sentence_info text drifts away from its timestamps/speakers: Chinese VAD chunks are joined without a space before timestamp_sentence splits on spaces

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Valutazione

Difficoltà
2/5
Tempo stimato
1-3 ore
Idoneità per principianti
72/100
Tipo di issue
Bug
Chiarezza
Specificata chiaramente
Stato di attività
Attiva
Stack tecnologico
python, pytorch
Ambito
audio-video-rtc, backend

Direzione di ricerca

Inizia da _join_vad_texts in funasr/auto/auto_model.py e timestamp_sentence in funasr/utils/timestamp_tools.py; segui come inference_with_vad fornisce testo e timestamp al separatore di frasi. Riproduci il problema con i due segmenti in cinese dell’issue e verifica che il testo di ogni frase resti allineato con i relativi timestamp e con il parlante, e che la punteggiatura resti corretta.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

Summary

With vad_model + punc_model (+ spk_model), the text of each sentence_info entry is shifted relative to its start/end/timestamp/spk. The shift grows by one unit at every VAD chunk boundary where two Chinese chunks meet, so on long audio the sentence text ends up far ahead of the time range and speaker it is attached to. The top-level text (punctuated) is correct; only sentence_info[*].text is wrong.

Version

  • funasr 1.4.16 (same code in 1.4.17 and current main)
  • AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc", spk_model="cam++"), Python 3.12, macOS, CPU

Minimal reproduction (no audio needed)

from funasr.auto.auto_model import _join_vad_texts
from funasr.utils.timestamp_tools import timestamp_sentence

chunks = ["今 天 天 气 很 好", "我 们 去 公 园 吧"]          # two VAD chunks
punc_input_text = _join_vad_texts(chunks)
print(repr(punc_input_text))   # '今 天 天 气 很 好我 们 去 公 园 吧'  ("好我" fused)

timestamps = [[i * 200, i * 200 + 200] for i in range(12)]
punc = [1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 3]                 # "，" after 好, "。" after 吧
print(len(punc_input_text.split()), "text units vs", len(timestamps), "timestamps")
for s in timestamp_sentence(punc, timestamps, punc_input_text):
    print(s["text"], s["start"], s["end"])

Output:

11 text units vs 12 timestamps
今天天气很好我， 0 1200
们去公园吧。 1200 2400

Expected 今天天气很好， / 我们去公园吧。.

Real-world impact

On a 19.5-minute Mandarin recording: 5299 timestamps and 5299 punctuation ids, but punc_input_text.split() yields only 5082 units (217 fused boundaries). Sentence text drifts up to ~217 characters (~50 s of speech) ahead of its timestamps, so:

  • sentences are cut 1–N characters off (e.g. 然， / 后用于…)
  • since speakers are distributed by time, text is attributed to the wrong speaker, often switching speaker mid-sentence

Cause

In AutoModel.inference_with_vad, punc_input_text = _join_vad_texts(...) intentionally joins Chinese chunks without a space (fine for the punctuation model, whose tokenizer splits CJK into characters, so punc_array length still matches). But the same string is then used as timestamp_text, and timestamp_sentence pairs units with timestamps via text_postprocessed.split(). The fused boundary units shift every following unit. The existing check punc_length != len(sentence_timestamps) doesn't catch it because the number of text units is never compared.

Suggested fix

Use a space-joined version of the chunk texts for timestamp_text (or have timestamp_sentence consume one unit per timestamp), and/or assert that the number of text units equals the number of timestamps.

Workaround we use for now: call generate(..., return_raw_text=True) and rebuild each sentence's text by taking len(sentence["timestamp"]) units from result["raw_text"].split() in order, keeping the trailing punctuation from sentence["text"]. With that, sentence boundaries and speaker turns line up correctly.

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