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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10406.263945578237\n"
]
}
],
"source": [
"import os\n",
"import librosa\n",
"import numpy as np\n",
"path = \"./9nine/nimi_sora/reference_audio\"\n",
"files = os.listdir(path)\n",
"time = 0\n",
"for file in files:\n",
" data, fs = librosa.load(path + \"/\" + file)\n",
" time += len(data) / fs\n",
"print(time)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.890627777777778"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"10406.26/60/60"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[False, False, False, False, True, True, True, True, True, True],\n",
" [False, False, False, False, True, True, True, True, True, True],\n",
" [False, False, False, False, True, True, True, True, True, True],\n",
" [False, False, False, False, True, True, True, True, True, True],\n",
" [False, False, False, False, False, True, True, True, True, True],\n",
" [False, False, False, False, False, False, True, True, True, True],\n",
" [False, False, False, False, False, False, False, True, True, True],\n",
" [False, False, False, False, False, False, False, False, True, True],\n",
" [False, False, False, False, False, False, False, False, False, True],\n",
" [False, False, False, False, False, False, False, False, False, False]])\n"
]
}
],
"source": [
"import torch\n",
"import torch.nn.functional as F\n",
"x = torch.randn(3, 4, 5)\n",
"y = torch.randn(3, 6, 5)\n",
"x_len = x.shape[1]\n",
"y_len = y.shape[1]\n",
"\n",
"x_attn_mask = F.pad(\n",
" torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device),\n",
" (0, y_len),\n",
" value=True, )\n",
"y_attn_mask = F.pad(\n",
" torch.triu(\n",
" torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),\n",
" diagonal=1, ),\n",
" (x_len, 0),\n",
" value=False, )\n",
"xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0)\n",
"bsz, src_len = x.shape[0], x_len + y_len\n",
"_xy_padding_mask = (ar_xy_padding_mask.view(bsz, 1, 1, src_len)\n",
" .expand(-1, self.num_head, -1, -1)\n",
" .reshape(bsz * self.num_head, 1, src_len))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[False, False, False, False, True, True, True, True, True, True],\n",
" [False, False, False, False, True, True, True, True, True, True],\n",
" [False, False, False, False, True, True, True, True, True, True],\n",
" [False, False, False, False, True, True, True, True, True, True],\n",
" [False, False, False, False, False, True, True, True, True, True],\n",
" [False, False, False, False, False, False, True, True, True, True],\n",
" [False, False, False, False, False, False, False, True, True, True],\n",
" [False, False, False, False, False, False, False, False, True, True],\n",
" [False, False, False, False, False, False, False, False, False, True],\n",
" [False, False, False, False, False, False, False, False, False, False]])\n"
]
}
],
"source": [
"import torch\n",
"import torch.nn.functional as F\n",
"from AR.models.utils import make_pad_mask\n",
"# self.ar_text_embedding = TokenEmbedding(\n",
"# self.embedding_dim, self.phoneme_vocab_size, self.p_dropout) vocab_size = 512, embedding_dim = 512\n",
"\n",
"# x = x + self.bert_proj(bert_feature.transpose(1,2))\n",
"# x = self.ar_text_position(x)\n",
"# x_mask = make_pad_mask(x_lens)\n",
"\n",
"# y_mask = make_pad_mask(y_lens)\n",
"# y_mask_int = y_mask.type(torch.int64)\n",
"# codes = y.type(torch.int64) * (1 - y_mask_int)\n",
"\n",
"# Training\n",
"# AR Decoder\n",
"# 将x(文本)和y(音频)的token拼起来，emb维度是512\n",
"# 测试一个简单的case\n",
"num_head = 8\n",
"\n",
"\n",
"x_len = 4\n",
"y_len = 6\n",
"\n",
"x_attn_mask = F.pad(\n",
" torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device),\n",
" (0, y_len),\n",
" value=True, )\n",
"y_attn_mask = F.pad(\n",
" torch.triu(\n",
" torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),\n",
" diagonal=1, ),\n",
" (x_len, 0),\n",
" value=False, )\n",
"xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0)\n",
"bsz, src_len = x.shape[0], x_len + y_len\n",
"print(xy_attn_mask)\n",
"# _xy_padding_mask = (ar_xy_padding_mask.view(bsz, 1, 1, src_len)\n",
"# .expand(-1, num_head, -1, -1)\n",
"# .reshape(bsz * num_head, 1, src_len))\n",
"# xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)\n",
"# new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)\n",
"# new_attn_mask.masked_fill_(xy_attn_mask, float(\"-inf\"))\n",
"# xy_attn_mask = new_attn_mask"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([324, 192])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch\n",
"import AR.models.t2s_model\n",
"# model = AR.models.t2s_model.Text2SemanticDecoder()\n",
"dict = torch.load(r\"D:\\pyprojs\\GPT-SoVITSs\\fork\\GPT-SoVITS-NIMI_SORA\\9nine\\nimi_sora\\sora_e5_s3275.pth\")\n",
"dict[\"weight\"][\"enc_p.text_embedding.weight\"].shape"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "vits",
"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": "3.10.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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