Merge pull request #2062 from Jackwaterveg/webdataset

[Audio] Add webdataset in paddlespeech.audio

examples/wenetspeech/asr1/conf/conformer.yaml

 ############################################
 #           Network Architecture           #
 ############################################
-cmvn_file: 
 cmvn_file_type: "json"
 # encoder related
 encoder: conformer
@@ -43,40 +42,42 @@ model_conf:
 ###########################################
 #                   Data                  #
 ###########################################
-train_manifest: data/manifest.train
-dev_manifest: data/manifest.dev
-test_manifest: data/manifest.test
+train_manifest: data/train_l/data.list
+dev_manifest: data/dev/data.list
+test_manifest: data/test_meeting/data.list
 
 ###########################################
 #              Dataloader                 #
 ###########################################
-vocab_filepath: data/lang_char/vocab.txt 
+use_stream_data: True
 unit_type: 'char'
+vocab_filepath: data/lang_char/vocab.txt 
 preprocess_config: conf/preprocess.yaml
+cmvn_file: data/mean_std.json
 spm_model_prefix: ''
 feat_dim: 80
 stride_ms: 10.0
 window_ms: 25.0
+dither: 0.1
 sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs 
-batch_size: 64
-maxlen_in: 512  # if input length  > maxlen-in, batchsize is automatically reduced
-maxlen_out: 150  # if output length > maxlen-out, batchsize is automatically reduced
-minibatches: 0 # for debug
-batch_count: auto
-batch_bins: 0 
-batch_frames_in: 0
-batch_frames_out: 0
-batch_frames_inout: 0
-num_workers: 0
-subsampling_factor: 1
+batch_size: 32
+minlen_in: 10
+maxlen_in: 1200  # if input length(number of frames) > maxlen-in, data is automatically removed
+minlen_out: 0
+maxlen_out: 150  # if output length(number of tokens) > maxlen-out, data is automatically removed
+resample_rate: 16000
+shuffle_size: 1500 # read number of 'shuffle_size' data as a chunk, shuffle the data in the chunk
+sort_size: 1000  # read number of 'sort_size' data as a chunk, sort the data in the chunk 
+num_workers: 8
+prefetch_factor: 10
+dist_sampler: True
 num_encs: 1
 
-
 ###########################################
 #                 Training                #
 ###########################################
-n_epoch: 240 
-accum_grad: 16
+n_epoch: 32 
+accum_grad: 32
 global_grad_clip: 5.0
 log_interval: 100
 checkpoint:

examples/wenetspeech/asr1/local/data.sh

@@ -2,6 +2,8 @@
 
 # Copyright 2021  Mobvoi Inc(Author: Di Wu, Binbin Zhang)
 #                 NPU, ASLP Group (Author: Qijie Shao)
+#
+# Modified from wenet(https://github.com/wenet-e2e/wenet)
 
 stage=-1
 stop_stage=100
@@ -30,7 +32,7 @@ mkdir -p data
 TARGET_DIR=${MAIN_ROOT}/dataset
 mkdir -p ${TARGET_DIR}
 
-if [ ${stage} -le -2 ] && [ ${stop_stage} -ge -2 ]; then
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
     # download data
     echo "Please follow https://github.com/wenet-e2e/WenetSpeech to download the data."
     exit 0;
@@ -44,86 +46,57 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
         data || exit 1;
 fi
 
-if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
-    # generate manifests
-    python3 ${TARGET_DIR}/aishell/aishell.py \
-    --manifest_prefix="data/manifest" \
-    --target_dir="${TARGET_DIR}/aishell"
-
-    if [ $? -ne 0 ]; then
-        echo "Prepare Aishell failed. Terminated."
-        exit 1
-    fi
-
-    for dataset in train dev test; do
-        mv data/manifest.${dataset} data/manifest.${dataset}.raw
-    done
-fi
-
-if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
-    # compute mean and stddev for normalizer
-    if $cmvn; then
-        full_size=`cat data/${train_set}/wav.scp | wc -l`
-        sampling_size=$((full_size / cmvn_sampling_divisor))
-        shuf -n $sampling_size data/$train_set/wav.scp \
-            > data/$train_set/wav.scp.sampled
-        num_workers=$(nproc)
-
-        python3 ${MAIN_ROOT}/utils/compute_mean_std.py \
-        --manifest_path="data/manifest.train.raw" \
-        --spectrum_type="fbank" \
-        --feat_dim=80 \
-        --delta_delta=false \
-        --stride_ms=10 \
-        --window_ms=25 \
-        --sample_rate=16000 \
-        --use_dB_normalization=False \
-        --num_samples=-1 \
-        --num_workers=${num_workers} \
-        --output_path="data/mean_std.json"
-
-        if [ $? -ne 0 ]; then
-            echo "Compute mean and stddev failed. Terminated."
-            exit 1
-        fi
-    fi
-fi
-
-dict=data/dict/lang_char.txt
+dict=data/lang_char/vocab.txt
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    # download data, generate manifests
-    # build vocabulary
-    python3 ${MAIN_ROOT}/utils/build_vocab.py \
-    --unit_type="char" \
-    --count_threshold=0 \
-    --vocab_path="data/lang_char/vocab.txt" \
-    --manifest_paths "data/manifest.train.raw"
-
-    if [ $? -ne 0 ]; then
-        echo "Build vocabulary failed. Terminated."
-        exit 1
-    fi
+    echo "Make a dictionary"
+    echo "dictionary: ${dict}"
+    mkdir -p $(dirname $dict)
+    echo "<blank>" > ${dict} # 0 will be used for "blank" in CTC
+    echo "<unk>" >> ${dict} # <unk> must be 1
+    echo "▁" >> ${dict} # ▁ is for space
+    utils/text2token.py -s 1 -n 1 --space "▁" data/${train_set}/text \
+        | cut -f 2- -d" " | tr " " "\n" \
+        | sort | uniq | grep -a -v -e '^\s*$' \
+        | grep -v "▁" \
+        | awk '{print $0}' >> ${dict} \
+        || exit 1;
+    echo "<eos>" >> $dict
 fi
 
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
-    # format manifest with tokenids, vocab size
-    for dataset in train dev test; do
-    {
-        python3 ${MAIN_ROOT}/utils/format_data.py \
-            --cmvn_path "data/mean_std.json" \
-            --unit_type "char" \
-            --vocab_path="data/vocab.txt" \
-            --manifest_path="data/manifest.${dataset}.raw" \
-            --output_path="data/manifest.${dataset}"
+  echo "Compute cmvn"
+  # Here we use all the training data, you can sample some some data to save time
+  # BUG!!! We should use the segmented data for CMVN
+  if $cmvn; then
+    full_size=`cat data/${train_set}/wav.scp | wc -l`
+    sampling_size=$((full_size / cmvn_sampling_divisor))
+    shuf -n $sampling_size data/$train_set/wav.scp \
+      > data/$train_set/wav.scp.sampled
+    python3 utils/compute_cmvn_stats.py \
+    --num_workers 16 \
+    --train_config $train_config \
+    --in_scp data/$train_set/wav.scp.sampled \
+    --out_cmvn data/$train_set/mean_std.json \
+    || exit 1;
+  fi
+fi
 
-        if [ $? -ne 0 ]; then
-            echo "Formt mnaifest failed. Terminated."
-            exit 1
-        fi
-    } &
-    done
-    wait
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+  echo "Making shards, please wait..."
+  RED='\033[0;31m'
+  NOCOLOR='\033[0m'
+  echo -e "It requires ${RED}1.2T ${NOCOLOR}space for $shards_dir, please make sure you have enough space"
+  echo -e "It takes about ${RED}12 ${NOCOLOR}hours with 32 threads"
+  for x in $dev_set $test_sets ${train_set}; do
+    dst=$shards_dir/$x
+    mkdir -p $dst
+    utils/make_filted_shard_list.py --num_node 1 --num_gpus_per_node 8 --num_utts_per_shard 1000 \
+      --do_filter --resample 16000  \
+      --num_threads 32 --segments data/$x/segments \
+      data/$x/wav.scp data/$x/text \
+      $(realpath $dst) data/$x/data.list
+  done
 fi
 
-echo "Aishell data preparation done."
+echo "Wenetspeech data preparation done."
 exit 0

examples/wenetspeech/asr1/local/train.sh

+#!/bin/bash
+
+profiler_options=
+benchmark_batch_size=0
+benchmark_max_step=0
+
+# seed may break model convergence
+seed=0
+
+source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+if [ ${seed} != 0  ]; then
+    export FLAGS_cudnn_deterministic=True
+    echo "using seed $seed & FLAGS_cudnn_deterministic=True ..."
+fi
+
+if [ $# -lt 2 ] && [ $# -gt 3 ];then
+    echo "usage: CUDA_VISIBLE_DEVICES=0 ${0} config_path ckpt_name ips(optional)"
+    exit -1
+fi
+
+config_path=$1
+ckpt_name=$2
+ips=$3
+
+if [ ! $ips ];then
+  ips_config=
+else
+  ips_config="--ips="${ips}
+fi
+echo ${ips_config}
+
+mkdir -p exp
+
+if [ ${ngpu} == 0 ]; then
+python3 -u ${BIN_DIR}/train.py \
+--ngpu ${ngpu} \
+--seed ${seed} \
+--config ${config_path} \
+--output exp/${ckpt_name} \
+--profiler-options "${profiler_options}" \
+--benchmark-batch-size ${benchmark_batch_size} \
+--benchmark-max-step ${benchmark_max_step}
+else
+NCCL_SOCKET_IFNAME=eth0 python3 -m paddle.distributed.launch --gpus=${CUDA_VISIBLE_DEVICES} ${ips_config} ${BIN_DIR}/train.py \
+--ngpu ${ngpu} \
+--seed ${seed} \
+--config ${config_path} \
+--output exp/${ckpt_name} \
+--profiler-options "${profiler_options}" \
+--benchmark-batch-size ${benchmark_batch_size} \
+--benchmark-max-step ${benchmark_max_step}
+fi
+
+
+if [ ${seed} != 0  ]; then
+    unset FLAGS_cudnn_deterministic
+fi
+
+if [ $? -ne 0 ]; then
+    echo "Failed in training!"
+    exit 1
+fi
+
+exit 0

examples/wenetspeech/asr1/local/wenetspeech_data_prep.sh

@@ -24,7 +24,7 @@ stage=1
 prefix=
 train_subset=L
 
-. ./tools/parse_options.sh || exit 1;
+. ./utils/parse_options.sh || exit 1;
 
 filter_by_id () {
   idlist=$1
@@ -132,4 +132,4 @@ if [ $stage -le 2 ]; then
   done
 fi
 
-echo "$0: Done"
+echo "$0: Done"
\ No newline at end of file

examples/wenetspeech/asr1/run.sh

@@ -7,6 +7,7 @@ gpus=0,1,2,3,4,5,6,7
 stage=0
 stop_stage=100
 conf_path=conf/conformer.yaml
+ips=  #xxx.xxx.xxx.xxx,xxx.xxx.xxx.xxx
 decode_conf_path=conf/tuning/decode.yaml
 average_checkpoint=true
 avg_num=10
@@ -26,7 +27,7 @@ fi
 
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # train model, all `ckpt` under `exp` dir
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path}  ${ckpt}
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path}  ${ckpt} ${ips}
 fi
 
 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then

paddlespeech/audio/streamdata/__init__.py

+# Copyright (c) 2017-2019 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# See the LICENSE file for licensing terms (BSD-style).
+# Modified from https://github.com/webdataset/webdataset
+#
+# flake8: noqa
+
+from .cache import (
+    cached_tarfile_samples,
+    cached_tarfile_to_samples,
+    lru_cleanup,
+    pipe_cleaner,
+)
+from .compat import WebDataset, WebLoader, FluidWrapper
+from .extradatasets import MockDataset, with_epoch, with_length
+from .filters import (
+    associate,
+    batched,
+    decode,
+    detshuffle,
+    extract_keys,
+    getfirst,
+    info,
+    map,
+    map_dict,
+    map_tuple,
+    pipelinefilter,
+    rename,
+    rename_keys,
+    audio_resample,
+    select,
+    shuffle,
+    slice,
+    to_tuple,
+    transform_with,
+    unbatched,
+    xdecode,
+    audio_data_filter,
+    audio_tokenize,
+    audio_resample,
+    audio_compute_fbank,
+    audio_spec_aug,
+    sort,
+    audio_padding,
+    audio_cmvn,
+    placeholder,
+)
+from .handlers import (
+    ignore_and_continue,
+    ignore_and_stop,
+    reraise_exception,
+    warn_and_continue,
+    warn_and_stop,
+)
+from .pipeline import DataPipeline
+from .shardlists import (
+    MultiShardSample,
+    ResampledShards,
+    SimpleShardList,
+    non_empty,
+    resampled,
+    shardspec,
+    single_node_only,
+    split_by_node,
+    split_by_worker,
+)
+from .tariterators import tarfile_samples, tarfile_to_samples
+from .utils import PipelineStage, repeatedly
+from .writer import ShardWriter, TarWriter, numpy_dumps
+from .mix import RandomMix, RoundRobin

