Bark_Voice_Cloning.ipynb

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  "cells": [
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KevinWang676/Bark-Voice-Cloning/blob/main/Bark_Voice_Cloning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
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      "execution_count": 1,
      "metadata": {
        "colab": {
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        },
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        "outputId": "4f97bc78-e9d8-4c73-fe95-c8aeabef73af"
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          "name": "stdout",
          "text": [
            "Cloning into 'Bark-Voice-Cloning'...\n",
            "remote: Enumerating objects: 178, done.\u001b[K\n",
            "remote: Counting objects: 100% (105/105), done.\u001b[K\n",
            "remote: Compressing objects: 100% (105/105), done.\u001b[K\n",
            "remote: Total 178 (delta 59), reused 0 (delta 0), pack-reused 73\u001b[K\n",
            "Receiving objects: 100% (178/178), 238.41 KiB | 1.31 MiB/s, done.\n",
            "Resolving deltas: 100% (67/67), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/KevinWang676/Bark-Voice-Cloning.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd Bark-Voice-Cloning"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "9Ul7ndP7qc0V",
        "outputId": "b474938b-c567-49ab-a641-a3a45d04e784"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Bark-Voice-Cloning\n"
          ]
        }
      ]
    },
    {
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        "pip install -r requirements.txt"
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        "outputId": "a7c3efc4-9ce5-4417-bc6e-fbb8fdac0c7e"
      },
      "execution_count": 4,
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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            "  Downloading pytorch_seed-0.2.0-py3-none-any.whl (4.2 kB)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 9)) (6.0)\n",
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            "Installing collected packages: tokenizers, sentencepiece, safetensors, pydub, linkify, funcy, ffmpy, bitarray, antlr4-python3-runtime, websockets, uc-micro-py, sox, semantic-version, python-multipart, portalocker, orjson, omegaconf, mutagen, multidict, h11, frozenlist, einops, colorama, beartype, async-timeout, aiofiles, yarl, uvicorn, starlette, scikit-learn, sacrebleu, mdit-py-plugins, linkify-it-py, hydra-core, huggingface-hub, httpcore, aiosignal, transformers, httpx, fastapi, aiohttp, gradio-client, gradio, vector-quantize-pytorch, local-attention, lion-pytorch, fairseq, encodec, ema-pytorch, accelerate, pytorch_seed, audiolm-pytorch\n",
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            "\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",
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            "\u001b[0mSuccessfully installed accelerate-0.20.3 aiofiles-23.1.0 aiohttp-3.8.4 aiosignal-1.3.1 antlr4-python3-runtime-4.8 async-timeout-4.0.2 audiolm-pytorch-1.2.0 beartype-0.14.1 bitarray-2.7.5 colorama-0.4.6 einops-0.6.1 ema-pytorch-0.2.3 encodec-0.1.1 fairseq-0.12.2 fastapi-0.97.0 ffmpy-0.3.0 frozenlist-1.3.3 funcy-2.0 gradio-3.35.0 gradio-client-0.2.7 h11-0.14.0 httpcore-0.17.2 httpx-0.24.1 huggingface-hub-0.15.1 hydra-core-1.0.7 linkify-1.4 linkify-it-py-2.0.2 lion-pytorch-0.1.2 local-attention-1.8.6 mdit-py-plugins-0.3.3 multidict-6.0.4 mutagen-1.46.0 omegaconf-2.0.6 orjson-3.9.1 portalocker-2.7.0 pydub-0.25.1 python-multipart-0.0.6 pytorch_seed-0.2.0 sacrebleu-2.3.1 safetensors-0.3.1 scikit-learn-0.24.0 semantic-version-2.10.0 sentencepiece-0.1.99 sox-1.4.1 starlette-0.27.0 tokenizers-0.13.3 transformers-4.30.2 uc-micro-py-1.0.2 uvicorn-0.22.0 vector-quantize-pytorch-1.6.18 websockets-11.0.3 yarl-1.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from cProfile import label\n",
        "import dataclasses\n",
        "from distutils.command.check import check\n",
        "from doctest import Example\n",
        "import gradio as gr\n",
        "import os\n",
        "import sys\n",
        "import numpy as np\n",
        "import logging\n",
        "import torch\n",
        "import pytorch_seed\n",
        "import time\n",
        "\n",
        "from xml.sax import saxutils\n",
        "from bark.api import generate_with_settings\n",
