2022-03-11 07:21:01 +01:00
{
2023-07-09 15:10:10 +02:00
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "_pIZ3ZXNp7cf"
},
"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."
]
2022-03-11 07:21:01 +01:00
},
2023-07-09 15:10:10 +02:00
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JrK20I32grP6"
},
"outputs": [],
"source": [
"#first follow the instructions in the README.md file under Local Installation\n",
"!pip3 install -r requirements.txt\n",
"# !python3 setup.py install"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "Gen09NM4hONQ"
},
"outputs": [
2022-03-11 07:21:01 +01:00
{
2023-07-09 15:10:10 +02:00
"name": "stderr",
"output_type": "stream",
"text": [
"/home/manmay/anaconda3/envs/tortoise/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"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",
"import IPython\n",
"\n",
"from tortoise.api import TextToSpeech\n",
"from tortoise.utils.audio import load_audio, load_voice, load_voices\n",
"\n",
"# This will download all the models used by Tortoise from the HF hub.\n",
"tts = TextToSpeech()\n",
"# If you want to use deepspeed the pass use_deepspeed=True nearly 2x faster than normal\n",
"# tts = TextToSpeech(use_deepspeed=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "bt_aoxONjfL2"
},
"outputs": [],
"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 a \"preset mode\" to determine quality. Options: {\"ultra_fast\", \"fast\" (default), \"standard\", \"high_quality\"}. See docs in api.py\n",
"preset = \"ultra_fast\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "SSleVnRAiEE2"
},
"outputs": [
2022-03-11 07:21:01 +01:00
{
2023-07-09 15:10:10 +02:00
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[0m\u001b[01;34mangie\u001b[0m/ \u001b[01;34mfreeman\u001b[0m/ \u001b[01;34mmyself\u001b[0m/ \u001b[01;34mtom\u001b[0m/ \u001b[01;34mtrain_grace\u001b[0m/\n",
"\u001b[01;34mapplejack\u001b[0m/ \u001b[01;34mgeralt\u001b[0m/ \u001b[01;34mpat\u001b[0m/ \u001b[01;34mtrain_atkins\u001b[0m/ \u001b[01;34mtrain_kennard\u001b[0m/\n",
"\u001b[01;34mcond_latent_example\u001b[0m/ \u001b[01;34mhalle\u001b[0m/ \u001b[01;34mpat2\u001b[0m/ \u001b[01;34mtrain_daws\u001b[0m/ \u001b[01;34mtrain_lescault\u001b[0m/\n",
"\u001b[01;34mdaniel\u001b[0m/ \u001b[01;34mjlaw\u001b[0m/ \u001b[01;34mrainbow\u001b[0m/ \u001b[01;34mtrain_dotrice\u001b[0m/ \u001b[01;34mtrain_mouse\u001b[0m/\n",
"\u001b[01;34mdeniro\u001b[0m/ \u001b[01;34mlj\u001b[0m/ \u001b[01;34msnakes\u001b[0m/ \u001b[01;34mtrain_dreams\u001b[0m/ \u001b[01;34mweaver\u001b[0m/\n",
"\u001b[01;34memma\u001b[0m/ \u001b[01;34mmol\u001b[0m/ \u001b[01;34mtim_reynolds\u001b[0m/ \u001b[01;34mtrain_empire\u001b[0m/ \u001b[01;34mwilliam\u001b[0m/\n"
]
2022-05-03 05:57:19 +02:00
},
{
2023-07-09 15:10:10 +02:00
"data": {
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
" <source src=\"data:audio/x-wav;base64,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
" Your browser does not support the audio element.\n",
" </audio>\n",
" "
2022-03-11 07:21:01 +01:00
],
2023-07-09 15:10:10 +02:00
"text/plain": [
"<IPython.lib.display.Audio object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Tortoise will attempt to mimic voices you provide. It comes pre-packaged\n",
"# with some voices you might recognize.\n",
"\n",
"# Let's 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 tortoise/voices\n",
"\n",
"IPython.display.Audio('tortoise/voices/tom/1.wav')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "KEXOKjIvn6NW"
},
"outputs": [
2022-03-11 07:21:01 +01:00
{
2023-07-09 15:10:10 +02:00
"name": "stdout",
"output_type": "stream",
"text": [
"Generating autoregressive samples..\n"
]
2022-05-03 05:57:19 +02:00
},
{
2023-07-09 15:10:10 +02:00
"name": "stderr",
"output_type": "stream",
"text": [
" 38%|███▊ | 6/16 [00:31<00:52, 5.20s/it]\n"