paddlespeech/audio/streamdata/autodecode.py

+#
+# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# This file is part of the WebDataset library.
+# See the LICENSE file for licensing terms (BSD-style).
+# Modified from https://github.com/webdataset/webdataset
+#
+
+"""Automatically decode webdataset samples."""
+
+import io, json, os, pickle, re, tempfile
+from functools import partial
+
+import numpy as np
+
+"""Extensions passed on to the image decoder."""
+image_extensions = "jpg jpeg png ppm pgm pbm pnm".split()
+
+
+################################################################
+# handle basic datatypes
+################################################################
+
+
+def paddle_loads(data):
+    """Load data using paddle.loads, importing paddle only if needed.
+
+    :param data: data to be decoded
+    """
+    import io
+
+    import paddle
+
+    stream = io.BytesIO(data)
+    return paddle.load(stream)
+
+
+def tenbin_loads(data):
+    from . import tenbin
+
+    return tenbin.decode_buffer(data)
+
+
+def msgpack_loads(data):
+    import msgpack
+
+    return msgpack.unpackb(data)
+
+
+def npy_loads(data):
+    import numpy.lib.format
+
+    stream = io.BytesIO(data)
+    return numpy.lib.format.read_array(stream)
+
+
+def cbor_loads(data):
+    import cbor
+
+    return cbor.loads(data)
+
+
+decoders = {
+    "txt": lambda data: data.decode("utf-8"),
+    "text": lambda data: data.decode("utf-8"),
+    "transcript": lambda data: data.decode("utf-8"),
+    "cls": lambda data: int(data),
+    "cls2": lambda data: int(data),
+    "index": lambda data: int(data),
+    "inx": lambda data: int(data),
+    "id": lambda data: int(data),
+    "json": lambda data: json.loads(data),
+    "jsn": lambda data: json.loads(data),
+    "pyd": lambda data: pickle.loads(data),
+    "pickle": lambda data: pickle.loads(data),
+    "pdparams": lambda data: paddle_loads(data),
+    "ten": tenbin_loads,
+    "tb": tenbin_loads,
+    "mp": msgpack_loads,
+    "msg": msgpack_loads,
+    "npy": npy_loads,
+    "npz": lambda data: np.load(io.BytesIO(data)),
+    "cbor": cbor_loads,
+}
+
+
+def basichandlers(key, data):
+    """Handle basic file decoding.
+
+    This function is usually part of the post= decoders.
+    This handles the following forms of decoding:
+
+    - txt -> unicode string
+    - cls cls2 class count index inx id -> int
+    - json jsn -> JSON decoding
+    - pyd pickle -> pickle decoding
+    - pdparams -> paddle.loads
+    - ten tenbin -> fast tensor loading
+    - mp messagepack msg -> messagepack decoding
+    - npy -> Python NPY decoding
+
+    :param key: file name extension
+    :param data: binary data to be decoded
+    """
+    extension = re.sub(r".*[.]", "", key)
+
+    if extension in decoders:
+        return decoders[extension](data)
+
+    return None
+
+
+################################################################
+# Generic extension handler.
+################################################################
+
+
+def call_extension_handler(key, data, f, extensions):
+    """Call the function f with the given data if the key matches the extensions.
+
+    :param key: actual key found in the sample
+    :param data: binary data
+    :param f: decoder function
+    :param extensions: list of matching extensions
+    """
+    extension = key.lower().split(".")
+    for target in extensions:
+        target = target.split(".")
+        if len(target) > len(extension):
+            continue
+        if extension[-len(target) :] == target:
+            return f(data)
+    return None
+
+
+def handle_extension(extensions, f):
+    """Return a decoder function for the list of extensions.
+
+    Extensions can be a space separated list of extensions.
+    Extensions can contain dots, in which case the corresponding number
+    of extension components must be present in the key given to f.
+    Comparisons are case insensitive.
+
+    Examples:
+    handle_extension("jpg jpeg", my_decode_jpg)  # invoked for any file.jpg
+    handle_extension("seg.jpg", special_case_jpg)  # invoked only for file.seg.jpg
+    """
+    extensions = extensions.lower().split()
+    return partial(call_extension_handler, f=f, extensions=extensions)
+
+
+################################################################
+# handle images
+################################################################
+
+imagespecs = {
+    "l8": ("numpy", "uint8", "l"),
+    "rgb8": ("numpy", "uint8", "rgb"),
+    "rgba8": ("numpy", "uint8", "rgba"),
+    "l": ("numpy", "float", "l"),
+    "rgb": ("numpy", "float", "rgb"),
+    "rgba": ("numpy", "float", "rgba"),
+    "paddlel8": ("paddle", "uint8", "l"),
+    "paddlergb8": ("paddle", "uint8", "rgb"),
+    "paddlergba8": ("paddle", "uint8", "rgba"),
+    "paddlel": ("paddle", "float", "l"),
+    "paddlergb": ("paddle", "float", "rgb"),
+    "paddle": ("paddle", "float", "rgb"),
+    "paddlergba": ("paddle", "float", "rgba"),
+    "pill": ("pil", None, "l"),
+    "pil": ("pil", None, "rgb"),
+    "pilrgb": ("pil", None, "rgb"),
+    "pilrgba": ("pil", None, "rgba"),
+}
+
+
+class ImageHandler:
+    """Decode image data using the given `imagespec`.
+
+    The `imagespec` specifies whether the image is decoded
+    to numpy/paddle/pi, decoded to uint8/float, and decoded
+    to l/rgb/rgba:
+
+    - l8: numpy uint8 l
+    - rgb8: numpy uint8 rgb
+    - rgba8: numpy uint8 rgba
+    - l: numpy float l
+    - rgb: numpy float rgb
+    - rgba: numpy float rgba
+    - paddlel8: paddle uint8 l
+    - paddlergb8: paddle uint8 rgb
+    - paddlergba8: paddle uint8 rgba
+    - paddlel: paddle float l
+    - paddlergb: paddle float rgb
+    - paddle: paddle float rgb
+    - paddlergba: paddle float rgba
+    - pill: pil None l
+    - pil: pil None rgb
+    - pilrgb: pil None rgb
+    - pilrgba: pil None rgba
+
+    """
+
+    def __init__(self, imagespec, extensions=image_extensions):
+        """Create an image handler.
+
+        :param imagespec: short string indicating the type of decoding
+        :param extensions: list of extensions the image handler is invoked for
+        """
+        if imagespec not in list(imagespecs.keys()):
+            raise ValueError("Unknown imagespec: %s" % imagespec)
+        self.imagespec = imagespec.lower()
+        self.extensions = extensions
+
+    def __call__(self, key, data):
+        """Perform image decoding.
+
+        :param key: file name extension
+        :param data: binary data
+        """
+        import PIL.Image
+
+        extension = re.sub(r".*[.]", "", key)
+        if extension.lower() not in self.extensions:
+            return None
+        imagespec = self.imagespec
+        atype, etype, mode = imagespecs[imagespec]
+        with io.BytesIO(data) as stream:
+            img = PIL.Image.open(stream)
+            img.load()
+            img = img.convert(mode.upper())
+        if atype == "pil":
+            return img
+        elif atype == "numpy":
+            result = np.asarray(img)
+            if result.dtype != np.uint8:
+                raise ValueError("ImageHandler: numpy image must be uint8")
+            if etype == "uint8":
+                return result
+            else:
+                return result.astype("f") / 255.0
+        elif atype == "paddle":
+            import paddle
+
+            result = np.asarray(img)
+            if result.dtype != np.uint8:
+                raise ValueError("ImageHandler: paddle image must be uint8")
+            if etype == "uint8":
+                result = np.array(result.transpose(2, 0, 1))
+                return paddle.tensor(result)
+            else:
+                result = np.array(result.transpose(2, 0, 1))
+                return paddle.tensor(result) / 255.0
+        return None
+
+
+def imagehandler(imagespec, extensions=image_extensions):
+    """Create an image handler.
+
+    This is just a lower case alias for ImageHander.
+
+    :param imagespec: textual image spec
+    :param extensions: list of extensions the handler should be applied for
+    """
+    return ImageHandler(imagespec, extensions)
+
+
+################################################################
+# torch video
+################################################################
+
+'''
+def torch_video(key, data):
+    """Decode video using the torchvideo library.
+
+    :param key: file name extension
+    :param data: data to be decoded
+    """
+    extension = re.sub(r".*[.]", "", key)
+    if extension not in "mp4 ogv mjpeg avi mov h264 mpg webm wmv".split():
+        return None
+
+    import torchvision.io
+
+    with tempfile.TemporaryDirectory() as dirname:
+        fname = os.path.join(dirname, f"file.{extension}")
+        with open(fname, "wb") as stream:
+            stream.write(data)
+        return torchvision.io.read_video(fname, pts_unit="sec")
+'''
+
+
+################################################################
+# paddlespeech.audio
+################################################################
+
+
+def paddle_audio(key, data):
+    """Decode audio using the paddlespeech.audio library.
+
+    :param key: file name extension
+    :param data: data to be decoded
+    """
+    extension = re.sub(r".*[.]", "", key)
+    if extension not in ["flac", "mp3", "sox", "wav", "m4a", "ogg", "wma"]:
+        return None
+
+    import paddlespeech.audio
+
+    with tempfile.TemporaryDirectory() as dirname:
+        fname = os.path.join(dirname, f"file.{extension}")
+        with open(fname, "wb") as stream:
+            stream.write(data)
+        return paddlespeech.audio.load(fname)
+
+
+################################################################
+# special class for continuing decoding
+################################################################
+
+
+class Continue:
+    """Special class for continuing decoding.
+
+    This is mostly used for decompression, as in:
+
+        def decompressor(key, data):
+            if key.endswith(".gz"):
+                return Continue(key[:-3], decompress(data))
+            return None
+    """
+
+    def __init__(self, key, data):
+        """__init__.
+
+        :param key:
+        :param data:
+        """
+        self.key, self.data = key, data
+
+
+def gzfilter(key, data):
+    """Decode .gz files.
+
+    This decodes compressed files and the continues decoding.
+
+    :param key: file name extension
+    :param data: binary data
+    """
+    import gzip
+
+    if not key.endswith(".gz"):
+        return None
+    decompressed = gzip.open(io.BytesIO(data)).read()
+    return Continue(key[:-3], decompressed)
+
+
+################################################################
+# decode entire training amples
+################################################################
+
+
+default_pre_handlers = [gzfilter]
+default_post_handlers = [basichandlers]
+
+
+class Decoder:
+    """Decode samples using a list of handlers.
+
+    For each key/data item, this iterates through the list of
+    handlers until some handler returns something other than None.
+    """
+
+    def __init__(self, handlers, pre=None, post=None, only=None, partial=False):
+        """Create a Decoder.
+
+        :param handlers: main list of handlers
+        :param pre: handlers called before the main list (.gz handler by default)
+        :param post: handlers called after the main list (default handlers by default)
+        :param only: a list of extensions; when give, only ignores files with those extensions
+        :param partial: allow partial decoding (i.e., don't decode fields that aren't of type bytes)
+        """
+        if isinstance(only, str):
+            only = only.split()
+        self.only = only if only is None else set(only)
+        if pre is None:
+            pre = default_pre_handlers
+        if post is None:
+            post = default_post_handlers
+        assert all(callable(h) for h in handlers), f"one of {handlers} not callable"
+        assert all(callable(h) for h in pre), f"one of {pre} not callable"
+        assert all(callable(h) for h in post), f"one of {post} not callable"
+        self.handlers = pre + handlers + post
+        self.partial = partial
+
+    def decode1(self, key, data):
+        """Decode a single field of a sample.
+
+        :param key: file name extension
+        :param data: binary data
+        """
+        key = "." + key
+        for f in self.handlers:
+            result = f(key, data)
+            if isinstance(result, Continue):
+                key, data = result.key, result.data
+                continue
+            if result is not None:
+                return result
+        return data
+
+    def decode(self, sample):
+        """Decode an entire sample.
+
+        :param sample: the sample, a dictionary of key value pairs
+        """
+        result = {}
+        assert isinstance(sample, dict), sample
+        for k, v in list(sample.items()):
+            if k[0] == "_":
+                if isinstance(v, bytes):
+                    v = v.decode("utf-8")
+                result[k] = v
+                continue
+            if self.only is not None and k not in self.only:
+                result[k] = v
+                continue
+            assert v is not None
+            if self.partial:
+                if isinstance(v, bytes):
+                    result[k] = self.decode1(k, v)
+                else:
+                    result[k] = v
+            else:
+                assert isinstance(v, bytes)
+                result[k] = self.decode1(k, v)
+        return result
+
+    def __call__(self, sample):
+        """Decode an entire sample.
+
+        :param sample: the sample
+        """
+        assert isinstance(sample, dict), (len(sample), sample)
+        return self.decode(sample)

paddlespeech/audio/streamdata/cache.py

+# Copyright (c) 2017-2019 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# See the LICENSE file for licensing terms (BSD-style).
+# Modified from https://github.com/webdataset/webdataset
+import itertools, os, random, re, sys
+from urllib.parse import urlparse
+
+from . import filters
+from . import gopen
+from .handlers import reraise_exception
+from .tariterators import tar_file_and_group_expander
+
+default_cache_dir = os.environ.get("WDS_CACHE", "./_cache")
+default_cache_size = float(os.environ.get("WDS_CACHE_SIZE", "1e18"))
+
+
+def lru_cleanup(cache_dir, cache_size, keyfn=os.path.getctime, verbose=False):
+    """Performs cleanup of the file cache in cache_dir using an LRU strategy,
+    keeping the total size of all remaining files below cache_size."""
+    if not os.path.exists(cache_dir):
+        return
+    total_size = 0
+    for dirpath, dirnames, filenames in os.walk(cache_dir):
+        for filename in filenames:
+            total_size += os.path.getsize(os.path.join(dirpath, filename))
+    if total_size <= cache_size:
+        return
+    # sort files by last access time
+    files = []
+    for dirpath, dirnames, filenames in os.walk(cache_dir):
+        for filename in filenames:
+            files.append(os.path.join(dirpath, filename))
+    files.sort(key=keyfn, reverse=True)
+    # delete files until we're under the cache size
+    while len(files) > 0 and total_size > cache_size:
+        fname = files.pop()
+        total_size -= os.path.getsize(fname)
+        if verbose:
+            print("# deleting %s" % fname, file=sys.stderr)
+        os.remove(fname)
+
+
+def download(url, dest, chunk_size=1024 ** 2, verbose=False):
+    """Download a file from `url` to `dest`."""
+    temp = dest + f".temp{os.getpid()}"
+    with gopen.gopen(url) as stream:
+        with open(temp, "wb") as f:
+            while True:
+                data = stream.read(chunk_size)
+                if not data:
+                    break
+                f.write(data)
+    os.rename(temp, dest)
+
+
+def pipe_cleaner(spec):
+    """Guess the actual URL from a "pipe:" specification."""
+    if spec.startswith("pipe:"):
+        spec = spec[5:]
+        words = spec.split(" ")
+        for word in words:
+            if re.match(r"^(https?|gs|ais|s3)", word):
+                return word
+    return spec
+
+
+def get_file_cached(
+    spec,
+    cache_size=-1,
+    cache_dir=None,
+    url_to_name=pipe_cleaner,
+    verbose=False,
+):
+    if cache_size == -1:
+        cache_size = default_cache_size
+    if cache_dir is None:
+        cache_dir = default_cache_dir
+    url = url_to_name(spec)
+    parsed = urlparse(url)
+    dirname, filename = os.path.split(parsed.path)
+    dirname = dirname.lstrip("/")
+    dirname = re.sub(r"[:/|;]", "_", dirname)
+    destdir = os.path.join(cache_dir, dirname)
+    os.makedirs(destdir, exist_ok=True)
+    dest = os.path.join(cache_dir, dirname, filename)
+    if not os.path.exists(dest):
+        if verbose:
+            print("# downloading %s to %s" % (url, dest), file=sys.stderr)
+        lru_cleanup(cache_dir, cache_size, verbose=verbose)
+        download(spec, dest, verbose=verbose)
+    return dest
+
+
+def get_filetype(fname):
+    with os.popen("file '%s'" % fname) as f:
+        ftype = f.read()
+    return ftype
+
+
+def check_tar_format(fname):
+    """Check whether a file is a tar archive."""
+    ftype = get_filetype(fname)
+    return "tar archive" in ftype or "gzip compressed" in ftype
+
+
+verbose_cache = int(os.environ.get("WDS_VERBOSE_CACHE", "0"))
+
+
+def cached_url_opener(
+    data,
+    handler=reraise_exception,
+    cache_size=-1,
+    cache_dir=None,
+    url_to_name=pipe_cleaner,
+    validator=check_tar_format,
+    verbose=False,
+    always=False,
+):
+    """Given a stream of url names (packaged in `dict(url=url)`), yield opened streams."""
+    verbose = verbose or verbose_cache
+    for sample in data:
+        assert isinstance(sample, dict), sample
+        assert "url" in sample
+        url = sample["url"]
+        attempts = 5
+        try:
+            if not always and os.path.exists(url):
+                dest = url
+            else:
+                dest = get_file_cached(
+                    url,
+                    cache_size=cache_size,
+                    cache_dir=cache_dir,
+                    url_to_name=url_to_name,
+                    verbose=verbose,
+                )
+            if verbose:
+                print("# opening %s" % dest, file=sys.stderr)
+            assert os.path.exists(dest)
+            if not validator(dest):
+                ftype = get_filetype(dest)
+                with open(dest, "rb") as f:
+                    data = f.read(200)
+                os.remove(dest)
+                raise ValueError(
+                    "%s (%s) is not a tar archive, but a %s, contains %s"
+                    % (dest, url, ftype, repr(data))
+                )
+            try:
+                stream = open(dest, "rb")
+                sample.update(stream=stream)
+                yield sample
+            except FileNotFoundError as exn:
+                # dealing with race conditions in lru_cleanup
+                attempts -= 1
+                if attempts > 0:
+                    time.sleep(random.random() * 10)
+                    continue
+                raise exn
+        except Exception as exn:
+            exn.args = exn.args + (url,)
+            if handler(exn):
+                continue
+            else:
+                break
+
+
+def cached_tarfile_samples(
+    src,
+    handler=reraise_exception,
+    cache_size=-1,
+    cache_dir=None,
+    verbose=False,
+    url_to_name=pipe_cleaner,
+    always=False,
+):
+    streams = cached_url_opener(
+        src,
+        handler=handler,
+        cache_size=cache_size,
+        cache_dir=cache_dir,
+        verbose=verbose,
+        url_to_name=url_to_name,
+        always=always,
+    )
+    samples = tar_file_and_group_expander(streams, handler=handler)
+    return samples
+
+
+cached_tarfile_to_samples = filters.pipelinefilter(cached_tarfile_samples)

paddlespeech/audio/streamdata/compat.py

+# Copyright (c) 2017-2019 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# See the LICENSE file for licensing terms (BSD-style).
+# Modified from https://github.com/webdataset/webdataset
+from dataclasses import dataclass
+from itertools import islice
+from typing import List
+
+import braceexpand, yaml
+
+from . import autodecode
+from . import cache, filters, shardlists, tariterators
+from .filters import reraise_exception
+from .pipeline import DataPipeline
+from .paddle_utils import DataLoader, IterableDataset
+
+
+class FluidInterface:
+    def batched(self, batchsize):
+        return self.compose(filters.batched(batchsize))
+
+    def dynamic_batched(self, max_frames_in_batch):
+        return self.compose(filter.dynamic_batched(max_frames_in_batch))
+
+    def unbatched(self):
+        return self.compose(filters.unbatched())
+
+    def listed(self, batchsize, partial=True):
+        return self.compose(filters.batched(), batchsize=batchsize, collation_fn=None)
+
+    def unlisted(self):
+        return self.compose(filters.unlisted())
+
+    def log_keys(self, logfile=None):
+        return self.compose(filters.log_keys(logfile))
+
+    def shuffle(self, size, **kw):
+        if size < 1:
+            return self
+        else:
+            return self.compose(filters.shuffle(size, **kw))
+
+    def map(self, f, handler=reraise_exception):
+        return self.compose(filters.map(f, handler=handler))
+
+    def decode(self, *args, pre=None, post=None, only=None, partial=False, handler=reraise_exception):
+        handlers = [autodecode.ImageHandler(x) if isinstance(x, str) else x for x in args]
+        decoder = autodecode.Decoder(handlers, pre=pre, post=post, only=only, partial=partial)
+        return self.map(decoder, handler=handler)
+
+    def map_dict(self, handler=reraise_exception, **kw):
+        return self.compose(filters.map_dict(handler=handler, **kw))
+
+    def select(self, predicate, **kw):
+        return self.compose(filters.select(predicate, **kw))
+
+    def to_tuple(self, *args, handler=reraise_exception):
+        return self.compose(filters.to_tuple(*args, handler=handler))
+
+    def map_tuple(self, *args, handler=reraise_exception):
+        return self.compose(filters.map_tuple(*args, handler=handler))
+
+    def slice(self, *args):
+        return self.compose(filters.slice(*args))
+
+    def rename(self, **kw):
+        return self.compose(filters.rename(**kw))
+
+    def rsample(self, p=0.5):
+        return self.compose(filters.rsample(p))
+
+    def rename_keys(self, *args, **kw):
+        return self.compose(filters.rename_keys(*args, **kw))
+
+    def extract_keys(self, *args, **kw):
+        return self.compose(filters.extract_keys(*args, **kw))
+
+    def xdecode(self, *args, **kw):
+        return self.compose(filters.xdecode(*args, **kw))
+
+    def audio_data_filter(self, *args, **kw):
+        return self.compose(filters.audio_data_filter(*args, **kw))
+    
+    def audio_tokenize(self, *args, **kw):
+        return self.compose(filters.audio_tokenize(*args, **kw))
+
+    def resample(self, *args, **kw):
+        return self.compose(filters.resample(*args, **kw)) 
+
+    def audio_compute_fbank(self, *args, **kw):
+        return self.compose(filters.audio_compute_fbank(*args, **kw))
+
+    def audio_spec_aug(self, *args, **kw):
+        return self.compose(filters.audio_spec_aug(*args, **kw))
+
+    def sort(self, size=500):
+        return self.compose(filters.sort(size))
+
+    def audio_padding(self):
+        return self.compose(filters.audio_padding())
+
+    def audio_cmvn(self, cmvn_file):
+        return self.compose(filters.audio_cmvn(cmvn_file))
+
+class WebDataset(DataPipeline, FluidInterface):
+    """Small fluid-interface wrapper for DataPipeline."""
+
+    def __init__(
+        self,
+        urls,
+        handler=reraise_exception,
+        resampled=False,
+        repeat=False,
+        shardshuffle=None,
+        cache_size=0,
+        cache_dir=None,
+        detshuffle=False,
+        nodesplitter=shardlists.single_node_only,
+        verbose=False,
+    ):
+        super().__init__()
+        if isinstance(urls, IterableDataset):
+            assert not resampled
+            self.append(urls)
+        elif isinstance(urls, str) and (urls.endswith(".yaml") or urls.endswith(".yml")):
+            with (open(urls)) as stream:
+                spec = yaml.safe_load(stream)
+            assert "datasets" in spec
+            self.append(shardlists.MultiShardSample(spec))
+        elif isinstance(urls, dict):
+            assert "datasets" in urls
+            self.append(shardlists.MultiShardSample(urls))
+        elif resampled:
+            self.append(shardlists.ResampledShards(urls))
+        else:
+            self.append(shardlists.SimpleShardList(urls))
+            self.append(nodesplitter)
+            self.append(shardlists.split_by_worker)
+            if shardshuffle is True:
+                shardshuffle = 100
+            if shardshuffle is not None:
+                if detshuffle:
+                    self.append(filters.detshuffle(shardshuffle))
+                else:
+                    self.append(filters.shuffle(shardshuffle))
+        if cache_size == 0:
+            self.append(tariterators.tarfile_to_samples(handler=handler))
+        else:
+            assert cache_size == -1 or cache_size > 0
+            self.append(
+                cache.cached_tarfile_to_samples(
+                    handler=handler,
+                    verbose=verbose,
+                    cache_size=cache_size,
+                    cache_dir=cache_dir,
+                )
+            )
+
+
+class FluidWrapper(DataPipeline, FluidInterface):
+    """Small fluid-interface wrapper for DataPipeline."""
+
+    def __init__(self, initial):
+        super().__init__()
+        self.append(initial)
+
+
+class WebLoader(DataPipeline, FluidInterface):
+    def __init__(self, *args, **kw):
+        super().__init__(DataLoader(*args, **kw))

