81 lines
3.5 KiB
Markdown
81 lines
3.5 KiB
Markdown
# Fun-ASR-Nano context, hotwords, and transcript correction notes
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Use this when deciding whether Fun-ASR-Nano can use semantic context to improve recognition quality.
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## Verified from upstream code/docs
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Sources checked:
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- `modelscope/FunASR` `funasr/models/fun_asr_nano/model.py`
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- `modelscope/FunASR` `funasr/models/fun_asr_nano/inference_vllm.py`
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- `modelscope/FunASR` `funasr/models/fun_asr_nano/inference_vllm_pipeline.py`
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- `modelscope/FunASR` `funasr/models/fun_asr_nano/inference_vllm_streaming.py`
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- `modelscope/FunASR` `docs/vllm_guide_zh.md`
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- `modelscope/FunASR` `docs/model_selection.md`
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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.
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The PyTorch path has a prompt builder roughly shaped as:
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```python
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def get_prompt(self, hotwords: list[str], language: str = None, itn: bool = True):
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if len(hotwords) > 0:
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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"
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prompt += f"热词列表：[{hotwords}]\n"
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else:
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prompt = ""
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if language is None:
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prompt += "语音转写"
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else:
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prompt += f"语音转写成{language}"
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if not itn:
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prompt += "，不进行文本规整"
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return prompt + "："
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```
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The vLLM path exposes the same idea via `hotwords` and `language`. The vLLM guide documents `language` and `hotwords` as `generate()` / API parameters.
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## What is enabled by default?
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Do **not** assume cross-segment semantic correction is automatically enabled for offline file transcription.
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In the bundled transcription script at the time of writing:
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- `funasr_common.py` calls `model.generate(input=..., batch_size=1)`.
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- It passes `language` only when the user specified a non-`auto` language.
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- It passes `use_itn=True` for Fun-ASR-Nano.
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- It does **not** pass `hotwords`.
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- It does **not** pass `prev_text` or a rolling context between VAD segments.
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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.
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## Practical guidance
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1. Use explicit `--language AUDIO_LANGUAGE_CODE` to avoid language ambiguity.
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2. If the content has known names, game terms, locations, item names, or recurring jargon, collect them as a hotword list before transcription.
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3. Prefer adding explicit script support such as `--hotword TERM` / `--hotwords-file FILE` over relying on vague “semantic correction”.
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4. Treat hotwords as biasing hints, not proof. They can improve proper nouns and domain terms but cannot fully fix unclear audio.
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5. For final captions, quotes, subtitles, narration, or published copy, still re-listen and manually correct the original audio segment.
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6. Do not silently invent hotwords. Ask the user for domain terms or extract candidate terms from existing project notes/transcripts and confirm them.
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## Possible future script enhancement
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Expose hotwords in the bundled CLI:
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```bash
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funasr-nano VIDEO_PATH \
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--track TRACK_INDEX \
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--language AUDIO_LANGUAGE_CODE \
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--hotword "帐篷" \
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--hotword "复活点" \
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--output-dir VIDEO_DIR
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```
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Then pass them to FunASR:
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```python
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gen_kw["hotwords"] = args.hotwords
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```
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For longer lists, support a UTF-8 text file with one hotword per line.
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