speech to text benchmark framework

Open Source AI Benchmarking toolkit for benchmarking speech to text services

Speech-To-Text forced-alignment Speech processing Universal PERformance Benchmark

Text-to-Speech Benchmark

SpeechGLUE is a speech version of the GLUE benchmark, driven by text-to-speech.

realtime speech to text benchmarks

Qwen3-ASR speech-to-text for llama.cpp — patch, GGUF models, and benchmarks

Curated list of papers, frameworks, benchmarks, and applications for multimodal AI agents (LLMs, text-to-image, speech, world models, etc.) on mobile and edge devices.

ASR-NL-benchmark is a python package to evaluate and compare the performance of speech-to-text for the Dutch language.

a place to discuss and create an independent speech-to-text benchmarking tool

Fake News Detection Fake News Detection in Python In this project, we have used various natural language processing techniques and machine learning algorithms to classify fake news articles using sci-kit libraries from python. Getting Started These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system. Prerequisites What things you need to install the software and how to install them: Python 3.6 This setup requires that your machine has python 3.6 installed on it. you can refer to this url https://www.python.org/downloads/ to download python. Once you have python downloaded and installed, you will need to setup PATH variables (if you want to run python program directly, detail instructions are below in how to run software section). To do that check this: https://www.pythoncentral.io/add-python-to-path-python-is-not-recognized-as-an-internal-or-external-command/. Setting up PATH variable is optional as you can also run program without it and more instruction are given below on this topic. Second and easier option is to download anaconda and use its anaconda prompt to run the commands. To install anaconda check this url https://www.anaconda.com/download/ You will also need to download and install below 3 packages after you install either python or anaconda from the steps above Sklearn (scikit-learn) numpy scipy if you have chosen to install python 3.6 then run below commands in command prompt/terminal to install these packages pip install -U scikit-learn pip install numpy pip install scipy if you have chosen to install anaconda then run below commands in anaconda prompt to install these packages conda install -c scikit-learn conda install -c anaconda numpy conda install -c anaconda scipy Dataset used The data source used for this project is LIAR dataset which contains 3 files with .tsv format for test, train and validation. Below is some description about the data files used for this project. LIAR: A BENCHMARK DATASET FOR FAKE NEWS DETECTION William Yang Wang, "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection, to appear in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), short paper, Vancouver, BC, Canada, July 30-August 4, ACL. the original dataset contained 13 variables/columns for train, test and validation sets as follows: Column 1: the ID of the statement ([ID].json). Column 2: the label. (Label class contains: True, Mostly-true, Half-true, Barely-true, FALSE, Pants-fire) Column 3: the statement. Column 4: the subject(s). Column 5: the speaker. Column 6: the speaker's job title. Column 7: the state info. Column 8: the party affiliation. Column 9-13: the total credit history count, including the current statement. 9: barely true counts. 10: false counts. 11: half true counts. 12: mostly true counts. 13: pants on fire counts. Column 14: the context (venue / location of the speech or statement). To make things simple we have chosen only 2 variables from this original dataset for this classification. The other variables can be added later to add some more complexity and enhance the features. Below are the columns used to create 3 datasets that have been in used in this project Column 1: Statement (News headline or text). Column 2: Label (Label class contains: True, False) You will see that newly created dataset has only 2 classes as compared to 6 from original classes. Below is method used for reducing the number of classes. Original -- New True -- True Mostly-true -- True Half-true -- True Barely-true -- False False -- False Pants-fire -- False The dataset used for this project were in csv format named train.csv, test.csv and valid.csv and can be found in repo. The original datasets are in "liar" folder in tsv format. File descriptions DataPrep.py This file contains all the pre processing functions needed to process all input documents and texts. First we read the train, test and validation data files then performed some pre processing like tokenizing, stemming etc. There are some exploratory data analysis is performed like response variable distribution and data quality checks like null or missing values etc. FeatureSelection.py In this file we have performed feature extraction and selection methods from sci-kit learn python libraries. For feature selection, we have used methods like simple bag-of-words and n-grams and then term frequency like tf-tdf weighting. we have also used word2vec and POS tagging to extract the features, though POS

🗣️ Transcription (speech to text) d’échanges téléphoniques adressés au support IT d’une entreprise

OpenAI Whisper benchmark against Deepgram on accuracy and latency

Speech to text model benchmarks

Auto inference + evaluation pipeline for benchmarking commercial and open-source speech-to-text models on archival audio

Citizen Voice: Speech-to-Text for Grievances -Improve open-source STT for citizen grievance calls (Hindi, English, Hinglish). Benchmark accuracy, implement enhancements (noise reduction, language models).

VoiceClone Benchmark: A comprehensive comparison of five state-of-the-art Text-to-Speech (TTS) models for voice cloning capabilities. This project showcases IndexTTS, Fish-Speech-1.5, SparkTTS, CosyVoice2, and F5-TTS using identical reference audio and synthesis text samples to evaluate voice similarity, naturalness, and expressiveness.

Real-time AI voice announcer for races, benchmarks, and long-running agent processes in Claude Code. Watches your logs, generates sports-style play-by-play commentary, and speaks it aloud using fast local text-to-speech (TTS). Powered by Claude. No cloud TTS APIs required.

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Benchmarking bunch of STT both online and offline.

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Speech to text model benchmarks

Benchmark for speech to text models.

Benchmarking Malaysian Speech-to-Text models.

Speech-to-text benchmarking using WER with clean speech dataset

The most comprehensive open Speech-to-Text benchmark