paddlespeech/audio/streamdata/extradatasets.py

+#
+# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# This file is part of the WebDataset library.
+# See the LICENSE file for licensing terms (BSD-style).
+# Modified from https://github.com/webdataset/webdataset
+#
+
+
+"""Train PyTorch models directly from POSIX tar archive.
+
+Code works locally or over HTTP connections.
+"""
+
+import itertools as itt
+import os
+import random
+import sys
+
+import braceexpand
+
+from . import utils
+from .paddle_utils import IterableDataset
+from .utils import PipelineStage
+
+
+class MockDataset(IterableDataset):
+    """MockDataset.
+
+    A mock dataset for performance testing and unit testing.
+    """
+
+    def __init__(self, sample, length):
+        """Create a mock dataset instance.
+
+        :param sample: the sample to be returned repeatedly
+        :param length: the length of the mock dataset
+        """
+        self.sample = sample
+        self.length = length
+
+    def __iter__(self):
+        """Return an iterator over this mock dataset."""
+        for i in range(self.length):
+            yield self.sample
+
+
+class repeatedly(IterableDataset, PipelineStage):
+    """Repeatedly yield samples from a dataset."""
+
+    def __init__(self, source, nepochs=None, nbatches=None, length=None):
+        """Create an instance of Repeatedly.
+
+        :param nepochs: repeat for a maximum of nepochs
+        :param nbatches: repeat for a maximum of nbatches
+        """
+        self.source = source
+        self.length = length
+        self.nbatches = nbatches
+
+    def invoke(self, source):
+        """Return an iterator that iterates repeatedly over a source."""
+        return utils.repeatedly(
+            source,
+            nepochs=self.nepochs,
+            nbatches=self.nbatches,
+        )
+
+
+class with_epoch(IterableDataset):
+    """Change the actual and nominal length of an IterableDataset.
+
+    This will continuously iterate through the original dataset, but
+    impose new epoch boundaries at the given length/nominal.
+    This exists mainly as a workaround for the odd logic in DataLoader.
+    It is also useful for choosing smaller nominal epoch sizes with
+    very large datasets.
+
+    """
+
+    def __init__(self, dataset, length):
+        """Chop the dataset to the given length.
+
+        :param dataset: IterableDataset
+        :param length: declared length of the dataset
+        :param nominal: nominal length of dataset (if different from declared)
+        """
+        super().__init__()
+        self.length = length
+        self.source = None
+
+    def __getstate__(self):
+        """Return the pickled state of the dataset.
+
+        This resets the dataset iterator, since that can't be pickled.
+        """
+        result = dict(self.__dict__)
+        result["source"] = None
+        return result
+
+    def invoke(self, dataset):
+        """Return an iterator over the dataset.
+
+        This iterator returns as many samples as given by the `length`
+        parameter.
+        """
+        if self.source is None:
+            self.source = iter(dataset)
+        for i in range(self.length):
+            try:
+                sample = next(self.source)
+            except StopIteration:
+                self.source = iter(dataset)
+                try:
+                    sample = next(self.source)
+                except StopIteration:
+                    return
+            yield sample
+        self.source = None
+
+
+class with_length(IterableDataset, PipelineStage):
+    """Repeatedly yield samples from a dataset."""
+
+    def __init__(self, dataset, length):
+        """Create an instance of Repeatedly.
+
+        :param dataset: source dataset
+        :param length: stated length
+        """
+        super().__init__()
+        self.dataset = dataset
+        self.length = length
+
+    def invoke(self, dataset):
+        """Return an iterator that iterates repeatedly over a source."""
+        return iter(dataset)
+
+    def __len__(self):
+        """Return the user specified length."""
+        return self.length

