sovits4_for_colab.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"2q0l56aFQhAM"},"source":["# Terms of Use\n","\n","### Please solve the authorization problem of the dataset on your own. You shall be solely responsible for any problems caused by the use of non-authorized datasets for training and all consequences thereof.The repository and its maintainer, svc develop team, have nothing to do with the consequences!\n","\n","1. This project is established for academic exchange purposes only and is intended for communication and learning purposes. It is not intended for production environments.\n","2. Any videos based on sovits that are published on video platforms must clearly indicate in the description that they are used for voice changing and specify the input source of the voice or audio, for example, using videos or audios published by others and separating the vocals as input source for conversion, which must provide clear original video or music links. If your own voice or other synthesized voices from other commercial vocal synthesis software are used as the input source for conversion, you must also explain it in the description.\n","3. You shall be solely responsible for any infringement problems caused by the input source. When using other commercial vocal synthesis software as input source, please ensure that you comply with the terms of use of the software. Note that many vocal synthesis engines clearly state in their terms of use that they cannot be used for input source conversion.\n","4. Continuing to use this project is deemed as agreeing to the relevant provisions stated in this repository README. This repository README has the obligation to persuade, and is not responsible for any subsequent problems that may arise.\n","5. If you distribute this repository's code or publish any results produced by this project publicly (including but not limited to video sharing platforms), please indicate the original author and code source (this repository).\n","6. If you use this project for any other plan, please contact and inform the author of this repository in advance. Thank you very much.\n"]},{"cell_type":"markdown","metadata":{"id":"M_RcDbVPhivj"},"source":["## **Note:**\n","## **Make sure there is no a directory named `sovits4data` in your google drive at the first time you use this notebook.**\n","## **It will be created to store some necessary files.** \n","## **For sure you can change it to another directory by modifying `sovits_data_dir` variable.**"]},{"cell_type":"markdown","metadata":{"id":"fHaw6hGEa_Nk"},"source":["# **Initialize environment**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0gQcIZ8RsOkn"},"outputs":[],"source":["#@title Connect to colab runtime and check GPU\n","\n","#@markdown # Connect to colab runtime and check GPU\n","\n","#@markdown\n","\n","!nvidia-smi"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0YUGpYrXhMck"},"outputs":[],"source":["#@title Clone repository and install requirements\n","\n","#@markdown # Clone repository and install requirements\n","\n","#@markdown\n","\n","#@markdown ### After the execution is completed, the runtime will **automatically restart**\n","\n","#@markdown\n","\n","!git clone https://github.com/svc-develop-team/so-vits-svc -b 4.0\n","!pip uninstall torchdata torchtext\n","!pip install --upgrade pip setuptools numpy numba\n","!pip install pyworld praat-parselmouth fairseq tensorboardX torchcrepe librosa==0.9.1\n","!pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117\n","%cd /content/so-vits-svc\n","!curl -L https://github.com/openvpi/vocoders/releases/download/nsf-hifigan-v1/nsf_hifigan_20221211.zip -o /content/so-vits-svc/nsf_hifigan_20221211.zip\n","!unzip nsf_hifigan_20221211.zip\n","!rm -rf pretrain/nsf_hifigan\n","!mv -v nsf_hifigan