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@@ -11,7 +11,6 @@ from typing import Union
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import kaldiio
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import numpy as np
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-import soundfile
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import torch
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import torchaudio
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from torch.utils.data.dataset import IterableDataset
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@@ -101,6 +100,7 @@ class IterableESPnetDataset(IterableDataset):
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[str, Dict[str, np.ndarray]], Dict[str, np.ndarray]
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] = None,
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float_dtype: str = "float32",
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+ fs: dict = None,
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int_dtype: str = "long",
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key_file: str = None,
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):
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@@ -116,6 +116,7 @@ class IterableESPnetDataset(IterableDataset):
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self.float_dtype = float_dtype
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self.int_dtype = int_dtype
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self.key_file = key_file
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+ self.fs = fs
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self.debug_info = {}
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non_iterable_list = []
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@@ -175,6 +176,15 @@ class IterableESPnetDataset(IterableDataset):
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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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if self.preprocess is not None:
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@@ -211,6 +221,15 @@ class IterableESPnetDataset(IterableDataset):
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f'Not supported audio type: {audio_type}')
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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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if self.preprocess is not None:
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@@ -302,6 +321,15 @@ class IterableESPnetDataset(IterableDataset):
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func = DATA_TYPES[_type]
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# Load entry
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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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if self.non_iterable_dataset is not None:
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# 2.b. Load data from non-iterable dataset
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@@ -335,4 +363,3 @@ class IterableESPnetDataset(IterableDataset):
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if count == 0:
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raise RuntimeError("No iteration")
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-
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hnluo