        "from bark.api import save_as_prompt\n",
        "from util.settings import Settings\n",
        "#import nltk\n",
        "\n",
        "from bark import SAMPLE_RATE\n",
        "from cloning.clonevoice import clone_voice\n",
        "from bark.generation import SAMPLE_RATE, preload_models, _load_history_prompt, codec_decode\n",
        "from scipy.io.wavfile import write as write_wav\n",
        "from util.parseinput import split_and_recombine_text, build_ssml, is_ssml, create_clips_from_ssml\n",
        "from datetime import datetime\n",
        "from tqdm.auto import tqdm\n",
        "from util.helper import create_filename, add_id3_tag\n",
        "from swap_voice import swap_voice_from_audio\n",
        "from training.training_prepare import prepare_semantics_from_text, prepare_wavs_from_semantics\n",
        "from training.train import training_prepare_files, train\n",
        "\n",
        "settings = Settings('config.yaml')\n",
        "\n",
        "def generate_text_to_speech(text, selected_speaker, text_temp, waveform_temp, eos_prob, quick_generation, complete_settings, seed, batchcount, progress=gr.Progress(track_tqdm=True)):\n",
        "    # Chunk the text into smaller pieces then combine the generated audio\n",
        "\n",
        "    # generation settings\n",
        "    if selected_speaker == 'None':\n",
        "        selected_speaker = None\n",
        "\n",
        "    voice_name = selected_speaker\n",
        "\n",
        "    if text == None or len(text) < 1:\n",
        "       if selected_speaker == None:\n",
        "            raise gr.Error('No text entered!')\n",
        "\n",
        "       # Extract audio data from speaker if no text and speaker selected\n",
        "       voicedata = _load_history_prompt(voice_name)\n",
        "       audio_arr = codec_decode(voicedata[\"fine_prompt\"])\n",
        "       result = create_filename(settings.output_folder_path, \"None\", \"extract\",\".wav\")\n",
        "       save_wav(audio_arr, result)\n",
        "       return result\n",
        "\n",
        "    if batchcount < 1:\n",
        "        batchcount = 1\n",
        "\n",
        "\n",
        "    silenceshort = np.zeros(int((float(settings.silence_sentence) / 1000.0) * SAMPLE_RATE), dtype=np.int16)  # quarter second of silence\n",
        "    silencelong = np.zeros(int((float(settings.silence_speakers) / 1000.0) * SAMPLE_RATE), dtype=np.float32)  # half a second of silence\n",
        "    use_last_generation_as_history = \"Use last generation as history\" in complete_settings\n",
        "    save_last_generation = \"Save generation as Voice\" in complete_settings\n",
        "    for l in range(batchcount):\n",
        "        currentseed = seed\n",
        "        if seed != None and seed > 2**32 - 1:\n",
        "            logger.warning(f\"Seed {seed} > 2**32 - 1 (max), setting to random\")\n",
        "            currentseed = None\n",
        "        if currentseed == None or currentseed <= 0:\n",
        "            currentseed = np.random.default_rng().integers(1, 2**32 - 1)\n",
        "        assert(0 < currentseed and currentseed < 2**32)\n",
        "\n",
        "        progress(0, desc=\"Generating\")\n",
        "\n",
        "        full_generation = None\n",
        "\n",
        "        all_parts = []\n",
        "        complete_text = \"\"\n",
        "        text = text.lstrip()\n",
        "        if is_ssml(text):\n",
        "            list_speak = create_clips_from_ssml(text)\n",
        "            prev_speaker = None\n",
        "            for i, clip in tqdm(enumerate(list_speak), total=len(list_speak)):\n",
        "                selected_speaker = clip[0]\n",
        "                # Add pause break between speakers\n",
        "                if i > 0 and selected_speaker != prev_speaker:\n",
        "                    all_parts += [silencelong.copy()]\n",
        "                prev_speaker = selected_speaker\n",
        "                text = clip[1]\n",
        "                text = saxutils.unescape(text)\n",
        "                if selected_speaker == \"None\":\n",
        "                    selected_speaker = None\n",
        "\n",
        "                print(f\"\\nGenerating Text ({i+1}/{len(list_speak)}) -> {selected_speaker} (Seed {currentseed}):`{text}`\")\n",
