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)
Source code for CsiNet and CRNet using Fully Connected Layer-Shared feedback architecture.

FCS-applications Source code for CsiNet and CRNet using the Fully Connected Layer-Shared feedback architecture. Introduction This repository contains

Boyuan Zhang 4 Oct 07, 2022
Local cross-platform machine translation GUI, based on CTranslate2

DesktopTranslator Local cross-platform machine translation GUI, based on CTranslate2 Download Windows Installer You can either download a ready-made W

Yasmin Moslem 29 Jan 05, 2023
Sentence boundary disambiguation tool for Japanese texts (日本語文境界判定器)

Bunkai Bunkai is a sentence boundary (SB) disambiguation tool for Japanese texts. Quick Start $ pip install bunkai $ echo -e '宿を予約しました♪!まだ2ヶ月も先だけど。早すぎ

Megagon Labs 160 Dec 23, 2022
Uncomplete archive of files from the European Nopsled Team

European Nopsled CTF Archive This is an archive of collected material from various Capture the Flag competitions that the European Nopsled team played

European Nopsled 4 Nov 24, 2021
本项目是作者们根据个人面试和经验总结出的自然语言处理(NLP)面试准备的学习笔记与资料,该资料目前包含 自然语言处理各领域的 面试题积累。

【关于 NLP】那些你不知道的事 作者:杨夕、芙蕖、李玲、陈海顺、twilight、LeoLRH、JimmyDU、艾春辉、张永泰、金金金 介绍 本项目是作者们根据个人面试和经验总结出的自然语言处理(NLP)面试准备的学习笔记与资料,该资料目前包含 自然语言处理各领域的 面试题积累。 目录架构 一、【

1.4k Dec 30, 2022
HAIS_2GNN: 3D Visual Grounding with Graph and Attention

HAIS_2GNN: 3D Visual Grounding with Graph and Attention This repository is for the HAIS_2GNN research project. Tao Gu, Yue Chen Introduction The motiv

Yue Chen 1 Nov 26, 2022
Fully featured implementation of Routing Transformer

Routing Transformer A fully featured implementation of Routing Transformer. The paper proposes using k-means to route similar queries / keys into the

Phil Wang 246 Jan 02, 2023
Implementation of Token Shift GPT - An autoregressive model that solely relies on shifting the sequence space for mixing

Token Shift GPT Implementation of Token Shift GPT - An autoregressive model that relies solely on shifting along the sequence dimension and feedforwar

Phil Wang 32 Oct 14, 2022
Simple translation demo showcasing our headliner package.

Headliner Demo This is a demo showcasing our Headliner package. In particular, we trained a simple seq2seq model on an English-German dataset. We didn

Axel Springer News Media & Tech GmbH & Co. KG - Ideas Engineering 16 Nov 24, 2022
hashily is a Python module that provides a variety of text decoding and encoding operations.

hashily is a python module that performs a variety of text decoding and encoding functions. It also various functions for encrypting and decrypting text using various ciphers.

DevMysT 5 Jul 17, 2022
Recognition of 38 speech commands in russian. Based on Yandex Cup 2021 ML Challenge: ASR

Speech_38_ru_commands Recognition of 38 speech commands in russian. Based on Yandex Cup 2021 ML Challenge: ASR Программа умеет распознавать 38 ключевы

Andrey 9 May 05, 2022
Refactored version of FastSpeech2

Refactored version of FastSpeech2. An implementation of Microsoft's "FastSpeech 2: Fast and High-Quality End-to-End Text to Speech"

ILJI CHOI 10 May 26, 2022
DataCLUE: 国内首个以数据为中心的AI测评(含模型分析报告)

DataCLUE 以数据为中心的AI测评(DataCLUE) DataCLUE: A Chinese Data-centric Language Evaluation Benchmark 内容导引 章节 描述 简介 介绍以数据为中心的AI测评(DataCLUE)的背景 任务描述 任务描述 实验结果

CLUE benchmark 135 Dec 22, 2022
Reproduction process of BERT on SST2 dataset

BERT-SST2-Prod Reproduction process of BERT on SST2 dataset 安装说明 下载代码库 git clone https://github.com/JunnYu/BERT-SST2-Prod 进入文件夹,安装requirements pip ins

yujun 1 Nov 18, 2021
Mlcode - Continuous ML API Integrations

mlcode Basic APIs for ML applications. Django REST Application Contains REST API

Sujith S 1 Jan 01, 2022
The source code of HeCo

HeCo This repo is for source code of KDD 2021 paper "Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning". Paper Link: htt

Nian Liu 106 Dec 27, 2022
official ( API ) for the zAmericanEnglish app in [ Google play ] and [ App store ]

official ( API ) for the zAmericanEnglish app in [ Google play ] and [ App store ]

Plugin 3 Jan 12, 2022
PyTorch implementation of "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" from Meta AI

data2vec-pytorch PyTorch implementation of "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" from Meta AI (F

Aryan Shekarlaban 105 Jan 04, 2023
Sentence Embeddings with BERT & XLNet

Sentence Transformers: Multilingual Sentence Embeddings using BERT / RoBERTa / XLM-RoBERTa & Co. with PyTorch This framework provides an easy method t

Ubiquitous Knowledge Processing Lab 9.1k Jan 02, 2023
Simple Python library, distributed via binary wheels with few direct dependencies, for easily using wav2vec 2.0 models for speech recognition

Wav2Vec2 STT Python Beta Software Simple Python library, distributed via binary wheels with few direct dependencies, for easily using wav2vec 2.0 mode

David Zurow 22 Dec 29, 2022