Utility for Google Text-To-Speech batch audio files generator. Ideal for prompt files creation with Google voices for application in offline IVRs

Overview

Google Text-To-Speech Batch Prompt File Maker

forthebadge forthebadge

Are you in the need of IVR prompts, but you have no voice actors? Let Google talk your prompts like a pro! This repository contains a tool for generating Google Text-To-Speech audio files in batch. It is ideal for offline prompts creation with Google voices for application in IVRs

In order to use this repository, clone the contents in your local environment with the following console command:

git clone https://github.com/ponchotitlan/google_text-to-speech_prompt_maker.git

Once cloned, follow the next steps for environment setup:

1) GCP account setup

Before adjusting up the contents of this project, it is neccesary to setup the Cloud Text-to-Speech API in your Google Cloud project:

  1. Follow the official documentation for activating this API and creating a Service Account
  2. Generate a JSON key associated to this Service Account
  3. Save this JSON key file in the same location as the contents of this repository

2) CSV and YAML files

Prepare a CSV document with the texts that you want to convert into prompt audio files. The CSV must have the following structure:

    <FILE NAME WITHOUT THE EXTENSION> , <PROMPT TEXT OR COMPLIANT SSML GRAMMAR>

An Excel export to CSV format should be enough for rendering a compatible structure, ever since the text within a cell is dumped between quotes if it contains spaces. An example of a compliant file with SSML prompts would look like the following:

    sample_prompt_01,"<speak>Welcome to ACME. How can I help you today?</speak>"
    sample_prompt_02,"<speak>Press 1 for sales. <break time=200ms/>Press 2 for Tech Support. <break time=200ms/>Or stay in the line for agent support</speak>"
    ...

Additionally, prepare a YAML document with the structure mentioned in the setup.yaml file included in this repository. The fields are the following:

# CSV format is: FILE_NAME , PROMPT_CONTENT
csv_prompts_file: <my_csv_file.csv>

google_settings:
    # ROUTE TO THE JSON KEY ASSOCIATED TO GCP. IF THE ROUTE HAS SPACES, ADD QUOTES TO THE VALUE
    JSON_key: <my_key.json>

    # PROMPT TYPE. ALLOWED VALUES ARE:
    # normal | SSML
    prompt_type: SSML

    # FILE FORMAT. ALLOWED VALUES ARE:
    # wav | mp3
    output_audio_format: wav

    # COMPLIANT LANGUAGE CODE. SEE https://cloud.google.com/text-to-speech/docs/voices FOR COMPATIBLE CODES
    language_code: es-US

    # COMPLIANT VOICE NAME. SEE https://cloud.google.com/text-to-speech/docs/voices FOR COMPATIBLE NAMES
    voice_name: es-US-Wavenet-C

    # COMPLIANT VOICE GENDER. SEE https://cloud.google.com/text-to-speech/docs/voices FOR COMPATIBLE GENDERS WITH THE SELECTED VOICE ABOVE
    voice_gender: MALE

    # COMPLIANT AUDIO ENCODING. SUPPORTED TYPES ARE:
    # AUDIO_ENCODING_UNSPECIFIED | LINEAR16 | MP3 | OGG_OPUS
    audio_encoding: LINEAR16

3) Dependencies installation

Install the requirements in a virtual environment with the following command:

pip install -r requirements.txt

4) Inline calling

The usage of the script requires the following inline elements:

usage: init.py [-h] [-b BATCH] configurationYAML

Batch prompt generation with Google TTS services

positional arguments:
  configurationYAML     YAML file with operation settings

optional arguments:
  -h, --help            show this help message and exit
  -b BATCH, --batch BATCH
                        Amount of rows in the CSV file to process at the same
                        time. Suggested max value is 100. Default is 10

An example is:

py init.py setup.yaml

The command prompt will show logs based on the status of each row:

✅ Prompt sample_prompt_04.WAV created successfully!
✅ Prompt sample_prompt_01.WAV created successfully!
✅ Prompt sample_prompt_03.WAV created successfully!
✅ Prompt sample_prompt_02.WAV created successfully!

The corresponding audio files will be saved in the same location where this script is executed.

5) Encoding for Cisco CVP Audio Elements

Unfortunately, Google Text-To-Speech service does not support the compulsory 8-bit μ-law encoding as per the Python SDK documentation (I am currently working on a Java version which does support this encoding. This option might be released in the Python SDK in the future). However, there are many online services such as this one for achieving the aforementioned. Audacity can also be used for the purpose. Follow this tutorial for compatible file conversion steps. There's a more straightforward tool which has been proven useful for me in order to process batch files with the CVP compatible settings.

The resulting files can later be uploaded into the Tomcat server for usage within a design in Cisco CallStudio. The route within the CVP Windows Server VM is the following:

    C:\Cisco\CVP\VXMLServer\Tomcat\webapps\CVP\audio

Please refer to the Official Cisco Documentation for more information.