        "                complete_text += text\n",
        "                with pytorch_seed.SavedRNG(currentseed):\n",
        "                    audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)\n",
        "                    currentseed = torch.random.initial_seed()\n",
        "                if len(list_speak) > 1:\n",
        "                    filename = create_filename(settings.output_folder_path, currentseed, \"audioclip\",\".wav\")\n",
        "                    save_wav(audio_array, filename)\n",
        "                    add_id3_tag(filename, text, selected_speaker, currentseed)\n",
        "\n",
        "                all_parts += [audio_array]\n",
        "        else:\n",
        "            texts = split_and_recombine_text(text, settings.input_text_desired_length, settings.input_text_max_length)\n",
        "            for i, text in tqdm(enumerate(texts), total=len(texts)):\n",
        "                print(f\"\\nGenerating Text ({i+1}/{len(texts)}) -> {selected_speaker} (Seed {currentseed}):`{text}`\")\n",
        "                complete_text += text\n",
        "                if quick_generation == True:\n",
        "                    with pytorch_seed.SavedRNG(currentseed):\n",
        "                        audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)\n",
        "                        currentseed = torch.random.initial_seed()\n",
        "                else:\n",
        "                    full_output = use_last_generation_as_history or save_last_generation\n",
        "                    if full_output:\n",
        "                        full_generation, audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob, output_full=True)\n",
        "                    else:\n",
        "                        audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)\n",
        "\n",
        "                # Noticed this in the HF Demo - convert to 16bit int -32767/32767 - most used audio format\n",
        "                # audio_array = (audio_array * 32767).astype(np.int16)\n",
        "\n",
        "                if len(texts) > 1:\n",
        "                    filename = create_filename(settings.output_folder_path, currentseed, \"audioclip\",\".wav\")\n",
        "                    save_wav(audio_array, filename)\n",
        "                    add_id3_tag(filename, text, selected_speaker, currentseed)\n",
        "\n",
        "                if quick_generation == False and (save_last_generation == True or use_last_generation_as_history == True):\n",
        "                    # save to npz\n",
        "                    voice_name = create_filename(settings.output_folder_path, seed, \"audioclip\", \".npz\")\n",
        "                    save_as_prompt(voice_name, full_generation)\n",
        "                    if use_last_generation_as_history:\n",
        "                        selected_speaker = voice_name\n",
        "\n",
        "                all_parts += [audio_array]\n",
        "                # Add short pause between sentences\n",
        "                if text[-1] in \"!?.\\n\" and i > 1:\n",
        "                    all_parts += [silenceshort.copy()]\n",
        "\n",
        "        # save & play audio\n",
        "        result = create_filename(settings.output_folder_path, currentseed, \"final\",\".wav\")\n",
        "        save_wav(np.concatenate(all_parts), result)\n",
        "        # write id3 tag with text truncated to 60 chars, as a precaution...\n",
        "        add_id3_tag(result, complete_text, selected_speaker, currentseed)\n",
        "\n",
        "    return result\n",
        "\n",
        "\n",
        "\n",
        "def save_wav(audio_array, filename):\n",
        "    write_wav(filename, SAMPLE_RATE, audio_array)\n",
        "\n",
        "def save_voice(filename, semantic_prompt, coarse_prompt, fine_prompt):\n",
        "    np.savez_compressed(\n",
        "        filename,\n",
        "        semantic_prompt=semantic_prompt,\n",
        "        coarse_prompt=coarse_prompt,\n",
        "        fine_prompt=fine_prompt\n",
        "    )\n",
        "\n",
        "\n",
        "def on_quick_gen_changed(checkbox):\n",
        "    if checkbox == False:\n",
        "        return gr.CheckboxGroup.update(visible=True)\n",
