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@ -3,7 +3,6 @@
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# This file is part of the WebDataset library.
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# See the LICENSE file for licensing terms (BSD-style).
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#
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# Modified from https://github.com/webdataset/webdataset
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# Modified from wenet(https://github.com/wenet-e2e/wenet)
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"""A collection of iterators for data transformations.
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@ -12,28 +11,29 @@ These functions are plain iterator functions. You can find curried versions
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in webdataset.filters, and you can find IterableDataset wrappers in
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webdataset.processing.
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"""
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import io
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from fnmatch import fnmatch
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import itertools
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import os
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import random
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import re
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import itertools, os, random, sys, time
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from functools import reduce, wraps
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import sys
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import time
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from fnmatch import fnmatch
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from functools import reduce
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import numpy as np
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import paddle
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from . import autodecode
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from . import utils
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from .paddle_utils import PaddleTensor
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from .utils import PipelineStage
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from . import utils
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from .. import backends
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from ..compliance import kaldi
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import paddle
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from ..transform.cmvn import GlobalCMVN
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from ..utils.tensor_utils import pad_sequence
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from ..transform.spec_augment import time_warp
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from ..transform.spec_augment import time_mask
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from ..transform.spec_augment import freq_mask
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from ..transform.spec_augment import time_mask
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from ..transform.spec_augment import time_warp
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from ..utils.tensor_utils import pad_sequence
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from .utils import PipelineStage
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class FilterFunction(object):
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"""Helper class for currying pipeline stages.
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@ -159,10 +159,12 @@ def transform_with(sample, transformers):
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result[i] = f(sample[i])
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return result
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###
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# Iterators
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###
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def _info(data, fmt=None, n=3, every=-1, width=50, stream=sys.stderr, name=""):
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"""Print information about the samples that are passing through.
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@ -278,10 +280,16 @@ def _log_keys(data, logfile=None):
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log_keys = pipelinefilter(_log_keys)
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def _minedecode(x):
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if isinstance(x, str):
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return autodecode.imagehandler(x)
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else:
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return x
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def _decode(data, *args, handler=reraise_exception, **kw):
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"""Decode data based on the decoding functions given as arguments."""
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decoder = lambda x: autodecode.imagehandler(x) if isinstance(x, str) else x
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decoder = _minedecode
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handlers = [decoder(x) for x in args]
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f = autodecode.Decoder(handlers, **kw)
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@ -325,15 +333,24 @@ def _rename(data, handler=reraise_exception, keep=True, **kw):
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for sample in data:
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try:
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if not keep:
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yield {k: getfirst(sample, v, missing_is_error=True) for k, v in kw.items()}
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yield {
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k: getfirst(sample, v, missing_is_error=True)
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for k, v in kw.items()
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}
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else:
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def listify(v):
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return v.split(";") if isinstance(v, str) else v
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to_be_replaced = {x for v in kw.values() for x in listify(v)}
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result = {k: v for k, v in sample.items() if k not in to_be_replaced}
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result.update({k: getfirst(sample, v, missing_is_error=True) for k, v in kw.items()})
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result = {
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k: v
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for k, v in sample.items() if k not in to_be_replaced
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}
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result.update({
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k: getfirst(sample, v, missing_is_error=True)
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for k, v in kw.items()
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})
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yield result
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except Exception as exn:
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if handler(exn):
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@ -381,7 +398,11 @@ def _map_dict(data, handler=reraise_exception, **kw):
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map_dict = pipelinefilter(_map_dict)
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def _to_tuple(data, *args, handler=reraise_exception, missing_is_error=True, none_is_error=None):
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def _to_tuple(data,
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*args,
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handler=reraise_exception,
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missing_is_error=True,
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none_is_error=None):
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"""Convert dict samples to tuples."""