]
2022-05-03 05:57:19 +02:00
},
{
2023-07-09 15:10:10 +02:00
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[4], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[39m# Load it and send it through Tortoise.\u001b[39;00m\n\u001b[1;32m 5\u001b[0m voice_samples, conditioning_latents \u001b[39m=\u001b[39m load_voice(voice)\n\u001b[0;32m----> 6\u001b[0m gen \u001b[39m=\u001b[39m tts\u001b[39m.\u001b[39;49mtts_with_preset(text, voice_samples\u001b[39m=\u001b[39;49mvoice_samples, conditioning_latents\u001b[39m=\u001b[39;49mconditioning_latents, \n\u001b[1;32m 7\u001b[0m preset\u001b[39m=\u001b[39;49mpreset)\n\u001b[1;32m 8\u001b[0m torchaudio\u001b[39m.\u001b[39msave(\u001b[39m'\u001b[39m\u001b[39mgenerated.wav\u001b[39m\u001b[39m'\u001b[39m, gen\u001b[39m.\u001b[39msqueeze(\u001b[39m0\u001b[39m)\u001b[39m.\u001b[39mcpu(), \u001b[39m24000\u001b[39m)\n\u001b[1;32m 9\u001b[0m IPython\u001b[39m.\u001b[39mdisplay\u001b[39m.\u001b[39mAudio(\u001b[39m'\u001b[39m\u001b[39mgenerated.wav\u001b[39m\u001b[39m'\u001b[39m)\n",
"File \u001b[0;32m/data/speech_synth/tortoise-tts/tortoise/api.py:329\u001b[0m, in \u001b[0;36mTextToSpeech.tts_with_preset\u001b[0;34m(self, text, preset, **kwargs)\u001b[0m\n\u001b[1;32m 327\u001b[0m settings\u001b[39m.\u001b[39mupdate(presets[preset])\n\u001b[1;32m 328\u001b[0m settings\u001b[39m.\u001b[39mupdate(kwargs) \u001b[39m# allow overriding of preset settings with kwargs\u001b[39;00m\n\u001b[0;32m--> 329\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtts(text, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49msettings)\n",
"File \u001b[0;32m/data/speech_synth/tortoise-tts/tortoise/api.py:412\u001b[0m, in \u001b[0;36mTextToSpeech.tts\u001b[0;34m(self, text, voice_samples, conditioning_latents, k, verbose, use_deterministic_seed, return_deterministic_state, num_autoregressive_samples, temperature, length_penalty, repetition_penalty, top_p, max_mel_tokens, cvvp_amount, diffusion_iterations, cond_free, cond_free_k, diffusion_temperature, **hf_generate_kwargs)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mGenerating autoregressive samples..\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 411\u001b[0m \u001b[39mfor\u001b[39;00m b \u001b[39min\u001b[39;00m tqdm(\u001b[39mrange\u001b[39m(num_batches), disable\u001b[39m=\u001b[39m\u001b[39mnot\u001b[39;00m verbose):\n\u001b[0;32m--> 412\u001b[0m codes \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mautoregressive\u001b[39m.\u001b[39;49minference_speech(auto_conditioning, text_tokens,\n\u001b[1;32m 413\u001b[0m do_sample\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m 414\u001b[0m top_p\u001b[39m=\u001b[39;49mtop_p,\n\u001b[1;32m 415\u001b[0m temperature\u001b[39m=\u001b[39;49mtemperature,\n\u001b[1;32m 416\u001b[0m num_return_sequences\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mautoregressive_batch_size,\n\u001b[1;32m 417\u001b[0m length_penalty\u001b[39m=\u001b[39;49mlength_penalty,\n\u001b[1;32m 418\u001b[0m repetition_penalty\u001b[39m=\u001b[39;49mrepetition_penalty,\n\u001b[1;32m 419\u001b[0m max_generate_length\u001b[39m=\u001b[39;49mmax_mel_tokens,\n\u001b[1;32m 420\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mhf_generate_kwargs)\n\u001b[1;32m 421\u001b[0m padding_needed \u001b[39m=\u001b[39m max_mel_tokens \u001b[39m-\u001b[39m codes\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m]\n\u001b[1;32m 422\u001b[0m codes \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mpad(codes, (\u001b[39m0\u001b[39m, padding_needed), value\u001b[39m=\u001b[39mstop_mel_token)\n",
