Multimodal Benchmark

English ASR WER

English ASR WER as evaluated on the LFM3-Omni-2B card: average word error rate (WER) in percent on the ESB / Open ASR benchmark suite; lower is better.

Category

Multimodal

Score type

percent

Scale

100%

higher is better

Models tested

2

What is English ASR WER?

English ASR WER is a benchmark for handling several input types together (images, documents, text). The official description says: "English ASR WER as evaluated on the LFM3-Omni-2B card: average word error rate (WER) in percent on the ESB / Open ASR benchmark suite; lower is better". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

English ASR WER measures handling several input types together (images, documents, text). In detail, it checks:

  • questions that mix text with images or document pages
  • reading and extracting information from screenshots and scans
  • understanding layout, titles, logos and figures in real documents
  • staying accurate when the text content alone is not enough

How to read the score

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Official methodology note: English ASR WER as evaluated on the LFM3-Omni-2B card: average word error rate (WER) in percent on the ESB / Open ASR benchmark suite; lower is better.

Frequently asked questions

What is the English ASR WER benchmark?

English ASR WER is a benchmark for handling several input types together (images, documents, text). The official description says: 'English ASR WER as evaluated on the LFM3-Omni-2B card: average word error rate (WER) in percent on the ESB / Open ASR benchmark suite; lower is better'. Below we describe in simple words what benchmarks of this type generally examine.

What does English ASR WER test?

English ASR WER mainly measures handling several input types together (images, documents, text). Concretely, it checks: questions that mix text with images or document pages; reading and extracting information from screenshots and scans; understanding layout, titles, logos and figures in real documents.

How is the English ASR WER score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get English ASR WER data and scores?

The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/english-asr-wer/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 2 models tested

Reset
# Model Score Date Source Verified
1 LFM3-Omni-2B
LiquidAI
6.36%
— Report Verified
2 LFM3-Audio-2B
LiquidAI
6.23%
— Report Verified