docs: clarify FunASR rolling context behavior
This commit is contained in:
@@ -132,7 +132,7 @@ 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).
|
||||
|
||||
**Context and hotwords:** Fun-ASR-Nano is an LLM-based ASR model and upstream code supports prompt-style `language` and `hotwords` hints. However, do not assume the current bundled script automatically performs cross-segment semantic correction: it currently passes `language` / `use_itn`, but does not pass external hotwords or rolling `prev_text` context between VAD segments. If the video contains known game terms, names, locations, item names, or other jargon, ask the user for a hotword list or derive candidate terms and confirm them before transcription. See `references/funasr-nano-context-hotwords.md`. Hotwords are biasing hints, not a substitute for final manual checking.
|
||||
**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:
|
||||
|
||||
|
||||
@@ -1,80 +0,0 @@
|
||||
# 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.
|
||||
@@ -0,0 +1,104 @@
|
||||
# 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.
|
||||
Reference in New Issue
Block a user