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huckiyang/agwer_asr_batch_test_v0

agwer_asr_batch_test_v0 Example batch inputs for the agwer CLI: 30 real ASR error-correction sessions (Whisper 5-best decodes from the MIT-licensed HyPoradise benchmark, WSJ, with the verbatim outputs of a real LLM corrector), wrapped in every input format agwer accepts: file format flag input.jsonl native agwer records (default) input.openai.jsonl OpenAI chat sessions (messages) --format openai input.anthropic.jsonl Anthropic Messages + structured output --format… See the full description on the dataset page: https://huggingface.co/datasets/huckiyang/agwer_asr_batch_test_v0.

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agwerasrbatchtestv0

Example batch inputs for the agwer CLI: 30 real ASR error-correction sessions (Whisper 5-best decodes from the MIT-licensed HyPoradise benchmark, WSJ, with the verbatim outputs of a real LLM corrector), wrapped in every input format agwer accepts:

fileformatflag
input.jsonlnative agwer records(default)
input.openai.jsonlOpenAI chat sessions (messages)--format openai
input.anthropic.jsonlAnthropic Messages + structured output--format anthropic
input.sharegpt.jsonlShareGPT conversations--format sharegpt
input.parquetcolumnar batch--format parquet

All five carry the same 30 sessions, so every format reports identical metrics. --format auto (the default) detects each of them.

bash
pip install "agwer[parquet]"
hf download huckiyang/agwer_asr_batch_test_v0 --repo-type dataset --local-dir batch
agwer batch/input.openai.jsonl
agwer batch/input.parquet --json

Expected report: WER 1-best 6.4%, corrected 3.1%, onb 4.8%, ocp 1.7%, RIR (rho) 2.000, HER 0.286.

In the chat formats, the corrected transcript is extracted from the last assistant turn (structured outputs with a "corrected" key are unwrapped); reference and nbest ride along as top-level keys.