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if none_is_error is None:
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none_is_error = missing_is_error
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@ -390,7 +411,10 @@ def _to_tuple(data, *args, handler=reraise_exception, missing_is_error=True, non
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for sample in data:
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try:
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result = tuple([getfirst(sample, f, missing_is_error=missing_is_error) for f in args])
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result = tuple([
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getfirst(sample, f, missing_is_error=missing_is_error)
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for f in args
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])
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if none_is_error and any(x is None for x in result):
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raise ValueError(f"to_tuple {args} got {sample.keys()}")
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yield result
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@ -463,19 +487,28 @@ rsample = pipelinefilter(_rsample)
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slice = pipelinefilter(itertools.islice)
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def _extract_keys(source, *patterns, duplicate_is_error=True, ignore_missing=False):
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def _extract_keys(source,
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*patterns,
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duplicate_is_error=True,
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ignore_missing=False):
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for sample in source:
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result = []
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for pattern in patterns:
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pattern = pattern.split(";") if isinstance(pattern, str) else pattern
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matches = [x for x in sample.keys() if any(fnmatch("." + x, p) for p in pattern)]
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pattern = pattern.split(";") if isinstance(pattern,
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str) else pattern
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matches = [
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x for x in sample.keys()
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if any(fnmatch("." + x, p) for p in pattern)
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]
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if len(matches) == 0:
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if ignore_missing:
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continue
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else:
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raise ValueError(f"Cannot find {pattern} in sample keys {sample.keys()}.")
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raise ValueError(
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f"Cannot find {pattern} in sample keys {sample.keys()}.")
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if len(matches) > 1 and duplicate_is_error:
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raise ValueError(f"Multiple sample keys {sample.keys()} match {pattern}.")
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raise ValueError(
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f"Multiple sample keys {sample.keys()} match {pattern}.")
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value = sample[matches[0]]
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result.append(value)
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yield tuple(result)
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@ -484,7 +517,12 @@ def _extract_keys(source, *patterns, duplicate_is_error=True, ignore_missing=Fal
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extract_keys = pipelinefilter(_extract_keys)
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def _rename_keys(source, *args, keep_unselected=False, must_match=True, duplicate_is_error=True, **kw):
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def _rename_keys(source,
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*args,
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keep_unselected=False,
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must_match=True,
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duplicate_is_error=True,
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**kw):
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renamings = [(pattern, output) for output, pattern in args]
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renamings += [(pattern, output) for output, pattern in kw.items()]
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for sample in source:
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@ -504,11 +542,15 @@ def _rename_keys(source, *args, keep_unselected=False, must_match=True, duplicat
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continue
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if new_name in new_sample:
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if duplicate_is_error:
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raise ValueError(f"Duplicate value in sample {sample.keys()} after rename.")
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raise ValueError(
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f"Duplicate value in sample {sample.keys()} after rename."
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)
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continue
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new_sample[new_name] = value
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if must_match and not all(matched.values()):
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raise ValueError(f"Not all patterns ({matched}) matched sample keys ({sample.keys()}).")
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raise ValueError(
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f"Not all patterns ({matched}) matched sample keys ({sample.keys()})."
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)
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yield new_sample
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@ -541,18 +583,18 @@ def find_decoder(decoders, path):
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if fname.startswith("__"):
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return lambda x: x
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for pattern, fun in decoders[::-1]:
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if fnmatch(fname.lower(), pattern) or fnmatch("." + fname.lower(), pattern):
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if fnmatch(fname.lower(), pattern) or fnmatch("." + fname.lower(),
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pattern):
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return fun
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return None
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def _xdecode(
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source,
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*args,
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must_decode=True,
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defaults=default_decoders,
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**kw,
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):
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source,
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*args,
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must_decode=True,
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defaults=default_decoders,
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**kw, ):
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decoders = list(defaults) + list(args)
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decoders += [("*." + k, v) for k, v in kw.items()]
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for sample in source:
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@ -575,18 +617,18 @@ def _xdecode(
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new_sample[path] = value
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yield new_sample
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xdecode = pipelinefilter(_xdecode)
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xdecode = pipelinefilter(_xdecode)
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def _audio_data_filter(source,
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frame_shift=10,
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max_length=10240,
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min_length=10,
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token_max_length=200,
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token_min_length=1,
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min_output_input_ratio=0.0005,
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max_output_input_ratio=1):
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frame_shift=10,
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max_length=10240,
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min_length=10,
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token_max_length=200,
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token_min_length=1,
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min_output_input_ratio=0.0005,
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max_output_input_ratio=1):
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""" Filter sample according to feature and label length
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Inplace operation.
