aroraakshit

import numpy as np

def softmax(x):
  """ Compute the softmax for each row of the input x
  Arguments:
  x -- A N dimensional veector or M X N dimensional numpy matrix.
  Return:
  x -- modified x in-place
  """
  

aroraakshit

name: rapids012
channels:
  - rapidsai
  - nvidia
  - conda-forge
  - defaults
  - numba
dependencies:
  - python=3.6
  - cudatoolkit=10.1

aroraakshit

#!/bin/bash

OS_NAME=$(lsb_release -is | tr '[:upper:]' '[:lower:]')
readonly OS_NAME
OS_DIST=$(lsb_release -cs)
readonly OS_DIST
CUDA_VERSION='10.2'
readonly CUDA_VERSION

readonly DEFAULT_NVIDIA_DEBIAN_GPU_DRIVER_VERSION='460.56'

aroraakshit

#!/bin/bash

# Dataproc configurations
readonly HADOOP_CONF_DIR='/etc/hadoop/conf'
readonly HIVE_CONF_DIR='/etc/hive/conf'
readonly SPARK_CONF_DIR='/etc/spark/conf'

function execute_with_retries() {
  local -r cmd=$1
  for ((i = 0; i < 10; i++)); do

aroraakshit

ICASSP 2024 MMITS-VC CHALENGE SUBMISSION FOR NVIDIA: SCALING NVIDIA’S MULTI-SPEAKER MULTI-LINGUAL TTS SYSTEMS WITH VOICE-CLONING TO INDIC LANGUAGES

Speaker	Initial data points	Duplicates, empty audio or transcripts	Final data points	Initial Durations of final data points (hours)	Final Durations after silence removal (hours)
Hindi_M	17798	1	17797	40.49	39.63
Hindi_F	16512	29	16483	40.21	38.16
Telugu_M	16939	0

aroraakshit

ICASSP 2023 LIMMITS CHALENGE SUBMISSION FOR VANI: VERY-LIGHTWEIGHT ACCENT-CONTROLLABLE TTS FOR NATIVE AND NON-NATIVE SPEAKERS WITH IDENTITY PRESERVATION

This gist includes:

• Dataset summary
• Model Parameter summary
• API usage

Dataset summary

Filelists for all tracks available here: https://drive.google.com/drive/folders/1u4Yri7KQmk8EiVNMIVJdZdvAVVNoiyb8?usp=sharing