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

Last update: Aug 19, 2021

Overview

Google Text-To-Speech Batch Prompt File Maker

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:

Follow the official documentation for activating this API and creating a Service Account
Generate a JSON key associated to this Service Account
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

Releases(v1.2.0)

v1.2.0(Aug 12, 2021)

Tested for 100 single-sentence concurrent records
Source code(tar.gz)
Source code(zip)

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

8 Dec 10, 2022

This python module is an easy-to-use port of the text normalization used in the paper "Not low-resource anymore: Aligner ensembling, batch filtering, and new datasets for Bengali-English machine translation". It is intended to be used for normalizing / cleaning Bengali and English text.

normalizer This python module is an easy-to-use port of the text normalization used in the paper "Not low-resource anymore: Aligner ensembling, batch

23 Nov 30, 2022

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

988 Jan 4, 2023

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.

186 Dec 24, 2022

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

1 Dec 20, 2021

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

29 Oct 16, 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

118 Dec 30, 2022

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

easySpeech 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

14 May 24, 2022

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

3 Nov 11, 2022

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

Related tags

Overview

Google Text-To-Speech Batch Prompt File Maker

1) GCP account setup

2) CSV and YAML files

3) Dependencies installation

4) Inline calling

5) Encoding for Cisco CVP Audio Elements

You might also like...

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)

This python module is an easy-to-use port of the text normalization used in the paper "Not low-resource anymore: Aligner ensembling, batch filtering, and new datasets for Bengali-English machine translation". It is intended to be used for normalizing / cleaning Bengali and English text.

voice2json is a collection of command-line tools for offline speech/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.

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

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

Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification"

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

Command Line Text-To-Speech using Google TTS

Releases(v1.2.0)

v1.2.0(Aug 12, 2021)

Owner

Ponchotitlán

A fast, efficient universal vector embedding utility package.

BERTAC (BERT-style transformer-based language model with Adversarially pretrained Convolutional neural network)

[ICLR 2021 Spotlight] Pytorch implementation for "Long-tailed Recognition by Routing Diverse Distribution-Aware Experts."

To classify the News into Real/Fake using Features from the Text Content of the article

Twewy-discord-chatbot - Build a Discord AI Chatbot that Speaks like Your Favorite Character

Unsupervised Language Modeling at scale for robust sentiment classification

a test times augmentation toolkit based on paddle2.0.

⚡ Automatically decrypt encryptions without knowing the key or cipher, decode encodings, and crack hashes ⚡

The official implementation of "BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?, ACL 2021 main conference"

Official PyTorch Implementation of paper "NeLF: Neural Light-transport Field for Single Portrait View Synthesis and Relighting", EGSR 2021.

ChainKnowledgeGraph, 产业链知识图谱包括A股上市公司、行业和产品共3类实体

Grading tools for Advanced NLP (11-711)Grading tools for Advanced NLP (11-711)

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

ElasticBERT: A pre-trained model with multi-exit transformer architecture.

Learn meanings behind words is a key element in NLP. This project concentrates on the disambiguation of preposition senses. Therefore, we train a bert-transformer model and surpass the state-of-the-art.

Code for hyperboloid embeddings for knowledge graph entities

Topic Inference with Zeroshot models

Prompt tuning toolkit for GPT-2 and GPT-Neo

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch

This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.