Crafted with ❤️ by Alfonso Sandoval - Cisco

You might also like...
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)

TOPSIS implementation in Python Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) CHING-LAI Hwang and Yoon introduced TOPSIS

voice2json is a collection of command-line tools for offline speech/intent recognition on Linux
voice2json is a collection of command-line tools for offline speech/intent recognition on Linux

Command-line tools for speech and intent recognition on Linux

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

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

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

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(v1.2.0)
Owner
Ponchotitlán
💻 ☕ 🥃 Let's talk about networks coding, automation and orchestration autour a cup of coffee, and a sip of tequila;
Ponchotitlán
Exploration of BERT-based models on twitter sentiment classifications

twitter-sentiment-analysis Explore the relationship between twitter sentiment of Tesla and its stock price/return. Explore the effect of different BER

Sammy Cui 2 Oct 02, 2022
A deep learning-based translation library built on Huggingface transformers

DL Translate A deep learning-based translation library built on Huggingface transformers and Facebook's mBART-Large 💻 GitHub Repository 📚 Documentat

Xing Han Lu 244 Dec 30, 2022
Multispeaker & Emotional TTS based on Tacotron 2 and Waveglow

This Repository contains a sample code for Tacotron 2, WaveGlow with multi-speaker, emotion embeddings together with a script for data preprocessing.

Ivan Didur 106 Jan 01, 2023
The source code of "Language Models are Few-shot Multilingual Learners" (MRL @ EMNLP 2021)

Language Models are Few-shot Multilingual Learners Paper This is the source code of the paper [Arxiv] [ACL Anthology]: This code has been written usin

Genta Indra Winata 45 Nov 21, 2022
A retro text-to-speech bot for Discord

hawking A retro text-to-speech bot for Discord, designed to work with all of the stuff you might've seen in Moonbase Alpha, using the existing command

Nick Schorr 23 Dec 25, 2022
A demo for end-to-end English and Chinese text spotting using ABCNet.

ABCNet_Chinese A demo for end-to-end English and Chinese text spotting using ABCNet. This is an old model that was trained a long ago, which serves as

Yuliang Liu 45 Oct 04, 2022
Neural network sequence labeling model

Sequence labeler This is a neural network sequence labeling system. Given a sequence of tokens, it will learn to assign labels to each token. Can be u

Marek Rei 250 Nov 03, 2022
Materials (slides, code, assignments) for the NYU class I teach on NLP and ML Systems (Master of Engineering).

FREE_7773 Repo containing material for the NYU class (Master of Engineering) I teach on NLP, ML Sys etc. For context on what the class is trying to ac

Jacopo Tagliabue 90 Dec 19, 2022
Python implementation of TextRank for phrase extraction and summarization of text documents

PyTextRank PyTextRank is a Python implementation of TextRank as a spaCy pipeline extension, used to: extract the top-ranked phrases from text document

derwen.ai 1.9k Jan 06, 2023
A crowdsourced dataset of dialogues grounded in social contexts involving utilization of commonsense.

A crowdsourced dataset of dialogues grounded in social contexts involving utilization of commonsense.

Alexa 62 Dec 20, 2022
Code Generation using a large neural network called GPT-J

CodeGenX is a Code Generation system powered by Artificial Intelligence! It is delivered to you in the form of a Visual Studio Code Extension and is Free and Open-source!

DeepGenX 389 Dec 31, 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
Meta learning algorithms to train cross-lingual NLI (multi-task) models

Meta learning algorithms to train cross-lingual NLI (multi-task) models

M.Hassan Mojab 4 Nov 20, 2022
Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx

Anchored CorEx: Hierarchical Topic Modeling with Minimal Domain Knowledge Correlation Explanation (CorEx) is a topic model that yields rich topics tha

Greg Ver Steeg 592 Dec 18, 2022
A sentence aligner for comparable corpora

About Yalign is a tool for extracting parallel sentences from comparable corpora. Statistical Machine Translation relies on parallel corpora (eg.. eur

Machinalis 128 Aug 24, 2022
AI Assistant for Building Reliable, High-performing and Fair Multilingual NLP Systems

AI Assistant for Building Reliable, High-performing and Fair Multilingual NLP Systems

Microsoft 37 Nov 29, 2022
Simple bots or Simbots is a library designed to create simple bots using the power of python. This library utilises Intent, Entity, Relation and Context model to create bots .

Simple bots or Simbots is a library designed to create simple chat bots using the power of python. This library utilises Intent, Entity, Relation and

14 Dec 15, 2021
CCF BDCI BERT系统调优赛题baseline(Pytorch版本)

CCF BDCI BERT系统调优赛题baseline(Pytorch版本) 此版本基于Pytorch后端的huggingface进行实现。由于此实现使用了Oneflow的dataloader作为数据读入的方式,因此也需要安装Oneflow。其它框架的数据读取可以参考OneflowDataloade

Ziqi Zhou 9 Oct 13, 2022
Transformer training code for sequential tasks

Sequential Transformer This is a code for training Transformers on sequential tasks such as language modeling. Unlike the original Transformer archite

Meta Research 578 Dec 13, 2022
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

ELECTRA Introduction ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using

Google Research 2.1k Dec 28, 2022