In this session, Eric Bezzam from Hugging Face will introduce the leaderboard, walk through its structure, and demonstrate how it can be used to evaluate and compare models under more realistic acoustic conditions.
Treble will then take you behind the scenes, exploring the data that powers the leaderboard and the levels of acoustic complexity required to move from controlled datasets to real world performance. If you are building or evaluating speech and audio systems, this session will give you a clearer view of what current benchmarks miss and how that is changing.
On the agenda for this Webinar:
1. Hugging Face: Introducing the Far Field ASR Leaderboard (FFASR)
Eric Bezzam presents the leaderboard, including its structure, key features, and how it can be used to evaluate and compare ASR models under more realistic conditions.
2. Treble: Why Far Field Data Matters
An inside look at the data powering FFASR and why far field audio is critical for building and evaluating robust audio AI systems. We will cover how this data is created and the level of acoustic complexity required to reflect real world performance.
3. Q&A
About Hugging Face
Hugging Face has become a central platform for the machine learning community, setting the standard for open, collaborative development in AI. Known for its model hub, evaluation tools, and commitment to open benchmarks, Hugging Face plays a key role in shaping how models are shared, tested, and improved across the ecosystem. The introduction of the Far Field ASR Leaderboard continues this direction, bringing more realistic evaluation practices into the open.
Meet the speakers
Audio ML Engineer - Hugging Face
Dr. Eric Bezzam
Eric Bezzam is an audio ML engineer at Hugging Face. He received his PhD from EPFL, and previously worked at Snips, Sonos, DSP Concepts, and Fraunhofer IDMT. He was one of the main developers of pyroomacoustics.
Senior Product Manager for the Treble SDK
Dr. Daniel Gert Nielsen
Dr. Daniel Gert Nielsen is a specialist in numerical vibro-acoustics, with a PhD focused on loudspeaker modeling and optimization. His expertise spans acoustic simulation for communication devices and synthetic data generation for machine learning applications. With a strong background in numerical methods and audio technology, he plays a key role in shaping advanced acoustic modeling solutions at Treble.
FFASR Leaderboard with Hugging Face and Treble

