From aa45aeeaa7e8abd11bc1be392b4547685645ca5e Mon Sep 17 00:00:00 2001
From: 语帆 <yf352572@alibaba-inc.com>
Date: 星期一, 04 三月 2024 14:29:52 +0800
Subject: [PATCH] atsr
---
funasr/auto/auto_model.py | 49 +++++++++++++++++++++++++++----------------------
1 files changed, 27 insertions(+), 22 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 78e47cc..9bb9ce0 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -23,7 +23,7 @@
from funasr.models.campplus.cluster_backend import ClusterBackend
except:
print("If you want to use the speaker diarization, please `pip install hdbscan`")
-
+import pdb
def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
"""
@@ -41,6 +41,7 @@
chars = string.ascii_letters + string.digits
if isinstance(data_in, str) and data_in.startswith('http'): # url
data_in = download_from_url(data_in)
+
if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
_, file_extension = os.path.splitext(data_in)
file_extension = file_extension.lower()
@@ -141,7 +142,7 @@
kwargs = download_model(**kwargs)
set_all_random_seed(kwargs.get("seed", 0))
-
+
device = kwargs.get("device", "cuda")
if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0:
device = "cpu"
@@ -161,19 +162,18 @@
vocab_size = len(tokenizer.token_list)
else:
vocab_size = -1
-
# build frontend
frontend = kwargs.get("frontend", None)
+
if frontend is not None:
frontend_class = tables.frontend_classes.get(frontend)
frontend = frontend_class(**kwargs["frontend_conf"])
kwargs["frontend"] = frontend
kwargs["input_size"] = frontend.output_size()
-
+
# build model
model_class = tables.model_classes.get(kwargs["model"])
model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
-
model.to(device)
# init_param
@@ -213,9 +213,9 @@
batch_size = kwargs.get("batch_size", 1)
# if kwargs.get("device", "cpu") == "cpu":
# batch_size = 1
-
+
key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
-
+
speed_stats = {}
asr_result_list = []
num_samples = len(data_list)
@@ -228,6 +228,7 @@
data_batch = data_list[beg_idx:end_idx]
key_batch = key_list[beg_idx:end_idx]
batch = {"data_in": data_batch, "key": key_batch}
+
if (end_idx - beg_idx) == 1 and kwargs.get("data_type", None) == "fbank": # fbank
batch["data_in"] = data_batch[0]
batch["data_lengths"] = input_len
@@ -379,12 +380,14 @@
result[k] = restored_data[j][k]
else:
result[k] += restored_data[j][k]
-
+
+ return_raw_text = kwargs.get('return_raw_text', False)
# step.3 compute punc model
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)
raw_text = copy.copy(result["text"])
+ if return_raw_text: result['raw_text'] = raw_text
result["text"] = punc_res[0]["text"]
else:
raw_text = None
@@ -403,26 +406,28 @@
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['text'],
- "timestamp": res['timestamp']})
+ 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['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'], \
- raw_text)
+ 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'],
+ raw_text,
+ return_raw_text=return_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'], \
- raw_text)
+ sentence_list = timestamp_sentence(punc_res[0]['punc_array'],
+ result['timestamp'],
+ raw_text,
+ return_raw_text=return_raw_text)
result['sentence_info'] = sentence_list
if "spk_embedding" in result: del result['spk_embedding']
--
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