speech_recognition/FunASR
1
0
Fork 0
mirror of https://github.com/modelscope/FunASR synced 2025-09-15 14:48:36 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Merge branch 'main' of github.com:alibaba-damo-academy/FunASR

Browse Source 
add
This commit is contained in:
游雁 2023-02-13 18:45:13 +08:00
commit 7f46849cad
1 changed files with 79 additions and 75 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

154
funasr/datasets/iterable_dataset.py
View File

@ -174,90 +174,94 @@ class IterableESPnetDataset(IterableDataset):
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()
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
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"
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).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
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()
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
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