[MohammadHijjawi97]

Summary

Fixes #3782.

When the rounded predicted token length is zero for every utterance in a batch, Paraformer.inference and ContextualParaformer.inference return a bare [], and SeacoParaformer.inference returns ([],). AutoModel.inference then replaces the whole batch with a single {"text": ""} without a key (Paraformer, ContextualParaformer), or adds nothing for it (SeACo-Paraformer, the same bug with a worse outcome). Reading inference_with_vad, the number of segment results then no longer matches the number of VAD segments, so the whole recording would be returned as an empty result, and with spk_model set results[_b] would raise IndexError.

The early return now produces one empty result per key, with the same fields the regular decoding loop produces for an empty hypothesis (text: "", plus timestamp: [] when timestamps are enabled, or token_int: [] without a tokenizer), and returns meta_data like the normal path. In Paraformer, the existing key normalisation is moved above the check so the early return uses the same keys.

Before (two utterances, batch_size=2, all token lengths 0):

[{'text': ''}]

After:

[{'key': 'a', 'text': ''}, {'key': 'b', 'text': ''}]

Type of change

• Bug fix
• Documentation
• Example or demo
• Runtime or deployment
• Benchmark or evaluation
• Model/training change

Validation

• python -m compileall funasr examples tests
• Docs or links checked
• Runtime/deployment command tested

Added tests/test_paraformer_empty_token_batch.py. It uses a real WavFrontend (no cmvn) and model stand-ins whose predictor returns zero tokens, so no weights are downloaded. It covers Paraformer (with and without timestamps, without a tokenizer), ContextualParaformer, SeacoParaformer (CifPredictorV3 and another predictor), and AutoModel.inference end to end. All 6 tests fail on main and pass with this change.

python -m pytest tests/test_paraformer_empty_token_batch.py tests/test_paraformer_timestamp_padding.py tests/test_paraformer_timestamp_contract.py tests/test_paraformer_hotword_file.py tests/test_auto_model.py -k "not merge_thr"

User impact

Users decoding short, silent or noise-only inputs in a batch get one keyed empty result per input instead of a single unkeyed row, so a batch of segments with no predicted tokens no longer collapses the batch's results.

Notes for reviewers

The batch-level early return still skips the decoder, so its behaviour is unchanged apart from the returned structure. When only some utterances in a batch have zero tokens, the existing loop already handles them, and this change does not touch that path.