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242 lines
8.7 KiB
242 lines
8.7 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import io
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import logging
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import sys
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import h5py
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import kaldiio
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import soundfile
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from deepspeech.io.reader import SoundHDF5File
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def file_reader_helper(
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rspecifier: str,
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filetype: str="mat",
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return_shape: bool=False,
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segments: str=None, ):
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"""Read uttid and array in kaldi style
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This function might be a bit confusing as "ark" is used
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for HDF5 to imitate "kaldi-rspecifier".
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Args:
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rspecifier: Give as "ark:feats.ark" or "scp:feats.scp"
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filetype: "mat" is kaldi-martix, "hdf5": HDF5
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return_shape: Return the shape of the matrix,
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instead of the matrix. This can reduce IO cost for HDF5.
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segments (str): The file format is
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"<segment-id> <recording-id> <start-time> <end-time>\n"
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"e.g. call-861225-A-0050-0065 call-861225-A 5.0 6.5\n"
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Returns:
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Generator[Tuple[str, np.ndarray], None, None]:
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Examples:
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Read from kaldi-matrix ark file:
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>>> for u, array in file_reader_helper('ark:feats.ark', 'mat'):
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... array
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Read from HDF5 file:
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>>> for u, array in file_reader_helper('ark:feats.h5', 'hdf5'):
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... array
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"""
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if filetype == "mat":
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return KaldiReader(
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rspecifier, return_shape=return_shape, segments=segments)
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elif filetype == "hdf5":
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return HDF5Reader(rspecifier, return_shape=return_shape)
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elif filetype == "sound.hdf5":
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return SoundHDF5Reader(rspecifier, return_shape=return_shape)
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elif filetype == "sound":
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return SoundReader(rspecifier, return_shape=return_shape)
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else:
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raise NotImplementedError(f"filetype={filetype}")
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class KaldiReader:
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def __init__(self, rspecifier, return_shape=False, segments=None):
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self.rspecifier = rspecifier
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self.return_shape = return_shape
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self.segments = segments
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def __iter__(self):
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with kaldiio.ReadHelper(
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self.rspecifier, segments=self.segments) as reader:
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for key, array in reader:
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if self.return_shape:
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array = array.shape
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yield key, array
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class HDF5Reader:
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def __init__(self, rspecifier, return_shape=False):
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if ":" not in rspecifier:
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raise ValueError('Give "rspecifier" such as "ark:some.ark: {}"'.
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format(self.rspecifier))
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self.rspecifier = rspecifier
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self.ark_or_scp, self.filepath = self.rspecifier.split(":", 1)
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if self.ark_or_scp not in ["ark", "scp"]:
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raise ValueError(f"Must be scp or ark: {self.ark_or_scp}")
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self.return_shape = return_shape
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def __iter__(self):
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if self.ark_or_scp == "scp":
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hdf5_dict = {}
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with open(self.filepath, "r", encoding="utf-8") as f:
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for line in f:
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key, value = line.rstrip().split(None, 1)
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if ":" not in value:
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raise RuntimeError(
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"scp file for hdf5 should be like: "
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'"uttid filepath.h5:key": {}({})'.format(
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line, self.filepath))
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path, h5_key = value.split(":", 1)
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hdf5_file = hdf5_dict.get(path)
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if hdf5_file is None:
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try:
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hdf5_file = h5py.File(path, "r")
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except Exception:
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logging.error("Error when loading {}".format(path))
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raise
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hdf5_dict[path] = hdf5_file
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try:
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data = hdf5_file[h5_key]
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except Exception:
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logging.error("Error when loading {} with key={}".
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format(path, h5_key))
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raise
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if self.return_shape:
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yield key, data.shape
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else:
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yield key, data[()]
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# Closing all files
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for k in hdf5_dict:
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try:
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hdf5_dict[k].close()
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except Exception:
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pass
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else:
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if self.filepath == "-":
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# Required h5py>=2.9
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filepath = io.BytesIO(sys.stdin.buffer.read())
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else:
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filepath = self.filepath
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with h5py.File(filepath, "r") as f:
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for key in f:
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if self.return_shape:
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yield key, f[key].shape
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else:
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yield key, f[key][()]
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class SoundHDF5Reader:
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def __init__(self, rspecifier, return_shape=False):
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if ":" not in rspecifier:
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raise ValueError('Give "rspecifier" such as "ark:some.ark: {}"'.
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format(rspecifier))
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self.ark_or_scp, self.filepath = rspecifier.split(":", 1)
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if self.ark_or_scp not in ["ark", "scp"]:
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raise ValueError(f"Must be scp or ark: {self.ark_or_scp}")
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self.return_shape = return_shape
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def __iter__(self):
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if self.ark_or_scp == "scp":
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hdf5_dict = {}
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with open(self.filepath, "r", encoding="utf-8") as f:
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for line in f:
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key, value = line.rstrip().split(None, 1)
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if ":" not in value:
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raise RuntimeError(
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"scp file for hdf5 should be like: "
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'"uttid filepath.h5:key": {}({})'.format(
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line, self.filepath))
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path, h5_key = value.split(":", 1)
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hdf5_file = hdf5_dict.get(path)
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if hdf5_file is None:
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try:
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hdf5_file = SoundHDF5File(path, "r")
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except Exception:
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logging.error("Error when loading {}".format(path))
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raise
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hdf5_dict[path] = hdf5_file
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try:
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data = hdf5_file[h5_key]
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except Exception:
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logging.error("Error when loading {} with key={}".
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format(path, h5_key))
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raise
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# Change Tuple[ndarray, int] -> Tuple[int, ndarray]
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# (soundfile style -> scipy style)
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array, rate = data
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if self.return_shape:
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array = array.shape
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yield key, (rate, array)
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# Closing all files
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for k in hdf5_dict:
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try:
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hdf5_dict[k].close()
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except Exception:
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pass
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else:
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if self.filepath == "-":
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# Required h5py>=2.9
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filepath = io.BytesIO(sys.stdin.buffer.read())
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else:
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filepath = self.filepath
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for key, (a, r) in SoundHDF5File(filepath, "r").items():
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if self.return_shape:
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a = a.shape
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yield key, (r, a)
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class SoundReader:
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def __init__(self, rspecifier, return_shape=False):
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if ":" not in rspecifier:
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raise ValueError('Give "rspecifier" such as "scp:some.scp: {}"'.
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format(rspecifier))
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self.ark_or_scp, self.filepath = rspecifier.split(":", 1)
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if self.ark_or_scp != "scp":
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raise ValueError('Only supporting "scp" for sound file: {}'.format(
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self.ark_or_scp))
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self.return_shape = return_shape
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def __iter__(self):
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with open(self.filepath, "r", encoding="utf-8") as f:
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for line in f:
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key, sound_file_path = line.rstrip().split(None, 1)
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# Assume PCM16
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array, rate = soundfile.read(sound_file_path, dtype="int16")
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# Change Tuple[ndarray, int] -> Tuple[int, ndarray]
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# (soundfile style -> scipy style)
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if self.return_shape:
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array = array.shape
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yield key, (rate, array)
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