"File \u001b[0;32m/data/speech_synth/tortoise-tts/tortoise/models/autoregressive.py:513\u001b[0m, in \u001b[0;36mUnifiedVoice.inference_speech\u001b[0;34m(self, speech_conditioning_latent, text_inputs, input_tokens, num_return_sequences, max_generate_length, typical_sampling, typical_mass, **hf_generate_kwargs)\u001b[0m\n\u001b[1;32m 511\u001b[0m logits_processor \u001b[39m=\u001b[39m LogitsProcessorList([TypicalLogitsWarper(mass\u001b[39m=\u001b[39mtypical_mass)]) \u001b[39mif\u001b[39;00m typical_sampling \u001b[39melse\u001b[39;00m LogitsProcessorList()\n\u001b[1;32m 512\u001b[0m max_length \u001b[39m=\u001b[39m trunc_index \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmax_mel_tokens \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m \u001b[39mif\u001b[39;00m max_generate_length \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m trunc_index \u001b[39m+\u001b[39m max_generate_length\n\u001b[0;32m--> 513\u001b[0m gen \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minference_model\u001b[39m.\u001b[39;49mgenerate(inputs, bos_token_id\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstart_mel_token, pad_token_id\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstop_mel_token, eos_token_id\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstop_mel_token,\n\u001b[1;32m 514\u001b[0m max_length\u001b[39m=\u001b[39;49mmax_length, logits_processor\u001b[39m=\u001b[39;49mlogits_processor,\n\u001b[1;32m 515\u001b[0m num_return_sequences\u001b[39m=\u001b[39;49mnum_return_sequences, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mhf_generate_kwargs)\n\u001b[1;32m 516\u001b[0m \u001b[39mreturn\u001b[39;00m gen[:, trunc_index:]\n",
"File \u001b[0;32m~/anaconda3/envs/tortoise/lib/python3.9/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m 113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorate_context\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 114\u001b[0m \u001b[39mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
"File \u001b[0;32m~/anaconda3/envs/tortoise/lib/python3.9/site-packages/transformers/generation_utils.py:1310\u001b[0m, in \u001b[0;36mGenerationMixin.generate\u001b[0;34m(self, inputs, max_length, min_length, do_sample, early_stopping, num_beams, temperature, top_k, top_p, typical_p, repetition_penalty, bad_words_ids, force_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, encoder_no_repeat_ngram_size, num_return_sequences, max_time, max_new_tokens, decoder_start_token_id, use_cache, num_beam_groups, diversity_penalty, prefix_allowed_tokens_fn, logits_processor, renormalize_logits, stopping_criteria, constraints, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, forced_bos_token_id, forced_eos_token_id, remove_invalid_values, synced_gpus, exponential_decay_length_penalty, **model_kwargs)\u001b[0m\n\u001b[1;32m 1302\u001b[0m input_ids, model_kwargs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_expand_inputs_for_generation(\n\u001b[1;32m 1303\u001b[0m input_ids,\n\u001b[1;32m 1304\u001b[0m expand_size\u001b[39m=\u001b[39mnum_return_sequences,\n\u001b[1;32m 1305\u001b[0m is_encoder_decoder\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig\u001b[39m.\u001b[39mis_encoder_decoder,\n\u001b[1;32m 1306\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mmodel_kwargs,\n\u001b[1;32m 1307\u001b[0m )\n\u001b[1;32m 1309\u001b[0m \u001b[39m# 12. run sample\u001b[39;00m\n\u001b[0;32m-> 1310\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msample(\n\u001b[1;32m 1311\u001b[0m input_ids,\n\u001b[1;32m 1312\u001b[0m logits_processor\u001b[39m=\u001b[39;49mlogits_processor,\n\u001b[1;32m 1313\u001b[0m logits_warper\u001b[39m=\u001b[39;49mlogits_warper,\n\u001b[1;32m 1314\u001b[0m stopping_criteria\u001b[39m=\u001b[39;49mstopping_criteria,\n\u001b[1;32m 1315\u001b[0m pad_token_id\u001b[39m=\u001b[39;49mpad_token_id,\n\u001b[1;32m 1316\u001b[0m eos_token_id\u001b[39m=\u001b[39;49meos_token_id,\n\u001b[1;32m 1317\u001b[0m output_scores\u001b[39m=\u001b[39;49moutput_scores,\n\u001b[1;32m 1318\u001b[0m return_dict_in_generate\u001b[39m=\u001b[39;49mreturn_dict_in_generate,\n\u001b[1;32m 1319\u001b[0m synced_gpus\u001b[39m=\u001b[39;49msynced_gpus,\n\u001b[1;32m 1320\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mmodel_kwargs,\n\u001b[1;32m 1321\u001b[0m )\n\u001b[1;32m 1323\u001b[0m \u001b[39melif\u001b[39;00m is_beam_gen_mode:\n\u001b[1;32m 1324\u001b[0m \u001b[39mif\u001b[39;00m num_return_sequences \u001b[39m>\u001b[39m num_beams:\n",
