Fun-ASR-Nano rolling context / `prev_text` notes

Use this when deciding whether Fun-ASR-Nano can use already-transcribed context from the same audio file to improve later sentence recognition.

User intent

This is not about manually adding fixed hotwords. The desired behavior is closer to rolling context or 2-pass-style refinement:

While processing one independent audio/video file, the model should use text already recognized from earlier audio as context for later audio.
When a current sentence is ambiguous because of unclear pronunciation, it may benefit from preceding vocabulary, phrasing, topic, and dialogue context.
This is analogous to streaming ASR systems where early partial text may be wrong, and a later stabilized/final pass revises the sentence with more context.

Verified upstream behavior

Sources checked:

modelscope/FunASR funasr/models/fun_asr_nano/model.py
modelscope/FunASR examples/industrial_data_pretraining/fun_asr_nano/demo2.py
modelscope/FunASR funasr/models/fun_asr_nano/inference_vllm_streaming.py
modelscope/FunASR docs/vllm_guide.md
modelscope/FunASR docs/vllm_guide_zh.md

Fun-ASR-Nano supports a prev_text mechanism in the model inference path. In model.py, prev_text is appended into the assistant-side prompt prefix before decoding:

if kwargs.get("prev_text", None) is not None:
    source_input += kwargs["prev_text"]

The official demo2.py demonstrates cumulative chunk transcription:

prev_text = ""
for idx, cum_duration in enumerate(cum_durations):
    audio, rate = load_audio(wav_path, 16000, duration=round(cum_duration, 3))
    prev_text = m.inference([torch.tensor(audio)], prev_text=prev_text, **kwargs)[0][0]["text"]
    if idx != len(cum_durations) - 1:
        prev_text = tokenizer.decode(tokenizer.encode(prev_text)[:-5]).replace("�", "")

The streaming vLLM code also uses a two-stage design:

Stage 1: generate early chunks without prev_text to find stable output.
Stage 2: use stable output as prev_text for later cumulative chunks.
It keeps a rollback/unfixed tail, so the last few characters may change as more audio arrives.

The vLLM guide explicitly describes this as:

first chunks: no prev_text
subsequent chunks: use stable output as prev_text

What the current bundled script does

The current scripts/transcription/funasr_common.py path calls ordinary offline AutoModel.generate(input=..., batch_size=1) on the extracted WAV file.

It currently passes:

input
batch_size=1
explicit language when non-auto
use_itn=True for Fun-ASR-Nano

It does not currently implement a chunked/cumulative pass that feeds prior recognized text back through prev_text.

Therefore:

Fun-ASR-Nano has model-level support for rolling text context.
Upstream examples show this mechanism in streaming/incremental usage.
But the current bundled offline script should not be described as automatically enabling whole-file rolling context correction.

Practical implication for this skill

When accuracy matters, especially for unclear speech, homophones, game terms, and dialogue continuity, do not assume the default offline transcript is the best possible use of Nano's context mechanism.

The skill should describe this honestly:

Current default offline transcription is a strong baseline.
It may still produce wrong words such as 帐篷 -> 账本 when source speech is unclear.
Fun-ASR-Nano has prev_text/rolling-context mechanisms upstream, but the bundled script does not yet expose a stable offline mode for that.
A future enhancement can evaluate a chunked/cumulative or 2-pass-like transcription mode that feeds stabilized earlier text as prev_text.
Such a mode should be benchmarked before becoming default, because cumulative re-encoding may be slower and may have different timestamp/segmentation behavior.

Future enhancement idea

A possible experimental mode:

funasr-nano VIDEO_PATH \
  --track TRACK_INDEX \
  --language AUDIO_LANGUAGE_CODE \
  --context-mode rolling \
  --rollback-chars 5 \
  --output-dir VIDEO_DIR

Implementation direction:

Split or cumulatively extend audio in chunks.
Keep a stable prefix and an unfixed tail, similar to upstream demo2.py / streaming vLLM logic.
Feed the stable prefix into prev_text for the next pass.
Preserve a normal offline mode because it is simpler, faster, and already works.
Compare quality on real user samples before enabling by default.

This is a transcription-mode experiment, not a hotword-list feature.

4.5 KiB Raw Blame History

Fun-ASR-Nano rolling context / prev_text notes