SambertHifigan.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "V100",
      "authorship_tag": "ABX9TyMro5JlPoA5ZsVAesl+IEoF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KevinWang676/Bark-Voice-Cloning/blob/main/SambertHifigan.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eWgvkZi3p9mS",
        "outputId": "87ba21df-1139-4684-8a4d-e86620159b6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Personal-TTS-v2'...\n",
            "remote: Enumerating objects: 893, done.\u001b[K\n",
            "remote: Counting objects: 100% (65/65), done.\u001b[K\n",
            "remote: Compressing objects: 100% (62/62), done.\u001b[K\n",
            "remote: Total 893 (delta 33), reused 2 (delta 0), pack-reused 828\u001b[K\n",
            "Receiving objects: 100% (893/893), 17.54 MiB | 18.38 MiB/s, done.\n",
            "Resolving deltas: 100% (76/76), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://huggingface.co/spaces/kevinwang676/Personal-TTS-v2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd Personal-TTS-v2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6N-eisYXqCdB",
        "outputId": "e992bc3c-3cfd-49f1-b942-3b37cadb8768"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Personal-TTS-v2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9IRHnwwvqFWB",
        "outputId": "d683c4a0-7b28-4a82-e0be-a6b201ca3ed1"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.\n",
            "Collecting multiprocess (from datasets<=2.13.0,>=2.8.0->modelscope->-r requirements.txt (line 2))\n",
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            "\u001b[?25hBuilding wheels for collected packages: openai-whisper, oss2, aliyun-python-sdk-core, crcmod\n",
            "  Building wheel for openai-whisper (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for openai-whisper: filename=openai_whisper-20230314-py3-none-any.whl size=796907 sha256=fd55dee3233c129a4681c5b820b257cab5fedf741f3adf50a3f3c09f0ddad5f9\n",
            "  Stored in directory: /root/.cache/pip/wheels/b2/13/5f/fe8245f6dc59df505879da4b2129932e342f02a80e6b87f27d\n",
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            "  Created wheel for oss2: filename=oss2-2.18.1-py3-none-any.whl size=115175 sha256=8b7b334b55645f00b7499778614d5c24fb488f44ddadc2b283091bcb6df6cbed\n",
            "  Stored in directory: /root/.cache/pip/wheels/54/b1/27/f9d5791a1f01ee0ad99c934aa0e1df3eaae375b5d5aa3da184\n",
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            "  Created wheel for aliyun-python-sdk-core: filename=aliyun_python_sdk_core-2.13.36-py3-none-any.whl size=533190 sha256=00b5d8a20c3f6abb7b8f30ddff77404cc6a20574874e93c355b9c9095b8540d8\n",
            "  Stored in directory: /root/.cache/pip/wheels/c0/f4/0e/87c534857132bd3bd2c4465c0b15b4db650cf6c15a876bda34\n",
            "  Building wheel for crcmod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for crcmod: filename=crcmod-1.7-cp310-cp310-linux_x86_64.whl size=31407 sha256=4324dc55d48a2343decd3ddb25e6c75786aaf5561dfd18c39e503b50cf0237ce\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/4c/07/72215c529bd59d67e3dac29711d7aba1b692f543c808ba9e86\n",
            "Successfully built openai-whisper oss2 aliyun-python-sdk-core crcmod\n",
            "Installing collected packages: crcmod, bitarray, addict, xxhash, typeguard, simplejson, pycryptodome, numpy, jmespath, ffmpeg-python, einops, dill, bitstring, yapf, tiktoken, tensorboardX, sox, pyarrow, multiprocess, huggingface-hub, aliyun-python-sdk-core, datasets, aliyun-python-sdk-kms, oss2, modelscope, pytorch_wavelets, openai-whisper\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.23.5\n",
            "    Uninstalling numpy-1.23.5:\n",
            "      Successfully uninstalled numpy-1.23.5\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 9.0.0\n",
            "    Uninstalling pyarrow-9.0.0:\n",
            "      Successfully uninstalled pyarrow-9.0.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pandas-gbq 0.17.9 requires pyarrow<10.0dev,>=3.0.0, but you have pyarrow 13.0.0 which is incompatible.\n",
            "plotnine 0.12.2 requires numpy>=1.23.0, but you have numpy 1.22.4 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed addict-2.4.0 aliyun-python-sdk-core-2.13.36 aliyun-python-sdk-kms-2.16.1 bitarray-2.8.1 bitstring-4.1.1 crcmod-1.7 datasets-2.13.0 dill-0.3.6 einops-0.6.1 ffmpeg-python-0.2.0 huggingface-hub-0.16.4 jmespath-0.10.0 modelscope-1.8.4 multiprocess-0.70.14 numpy-1.22.4 openai-whisper-20230314 oss2-2.18.1 pyarrow-13.0.0 pycryptodome-3.18.0 pytorch_wavelets-1.3.0 simplejson-3.19.1 sox-1.4.1 tensorboardX-2.6.2.2 tiktoken-0.3.1 typeguard-2.3.1 xxhash-3.3.0 yapf-0.40.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXXjEHxbqj4z",
