thomwolf

""" To use: install LLM studio (or Ollama), clone OpenVoice, run this script in the OpenVoice directory
    git clone https://github.com/myshell-ai/OpenVoice
    cd OpenVoice
    git clone https://huggingface.co/myshell-ai/OpenVoice
    cp -r OpenVoice/* .
    pip install whisper pynput pyaudio
"""

from openai import OpenAI
import time

hollance

To allow the Hugging Face version of Whisper to predict word-level timestamps, a new property alignment_heads must be added to the GenerationConfig object. This is a list of [layer, head] pairs that select the cross-attention heads that are highly correlated to word-level timing.

If your Whisper checkpoint does not have the alignment_heads property yet, it can be added in two possible ways.

Method 1. Change the model.generation_config property:

# load the model
model = WhisperForConditionalGeneration.from_pretrained("your_checkpoint")

yoavg

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

juanmc2005

# Copyright 2017 Ravi Sojitra. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

zcaceres

Faster, Better Speech Recognition with Wav2Letter's Auto Segmentation Criterion

In 2016, Facebook AI Research (FAIR) broke new ground with Wav2Letter, a fully convolutional speech recognition system.

In Wav2Letter, FAIR showed that systems based on convolutional neural networks (CNNs) could person as well as traditional recurrent neural network-based approaches.

In this article, we'll focus on an understudied module at the core of Wav2Letter: the Auto Segmentation (ASG) Criterion.

XinDongol

Inside profiler

import torch
import torchvision.models as models

model = models.densenet121(pretrained=True)
x = torch.randn((1, 3, 224, 224), requires_grad=True)

with torch.autograd.profiler.profile(use_cuda=True) as prof:
    model(x)

lattner

Swift Concurrency Manifesto

Author: Chris Lattner

Chinese Translation by Jason Yu

Contents

• Introduction
• Overall Vision

ianthetechie

Twilio Asterisk Secure Trunking HOWTO

This is a short guide on how to set up an encrypted VoIP system using Twilio and Asterisk. I was a little annoyed that just about everything these days still uses unencrypted RTP for media (though just about everyone supports SIP over TLS). So I spent a weekend looking at options, and settled on a totally overkill solution involving Twilio's secure trunking to an Asterisk PBX. While all bets are off once it hits the PSTN, at least you won't be blasting your conversations over the internet in clear text.

pahud

$ aws --region=ap-northeast-2 ec2 describe-spot-price-history --instance-types c4.large --start-time=$(date +%s) --product-descriptions="Linux/UNIX" --query 'SpotPriceHistory[*].{az:AvailabilityZone, price:SpotPrice}'
[
    {
        "price": "0.024900", 
        "az": "ap-northeast-2a"
    }, 
 {

htp

curl --include \
     --no-buffer \
     --header "Connection: Upgrade" \
     --header "Upgrade: websocket" \
     --header "Host: example.com:80" \
     --header "Origin: http://example.com:80" \
     --header "Sec-WebSocket-Key: SGVsbG8sIHdvcmxkIQ==" \
     --header "Sec-WebSocket-Version: 13" \
     http://example.com:80/