{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5m_Xf_2NY6mI"
},
"outputs": [],
"source": [
"import torch, torchaudio\n",
"import requests\n",
"import IPython.display as display"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GGiC0rT2hoik"
},
"outputs": [],
"source": [
"!apt-get install -y espeak libsndfile1-dev\n",
"!pip install flask\n",
"!pip install python-socketio\n",
"!pip install eventlet\n",
"!pip install unidecode\n",
"!pip install phonemizer\n",
"!pip install retry\n",
"!pip install flask\n",
"!pip install flask_cors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WO8XzrFMZGoj"
},
"outputs": [],
"source": [
"# hubert = torch.hub.load(\"bshall/hubert:main\", \"hubert_soft\").cuda()\n",
"# acoustic = torch.hub.load(\"bshall/acoustic-model:main\", \"hubert_soft\").cuda()\n",
"# hifigan = torch.hub.load(\"bshall/hifigan:main\", \"hifigan_hubert_soft\").cuda()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "v7xM7CnEZMTL"
},
"outputs": [],
"source": [
"# with open(\"example.wav\", \"wb\") as file:\n",
"# response = requests.get(\"https://drive.google.com/uc?export=preview&id=1Y3KuPAhB5VcsmIaokBVKu3LUEZOfhSu8\")\n",
"# file.write(response.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UZox3YDVZOya"
},
"outputs": [],
"source": [
"# source, sr = torchaudio.load(\"emotion059.wav\")\n",
"# source = torchaudio.functional.resample(source, sr, 16000)\n",
"# source = source.unsqueeze(0).cuda()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dEZ9_zCKnXpZ"
},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DSAA2CMfZY7C"
},
"outputs": [],
"source": [
"# with torch.inference_mode():\n",
"# # Extract speech units\n",
"# units = hubert.units(source)\n",
"# # Generate target spectrogram\n",
"# mel = acoustic.generate(units).transpose(1, 2)\n",
"# # Generate audio waveform\n",
"# target = hifigan(mel)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VCwjKdIUZZoi"
},
"outputs": [],
"source": [
"# display.Audio(target.squeeze().cpu(), rate=16000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vjJs35ifZbSK"
},
"outputs": [],
"source": [
"# data = target.squeeze().cpu()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0SuFlButeKXG"
},
"outputs": [],
"source": [
"# display.Audio(data, rate=16000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vFwF5Uh0eMLV"
},
"outputs": [],
"source": [
"# dest = torchaudio.functional.resample(target, 16000,24000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eIWedhF6ebuV"
},
"outputs": [],
"source": [
"# display.Audio(dest.squeeze().cpu(), rate=24000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XkCO-j9teccu"
},
"outputs": [],
"source": [
"# dest"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lzo_ZWmAjaby",
"outputId": "ed0af7dc-1614-4d28-e9b4-b3a600cafd88"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"fatal: destination path 'voice-changer' already exists and is not an empty directory.\n",
"\u001b[0m\u001b[01;34massets\u001b[0m/ \u001b[01;32mfavicon.ico\u001b[0m* \u001b[01;32mindex.js\u001b[0m*\n",
"\u001b[01;32mcoffee.png\u001b[0m* \u001b[01;32mindex.html\u001b[0m* \u001b[01;32mindex.js.LICENSE.txt\u001b[0m*\n"
]
}
],
"source": [
"# (3) リポジトリのクローン\n",
"!git clone https://github.com/w-okada/voice-changer.git\n",
"%ls voice-changer/frontend/dist\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"id": "8-z9j4e_j-Wb"
},
"outputs": [],
"source": [
"# (4-1) 設定ファイルの配置\n",
"!cp voice-changer/template/setting_colab.json voice-changer/frontend/dist/assets/setting.json\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"id": "-iPiSzvAepCl"
},
"outputs": [],
"source": [
"# (6-1) サーバの起動\n",
"PORT=8087\n",
"get_ipython().system_raw(f'python3 serverFlask.py {PORT} >foo 2>&1 &')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IiWSwDjQidc7",
"outputId": "2bb83f5f-965c-4b54-ac6c-7fd407daa5dc"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Using cache found in /root/.cache/torch/hub/bshall_hubert_main\n",
"Using cache found in /root/.cache/torch/hub/bshall_acoustic-model_main\n",
"Using cache found in /root/.cache/torch/hub/bshall_hifigan_main\n",
"[2022-09-14 06:41:12,893] INFO in serverFlask: INITIALIZE MODEL\n",
"[2022-09-14 06:41:12,893] INFO in serverFlask: START APP\n",
"Removing weight norm...\n",
" * Serving Flask app \"serverFlask\" (lazy loading)\n",
" * Environment: production\n",
" WARNING: This is a development server. Do not use it in a production deployment.\n",
" Use a production WSGI server instead.\n",
" * Debug mode: on\n",
"[2022-09-14 06:41:12,902] INFO in _internal: * Running on http://0.0.0.0:8087/ (Press CTRL+C to quit)\n",
"[2022-09-14 06:41:12,903] INFO in _internal: * Restarting with stat\n"
]
}
],
"source": [
"# (6-2) サーバの起動確認 (Ctrl+Retで実行)\n",
"!cat foo"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 205
},
"id": "WWn3HJlpin4R",
"outputId": "ab69bc12-969b-46b8-8705-6ef84c4ab34f"
},
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mserve_kernel_port_as_window\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPORT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'PORT' is not defined"
]
}
],
"source": [
"# (7) プロキシを起動\n",
"from google.colab import output\n",
"\n",
"output.serve_kernel_port_as_window(PORT)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "k9OqZ-hLjKIx",
"outputId": "28f3b99d-29cc-4581-c6f8-9ff0f55359a1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[0m\u001b[01;34massets\u001b[0m/ \u001b[01;32mfavicon.ico\u001b[0m* \u001b[01;32mindex.js\u001b[0m*\n",
"\u001b[01;32mcoffee.png\u001b[0m* \u001b[01;32mindex.html\u001b[0m* \u001b[01;32mindex.js.LICENSE.txt\u001b[0m*\n"
]
}
],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"provenance": [],
"authorship_tag": "ABX9TyPxoYe+Y2QsMoX8N7iTlceN",
"include_colab_link": true
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}