easySpeech is an open-source Python wrapper for google speech to text API that doesn't require PyAudio(So you especially windows user don't have to deal with the errors while installing PyAudio) and also works with hugging face transformers

Overview

easySpeech


GitHub issues GitHub forks GitHub stars GitHub license GitHub last commit GitHub contributors Downloads


easySpeech is an open source python wrapper for google speech to text api that doesn't require PyAaudio(So you specially windows user don't have to deal with the errors while installing PyAudio) and also works with hugging face transformers

Installation

You can install easySpeech very easily using the following command

pip3 install easySpeech

Usage

  • Using google speech to text api
    By default easySpeech comes with a default api key which you can for testing purposes using the following code.
from easySpeech import speech
a=speech.speech('google')
print(a)

For production purpose use your own key because google can revoke the default api key at any time. Get your own api key from http://www.chromium.org/developers/how-tos/api-keys and use the following code

from easySpeech import speech
a=speech.speech('google',key="your api key")
print(a)

Specifying the duration of speech recognition in seconds(default value is 5 seconds)

from easySpeech import speech
a=speech.speech('google',duration = 10)
print(a)

Specifying the sample frequency(default is 44100)

from easySpeech import speech
a=speech.speech('google',duration = 10,freq = 44100)
print(a)

Specifying the language(works only for google speech api and default is english)

from easySpeech import speech
a=speech.speech('google',language="en-US")
print(a)

Converting an audio file to text(Currently it supports only wav file)

from easySpeech import speech
a=speech.google_audio('recording.wav')
print(a)
  • Using hugging face transformers(works offline and no need of any kind of api key) For using easySpeech with hugging face transformers use the following code.
from easySpeech import speech
a=speech.speech('ml')
print(a)

Specifying the duration of speech recognition in seconds(default valus is 5 seconds)

from easySpeech import speech
a=speech.speech('ml',duration = 10)
print(a)

Specifying the sample frequency(default is 44100)

from easySpeech import speech
a=speech.speech('ml',duration = 10,freq = 44100)
print(a)

Converting an audio file to text(Currently it supports only wav file)

from easySpeech import ml
a=ml.ml('recording.wav')
print(a)
  • Recording audio
    For recording audio use the following code
from easySpeech import speech
speech.recorder('recording.wav')

For recording audio with a specific frequency use the following code(default is 44100)

from easySpeech import speech
speech.recorder('recording.wav',freq = 50000)

For recording audio for a specific duration use the following code(default is 5s)

from easySpeech import speech
speech.recorder('recording.wav',duration = 50)

How to contribute

Since it is a free software , you can contribute to make it better. New contributors are always welcome, whether you write code, create resources, report bugs, or suggest features.

The easySpeech is written primarily in Python3x

Have a look at the open issues to find a mission that resonates with you.


Contact

Email: [email protected]
If you find any bug make a issue immediately.

License

easySpeech is lisenced under MIT license

MIT License | Copyright (c) 2021 SaptakBhoumik

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software
You might also like...
Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classification tasks of Chinese long text and short text, and supports sequence annotation tasks such as Chinese named entity recognition, part of speech tagging and word segmentation.

Pytorch-NLU,一个中文文本分类、序列标注工具包,支持中文长文本、短文本的多类、多标签分类任务,支持中文命名实体识别、词性标注、分词等序列标注任务。 Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classification tasks of Chinese long text and short text, and supports sequence annotation tasks such as Chinese named entity recognition, part of speech tagging and word segmentation.

A Python module made to simplify the usage of Text To Speech and Speech Recognition.
A Python module made to simplify the usage of Text To Speech and Speech Recognition.

Nav Module The solution for voice related stuff in Python Nav is a Python module which simplifies voice related stuff in Python. Just import the Modul

A python script to prefab your scripts/text files, and re create them with ease and not have to open your browser to copy code or write code yourself
A python script to prefab your scripts/text files, and re create them with ease and not have to open your browser to copy code or write code yourself

Scriptfab - What is it? A python script to prefab your scripts/text files, and re create them with ease and not have to open your browser to copy code

A Python wrapper for simple offline real-time dictation (speech-to-text) and speaker-recognition using Vosk.

Simple-Vosk A Python wrapper for simple offline real-time dictation (speech-to-text) and speaker-recognition using Vosk. Check out the official Vosk G

PocketSphinx is a lightweight speech recognition engine, specifically tuned for handheld and mobile devices, though it works equally well on the desktop

PocketSphinx 5prealpha This is PocketSphinx, one of Carnegie Mellon University's open source large vocabulary, speaker-independent continuous speech r

Code for ACL 2022 main conference paper "STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation".

STEMM: Self-learning with Speech-Text Manifold Mixup for Speech Translation This is a PyTorch implementation for the ACL 2022 main conference paper ST

Creating an Audiobook (mp3 file) using a Ebook (epub) using BeautifulSoup and Google Text to Speech

epub2audiobook Creating an Audiobook (mp3 file) using a Ebook (epub) using BeautifulSoup and Google Text to Speech Input examples qual a pasta do seu

Command Line Text-To-Speech using Google TTS
Command Line Text-To-Speech using Google TTS

cli-tts Thanks to gTTS by @pndurette! This is an interactive command line text-to-speech tool using Google TTS. Just type text and the voice will be p

Releases(v1.0.2)
  • v1.0.2(Jun 3, 2021)

    easySpeech is an open-source Python wrapper for google speech to text API that doesn't require PyAudio(So you especially windows user don't have to deal with the errors while installing PyAudio) and also works with hugging face transformers. You can also use it to record sound. What's new

    1. It is now even more easy to use
    2. Minor bug fix
    Source code(tar.gz)
    Source code(zip)
  • v1.0.1(Jun 1, 2021)

    easySpeech is an open-source Python wrapper for google speech to text API that doesn't require PyAudio(So you especially windows user don't have to deal with the errors while installing PyAudio) and also works with hugging face transformers. You can also use it to record sound.

