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
TTS is a library for advanced Text-to-Speech generation.

TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality. TTS comes with pretra

Mozilla 6.5k Jan 08, 2023
"Investigating the Limitations of Transformers with Simple Arithmetic Tasks", 2021

transformers-arithmetic This repository contains the code to reproduce the experiments from the paper: Nogueira, Jiang, Lin "Investigating the Limitat

Castorini 33 Nov 16, 2022
Faster, modernized fork of the language identification tool langid.py

py3langid py3langid is a fork of the standalone language identification tool langid.py by Marco Lui. Original license: BSD-2-Clause. Fork license: BSD

Adrien Barbaresi 12 Nov 05, 2022
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language mod

20.5k Jan 08, 2023
NAACL 2022: MCSE: Multimodal Contrastive Learning of Sentence Embeddings

MCSE: Multimodal Contrastive Learning of Sentence Embeddings This repository contains code and pre-trained models for our NAACL-2022 paper MCSE: Multi

Saarland University Spoken Language Systems Group 39 Nov 15, 2022
An automated program that helps customers of Pizza Palour place their pizza orders

PIzza_Order_Assistant Introduction An automated program that helps customers of Pizza Palour place their pizza orders. The program uses voice commands

Tindi Sommers 1 Dec 26, 2021
Japanese NLP Library

Japanese NLP Library Back to Home Contents 1 Requirements 1.1 Links 1.2 Install 1.3 History 2 Libraries and Modules 2.1 Tokenize jTokenize.py 2.2 Cabo

Pulkit Kathuria 144 Dec 27, 2022
Train 🤗transformers with DeepSpeed: ZeRO-2, ZeRO-3

Fork from https://github.com/huggingface/transformers/tree/86d5fb0b360e68de46d40265e7c707fe68c8015b/examples/pytorch/language-modeling at 2021.05.17.

Junbum Lee 12 Oct 26, 2022
A Word Level Transformer layer based on PyTorch and 🤗 Transformers.

Transformer Embedder A Word Level Transformer layer based on PyTorch and 🤗 Transformers. How to use Install the library from PyPI: pip install transf

Riccardo Orlando 27 Nov 20, 2022
Basic yet complete Machine Learning pipeline for NLP tasks

Basic yet complete Machine Learning pipeline for NLP tasks This repository accompanies the article on building basic yet complete ML pipelines for sol

Ivan 20 Aug 22, 2022
Code for the paper "BERT Loses Patience: Fast and Robust Inference with Early Exit".

Patience-based Early Exit Code for the paper "BERT Loses Patience: Fast and Robust Inference with Early Exit". NEWS: We now have a better and tidier i

Kevin Canwen Xu 54 Jan 04, 2023
🤗🖼️ HuggingPics: Fine-tune Vision Transformers for anything using images found on the web.

🤗 🖼️ HuggingPics Fine-tune Vision Transformers for anything using images found on the web. Check out the video below for a walkthrough of this proje

Nathan Raw 185 Dec 21, 2022
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN

artificial intelligence cosmic love and attention fire in the sky a pyramid made of ice a lonely house in the woods marriage in the mountains lantern

Phil Wang 2.3k Jan 01, 2023
Code of paper: A Recurrent Vision-and-Language BERT for Navigation

Recurrent VLN-BERT Code of the Recurrent-VLN-BERT paper: A Recurrent Vision-and-Language BERT for Navigation Yicong Hong, Qi Wu, Yuankai Qi, Cristian

YicongHong 109 Dec 21, 2022
Contains the code and data for our #ICSE2022 paper titled as "CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences"

CodeFill This repository contains the code for our paper titled as "CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Namin

Software Analytics Lab 11 Oct 31, 2022
天池中药说明书实体识别挑战冠军方案;中文命名实体识别;NER; BERT-CRF & BERT-SPAN & BERT-MRC;Pytorch

天池中药说明书实体识别挑战冠军方案;中文命名实体识别;NER; BERT-CRF & BERT-SPAN & BERT-MRC;Pytorch

zxx飞翔的鱼 751 Dec 30, 2022
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
Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.

Pretrained Language Model This repository provides the latest pretrained language models and its related optimization techniques developed by Huawei N

HUAWEI Noah's Ark Lab 2.6k Jan 08, 2023
A fast Text-to-Speech (TTS) model. Work well for English, Mandarin/Chinese, Japanese, Korean, Russian and Tibetan (so far). 快速语音合成模型,适用于英语、普通话/中文、日语、韩语、俄语和藏语(当前已测试)。

简体中文 | English 并行语音合成 [TOC] 新进展 2021/04/20 合并 wavegan 分支到 main 主分支,删除 wavegan 分支! 2021/04/13 创建 encoder 分支用于开发语音风格迁移模块! 2021/04/13 softdtw 分支 支持使用 Sof

Atomicoo 161 Dec 19, 2022
Convolutional 2D Knowledge Graph Embeddings resources

ConvE Convolutional 2D Knowledge Graph Embeddings resources. Paper: Convolutional 2D Knowledge Graph Embeddings Used in the paper, but do not use thes

Tim Dettmers 586 Dec 24, 2022