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@@ -174,90 +174,94 @@ class IterableESPnetDataset(IterableDataset):
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def __iter__(self) -> Iterator[Tuple[Union[str, int], Dict[str, np.ndarray]]]:
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count = 0
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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"):
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+ linenum = len(self.path_name_type_list)
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data = {}
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- value = self.path_name_type_list[0][0]
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- uid = 'utt_id'
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- name = self.path_name_type_list[0][1]
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- _type = self.path_name_type_list[0][2]
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- func = DATA_TYPES[_type]
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- array = func(value)
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- if self.fs is not None and name == "speech":
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- audio_fs = self.fs["audio_fs"]
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- model_fs = self.fs["model_fs"]
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- if audio_fs is not None and model_fs is not None:
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- array = torch.from_numpy(array)
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- array = array.unsqueeze(0)
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- array = torchaudio.transforms.Resample(orig_freq=audio_fs,
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- new_freq=model_fs)(array)
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- array = array.squeeze(0).numpy()
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- data[name] = array
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-
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- if self.preprocess is not None:
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- data = self.preprocess(uid, data)
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- for name in data:
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- count += 1
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- value = data[name]
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- if not isinstance(value, np.ndarray):
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- raise RuntimeError(
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- f'All values must be converted to np.ndarray object '
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- f'by preprocessing, but "{name}" is still {type(value)}.')
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- # Cast to desired type
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- if value.dtype.kind == 'f':
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- value = value.astype(self.float_dtype)
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- elif value.dtype.kind == 'i':
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- value = value.astype(self.int_dtype)
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- else:
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- raise NotImplementedError(
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- f'Not supported dtype: {value.dtype}')
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- data[name] = value
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+ for i in range(linenum):
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+ value = self.path_name_type_list[i][0]
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+ uid = 'utt_id'
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+ name = self.path_name_type_list[i][1]
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+ _type = self.path_name_type_list[i][2]
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+ func = DATA_TYPES[_type]
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+ array = func(value)
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+ if self.fs is not None and (name == "speech" or name == "ref_speech"):
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+ audio_fs = self.fs["audio_fs"]
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+ model_fs = self.fs["model_fs"]
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+ if audio_fs is not None and model_fs is not None:
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+ array = torch.from_numpy(array)
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+ array = array.unsqueeze(0)
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+ array = torchaudio.transforms.Resample(orig_freq=audio_fs,
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+ new_freq=model_fs)(array)
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+ array = array.squeeze(0).numpy()
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+ data[name] = array
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+
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+ if self.preprocess is not None:
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+ data = self.preprocess(uid, data)
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+ for name in data:
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+ count += 1
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+ value = data[name]
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+ if not isinstance(value, np.ndarray):
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+ raise RuntimeError(
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+ f'All values must be converted to np.ndarray object '
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+ f'by preprocessing, but "{name}" is still {type(value)}.')
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+ # Cast to desired type
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+ if value.dtype.kind == 'f':
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+ value = value.astype(self.float_dtype)
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+ elif value.dtype.kind == 'i':
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+ value = value.astype(self.int_dtype)
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+ else:
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+ raise NotImplementedError(
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+ f'Not supported dtype: {value.dtype}')
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+ data[name] = value
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yield uid, data
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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"):
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+ linenum = len(self.path_name_type_list)
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data = {}
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- value = self.path_name_type_list[0][0]
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- uid = os.path.basename(self.path_name_type_list[0][0]).split(".")[0]
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- name = self.path_name_type_list[0][1]
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- _type = self.path_name_type_list[0][2]
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- if _type == "sound":
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- audio_type = os.path.basename(value).split(".")[1].lower()
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- if audio_type not in SUPPORT_AUDIO_TYPE_SETS:
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- raise NotImplementedError(
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- f'Not supported audio type: {audio_type}')
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- if audio_type == "pcm":
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- _type = "pcm"
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-
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- func = DATA_TYPES[_type]
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- array = func(value)
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- if self.fs is not None and name == "speech":
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- audio_fs = self.fs["audio_fs"]
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- model_fs = self.fs["model_fs"]
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- if audio_fs is not None and model_fs is not None:
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- array = torch.from_numpy(array)
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- array = array.unsqueeze(0)
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- array = torchaudio.transforms.Resample(orig_freq=audio_fs,
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- new_freq=model_fs)(array)
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- array = array.squeeze(0).numpy()
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- data[name] = array
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-
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- if self.preprocess is not None:
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- data = self.preprocess(uid, data)
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- for name in data:
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- count += 1
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- value = data[name]
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- if not isinstance(value, np.ndarray):
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- raise RuntimeError(
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- f'All values must be converted to np.ndarray object '
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- f'by preprocessing, but "{name}" is still {type(value)}.')
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- # Cast to desired type
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- if value.dtype.kind == 'f':
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- value = value.astype(self.float_dtype)
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- elif value.dtype.kind == 'i':
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- value = value.astype(self.int_dtype)
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- else:
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- raise NotImplementedError(
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- f'Not supported dtype: {value.dtype}')
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- data[name] = value
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+ for i in range(linenum):
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+ value = self.path_name_type_list[i][0]
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+ uid = os.path.basename(self.path_name_type_list[i][0]).split(".")[0]
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+ name = self.path_name_type_list[i][1]
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+ _type = self.path_name_type_list[i][2]
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+ if _type == "sound":
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+ audio_type = os.path.basename(value).split(".")[1].lower()
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+ if audio_type not in SUPPORT_AUDIO_TYPE_SETS:
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+ raise NotImplementedError(
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+ f'Not supported audio type: {audio_type}')
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+ if audio_type == "pcm":
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+ _type = "pcm"
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+
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+ func = DATA_TYPES[_type]
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+ array = func(value)
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+ if self.fs is not None and (name == "speech" or name == "ref_speech"):
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+ audio_fs = self.fs["audio_fs"]
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+ model_fs = self.fs["model_fs"]
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+ if audio_fs is not None and model_fs is not None:
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+ array = torch.from_numpy(array)
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+ array = array.unsqueeze(0)
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+ array = torchaudio.transforms.Resample(orig_freq=audio_fs,
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+ new_freq=model_fs)(array)
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+ array = array.squeeze(0).numpy()
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+ data[name] = array
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+
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+ if self.preprocess is not None:
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+ data = self.preprocess(uid, data)
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+ for name in data:
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+ count += 1
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+ value = data[name]
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+ if not isinstance(value, np.ndarray):
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+ raise RuntimeError(
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+ f'All values must be converted to np.ndarray object '
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+ f'by preprocessing, but "{name}" is still {type(value)}.')
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+ # Cast to desired type
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+ if value.dtype.kind == 'f':
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+ value = value.astype(self.float_dtype)
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+ elif value.dtype.kind == 'i':
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+ value = value.astype(self.int_dtype)
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+ else:
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+ raise NotImplementedError(
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+ f'Not supported dtype: {value.dtype}')
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+ data[name] = value
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yield uid, data
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