paddlespeech/audio/streamdata/filters.py

+# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# This file is part of the WebDataset library.
+# See the LICENSE file for licensing terms (BSD-style).
+#
+
+# Modified from https://github.com/webdataset/webdataset
+# Modified from wenet(https://github.com/wenet-e2e/wenet)
+"""A collection of iterators for data transformations.
+
+These functions are plain iterator functions. You can find curried versions
+in webdataset.filters, and you can find IterableDataset wrappers in
+webdataset.processing.
+"""
+
+import io
+from fnmatch import fnmatch
+import re
+import itertools, os, random, sys, time
+from functools import reduce, wraps
+
+import numpy as np
+
+from . import autodecode
+from . import  utils
+from .paddle_utils import PaddleTensor
+from .utils import PipelineStage
+
+from .. import backends
+from ..compliance import kaldi
+import paddle
+from ..transform.cmvn import GlobalCMVN
+from ..utils.tensor_utils import pad_sequence
+from ..transform.spec_augment import time_warp
+from ..transform.spec_augment import time_mask
+from ..transform.spec_augment import freq_mask
+
+class FilterFunction(object):
+    """Helper class for currying pipeline stages.
+
+    We use this roundabout construct becauce it can be pickled.
+    """
+
+    def __init__(self, f, *args, **kw):
+        """Create a curried function."""
+        self.f = f
+        self.args = args
+        self.kw = kw
+
+    def __call__(self, data):
+        """Call the curried function with the given argument."""
+        return self.f(data, *self.args, **self.kw)
+
+    def __str__(self):
+        """Compute a string representation."""
+        return f"<{self.f.__name__} {self.args} {self.kw}>"
+
+    def __repr__(self):
+        """Compute a string representation."""
+        return f"<{self.f.__name__} {self.args} {self.kw}>"
+
+
+class RestCurried(object):
+    """Helper class for currying pipeline stages.
+
+    We use this roundabout construct because it can be pickled.
+    """
+
+    def __init__(self, f):
+        """Store the function for future currying."""
+        self.f = f
+
+    def __call__(self, *args, **kw):
+        """Curry with the given arguments."""
+        return FilterFunction(self.f, *args, **kw)
+
+
+def pipelinefilter(f):
+    """Turn the decorated function into one that is partially applied for
+    all arguments other than the first."""
+    result = RestCurried(f)
+    return result
+
+
+def reraise_exception(exn):
+    """Reraises the given exception; used as a handler.
+
+    :param exn: exception
+    """
+    raise exn
+
+
+def identity(x):
+    """Return the argument."""
+    return x
+
+
+def compose2(f, g):
+    """Compose two functions, g(f(x))."""
+    return lambda x: g(f(x))
+
+
+def compose(*args):
+    """Compose a sequence of functions (left-to-right)."""
+    return reduce(compose2, args)
+
+
+def pipeline(source, *args):
+    """Write an input pipeline; first argument is source, rest are filters."""
+    if len(args) == 0:
+        return source
+    return compose(*args)(source)
+
+
+def getfirst(a, keys, default=None, missing_is_error=True):
+    """Get the first matching key from a dictionary.
+
+    Keys can be specified as a list, or as a string of keys separated by ';'.
+    """
+    if isinstance(keys, str):
+        assert " " not in keys
+        keys = keys.split(";")
+    for k in keys:
+        if k in a:
+            return a[k]
+    if missing_is_error:
+        raise ValueError(f"didn't find {keys} in {list(a.keys())}")
+    return default
+
+
+def parse_field_spec(fields):
+    """Parse a specification for a list of fields to be extracted.
+
+    Keys are separated by spaces in the spec. Each key can itself
+    be composed of key alternatives separated by ';'.
+    """
+    if isinstance(fields, str):
+        fields = fields.split()
+    return [field.split(";") for field in fields]
+
+
+def transform_with(sample, transformers):
+    """Transform a list of values using a list of functions.
+
+    sample: list of values
+    transformers: list of functions
+
+    If there are fewer transformers than inputs, or if a transformer
+    function is None, then the identity function is used for the
+    corresponding sample fields.
+    """
+    if transformers is None or len(transformers) == 0:
+        return sample
+    result = list(sample)
+    assert len(transformers) <= len(sample)
+    for i in range(len(transformers)):  # skipcq: PYL-C0200
+        f = transformers[i]
+        if f is not None:
+            result[i] = f(sample[i])
+    return result
+
+###
+# Iterators
+###
+
+def _info(data, fmt=None, n=3, every=-1, width=50, stream=sys.stderr, name=""):
+    """Print information about the samples that are passing through.
+
+    :param data: source iterator
+    :param fmt: format statement (using sample dict as keyword)
+    :param n: when to stop
+    :param every: how often to print
+    :param width: maximum width
+    :param stream: output stream
+    :param name: identifier printed before any output
+    """
+    for i, sample in enumerate(data):
+        if i < n or (every > 0 and (i + 1) % every == 0):
+            if fmt is None:
+                print("---", name, file=stream)
+                for k, v in sample.items():
+                    print(k, repr(v)[:width], file=stream)
+            else:
+                print(fmt.format(**sample), file=stream)
+        yield sample
+
+
+info = pipelinefilter(_info)
+
+
+def pick(buf, rng):
+    k = rng.randint(0, len(buf) - 1)
+    sample = buf[k]
+    buf[k] = buf[-1]
+    buf.pop()
+    return sample
+
+
+def _shuffle(data, bufsize=1000, initial=100, rng=None, handler=None):
+    """Shuffle the data in the stream.
+
+    This uses a buffer of size `bufsize`. Shuffling at
+    startup is less random; this is traded off against
+    yielding samples quickly.
+
+    data: iterator
+    bufsize: buffer size for shuffling
+    returns: iterator
+    rng: either random module or random.Random instance
+
+    """
+    if rng is None:
+        rng = random.Random(int((os.getpid() + time.time()) * 1e9))
+    initial = min(initial, bufsize)
+    buf = []
+    for sample in data:
+        buf.append(sample)
+        if len(buf) < bufsize:
+            try:
+                buf.append(next(data))  # skipcq: PYL-R1708
+            except StopIteration:
+                pass
+        if len(buf) >= initial:
+            yield pick(buf, rng)
+    while len(buf) > 0:
+        yield pick(buf, rng)
+
+
+shuffle = pipelinefilter(_shuffle)
+
+
+class detshuffle(PipelineStage):
+    def __init__(self, bufsize=1000, initial=100, seed=0, epoch=-1):
+        self.bufsize = bufsize
+        self.initial = initial
+        self.seed = seed
+        self.epoch = epoch
+
+    def run(self, src):
+        self.epoch += 1
+        rng = random.Random()
+        rng.seed((self.seed, self.epoch))
+        return _shuffle(src, self.bufsize, self.initial, rng)
+
+
+def _select(data, predicate):
+    """Select samples based on a predicate.
+
+    :param data: source iterator
+    :param predicate: predicate (function)
+    """
+    for sample in data:
+        if predicate(sample):
+            yield sample
+
+
+select = pipelinefilter(_select)
+
+
+def _log_keys(data, logfile=None):
+    import fcntl
+
+    if logfile is None or logfile == "":
+        for sample in data:
+            yield sample
+    else:
+        with open(logfile, "a") as stream:
+            for i, sample in enumerate(data):
+                buf = f"{i}\t{sample.get('__worker__')}\t{sample.get('__rank__')}\t{sample.get('__key__')}\n"
+                try:
+                    fcntl.flock(stream.fileno(), fcntl.LOCK_EX)
+                    stream.write(buf)
+                finally:
+                    fcntl.flock(stream.fileno(), fcntl.LOCK_UN)
+                yield sample
+
+
+log_keys = pipelinefilter(_log_keys)
+
+
+def _decode(data, *args, handler=reraise_exception, **kw):
+    """Decode data based on the decoding functions given as arguments."""
+
+    decoder = lambda x: autodecode.imagehandler(x) if isinstance(x, str) else x
+    handlers = [decoder(x) for x in args]
+    f = autodecode.Decoder(handlers, **kw)
+
+    for sample in data:
+        assert isinstance(sample, dict), sample
+        try:
+            decoded = f(sample)
+        except Exception as exn:  # skipcq: PYL-W0703
+            if handler(exn):
+                continue
+            else:
+                break
+        yield decoded
+
+
+decode = pipelinefilter(_decode)
+
+
+def _map(data, f, handler=reraise_exception):
+    """Map samples."""
+    for sample in data:
+        try:
+            result = f(sample)
+        except Exception as exn:
+            if handler(exn):
+                continue
+            else:
+                break
+        if result is None:
+            continue
+        if isinstance(sample, dict) and isinstance(result, dict):
+            result["__key__"] = sample.get("__key__")
+        yield result
+
+
+map = pipelinefilter(_map)
+
+
+def _rename(data, handler=reraise_exception, keep=True, **kw):
+    """Rename samples based on keyword arguments."""
+    for sample in data:
+        try:
+            if not keep:
+                yield {k: getfirst(sample, v, missing_is_error=True) for k, v in kw.items()}
+            else:
+
+                def listify(v):
+                    return v.split(";") if isinstance(v, str) else v
+
+                to_be_replaced = {x for v in kw.values() for x in listify(v)}
+                result = {k: v for k, v in sample.items() if k not in to_be_replaced}
+                result.update({k: getfirst(sample, v, missing_is_error=True) for k, v in kw.items()})
+                yield result
+        except Exception as exn:
+            if handler(exn):
+                continue
+            else:
+                break
+
+
+rename = pipelinefilter(_rename)
+
+
+def _associate(data, associator, **kw):
+    """Associate additional data with samples."""
+    for sample in data:
+        if callable(associator):
+            extra = associator(sample["__key__"])
+        else:
+            extra = associator.get(sample["__key__"], {})
+        sample.update(extra)  # destructive
+        yield sample
+
+
+associate = pipelinefilter(_associate)
+
+
+def _map_dict(data, handler=reraise_exception, **kw):
+    """Map the entries in a dict sample with individual functions."""
+    assert len(list(kw.keys())) > 0
+    for key, f in kw.items():
+        assert callable(f), (key, f)
+
+    for sample in data:
+        assert isinstance(sample, dict)
+        try:
+            for k, f in kw.items():
+                sample[k] = f(sample[k])
+        except Exception as exn:
+            if handler(exn):
+                continue
+            else:
+                break
+        yield sample
+
+
+map_dict = pipelinefilter(_map_dict)
+
+
+def _to_tuple(data, *args, handler=reraise_exception, missing_is_error=True, none_is_error=None):
+    """Convert dict samples to tuples."""
+    if none_is_error is None:
+        none_is_error = missing_is_error
+    if len(args) == 1 and isinstance(args[0], str) and " " in args[0]:
+        args = args[0].split()
+
+    for sample in data:
+        try:
+            result = tuple([getfirst(sample, f, missing_is_error=missing_is_error) for f in args])
+            if none_is_error and any(x is None for x in result):
+                raise ValueError(f"to_tuple {args} got {sample.keys()}")
+            yield result
+        except Exception as exn:
+            if handler(exn):
+                continue
+            else:
+                break
+
+
+to_tuple = pipelinefilter(_to_tuple)
+
+
+def _map_tuple(data, *args, handler=reraise_exception):
+    """Map the entries of a tuple with individual functions."""
+    args = [f if f is not None else utils.identity for f in args]
+    for f in args:
+        assert callable(f), f
+    for sample in data:
+        assert isinstance(sample, (list, tuple))
+        sample = list(sample)
+        n = min(len(args), len(sample))
+        try:
+            for i in range(n):
+                sample[i] = args[i](sample[i])
+        except Exception as exn:
+            if handler(exn):
+                continue
+            else:
+                break
+        yield tuple(sample)
+
+
+map_tuple = pipelinefilter(_map_tuple)
+
+
+def _unlisted(data):
+    """Turn batched data back into unbatched data."""
+    for batch in data:
+        assert isinstance(batch, list), sample
+        for sample in batch:
+            yield sample
+
+
+unlisted = pipelinefilter(_unlisted)
+
+
+def _unbatched(data):
+    """Turn batched data back into unbatched data."""
+    for sample in data:
+        assert isinstance(sample, (tuple, list)), sample
+        assert len(sample) > 0
+        for i in range(len(sample[0])):
+            yield tuple(x[i] for x in sample)
+
+
+unbatched = pipelinefilter(_unbatched)
+
+
+def _rsample(data, p=0.5):
+    """Randomly subsample a stream of data."""
+    assert p >= 0.0 and p <= 1.0
+    for sample in data:
+        if random.uniform(0.0, 1.0) < p:
+            yield sample
+
+
+rsample = pipelinefilter(_rsample)
+
+slice = pipelinefilter(itertools.islice)
+
+
+def _extract_keys(source, *patterns, duplicate_is_error=True, ignore_missing=False):
+    for sample in source:
+        result = []
+        for pattern in patterns:
+            pattern = pattern.split(";") if isinstance(pattern, str) else pattern
+            matches = [x for x in sample.keys() if any(fnmatch("." + x, p) for p in pattern)]
+            if len(matches) == 0:
+                if ignore_missing:
+                    continue
+                else:
+                    raise ValueError(f"Cannot find {pattern} in sample keys {sample.keys()}.")
+            if len(matches) > 1 and duplicate_is_error:
+                raise ValueError(f"Multiple sample keys {sample.keys()} match {pattern}.")
+            value = sample[matches[0]]
+            result.append(value)
+        yield tuple(result)
+
+
+extract_keys = pipelinefilter(_extract_keys)
+
+
+def _rename_keys(source, *args, keep_unselected=False, must_match=True, duplicate_is_error=True, **kw):
+    renamings = [(pattern, output) for output, pattern in args]
+    renamings += [(pattern, output) for output, pattern in kw.items()]
+    for sample in source:
+        new_sample = {}
+        matched = {k: False for k, _ in renamings}
+        for path, value in sample.items():
+            fname = re.sub(r".*/", "", path)
+            new_name = None
+            for pattern, name in renamings[::-1]:
+                if fnmatch(fname.lower(), pattern):
+                    matched[pattern] = True
+                    new_name = name
+                    break
+            if new_name is None:
+                if keep_unselected:
+                    new_sample[path] = value
+                continue
+            if new_name in new_sample:
+                if duplicate_is_error:
+                    raise ValueError(f"Duplicate value in sample {sample.keys()} after rename.")
+                continue
+            new_sample[new_name] = value
+        if must_match and not all(matched.values()):
+            raise ValueError(f"Not all patterns ({matched}) matched sample keys ({sample.keys()}).")
+
+        yield new_sample
+
+
+rename_keys = pipelinefilter(_rename_keys)
+
+
+def decode_bin(stream):
+    return stream.read()
+
+
+def decode_text(stream):
+    binary = stream.read()
+    return binary.decode("utf-8")
+
+
+def decode_pickle(stream):
+    return pickle.load(stream)
+
+
+default_decoders = [
+    ("*.bin", decode_bin),
+    ("*.txt", decode_text),
+    ("*.pyd", decode_pickle),
+]
+
+
+def find_decoder(decoders, path):
+    fname = re.sub(r".*/", "", path)
+    if fname.startswith("__"):
+        return lambda x: x
+    for pattern, fun in decoders[::-1]:
+        if fnmatch(fname.lower(), pattern) or fnmatch("." + fname.lower(), pattern):
+            return fun
+    return None
+
+
+def _xdecode(
+    source,
+    *args,
+    must_decode=True,
+    defaults=default_decoders,
+    **kw,
+):
+    decoders = list(defaults) + list(args)
+    decoders += [("*." + k, v) for k, v in kw.items()]
+    for sample in source:
+        new_sample = {}
+        for path, data in sample.items():
+            if path.startswith("__"):
+                new_sample[path] = data
+                continue
+            decoder = find_decoder(decoders, path)
+            if decoder is False:
+                value = data
+            elif decoder is None:
+                if must_decode:
+                    raise ValueError(f"No decoder found for {path}.")
+                value = data
+            else:
+                if isinstance(data, bytes):
+                    data = io.BytesIO(data)
+                value = decoder(data)
+            new_sample[path] = value
+        yield new_sample
+
+xdecode = pipelinefilter(_xdecode)
+
+
+
+def _audio_data_filter(source,
+           frame_shift=10,
+           max_length=10240,
+           min_length=10,
+           token_max_length=200,
+           token_min_length=1,
+           min_output_input_ratio=0.0005,
+           max_output_input_ratio=1):
+    """ Filter sample according to feature and label length
+        Inplace operation.
+
+        Args::
+            source: Iterable[{fname, wav, label, sample_rate}]
+            frame_shift: length of frame shift (ms)
+            max_length: drop utterance which is greater than max_length(10ms)
+            min_length: drop utterance which is less than min_length(10ms)
+            token_max_length: drop utterance which is greater than
+                token_max_length, especially when use char unit for
+                english modeling
+            token_min_length: drop utterance which is
+                less than token_max_length
+            min_output_input_ratio: minimal ration of
+                token_length / feats_length(10ms)
+            max_output_input_ratio: maximum ration of
+                token_length / feats_length(10ms)
+
+        Returns:
+            Iterable[{fname, wav, label, sample_rate}]
+    """
+    for sample in source:
+        assert 'sample_rate' in sample
+        assert 'wav' in sample
+        assert 'label' in sample
+        # sample['wav'] is paddle.Tensor, we have 100 frames every second (default)
+        num_frames = sample['wav'].shape[1] / sample['sample_rate'] * (1000 / frame_shift)
+        if num_frames < min_length:
+            continue
+        if num_frames > max_length:
+            continue
+        if len(sample['label']) < token_min_length:
+            continue
+        if len(sample['label']) > token_max_length:
+            continue
+        if num_frames != 0:
+            if len(sample['label']) / num_frames < min_output_input_ratio:
+                continue
+            if len(sample['label']) / num_frames > max_output_input_ratio:
+                continue
+        yield sample
+
+audio_data_filter = pipelinefilter(_audio_data_filter)
+
+def _audio_tokenize(source,
+             symbol_table,
+             bpe_model=None,
+             non_lang_syms=None,
+             split_with_space=False):
+    """ Decode text to chars or BPE
+        Inplace operation
+
+        Args:
+            source: Iterable[{fname, wav, txt, sample_rate}]
+
+        Returns:
+            Iterable[{fname, wav, txt, tokens, label, sample_rate}]
+    """
+    if non_lang_syms is not None:
+        non_lang_syms_pattern = re.compile(r"(\[[^\[\]]+\]|<[^<>]+>|{[^{}]+})")
+    else:
+        non_lang_syms = {}
+        non_lang_syms_pattern = None
+
+    if bpe_model is not None:
+        import sentencepiece as spm
+        sp = spm.SentencePieceProcessor()
+        sp.load(bpe_model)
+    else:
+        sp = None
+
+    for sample in source:
+        assert 'txt' in sample
+        txt = sample['txt'].strip()
+        if non_lang_syms_pattern is not None:
+            parts = non_lang_syms_pattern.split(txt.upper())
+            parts = [w for w in parts if len(w.strip()) > 0]
+        else:
+            parts = [txt]
+
+        label = []
+        tokens = []
+        for part in parts:
+            if part in non_lang_syms:
+                tokens.append(part)
+            else:
+                if bpe_model is not None:
+                    tokens.extend(__tokenize_by_bpe_model(sp, part))
+                else:
+                    if split_with_space:
+                        part = part.split(" ")
+                    for ch in part:
+                        if ch == ' ':
+                            ch = "<space>"
+                        tokens.append(ch)
+
+        for ch in tokens:
+            if ch in symbol_table:
+                label.append(symbol_table[ch])
+            elif '<unk>' in symbol_table:
+                label.append(symbol_table['<unk>'])
+
+        sample['tokens'] = tokens
+        sample['label'] = label
+        yield sample
+
+audio_tokenize = pipelinefilter(_audio_tokenize)
+
+def _audio_resample(source, resample_rate=16000):
+    """ Resample data.
+        Inplace operation.
+
+        Args:
+            data: Iterable[{fname, wav, label, sample_rate}]
+            resample_rate: target resample rate
+
+        Returns:
+            Iterable[{fname, wav, label, sample_rate}]
+    """
+    for sample in source:
+        assert 'sample_rate' in sample
+        assert 'wav' in sample
+        sample_rate = sample['sample_rate']
+        waveform = sample['wav']
+        if sample_rate != resample_rate:
+            sample['sample_rate'] = resample_rate
+            sample['wav'] = paddle.to_tensor(backends.soundfile_backend.resample(
+                waveform.numpy(), src_sr = sample_rate, target_sr = resample_rate
+            ))
+        yield sample
+
+audio_resample = pipelinefilter(_audio_resample)
+
+def _audio_compute_fbank(source,
+                  num_mel_bins=80,
+                  frame_length=25,
+                  frame_shift=10,
+                  dither=0.0):
+    """ Extract fbank
+
+        Args:
+            source: Iterable[{fname, wav, label, sample_rate}]
+            num_mel_bins: number of mel filter bank
+            frame_length: length of one frame (ms)
+            frame_shift: length of frame shift (ms)
+            dither: value of dither
+
+        Returns:
+            Iterable[{fname, feat, label}]
+    """
+    for sample in source:
+        assert 'sample_rate' in sample
+        assert 'wav' in sample
+        assert 'fname' in sample
+        assert 'label' in sample
+        sample_rate = sample['sample_rate']
+        waveform = sample['wav']
+        waveform = waveform * (1 << 15)
+        # Only keep fname, feat, label
+        mat = kaldi.fbank(waveform,
+                          n_mels=num_mel_bins,
+                          frame_length=frame_length,
+                          frame_shift=frame_shift,
+                          dither=dither,
+                          energy_floor=0.0,
+                          sr=sample_rate)
+        yield dict(fname=sample['fname'], label=sample['label'], feat=mat)
+
+
+audio_compute_fbank = pipelinefilter(_audio_compute_fbank)
+
+def _audio_spec_aug(source,
+            max_w=5, 
+            w_inplace=True, 
+            w_mode="PIL",
+            max_f=30,
+            num_f_mask=2, 
+            f_inplace=True, 
+            f_replace_with_zero=False,
+            max_t=40, 
+            num_t_mask=2, 
+            t_inplace=True, 
+            t_replace_with_zero=False,):
+    """ Do spec augmentation
+        Inplace operation
+
+        Args:
+            source: Iterable[{fname, feat, label}]
+            max_w: max width of time warp
+            w_inplace: whether to inplace the original data while time warping
+            w_mode: time warp mode
+            max_f: max width of freq mask
+            num_f_mask: number of freq mask to apply
+            f_inplace: whether to inplace the original data while frequency masking
+            f_replace_with_zero: use zero to mask
+            max_t: max width of time mask
+            num_t_mask: number of time mask to apply
+            t_inplace: whether to inplace the original data while time masking
+            t_replace_with_zero: use zero to mask
+            
+        Returns
+            Iterable[{fname, feat, label}]
+     """
+    for sample in source:
+        x = sample['feat']
+        x = x.numpy()
+        x = time_warp(x, max_time_warp=max_w, inplace = w_inplace, mode= w_mode)
+        x = freq_mask(x, F = max_f, n_mask = num_f_mask, inplace = f_inplace, replace_with_zero = f_replace_with_zero)
+        x = time_mask(x, T = max_t, n_mask = num_t_mask, inplace = t_inplace, replace_with_zero = t_replace_with_zero)
+        sample['feat'] = paddle.to_tensor(x, dtype=paddle.float32)
+        yield sample
+
+audio_spec_aug = pipelinefilter(_audio_spec_aug)
+
+
+def _sort(source, sort_size=500):
+    """ Sort the data by feature length.
+        Sort is used after shuffle and before batch, so we can group
+        utts with similar lengths into a batch, and `sort_size` should
+        be less than `shuffle_size`
+
+        Args:
+            source: Iterable[{fname, feat, label}]
+            sort_size: buffer size for sort
+
+        Returns:
+            Iterable[{fname, feat, label}]
+    """
+
+    buf = []
+    for sample in source:
+        buf.append(sample)
+        if len(buf) >= sort_size:
+            buf.sort(key=lambda x: x['feat'].shape[0])
+            for x in buf:
+                yield x
+            buf = []
+    # The sample left over
+    buf.sort(key=lambda x: x['feat'].shape[0])
+    for x in buf:
+        yield x
+
+sort = pipelinefilter(_sort)
+
+def _batched(source, batch_size=16):
+    """ Static batch the data by `batch_size`
+
+        Args:
+            data: Iterable[{fname, feat, label}]
+            batch_size: batch size
+
+        Returns:
+            Iterable[List[{fname, feat, label}]]
+    """
+    buf = []
+    for sample in source:
+        buf.append(sample)
+        if len(buf) >= batch_size:
+            yield buf
+            buf = []
+    if len(buf) > 0:
+        yield buf
+
+batched = pipelinefilter(_batched)
+
+def dynamic_batched(source, max_frames_in_batch=12000):
+    """ Dynamic batch the data until the total frames in batch
+        reach `max_frames_in_batch`
+
+        Args:
+            source: Iterable[{fname, feat, label}]
+            max_frames_in_batch: max_frames in one batch
+
+        Returns:
+            Iterable[List[{fname, feat, label}]]
+    """
+    buf = []
+    longest_frames = 0
+    for sample in source:
+        assert 'feat' in sample
+        assert isinstance(sample['feat'], paddle.Tensor)
+        new_sample_frames = sample['feat'].size(0)
+        longest_frames = max(longest_frames, new_sample_frames)
+        frames_after_padding = longest_frames * (len(buf) + 1)
+        if frames_after_padding > max_frames_in_batch:
+            yield buf
+            buf = [sample]
+            longest_frames = new_sample_frames
+        else:
+            buf.append(sample)
+    if len(buf) > 0:
+        yield buf
+
+
+def _audio_padding(source):
+    """ Padding the data into training data
+
+        Args:
+            source: Iterable[List[{fname, feat, label}]]
+
+        Returns:
+            Iterable[Tuple(fname, feats, labels, feats lengths, label lengths)]
+    """
+    for sample in source:
+        assert isinstance(sample, list)
+        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")
+        sorted_feats = [sample[i]['feat'] for i in order]
+        sorted_keys = [sample[i]['fname'] for i in order]
+        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)
+
+audio_padding = pipelinefilter(_audio_padding)
+
+def _audio_cmvn(source, cmvn_file):
+    global_cmvn = GlobalCMVN(cmvn_file)
+    for batch in source:
+        sorted_keys, padded_feats, feats_lengths, padding_labels, label_lengths = batch
+        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)
+
+audio_cmvn = pipelinefilter(_audio_cmvn)
+
+def _placeholder(source):
+    for data in source:
+        yield data
+
+placeholder = pipelinefilter(_placeholder)

