From 571dc8b55a9b036a5b36f968bb3a5baf5858e395 Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期三, 21 二月 2024 14:42:36 +0800
Subject: [PATCH] update raw_text handling
---
funasr/auto/auto_model.py | 25 ++++++++++++++++++-------
1 files changed, 18 insertions(+), 7 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index f59bb6b..78e47cc 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -1,14 +1,13 @@
import json
import time
+import copy
import torch
-import hydra
import random
import string
import logging
import os.path
import numpy as np
from tqdm import tqdm
-from omegaconf import DictConfig, OmegaConf, ListConfig
from funasr.register import tables
from funasr.utils.load_utils import load_bytes
@@ -17,7 +16,7 @@
from funasr.utils.vad_utils import slice_padding_audio_samples
from funasr.train_utils.set_all_random_seed import set_all_random_seed
from funasr.train_utils.load_pretrained_model import load_pretrained_model
-from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+from funasr.utils.load_utils import load_audio_text_image_video
from funasr.utils.timestamp_tools import timestamp_sentence
from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
try:
@@ -385,11 +384,15 @@
if self.punc_model is not None:
self.punc_kwargs.update(cfg)
punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, disable_pbar=True, **cfg)
- import copy; raw_text = copy.copy(result["text"])
+ raw_text = copy.copy(result["text"])
result["text"] = punc_res[0]["text"]
+ else:
+ raw_text = None
# speaker embedding cluster after resorted
if self.spk_model is not None and kwargs.get('return_spk_res', True):
+ if raw_text is None:
+ logging.error("Missing punc_model, which is required by spk_model.")
all_segments = sorted(all_segments, key=lambda x: x[0])
spk_embedding = result['spk_embedding']
labels = self.cb_model(spk_embedding.cpu(), oracle_num=kwargs.get('preset_spk_num', None))
@@ -398,20 +401,28 @@
if self.spk_mode == 'vad_segment': # recover sentence_list
sentence_list = []
for res, vadsegment in zip(restored_data, vadsegments):
+ if 'timestamp' not in res:
+ logging.error("Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \
+ and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
+ can predict timestamp, and speaker diarization relies on timestamps.")
sentence_list.append({"start": vadsegment[0],\
"end": vadsegment[1],
- "sentence": res['raw_text'],
+ "sentence": res['text'],
"timestamp": res['timestamp']})
elif self.spk_mode == 'punc_segment':
+ if 'timestamp' not in result:
+ logging.error("Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \
+ and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
+ can predict timestamp, and speaker diarization relies on timestamps.")
sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
result['timestamp'], \
- result['raw_text'])
+ raw_text)
distribute_spk(sentence_list, sv_output)
result['sentence_info'] = sentence_list
elif kwargs.get("sentence_timestamp", False):
sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
result['timestamp'], \
- result['raw_text'])
+ raw_text)
result['sentence_info'] = sentence_list
if "spk_embedding" in result: del result['spk_embedding']
--
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