        "outputId": "2219e53a-15ab-4760-e636-a6a24afd959e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=3ddfe44918d2a64dab484200c6108a113e0e89a47693d3e8d6d3fdb66a35ae22\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, semantic-version, python-multipart, orjson, h11, aiofiles, uvicorn, starlette, httpcore, httpx, fastapi, gradio-client, gradio\n",
            "Successfully installed aiofiles-23.2.1 fastapi-0.103.0 ffmpy-0.3.1 gradio-3.41.2 gradio-client-0.5.0 h11-0.14.0 httpcore-0.17.3 httpx-0.24.1 orjson-3.9.5 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.27.0 uvicorn-0.23.2 websockets-11.0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import subprocess\n",
        "import random\n",
        "import os\n",
        "from pathlib import Path\n",
        "import librosa\n",
        "from scipy.io import wavfile\n",
        "import numpy as np\n",
        "import torch\n",
        "import csv\n",
        "import whisper\n",
        "import gradio as gr\n",
        "\n",
        "os.system(\"pip install --upgrade Cython==0.29.35\")\n",
        "os.system(\"pip install pysptk --no-build-isolation\")\n",
        "os.system(\"pip install kantts -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html\")\n",
        "os.system(\"pip install tts-autolabel -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html\")\n",
        "os.system(\"apt-get install sox\")\n",
        "\n",
        "os.system(\"git clone https://github.com/fbcotter/pytorch_wavelets\")\n",
        "os.system(\"cd pytorch_wavelets\")\n",
        "os.system(\"pip install .\")\n",
        "\n",
        "os.system(\"pip install modelscope==1.8.4\")\n",
        "\n",
        "import sox\n",
        "\n",
        "def split_long_audio(model, filepaths, save_dir=\"data_dir\", out_sr=44100):\n",
        "    if isinstance(filepaths, str):\n",
        "        filepaths = [filepaths]\n",
        "\n",
        "    for file_idx, filepath in enumerate(filepaths):\n",
        "\n",
        "        save_path = Path(save_dir)\n",
        "        save_path.mkdir(exist_ok=True, parents=True)\n",
        "\n",
        "        print(f\"Transcribing file {file_idx}: '{filepath}' to segments...\")\n",
        "        result = model.transcribe(filepath, word_timestamps=True, task=\"transcribe\", beam_size=5, best_of=5)\n",
        "        segments = result['segments']\n",
        "\n",
        "        wav, sr = librosa.load(filepath, sr=None, offset=0, duration=None, mono=True)\n",
        "        wav, _ = librosa.effects.trim(wav, top_db=20)\n",
        "        peak = np.abs(wav).max()\n",
        "        if peak > 1.0:\n",
        "            wav = 0.98 * wav / peak\n",
        "        wav2 = librosa.resample(wav, orig_sr=sr, target_sr=out_sr)\n",
        "        wav2 /= max(wav2.max(), -wav2.min())\n",
        "\n",
        "        for i, seg in enumerate(segments):\n",
        "            start_time = seg['start']\n",
        "            end_time = seg['end']\n",
        "            wav_seg = wav2[int(start_time * out_sr):int(end_time * out_sr)]\n",
        "            wav_seg_name = f\"{file_idx}_{i}.wav\"\n",
        "            out_fpath = save_path / wav_seg_name\n",
        "            wavfile.write(out_fpath, rate=out_sr, data=(wav_seg * np.iinfo(np.int16).max).astype(np.int16))\n",
        "\n",
        "whisper_size = \"medium\"\n",
        "whisper_model = whisper.load_model(whisper_size)\n",
        "\n",
        "from modelscope.tools import run_auto_label\n",
        "\n",
        "from modelscope.models.audio.tts import SambertHifigan\n",
        "from modelscope.pipelines import pipeline\n",
        "from modelscope.utils.constant import Tasks\n",
        "\n",
        "from modelscope.metainfo import Trainers\n",
        "from modelscope.trainers import build_trainer\n",
        "from modelscope.utils.audio.audio_utils import TtsTrainType\n",
        "\n",
        "pretrained_model_id = 'damo/speech_personal_sambert-hifigan_nsf_tts_zh-cn_pretrain_16k'\n",
        "\n",
        "dataset_id = \"./output_training_data/\"\n",
        "pretrain_work_dir = \"./pretrain_work_dir/\"\n",
        "\n",
        "\n",
        "def auto_label(audio):\n",
        "    try:\n",
        "        split_long_audio(whisper_model, audio, \"test_wavs\")\n",
        "        os.makedirs(\"output_training_data\", exist_ok=True)\n",
        "        input_wav = \"./test_wavs/\"\n",
        "        output_data = \"./output_training_data/\"\n",
        "        ret, report = run_auto_label(input_wav=input_wav, work_dir=output_data, resource_revision=\"v1.0.7\")\n",
        "\n",
        "    except Exception:\n",
        "        pass\n",
        "    return \"标注成功\"\n",
        "\n",
        "\n",
        "def train(a):\n",