paddlespeech/audio/streamdata/gopen.py

+#
+# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
+# This file is part of the WebDataset library.
+# See the LICENSE file for licensing terms (BSD-style).
+#
+
+
+"""Open URLs by calling subcommands."""
+
+import os, sys, re
+from subprocess import PIPE, Popen
+from urllib.parse import urlparse
+
+# global used for printing additional node information during verbose output
+info = {}
+
+
+class Pipe:
+    """Wrapper class for subprocess.Pipe.
+
+    This class looks like a stream from the outside, but it checks
+    subprocess status and handles timeouts with exceptions.
+    This way, clients of the class do not need to know that they are
+    dealing with subprocesses.
+
+    :param *args: passed to `subprocess.Pipe`
+    :param **kw: passed to `subprocess.Pipe`
+    :param timeout: timeout for closing/waiting
+    :param ignore_errors: don't raise exceptions on subprocess errors
+    :param ignore_status: list of status codes to ignore
+    """
+
+    def __init__(
+        self,
+        *args,
+        mode=None,
+        timeout=7200.0,
+        ignore_errors=False,
+        ignore_status=[],
+        **kw,
+    ):
+        """Create an IO Pipe."""
+        self.ignore_errors = ignore_errors
+        self.ignore_status = [0] + ignore_status
+        self.timeout = timeout
+        self.args = (args, kw)
+        if mode[0] == "r":
+            self.proc = Popen(*args, stdout=PIPE, **kw)
+            self.stream = self.proc.stdout
+            if self.stream is None:
+                raise ValueError(f"{args}: couldn't open")
+        elif mode[0] == "w":
+            self.proc = Popen(*args, stdin=PIPE, **kw)
+            self.stream = self.proc.stdin
+            if self.stream is None:
+                raise ValueError(f"{args}: couldn't open")
+        self.status = None
+
+    def __str__(self):
+        return f"<Pipe {self.args}>"
+
+    def check_status(self):
+        """Poll the process and handle any errors."""
+        status = self.proc.poll()
+        if status is not None:
+            self.wait_for_child()
+
+    def wait_for_child(self):
+        """Check the status variable and raise an exception if necessary."""
+        verbose = int(os.environ.get("GOPEN_VERBOSE", 0))
+        if self.status is not None and verbose:
+            # print(f"(waiting again [{self.status} {os.getpid()}:{self.proc.pid}])", file=sys.stderr)
+            return
+        self.status = self.proc.wait()
+        if verbose:
+            print(
+                f"pipe exit [{self.status} {os.getpid()}:{self.proc.pid}] {self.args} {info}",
+                file=sys.stderr,
+            )
+        if self.status not in self.ignore_status and not self.ignore_errors:
+            raise Exception(f"{self.args}: exit {self.status} (read) {info}")
+
+    def read(self, *args, **kw):
+        """Wrap stream.read and checks status."""
+        result = self.stream.read(*args, **kw)
+        self.check_status()
+        return result
+
+    def write(self, *args, **kw):
+        """Wrap stream.write and checks status."""
+        result = self.stream.write(*args, **kw)
+        self.check_status()
+        return result
+
+    def readLine(self, *args, **kw):
+        """Wrap stream.readLine and checks status."""
+        result = self.stream.readLine(*args, **kw)
+        self.status = self.proc.poll()
+        self.check_status()
+        return result
+
+    def close(self):
+        """Wrap stream.close, wait for the subprocess, and handle errors."""
+        self.stream.close()
+        self.status = self.proc.wait(self.timeout)
+        self.wait_for_child()
+
+    def __enter__(self):
+        """Context handler."""
+        return self
+
+    def __exit__(self, etype, value, traceback):
+        """Context handler."""
+        self.close()
+
+
+def set_options(
+    obj, timeout=None, ignore_errors=None, ignore_status=None, handler=None
+):
+    """Set options for Pipes.
+
+    This function can be called on any stream. It will set pipe options only
+    when its argument is a pipe.
+
+    :param obj: any kind of stream
+    :param timeout: desired timeout
+    :param ignore_errors: desired ignore_errors setting
+    :param ignore_status: desired ignore_status setting
+    :param handler: desired error handler
+    """
+    if not isinstance(obj, Pipe):
+        return False
+    if timeout is not None:
+        obj.timeout = timeout
+    if ignore_errors is not None:
+        obj.ignore_errors = ignore_errors
+    if ignore_status is not None:
+        obj.ignore_status = ignore_status
+    if handler is not None:
+        obj.handler = handler
+    return True
+
+
+def gopen_file(url, mode="rb", bufsize=8192):
+    """Open a file.
+
+    This works for local files, files over HTTP, and pipe: files.
+
+    :param url: URL to be opened
+    :param mode: mode to open it with
+    :param bufsize: requested buffer size
+    """
+    return open(url, mode)
+
+
+def gopen_pipe(url, mode="rb", bufsize=8192):
+    """Use gopen to open a pipe.
+
+    :param url: a pipe: URL
+    :param mode: desired mode
+    :param bufsize: desired buffer size
+    """
+    assert url.startswith("pipe:")
+    cmd = url[5:]
+    if mode[0] == "r":
+        return Pipe(
+            cmd,
+            mode=mode,
+            shell=True,
+            bufsize=bufsize,
+            ignore_status=[141],
+        )  # skipcq: BAN-B604
+    elif mode[0] == "w":
+        return Pipe(
+            cmd,
+            mode=mode,
+            shell=True,
+            bufsize=bufsize,
+            ignore_status=[141],
+        )  # skipcq: BAN-B604
+    else:
+        raise ValueError(f"{mode}: unknown mode")
+
+
+def gopen_curl(url, mode="rb", bufsize=8192):
+    """Open a URL with `curl`.
+
+    :param url: url (usually, http:// etc.)
+    :param mode: file mode
+    :param bufsize: buffer size
+    """
+    if mode[0] == "r":
+        cmd = f"curl -s -L '{url}'"
+        return Pipe(
+            cmd,
+            mode=mode,
+            shell=True,
+            bufsize=bufsize,
+            ignore_status=[141, 23],
+        )  # skipcq: BAN-B604
+    elif mode[0] == "w":
+        cmd = f"curl -s -L -T - '{url}'"
+        return Pipe(
+            cmd,
+            mode=mode,
+            shell=True,
+            bufsize=bufsize,
+            ignore_status=[141, 26],
+        )  # skipcq: BAN-B604
+    else:
+        raise ValueError(f"{mode}: unknown mode")
+
+
+def gopen_htgs(url, mode="rb", bufsize=8192):
+    """Open a URL with `curl`.
+
+    :param url: url (usually, http:// etc.)
+    :param mode: file mode
+    :param bufsize: buffer size
+    """
+    if mode[0] == "r":
+        url = re.sub(r"(?i)^htgs://", "gs://", url)
+        cmd = f"curl -s -L '{url}'"
+        return Pipe(
+            cmd,
+            mode=mode,
+            shell=True,
+            bufsize=bufsize,
+            ignore_status=[141, 23],
+        )  # skipcq: BAN-B604
+    elif mode[0] == "w":
+        raise ValueError(f"{mode}: cannot write")
+    else:
+        raise ValueError(f"{mode}: unknown mode")
+
+
+
+def gopen_gsutil(url, mode="rb", bufsize=8192):
+    """Open a URL with `curl`.
+
+    :param url: url (usually, http:// etc.)
+    :param mode: file mode
+    :param bufsize: buffer size
+    """
+    if mode[0] == "r":
+        cmd = f"gsutil cat '{url}'"
+        return Pipe(
+            cmd,
+            mode=mode,
+            shell=True,
+            bufsize=bufsize,
+            ignore_status=[141, 23],
+        )  # skipcq: BAN-B604
+    elif mode[0] == "w":
+        cmd = f"gsutil cp - '{url}'"
+        return Pipe(
+            cmd,
+            mode=mode,
+            shell=True,
+            bufsize=bufsize,
+            ignore_status=[141, 26],
+        )  # skipcq: BAN-B604
+    else:
+        raise ValueError(f"{mode}: unknown mode")
+
+
+
+def gopen_error(url, *args, **kw):
+    """Raise a value error.
+
+    :param url: url
+    :param args: other arguments
+    :param kw: other keywords
+    """
+    raise ValueError(f"{url}: no gopen handler defined")
+
+
+"""A dispatch table mapping URL schemes to handlers."""
+gopen_schemes = dict(
+    __default__=gopen_error,
+    pipe=gopen_pipe,
+    http=gopen_curl,
+    https=gopen_curl,
+    sftp=gopen_curl,
+    ftps=gopen_curl,
+    scp=gopen_curl,
+    gs=gopen_gsutil,
+    htgs=gopen_htgs,
+)
+
+
+def gopen(url, mode="rb", bufsize=8192, **kw):
+    """Open the URL.
+
+    This uses the `gopen_schemes` dispatch table to dispatch based
+    on scheme.
+
+    Support for the following schemes is built-in: pipe, file,
+    http, https, sftp, ftps, scp.
+
+    When no scheme is given the url is treated as a file.
+
+    You can use the OPEN_VERBOSE argument to get info about
+    files being opened.
+
+    :param url: the source URL
+    :param mode: the mode ("rb", "r")
+    :param bufsize: the buffer size
+    """
+    global fallback_gopen
+    verbose = int(os.environ.get("GOPEN_VERBOSE", 0))
+    if verbose:
+        print("GOPEN", url, info, file=sys.stderr)
+    assert mode in ["rb", "wb"], mode
+    if url == "-":
+        if mode == "rb":
+            return sys.stdin.buffer
+        elif mode == "wb":
+            return sys.stdout.buffer
+        else:
+            raise ValueError(f"unknown mode {mode}")
+    pr = urlparse(url)
+    if pr.scheme == "":
+        bufsize = int(os.environ.get("GOPEN_BUFFER", -1))
+        return open(url, mode, buffering=bufsize)
+    if pr.scheme == "file":
+        bufsize = int(os.environ.get("GOPEN_BUFFER", -1))
+        return open(pr.path, mode, buffering=bufsize)
+    handler = gopen_schemes["__default__"]
+    handler = gopen_schemes.get(pr.scheme, handler)
+    return handler(url, mode, bufsize, **kw)
+
+
+def reader(url, **kw):
+    """Open url with gopen and mode "rb".
+
+    :param url: source URL
+    :param kw: other keywords forwarded to gopen
+    """
+    return gopen(url, "rb", **kw)

paddlespeech/audio/streamdata/handlers.py

+#
+# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
+# This file is part of the WebDataset library.
+# See the LICENSE file for licensing terms (BSD-style).
+#
+
+"""Pluggable exception handlers.
+
+These are functions that take an exception as an argument and then return...
+
+- the exception (in order to re-raise it)
+- True (in order to continue and ignore the exception)
+- False (in order to ignore the exception and stop processing)
+
+They are used as handler= arguments in much of the library.
+"""
+
+import time, warnings
+
+
+def reraise_exception(exn):
+    """Call in an exception handler to re-raise the exception."""
+    raise exn
+
+
+def ignore_and_continue(exn):
+    """Call in an exception handler to ignore any exception and continue."""
+    return True
+
+
+def warn_and_continue(exn):
+    """Call in an exception handler to ignore any exception, isssue a warning, and continue."""
+    warnings.warn(repr(exn))
+    time.sleep(0.5)
+    return True
+
+
+def ignore_and_stop(exn):
+    """Call in an exception handler to ignore any exception and stop further processing."""
+    return False
+
+
+def warn_and_stop(exn):
+    """Call in an exception handler to ignore any exception and stop further processing."""
+    warnings.warn(repr(exn))
+    time.sleep(0.5)
+    return False

paddlespeech/audio/streamdata/mix.py

+#
+# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# This file is part of the WebDataset library.
+# See the LICENSE file for licensing terms (BSD-style).
+# Modified from https://github.com/webdataset/webdataset
+#
+
+"""Classes for mixing samples from multiple sources."""
+
+import itertools, os, random, time, sys
+from functools import reduce, wraps
+
+import numpy as np
+
+from . import autodecode, utils
+from .paddle_utils import PaddleTensor, IterableDataset
+from .utils import PipelineStage
+
+
+def round_robin_shortest(*sources):
+    i = 0
+    while True:
+        try:
+            sample = next(sources[i % len(sources)])
+            yield sample
+        except StopIteration:
+            break
+        i += 1
+
+
+def round_robin_longest(*sources):
+    i = 0
+    while len(sources) > 0:
+        try:
+            sample = next(sources[i])
+            i += 1
+            yield sample
+        except StopIteration:
+            del sources[i]
+
+
+class RoundRobin(IterableDataset):
+    def __init__(self, datasets, longest=False):
+        self.datasets = datasets
+        self.longest = longest
+
+    def __iter__(self):
+        """Return an iterator over the sources."""
+        sources = [iter(d) for d in self.datasets]
+        if self.longest:
+            return round_robin_longest(*sources)
+        else:
+            return round_robin_shortest(*sources)
+
+
+def random_samples(sources, probs=None, longest=False):
+    if probs is None:
+        probs = [1] * len(sources)
+    else:
+        probs = list(probs)
+    while len(sources) > 0:
+        cum = (np.array(probs) / np.sum(probs)).cumsum()
+        r = random.random()
+        i = np.searchsorted(cum, r)
+        try:
+            yield next(sources[i])
+        except StopIteration:
+            if longest:
+                del sources[i]
+                del probs[i]
+            else:
+                break
+
+
+class RandomMix(IterableDataset):
+    def __init__(self, datasets, probs=None, longest=False):
+        self.datasets = datasets
+        self.probs = probs
+        self.longest = longest
+
+    def __iter__(self):
+        """Return an iterator over the sources."""
+        sources = [iter(d) for d in self.datasets]
+        return random_samples(sources, self.probs, longest=self.longest)

paddlespeech/audio/streamdata/paddle_utils.py

+#
+# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# This file is part of the WebDataset library.
+# See the LICENSE file for licensing terms (BSD-style).
+# Modified from https://github.com/webdataset/webdataset
+#
+
+"""Mock implementations of paddle interfaces when paddle is not available."""
+
+
+try:
+    from paddle.io import DataLoader, IterableDataset
+except ModuleNotFoundError:
+
+    class IterableDataset:
+        """Empty implementation of IterableDataset when paddle is not available."""
+
+        pass
+
+    class DataLoader:
+        """Empty implementation of DataLoader when paddle is not available."""
+
+        pass
+
+try:
+    from paddle import Tensor as PaddleTensor
+except ModuleNotFoundError:
+
+    class TorchTensor:
+        """Empty implementation of PaddleTensor when paddle is not available."""
+
+        pass

paddlespeech/audio/streamdata/pipeline.py

+# Copyright (c) 2017-2019 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# See the LICENSE file for licensing terms (BSD-style).
+# Modified from https://github.com/webdataset/webdataset
+#%%
+import copy, os, random, sys, time
+from dataclasses import dataclass
+from itertools import islice
+from typing import List
+
+import braceexpand, yaml
+
+from .handlers import reraise_exception
+from .paddle_utils import DataLoader, IterableDataset
+from .utils import PipelineStage
+
+
+def add_length_method(obj):
+    def length(self):
+        return self.size
+
+    Combined = type(
+        obj.__class__.__name__ + "_Length",
+        (obj.__class__, IterableDataset),
+        {"__len__": length},
+    )
+    obj.__class__ = Combined
+    return obj
+
+
+class DataPipeline(IterableDataset, PipelineStage):
+    """A pipeline starting with an IterableDataset and a series of filters."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+        self.pipeline = []
+        self.length = -1
+        self.repetitions = 1
+        self.nsamples = -1
+        for arg in args:
+            if arg is None:
+                continue
+            if isinstance(arg, list):
+                self.pipeline.extend(arg)
+            else:
+                self.pipeline.append(arg)
+
+    def invoke(self, f, *args, **kwargs):
+        """Apply a pipeline stage, possibly to the output of a previous stage."""
+        if isinstance(f, PipelineStage):
+            return f.run(*args, **kwargs)
+        if isinstance(f, (IterableDataset, DataLoader)) and len(args) == 0:
+            return iter(f)
+        if isinstance(f, list):
+            return iter(f)
+        if callable(f):
+            result = f(*args, **kwargs)
+            return result
+        raise ValueError(f"{f}: not a valid pipeline stage")
+
+    def iterator1(self):
+        """Create an iterator through one epoch in the pipeline."""
+        source = self.invoke(self.pipeline[0])
+        for step in self.pipeline[1:]:
+            source = self.invoke(step, source)
+        return source
+
+    def iterator(self):
+        """Create an iterator through the entire dataset, using the given number of repetitions."""
+        for i in range(self.repetitions):
+            for sample in self.iterator1():
+                yield sample
+
+    def __iter__(self):
+        """Create an iterator through the pipeline, repeating and slicing as requested."""
+        if self.repetitions != 1:
+            if self.nsamples > 0:
+                return islice(self.iterator(), self.nsamples)
+            else:
+                return self.iterator()
+        else:
+            return self.iterator()
+
+    def stage(self, i):
+        """Return pipeline stage i."""
+        return self.pipeline[i]
+
+    def append(self, f):
+        """Append a pipeline stage (modifies the object)."""
+        self.pipeline.append(f)
+        return self
+
+    def append_list(self, *args):
+        for arg in args:
+            self.pipeline.append(arg)
+        return self
+
+    def compose(self, *args):
+        """Append a pipeline stage to a copy of the pipeline and returns the copy."""
+        result = copy.copy(self)
+        for arg in args:
+            result.append(arg)
+        return result
+
+    def with_length(self, n):
+        """Add a __len__ method returning the desired value.
+
+        This does not change the actual number of samples in an epoch.
+        PyTorch IterableDataset should not have a __len__ method.
+        This is provided only as a workaround for some broken training environments
+        that require a __len__ method.
+        """
+        self.size = n
+        return add_length_method(self)
+
+    def with_epoch(self, nsamples=-1, nbatches=-1):
+        """Change the epoch to return the given number of samples/batches.
+
+        The two arguments mean the same thing."""
+        self.repetitions = sys.maxsize
+        self.nsamples = max(nsamples, nbatches)
+        return self
+
+    def repeat(self, nepochs=-1, nbatches=-1):
+        """Repeat iterating through the dataset for the given #epochs up to the given #samples."""
+        if nepochs > 0:
+            self.repetitions = nepochs
+            self.nsamples = nbatches
+        else:
+            self.repetitions = sys.maxsize
+            self.nsamples = nbatches
+        return self