pretrain\n","!curl -L https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr -o /content/so-vits-svc/hubert/checkpoint_best_legacy_500.pt\n","exit()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wmUkpUmfn_Hs"},"outputs":[],"source":["#@title Mount google drive and select which directories to sync with google drive\n","\n","#@markdown # Mount google drive and select which directories to sync with google drive\n","\n","#@markdown\n","\n","from google.colab import drive\n","drive.mount(\"/content/drive\")\n","\n","#@markdown Directory to store **necessary files**, dont miss the slash at the end👇.\n","sovits_data_dir = \"/content/drive/MyDrive/sovits4data/\"  #@param {type:\"string\"}\n","#@markdown By default it will create a `sovits4data/` folder in your google drive.\n","RAW_DIR = sovits_data_dir + \"raw/\"\n","RESULTS_DIR = sovits_data_dir + \"results/\"\n","FILELISTS_DIR = sovits_data_dir + \"filelists/\"\n","CONFIGS_DIR = sovits_data_dir + \"configs/\"\n","LOGS_DIR = sovits_data_dir + \"logs/44k/\"\n","\n","#@markdown\n","\n","#@markdown ### These folders will be synced with your google drvie\n","\n","#@markdown　### **Strongly recommend to check all.**\n","\n","#@markdown Sync **input audios** and **output audios**\n","sync_raw_and_results = True  #@param {type:\"boolean\"}\n","if sync_raw_and_results:\n","  !mkdir -p {RAW_DIR}\n","  !mkdir -p {RESULTS_DIR}\n","  !rm -rf /content/so-vits-svc/raw\n","  !rm -rf /content/so-vits-svc/results\n","  !ln -s {RAW_DIR} /content/so-vits-svc/raw\n","  !ln -s {RESULTS_DIR} /content/so-vits-svc/results\n","\n","#@markdown Sync **config** and **models**\n","sync_configs_and_logs = True  #@param {type:\"boolean\"}\n","if sync_configs_and_logs:\n","  !mkdir -p {FILELISTS_DIR}\n","  !mkdir -p {CONFIGS_DIR}\n","  !mkdir -p {LOGS_DIR}\n","  !rm -rf /content/so-vits-svc/filelists\n","  !rm -rf /content/so-vits-svc/configs\n","  !rm -rf /content/so-vits-svc/logs/44k\n","  !ln -s {FILELISTS_DIR} /content/so-vits-svc/filelists\n","  !ln -s {CONFIGS_DIR} /content/so-vits-svc/configs\n","  !ln -s {LOGS_DIR} /content/so-vits-svc/logs/44k"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"G_PMPCN6wvgZ"},"outputs":[],"source":["#@title Get pretrained model(Optional but strongly recommend).\n","\n","#@markdown # Get pretrained model(Optional but strongly recommend).\n","\n","#@markdown\n","\n","#@markdown - Pre-trained model files: `G_0.pth` `D_0.pth`\n","#@markdown   - Place them under /sovits4data/logs/44k/ in your google drive manualy\n","\n","#@markdown Get them from svc-develop-team(TBD) or anywhere else.\n","\n","#@markdown Although the pretrained model generally does not cause any copyright problems, please pay attention to it. For example, ask the author in advance, or the author has indicated the feasible use in the description clearly.\n","\n","!pwd"]},{"cell_type":"markdown","metadata":{"id":"k1qadJBFehMo"},"source":["# **Dataset preprocessing**"]},{"cell_type":"markdown","metadata":{"id":"kBlju6Q3lSM6"},"source":["Pack and upload your raw dataset(dataset_raw/) to your google drive.\n","\n","Makesure the file structure in your zip file looks like this:\n","\n","```\n","YourZIPforSingleSpeakers.zip\n","└───speaker\n","    ├───xxx1-xxx1.wav\n","    ├───...\n","    └───Lxx-0xx8.wav\n","```\n","\n","```\n","YourZIPforMultipleSpeakers.zip\n","├───speaker0\n","│   ├───xxx1-xxx1.wav\n","│   ├───...\n","│   └───Lxx-0xx8.wav\n","└───speaker1\n","    ├───xx2-0xxx2.wav\n","    ├───...\n","    └───xxx7-xxx007.wav\n","```\n","\n","**Even if there is only one speaker, a folder named `{speaker_name}` is needed.