This project provides a comprehensive benchmarking platform for Automatic Speech Recognition (ASR) systems, targeting researchers and developers in the ASR field. It aims to foster improvement in ASR technology by enabling easy comparison, reproduction, and examination of different models across a wide array of speech tasks and scenarios.

How It Works

The platform comprises three core components: a TestSet Zoo with diverse academic and custom-curated datasets (including Chinese datasets with detailed scenario and topic information), a Model Zoo featuring both commercial cloud APIs and popular open-source local models, and a standardized Benchmarking Pipeline for data preparation, recognition, post-processing, and error rate evaluation. This integrated approach ensures consistent and reproducible benchmarking.

Quick Start & Requirements

• Installation and usage details are not explicitly provided in the README.
• The project requires access to various ASR models (cloud APIs or local model files) and the specified datasets.
• Specific hardware or software prerequisites are not detailed.

Highlighted Details

• Extensive TestSet Zoo includes 26 curated Chinese datasets covering diverse scenarios (e.g., news, interviews, live broadcasts, lectures, podcasts) and topics, alongside popular English datasets like LibriSpeech and GigaSpeech.
• Model Zoo lists numerous English and Chinese ASR models, including major cloud providers (Alibaba, Amazon, Google, Microsoft, Tencent) and local models (Coqui, DeepSpeech, NeMo, Vosk, Whisper, Paraformer).
• Detailed benchmarking results (Character Error Rate - CER) are presented for various models across different test sets, allowing for direct comparison.
• The platform supports both "unlocked" (publicly available) and "locked" (restricted access) test sets, offering a broader evaluation scope.

Maintenance & Community

• Contact email: leaderboard@speechio.ai.
• No specific community channels (Discord, Slack) or roadmap information are provided in the README.

Licensing & Compatibility

• The README does not specify a license for the project code or the datasets.
• Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

• The README lacks explicit instructions for setup, installation, or running the benchmarking pipeline.
• Information regarding project maintenance, contribution guidelines, or community engagement is absent.
• The availability status of some listed models (e.g., bilibili_api_zh(*)) is marked as not universally available to the public yet.