fix notebook

This commit is contained in:
James Betker 2022-04-25 21:17:49 -06:00
parent 8606680545
commit f3e17662cc

View File

@ -34,88 +34,9 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"id": "JrK20I32grP6", "id": "JrK20I32grP6"
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "44f55dca-5d0a-405e-a4cc-54bc8e16b780"
}, },
"outputs": [ "outputs": [],
{
"output_type": "stream",
"name": "stdout",
"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",
"Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 1)) (1.10.0+cu111)\n",
"Requirement already satisfied: torchaudio in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (0.10.0+cu111)\n",
"Collecting rotary_embedding_torch\n",
" Downloading rotary_embedding_torch-0.1.5-py3-none-any.whl (4.1 kB)\n",
"Collecting transformers\n",
" Downloading transformers-4.18.0-py3-none-any.whl (4.0 MB)\n",
"\u001b[K |████████████████████████████████| 4.0 MB 5.3 MB/s \n",
"\u001b[?25hCollecting tokenizers\n",
" Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
"\u001b[K |████████████████████████████████| 6.6 MB 31.3 MB/s \n",
"\u001b[?25hRequirement already satisfied: inflect in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 6)) (2.1.0)\n",
"Collecting progressbar\n",
" Downloading progressbar-2.5.tar.gz (10 kB)\n",
"Collecting einops\n",
" Downloading einops-0.4.1-py3-none-any.whl (28 kB)\n",
"Collecting unidecode\n",
" Downloading Unidecode-1.3.4-py3-none-any.whl (235 kB)\n",
"\u001b[K |████████████████████████████████| 235 kB 44.3 MB/s \n",
"\u001b[?25hCollecting entmax\n",
" Downloading entmax-1.0.tar.gz (7.2 kB)\n",
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"Collecting sacremoses\n",
" Downloading sacremoses-0.0.49-py3-none-any.whl (895 kB)\n",
"\u001b[K |████████████████████████████████| 895 kB 36.6 MB/s \n",
"\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n",
" Downloading huggingface_hub-0.5.1-py3-none-any.whl (77 kB)\n",
"\u001b[K |████████████████████████████████| 77 kB 6.3 MB/s \n",
"\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers->-r requirements.txt (line 4)) (3.6.0)\n",
"Collecting pyyaml>=5.1\n",
" Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
"\u001b[K |████████████████████████████████| 596 kB 38.9 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers->-r requirements.txt (line 4)) (1.21.6)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers->-r requirements.txt (line 4)) (2.23.0)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers->-r requirements.txt (line 4)) (2019.12.20)\n",
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"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers->-r requirements.txt (line 4)) (3.0.4)\n",
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"Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers->-r requirements.txt (line 4)) (7.1.2)\n",
"Building wheels for collected packages: progressbar, entmax\n",
" Building wheel for progressbar (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for progressbar: filename=progressbar-2.5-py3-none-any.whl size=12082 sha256=bb7d90605d0bf4d89aedc46bd8ed39538f55e00ee70fa382c1af81f142f08fa8\n",
" Stored in directory: /root/.cache/pip/wheels/f0/fd/1f/3e35ed57e94cd8ced38dd46771f1f0f94f65fec548659ed855\n",
" Building wheel for entmax (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for entmax: filename=entmax-1.0-py3-none-any.whl size=11015 sha256=5e2cf723e790ec941984d2030eb3231e1ae3ce75231709391a13edcd2bfb4770\n",
" Stored in directory: /root/.cache/pip/wheels/f7/e8/0d/acc29c2f66e69a1f42483347fa8545c293dec12325ee161716\n",
"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": [ "source": [
"!git clone https://github.com/neonbjb/tortoise-tts.git\n", "!git clone https://github.com/neonbjb/tortoise-tts.git\n",
"%cd tortoise-tts\n", "%cd tortoise-tts\n",
@ -138,97 +59,10 @@
"tts = TextToSpeech()" "tts = TextToSpeech()"
], ],
"metadata": { "metadata": {
"id": "Gen09NM4hONQ", "id": "Gen09NM4hONQ"
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "35c1fb4b-5998-4e75-9ec9-29521b301db6"
}, },
"execution_count": null, "execution_count": null,
"outputs": [ "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", "cell_type": "code",
@ -239,28 +73,10 @@
"%ls voices" "%ls voices"
], ],
"metadata": { "metadata": {
"id": "SSleVnRAiEE2", "id": "SSleVnRAiEE2"
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e1eb09e2-1b68-4f81-b679-edb97538da39"
}, },
"execution_count": null, "execution_count": null,
"outputs": [ "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", "cell_type": "code",
@ -302,40 +118,10 @@
"torchaudio.save('generated.wav', gen.squeeze(0).cpu(), 24000)" "torchaudio.save('generated.wav', gen.squeeze(0).cpu(), 24000)"
], ],
"metadata": { "metadata": {
"id": "KEXOKjIvn6NW", "id": "KEXOKjIvn6NW"
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7977bfd7-9fbc-41f7-d3ac-25fd4e350049"
}, },
"execution_count": null, "execution_count": null,
"outputs": [ "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", "cell_type": "code",
@ -346,7 +132,7 @@
"#\n", "#\n",
"# Lets see what it would sound like if Picard and Kirk had a kid with a penchant for philosophy:\n", "# Lets see what it would sound like if Picard and Kirk had a kid with a penchant for philosophy:\n",
"conds = []\n", "conds = []\n",
"for v in ['patrick_stewart', 'william_shatner']:\n", "for v in ['pat', 'william']:\n",
" cond_paths = voices[v]\n", " cond_paths = voices[v]\n",
" for cond_path in cond_paths:\n", " for cond_path in cond_paths:\n",
" c = load_audio(cond_path, 22050)\n", " c = load_audio(cond_path, 22050)\n",
@ -356,40 +142,10 @@
"torchaudio.save('captain_kirkard.wav', gen.squeeze(0).cpu(), 24000)" "torchaudio.save('captain_kirkard.wav', gen.squeeze(0).cpu(), 24000)"
], ],
"metadata": { "metadata": {
"colab": { "id": "fYTk8KUezUr5"
"base_uri": "https://localhost:8080/"
},
"id": "fYTk8KUezUr5",
"outputId": "8a07f251-c90f-4e6a-c204-132b737dfff8"
}, },
"execution_count": null, "execution_count": null,
"outputs": [ "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"
]
}
]
} }
] ]
} }