        "    try:\n",
        "        os.makedirs(\"pretrain_work_dir\", exist_ok=True)\n",
        "\n",
        "        train_info = {\n",
        "            TtsTrainType.TRAIN_TYPE_SAMBERT: {  # 配置训练AM（sambert）模型\n",
        "                'train_steps': 52,               # 训练多少个step\n",
        "                'save_interval_steps': 50,       # 每训练多少个step保存一次checkpoint\n",
        "                'log_interval': 10               # 每训练多少个step打印一次训练日志\n",
        "            }\n",
        "        }\n",
        "\n",
        "        # 配置训练参数，指定数据集，临时工作目录和train_info\n",
        "        kwargs = dict(\n",
        "            model=pretrained_model_id,                  # 指定要finetune的模型\n",
        "            model_revision = \"v1.0.6\",\n",
        "            work_dir=pretrain_work_dir,                 # 指定临时工作目录\n",
        "            train_dataset=dataset_id,                   # 指定数据集id\n",
        "            train_type=train_info                       # 指定要训练类型及参数\n",
        "        )\n",
        "\n",
        "        trainer = build_trainer(Trainers.speech_kantts_trainer,\n",
        "                            default_args=kwargs)\n",
        "\n",
        "        trainer.train()\n",
        "\n",
        "    except Exception:\n",
        "        pass\n",
        "\n",
        "    return \"训练完成\"\n",
        "\n",
        "\n",
        "import random\n",
        "\n",
        "def infer(text):\n",
        "\n",
        "  model_dir = os.path.abspath(\"./pretrain_work_dir\")\n",
        "\n",
        "  custom_infer_abs = {\n",
        "      'voice_name':\n",
        "      'F7',\n",
        "      'am_ckpt':\n",
        "      os.path.join(model_dir, 'tmp_am', 'ckpt'),\n",
        "      'am_config':\n",
        "      os.path.join(model_dir, 'tmp_am', 'config.yaml'),\n",
        "      'voc_ckpt':\n",
        "      os.path.join(model_dir, 'orig_model', 'basemodel_16k', 'hifigan', 'ckpt'),\n",
        "      'voc_config':\n",
        "      os.path.join(model_dir, 'orig_model', 'basemodel_16k', 'hifigan',\n",
        "              'config.yaml'),\n",
        "      'audio_config':\n",
        "      os.path.join(model_dir, 'data', 'audio_config.yaml'),\n",
        "      'se_file':\n",
        "      os.path.join(model_dir, 'data', 'se', 'se.npy')\n",
        "  }\n",
        "  kwargs = {'custom_ckpt': custom_infer_abs}\n",
        "\n",
        "  model_id = SambertHifigan(os.path.join(model_dir, \"orig_model\"), **kwargs)\n",
        "\n",
        "  inference = pipeline(task=Tasks.text_to_speech, model=model_id)\n",
        "  output = inference(input=text)\n",
        "\n",
        "  filename = str(random.randint(1, 1000000000000))\n",
        "\n",
        "  with open(filename + \"myfile.wav\", mode='bx') as f:\n",
        "      f.write(output[\"output_wav\"])\n",
        "  return filename + \"myfile.wav\"\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "app = gr.Blocks()\n",
        "\n",
        "with app:\n",
        "    gr.Markdown(\"# <center>🥳🎶🎡 - Sambert中文声音克隆</center>\")\n",
        "    gr.Markdown(\"## <center>🌟 - 训练3分钟，推理5秒钟，中英自然发音 </center>\")\n",
        "    gr.Markdown(\"### <center>🌊 - 更多精彩应用，敬请关注[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕</center>\")\n",
        "\n",
        "    with gr.Row():\n",
        "      inp1 = gr.Audio(type=\"filepath\", label=\"请上传一段音频\")\n",
        "      out1 = gr.Textbox(label=\"标注情况\", lines=1, interactive=False)\n",
        "\n",
        "      out2 = gr.Textbox(label=\"训练情况\", lines=1, interactive=False)\n",
        "      inp2 = gr.Textbox(label=\"文本\", lines=3)\n",
        "      out3 = gr.Audio(type=\"filepath\", label=\"合成的音频\")\n",
        "      btn1 = gr.Button(\"1.标注数据\")\n",
        "      btn2 = gr.Button(\"2.开始训练\")\n",
        "      btn3 = gr.Button(\"3.一键推理\", variant=\"primary\")\n",
        "\n",
        "      btn1.click(auto_label, inp1, out1)\n",
        "      btn2.click(train, out1, out2)\n",
        "      btn3.click(infer, inp2, out3)\n",
        "\n",
        "    gr.Markdown(\"### <center>注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。</center>\")\n",
        "    gr.HTML('''\n",
        "        <div class=\"footer\">\n",
        "                    <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘\n",
        "                    </p>\n",
        "        </div>\n",
        "    ''')\n",
        "app.launch(show_error=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "a7yFvYXXqIVB",
        "outputId": "c6bfa068-61c4-4345-9a16-18473e3ae697"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7861, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H3GOFgHCqheH"
      },
      "execution_count": 11,
      "outputs": []
    }
  ]
}