"File \u001b[0;32m~/anaconda3/envs/tortoise/lib/python3.9/site-packages/transformers/generation_utils.py:1926\u001b[0m, in \u001b[0;36mGenerationMixin.sample\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, logits_warper, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, **model_kwargs)\u001b[0m\n\u001b[1;32m 1923\u001b[0m model_inputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprepare_inputs_for_generation(input_ids, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mmodel_kwargs)\n\u001b[1;32m 1925\u001b[0m \u001b[39m# forward pass to get next token\u001b[39;00m\n\u001b[0;32m-> 1926\u001b[0m outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m(\n\u001b[1;32m 1927\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mmodel_inputs,\n\u001b[1;32m 1928\u001b[0m return_dict\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m 1929\u001b[0m output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[1;32m 1930\u001b[0m output_hidden_states\u001b[39m=\u001b[39;49moutput_hidden_states,\n\u001b[1;32m 1931\u001b[0m )\n\u001b[1;32m 1933\u001b[0m \u001b[39mif\u001b[39;00m synced_gpus \u001b[39mand\u001b[39;00m this_peer_finished:\n\u001b[1;32m 1934\u001b[0m cur_len \u001b[39m=\u001b[39m cur_len \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m\n",
"File \u001b[0;32m~/anaconda3/envs/tortoise/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
"File \u001b[0;32m/data/speech_synth/tortoise-tts/tortoise/models/autoregressive.py:142\u001b[0m, in \u001b[0;36mGPT2InferenceModel.forward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 139\u001b[0m emb \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39membeddings(input_ids)\n\u001b[1;32m 140\u001b[0m emb \u001b[39m=\u001b[39m emb \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtext_pos_embedding\u001b[39m.\u001b[39mget_fixed_embedding(attention_mask\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m]\u001b[39m-\u001b[39mmel_len, attention_mask\u001b[39m.\u001b[39mdevice)\n\u001b[0;32m--> 142\u001b[0m transformer_outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtransformer(\n\u001b[1;32m 143\u001b[0m inputs_embeds\u001b[39m=\u001b[39;49memb,\n\u001b[1;32m 144\u001b[0m past_key_values\u001b[39m=\u001b[39;49mpast_key_values,\n\u001b[1;32m 145\u001b[0m attention_mask\u001b[39m=\u001b[39;49mattention_mask,\n\u001b[1;32m 146\u001b[0m token_type_ids\u001b[39m=\u001b[39;49mtoken_type_ids,\n\u001b[1;32m 147\u001b[0m position_ids\u001b[39m=\u001b[39;49mposition_ids,\n\u001b[1;32m 148\u001b[0m head_mask\u001b[39m=\u001b[39;49mhead_mask,\n\u001b[1;32m 149\u001b[0m encoder_hidden_states\u001b[39m=\u001b[39;49mencoder_hidden_states,\n\u001b[1;32m 150\u001b[0m encoder_attention_mask\u001b[39m=\u001b[39;49mencoder_attention_mask,\n\u001b[1;32m 151\u001b[0m use_cache\u001b[39m=\u001b[39;49muse_cache,\n\u001b[1;32m 152\u001b[0m output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[1;32m 153\u001b[0m output_hidden_states\u001b[39m=\u001b[39;49moutput_hidden_states,\n\u001b[1;32m 154\u001b[0m return_dict\u001b[39m=\u001b[39;49mreturn_dict,\n\u001b[1;32m 155\u001b[0m )\n\u001b[1;32m 156\u001b[0m hidden_states \u001b[39m=\u001b[39m transformer_outputs[\u001b[39m0\u001b[39m]\n\u001b[1;32m 158\u001b[0m \u001b[39m# Set device for model parallelism\u001b[39;00m\n",
"File \u001b[0;32m~/anaconda3/envs/tortoise/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