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@ -613,7 +655,8 @@ def _audio_data_filter(source,
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assert 'wav' in sample
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assert 'label' in sample
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# sample['wav'] is paddle.Tensor, we have 100 frames every second (default)
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num_frames = sample['wav'].shape[1] / sample['sample_rate'] * (1000 / frame_shift)
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num_frames = sample['wav'].shape[1] / sample['sample_rate'] * (
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1000 / frame_shift)
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if num_frames < min_length:
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continue
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if num_frames > max_length:
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@ -629,13 +672,15 @@ def _audio_data_filter(source,
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continue
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yield sample
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audio_data_filter = pipelinefilter(_audio_data_filter)
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def _audio_tokenize(source,
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symbol_table,
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bpe_model=None,
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non_lang_syms=None,
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split_with_space=False):
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symbol_table,
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bpe_model=None,
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non_lang_syms=None,
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split_with_space=False):
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""" Decode text to chars or BPE
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Inplace operation
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@ -693,8 +738,10 @@ def _audio_tokenize(source,
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sample['label'] = label
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yield sample
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audio_tokenize = pipelinefilter(_audio_tokenize)
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def _audio_resample(source, resample_rate=16000):
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""" Resample data.
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Inplace operation.
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@ -713,18 +760,22 @@ def _audio_resample(source, resample_rate=16000):
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waveform = sample['wav']
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if sample_rate != resample_rate:
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sample['sample_rate'] = resample_rate
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sample['wav'] = paddle.to_tensor(backends.soundfile_backend.resample(
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waveform.numpy(), src_sr = sample_rate, target_sr = resample_rate
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))
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sample['wav'] = paddle.to_tensor(
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backends.soundfile_backend.resample(
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waveform.numpy(),
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src_sr=sample_rate,
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target_sr=resample_rate))
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yield sample
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audio_resample = pipelinefilter(_audio_resample)
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def _audio_compute_fbank(source,
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num_mel_bins=80,
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frame_length=25,
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frame_shift=10,
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dither=0.0):
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num_mel_bins=80,
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frame_length=25,
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frame_shift=10,
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dither=0.0):
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""" Extract fbank
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Args:
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@ -746,30 +797,33 @@ def _audio_compute_fbank(source,
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waveform = sample['wav']
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waveform = waveform * (1 << 15)
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# Only keep fname, feat, label
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mat = kaldi.fbank(waveform,
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n_mels=num_mel_bins,
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frame_length=frame_length,
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frame_shift=frame_shift,
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dither=dither,
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energy_floor=0.0,
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sr=sample_rate)
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mat = kaldi.fbank(
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waveform,
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n_mels=num_mel_bins,
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frame_length=frame_length,
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frame_shift=frame_shift,
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dither=dither,
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energy_floor=0.0,
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sr=sample_rate)
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yield dict(fname=sample['fname'], label=sample['label'], feat=mat)
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audio_compute_fbank = pipelinefilter(_audio_compute_fbank)
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def _audio_spec_aug(source,
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max_w=5,
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w_inplace=True,
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w_mode="PIL",
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max_f=30,
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num_f_mask=2,
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f_inplace=True,
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f_replace_with_zero=False,
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max_t=40,
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num_t_mask=2,
