snakers4

class BertEmbeddingBag(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings.
    """
    def __init__(self, config):
        super(BertEmbeddingBag, self).__init__()

        # self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
        ngram_matrix=np.load(config.ngram_matrix_path)

        self.old_bag = config.old_bag

snakers4

diff --git a/include/contrib/moderngpu/include/device/intrinsics.cuh b/include/contrib/moderngpu/include/device/intrinsics.cuh
index a601443..c212a38 100644
--- a/include/contrib/moderngpu/include/device/intrinsics.cuh
+++ b/include/contrib/moderngpu/include/device/intrinsics.cuh
@@ -112,7 +112,7 @@ __device__ __forceinline__ float shfl_up(float var,
 	unsigned int delta, int width = 32) {
 
 #if __CUDA_ARCH__ >= 300
-	var = __shfl_up(var, delta, width);
+	var = __shfl_up_sync(0xFFFFFFFF, var, delta, width);

snakers4

import torch
torch.set_num_threads(1)
from pprint import pprint

model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
                              model='silero_vad',
                              force_reload=True)

(get_speech_ts,
 _, read_audio,

snakers4

import torch
import itertools


# Based on https://github.com/bfs18/tacotron2


def grads_for_params(loss, parameters, optimizer):
    optimizer.zero_grad()
    loss.backward(retain_graph=True)

snakers4

import torch

model, example_texts, languages, punct, apply_te = torch.hub.load(repo_or_dir='snakers4/silero-models',
                                                                  model='silero_te')

input_text = input('Enter input text\n')
apply_te(input_text, lan='en')

snakers4

Original	Model
She heard Missis Gibson talking on in a sweet monotone, and wished to attend to what she was saying, but the Squires visible annoyance struck sharper on her mind.	She heard Missis Gibson talking on in a sweet monotone and wished to attend to what she was saying, but the squires visible ann

snakers4

snakers4

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "1XEMm5oo36Sm"
      },
      "outputs": [],
      "source": [

snakers4

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"vA0-1iElD-wr","executionInfo":{"status":"ok","timestamp":1681303032533,"user_tz":-180,"elapsed":10963,"user":{"displayName":"Senhor Maestro","userId":"08176940519269874318"}}},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import torch\n","\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","from sklearn.model_selection import train_test_split\n","from sklearn.feature_selection import mutual_info_regression\n","from sklearn.metrics import accuracy_score\n","#from catboost import CatBoostClassifier"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2rjmy9OpEYEr","executionInfo":{"status":"ok","timestamp":1681303100348,"user_tz":-180,"elapsed":19790,"user":{"displayName":"Senhor Maestro","userId":"08176940519269874318"}},"outputId":"cac0e451-2165-406b-cba1-f8c3bf6d480f"},"execution_count":