fakufaku

import torch
import paderbox
import numpy as np
from scipy.signal import hamming, blackman, get_window, hann
import matplotlib.pyplot as plt


f = 1500.0  # exactly periodic
fs = 48000.0
nfft = 512

pzelasko

#!/usr/bin/env bash

# Common steps
conda create -n k2 python=3.8
conda activate k2 
conda install -c nvidia cudnn=7.6.5 cudatoolkit=10.2
conda install -c pytorch pytorch torchaudio
pip install cmake
mkdir build
pushd build 

SuperShinyEyes

def f1_loss(y_true:torch.Tensor, y_pred:torch.Tensor, is_training=False) -> torch.Tensor:
    '''Calculate F1 score. Can work with gpu tensors
    
    The original implmentation is written by Michal Haltuf on Kaggle.
    
    Returns
    -------
    torch.Tensor
        `ndim` == 1. 0 <= val <= 1
    

thomwolf

def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (vocabulary size)
            top_k >0: keep only top k tokens with highest probability (top-k filtering).
            top_p >0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
    """
    assert logits.dim() == 1  # batch size 1 for now - could be updated for more but the code would be less clear
    top_k = min(top_k, logits.size(-1))  # Safety check

kfcobrien

tsiege

ANNOUNCEMENT

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