241 lines
9.8 KiB
Plaintext
241 lines
9.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"VoiceCraft Inference Text To Speech Demo\n",
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"==="
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Select 'voicecraft' as the kernel"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# import libs\n",
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"# if this throws an error, something went wrong installing dependencies or changing the kernel above!\n",
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"import os\n",
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"os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\" \n",
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"os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
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"os.environ[\"USER\"] = \"me\" # TODO change this to your username\n",
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"\n",
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"import torch\n",
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"import torchaudio\n",
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"import numpy as np\n",
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"import random\n",
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"from argparse import Namespace\n",
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"\n",
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"from data.tokenizer import (\n",
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" AudioTokenizer,\n",
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" TextTokenizer,\n",
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")\n",
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"from huggingface_hub import hf_hub_download"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# # install MFA models and dictionaries if you haven't done so already, already done in the dockerfile or envrionment setup\n",
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"# !source ~/.bashrc && \\\n",
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"# conda activate voicecraft && \\\n",
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"# mfa model download dictionary english_us_arpa && \\\n",
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"# mfa model download acoustic english_us_arpa"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Dora directory: /tmp/audiocraft_me\n"
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]
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}
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],
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"source": [
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"# load model, encodec, and phn2num\n",
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"# # load model, tokenizer, and other necessary files\n",
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"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
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"voicecraft_name=\"830M_TTSEnhanced.pth\" # or giga330M.pth, 330M_TTSEnhanced.pth, giga830M.pth\n",
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"\n",
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"# the new way of loading the model, with huggingface, recommended\n",
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"from models import voicecraft\n",
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"model = voicecraft.VoiceCraft.from_pretrained(f\"pyp1/VoiceCraft_{voicecraft_name.replace('.pth', '')}\")\n",
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"phn2num = model.args.phn2num\n",
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"config = vars(model.args)\n",
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"model.to(device)\n",
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"\n",
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"\n",
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"# # the old way of loading the model\n",
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"# from models import voicecraft\n",
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"# filepath = hf_hub_download(repo_id=\"pyp1/VoiceCraft\", filename=voicecraft_name, repo_type=\"model\")\n",
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"# ckpt = torch.load(filepath, map_location=\"cpu\")\n",
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"# model = voicecraft.VoiceCraft(ckpt[\"config\"])\n",
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"# model.load_state_dict(ckpt[\"model\"])\n",
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"# config = vars(model.args)\n",
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"# phn2num = ckpt[\"phn2num\"]\n",
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"# model.to(device)\n",
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"# model.eval()\n",
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"\n",
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"\n",
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"encodec_fn = \"./pretrained_models/encodec_4cb2048_giga.th\"\n",
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"if not os.path.exists(encodec_fn):\n",
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" os.system(f\"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th\")\n",
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" os.system(f\"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th\")\n",
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"audio_tokenizer = AudioTokenizer(signature=encodec_fn, device=device) # will also put the neural codec model on gpu\n",
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"\n",
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"text_tokenizer = TextTokenizer(backend=\"espeak\")\n"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Prepare your audio\n",
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"# point to the original audio whose speech you want to clone\n",
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"# write down the transcript for the file, or run whisper to get the transcript (and you can modify it if it's not accurate), save it as a .txt file\n",
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"orig_audio = \"./demo/5895_34622_000026_000002.wav\"\n",
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"orig_transcript = \"Gwynplaine had, besides, for his work and for his feats of strength, round his neck and over his shoulders, an esclavine of leather.\"\n",
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"\n",
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"# move the audio and transcript to temp folder\n",
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"temp_folder = \"./demo/temp\"\n",
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"os.makedirs(temp_folder, exist_ok=True)\n",
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"os.system(f\"cp {orig_audio} {temp_folder}\")\n",
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"filename = os.path.splitext(orig_audio.split(\"/\")[-1])[0]\n",
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"with open(f\"{temp_folder}/{filename}.txt\", \"w\") as f:\n",
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" f.write(orig_transcript)\n",
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"# run MFA to get the alignment\n",
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"align_temp = f\"{temp_folder}/mfa_alignments\"\n",
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"!source ~/.bashrc && \\\n",
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" conda activate voicecraft && \\\n",
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" mfa align -v --clean -j 1 --output_format csv {temp_folder} \\\n",
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" english_us_arpa english_us_arpa {align_temp}\n",
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"\n",
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"# # if the above fails, it could be because the audio is too hard for the alignment model, increasing the beam size usually solves the issue\n",
