docs: document long outputs and FunASR hotwords

2026-07-14 17:00:27 +08:00
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+# Fun-ASR-Nano context, hotwords, and transcript correction notes
+
+Use this when deciding whether Fun-ASR-Nano can use semantic context to improve recognition quality.
+
+## Verified from upstream code/docs
+
+Sources checked:
+
+- `modelscope/FunASR` `funasr/models/fun_asr_nano/model.py`
+- `modelscope/FunASR` `funasr/models/fun_asr_nano/inference_vllm.py`
+- `modelscope/FunASR` `funasr/models/fun_asr_nano/inference_vllm_pipeline.py`
+- `modelscope/FunASR` `funasr/models/fun_asr_nano/inference_vllm_streaming.py`
+- `modelscope/FunASR` `docs/vllm_guide_zh.md`
+- `modelscope/FunASR` `docs/model_selection.md`
+
+The upstream model is LLM-based: a SenseVoice-style audio encoder plus an LLM decoder. The upstream model-selection guide describes Fun-ASR-Nano as stronger on hard cases, context, and proper nouns.
+
+The PyTorch path has a prompt builder roughly shaped as:
+
+```python
+def get_prompt(self, hotwords: list[str], language: str = None, itn: bool = True):
+    if len(hotwords) > 0:
+        prompt = "请结合上下文信息，更加准确地完成语音转写任务。如果没有相关信息，我们会留空。\n\n\n**上下文信息：**\n\n\n"
+        prompt += f"热词列表：[{hotwords}]\n"
+    else:
+        prompt = ""
+    if language is None:
+        prompt += "语音转写"
+    else:
+        prompt += f"语音转写成{language}"
+    if not itn:
+        prompt += "，不进行文本规整"
+    return prompt + "："
+```
+
+The vLLM path exposes the same idea via `hotwords` and `language`. The vLLM guide documents `language` and `hotwords` as `generate()` / API parameters.
+
+## What is enabled by default?
+
+Do **not** assume cross-segment semantic correction is automatically enabled for offline file transcription.
+
+In the bundled transcription script at the time of writing:
+
+- `funasr_common.py` calls `model.generate(input=..., batch_size=1)`.
+- It passes `language` only when the user specified a non-`auto` language.
+- It passes `use_itn=True` for Fun-ASR-Nano.
+- It does **not** pass `hotwords`.
+- It does **not** pass `prev_text` or a rolling context between VAD segments.
+
+Therefore, the current script uses Nano's model-internal contextual ability, language hint, and ITN, but it does not provide external domain context or previous transcript text. The transcript may still contain homophone/near-homophone mistakes such as `帐篷` -> `账本` when the source audio is unclear.
+
+## Practical guidance
+
+1. Use explicit `--language AUDIO_LANGUAGE_CODE` to avoid language ambiguity.
+2. If the content has known names, game terms, locations, item names, or recurring jargon, collect them as a hotword list before transcription.
+3. Prefer adding explicit script support such as `--hotword TERM` / `--hotwords-file FILE` over relying on vague “semantic correction”.
+4. Treat hotwords as biasing hints, not proof. They can improve proper nouns and domain terms but cannot fully fix unclear audio.
+5. For final captions, quotes, subtitles, narration, or published copy, still re-listen and manually correct the original audio segment.
+6. Do not silently invent hotwords. Ask the user for domain terms or extract candidate terms from existing project notes/transcripts and confirm them.
+
+## Possible future script enhancement
+
+Expose hotwords in the bundled CLI:
+
+```bash
+funasr-nano VIDEO_PATH \
+  --track TRACK_INDEX \
+  --language AUDIO_LANGUAGE_CODE \
+  --hotword "帐篷" \
+  --hotword "复活点" \
+  --output-dir VIDEO_DIR
+```
+
+Then pass them to FunASR:
+
+```python
+gen_kw["hotwords"] = args.hotwords
+```
+
+For longer lists, support a UTF-8 text file with one hotword per line.