        "    return gr.CheckboxGroup.update(visible=False)\n",
        "\n",
        "def delete_output_files(checkbox_state):\n",
        "    if checkbox_state:\n",
        "        outputs_folder = os.path.join(os.getcwd(), settings.output_folder_path)\n",
        "        if os.path.exists(outputs_folder):\n",
        "            purgedir(outputs_folder)\n",
        "    return False\n",
        "\n",
        "\n",
        "# https://stackoverflow.com/a/54494779\n",
        "def purgedir(parent):\n",
        "    for root, dirs, files in os.walk(parent):\n",
        "        for item in files:\n",
        "            # Delete subordinate files\n",
        "            filespec = os.path.join(root, item)\n",
        "            os.unlink(filespec)\n",
        "        for item in dirs:\n",
        "            # Recursively perform this operation for subordinate directories\n",
        "            purgedir(os.path.join(root, item))\n",
        "\n",
        "def convert_text_to_ssml(text, selected_speaker):\n",
        "    return build_ssml(text, selected_speaker)\n",
        "\n",
        "\n",
        "def training_prepare(selected_step, num_text_generations, progress=gr.Progress(track_tqdm=True)):\n",
        "    if selected_step == prepare_training_list[0]:\n",
        "        prepare_semantics_from_text()\n",
        "    else:\n",
        "        prepare_wavs_from_semantics()\n",
        "    return None\n",
        "\n",
        "\n",
        "def start_training(save_model_epoch, max_epochs, progress=gr.Progress(track_tqdm=True)):\n",
        "    training_prepare_files(\"./training/data/\", \"./training/data/checkpoint/hubert_base_ls960.pt\")\n",
        "    train(\"./training/data/\", save_model_epoch, max_epochs)\n",
        "    return None\n",
        "\n",
        "\n",
        "\n",
        "def apply_settings(themes, input_server_name, input_server_port, input_server_public, input_desired_len, input_max_len, input_silence_break, input_silence_speaker):\n",
        "    settings.selected_theme = themes\n",
        "    settings.server_name = input_server_name\n",
        "    settings.server_port = input_server_port\n",
        "    settings.server_share = input_server_public\n",
        "    settings.input_text_desired_length = input_desired_len\n",
        "    settings.input_text_max_length = input_max_len\n",
        "    settings.silence_sentence = input_silence_break\n",
        "    settings.silence_speaker = input_silence_speaker\n",
        "    settings.save()\n",
        "\n",
        "def restart():\n",
        "    global restart_server\n",
        "    restart_server = True\n",
        "\n",
        "\n",
        "def create_version_html():\n",
        "    python_version = \".\".join([str(x) for x in sys.version_info[0:3]])\n",
        "    versions_html = f\"\"\"\n",
        "python: <span title=\"{sys.version}\">{python_version}</span>\n",
        " •\n",
        "torch: {getattr(torch, '__long_version__',torch.__version__)}\n",
        " •\n",
        "gradio: {gr.__version__}\n",
        "\"\"\"\n",
        "    return versions_html\n",
        "\n",
        "\n",
        "\n",
        "logger = logging.getLogger(__name__)\n",
        "APPTITLE = \"Bark Voice Cloning UI\"\n",
        "\n",
        "\n",
        "autolaunch = False\n",
        "\n",
        "if len(sys.argv) > 1:\n",
        "    autolaunch = \"-autolaunch\" in sys.argv\n",
        "\n",
        "if torch.cuda.is_available() == False:\n",
        "    os.environ['BARK_FORCE_CPU'] = 'True'\n",
        "    logger.warning(\"No CUDA detected, fallback to CPU!\")\n",
        "\n",
        "print(f'smallmodels={os.environ.get(\"SUNO_USE_SMALL_MODELS\", False)}')\n",
        "print(f'enablemps={os.environ.get(\"SUNO_ENABLE_MPS\", False)}')\n",
        "print(f'offloadcpu={os.environ.get(\"SUNO_OFFLOAD_CPU\", False)}')\n",
        "print(f'forcecpu={os.environ.get(\"BARK_FORCE_CPU\", False)}')\n",
        "print(f'autolaunch={autolaunch}\\n\\n')\n",
        "\n",
        "#print(\"Updating nltk\\n\")\n",
        "#nltk.download('punkt')\n",
        "\n",
        "print(\"Preloading Models\\n\")\n",
        "preload_models()\n",
        "\n",
        "available_themes = [\"Default\", \"gradio/glass\", \"gradio/monochrome\", \"gradio/seafoam\", \"gradio/soft\", \"gstaff/xkcd\", \"freddyaboulton/dracula_revamped\", \"ysharma/steampunk\"]\n",
