From 6427c834dfd97b1f05c6659cdc7ccf010bf82fe1 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期一, 24 四月 2023 19:50:07 +0800
Subject: [PATCH] update
---
funasr/datasets/iterable_dataset.py | 182 ++++++++++++++++++++++++---------------------
1 files changed, 97 insertions(+), 85 deletions(-)
diff --git a/funasr/datasets/iterable_dataset.py b/funasr/datasets/iterable_dataset.py
index 3798280..4b2fb1a 100644
--- a/funasr/datasets/iterable_dataset.py
+++ b/funasr/datasets/iterable_dataset.py
@@ -67,7 +67,7 @@
return load_bytes(bytes)
DATA_TYPES = {
- "sound": lambda x: torchaudio.load(x)[0][0].numpy(),
+ "sound": lambda x: torchaudio.load(x)[0].numpy(),
"pcm": load_pcm,
"kaldi_ark": load_kaldi,
"bytes": load_bytes,
@@ -107,6 +107,7 @@
] = None,
float_dtype: str = "float32",
fs: dict = None,
+ mc: bool = False,
int_dtype: str = "long",
key_file: str = None,
):
@@ -123,6 +124,7 @@
self.int_dtype = int_dtype
self.key_file = key_file
self.fs = fs
+ self.mc = mc
self.debug_info = {}
non_iterable_list = []
@@ -175,90 +177,97 @@
def __iter__(self) -> Iterator[Tuple[Union[str, int], Dict[str, np.ndarray]]]:
count = 0
if len(self.path_name_type_list) != 0 and (self.path_name_type_list[0][2] == "bytes" or self.path_name_type_list[0][2] == "waveform"):
+ linenum = len(self.path_name_type_list)
data = {}
- value = self.path_name_type_list[0][0]
- uid = 'utt_id'
- name = self.path_name_type_list[0][1]
- _type = self.path_name_type_list[0][2]
- func = DATA_TYPES[_type]
- array = func(value)
- if self.fs is not None and name == "speech":
- audio_fs = self.fs["audio_fs"]
- model_fs = self.fs["model_fs"]
- if audio_fs is not None and model_fs is not None:
- array = torch.from_numpy(array)
- array = array.unsqueeze(0)
- array = torchaudio.transforms.Resample(orig_freq=audio_fs,
- new_freq=model_fs)(array)
- array = array.squeeze(0).numpy()
- data[name] = array
+ for i in range(linenum):
+ value = self.path_name_type_list[i][0]
+ uid = 'utt_id'
+ name = self.path_name_type_list[i][1]
+ _type = self.path_name_type_list[i][2]
+ func = DATA_TYPES[_type]
+ array = func(value)
+ if self.fs is not None and (name == "speech" or name == "ref_speech"):
+ audio_fs = self.fs["audio_fs"]
+ model_fs = self.fs["model_fs"]
+ if audio_fs is not None and model_fs is not None:
+ array = torch.from_numpy(array)
+ array = array.unsqueeze(0)
+ array = torchaudio.transforms.Resample(orig_freq=audio_fs,
+ new_freq=model_fs)(array)
+ array = array.squeeze(0).numpy()
- if self.preprocess is not None:
- data = self.preprocess(uid, data)
- for name in data:
- count += 1
- value = data[name]
- if not isinstance(value, np.ndarray):
- raise RuntimeError(
- f'All values must be converted to np.ndarray object '
- f'by preprocessing, but "{name}" is still {type(value)}.')
- # Cast to desired type
- if value.dtype.kind == 'f':
- value = value.astype(self.float_dtype)
- elif value.dtype.kind == 'i':
- value = value.astype(self.int_dtype)
- else:
- raise NotImplementedError(
- f'Not supported dtype: {value.dtype}')
- data[name] = value
+ data[name] = array
+
+ if self.preprocess is not None:
+ data = self.preprocess(uid, data)
+ for name in data:
+ count += 1
+ value = data[name]
+ if not isinstance(value, np.ndarray):
+ raise RuntimeError(
+ f'All values must be converted to np.ndarray object '
+ f'by preprocessing, but "{name}" is still {type(value)}.')