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"# !source ~/.bashrc && \\\n",
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"# conda activate voicecraft && \\\n",
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"# mfa align -v --clean -j 1 --output_format csv {temp_folder} \\\n",
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"# english_us_arpa english_us_arpa {align_temp} --beam 1000 --retry_beam 2000\n",
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"\n"
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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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"outputs": [],
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"source": [
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"# take a look at demo/temp/mfa_alignment, decide which part of the audio to use as prompt\n",
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"cut_off_sec = 3.6 # NOTE: according to forced-alignment file demo/temp/mfa_alignments/5895_34622_000026_000002.wav, the word \"strength\" stop as 3.561 sec, so we use first 3.6 sec as the prompt. this should be different for different audio\n",
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"target_transcript = \"Gwynplaine had, besides, for his work and for his feats of strength, I cannot believe that the same model can also do text to speech synthesis too!\"\n",
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"# NOTE: 3 sec of reference is generally enough for high quality voice cloning, but longer is generally better, try e.g. 3~6 sec.\n",
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"audio_fn = f\"{temp_folder}/{filename}.wav\"\n",
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"info = torchaudio.info(audio_fn)\n",
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"audio_dur = info.num_frames / info.sample_rate\n",
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"\n",
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"assert cut_off_sec < audio_dur, f\"cut_off_sec {cut_off_sec} is larger than the audio duration {audio_dur}\"\n",
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"prompt_end_frame = int(cut_off_sec * info.sample_rate)\n",
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"\n",
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"# run the model to get the output\n",
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"# hyperparameters for inference\n",
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"codec_audio_sr = 16000\n",
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"codec_sr = 50\n",
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"top_k = 0\n",
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"top_p = 0.9 # can also try 0.8, but 0.9 seems to work better\n",
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"temperature = 1\n",
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"silence_tokens=[1388,1898,131]\n",
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"kvcache = 1 # NOTE if OOM, change this to 0, or try the 330M model\n",
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"\n",
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"# NOTE adjust the below three arguments if the generation is not as good\n",
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"stop_repetition = 3 # NOTE if the model generate long silence, reduce the stop_repetition to 3, 2 or even 1\n",
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"sample_batch_size = 3 # NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 4 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.\n",
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"seed = 1 # change seed if you are still unhappy with the result\n",
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"\n",
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"def seed_everything(seed):\n",
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" os.environ['PYTHONHASHSEED'] = str(seed)\n",
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" random.seed(seed)\n",
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" np.random.seed(seed)\n",
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" torch.manual_seed(seed)\n",
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" torch.cuda.manual_seed(seed)\n",
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" torch.backends.cudnn.benchmark = False\n",
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" torch.backends.cudnn.deterministic = True\n",
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"seed_everything(seed)\n",
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"\n",
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"decode_config = {'top_k': top_k, 'top_p': top_p, 'temperature': temperature, 'stop_repetition': stop_repetition, 'kvcache': kvcache, \"codec_audio_sr\": codec_audio_sr, \"codec_sr\": codec_sr, \"silence_tokens\": silence_tokens, \"sample_batch_size\": sample_batch_size}\n",
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"from inference_tts_scale import inference_one_sample\n",
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"concated_audio, gen_audio = inference_one_sample(model, Namespace(**config), phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, device, decode_config, prompt_end_frame)\n",
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" \n",
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"# save segments for comparison\n",
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"concated_audio, gen_audio = concated_audio[0].cpu(), gen_audio[0].cpu()\n",
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"# logging.info(f\"length of the resynthesize orig audio: {orig_audio.shape}\")\n",
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"\n",
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"\n",
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"# display the audio\n",
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"from IPython.display import Audio\n",
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"print(\"concatenate prompt and generated:\")\n",
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"display(Audio(concated_audio, rate=codec_audio_sr))\n",
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"\n",
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"print(\"generated:\")\n",
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"display(Audio(gen_audio, rate=codec_audio_sr))\n",
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"\n",
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"# # save the audio\n",
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"# # output_dir\n",
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"# output_dir = \"/home/pyp/VoiceCraft/demo/generated_tts\"\n",
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"# os.makedirs(output_dir, exist_ok=True)\n",
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"# seg_save_fn_gen = f\"{output_dir}/{os.path.basename(audio_fn)[:-4]}_gen_seed{seed}.wav\"\n",
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"# seg_save_fn_concat = f\"{output_dir}/{os.path.basename(audio_fn)[:-4]}_concat_seed{seed}.wav\" \n",
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"\n",
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"# torchaudio.save(seg_save_fn_gen, gen_audio, codec_audio_sr)\n",
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"# torchaudio.save(seg_save_fn_concat, concated_audio, codec_audio_sr)\n",
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"\n",
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"# you are might get warnings like WARNING:phonemizer:words count mismatch on 300.0% of the lines (3/1), this can be safely ignored"
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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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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "voicecraft",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.18"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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