forked from mrq/tortoise-tts
Colab notebook (part 1)
This commit is contained in:
parent
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27
main.py
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27
main.py
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import webui as mrq
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if __name__ == "__main__":
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mrq.args = mrq.setup_args()
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if mrq.args.listen_path is not None and mrq.args.listen_path != "/":
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import uvicorn
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uvicorn.run("main:app", host=mrq.args.listen_host, port=mrq.args.listen_port if not None else 8000)
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else:
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mrq.webui = mrq.setup_gradio()
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mrq.webui.launch(share=mrq.args.share, prevent_thread_lock=True, server_name=mrq.args.listen_host, server_port=mrq.args.listen_port)
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mrq.tts = mrq.setup_tortoise()
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mrq.webui.block_thread()
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elif __name__ == "main":
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from fastapi import FastAPI
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import gradio as gr
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import sys
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sys.argv = [sys.argv[0]]
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app = FastAPI()
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mrq.args = mrq.setup_args()
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mrq.webui = mrq.setup_gradio()
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app = gr.mount_gradio_app(app, mrq.webui, path=mrq.args.listen_path)
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mrq.tts = mrq.setup_tortoise()
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call .\tortoise-venv\Scripts\activate.bat
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call .\tortoise-venv\Scripts\activate.bat
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python app.py
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python main.py
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deactivate
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deactivate
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pause
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pause
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2
start.sh
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start.sh
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source ./tortoise-venv/bin/activate
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source ./tortoise-venv/bin/activate
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python ./app.py
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python3 ./main.py
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deactivate
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deactivate
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186
tortoise_tts.ipynb
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tortoise_tts.ipynb
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{
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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"private_outputs":true,"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"markdown","source":["## Initialization"],"metadata":{"id":"ni41hmE03DL6"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"FtsMKKfH18iM"},"outputs":[],"source":["!git clone https://git.ecker.tech/mrq/tortoise-tts/\n","%cd tortoise-tts\n","!python -m pip install --upgrade pip\n","!pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116\n","!python -m pip install -r ./requirements.txt\n","!pip install Pillow==9.0.0 # errors out only when importing\n","!python setup.py install"]},{"cell_type":"markdown","source":["## Running"],"metadata":{"id":"o1gkfw3B3JSk"}},{"cell_type":"code","source":["import webui as mrq\n","\n","mrq.args = mrq.setup_args()\n","mrq.webui = mrq.setup_gradio()\n","mrq.webui.launch(share=True, prevent_thread_lock=True)\n","mrq.tts = mrq.setup_tortoise()\n","mrq.webui.block_thread()"],"metadata":{"id":"c_EQZLTA19c7"},"execution_count":null,"outputs":[]}]}
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "tortoise-tts.ipynb",
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"provenance": [],
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"collapsed_sections": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"Welcome to Tortoise! 🐢🐢🐢🐢\n",
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"\n",
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"Before you begin, I **strongly** recommend you turn on a GPU runtime.\n",
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"\n",
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"There's a reason this is called \"Tortoise\" - this model takes up to a minute to perform inference for a single sentence on a GPU. Expect waits on the order of hours on a CPU."
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],
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"metadata": {
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"id": "_pIZ3ZXNp7cf"
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "JrK20I32grP6"
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},
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"outputs": [],
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"source": [
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"!git clone https://github.com/neonbjb/tortoise-tts.git\n",
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"%cd tortoise-tts\n",
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"!pip3 install -r requirements.txt\n",
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"!python3 setup.py install"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"# Imports used through the rest of the notebook.\n",
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"import torch\n",
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"import torchaudio\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"\n",
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"import IPython\n",
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"\n",
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"from tortoise.api import TextToSpeech\n",
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"from tortoise.utils.audio import load_audio, load_voice, load_voices\n",
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"\n",
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"# This will download all the models used by Tortoise from the HF hub.\n",
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"tts = TextToSpeech()"
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],
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"metadata": {
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"id": "Gen09NM4hONQ"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# This is the text that will be spoken.\n",
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"text = \"Joining two modalities results in a surprising increase in generalization! What would happen if we combined them all?\"\n",
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"\n",
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"# Here's something for the poetically inclined.. (set text=)\n",
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"\"\"\"\n",
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"Then took the other, as just as fair,\n",
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"And having perhaps the better claim,\n",
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"Because it was grassy and wanted wear;\n",
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"Though as for that the passing there\n",
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"Had worn them really about the same,\"\"\"\n",
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"\n",
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"# Pick a \"preset mode\" to determine quality. Options: {\"ultra_fast\", \"fast\" (default), \"standard\", \"high_quality\"}. See docs in api.py\n",
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"preset = \"fast\""
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],
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"metadata": {
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"id": "bt_aoxONjfL2"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Tortoise will attempt to mimic voices you provide. It comes pre-packaged\n",
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"# with some voices you might recognize.\n",
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"\n",
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"# Let's list all the voices available. These are just some random clips I've gathered\n",
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"# from the internet as well as a few voices from the training dataset.\n",
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"# Feel free to add your own clips to the voices/ folder.\n",