paddlespeech/audio/streamdata/shardlists.py

+#
+# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# This file is part of the WebDataset library.
+# See the LICENSE file for licensing terms (BSD-style).
+#
+
+# Modified from https://github.com/webdataset/webdataset
+
+"""Train PyTorch models directly from POSIX tar archive.
+
+Code works locally or over HTTP connections.
+"""
+
+import os, random, sys, time
+from dataclasses import dataclass, field
+from itertools import islice
+from typing import List
+
+import braceexpand, yaml
+
+from . import utils
+from .filters import pipelinefilter
+from .paddle_utils import IterableDataset
+
+
+from ..utils.log import Logger
+logger = Logger(__name__)
+def expand_urls(urls):
+    if isinstance(urls, str):
+        urllist = urls.split("::")
+        result = []
+        for url in urllist:
+            result.extend(braceexpand.braceexpand(url))
+        return result
+    else:
+        return list(urls)
+
+
+class SimpleShardList(IterableDataset):
+    """An iterable dataset yielding a list of urls."""
+
+    def __init__(self, urls, seed=None):
+        """Iterate through the list of shards.
+
+        :param urls: a list of URLs as a Python list or brace notation string
+        """
+        super().__init__()
+        urls = expand_urls(urls)
+        self.urls = urls
+        assert isinstance(self.urls[0], str)
+        self.seed = seed
+
+    def __len__(self):
+        return len(self.urls)
+
+    def __iter__(self):
+        """Return an iterator over the shards."""
+        urls = self.urls.copy()
+        if self.seed is not None:
+            random.Random(self.seed).shuffle(urls)
+        for url in urls:
+            yield dict(url=url)
+
+
+def split_by_node(src, group=None):
+    rank, world_size, worker, num_workers = utils.paddle_worker_info(group=group)
+    logger.info(f"world_size:{world_size}, rank:{rank}")
+    if world_size > 1:
+        for s in islice(src, rank, None, world_size):
+            yield s
+    else:
+        for s in src:
+            yield s
+
+
+def single_node_only(src, group=None):
+    rank, world_size, worker, num_workers = utils.paddle_worker_info(group=group)
+    if world_size > 1:
+        raise ValueError("input pipeline needs to be reconfigured for multinode training")
+    for s in src:
+        yield s
+
+
+def split_by_worker(src):
+    rank, world_size, worker, num_workers = utils.paddle_worker_info()
+    logger.info(f"num_workers:{num_workers}, worker:{worker}")
+    if num_workers > 1:
+        for s in islice(src, worker, None, num_workers):
+            yield s
+    else:
+        for s in src:
+            yield s
+
+
+def resampled_(src, n=sys.maxsize):
+    import random
+
+    seed = time.time()
+    try:
+        seed = open("/dev/random", "rb").read(20)
+    except Exception as exn:
+        print(repr(exn)[:50], file=sys.stderr)
+    rng = random.Random(seed)
+    print("# resampled loading", file=sys.stderr)
+    items = list(src)
+    print(f"# resampled got {len(items)} samples, yielding {n}", file=sys.stderr)
+    for i in range(n):
+        yield rng.choice(items)
+
+
+resampled = pipelinefilter(resampled_)
+
+
+def non_empty(src):
+    count = 0
+    for s in src:
+        yield s
+        count += 1
+    if count == 0:
+        raise ValueError("pipeline stage received no data at all and this was declared as an error")
+
+
+@dataclass
+class MSSource:
+    """Class representing a data source."""
+
+    name: str = ""
+    perepoch: int = -1
+    resample: bool = False
+    urls: List[str] = field(default_factory=list)
+
+
+default_rng = random.Random()
+
+
+def expand(s):
+    return os.path.expanduser(os.path.expandvars(s))
+
+
+class MultiShardSample(IterableDataset):
+    def __init__(self, fname):
+        """Construct a shardlist from multiple sources using a YAML spec."""
+        self.epoch = -1
+class MultiShardSample(IterableDataset):
+    def __init__(self, fname):
+        """Construct a shardlist from multiple sources using a YAML spec."""
+        self.epoch = -1
+        self.parse_spec(fname)
+
+    def parse_spec(self, fname):
+        self.rng = default_rng  # capture default_rng if we fork
+        if isinstance(fname, dict):
+            spec = fname
+            fname = "{dict}"
+        else:
+            with open(fname) as stream:
+                spec = yaml.safe_load(stream)
+        assert set(spec.keys()).issubset(set("prefix datasets buckets".split())), list(spec.keys())
+        prefix = expand(spec.get("prefix", ""))
+        self.sources = []
+        for ds in spec["datasets"]:
+            assert set(ds.keys()).issubset(set("buckets name shards resample choose".split())), list(
+                ds.keys()
+            )
+            buckets = ds.get("buckets", spec.get("buckets", []))
+            if isinstance(buckets, str):
+                buckets = [buckets]
+            buckets = [expand(s) for s in buckets]
+            if buckets == []:
+                buckets = [""]
+            assert len(buckets) == 1, f"{buckets}: FIXME support for multiple buckets unimplemented"
+            bucket = buckets[0]
+            name = ds.get("name", "@" + bucket)
+            urls = ds["shards"]
+            if isinstance(urls, str):
+                urls = [urls]
+            # urls = [u for url in urls for u in braceexpand.braceexpand(url)]
+            urls = [
+                prefix + os.path.join(bucket, u) for url in urls for u in braceexpand.braceexpand(expand(url))
+            ]
+            resample = ds.get("resample", -1)
+            nsample = ds.get("choose", -1)
+            if nsample > len(urls):
+                raise ValueError(f"perepoch {nsample} must be no greater than the number of shards")
+            if (nsample > 0) and (resample > 0):
+                raise ValueError("specify only one of perepoch or choose")
+            entry = MSSource(name=name, urls=urls, perepoch=nsample, resample=resample)
+            self.sources.append(entry)
+            print(f"# {name} {len(urls)} {nsample}", file=sys.stderr)
+
+    def set_epoch(self, seed):
+        """Set the current epoch (for consistent shard selection among nodes)."""
+        self.rng = random.Random(seed)
+
+    def get_shards_for_epoch(self):
+        result = []
+        for source in self.sources:
+            if source.resample > 0:
+                # sample with replacement
+                l = self.rng.choices(source.urls, k=source.resample)
+            elif source.perepoch > 0:
+                # sample without replacement
+                l = list(source.urls)
+                self.rng.shuffle(l)
+                l = l[: source.perepoch]
+            else:
+                l = list(source.urls)
+            result += l
+        self.rng.shuffle(result)
+        return result
+
+    def __iter__(self):
+        shards = self.get_shards_for_epoch()
+        for shard in shards:
+            yield dict(url=shard)
+
+
+def shardspec(spec):
+    if spec.endswith(".yaml"):
+        return MultiShardSample(spec)
+    else:
+        return SimpleShardList(spec)
+
+
+class ResampledShards(IterableDataset):
+    """An iterable dataset yielding a list of urls."""
+
+    def __init__(
+        self,
+        urls,
+        nshards=sys.maxsize,
+        worker_seed=None,
+        deterministic=False,
+    ):
+        """Sample shards from the shard list with replacement.
+
+        :param urls: a list of URLs as a Python list or brace notation string
+        """
+        super().__init__()
+        urls = expand_urls(urls)
+        self.urls = urls
+        assert isinstance(self.urls[0], str)
+        self.nshards = nshards
+        self.worker_seed = utils.paddle_worker_seed if worker_seed is None else worker_seed
+        self.deterministic = deterministic
+        self.epoch = -1
+
+    def __iter__(self):
+        """Return an iterator over the shards."""
+        self.epoch += 1
+        if self.deterministic:
+            seed = utils.make_seed(self.worker_seed(), self.epoch)
+        else:
+            seed = utils.make_seed(self.worker_seed(), self.epoch, os.getpid(), time.time_ns(), os.urandom(4))
+        if os.environ.get("WDS_SHOW_SEED", "0") == "1":
+            print(f"# ResampledShards seed {seed}")
+        self.rng = random.Random(seed)
+        for _ in range(self.nshards):
+            index = self.rng.randint(0, len(self.urls) - 1)
+            yield dict(url=self.urls[index])

paddlespeech/audio/streamdata/tariterators.py

+#
+# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# This file is part of the WebDataset library.
+# See the LICENSE file for licensing terms (BSD-style).
+
+# Modified from https://github.com/webdataset/webdataset
+# Modified from wenet(https://github.com/wenet-e2e/wenet)
+
+"""Low level iteration functions for tar archives."""
+
+import random, re, tarfile
+
+import braceexpand
+
+from . import filters
+from . import gopen
+from .handlers import reraise_exception
+
+trace = False
+meta_prefix = "__"
+meta_suffix = "__"
+
+import paddlespeech
+import paddle
+import numpy as np
+
+AUDIO_FORMAT_SETS = set(['flac', 'mp3', 'm4a', 'ogg', 'opus', 'wav', 'wma'])
+
+def base_plus_ext(path):
+    """Split off all file extensions.
+
+    Returns base, allext.
+
+    :param path: path with extensions
+    :param returns: path with all extensions removed
+
+    """
+    match = re.match(r"^((?:.*/|)[^.]+)[.]([^/]*)$", path)
+    if not match:
+        return None, None
+    return match.group(1), match.group(2)
+
+
+def valid_sample(sample):
+    """Check whether a sample is valid.
+
+    :param sample: sample to be checked
+    """
+    return (
+        sample is not None
+        and isinstance(sample, dict)
+        and len(list(sample.keys())) > 0
+        and not sample.get("__bad__", False)
+    )
+
+
+# FIXME: UNUSED
+def shardlist(urls, *, shuffle=False):
+    """Given a list of URLs, yields that list, possibly shuffled."""
+    if isinstance(urls, str):
+        urls = braceexpand.braceexpand(urls)
+    else:
+        urls = list(urls)
+    if shuffle:
+        random.shuffle(urls)
+    for url in urls:
+        yield dict(url=url)
+
+
+def url_opener(data, handler=reraise_exception, **kw):
+    """Given a stream of url names (packaged in `dict(url=url)`), yield opened streams."""
+    for sample in data:
+        assert isinstance(sample, dict), sample
+        assert "url" in sample
+        url = sample["url"]
+        try:
+            stream = gopen.gopen(url, **kw)
+            sample.update(stream=stream)
+            yield sample
+        except Exception as exn:
+            exn.args = exn.args + (url,)
+            if handler(exn):
+                continue
+            else:
+                break
+
+
+def tar_file_iterator(
+    fileobj, skip_meta=r"__[^/]*__($|/)", handler=reraise_exception
+):
+    """Iterate over tar file, yielding filename, content pairs for the given tar stream.
+
+    :param fileobj: byte stream suitable for tarfile
+    :param skip_meta: regexp for keys that are skipped entirely (Default value = r"__[^/]*__($|/)")
+
+    """
+    stream = tarfile.open(fileobj=fileobj, mode="r:*")
+    for tarinfo in stream:
+        fname = tarinfo.name
+        try:
+            if not tarinfo.isreg():
+                continue
+            if fname is None:
+                continue
+            if (
+                "/" not in fname
+                and fname.startswith(meta_prefix)
+                and fname.endswith(meta_suffix)
+            ):
+                # skipping metadata for now
+                continue
+            if skip_meta is not None and re.match(skip_meta, fname):
+                continue
+
+            name = tarinfo.name
+            pos = name.rfind('.')
+            assert pos > 0
+            prefix, postfix = name[:pos], name[pos + 1:]
+            if postfix == 'wav':
+                waveform, sample_rate = paddlespeech.audio.load(stream.extractfile(tarinfo), normal=False)
+                result = dict(fname=prefix, wav=waveform, sample_rate = sample_rate)
+            else:
+                txt = stream.extractfile(tarinfo).read().decode('utf8').strip()
+                result = dict(fname=prefix, txt=txt)
+            #result = dict(fname=fname, data=data)
+            yield result
+            stream.members = []
+        except Exception as exn:
+            if hasattr(exn, "args") and len(exn.args) > 0:
+                exn.args = (exn.args[0] + " @ " + str(fileobj),) + exn.args[1:]
+            if handler(exn):
+                continue
+            else:
+                break
+    del stream
+
+def tar_file_and_group_iterator(
+    fileobj, skip_meta=r"__[^/]*__($|/)", handler=reraise_exception
+):
+    """ Expand a stream of open tar files into a stream of tar file contents.
+        And groups the file with same prefix
+
+        Args:
+            data: Iterable[{src, stream}]
+
+        Returns:
+            Iterable[{key, wav, txt, sample_rate}]
+    """
+    stream = tarfile.open(fileobj=fileobj, mode="r:*")
+    prev_prefix = None
+    example = {}
+    valid = True
+    for tarinfo in stream:
+        name = tarinfo.name
+        pos = name.rfind('.')
+        assert pos > 0
+        prefix, postfix = name[:pos], name[pos + 1:]
+        if prev_prefix is not None and prefix != prev_prefix:
+            example['fname'] = prev_prefix
+            if valid:
+                yield example
+            example = {}
+            valid = True
+        with stream.extractfile(tarinfo) as file_obj:
+            try:
+                if postfix == 'txt':
+                    example['txt'] = file_obj.read().decode('utf8').strip()
+                elif postfix in AUDIO_FORMAT_SETS:
+                    waveform, sample_rate = paddlespeech.audio.load(file_obj, normal=False)
+                    waveform = paddle.to_tensor(np.expand_dims(np.array(waveform),0), dtype=paddle.float32)
+
+                    example['wav'] = waveform
+                    example['sample_rate'] = sample_rate
+                else:
+                    example[postfix] = file_obj.read()
+            except Exception as exn:
+                if hasattr(exn, "args") and len(exn.args) > 0:
+                    exn.args = (exn.args[0] + " @ " + str(fileobj),) + exn.args[1:]
+                if handler(exn):
+                    continue
+                else:
+                    break
+                valid = False
+              #  logging.warning('error to parse {}'.format(name))
+        prev_prefix = prefix
+    if prev_prefix is not None:
+        example['fname'] = prev_prefix
+        yield example
+    stream.close()
+
+def tar_file_expander(data, handler=reraise_exception):
+    """Expand a stream of open tar files into a stream of tar file contents.
+
+    This returns an iterator over (filename, file_contents).
+    """
+    for source in data:
+        url = source["url"]
+        try:
+            assert isinstance(source, dict)
+            assert "stream" in source
+            for sample in tar_file_iterator(source["stream"]):
+                assert (
+                    isinstance(sample, dict) and "data" in sample and "fname" in sample
+                )
+                sample["__url__"] = url
+                yield sample
+        except Exception as exn:
+            exn.args = exn.args + (source.get("stream"), source.get("url"))
+            if handler(exn):
+                continue
+            else:
+                break
+
+
+
+
+def tar_file_and_group_expander(data, handler=reraise_exception):
+    """Expand a stream of open tar files into a stream of tar file contents.
+
+    This returns an iterator over (filename, file_contents).
+    """
+    for source in data:
+        url = source["url"]
+        try:
+            assert isinstance(source, dict)
+            assert "stream" in source
+            for sample in tar_file_and_group_iterator(source["stream"]):
+                assert (
+                    isinstance(sample, dict) and "wav" in sample and "txt" in sample and "fname" in sample
+                )
+                sample["__url__"] = url
+                yield sample
+        except Exception as exn:
+            exn.args = exn.args + (source.get("stream"), source.get("url"))
+            if handler(exn):
+                continue
+            else:
+                break
+
+
+def group_by_keys(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
+    """Return function over iterator that groups key, value pairs into samples.
+
+    :param keys: function that splits the key into key and extension (base_plus_ext)
+    :param lcase: convert suffixes to lower case (Default value = True)
+    """
+    current_sample = None
+    for filesample in data:
+        assert isinstance(filesample, dict)
+        fname, value = filesample["fname"], filesample["data"]
+        prefix, suffix = keys(fname)
+        if trace:
+            print(
+                prefix,
+                suffix,
+                current_sample.keys() if isinstance(current_sample, dict) else None,
+            )
+        if prefix is None:
+            continue
+        if lcase:
+            suffix = suffix.lower()
+        if current_sample is None or prefix != current_sample["__key__"]:
+            if valid_sample(current_sample):
+                yield current_sample
+            current_sample = dict(__key__=prefix, __url__=filesample["__url__"])
+        if suffix in current_sample:
+            raise ValueError(
+                f"{fname}: duplicate file name in tar file {suffix} {current_sample.keys()}"
+            )
+        if suffixes is None or suffix in suffixes:
+            current_sample[suffix] = value
+    if valid_sample(current_sample):
+        yield current_sample
+
+
+def tarfile_samples(src, handler=reraise_exception):
+    streams = url_opener(src, handler=handler)
+    samples = tar_file_and_group_expander(streams, handler=handler)
+    return samples
+
+
+tarfile_to_samples = filters.pipelinefilter(tarfile_samples)

