Practical overview
When it fits
Use it when you have a supported dataset and want to evaluate one or more speech-to-text engines through a Python command-line workflow.
Who it is for
- Teams comparing speech recognition engines on shared datasets.
- Developers evaluating accuracy, compute efficiency or streaming responsiveness.
- Researchers preparing repeatable speech recognition evaluation workflows.
Documented uses
- Compare supported speech-to-text engines using a chosen dataset and language.
- Measure transcript errors with Word Error Rate or Character Error Rate.
- Assess punctuation errors, local-engine compute use and model size where applicable.
- Measure word-emission latency for supported streaming engines.
Requirements & limits
The repository is public and licensed under Apache-2.0. The README documents a CLI workflow using Python 3.12 on Ubuntu 22.04; cloud-engine use requires provider-specific configuration and credentials.
- The README lists dataset identifiers and supported languages, so confirm that your intended dataset and language are included.
- Metric coverage depends on engine type: core-hour and model size are omitted for cloud engines, and latency is measured only for streaming engines.
- Cloud benchmarks require service-specific credentials; the documented Amazon Transcribe example uses an AWS profile and location.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare the benchmark environment
Set up the documented prerequisites for using the Python benchmark.
Before you start
- Ubuntu 22.04 and Python 3.12 are the documented development and test environment.
- FFmpeg
- A downloaded supported dataset
Steps
Install FFmpeg.
Download a supported dataset.
Install the repository requirements with the command below.
pip3 install -r requirements.txt
Expected outcome
The documented software and dataset prerequisites are in place for proceeding to the engine-specific benchmark instructions.
Guide limitations
- These are setup instructions; they do not establish that a benchmark run succeeded.
- The README's benchmark commands require dataset, language and engine-specific configuration.
Run a documented Amazon Transcribe benchmark
Use the README's command structure to benchmark Amazon Transcribe on a supported dataset.
Before you start
- The installed requirements, FFmpeg and a downloaded supported dataset
- Dataset identifier and dataset folder path
- Target language
- AWS profile and AWS location
Steps
Choose a supported dataset identifier and provide its folder path.
Set the target language, AWS profile and AWS location.
Run the command below with
--dataset,--dataset-folder,--language,--engine AMAZON_TRANSCRIBE,--aws-profileand--aws-location.python3 benchmark.py
Expected outcome
The command invokes the documented benchmark workflow for Amazon Transcribe using the selected dataset and language.
Guide limitations
- AWS account configuration and credentials are required.
- The source gives the command template but does not report a run outcome.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- Picovoice/speech-to-text-benchmark · Official homepage title · retained in its source language