        "tokenizer_language_list = [\"de\",\"en\", \"pl\"]\n",
        "prepare_training_list = [\"Step 1: Semantics from Text\",\"Step 2: WAV from Semantics\"]\n",
        "\n",
        "seed = -1\n",
        "server_name = settings.server_name\n",
        "if len(server_name) < 1:\n",
        "    server_name = None\n",
        "server_port = settings.server_port\n",
        "if server_port <= 0:\n",
        "    server_port = None\n",
        "global run_server\n",
        "global restart_server\n",
        "\n",
        "run_server = True\n",
        "\n",
        "while run_server:\n",
        "    # Collect all existing speakers/voices in dir\n",
        "    speakers_list = []\n",
        "\n",
        "    for root, dirs, files in os.walk(\"./bark/assets/prompts\"):\n",
        "        for file in files:\n",
        "            if file.endswith(\".npz\"):\n",
        "                pathpart = root.replace(\"./bark/assets/prompts\", \"\")\n",
        "                name = os.path.join(pathpart, file[:-4])\n",
        "                if name.startswith(\"/\") or name.startswith(\"\\\\\"):\n",
        "                     name = name[1:]\n",
        "                speakers_list.append(name)\n",
        "\n",
        "    speakers_list = sorted(speakers_list, key=lambda x: x.lower())\n",
        "    speakers_list.insert(0, 'None')\n",
        "\n",
        "    print(f'Launching {APPTITLE} Server')\n",
        "\n",
        "    # Create Gradio Blocks\n",
        "\n",
        "    with gr.Blocks(title=f\"{APPTITLE}\", mode=f\"{APPTITLE}\", theme=settings.selected_theme) as barkgui:\n",
        "        gr.Markdown(\"# <center>🐶🎶⭐ - Bark Voice Cloning</center>\")\n",
        "        gr.Markdown(\"## <center>🤗 - If you like this space, please star my [github repo](https://github.com/KevinWang676/Bark-Voice-Cloning)</center>\")\n",
        "        gr.Markdown(\"### <center>🎡 - Based on [bark-gui](https://github.com/C0untFloyd/bark-gui)</center>\")\n",
        "        gr.Markdown(f\"\"\" You can duplicate and use it with a GPU: <a href=\"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}?duplicate=true\"><img style=\"display: inline; margin-top: 0em; margin-bottom: 0em\" src=\"https://bit.ly/3gLdBN6\" alt=\"Duplicate Space\" /></a>\n",
        "                         or open in [Colab](https://colab.research.google.com/github/KevinWang676/Bark-Voice-Cloning/blob/main/Bark_Voice_Cloning_UI.ipynb) for quick start 🌟\n",
        "                    \"\"\")\n",
        "\n",
        "        with gr.Tab(\"🎙️ - Clone Voice\"):\n",
        "            with gr.Row():\n",
        "                input_audio_filename = gr.Audio(label=\"Input audio.wav\", source=\"upload\", type=\"filepath\")\n",
        "            #transcription_text = gr.Textbox(label=\"Transcription Text\", lines=1, placeholder=\"Enter Text of your Audio Sample here...\")\n",
        "            with gr.Row():\n",
        "                with gr.Column():\n",
        "                    initialname = \"/content/Bark-Voice-Cloning/bark/assets/prompts/file\"\n",
        "                    output_voice = gr.Textbox(label=\"Filename of trained Voice (do not change the initial name)\", lines=1, placeholder=initialname, value=initialname, visible=False)\n",
        "                with gr.Column():\n",
        "                    tokenizerlang = gr.Dropdown(tokenizer_language_list, label=\"Base Language Tokenizer\", value=tokenizer_language_list[1], visible=False)\n",
        "            with gr.Row():\n",
        "                clone_voice_button = gr.Button(\"Create Voice\", variant=\"primary\")\n",
        "            with gr.Row():\n",
        "                dummy = gr.Text(label=\"Progress\")\n",
        "                npz_file = gr.File(label=\".npz file\")\n",
        "            speakers_list.insert(0, npz_file) # add prompt\n",
        "\n",
        "        with gr.Tab(\"🎵 - TTS\"):\n",
        "            with gr.Row():\n",
        "                with gr.Column():\n",
        "                    placeholder = \"Enter text here.\"\n",
        "                    input_text = gr.Textbox(label=\"Input Text\", lines=4, placeholder=placeholder)\n",
        "                    convert_to_ssml_button = gr.Button(\"Convert Input Text to SSML\")\n",
        "                with gr.Column():\n",