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t_inplace=True,
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t_replace_with_zero=False,):
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def _audio_spec_aug(
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source,
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max_w=5,
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w_inplace=True,
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w_mode="PIL",
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max_f=30,
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num_f_mask=2,
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f_inplace=True,
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f_replace_with_zero=False,
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max_t=40,
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num_t_mask=2,
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t_inplace=True,
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t_replace_with_zero=False, ):
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""" Do spec augmentation
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Inplace operation
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@ -793,12 +847,23 @@ def _audio_spec_aug(source,
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for sample in source:
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x = sample['feat']
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x = x.numpy()
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x = time_warp(x, max_time_warp=max_w, inplace = w_inplace, mode= w_mode)
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x = freq_mask(x, F = max_f, n_mask = num_f_mask, inplace = f_inplace, replace_with_zero = f_replace_with_zero)
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x = time_mask(x, T = max_t, n_mask = num_t_mask, inplace = t_inplace, replace_with_zero = t_replace_with_zero)
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x = time_warp(x, max_time_warp=max_w, inplace=w_inplace, mode=w_mode)
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x = freq_mask(
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x,
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F=max_f,
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n_mask=num_f_mask,
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inplace=f_inplace,
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replace_with_zero=f_replace_with_zero)
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x = time_mask(
|
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x,
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T=max_t,
|
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n_mask=num_t_mask,
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inplace=t_inplace,
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replace_with_zero=t_replace_with_zero)
|
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|
sample['feat'] = paddle.to_tensor(x, dtype=paddle.float32)
|
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|
yield sample
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audio_spec_aug = pipelinefilter(_audio_spec_aug)
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|
@ -829,8 +894,10 @@ def _sort(source, sort_size=500):
|
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for x in buf:
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|
yield x
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sort = pipelinefilter(_sort)
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def _batched(source, batch_size=16):
|
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|
|
""" Static batch the data by `batch_size`
|
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|
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|
|
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|
@ -850,8 +917,10 @@ def _batched(source, batch_size=16):
|
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|
if len(buf) > 0:
|
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|
|
yield buf
|
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|
batched = pipelinefilter(_batched)
|
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|
|
def dynamic_batched(source, max_frames_in_batch=12000):
|
|
|
|
|
""" Dynamic batch the data until the total frames in batch
|
|
|
|
|
reach `max_frames_in_batch`
|
|
|
|
|
@ -892,8 +961,8 @@ def _audio_padding(source):
|
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|
|
|
"""
|
|
|
|
|
for sample in source:
|
|
|
|
|
assert isinstance(sample, list)
|
|
|
|
|
feats_length = paddle.to_tensor([x['feat'].shape[0] for x in sample],
|
|
|
|
|
dtype="int64")
|
|
|
|
|
feats_length = paddle.to_tensor(
|
|
|
|
|
[x['feat'].shape[0] for x in sample], dtype="int64")
|
|
|
|
|
order = paddle.argsort(feats_length, descending=True)
|
|
|
|
|
feats_lengths = paddle.to_tensor(
|
|
|
|
|
[sample[i]['feat'].shape[0] for i in order], dtype="int64")
|
|
|
|
|
@ -902,20 +971,20 @@ def _audio_padding(source):
|
|
|
|
|
sorted_labels = [
|
|
|
|
|
paddle.to_tensor(sample[i]['label'], dtype="int32") for i in order
|
|
|
|
|
]
|
|
|
|
|
label_lengths = paddle.to_tensor([x.shape[0] for x in sorted_labels],
|
|
|
|
|
dtype="int64")
|
|
|
|
|
padded_feats = pad_sequence(sorted_feats,
|
|
|
|
|
batch_first=True,
|
|
|
|
|
padding_value=0)
|
|
|
|
|
padding_labels = pad_sequence(sorted_labels,
|
|
|
|
|
batch_first=True,
|
|
|
|
|
padding_value=-1)
|
|
|
|
|
|
|
|
|
|
yield (sorted_keys, padded_feats, feats_lengths, padding_labels,
|
|
|
|
|
label_lengths = paddle.to_tensor(
|
|
|
|
|
[x.shape[0] for x in sorted_labels], dtype="int64")
|
|
|
|
|
padded_feats = pad_sequence(
|
|
|
|
|
sorted_feats, batch_first=True, padding_value=0)
|
|
|
|
|
padding_labels = pad_sequence(
|
|
|
|
|
sorted_labels, batch_first=True, padding_value=-1)
|
|
|
|
|
|
|
|
|
|
yield (sorted_keys, padded_feats, feats_lengths, padding_labels,
|
|
|
|
|
label_lengths)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
audio_padding = pipelinefilter(_audio_padding)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def _audio_cmvn(source, cmvn_file):
|
|
|
|
|
global_cmvn = GlobalCMVN(cmvn_file)
|
|
|
|
|
for batch in source:
|
|
|
|
|
@ -923,13 +992,16 @@ def _audio_cmvn(source, cmvn_file):
|
|
|
|
|
padded_feats = padded_feats.numpy()
|
|
|
|
|
padded_feats = global_cmvn(padded_feats)
|
|
|
|
|
padded_feats = paddle.to_tensor(padded_feats, dtype=paddle.float32)
|
|
|
|
|
yield (sorted_keys, padded_feats, feats_lengths, padding_labels,
|
|
|
|
|
label_lengths)
|
|
|
|
|
yield (sorted_keys, padded_feats, feats_lengths, padding_labels,
|
|
|
|
|
label_lengths)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
audio_cmvn = pipelinefilter(_audio_cmvn)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def _placeholder(source):
|
|
|
|
|
for data in source:
|
|
|
|
|
yield data
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
placeholder = pipelinefilter(_placeholder)
|
|
|
|
|
|
TianYuan
2 years ago
committed by
GitHub