paddlespeech/audio/streamdata/utils.py

+#
+# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# This file is part of the WebDataset library.
+# See the LICENSE file for licensing terms (BSD-style).
+#
+
+# Modified from https://github.com/webdataset/webdataset
+
+"""Miscellaneous utility functions."""
+
+import importlib
+import itertools as itt
+import os
+import re
+import sys
+from typing import Any, Callable, Iterator, Optional, Union
+
+from ..utils.log import Logger
+
+logger = Logger(__name__)
+
+def make_seed(*args):
+    seed = 0
+    for arg in args:
+        seed = (seed * 31 + hash(arg)) & 0x7FFFFFFF
+    return seed
+
+
+class PipelineStage:
+    def invoke(self, *args, **kw):
+        raise NotImplementedError
+
+
+def identity(x: Any) -> Any:
+    """Return the argument as is."""
+    return x
+
+
+def safe_eval(s: str, expr: str = "{}"):
+    """Evaluate the given expression more safely."""
+    if re.sub("[^A-Za-z0-9_]", "", s) != s:
+        raise ValueError(f"safe_eval: illegal characters in: '{s}'")
+    return eval(expr.format(s))
+
+
+def lookup_sym(sym: str, modules: list):
+    """Look up a symbol in a list of modules."""
+    for mname in modules:
+        module = importlib.import_module(mname, package="webdataset")
+        result = getattr(module, sym, None)
+        if result is not None:
+            return result
+    return None
+
+
+def repeatedly0(
+    loader: Iterator, nepochs: int = sys.maxsize, nbatches: int = sys.maxsize
+):
+    """Repeatedly returns batches from a DataLoader."""
+    for epoch in range(nepochs):
+        for sample in itt.islice(loader, nbatches):
+            yield sample
+
+
+def guess_batchsize(batch: Union[tuple, list]):
+    """Guess the batch size by looking at the length of the first element in a tuple."""
+    return len(batch[0])
+
+
+def repeatedly(
+    source: Iterator,
+    nepochs: int = None,
+    nbatches: int = None,
+    nsamples: int = None,
+    batchsize: Callable[..., int] = guess_batchsize,
+):
+    """Repeatedly yield samples from an iterator."""
+    epoch = 0
+    batch = 0
+    total = 0
+    while True:
+        for sample in source:
+            yield sample
+            batch += 1
+            if nbatches is not None and batch >= nbatches:
+                return
+            if nsamples is not None:
+                total += guess_batchsize(sample)
+                if total >= nsamples:
+                    return
+        epoch += 1
+        if nepochs is not None and epoch >= nepochs:
+            return
+
+def paddle_worker_info(group=None):
+    """Return node and worker info for PyTorch and some distributed environments."""
+    rank = 0
+    world_size = 1
+    worker = 0
+    num_workers = 1
+    if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
+        rank = int(os.environ["RANK"])
+        world_size = int(os.environ["WORLD_SIZE"])
+    else:
+        try:
+            import paddle.distributed
+            group = group or paddle.distributed.get_group()
+            rank = paddle.distributed.get_rank()
+            world_size = paddle.distributed.get_world_size()
+        except ModuleNotFoundError:
+            pass
+    if "WORKER" in os.environ and "NUM_WORKERS" in os.environ:
+        worker = int(os.environ["WORKER"])
+        num_workers = int(os.environ["NUM_WORKERS"])
+    else:
+        try:
+            from paddle.io import get_worker_info
+            worker_info = paddle.io.get_worker_info()
+            if worker_info is not None:
+                worker = worker_info.id
+                num_workers = worker_info.num_workers
+        except ModuleNotFoundError as E:
+            logger.info(f"not found {E}")
+            exit(-1)
+
+    return rank, world_size, worker, num_workers
+
+def paddle_worker_seed(group=None):
+    """Compute a distinct, deterministic RNG seed for each worker and node."""
+    rank, world_size, worker, num_workers = paddle_worker_info(group=group)
+    return rank * 1000 + worker

paddlespeech/audio/streamdata/writer.py

+#
+# Copyright (c) 2017-2021 NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# This file is part of the WebDataset library.
+# See the LICENSE file for licensing terms (BSD-style).
+# Modified from https://github.com/webdataset/webdataset
+#
+
+"""Classes and functions for writing tar files and WebDataset files."""
+
+import io, json, pickle, re, tarfile, time
+from typing import Any, Callable, Optional, Union
+
+import numpy as np
+
+from . import gopen
+
+
+def imageencoder(image: Any, format: str = "PNG"):  # skipcq: PYL-W0622
+    """Compress an image using PIL and return it as a string.
+
+    Can handle float or uint8 images.
+
+    :param image: ndarray representing an image
+    :param format: compression format (PNG, JPEG, PPM)
+
+    """
+    import PIL
+
+    assert isinstance(image, (PIL.Image.Image, np.ndarray)), type(image)
+
+    if isinstance(image, np.ndarray):
+        if image.dtype in [np.dtype("f"), np.dtype("d")]:
+            if not (np.amin(image) > -0.001 and np.amax(image) < 1.001):
+                raise ValueError(
+                    f"image values out of range {np.amin(image)} {np.amax(image)}"
+                )
+            image = np.clip(image, 0.0, 1.0)
+            image = np.array(image * 255.0, "uint8")
+        assert image.ndim in [2, 3]
+        if image.ndim == 3:
+            assert image.shape[2] in [1, 3]
+        image = PIL.Image.fromarray(image)
+    if format.upper() == "JPG":
+        format = "JPEG"
+    elif format.upper() in ["IMG", "IMAGE"]:
+        format = "PPM"
+    if format == "JPEG":
+        opts = dict(quality=100)
+    else:
+        opts = {}
+    with io.BytesIO() as result:
+        image.save(result, format=format, **opts)
+        return result.getvalue()
+
+
+def bytestr(data: Any):
+    """Convert data into a bytestring.
+
+    Uses str and ASCII encoding for data that isn't already in string format.
+
+    :param data: data
+    """
+    if isinstance(data, bytes):
+        return data
+    if isinstance(data, str):
+        return data.encode("ascii")
+    return str(data).encode("ascii")
+
+def paddle_dumps(data: Any):
+    """Dump data into a bytestring using paddle.dumps.
+
+    This delays importing paddle until needed.
+
+    :param data: data to be dumped
+    """
+    import io
+
+    import paddle
+
+    stream = io.BytesIO()
+    paddle.save(data, stream)
+    return stream.getvalue()
+
+def numpy_dumps(data: np.ndarray):
+    """Dump data into a bytestring using numpy npy format.
+
+    :param data: data to be dumped
+    """
+    import io
+
+    import numpy.lib.format
+
+    stream = io.BytesIO()
+    numpy.lib.format.write_array(stream, data)
+    return stream.getvalue()
+
+
+def numpy_npz_dumps(data: np.ndarray):
+    """Dump data into a bytestring using numpy npz format.
+
+    :param data: data to be dumped
+    """
+    import io
+
+    stream = io.BytesIO()
+    np.savez_compressed(stream, **data)
+    return stream.getvalue()
+
+
+def tenbin_dumps(x):
+    from . import tenbin
+
+    if isinstance(x, list):
+        return memoryview(tenbin.encode_buffer(x))
+    else:
+        return memoryview(tenbin.encode_buffer([x]))
+
+
+def cbor_dumps(x):
+    import cbor
+
+    return cbor.dumps(x)
+
+
+def mp_dumps(x):
+    import msgpack
+
+    return msgpack.packb(x)
+
+
+def add_handlers(d, keys, value):
+    if isinstance(keys, str):
+        keys = keys.split()
+    for k in keys:
+        d[k] = value
+
+
+def make_handlers():
+    """Create a list of handlers for encoding data."""
+    handlers = {}
+    add_handlers(
+        handlers, "cls cls2 class count index inx id", lambda x: str(x).encode("ascii")
+    )
+    add_handlers(handlers, "txt text transcript", lambda x: x.encode("utf-8"))
+    add_handlers(handlers, "html htm", lambda x: x.encode("utf-8"))
+    add_handlers(handlers, "pyd pickle", pickle.dumps)
+    add_handlers(handlers, "pdparams", paddle_dumps)
+    add_handlers(handlers, "npy", numpy_dumps)
+    add_handlers(handlers, "npz", numpy_npz_dumps)
+    add_handlers(handlers, "ten tenbin tb", tenbin_dumps)
+    add_handlers(handlers, "json jsn", lambda x: json.dumps(x).encode("utf-8"))
+    add_handlers(handlers, "mp msgpack msg", mp_dumps)
+    add_handlers(handlers, "cbor", cbor_dumps)
+    add_handlers(handlers, "jpg jpeg img image", lambda data: imageencoder(data, "jpg"))
+    add_handlers(handlers, "png", lambda data: imageencoder(data, "png"))
+    add_handlers(handlers, "pbm", lambda data: imageencoder(data, "pbm"))
+    add_handlers(handlers, "pgm", lambda data: imageencoder(data, "pgm"))
+    add_handlers(handlers, "ppm", lambda data: imageencoder(data, "ppm"))
+    return handlers
+
+
+default_handlers = make_handlers()
+
+
+def encode_based_on_extension1(data: Any, tname: str, handlers: dict):
+    """Encode data based on its extension and a dict of handlers.
+
+    :param data: data
+    :param tname: file extension
+    :param handlers: handlers
+    """
+    if tname[0] == "_":
+        if not isinstance(data, str):
+            raise ValueError("the values of metadata must be of string type")
+        return data
+    extension = re.sub(r".*\.", "", tname).lower()
+    if isinstance(data, bytes):
+        return data
+    if isinstance(data, str):
+        return data.encode("utf-8")
+    handler = handlers.get(extension)
+    if handler is None:
+        raise ValueError(f"no handler found for {extension}")
+    return handler(data)
+
+
+def encode_based_on_extension(sample: dict, handlers: dict):
+    """Encode an entire sample with a collection of handlers.
+
+    :param sample: data sample (a dict)
+    :param handlers: handlers for encoding
+    """
+    return {
+        k: encode_based_on_extension1(v, k, handlers) for k, v in list(sample.items())
+    }
+
+
+def make_encoder(spec: Union[bool, str, dict, Callable]):
+    """Make an encoder function from a specification.
+
+    :param spec: specification
+    """
+    if spec is False or spec is None:
+
+        def encoder(x):
+            """Do not encode at all."""
+            return x
+
+    elif callable(spec):
+        encoder = spec
+    elif isinstance(spec, dict):
+
+        def f(sample):
+            """Encode based on extension."""
+            return encode_based_on_extension(sample, spec)
+
+        encoder = f
+
+    elif spec is True:
+        handlers = default_handlers
+
+        def g(sample):
+            """Encode based on extension."""
+            return encode_based_on_extension(sample, handlers)
+
+        encoder = g
+
+    else:
+        raise ValueError(f"{spec}: unknown decoder spec")
+    if not callable(encoder):
+        raise ValueError(f"{spec} did not yield a callable encoder")
+    return encoder
+
+
+class TarWriter:
+    """A class for writing dictionaries to tar files.
+
+    :param fileobj: fileobj: file name for tar file (.tgz/.tar) or open file descriptor
+    :param encoder: sample encoding (Default value = True)
+    :param compress:  (Default value = None)
+
+    `True` will use an encoder that behaves similar to the automatic
+    decoder for `Dataset`. `False` disables encoding and expects byte strings
+    (except for metadata, which must be strings). The `encoder` argument can
+    also be a `callable`, or a dictionary mapping extensions to encoders.
+
+    The following code will add two file to the tar archive: `a/b.png` and
+    `a/b.output.png`.
+
+    ```Python
+        tarwriter = TarWriter(stream)
+        image = imread("b.jpg")
+        image2 = imread("b.out.jpg")
+        sample = {"__key__": "a/b", "png": image, "output.png": image2}
+        tarwriter.write(sample)
+    ```
+    """
+
+    def __init__(
+        self,
+        fileobj,
+        user: str = "bigdata",
+        group: str = "bigdata",
+        mode: int = 0o0444,
+        compress: Optional[bool] = None,
+        encoder: Union[None, bool, Callable] = True,
+        keep_meta: bool = False,
+    ):
+        """Create a tar writer.
+
+        :param fileobj: stream to write data to
+        :param user: user for tar files
+        :param group: group for tar files
+        :param mode: mode for tar files
+        :param compress: desired compression
+        :param encoder: encoder function
+        :param keep_meta: keep metadata (entries starting with "_")
+        """
+        if isinstance(fileobj, str):
+            if compress is False:
+                tarmode = "w|"
+            elif compress is True:
+                tarmode = "w|gz"
+            else:
+                tarmode = "w|gz" if fileobj.endswith("gz") else "w|"
+            fileobj = gopen.gopen(fileobj, "wb")
+            self.own_fileobj = fileobj
+        else:
+            tarmode = "w|gz" if compress is True else "w|"
+            self.own_fileobj = None
+        self.encoder = make_encoder(encoder)
+        self.keep_meta = keep_meta
+        self.stream = fileobj
+        self.tarstream = tarfile.open(fileobj=fileobj, mode=tarmode)
+
+        self.user = user
+        self.group = group
+        self.mode = mode
+        self.compress = compress
+
+    def __enter__(self):
+        """Enter context."""
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Exit context."""
+        self.close()
+
+    def close(self):
+        """Close the tar file."""
+        self.tarstream.close()
+        if self.own_fileobj is not None:
+            self.own_fileobj.close()
+            self.own_fileobj = None
+
+    def write(self, obj):
+        """Write a dictionary to the tar file.
+
+        :param obj: dictionary of objects to be stored
+        :returns: size of the entry
+
+        """
+        total = 0
+        obj = self.encoder(obj)
+        if "__key__" not in obj:
+            raise ValueError("object must contain a __key__")
+        for k, v in list(obj.items()):
+            if k[0] == "_":
+                continue
+            if not isinstance(v, (bytes, bytearray, memoryview)):
+                raise ValueError(
+                    f"{k} doesn't map to a bytes after encoding ({type(v)})"
+                )
+        key = obj["__key__"]
+        for k in sorted(obj.keys()):
+            if k == "__key__":
+                continue
+            if not self.keep_meta and k[0] == "_":
+                continue
+            v = obj[k]
+            if isinstance(v, str):
+                v = v.encode("utf-8")
+            now = time.time()
+            ti = tarfile.TarInfo(key + "." + k)
+            ti.size = len(v)
+            ti.mtime = now
+            ti.mode = self.mode
+            ti.uname = self.user
+            ti.gname = self.group
+            if not isinstance(v, (bytes, bytearray, memoryview)):
+                raise ValueError(f"converter didn't yield bytes: {k}, {type(v)}")
+            stream = io.BytesIO(v)
+            self.tarstream.addfile(ti, stream)
+            total += ti.size
+        return total
+
+
+class ShardWriter:
+    """Like TarWriter but splits into multiple shards."""
+
+    def __init__(
+        self,
+        pattern: str,
+        maxcount: int = 100000,
+        maxsize: float = 3e9,
+        post: Optional[Callable] = None,
+        start_shard: int = 0,
+        **kw,
+    ):
+        """Create a ShardWriter.
+
+        :param pattern: output file pattern
+        :param maxcount: maximum number of records per shard (Default value = 100000)
+        :param maxsize: maximum size of each shard (Default value = 3e9)
+        :param kw: other options passed to TarWriter
+        """
+        self.verbose = 1
+        self.kw = kw
+        self.maxcount = maxcount
+        self.maxsize = maxsize
+        self.post = post
+
+        self.tarstream = None
+        self.shard = start_shard
+        self.pattern = pattern
+        self.total = 0
+        self.count = 0
+        self.size = 0
+        self.fname = None
+        self.next_stream()
+
+    def next_stream(self):
+        """Close the current stream and move to the next."""
+        self.finish()
+        self.fname = self.pattern % self.shard
+        if self.verbose:
+            print(
+                "# writing",
+                self.fname,
+                self.count,
+                "%.1f GB" % (self.size / 1e9),
+                self.total,
+            )
+        self.shard += 1
+        stream = open(self.fname, "wb")
+        self.tarstream = TarWriter(stream, **self.kw)
+        self.count = 0
+        self.size = 0
+
+    def write(self, obj):
+        """Write a sample.
+
+        :param obj: sample to be written
+        """
+        if (
+            self.tarstream is None
+            or self.count >= self.maxcount
+            or self.size >= self.maxsize
+        ):
+            self.next_stream()
+        size = self.tarstream.write(obj)
+        self.count += 1
+        self.total += 1
+        self.size += size
+
+    def finish(self):
+        """Finish all writing (use close instead)."""
+        if self.tarstream is not None:
+            self.tarstream.close()
+            assert self.fname is not None
+            if callable(self.post):
+                self.post(self.fname)
+            self.tarstream = None
+
+    def close(self):
+        """Close the stream."""
+        self.finish()
+        del self.tarstream
+        del self.shard
+        del self.count
+        del self.size
+
+    def __enter__(self):
+        """Enter context."""
+        return self
+
+    def __exit__(self, *args, **kw):
+        """Exit context."""
+        self.close()