        "                        seedcomponent = gr.Number(label=\"Seed (default -1 = Random)\", precision=0, value=-1)\n",
        "                        batchcount = gr.Number(label=\"Batch count\", precision=0, value=1)\n",
        "\n",
        "            with gr.Row():\n",
        "                with gr.Column():\n",
        "                    gr.Markdown(\"[Voice Prompt Library](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c)\")\n",
        "                    speaker = gr.Dropdown(speakers_list, value=speakers_list[0], label=\"Voice (Choose “file” if you wanna use the custom voice)\")\n",
        "\n",
        "                with gr.Column():\n",
        "                    text_temp = gr.Slider(0.1, 1.0, value=0.6, label=\"Generation Temperature\", info=\"1.0 more diverse, 0.1 more conservative\")\n",
        "                    waveform_temp = gr.Slider(0.1, 1.0, value=0.7, label=\"Waveform temperature\", info=\"1.0 more diverse, 0.1 more conservative\")\n",
        "\n",
        "            with gr.Row():\n",
        "                with gr.Column():\n",
        "                    quick_gen_checkbox = gr.Checkbox(label=\"Quick Generation\", value=True)\n",
        "                    settings_checkboxes = [\"Use last generation as history\", \"Save generation as Voice\"]\n",
        "                    complete_settings = gr.CheckboxGroup(choices=settings_checkboxes, value=settings_checkboxes, label=\"Detailed Generation Settings\", type=\"value\", interactive=True, visible=False)\n",
        "                with gr.Column():\n",
        "                    eos_prob = gr.Slider(0.0, 0.5, value=0.05, label=\"End of sentence probability\")\n",
        "\n",
        "            with gr.Row():\n",
        "                with gr.Column():\n",
        "                    tts_create_button = gr.Button(\"Generate\", variant=\"primary\")\n",
        "                with gr.Column():\n",
        "                    hidden_checkbox = gr.Checkbox(visible=False)\n",
        "                    button_stop_generation = gr.Button(\"Stop generation\")\n",
        "            with gr.Row():\n",
        "                output_audio = gr.Audio(label=\"Generated Audio\", type=\"filepath\")\n",
        "\n",
        "        with gr.Tab(\"🔮 - Voice Conversion\"):\n",
        "            with gr.Row():\n",
        "                 swap_audio_filename = gr.Audio(label=\"Input audio.wav to swap voice\", source=\"upload\", type=\"filepath\")\n",
        "            with gr.Row():\n",
        "                 with gr.Column():\n",
        "                     swap_tokenizer_lang = gr.Dropdown(tokenizer_language_list, label=\"Base Language Tokenizer\", value=tokenizer_language_list[1])\n",
        "                     swap_seed = gr.Number(label=\"Seed (default -1 = Random)\", precision=0, value=-1)\n",
        "                 with gr.Column():\n",
        "                     speaker_swap = gr.Dropdown(speakers_list, value=speakers_list[0], label=\"Voice (Choose “file” if you wanna use the custom voice)\")\n",
        "                     swap_batchcount = gr.Number(label=\"Batch count\", precision=0, value=1)\n",
        "            with gr.Row():\n",
        "                swap_voice_button = gr.Button(\"Generate\", variant=\"primary\")\n",
        "            with gr.Row():\n",
        "                output_swap = gr.Audio(label=\"Generated Audio\", type=\"filepath\")\n",
        "\n",
        "\n",
        "        quick_gen_checkbox.change(fn=on_quick_gen_changed, inputs=quick_gen_checkbox, outputs=complete_settings)\n",
        "        convert_to_ssml_button.click(convert_text_to_ssml, inputs=[input_text, speaker],outputs=input_text)\n",
        "        gen_click = tts_create_button.click(generate_text_to_speech, inputs=[input_text, speaker, text_temp, waveform_temp, eos_prob, quick_gen_checkbox, complete_settings, seedcomponent, batchcount],outputs=output_audio)\n",
        "        button_stop_generation.click(fn=None, inputs=None, outputs=None, cancels=[gen_click])\n",
        "\n",
        "\n",
        "\n",
        "        swap_voice_button.click(swap_voice_from_audio, inputs=[swap_audio_filename, speaker_swap, swap_tokenizer_lang, swap_seed, swap_batchcount], outputs=output_swap)\n",
        "        clone_voice_button.click(clone_voice, inputs=[input_audio_filename, output_voice], outputs=[dummy, npz_file])\n",