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"%ls tortoise/voices\n",
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"\n",
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"IPython.display.Audio('tortoise/voices/tom/1.wav')"
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],
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"metadata": {
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"id": "SSleVnRAiEE2"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Pick one of the voices from the output above\n",
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"voice = 'tom'\n",
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"\n",
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"# Load it and send it through Tortoise.\n",
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"voice_samples, conditioning_latents = load_voice(voice)\n",
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"gen = tts.tts_with_preset(text, voice_samples=voice_samples, conditioning_latents=conditioning_latents, \n",
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" preset=preset)\n",
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"torchaudio.save('generated.wav', gen.squeeze(0).cpu(), 24000)\n",
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"IPython.display.Audio('generated.wav')"
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],
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"metadata": {
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"id": "KEXOKjIvn6NW"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Tortoise can also generate speech using a random voice. The voice changes each time you execute this!\n",
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"# (Note: random voices can be prone to strange utterances)\n",
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"gen = tts.tts_with_preset(text, voice_samples=None, conditioning_latents=None, preset=preset)\n",
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"torchaudio.save('generated.wav', gen.squeeze(0).cpu(), 24000)\n",
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"IPython.display.Audio('generated.wav')"
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],
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"metadata": {
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"id": "16Xs2SSC3BXa"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# You can also combine conditioning voices. Combining voices produces a new voice\n",
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"# with traits from all the parents.\n",
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"#\n",
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"# Lets see what it would sound like if Picard and Kirk had a kid with a penchant for philosophy:\n",
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"voice_samples, conditioning_latents = load_voices(['pat', 'william'])\n",
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"\n",
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"gen = tts.tts_with_preset(\"They used to say that if man was meant to fly, he’d have wings. But he did fly. He discovered he had to.\", \n",
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" voice_samples=None, conditioning_latents=None, preset=preset)\n",
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"torchaudio.save('captain_kirkard.wav', gen.squeeze(0).cpu(), 24000)\n",
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"IPython.display.Audio('captain_kirkard.wav')"
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],
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"metadata": {
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"id": "fYTk8KUezUr5"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"del tts # Will break other cells, but necessary to conserve RAM if you want to run this cell.\n",
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"\n",
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"# Tortoise comes with some scripts that does a lot of the lifting for you. For example,\n",
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"# read.py will read a text file for you.\n",
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"!python3 tortoise/read.py --voice=train_atkins --textfile=tortoise/data/riding_hood.txt --preset=ultra_fast --output_path=.\n",
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"\n",
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"IPython.display.Audio('train_atkins/combined.wav')\n",
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"# This will take awhile.."
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],
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"metadata": {
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"id": "t66yqWgu68KL"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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}
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from tortoise.utils.audio import load_audio, load_voice, load_voices
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from tortoise.utils.audio import load_audio, load_voice, load_voices
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from tortoise.utils.text import split_and_recombine_text
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from tortoise.utils.text import split_and_recombine_text
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args = None
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webui = None
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tts = None
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def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, breathing_room, cvvp_weight, experimentals, progress=gr.Progress(track_tqdm=True)):
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def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidates, num_autoregressive_samples, diffusion_iterations, temperature, diffusion_sampler, breathing_room, cvvp_weight, experimentals, progress=gr.Progress(track_tqdm=True)):
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try:
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try:
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tts
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tts
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@ -424,7 +428,7 @@ def setup_args():
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args.listen_host = None
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args.listen_host = None
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args.listen_port = None
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args.listen_port = None
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args.listen_path = None
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args.listen_path = None
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if args.listen is not None:
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if args.listen:
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match = re.findall(r"^(?:(.+?):(\d+))?(\/.+?)?$", args.listen)[0]
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match = re.findall(r"^(?:(.+?):(\d+))?(\/.+?)?$", args.listen)[0]
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args.listen_host = match[0] if match[0] != "" else "127.0.0.1"
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args.listen_host = match[0] if match[0] != "" else "127.0.0.1"
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webui.queue(concurrency_count=args.concurrency_count)
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webui.queue(concurrency_count=args.concurrency_count)
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return webui
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return webui
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if __name__ == "__main__":
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args = setup_args()
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if args.listen_path is not None and args.listen_path != "/":
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import uvicorn
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uvicorn.run("app:app", host=args.listen_host, port=args.listen_port if not None else 8000)
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else:
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webui = setup_gradio()
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webui.launch(share=args.share, prevent_thread_lock=True, server_name=args.listen_host, server_port=args.listen_port)
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tts = setup_tortoise()
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webui.block_thread()
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elif __name__ == "app":
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import sys
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from fastapi import FastAPI
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sys.argv = [sys.argv[0]]
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app = FastAPI()
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args = setup_args()
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webui = setup_gradio()
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app = gr.mount_gradio_app(app, webui, path=args.listen_path)
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tts = setup_tortoise()
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Block a user