"File \u001b[0;32m~/anaconda3/envs/tortoise/lib/python3.9/site-packages/transformers/models/gpt2/modeling_gpt2.py:889\u001b[0m, in \u001b[0;36mGPT2Model.forward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 879\u001b[0m outputs \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mutils\u001b[39m.\u001b[39mcheckpoint\u001b[39m.\u001b[39mcheckpoint(\n\u001b[1;32m 880\u001b[0m create_custom_forward(block),\n\u001b[1;32m 881\u001b[0m hidden_states,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 886\u001b[0m encoder_attention_mask,\n\u001b[1;32m 887\u001b[0m )\n\u001b[1;32m 888\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 889\u001b[0m outputs \u001b[39m=\u001b[39m block(\n\u001b[1;32m 890\u001b[0m hidden_states,\n\u001b[1;32m 891\u001b[0m layer_past\u001b[39m=\u001b[39;49mlayer_past,\n\u001b[1;32m 892\u001b[0m attention_mask\u001b[39m=\u001b[39;49mattention_mask,\n\u001b[1;32m 893\u001b[0m head_mask\u001b[39m=\u001b[39;49mhead_mask[i],\n\u001b[1;32m 894\u001b[0m encoder_hidden_states\u001b[39m=\u001b[39;49mencoder_hidden_states,\n\u001b[1;32m 895\u001b[0m encoder_attention_mask\u001b[39m=\u001b[39;49mencoder_attention_mask,\n\u001b[1;32m 896\u001b[0m use_cache\u001b[39m=\u001b[39;49muse_cache,\n\u001b[1;32m 897\u001b[0m output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[1;32m 898\u001b[0m )\n\u001b[1;32m 900\u001b[0m hidden_states \u001b[39m=\u001b[39m outputs[\u001b[39m0\u001b[39m]\n\u001b[1;32m 901\u001b[0m \u001b[39mif\u001b[39;00m use_cache \u001b[39mis\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n",
"File \u001b[0;32m~/anaconda3/envs/tortoise/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
"File \u001b[0;32m~/anaconda3/envs/tortoise/lib/python3.9/site-packages/transformers/models/gpt2/modeling_gpt2.py:390\u001b[0m, in \u001b[0;36mGPT2Block.forward\u001b[0;34m(self, hidden_states, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m 388\u001b[0m residual \u001b[39m=\u001b[39m hidden_states\n\u001b[1;32m 389\u001b[0m hidden_states \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mln_1(hidden_states)\n\u001b[0;32m--> 390\u001b[0m attn_outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mattn(\n\u001b[1;32m 391\u001b[0m hidden_states,\n\u001b[1;32m 392\u001b[0m layer_past\u001b[39m=\u001b[39;49mlayer_past,\n\u001b[1;32m 393\u001b[0m attention_mask\u001b[39m=\u001b[39;49mattention_mask,\n\u001b[1;32m 394\u001b[0m head_mask\u001b[39m=\u001b[39;49mhead_mask,\n\u001b[1;32m 395\u001b[0m use_cache\u001b[39m=\u001b[39;49muse_cache,\n\u001b[1;32m 396\u001b[0m output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[1;32m 397\u001b[0m )\n\u001b[1;32m 398\u001b[0m attn_output \u001b[39m=\u001b[39m attn_outputs[\u001b[39m0\u001b[39m] \u001b[39m# output_attn: a, present, (attentions)\u001b[39;00m\n\u001b[1;32m 399\u001b[0m outputs \u001b[39m=\u001b[39m attn_outputs[\u001b[39m1\u001b[39m:]\n",
"File \u001b[0;32m~/anaconda3/envs/tortoise/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
"File \u001b[0;32m~/anaconda3/envs/tortoise/lib/python3.9/site-packages/transformers/models/gpt2/modeling_gpt2.py:290\u001b[0m, in \u001b[0;36mGPT2Attention.forward\u001b[0;34m(self, hidden_states, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m 287\u001b[0m new_shape \u001b[39m=\u001b[39m tensor\u001b[39m.\u001b[39msize()[:\u001b[39m-\u001b[39m\u001b[39m2\u001b[39m] \u001b[39m+\u001b[39m (num_heads \u001b[39m*\u001b[39m attn_head_size,)\n\u001b[1;32m 288\u001b[0m \u001b[39mreturn\u001b[39;00m tensor\u001b[39m.\u001b[39mview(new_shape)\n\u001b[0;32m--> 290\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\n\u001b[1;32m 291\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[1;32m 292\u001b[0m hidden_states: Optional[Tuple[torch\u001b[39m.