        "\n",
        "\n",
        "        restart_server = False\n",
        "        try:\n",
        "            barkgui.queue().launch(show_error=True)\n",
        "        except:\n",
        "            restart_server = True\n",
        "            run_server = False\n",
        "        try:\n",
        "            while restart_server == False:\n",
        "                time.sleep(1.0)\n",
        "        except (KeyboardInterrupt, OSError):\n",
        "            print(\"Keyboard interruption in main thread... closing server.\")\n",
        "            run_server = False\n",
        "        barkgui.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "C6a5-_daqmrw",
        "outputId": "d5cb11d5-05b3-487a-8c6a-9219eb7729b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "smallmodels=False\n",
            "enablemps=False\n",
            "offloadcpu=False\n",
            "forcecpu=False\n",
            "autolaunch=False\n",
            "\n",
            "\n",
            "Preloading Models\n",
            "\n",
            "Downloading text suno/bark remote model file https://huggingface.co/suno/bark/resolve/main/text_2.pt text_2.pt to ./models\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading text_2.pt:   0%|          | 0.00/5.35G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2b842320f41454b80194c86488a97b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading text model from ./models/text_2.pt to cuda\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/996k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90207977a1bb4edfb6019410f218510b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6410b7f03944856814a58a621df24f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/625 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1218cfbc9ce74ee8a5a0dce0a5cd22be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading coarse suno/bark remote model file https://huggingface.co/suno/bark/resolve/main/coarse_2.pt coarse_2.pt to ./models\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading coarse_2.pt:   0%|          | 0.00/3.93G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "efe25a38898841ab8a2a6c10437c84de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading coarse model from ./models/coarse_2.pt to cuda\n",
            "Downloading fine suno/bark remote model file https://huggingface.co/suno/bark/resolve/main/fine_2.pt fine_2.pt to ./models\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading fine_2.pt:   0%|          | 0.00/3.74G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a21a370a7fe427f873d807359946c3a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading fine model from ./models/fine_2.pt to cuda\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th\" to /root/.cache/torch/hub/checkpoints/encodec_24khz-d7cc33bc.th\n",
            "100%|██████████| 88.9M/88.9M [00:00<00:00, 142MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Launching Bark Voice Cloning UI Server\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)_schema%401.0.0.json:   0%|          | 0.00/13.0k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e4128bc3248d4b07b0b357790afe9a32"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://badaf720a19ad82c4e.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://badaf720a19ad82c4e.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading HuBERT base model from https://dl.fbaipublicfiles.com/hubert/hubert_base_ls960.pt\n",
            "Downloaded HuBERT\n",
            "en_tokenizer.pth not found. Downloading HuBERT custom tokenizer\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)rt_base_ls960_14.pth:   0%|          | 0.00/104M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0b88c648cbef43d5aef20053ba9ac9e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloaded tokenizer\n",
            "Loading Hubert ./models/hubert/hubert.pt\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cc838ab04e70485f931d95a293cb0f35"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Generating Text (1/1) -> file (Seed 1966431797):`We've always tried to be at the intersection of technology and liberal arts, to be able to get the best of both`\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KMtSvf8fq_Ww"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}