MiniXC

; Some pipe(|) separated metadata about all LibriVox readers, whose work was used
; in the corpus.
;
; The meaning of the fields in left-to-right order is as follows:
;
; reader_id: the ID of the reader in the LibriVox's database
; gender: 'F' for female, 'M' for male
; subset: the corpus subset to which the reader's audio is assigned
; duration: total number of minutes of speech by the reader, included in the corpus
; name: the name under which the reader is registered in LibriVox

MiniXC

#!/bin/bash

if ! test -f /etc/hosts.bak; then
    sudo cp /etc/hosts /etc/hosts.bak
fi

sudo cp /etc/hosts.bak /etc/hosts

YAML_INFO="
`gcloud compute tpus tpu-vm list --zone=us-central1-f --format=yaml`

MiniXC

# add to your bashrc

function pyroblast() {
    pkill -f ".*python.*"
    pkill -f ".*wandb.*"
    accelerate launch "$@"
}

MiniXC

import json
import sys

from accelerate import Accelerator
import torch
from tqdm.auto import tqdm
from datasets import load_dataset
from speech_collator import SpeechCollator
from speech_collator.measures import EnergyMeasure, PitchMeasure, SRMRMeasure, SNRMeasure
from transformers import HfArgumentParser

MiniXC

possibly later

• include what the graph is showing in the title (better for screenshots)
• option to remove outliers (came across some in the data)
• moving average (although we have to be careful with that)

bigger changes

• more than one tag at the same time
• control for tags other than fandom (e.g. a pairing)

small changes

MiniXC

if [ $# -eq 0 ]; then
    tmux new-session -d -s ram_police
    tmux send-keys -t ram_police "bash ram_police.sh true" ENTER
    tmux capture-pane -t ram_police
else
    free_ram=$(free -g | grep -m1 -E "[[:digit:]]" | tail -c4)
    ram_limit=10
    
    clear
    echo "RAM POLICE WITH $ram_limit GB MIN."

MiniXC

MiniXC

,y_pred,y_pred_rounded,fips,date,y_true,week
0,-0.043328024446964264,0,1001,2017-06-30,0.0,0
1,0.507280170917511,1,1001,2017-12-27,1.0,0
2,0.028431229293346405,0,1001,2018-06-25,0.0,0
3,-0.017092008143663406,0,1003,2017-06-30,0.0,0
4,0.6950212121009827,1,1003,2017-12-27,1.0382,0
5,0.31889382004737854,0,1003,2018-06-25,0.382,0
6,-0.0005514370277523994,0,1005,2017-06-30,0.0,0
7,0.820785403251648,1,1005,2017-12-27,1.0974,0
8,0.07871026545763016,0,1005,2018-06-25,0.0,0

MiniXC

,y_pred,y_pred_rounded,fips,date,y_true,week
0,0.03677709400653839,0,1001,2017-06-30,0.0,0
1,0.42399361729621887,0,1001,2017-12-27,1.0,0
2,0.017427001148462296,0,1001,2018-06-25,0.0,0
3,0.036498844623565674,0,1003,2017-06-30,0.0,0
4,0.7784255743026733,1,1003,2017-12-27,1.0382,0
5,0.3446190059185028,0,1003,2018-06-25,0.382,0
6,0.03354061394929886,0,1005,2017-06-30,0.0,0
7,0.744026780128479,1,1005,2017-12-27,1.0974,0
8,0.03732544928789139,0,1005,2018-06-25,0.0,0

MiniXC

import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F

class FocalLoss(nn.Module):
    """ see https://arxiv.org/abs/1708.02002
    based on https://github.com/AdeelH/pytorch-multi-class-focal-loss/blob/master/focal_loss.py
    """
    def __init__(self,