From 557b913c5d78945e49cb2ac2bf254a2de40b6cd5 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 14 三月 2024 11:48:31 +0800
Subject: [PATCH] v1.0.16
---
funasr/auto/auto_model.py | 80 +++++++++++++++++++++++++++++++--------
1 files changed, 63 insertions(+), 17 deletions(-)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
index 70d09df..2df1910 100644
--- a/funasr/auto/auto_model.py
+++ b/funasr/auto/auto_model.py
@@ -14,6 +14,7 @@
import numpy as np
from tqdm import tqdm
+from funasr.utils.misc import deep_update
from funasr.register import tables
from funasr.utils.load_utils import load_bytes
from funasr.download.file import download_from_url
@@ -23,12 +24,13 @@
from funasr.utils.load_utils import load_audio_text_image_video
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.models.campplus.utils import sv_chunk, postprocess, distribute_spk
+from funasr.utils import export_utils
try:
+ from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
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):
"""
@@ -41,7 +43,7 @@
"""
data_list = []
key_list = []
- filelist = [".scp", ".txt", ".json", ".jsonl"]
+ filelist = [".scp", ".txt", ".json", ".jsonl", ".text"]
chars = string.ascii_letters + string.digits
if isinstance(data_in, str) and data_in.startswith('http'): # url
@@ -98,7 +100,7 @@
def __init__(self, **kwargs):
if not kwargs.get("disable_log", True):
tables.print()
-
+
model, kwargs = self.build_model(**kwargs)
# if vad_model is not None, build vad model else None
@@ -153,31 +155,32 @@
device = "cpu"
kwargs["batch_size"] = 1
kwargs["device"] = device
-
- if kwargs.get("ncpu", None):
- torch.set_num_threads(kwargs.get("ncpu"))
+
+ torch.set_num_threads(kwargs.get("ncpu", 4))
# build tokenizer
tokenizer = kwargs.get("tokenizer", None)
if tokenizer is not None:
tokenizer_class = tables.tokenizer_classes.get(tokenizer)
- tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
- kwargs["tokenizer"] = tokenizer
+ tokenizer_conf = kwargs.get("tokenizer_conf", {})
+ tokenizer = tokenizer_class(**tokenizer_conf)
+
kwargs["token_list"] = tokenizer.token_list if hasattr(tokenizer, "token_list") else None
kwargs["token_list"] = tokenizer.get_vocab() if hasattr(tokenizer, "get_vocab") else kwargs["token_list"]
vocab_size = len(kwargs["token_list"]) if kwargs["token_list"] is not None else -1
else:
vocab_size = -1
+ kwargs["tokenizer"] = tokenizer
+
# build frontend
frontend = kwargs.get("frontend", None)
kwargs["input_size"] = 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() if hasattr(frontend, "output_size") else None
-
+ kwargs["frontend"] = frontend
# build model
model_class = tables.model_classes.get(kwargs["model"])
model = model_class(**kwargs, **kwargs.get("model_conf", {}), vocab_size=vocab_size)
@@ -203,7 +206,7 @@
def __call__(self, *args, **cfg):
kwargs = self.kwargs
- kwargs.update(cfg)
+ deep_update(kwargs, cfg)
res = self.model(*args, kwargs)
return res
@@ -216,7 +219,7 @@
def inference(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
kwargs = self.kwargs if kwargs is None else kwargs
- kwargs.update(cfg)
+ deep_update(kwargs, cfg)
model = self.model if model is None else model
model.eval()
@@ -245,7 +248,10 @@
time1 = time.perf_counter()
with torch.no_grad():
- results, meta_data = model.inference(**batch, **kwargs)
+ res = model.inference(**batch, **kwargs)
+ if isinstance(res, (list, tuple)):
+ results = res[0]
+ meta_data = res[1] if len(res) > 1 else {}
time2 = time.perf_counter()
asr_result_list.extend(results)
@@ -276,7 +282,7 @@
def inference_with_vad(self, input, input_len=None, **cfg):
kwargs = self.kwargs
# step.1: compute the vad model
- self.vad_kwargs.update(cfg)
+ deep_update(self.vad_kwargs, cfg)
beg_vad = time.time()
res = self.inference(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
end_vad = time.time()
@@ -284,7 +290,7 @@
# step.2 compute asr model
model = self.model
- kwargs.update(cfg)
+ deep_update(kwargs, cfg)
batch_size = int(kwargs.get("batch_size_s", 300))*1000
batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
kwargs["batch_size"] = batch_size
@@ -396,7 +402,7 @@
if return_raw_text:
result['raw_text'] = ''
else:
- self.punc_kwargs.update(cfg)
+ 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
@@ -464,3 +470,43 @@
# f"time_escape_all: {time_escape_total_all_samples:0.3f}")
return results_ret_list
+ def export(self, input=None, **cfg):
+
+ """
+
+ :param input:
+ :param type:
+ :param quantize:
+ :param fallback_num:
+ :param calib_num:
+ :param opset_version:
+ :param cfg:
+ :return:
+ """
+
+ device = cfg.get("device", "cpu")
+ model = self.model.to(device=device)
+ kwargs = self.kwargs
+ deep_update(kwargs, cfg)
+ kwargs["device"] = device
+ del kwargs["model"]
+ model.eval()
+
+ type = kwargs.get("type", "onnx")
+
+ key_list, data_list = prepare_data_iterator(input, input_len=None, data_type=kwargs.get("data_type", None), key=None)
+
+ 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)
+
+ return export_dir
\ No newline at end of file
--
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