From d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99 Mon Sep 17 00:00:00 2001
From: liugz18 <57401541+liugz18@users.noreply.github.com>
Date: 星期四, 18 七月 2024 21:34:55 +0800
Subject: [PATCH] Rename 'res' in line 514 to avoid with naming conflict with line 365
---
funasr/auto/auto_model.py | 122 +++++++++++++++++++++++++---------------
1 files changed, 76 insertions(+), 46 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 7b5a02f..75324dc 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -19,7 +19,8 @@
from funasr.utils.load_utils import load_bytes
from funasr.download.file import download_from_url
from funasr.utils.timestamp_tools import timestamp_sentence
-from funasr.download.download_from_hub import download_model
+from funasr.utils.timestamp_tools import timestamp_sentence_en
+from funasr.download.download_model_from_hub import download_model
from funasr.utils.vad_utils import slice_padding_audio_samples
from funasr.utils.vad_utils import merge_vad
from funasr.utils.load_utils import load_audio_text_image_video
@@ -91,7 +92,8 @@
if isinstance(data_i, str) and os.path.exists(data_i):
key = misc.extract_filename_without_extension(data_i)
else:
- key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
+ if key is None:
+ key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
key_list.append(key)
else: # raw text; audio sample point, fbank; bytes
@@ -109,11 +111,15 @@
def __init__(self, **kwargs):
+ try:
+ from funasr.utils.version_checker import check_for_update
+
+ check_for_update()
+ except:
+ pass
+
log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
logging.basicConfig(level=log_level)
-
- if not kwargs.get("disable_log", True):
- tables.print()
model, kwargs = self.build_model(**kwargs)
@@ -162,7 +168,8 @@
self.spk_kwargs = spk_kwargs
self.model_path = kwargs.get("model_path")
- def build_model(self, **kwargs):
+ @staticmethod
+ def build_model(**kwargs):
assert "model" in kwargs
if "model_conf" not in kwargs:
logging.info("download models from model hub: {}".format(kwargs.get("hub", "ms")))
@@ -208,11 +215,11 @@
kwargs["frontend"] = frontend
# build model
model_class = tables.model_classes.get(kwargs["model"])
+ assert model_class is not None, f'{kwargs["model"]} is not registered'
model_conf = {}
deep_update(model_conf, kwargs.get("model_conf", {}))
deep_update(model_conf, kwargs)
model = model_class(**model_conf, vocab_size=vocab_size)
- model.to(device)
# init_param
init_param = kwargs.get("init_param", None)
@@ -233,6 +240,13 @@
# fp16
if kwargs.get("fp16", False):
model.to(torch.float16)
+ elif kwargs.get("bf16", False):
+ model.to(torch.bfloat16)
+ model.to(device)
+
+ if not kwargs.get("disable_log", True):
+ tables.print()
+
return model, kwargs
def __call__(self, *args, **cfg):
@@ -301,7 +315,7 @@
speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
description = f"{speed_stats}, "
if pbar:
- pbar.update(1)
+ pbar.update(end_idx - beg_idx)
pbar.set_description(description)
time_speech_total += batch_data_time
time_escape_total += time_escape
@@ -325,7 +339,9 @@
# FIX(gcf): concat the vad clips for sense vocie model for better aed
if kwargs.get("merge_vad", False):
for i in range(len(res)):
- res[i]["value"] = merge_vad(res[i]["value"], kwargs.get("merge_length", 15000))
+ res[i]["value"] = merge_vad(
+ res[i]["value"], kwargs.get("merge_length_s", 15) * 1000
+ )
# step.2 compute asr model
model = self.model
@@ -365,6 +381,9 @@
if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])
+
+ if kwargs["device"] == "cpu":
+ batch_size = 0
beg_idx = 0
beg_asr_total = time.time()
@@ -427,6 +446,10 @@
# f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
# f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
+ if len(results_sorted) != n:
+ results_ret_list.append({"key": key, "text": "", "timestamp": []})
+ logging.info("decoding, utt: {}, empty result".format(key))
+ continue
restored_data = [0] * n
for j in range(n):
index = sorted_data[j][1]
@@ -460,23 +483,20 @@
else:
result[k] += restored_data[j][k]
+ if not len(result["text"].strip()):
+ continue
return_raw_text = kwargs.get("return_raw_text", False)
# step.3 compute punc model
+ raw_text = None
if self.punc_model is not None:
- if not len(result["text"].strip()):
- if return_raw_text:
- result["raw_text"] = ""
- else:
- deep_update(self.punc_kwargs, cfg)
- punc_res = self.inference(
- result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **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
+ deep_update(self.punc_kwargs, cfg)
+ punc_res = self.inference(
+ result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg
+ )
+ raw_text = copy.copy(result["text"])
+ if return_raw_text:
+ result["raw_text"] = raw_text
+ result["text"] = punc_res[0]["text"]
# speaker embedding cluster after resorted
if self.spk_model is not None and kwargs.get("return_spk_res", True):
@@ -491,8 +511,8 @@
sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
if self.spk_mode == "vad_segment": # recover sentence_list
sentence_list = []
- for res, vadsegment in zip(restored_data, vadsegments):
- if "timestamp" not in res:
+ for rest, vadsegment in zip(restored_data, vadsegments):
+ if "timestamp" not in rest:
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'\
@@ -502,8 +522,8 @@
{
"start": vadsegment[0],
"end": vadsegment[1],
- "sentence": res["text"],
- "timestamp": res["timestamp"],
+ "sentence": rest["text"],
+ "timestamp": rest["timestamp"],
}
)
elif self.spk_mode == "punc_segment":
@@ -513,24 +533,40 @@
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,
- )
+ if kwargs.get("en_post_proc", False):
+ sentence_list = timestamp_sentence_en(
+ punc_res[0]["punc_array"],
+ result["timestamp"],
+ raw_text,
+ return_raw_text=return_raw_text,
+ )
+ else:
+ 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):
if not len(result["text"].strip()):
sentence_list = []
else:
- sentence_list = timestamp_sentence(
- punc_res[0]["punc_array"],
- result["timestamp"],
- raw_text,
- return_raw_text=return_raw_text,
- )
+ if kwargs.get("en_post_proc", False):
+ sentence_list = timestamp_sentence_en(
+ punc_res[0]["punc_array"],
+ result["timestamp"],
+ raw_text,
+ return_raw_text=return_raw_text,
+ )
+ else:
+ 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"]
@@ -582,12 +618,6 @@
)
with torch.no_grad():
-
- if type == "onnx":
- export_dir = export_utils.export_onnx(model=model, data_in=data_list, **kwargs)
- else:
- export_dir = export_utils.export_torchscripts(
- model=model, data_in=data_list, **kwargs
- )
+ export_dir = export_utils.export(model=model, data_in=data_list, **kwargs)
return export_dir
--
Gitblit v1.9.1