 | args.rms_mix_rate, 61 | args.protect, 62 | ) 63 | wavfile.write(args.opt_path, wav_opt[0], wav_opt[1]) 64 | 65 | 66 | if __name__ == "__main__": 67 | main() 68 | -------------------------------------------------------------------------------- /tools/cmd/onnx/export.py: -------------------------------------------------------------------------------- 1 | from rvc.onnx import export_onnx 2 | 3 | export_onnx("pt/Justin Bieber.pth", "pt/TestRvc_Rvc.onnx") 4 | -------------------------------------------------------------------------------- /tools/cmd/onnx/infer.py: -------------------------------------------------------------------------------- 1 | import librosa 2 | 3 | from rvc.onnx import RVC 4 | 5 | from infer.lib.audio import save_audio 6 | 7 | hop_size = 512 8 | sampling_rate = 40000 # 采样率 9 | f0_up_key = 0 # 升降调 10 | sid = 0 # 角色ID 11 | f0_method = "dio" # F0提取算法 12 | model_path = "exported_model.onnx" # 模型的完整路径 13 | vec_path = "vec-256-layer-9.onnx" # 需要onnx的vec模型 14 | wav_path = "123.wav" # 输入路径或ByteIO实例 15 | out_path = "out.wav" # 输出路径或ByteIO实例 16 | 17 | model = RVC(model_path, vec_path=vec_path, hop_len=hop_size, device="cuda") 18 | 19 | wav, sr = librosa.load(wav_path, sr=sampling_rate) 20 | 21 | audio = model.infer(wav, sr, sampling_rate, sid, f0_method, f0_up_key) 22 | 23 | save_audio(out_path, audio, sampling_rate) 24 | -------------------------------------------------------------------------------- /tools/cmd/train-index-v2.py: -------------------------------------------------------------------------------- 1 | """ 2 | 格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个 3 | """ 4 | 5 | import os 6 | import traceback 7 | import logging 8 | 9 | logger = logging.getLogger(__name__) 10 | 11 | from multiprocessing import cpu_count 12 | 13 | import faiss 14 | import numpy as np 15 | from sklearn.cluster import MiniBatchKMeans 16 | 17 | # ###########如果是原始特征要先写save 18 | n_cpu = 0 19 | if n_cpu == 0: 20 | n_cpu = cpu_count() 21 | inp_root = r"./logs/anz/3_feature768" 22 | npys = [] 23 | listdir_res = list(os.listdir(inp_root)) 24 | for name in sorted(listdir_res): 25 | phone = np.load("%s/%s" % (inp_root, name)) 26 | npys.append(phone) 27 | big_npy = np.concatenate(npys, 0) 28 | big_npy_idx = np.arange(big_npy.shape[0]) 29 | np.random.shuffle(big_npy_idx) 30 | big_npy = big_npy[big_npy_idx] 31 | logger.debug(big_npy.shape) # (6196072, 192)#fp32#4.43G 32 | if big_npy.shape[0] > 2e5: 33 | # if(1): 34 | info = "Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0] 35 | logger.info(info) 36 | try: 37 | big_npy = ( 38 | MiniBatchKMeans( 39 | n_clusters=10000, 40 | verbose=True, 41 | batch_size=256 * n_cpu, 42 | compute_labels=False, 43 | init="random", 44 | ) 45 | .fit(big_npy) 46 | .cluster_centers_ 47 | ) 48 | except: 49 | info = traceback.format_exc() 50 | logger.warning(info) 51 | 52 | np.save("tools/infer/big_src_feature_mi.npy", big_npy) 53 | 54 | ##################train+add 55 | # big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy") 56 | n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) 57 | index = faiss.index_factory(768, "IVF%s,Flat" % n_ivf) # mi 58 | logger.info("Training...") 59 | index_ivf = faiss.extract_index_ivf(index) # 60 | index_ivf.nprobe = 1 61 | index.train(big_npy) 62 | faiss.write_index( 63 | index, "tools/infer/trained_IVF%s_Flat_baseline_src_feat_v2.index" % (n_ivf) 64 | ) 65 | logger.info("Adding...") 66 | batch_size_add = 8192 67 | for i in range(0, big_npy.shape[0], batch_size_add): 68 | index.add(big_npy[i : i + batch_size_add]) 69 | faiss.write_index( 70 | index, "tools/infer/added_IVF%s_Flat_mi_baseline_src_feat.index" % (n_ivf) 71 | ) 72 | """ 73 | 大小（都是FP32） 74 | big_src_feature 2.95G 75 | (3098036, 256) 76 | big_emb 4.43G 77 | (6196072, 192) 78 | big_emb双倍是因为求特征要repeat后再加pitch 79 | 80 | """ 81 | -------------------------------------------------------------------------------- /tools/cmd/train-index.py: -------------------------------------------------------------------------------- 1 | """ 2 | 格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个 3 | """ 4 | 5 | import os 6 | import logging 7 | 8 | logger = logging.getLogger(__name__) 9 | 10 | import faiss 11 | import numpy as np 12 | 13 | # ###########如果是原始特征要先写save 14 | inp_root = r"E:\codes\py39\dataset\mi\2-co256" 15 | npys = [] 16 | for name in sorted(list(os.listdir(inp_root))): 17 | phone = np.load("%s/%s" % (inp_root, name)) 18 | npys.append(phone) 19 | big_npy = np.concatenate(npys, 0) 20 | logger.debug(big_npy.shape) # (6196072, 192)#fp32#4.43G 21 | np.save("infer/big_src_feature_mi.npy", big_npy) 22 | 23 | ##################train+add 24 | # big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy") 25 | logger.debug(big_npy.shape) 26 | index = faiss.index_factory(256, "IVF512,Flat") # mi 27 | logger.info("Training...") 28 | index_ivf = faiss.extract_index_ivf(index) # 29 | index_ivf.nprobe = 9 30 | index.train(big_npy) 31 | faiss.write_index(index, "infer/trained_IVF512_Flat_mi_baseline_src_feat.index") 32 | logger.info("Adding...") 33 | index.add(big_npy) 34 | faiss.write_index(index, "infer/added_IVF512_Flat_mi_baseline_src_feat.index") 35 | """ 36 | 大小（都是FP32） 37 | big_src_feature 2.95G 38 | (3098036, 256) 39 | big_emb 4.43G 40 | (6196072, 192) 41 | big_emb双倍是因为求特征要repeat后再加pitch 42 | 43 | """ 44 | -------------------------------------------------------------------------------- /tools/cmd/trans_weights.py: -------------------------------------------------------------------------------- 1 | import pdb 2 | 3 | import torch 4 | 5 | # a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-suc\G_1000.pth")["model"]#sim_nsf# 6 | # a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-freeze-vocoder-flow-enc_q\G_1000.pth")["model"]#sim_nsf# 7 | # a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-freeze-vocoder\G_1000.pth")["model"]#sim_nsf# 8 | # a=torch.load(r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-test\G_1000.pth")["model"]#sim_nsf# 9 | a = torch.load( 10 | r"E:\codes\py39\vits_vc_gpu_train\logs\ft-mi-no_opt-no_dropout\G_1000.pth" 11 | )[ 12 | "model" 13 | ] # sim_nsf# 14 | for key in a.keys(): 15 | a[key] = a[key].half() 16 | # torch.save(a,"ft-mi-freeze-vocoder_true_1k.pt")# 17 | # torch.save(a,"ft-mi-sim1k.pt")# 18 | torch.save(a, "ft-mi-no_opt-no_dropout.pt") # 19 | --------------------------------------------------------------------------------