    Source code(tar.gz)
    Source code(zip)
Ray-based parallel data preprocessing for NLP and ML.

Wrangl Ray-based parallel data preprocessing for NLP and ML. pip install wrangl # for latest pip install git+https://github.com/vzhong/wrangl See exa

Victor Zhong 33 Dec 27, 2022
Toward a Visual Concept Vocabulary for GAN Latent Space, ICCV 2021

Toward a Visual Concept Vocabulary for GAN Latent Space Code and data from the ICCV 2021 paper Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Kl

Sarah Schwettmann 13 Dec 23, 2022
Finally, some decent sample sentences

tts-dataset-prompts This repository aims to be a decent set of sentences for people looking to clone their own voices (e.g. using Tacotron 2). Each se

hecko 19 Dec 13, 2022
Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks.

Multilabel time series classification with LSTM Tensorflow implementation of model discussed in the following paper: Learning to Diagnose with LSTM Re

Aaqib 552 Nov 28, 2022
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.09

Keon Lee 142 Jan 06, 2023
Almost State-of-the-art Text Generation library

Ps: we are adding transformer model soon Text Gen 🐐 Almost State-of-the-art Text Generation library Text gen is a python library that allow you build

Emeka boris ama 63 Jun 24, 2022
MMDA - multimodal document analysis

MMDA - multimodal document analysis

AI2 75 Jan 04, 2023
Watson Natural Language Understanding and Knowledge Studio

Material de demonstração dos serviços: Watson Natural Language Understanding e Knowledge Studio Visão Geral: https://www.ibm.com/br-pt/cloud/watson-na

Vanderlei Munhoz 4 Oct 24, 2021
A Transformer Implementation that is easy to understand and customizable.

Simple Transformer I've written a series of articles on the transformer architecture and language models on Medium. This repository contains an implem

Naoki Shibuya 4 Jan 20, 2022
A BERT-based reverse dictionary of Korean proverbs

Wisdomify A BERT-based reverse-dictionary of Korean proverbs. 김유빈 : 모델링 / 데이터 수집 / 프로젝트 설계 / back-end 김종윤 : 데이터 수집 / 프로젝트 설계 / front-end / back-end 임용

94 Dec 08, 2022
Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification"

PTR Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification" If you use the code, please cite the following paper: @art

THUNLP 118 Dec 30, 2022
Conditional Transformer Language Model for Controllable Generation

CTRL - A Conditional Transformer Language Model for Controllable Generation Authors: Nitish Shirish Keskar, Bryan McCann, Lav Varshney, Caiming Xiong,

Salesforce 1.7k Dec 28, 2022
Stanford CoreNLP provides a set of natural language analysis tools written in Java

Stanford CoreNLP Stanford CoreNLP provides a set of natural language analysis tools written in Java. It can take raw human language text input and giv

Stanford NLP 8.8k Jan 07, 2023
Binaural Speech Synthesis

Binaural Speech Synthesis This repository contains code to train a mono-to-binaural neural sound renderer. If you use this code or the provided datase

Facebook Research 135 Dec 18, 2022
Final Project Bootcamp Zero

The Quest (Pygame) Descripción Este es el repositorio de código The-Quest para el proyecto final Bootcamp Zero de KeepCoding. El juego consiste en la

Seven-z01 1 Mar 02, 2022
Korea Spell Checker

한국어 문서 koSpellPy Korean Spell checker How to use Install pip install kospellpy Use from kospellpy import spell_init spell_checker = spell_init() # d

kangsukmin 2 Oct 20, 2021
Text Normalization(文本正则化)

Text Normalization(文本正则化) 任务描述:通过机器学习算法将英文文本的“手写”形式转换成“口语“形式,例如“6ft”转换成“six feet”等 实验结果 XGBoost + bag-of-words: 0.99159 XGBoost+Weights+rules:0.99002

Jason_Zhang 0 Feb 26, 2022
CDLA: A Chinese document layout analysis (CDLA) dataset

CDLA: A Chinese document layout analysis (CDLA) dataset 介绍 CDLA是一个中文文档版面分析数据集,面向中文文献类(论文)场景。包含以下10个label: 正文 标题 图片 图片标题 表格 表格标题 页眉 页脚 注释 公式 Text Title

buptlihang 84 Dec 28, 2022
APEACH: Attacking Pejorative Expressions with Analysis on Crowd-generated Hate Speech Evaluation Datasets

APEACH - Korean Hate Speech Evaluation Datasets APEACH is the first crowd-generated Korean evaluation dataset for hate speech detection. Sentences of

Kevin-Yang 70 Dec 06, 2022
A fast and lightweight python-based CTC beam search decoder for speech recognition.

pyctcdecode A fast and feature-rich CTC beam search decoder for speech recognition written in Python, providing n-gram (kenlm) language model support

Kensho 315 Dec 21, 2022