From 7ea3836893bfdf1aac03952bb1ff2da2c6ef6e57 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期二, 01 八月 2023 14:18:32 +0800
Subject: [PATCH] update
---
funasr/bin/asr_infer.py | 52 +---------------------------------------------------
1 files changed, 1 insertions(+), 51 deletions(-)
diff --git a/funasr/bin/asr_infer.py b/funasr/bin/asr_infer.py
index 0ce8dd8..02ca63d 100644
--- a/funasr/bin/asr_infer.py
+++ b/funasr/bin/asr_infer.py
@@ -22,9 +22,7 @@
import requests
import torch
from packaging.version import parse as V
-from typeguard import check_argument_types
-from typeguard import check_return_type
-from funasr.build_utils.build_model_from_file import build_model_from_file
+from funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.models.e2e_asr_contextual_paraformer import NeatContextualParaformer
from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
from funasr.models.frontend.wav_frontend import WavFrontend, WavFrontendOnline
@@ -78,7 +76,6 @@
frontend_conf: dict = None,
**kwargs,
):
- assert check_argument_types()
# 1. Build ASR model
scorers = {}
@@ -192,7 +189,6 @@
text, token, token_int, hyp
"""
- assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
@@ -248,7 +244,6 @@
text = None
results.append((text, token, token_int, hyp))
- assert check_return_type(results)
return results
@@ -289,7 +284,6 @@
decoding_ind: int = 0,
**kwargs,
):
- assert check_argument_types()
# 1. Build ASR model
scorers = {}
@@ -415,7 +409,6 @@
text, token, token_int, hyp
"""
- assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
@@ -522,7 +515,6 @@
vad_offset=begin_time)
results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor))
- # assert check_return_type(results)
return results
def generate_hotwords_list(self, hotword_list_or_file):
@@ -662,7 +654,6 @@
hotword_list_or_file: str = None,
**kwargs,
):
- assert check_argument_types()
# 1. Build ASR model
scorers = {}
@@ -782,7 +773,6 @@
text, token, token_int, hyp
"""
- assert check_argument_types()
results = []
cache_en = cache["encoder"]
if speech.shape[1] < 16 * 60 and cache_en["is_final"]:
@@ -877,7 +867,6 @@
results.append(postprocessed_result)
- # assert check_return_type(results)
return results
@@ -918,7 +907,6 @@
frontend_conf: dict = None,
**kwargs,
):
- assert check_argument_types()
# 1. Build ASR model
scorers = {}
@@ -1042,7 +1030,6 @@
text, token, token_int, hyp
"""
- assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
@@ -1110,7 +1097,6 @@
text = None
results.append((text, token, token_int, hyp))
- assert check_return_type(results)
return results
@@ -1149,7 +1135,6 @@
streaming: bool = False,
**kwargs,
):
- assert check_argument_types()
# 1. Build ASR model
scorers = {}
@@ -1254,7 +1239,6 @@
text, token, token_int, hyp
"""
- assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
@@ -1304,7 +1288,6 @@
text = None
results.append((text, token, token_int, hyp))
- assert check_return_type(results)
return results
@@ -1361,7 +1344,6 @@
"""Construct a Speech2Text object."""
super().__init__()
- assert check_argument_types()
asr_model, asr_train_args = build_model_from_file(
asr_train_config, asr_model_file, cmvn_file, device
)
@@ -1540,7 +1522,6 @@
Returns:
nbest_hypothesis: N-best hypothesis.
"""
- assert check_argument_types()
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
@@ -1572,7 +1553,6 @@
Returns:
nbest_hypothesis: N-best hypothesis.
"""
- assert check_argument_types()
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
@@ -1614,35 +1594,8 @@
text = None
results.append((text, token, token_int, hyp))
- assert check_return_type(results)
return results
-
- @staticmethod
- def from_pretrained(
- model_tag: Optional[str] = None,
- **kwargs: Optional[Any],
- ) -> Speech2Text:
- """Build Speech2Text instance from the pretrained model.
- Args:
- model_tag: Model tag of the pretrained models.
- Return:
- : Speech2Text instance.
- """
- if model_tag is not None:
- try:
- from espnet_model_zoo.downloader import ModelDownloader
-
- except ImportError:
- logging.error(
- "`espnet_model_zoo` is not installed. "
- "Please install via `pip install -U espnet_model_zoo`."
- )
- raise
- d = ModelDownloader()
- kwargs.update(**d.download_and_unpack(model_tag))
-
- return Speech2TextTransducer(**kwargs)
class Speech2TextSAASR:
@@ -1681,7 +1634,6 @@
frontend_conf: dict = None,
**kwargs,
):
- assert check_argument_types()
# 1. Build ASR model
scorers = {}
@@ -1799,7 +1751,6 @@
text, text_id, token, token_int, hyp
"""
- assert check_argument_types()
# Input as audio signal
if isinstance(speech, np.ndarray):
@@ -1892,5 +1843,4 @@
results.append((text, text_id, token, token_int, hyp))
- assert check_return_type(results)
return results
--
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