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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"\n",
"sys.path.append('../../')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Init model and load weights\n",
"Model ready\n",
"Globbed 12 wav files.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████| 1/1 [00:00<00:00, 11.08it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"wav.shape: (97681,)\n",
"acoustic_token: tensor([[[ 11, 591, 281, 629],\n",
" [733, 591, 401, 139],\n",
" [500, 591, 733, 600],\n",
" ...,\n",
" [733, 591, 451, 346],\n",
" [733, 591, 401, 139],\n",
" [386, 591, 281, 461]]], device='cuda:0')\n",
"acoustic_token.shape: torch.Size([1, 305, 4])\n",
"acoustic_token.dtype: torch.int64\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import glob\n",
"import json\n",
"import os\n",
"from pathlib import Path\n",
"\n",
"import librosa\n",
"import torch\n",
"from academicodec.models.hificodec.vqvae import VQVAE\n",
"from librosa.util import normalize\n",
"from tqdm import tqdm\n",
"\n",
"ckpt_path = './checkpoint/HiFi-Codec-24k-320d'\n",
"config_path = './config_24k_320d.json'\n",
"with open(config_path, 'r') as f:\n",
" config = json.load(f)\n",
" sample_rate = config['sampling_rate']\n",
"\n",
"outputdir = './output'\n",
"inputdir = './test_wav'\n",
"num = 1024\n",
"\n",
"if __name__ == '__main__':\n",
" Path(outputdir).mkdir(parents=True, exist_ok=True)\n",
" print(\"Init model and load weights\")\n",
" # make sure you downloaded the weights from https://huggingface.co/Dongchao/AcademiCodec/blob/main/HiFi-Codec-24k-320d \n",
" # and put it in ./checkpoint/\n",
" model = VQVAE(\n",
" config_path,\n",
" ckpt_path,\n",
" with_encoder=True)\n",
" model.cuda()\n",
" model.eval()\n",
" print(\"Model ready\")\n",
"\n",
" wav_paths = glob.glob(f\"{inputdir}/*.wav\")[:num]\n",
" print(f\"Globbed {len(wav_paths)} wav files.\")\n",
" fid_to_acoustic_token = {}\n",
" for wav_path in tqdm(wav_paths[:1]):\n",
" wav, sr = librosa.load(wav_path, sr=sample_rate)\n",
" print(\"wav.shape:\",wav.shape)\n",
" assert sr == sample_rate\n",
" fid = os.path.basename(wav_path)[:-4]\n",
" wav = normalize(wav) * 0.95\n",
" wav = torch.FloatTensor(wav).unsqueeze(0)\n",
" wav = wav.to(torch.device('cuda'))\n",
" acoustic_token = model.encode(wav)\n",
" print(\"acoustic_token:\",acoustic_token)\n",
" print(\"acoustic_token.shape:\",acoustic_token.shape)\n",
" print(\"acoustic_token.dtype:\",acoustic_token.dtype)\n",
" fid = os.path.basename(wav_path)[:-4]\n",
" fid_to_acoustic_token[fid] = acoustic_token\n",
"\n",
" torch.save(fid_to_acoustic_token,\n",
" os.path.join(outputdir, 'fid_to_acoustic_token.pth'))\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.7.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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