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{
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"name": "tortoise-tts.ipynb",
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{
"cell_type": "markdown",
"source": [
"Welcome to Tortoise! 🐢🐢🐢🐢\n",
"\n",
"Before you begin, I **strongly** recommend you turn on a GPU runtime.\n",
"\n",
"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."
],
"metadata": {
"id": "_pIZ3ZXNp7cf"
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"text": [
"Cloning into 'tortoise-tts'...\n",
"remote: Enumerating objects: 736, done.\u001b[K\n",
"remote: Counting objects: 100% (23/23), done.\u001b[K\n",
"remote: Compressing objects: 100% (15/15), done.\u001b[K\n",
"remote: Total 736 (delta 10), reused 20 (delta 8), pack-reused 713\u001b[K\n",
"Receiving objects: 100% (736/736), 348.62 MiB | 24.08 MiB/s, done.\n",
"Resolving deltas: 100% (161/161), done.\n",
"/content/tortoise-tts\n",
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"Building wheels for collected packages: progressbar, entmax\n",
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" Building wheel for entmax (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"Successfully built progressbar entmax\n",
"Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, einops, unidecode, transformers, rotary-embedding-torch, progressbar, entmax\n",
" Attempting uninstall: pyyaml\n",
" Found existing installation: PyYAML 3.13\n",
" Uninstalling PyYAML-3.13:\n",
" Successfully uninstalled PyYAML-3.13\n",
"Successfully installed einops-0.4.1 entmax-1.0 huggingface-hub-0.5.1 progressbar-2.5 pyyaml-6.0 rotary-embedding-torch-0.1.5 sacremoses-0.0.49 tokenizers-0.12.1 transformers-4.18.0 unidecode-1.3.4\n"
]
}
],
"source": [
"!git clone https://github.com/neonbjb/tortoise-tts.git\n",
"%cd tortoise-tts\n",
"!pip install -r requirements.txt"
]
},
{
"cell_type": "code",
"source": [
"# Imports used through the rest of the notebook.\n",
"import torch\n",
"import torchaudio\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"from api import TextToSpeech\n",
"from utils.audio import load_audio, get_voices\n",
"\n",
"# This will download all the models used by Tortoise from the HF hub.\n",
"tts = TextToSpeech()"
],
"metadata": {
"id": "Gen09NM4hONQ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "35c1fb4b-5998-4e75-9ec9-29521b301db6"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading autoregressive.pth from https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/autoregressive.pth...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Done.\n",
"Downloading clvp.pth from https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/clvp.pth...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Done.\n",
"Downloading cvvp.pth from https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/cvvp.pth...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Done.\n",
"Downloading diffusion_decoder.pth from https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/diffusion_decoder.pth...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Done.\n",
"Downloading vocoder.pth from https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/vocoder.pth...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Done.\n",
"Removing weight norm...\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# List all the voices available. These are just some random clips I've gathered\n",
"# from the internet as well as a few voices from the training dataset.\n",
"# Feel free to add your own clips to the voices/ folder.\n",
"%ls voices"
],
"metadata": {
"id": "SSleVnRAiEE2",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e1eb09e2-1b68-4f81-b679-edb97538da39"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[0m\u001b[01;34mangelina_jolie\u001b[0m/ \u001b[01;34mhalle_barry\u001b[0m/ \u001b[01;34mlj\u001b[0m/ \u001b[01;34msamuel_jackson\u001b[0m/\n",
"\u001b[01;34matkins\u001b[0m/ \u001b[01;34mharris\u001b[0m/ \u001b[01;34mmol\u001b[0m/ \u001b[01;34msigourney_weaver\u001b[0m/\n",
"\u001b[01;34mcarlin\u001b[0m/ \u001b[01;34mhenry_cavill\u001b[0m/ \u001b[01;34mmorgan_freeman\u001b[0m/ \u001b[01;34mtom_hanks\u001b[0m/\n",
"\u001b[01;34mdaniel_craig\u001b[0m/ \u001b[01;34mjennifer_lawrence\u001b[0m/ \u001b[01;34mmyself\u001b[0m/ \u001b[01;34mwilliam_shatner\u001b[0m/\n",
"\u001b[01;34mdotrice\u001b[0m/ \u001b[01;34mjohn_krasinski\u001b[0m/ \u001b[01;34motto\u001b[0m/\n",
"\u001b[01;34memma_stone\u001b[0m/ \u001b[01;34mkennard\u001b[0m/ \u001b[01;34mpatrick_stewart\u001b[0m/\n",
"\u001b[01;34mgrace\u001b[0m/ \u001b[01;34mlescault\u001b[0m/ \u001b[01;34mrobert_deniro\u001b[0m/\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# This is the text that will be spoken.\n",
"text = \"Joining two modalities results in a surprising increase in generalization! What would happen if we combined them all?\"\n",
"\n",
"# Here's something for the poetically inclined.. (set text=)\n",
"\"\"\"\n",
"Then took the other, as just as fair,\n",
"And having perhaps the better claim,\n",
"Because it was grassy and wanted wear;\n",
"Though as for that the passing there\n",
"Had worn them really about the same,\"\"\"\n",
"\n",
"# Pick one of the voices from above\n",
"voice = 'dotrice'\n",
"# Pick a \"preset mode\" to determine quality. Options: {\"ultra_fast\", \"fast\" (default), \"standard\", \"high_quality\"}. See docs in api.py\n",
"preset = \"fast\""
],
"metadata": {
"id": "bt_aoxONjfL2"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Fetch the voice references and forward execute!\n",
"voices = get_voices()\n",
"cond_paths = voices[voice]\n",
"conds = []\n",
"for cond_path in cond_paths:\n",
" c = load_audio(cond_path, 22050)\n",
" conds.append(c)\n",
"\n",
"gen = tts.tts_with_preset(text, conds, preset)\n",
"torchaudio.save('generated.wav', gen.squeeze(0).cpu(), 24000)"
],
"metadata": {
"id": "KEXOKjIvn6NW",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7977bfd7-9fbc-41f7-d3ac-25fd4e350049"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 6/6 [01:18<00:00, 13.11s/it]\n",
"/usr/local/lib/python3.7/dist-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
" warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
"/content/tortoise-tts/models/autoregressive.py:359: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
" mel_lengths = wav_lengths // self.mel_length_compression\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Performing vocoding..\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 32/32 [00:16<00:00, 1.94it/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# You can add as many conditioning voices as you want together. Combining\n",
"# clips from multiple voices takes the mean of the latent space for all\n",
"# voices. This creates a novel voice that is a combination of the two inputs.\n",
"#\n",
"# Lets see what it would sound like if Picard and Kirk had a kid with a penchant for philosophy:\n",
"conds = []\n",
"for v in ['patrick_stewart', 'william_shatner']:\n",
" cond_paths = voices[v]\n",
" for cond_path in cond_paths:\n",
" c = load_audio(cond_path, 22050)\n",
" conds.append(c)\n",
"\n",
"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.\", conds, preset)\n",
"torchaudio.save('captain_kirkard.wav', gen.squeeze(0).cpu(), 24000)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fYTk8KUezUr5",
"outputId": "8a07f251-c90f-4e6a-c204-132b737dfff8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 6/6 [01:45<00:00, 17.62s/it]\n",
"/usr/local/lib/python3.7/dist-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
" warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
"/content/tortoise-tts/models/autoregressive.py:359: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
" mel_lengths = wav_lengths // self.mel_length_compression\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Performing vocoding..\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 32/32 [00:16<00:00, 2.00it/s]\n"
]
}
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}
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} |