From d2c1204d91d7c98be7998e3966bd82e22750293b Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 04 三月 2024 17:50:29 +0800
Subject: [PATCH] Revert "Dev yf" (#1418)
---
funasr/utils/load_utils.py | 28 ++++++++++++----------------
1 files changed, 12 insertions(+), 16 deletions(-)
diff --git a/funasr/utils/load_utils.py b/funasr/utils/load_utils.py
index 84c38f9..7748172 100644
--- a/funasr/utils/load_utils.py
+++ b/funasr/utils/load_utils.py
@@ -13,25 +13,29 @@
from funasr.download.file import download_from_url
except:
print("urllib is not installed, if you infer from url, please install it first.")
-import pdb
+
def load_audio_text_image_video(data_or_path_or_list, fs: int = 16000, audio_fs: int = 16000, data_type="sound", tokenizer=None, **kwargs):
if isinstance(data_or_path_or_list, (list, tuple)):
if data_type is not None and isinstance(data_type, (list, tuple)):
+
data_types = [data_type] * len(data_or_path_or_list)
data_or_path_or_list_ret = [[] for d in data_type]
for i, (data_type_i, data_or_path_or_list_i) in enumerate(zip(data_types, data_or_path_or_list)):
+
for j, (data_type_j, data_or_path_or_list_j) in enumerate(zip(data_type_i, data_or_path_or_list_i)):
+
data_or_path_or_list_j = load_audio_text_image_video(data_or_path_or_list_j, fs=fs, audio_fs=audio_fs, data_type=data_type_j, tokenizer=tokenizer, **kwargs)
data_or_path_or_list_ret[j].append(data_or_path_or_list_j)
return data_or_path_or_list_ret
else:
return [load_audio_text_image_video(audio, fs=fs, audio_fs=audio_fs, data_type=data_type, **kwargs) for audio in data_or_path_or_list]
+
if isinstance(data_or_path_or_list, str) and data_or_path_or_list.startswith('http'): # download url to local file
data_or_path_or_list = download_from_url(data_or_path_or_list)
-
+
if isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list): # local file
if data_type is None or data_type == "sound":
data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list)
@@ -52,22 +56,10 @@
data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point
data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze() # [n_samples,]
- elif isinstance(data_or_path_or_list, str) and data_type == "kaldi_ark":
- data_mat = kaldiio.load_mat(data_or_path_or_list)
- if isinstance(data_mat, tuple):
- audio_fs, mat = data_mat
- else:
- mat = data_mat
- if mat.dtype == 'int16' or mat.dtype == 'int32':
- mat = mat.astype(np.float64)
- mat = mat / 32768
- if mat.ndim ==2:
- mat = mat[:,0]
- data_or_path_or_list = mat
else:
pass
# print(f"unsupport data type: {data_or_path_or_list}, return raw data")
-
+
if audio_fs != fs and data_type != "text":
resampler = torchaudio.transforms.Resample(audio_fs, fs)
data_or_path_or_list = resampler(data_or_path_or_list[None, :])[0, :]
@@ -89,6 +81,8 @@
return array
def extract_fbank(data, data_len = None, data_type: str="sound", frontend=None, **kwargs):
+ # import pdb;
+ # pdb.set_trace()
if isinstance(data, np.ndarray):
data = torch.from_numpy(data)
if len(data.shape) < 2:
@@ -106,7 +100,9 @@
data_list.append(data_i)
data_len.append(data_i.shape[0])
data = pad_sequence(data_list, batch_first=True) # data: [batch, N]
-
+ # import pdb;
+ # pdb.set_trace()
+ # if data_type == "sound":
data, data_len = frontend(data, data_len, **kwargs)
if isinstance(data_len, (list, tuple)):
--
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