docs: remove unimplemented FunASR context mode

SKILL.md

@@ -132,7 +132,6 @@ uv run --directory SKILL_DIR/scripts/transcription funasr-nano VIDEO_PATH --list
 
 Where `VIDEO_DIR` is the directory containing the video file, `SKILL_DIR` is the skill installation directory, and `AUDIO_LANGUAGE_CODE` is the explicit transcription language (for example `zh` for Mandarin Chinese).
 
-**Rolling context / semantic correction:** Fun-ASR-Nano has upstream support for `prev_text`-style rolling context in streaming/incremental examples: earlier stabilized text can be fed back as context for later decoding, similar to a 2-pass-style refinement. However, do not assume the current bundled offline script automatically enables whole-file rolling semantic correction: it currently calls ordinary `AutoModel.generate(...)` on the extracted WAV and does not run a cumulative chunked pass with `prev_text`. See `references/funasr-nano-rolling-context.md`. Treat the current transcript as a strong baseline, not the final word when unclear speech or homophones matter.
 
 **Common OBS multi-track audio layouts.** When the user says "multi-track" or mentions OBS recording, these are the typical stream layouts:
 

references/funasr-nano-rolling-context.md

@@ -1,104 +0,0 @@
-# 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:
-
-```python
-if kwargs.get("prev_text", None) is not None:
-    source_input += kwargs["prev_text"]
-```
-
-The official `demo2.py` demonstrates cumulative chunk transcription:
-
-```python
-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:
-
-1. Current default offline transcription is a strong baseline.
-2. It may still produce wrong words such as `帐篷` -> `账本` when source speech is unclear.
-3. Fun-ASR-Nano has `prev_text`/rolling-context mechanisms upstream, but the bundled script does not yet expose a stable offline mode for that.
-4. A future enhancement can evaluate a chunked/cumulative or 2-pass-like transcription mode that feeds stabilized earlier text as `prev_text`.
-5. 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:
-
-```bash
-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.