+ # Cast to desired type
+ if value.dtype.kind == 'f':
+ value = value.astype(self.float_dtype)
+ elif value.dtype.kind == 'i':
+ value = value.astype(self.int_dtype)
+ else:
+ raise NotImplementedError(
+ f'Not supported dtype: {value.dtype}')
+ data[name] = value
yield uid, data
elif len(self.path_name_type_list) != 0 and self.path_name_type_list[0][2] == "sound" and not self.path_name_type_list[0][0].lower().endswith(".scp"):
+ linenum = len(self.path_name_type_list)
data = {}
- value = self.path_name_type_list[0][0]
- uid = os.path.basename(self.path_name_type_list[0][0]).split(".")[0]
- name = self.path_name_type_list[0][1]
- _type = self.path_name_type_list[0][2]
- if _type == "sound":
- audio_type = os.path.basename(value).split(".")[1].lower()
- if audio_type not in SUPPORT_AUDIO_TYPE_SETS:
- raise NotImplementedError(
- f'Not supported audio type: {audio_type}')
- if audio_type == "pcm":
- _type = "pcm"
-
- func = DATA_TYPES[_type]
- array = func(value)
- if self.fs is not None and name == "speech":
- audio_fs = self.fs["audio_fs"]
- model_fs = self.fs["model_fs"]
- if audio_fs is not None and model_fs is not None:
- array = torch.from_numpy(array)
- array = array.unsqueeze(0)
- array = torchaudio.transforms.Resample(orig_freq=audio_fs,
- new_freq=model_fs)(array)
- array = array.squeeze(0).numpy()
- data[name] = array
-
- if self.preprocess is not None:
- data = self.preprocess(uid, data)
- for name in data:
- count += 1
- value = data[name]
- if not isinstance(value, np.ndarray):
- raise RuntimeError(
- f'All values must be converted to np.ndarray object '
- f'by preprocessing, but "{name}" is still {type(value)}.')
- # Cast to desired type
- if value.dtype.kind == 'f':
- value = value.astype(self.float_dtype)
- elif value.dtype.kind == 'i':
- value = value.astype(self.int_dtype)
+ for i in range(linenum):
+ value = self.path_name_type_list[i][0]
+ uid = os.path.basename(self.path_name_type_list[i][0]).split(".")[0]
+ name = self.path_name_type_list[i][1]
+ _type = self.path_name_type_list[i][2]
+ if _type == "sound":
+ audio_type = os.path.basename(value).lower()
+ if audio_type.rfind(".pcm") >= 0:
+ _type = "pcm"
+ func = DATA_TYPES[_type]
+ array = func(value)
+ if self.fs is not None and (name == "speech" or name == "ref_speech"):
+ audio_fs = self.fs["audio_fs"]
+ model_fs = self.fs["model_fs"]
+ if audio_fs is not None and model_fs is not None:
+ array = torch.from_numpy(array)
+ array = torchaudio.transforms.Resample(orig_freq=audio_fs,
+ new_freq=model_fs)(array)
+ array = array.numpy()
+
+ if _type == "sound":
+ if self.mc:
+ data[name] = array.transpose((1, 0))
+ else:
+ data[name] = array[0]
else:
- raise NotImplementedError(
- f'Not supported dtype: {value.dtype}')
- data[name] = value
+ data[name] = array
+
+ if self.preprocess is not None:
+ data = self.preprocess(uid, data)
+ for name in data:
+ count += 1
+ value = data[name]
+ if not isinstance(value, np.ndarray):
+ raise RuntimeError(
+ f'All values must be converted to np.ndarray object '
+ f'by preprocessing, but "{name}" is still {type(value)}.')
+ # Cast to desired type
+ if value.dtype.kind == 'f':
+ value = value.astype(self.float_dtype)
+ elif value.dtype.kind == 'i':
+ value = value.astype(self.int_dtype)
+ else:
+ raise NotImplementedError(
+ f'Not supported dtype: {value.dtype}')
+ data[name] = value
yield uid, data
@@ -323,11 +332,8 @@
# 2.a. Load data streamingly
for value, (path, name, _type) in zip(values, self.path_name_type_list):
if _type == "sound":
- audio_type = os.path.basename(value).split(".")[1].lower()
- if audio_type not in SUPPORT_AUDIO_TYPE_SETS:
- raise NotImplementedError(
- f'Not supported audio type: {audio_type}')
- if audio_type == "pcm":
+ audio_type = os.path.basename(value).lower()
+ if audio_type.rfind(".pcm") >= 0:
_type = "pcm"
func = DATA_TYPES[_type]
# Load entry
@@ -337,11 +343,16 @@
model_fs = self.fs["model_fs"]
if audio_fs is not None and model_fs is not None:
array = torch.from_numpy(array)
- array = array.unsqueeze(0)
array = torchaudio.transforms.Resample(orig_freq=audio_fs,
new_freq=model_fs)(array)
- array = array.squeeze(0).numpy()
- data[name] = array
+ array = array.numpy()
+ if _type == "sound":
+ if self.mc:
+ data[name] = array.transpose((1, 0))
+ else:
+ data[name] = array[0]
+ else:
+ data[name] = array
if self.non_iterable_dataset is not None:
# 2.b. Load data from non-iterable dataset
_, from_non_iterable = self.non_iterable_dataset[uid]
@@ -374,3 +385,4 @@
if count == 0:
raise RuntimeError("No iteration")
+
--
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