\u001b[39mFloatTensor]],\n\u001b[1;32m 293\u001b[0m layer_past: Optional[Tuple[torch\u001b[39m.\u001b[39mTensor]] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 294\u001b[0m attention_mask: Optional[torch\u001b[39m.\u001b[39mFloatTensor] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 295\u001b[0m head_mask: Optional[torch\u001b[39m.\u001b[39mFloatTensor] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 296\u001b[0m encoder_hidden_states: Optional[torch\u001b[39m.\u001b[39mTensor] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 297\u001b[0m encoder_attention_mask: Optional[torch\u001b[39m.\u001b[39mFloatTensor] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 298\u001b[0m use_cache: Optional[\u001b[39mbool\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m,\n\u001b[1;32m 299\u001b[0m output_attentions: Optional[\u001b[39mbool\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m,\n\u001b[1;32m 300\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tuple[Union[torch\u001b[39m.\u001b[39mTensor, Tuple[torch\u001b[39m.\u001b[39mTensor]], \u001b[39m.\u001b[39m\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m]:\n\u001b[1;32m 301\u001b[0m \u001b[39mif\u001b[39;00m encoder_hidden_states \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 302\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mhasattr\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mq_attn\u001b[39m\u001b[39m\"\u001b[39m):\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
2022-03-11 07:21:01 +01:00
}
2023-07-09 15:10:10 +02:00
],
"source": [
"# Pick one of the voices from the output above\n",
"voice = 'tom'\n",
"\n",
"# Load it and send it through Tortoise.\n",
"voice_samples, conditioning_latents = load_voice(voice)\n",
"gen = tts.tts_with_preset(text, voice_samples=voice_samples, conditioning_latents=conditioning_latents, \n",
" preset=preset)\n",
"torchaudio.save('generated.wav', gen.squeeze(0).cpu(), 24000)\n",
"IPython.display.Audio('generated.wav')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "16Xs2SSC3BXa"
},
"outputs": [],
"source": [
"# Tortoise can also generate speech using a random voice. The voice changes each time you execute this!\n",
"# (Note: random voices can be prone to strange utterances)\n",
"gen = tts.tts_with_preset(text, voice_samples=None, conditioning_latents=None, preset=preset)\n",
"torchaudio.save('generated.wav', gen.squeeze(0).cpu(), 24000)\n",
"IPython.display.Audio('generated.wav')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fYTk8KUezUr5"
},
"outputs": [],
"source": [
"# You can also combine conditioning voices. Combining voices produces a new voice\n",
"# with traits from all the parents.\n",
"#\n",
"# Lets see what it would sound like if Picard and Kirk had a kid with a penchant for philosophy:\n",
"voice_samples, conditioning_latents = load_voices(['pat', 'william'])\n",
"\n",
"gen = tts.tts_with_preset(\"They used to say that if man was meant to fly, he’
" voice_samples=None, conditioning_latents=None, preset=preset)\n",
"torchaudio.save('captain_kirkard.wav', gen.squeeze(0).cpu(), 24000)\n",
"IPython.display.Audio('captain_kirkard.wav')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "t66yqWgu68KL"
},
"outputs": [],
"source": [
"del tts # Will break other cells, but necessary to conserve RAM if you want to run this cell.\n",
"\n",
"# Tortoise comes with some scripts that does a lot of the lifting for you. For example,\n",
"# read.py will read a text file for you.\n",
"!python3 tortoise/read.py --voice=train_atkins --textfile=tortoise/data/riding_hood.txt --preset=ultra_fast --output_path=.\n",
"\n",
"IPython.display.Audio('train_atkins/combined.wav')\n",
"# This will take awhile.."
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "tortoise-tts.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