**\n","\n","![1.png](data:image/png;base64,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)\n","\n","![2.png](data:image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)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"U05CXlAipvJR"},"outputs":[],"source":["#@title Get raw dataset from google drive\n","\n","#@markdown # Get raw dataset from google drive\n","\n","#@markdown\n","\n","#@markdown Directory where **your zip file** located in, dont miss the slash at the end👇.\n","sovits_data_dir = \"/content/drive/MyDrive/sovits4data/\"  #@param {type:\"string\"}\n","#@markdown Filename of **your zip file**, do NOT be \"dataset.zip\"\n","zip_filename = \"YourZIPFilenameofRawDataset.zip\"  #@param {type:\"string\"}\n","ZIP_PATH = sovits_data_dir + zip_filename\n","\n","!unzip -od /content/so-vits-svc/dataset_raw {ZIP_PATH}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_ThKTzYs5CfL"},"outputs":[],"source":["#@title Resample to 44100Hz and mono\n","\n","#@markdown # Resample to 44100Hz and mono\n","\n","#@markdown\n","\n","%cd /content/so-vits-svc\n","!python resample.py"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"svITReeL5N8K"},"outputs":[],"source":["#@title Divide filelists and generate config.json\n","\n","#@markdown # Divide filelists and generate config.json\n","\n","#@markdown\n","\n","%cd /content/so-vits-svc\n","!python preprocess_flist_config.py"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xHUXMi836DMe"},"outputs":[],"source":["#@title Generate hubert and f0\n","\n","#@markdown # Generate hubert and f0\n","\n","#@markdown\n","\n","%cd /content/so-vits-svc\n","!python preprocess_hubert_f0.py"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Wo4OTmTAUXgj"},"outputs":[],"source":["#@title Save the preprocessed dataset to google drive\n","\n","#@markdown # Save the preprocessed dataset to google drive\n","\n","#@markdown\n","\n","#@markdown You can save the dataset and related files to your google drive for the next training\n","\n","#@markdown **Directory for saving**, dont miss the slash at the end👇.\n","sovits_data_dir = \"/content/drive/MyDrive/sovits4data/\" #@param {type:\"string\"}\n","\n","#@markdown There will be a `dataset.zip` contained `dataset/` in your google drive, which is preprocessed data.\n","\n","!mkdir -p {sovits_data_dir}\n","!zip -r dataset.zip /content/so-vits-svc/dataset\n","!cp -vr dataset.zip \"{sovits_data_dir}\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P2G6v_6zblWK"},"outputs":[],"source":["#@title Unzip preprocessed dataset from google drive directly if you have preprocessed already.\n","\n","#@markdown # Unzip preprocessed dataset from google drive directly if you have preprocessed already.\n","\n","#@markdown\n","\n","#@markdown Directory where **your preprocessed dataset** located in, dont miss the slash at the end👇.\n","sovits_data_dir = \"/content/drive/MyDrive/sovits4data/\" #@param {type:\"string\"}\n","CONFIG = sovits_data_dir + \"configs/\"\n","FILELISTS = sovits_data_dir + \"filelists/\"\n","DATASET = sovits_data_dir + \"dataset.zip\"\n","\n","!cp -vr {CONFIG} /content/so-vits-svc/\n","!cp -vr {FILELISTS} /content/so-vits-svc/\n","!unzip {DATASET} -d /"]},{"cell_type":"markdown","metadata":{"id":"ENoH-pShel7w"},"source":["# **Trainning**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-hEFFTCfZf57"},"outputs":[],"source":["#@title Start training\n","\n","#@markdown # Start training\n","\n","#@markdown If you want to use pre-trained models, upload them to /sovits4data/logs/44k/ in your google drive manualy.\n","\n","#@markdown\n","\n","#@markdown Whether to enable tensorboard\n","tensorboard_on = True  #@param {type:\"boolean\"}\n","\n","if tensorboard_on:\n","  %load_ext tensorboard\n","  %tensorboard --logdir logs/44k\n","\n","%cd /content/so-vits-svc\n","!python train.py -c configs/config.json -m 44k"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZThaMxmIJgWy"},"outputs":[],"source":["#@title Train cluster model (Optional)\n","\n","#@markdown # Train cluster model (Optional)\n","\n","#@markdown #### Details see [README.md#cluster-based-timbre-leakage-control](https://github.com/svc-develop-team/so-vits-svc#cluster-based-timbre-leakage-control)\n","\n","#@markdown\n","\n","%cd /content/so-vits-svc\n","!python cluster/train_cluster.py"]},{"cell_type":"markdown","metadata":{"id":"oCnbX-OT897k"},"source":["# **Inference**\n","### Upload wav files from this notebook\n","### **OR**\n","### Upload to `sovits4data/raw/` in your google drive manualy (should be faster)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"elapsed":94633,"status":"ok","timestamp":1678591088790,"user":{"displayName":"謬紗特","userId":"09445825975794260265"},"user_tz":-480},"id":"XUsmGkgCMD_Q","outputId":"8bbfde13-030a-4ba0-bbdb-7eb6b89c02b4"},"outputs":[{"data":{"text/html":["\n","     <input type=\"file\" id=\"files-248d7a62-b1f9-4a61-a516-b2dc2fd0d57f\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-248d7a62-b1f9-4a61-a516-b2dc2fd0d57f\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Saving YourWAVFile.wav to YourWAVFile.wav\n"]}],"source":["#@title Upload wav files, the filename should not contain any special symbols like `#` `$` `(` `)`\n","\n","#@markdown # Upload wav files, the filename should not contain any special symbols like `#` `$` `(` `)`\n","\n","#@markdown\n","\n","%cd /content/so-vits-svc\n","%run wav_upload.py --type audio"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dYnKuKTIj3z1"},"outputs":[],"source":["#@title Start inference (and download)\n","\n","#@markdown # Start inference (and download)\n","\n","#@markdown Parameters see [README.MD#Inference](https://github.com/svc-develop-team/so-vits-svc#-inference)\n","\n","#@markdown\n","\n","wav_filename = \"YourWAVFile.wav\"  #@param {type:\"string\"}\n","model_filename = \"G_210000.pth\"  #@param {type:\"string\"}\n","model_path = \"/content/so-vits-svc/logs/44k/\" + model_filename\n","speaker = \"YourSpeaker\"  #@param {type:\"string\"}\n","trans = \"0\"  #@param {type:\"string\"}\n","cluster_infer_ratio = \"0\"  #@param {type:\"string\"}\n","auto_predict_f0 = False  #@param {type:\"boolean\"}\n","apf = \"\"\n","if auto_predict_f0:\n","  apf = \" -a \"\n","f0_mean_pooling = False  #@param {type:\"boolean\"}\n","fmp = \"\"\n","if f0_mean_pooling:\n","  fmp = \" -fmp \"\n","enhance = False  #@param {type:\"boolean\"}\n","ehc = \"\"\n","if enhance:\n","  ehc = \" -eh \"\n","#@markdown\n","\n","#@markdown Generally keep default:\n","config_filename = \"config.json\"  #@param {type:\"string\"}\n","config_path = \"/content/so-vits-svc/configs/\" + config_filename\n","kmeans_filenname = \"kmeans_10000.pt\"  #@param {type:\"string\"}\n","kmeans_path = \"/content/so-vits-svc/logs/44k/\" + kmeans_filenname\n","slice_db = \"-40\"  #@param {type:\"string\"}\n","wav_format = \"flac\"  #@param {type:\"string\"}\n","wav_output = \"/content/so-vits-svc/results/\" + wav_filename + \"_\" + trans + \"key\" + \"_\" + speaker + \".\" + wav_format\n","\n","%cd /content/so-vits-svc\n","!python inference_main.py -n {wav_filename} -m {model_path} -s {speaker} -t {trans} -cr {cluster_infer_ratio} -c {config_path} -cm {kmeans_path} -sd {slice_db} -wf {wav_format} {apf} {fmp} {ehc}\n","\n","#@markdown\n","\n","#@markdown If you dont want to download from here, uncheck this.\n","download_after_inference = True  #@param {type:\"boolean\"}\n","\n","if download_after_inference:\n","  from google.colab import files\n","  files.download(wav_output)"]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"19fxpo-ZoL_ShEUeZIZi6Di-YioWrEyhR","timestamp":1678516497580},{"file_id":"1rCUOOVG7-XQlVZuWRAj5IpGrMM8t07pE","timestamp":1673086970071},{"file_id":"1Ul5SmzWiSHBj0MaKA0B682C-RZKOycwF","timestamp":1670483515921}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}