paddlespeech/audio/text/text_featurizer.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains the text featurizer class."""
+from pprint import pformat
+from typing import Union
+
+import sentencepiece as spm
+
+from .utility import BLANK
+from .utility import EOS
+from .utility import load_dict
+from .utility import MASKCTC
+from .utility import SOS
+from .utility import SPACE
+from .utility import UNK
+from ..utils.log import Logger
+
+logger = Logger(__name__)
+
+__all__ = ["TextFeaturizer"]
+
+
+class TextFeaturizer():
+    def __init__(self, unit_type, vocab, spm_model_prefix=None, maskctc=False):
+        """Text featurizer, for processing or extracting features from text.
+
+        Currently, it supports char/word/sentence-piece level tokenizing and conversion into
+        a list of token indices. Note that the token indexing order follows the
+        given vocabulary file.
+
+        Args:
+            unit_type (str): unit type, e.g. char, word, spm
+            vocab Option[str, list]: Filepath to load vocabulary for token indices conversion, or vocab list.
+            spm_model_prefix (str, optional): spm model prefix. Defaults to None.
+        """
+        assert unit_type in ('char', 'spm', 'word')
+        self.unit_type = unit_type
+        self.unk = UNK
+        self.maskctc = maskctc
+
+        if vocab:
+            self.vocab_dict, self._id2token, self.vocab_list, self.unk_id, self.eos_id, self.blank_id = self._load_vocabulary_from_file(
+                vocab, maskctc)
+            self.vocab_size = len(self.vocab_list)
+        else:
+            logger.warning("TextFeaturizer: not have vocab file or vocab list.")
+
+        if unit_type == 'spm':
+            spm_model = spm_model_prefix + '.model'
+            self.sp = spm.SentencePieceProcessor()
+            self.sp.Load(spm_model)
+
+    def tokenize(self, text, replace_space=True):
+        if self.unit_type == 'char':
+            tokens = self.char_tokenize(text, replace_space)
+        elif self.unit_type == 'word':
+            tokens = self.word_tokenize(text)
+        else:  # spm
+            tokens = self.spm_tokenize(text)
+        return tokens
+
+    def detokenize(self, tokens):
+        if self.unit_type == 'char':
+            text = self.char_detokenize(tokens)
+        elif self.unit_type == 'word':
+            text = self.word_detokenize(tokens)
+        else:  # spm
+            text = self.spm_detokenize(tokens)
+        return text
+
+    def featurize(self, text):
+        """Convert text string to a list of token indices.
+
+        Args:
+            text (str): Text to process.
+
+        Returns:
+            List[int]: List of token indices.
+        """
+        tokens = self.tokenize(text)
+        ids = []
+        for token in tokens:
+            if token not in self.vocab_dict:
+                logger.debug(f"Text Token: {token} -> {self.unk}")
+                token = self.unk
+            ids.append(self.vocab_dict[token])
+        return ids
+
+    def defeaturize(self, idxs):
+        """Convert a list of token indices to text string,
+        ignore index after eos_id.
+
+        Args:
+            idxs (List[int]): List of token indices.
+
+        Returns:
+            str: Text.
+        """
+        tokens = []
+        for idx in idxs:
+            if idx == self.eos_id:
+                break
+            tokens.append(self._id2token[idx])
+        text = self.detokenize(tokens)
+        return text
+
+    def char_tokenize(self, text, replace_space=True):
+        """Character tokenizer.
+
+        Args:
+            text (str): text string.
+            replace_space (bool): False only used by build_vocab.py.
+
+        Returns:
+            List[str]: tokens.
+        """
+        text = text.strip()
+        if replace_space:
+            text_list = [SPACE if item == " " else item for item in list(text)]
+        else:
+            text_list = list(text)
+        return text_list
+
+    def char_detokenize(self, tokens):
+        """Character detokenizer.
+
+        Args:
+            tokens (List[str]): tokens.
+
+        Returns:
+           str: text string.
+        """
+        tokens = [t.replace(SPACE, " ") for t in tokens]
+        return "".join(tokens)
+
+    def word_tokenize(self, text):
+        """Word tokenizer, separate by <space>."""
+        return text.strip().split()
+
+    def word_detokenize(self, tokens):
+        """Word detokenizer, separate by <space>."""
+        return " ".join(tokens)
+
+    def spm_tokenize(self, text):
+        """spm tokenize.
+
+        Args:
+            text (str): text string.
+
+        Returns:
+            List[str]: sentence pieces str code
+        """
+        stats = {"num_empty": 0, "num_filtered": 0}
+
+        def valid(line):
+            return True
+
+        def encode(l):
+            return self.sp.EncodeAsPieces(l)
+
+        def encode_line(line):
+            line = line.strip()
+            if len(line) > 0:
+                line = encode(line)
+                if valid(line):
+                    return line
+                else:
+                    stats["num_filtered"] += 1
+            else:
+                stats["num_empty"] += 1
+            return None
+
+        enc_line = encode_line(text)
+        return enc_line
+
+    def spm_detokenize(self, tokens, input_format='piece'):
+        """spm detokenize.
+
+        Args:
+            ids (List[str]): tokens.
+
+        Returns:
+            str: text
+        """
+        if input_format == "piece":
+
+            def decode(l):
+                return "".join(self.sp.DecodePieces(l))
+        elif input_format == "id":
+
+            def decode(l):
+                return "".join(self.sp.DecodeIds(l))
+
+        return decode(tokens)
+
+    def _load_vocabulary_from_file(self, vocab: Union[str, list],
+                                   maskctc: bool):
+        """Load vocabulary from file."""
+        if isinstance(vocab, list):
+            vocab_list = vocab
+        else:
+            vocab_list = load_dict(vocab, maskctc)
+        assert vocab_list is not None
+        logger.debug(f"Vocab: {pformat(vocab_list)}")
+
+        id2token = dict(
+            [(idx, token) for (idx, token) in enumerate(vocab_list)])
+        token2id = dict(
+            [(token, idx) for (idx, token) in enumerate(vocab_list)])
+
+        blank_id = vocab_list.index(BLANK) if BLANK in vocab_list else -1
+        maskctc_id = vocab_list.index(MASKCTC) if MASKCTC in vocab_list else -1
+        unk_id = vocab_list.index(UNK) if UNK in vocab_list else -1
+        eos_id = vocab_list.index(EOS) if EOS in vocab_list else -1
+        sos_id = vocab_list.index(SOS) if SOS in vocab_list else -1
+        space_id = vocab_list.index(SPACE) if SPACE in vocab_list else -1
+
+        logger.info(f"BLANK id: {blank_id}")
+        logger.info(f"UNK id: {unk_id}")
+        logger.info(f"EOS id: {eos_id}")
+        logger.info(f"SOS id: {sos_id}")
+        logger.info(f"SPACE id: {space_id}")
+        logger.info(f"MASKCTC id: {maskctc_id}")
+        return token2id, id2token, vocab_list, unk_id, eos_id, blank_id

paddlespeech/audio/text/utility.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains data helper functions."""
+import json
+import math
+import tarfile
+from collections import namedtuple
+from typing import List
+from typing import Optional
+from typing import Text
+
+import jsonlines
+import numpy as np
+
+from paddlespeech.s2t.utils.log import Log
+
+logger = Log(__name__).getlog()
+
+__all__ = [
+    "load_dict", "load_cmvn", "read_manifest", "rms_to_db", "rms_to_dbfs",
+    "max_dbfs", "mean_dbfs", "gain_db_to_ratio", "normalize_audio", "SOS",
+    "EOS", "UNK", "BLANK", "MASKCTC", "SPACE", "convert_samples_to_float32",
+    "convert_samples_from_float32"
+]
+
+IGNORE_ID = -1
+# `sos` and `eos` using same token
+SOS = "<eos>"
+EOS = SOS
+UNK = "<unk>"
+BLANK = "<blank>"
+MASKCTC = "<mask>"
+SPACE = "<space>"
+
+
+def load_dict(dict_path: Optional[Text], maskctc=False) -> Optional[List[Text]]:
+    if dict_path is None:
+        return None
+
+    with open(dict_path, "r") as f:
+        dictionary = f.readlines()
+    # first token is `<blank>`
+    # multi line: `<blank> 0\n`
+    # one line: `<blank>`
+    # space is relpace with <space>
+    char_list = [entry[:-1].split(" ")[0] for entry in dictionary]
+    if BLANK not in char_list:
+        char_list.insert(0, BLANK)
+    if EOS not in char_list:
+        char_list.append(EOS)
+    # for non-autoregressive maskctc model
+    if maskctc and MASKCTC not in char_list:
+        char_list.append(MASKCTC)
+    return char_list
+
+
+def read_manifest(
+        manifest_path,
+        max_input_len=float('inf'),
+        min_input_len=0.0,
+        max_output_len=float('inf'),
+        min_output_len=0.0,
+        max_output_input_ratio=float('inf'),
+        min_output_input_ratio=0.0, ):
+    """Load and parse manifest file.
+
+    Args:
+        manifest_path ([type]): Manifest file to load and parse.
+        max_input_len ([type], optional): maximum output seq length,
+            in seconds for raw wav, in frame numbers for feature data.
+            Defaults to float('inf').
+        min_input_len (float, optional): minimum input seq length,
+            in seconds for raw wav, in frame numbers for feature data.
+            Defaults to 0.0.
+        max_output_len (float, optional): maximum input seq length,
+            in modeling units. Defaults to 500.0.
+        min_output_len (float, optional): minimum input seq length,
+            in modeling units. Defaults to 0.0.
+        max_output_input_ratio (float, optional):
+            maximum output seq length/output seq length ratio. Defaults to 10.0.
+        min_output_input_ratio (float, optional):
+            minimum output seq length/output seq length ratio. Defaults to 0.05.
+
+    Raises:
+        IOError: If failed to parse the manifest.
+
+    Returns:
+        List[dict]: Manifest parsing results.
+    """
+    manifest = []
+    with jsonlines.open(manifest_path, 'r') as reader:
+        for json_data in reader:
+            feat_len = json_data["input"][0]["shape"][
+                0] if "input" in json_data and "shape" in json_data["input"][
+                    0] else 1.0
+            token_len = json_data["output"][0]["shape"][
+                0] if "output" in json_data and "shape" in json_data["output"][
+                    0] else 1.0
+            conditions = [
+                feat_len >= min_input_len,
+                feat_len <= max_input_len,
+                token_len >= min_output_len,
+                token_len <= max_output_len,
+                token_len / feat_len >= min_output_input_ratio,
+                token_len / feat_len <= max_output_input_ratio,
+            ]
+            if all(conditions):
+                manifest.append(json_data)
+    return manifest
+
+
+# Tar File read
+TarLocalData = namedtuple('TarLocalData', ['tar2info', 'tar2object'])
+
+
+def parse_tar(file):
+    """Parse a tar file to get a tarfile object
+    and a map containing tarinfoes
+    """
+    result = {}
+    f = tarfile.open(file)
+    for tarinfo in f.getmembers():
+        result[tarinfo.name] = tarinfo
+    return f, result
+
+
+def subfile_from_tar(file, local_data=None):
+    """Get subfile object from tar.
+
+    tar:tarpath#filename
+
+    It will return a subfile object from tar file
+    and cached tar file info for next reading request.
+    """
+    tarpath, filename = file.split(':', 1)[1].split('#', 1)
+
+    if local_data is None:
+        local_data = TarLocalData(tar2info={}, tar2object={})
+
+    assert isinstance(local_data, TarLocalData)
+
+    if 'tar2info' not in local_data.__dict__:
+        local_data.tar2info = {}
+    if 'tar2object' not in local_data.__dict__:
+        local_data.tar2object = {}
+
+    if tarpath not in local_data.tar2info:
+        fobj, infos = parse_tar(tarpath)
+        local_data.tar2info[tarpath] = infos
+        local_data.tar2object[tarpath] = fobj
+    else:
+        fobj = local_data.tar2object[tarpath]
+        infos = local_data.tar2info[tarpath]
+    return fobj.extractfile(infos[filename])
+
+
+def rms_to_db(rms: float):
+    """Root Mean Square to dB.
+
+    Args:
+        rms ([float]): root mean square
+
+    Returns:
+        float: dB
+    """
+    return 20.0 * math.log10(max(1e-16, rms))
+
+
+def rms_to_dbfs(rms: float):
+    """Root Mean Square to dBFS.
+    https://fireattack.wordpress.com/2017/02/06/replaygain-loudness-normalization-and-applications/
+    Audio is mix of sine wave, so 1 amp sine wave's Full scale is 0.7071, equal to -3.0103dB.
+
+    dB = dBFS + 3.0103
+    dBFS = db - 3.0103
+    e.g. 0 dB = -3.0103 dBFS
+
+    Args:
+        rms ([float]): root mean square
+
+    Returns:
+        float: dBFS
+    """
+    return rms_to_db(rms) - 3.0103
+
+
+def max_dbfs(sample_data: np.ndarray):
+    """Peak dBFS based on the maximum energy sample.
+
+    Args:
+        sample_data ([np.ndarray]): float array, [-1, 1].
+
+    Returns:
+        float: dBFS
+    """
+    # Peak dBFS based on the maximum energy sample. Will prevent overdrive if used for normalization.
+    return rms_to_dbfs(max(abs(np.min(sample_data)), abs(np.max(sample_data))))
+
+
+def mean_dbfs(sample_data):
+    """Peak dBFS based on the RMS energy.
+
+    Args:
+        sample_data ([np.ndarray]): float array, [-1, 1].
+
+    Returns:
+        float: dBFS
+    """
+    return rms_to_dbfs(
+        math.sqrt(np.mean(np.square(sample_data, dtype=np.float64))))
+
+
+def gain_db_to_ratio(gain_db: float):
+    """dB to ratio
+
+    Args:
+        gain_db (float): gain in dB
+
+    Returns:
+        float: scale in amp
+    """
+    return math.pow(10.0, gain_db / 20.0)
+
+
+def normalize_audio(sample_data: np.ndarray, dbfs: float=-3.0103):
+    """Nomalize audio to dBFS.
+
+    Args:
+        sample_data (np.ndarray): input wave samples, [-1, 1].
+        dbfs (float, optional): target dBFS. Defaults to -3.0103.
+
+    Returns:
+        np.ndarray: normalized wave
+    """
+    return np.maximum(
+        np.minimum(sample_data * gain_db_to_ratio(dbfs - max_dbfs(sample_data)),
+                   1.0), -1.0)
+
+
+def _load_json_cmvn(json_cmvn_file):
+    """ Load the json format cmvn stats file and calculate cmvn
+
+    Args:
+        json_cmvn_file: cmvn stats file in json format
+
+    Returns:
+        a numpy array of [means, vars]
+    """
+    with open(json_cmvn_file) as f:
+        cmvn_stats = json.load(f)
+
+    means = cmvn_stats['mean_stat']
+    variance = cmvn_stats['var_stat']
+    count = cmvn_stats['frame_num']
+    for i in range(len(means)):
+        means[i] /= count
+        variance[i] = variance[i] / count - means[i] * means[i]
+        if variance[i] < 1.0e-20:
+            variance[i] = 1.0e-20
+        variance[i] = 1.0 / math.sqrt(variance[i])
+    cmvn = np.array([means, variance])
+    return cmvn
+
+
+def _load_kaldi_cmvn(kaldi_cmvn_file):
+    """ Load the kaldi format cmvn stats file and calculate cmvn
+
+    Args:
+        kaldi_cmvn_file:  kaldi text style global cmvn file, which
+           is generated by:
+           compute-cmvn-stats --binary=false scp:feats.scp global_cmvn
+
+    Returns:
+        a numpy array of [means, vars]
+    """
+    means = []
+    variance = []
+    with open(kaldi_cmvn_file, 'r') as fid:
+        # kaldi binary file start with '\0B'
+        if fid.read(2) == '\0B':
+            logger.error('kaldi cmvn binary file is not supported, please '
+                         'recompute it by: compute-cmvn-stats --binary=false '
+                         ' scp:feats.scp global_cmvn')
+            sys.exit(1)
+        fid.seek(0)
+        arr = fid.read().split()
+        assert (arr[0] == '[')
+        assert (arr[-2] == '0')
+        assert (arr[-1] == ']')
+        feat_dim = int((len(arr) - 2 - 2) / 2)
+        for i in range(1, feat_dim + 1):
+            means.append(float(arr[i]))
+        count = float(arr[feat_dim + 1])
+        for i in range(feat_dim + 2, 2 * feat_dim + 2):
+            variance.append(float(arr[i]))
+
+    for i in range(len(means)):
+        means[i] /= count
+        variance[i] = variance[i] / count - means[i] * means[i]
+        if variance[i] < 1.0e-20:
+            variance[i] = 1.0e-20
+        variance[i] = 1.0 / math.sqrt(variance[i])
+    cmvn = np.array([means, variance])
+    return cmvn
+
+
+def load_cmvn(cmvn_file: str, filetype: str):
+    """load cmvn from file.
+
+    Args:
+        cmvn_file (str): cmvn path.
+        filetype (str): file type, optional[npz, json, kaldi].
+
+    Raises:
+        ValueError: file type not support.
+
+    Returns:
+        Tuple[np.ndarray, np.ndarray]: mean, istd
+    """
+    assert filetype in ['npz', 'json', 'kaldi'], filetype
+    filetype = filetype.lower()
+    if filetype == "json":
+        cmvn = _load_json_cmvn(cmvn_file)
+    elif filetype == "kaldi":
+        cmvn = _load_kaldi_cmvn(cmvn_file)
+    elif filetype == "npz":
+        eps = 1e-14
+        npzfile = np.load(cmvn_file)
+        mean = np.squeeze(npzfile["mean"])
+        std = np.squeeze(npzfile["std"])
+        istd = 1 / (std + eps)
+        cmvn = [mean, istd]
+    else:
+        raise ValueError(f"cmvn file type no support: {filetype}")
+    return cmvn[0], cmvn[1]
+
+
+def convert_samples_to_float32(samples):
+    """Convert sample type to float32.
+
+    Audio sample type is usually integer or float-point.
+    Integers will be scaled to [-1, 1] in float32.
+
+    PCM16 -> PCM32
+    """
+    float32_samples = samples.astype('float32')
+    if samples.dtype in np.sctypes['int']:
+        bits = np.iinfo(samples.dtype).bits
+        float32_samples *= (1. / 2**(bits - 1))
+    elif samples.dtype in np.sctypes['float']:
+        pass
+    else:
+        raise TypeError("Unsupported sample type: %s." % samples.dtype)
+    return float32_samples
+
+
+def convert_samples_from_float32(samples, dtype):
+    """Convert sample type from float32 to dtype.
+
+    Audio sample type is usually integer or float-point. For integer
+    type, float32 will be rescaled from [-1, 1] to the maximum range
+    supported by the integer type.
+
+    PCM32 -> PCM16
+    """
+    dtype = np.dtype(dtype)
+    output_samples = samples.copy()
+    if dtype in np.sctypes['int']:
+        bits = np.iinfo(dtype).bits
+        output_samples *= (2**(bits - 1) / 1.)
+        min_val = np.iinfo(dtype).min
+        max_val = np.iinfo(dtype).max
+        output_samples[output_samples > max_val] = max_val
+        output_samples[output_samples < min_val] = min_val
+    elif samples.dtype in np.sctypes['float']:
+        min_val = np.finfo(dtype).min
+        max_val = np.finfo(dtype).max
+        output_samples[output_samples > max_val] = max_val
+        output_samples[output_samples < min_val] = min_val
+    else:
+        raise TypeError("Unsupported sample type: %s." % samples.dtype)
+    return output_samples.astype(dtype)

paddlespeech/s2t/transform/functional.py → paddlespeech/audio/transform/functional.py

@@ -14,8 +14,8 @@
 # Modified from espnet(https://github.com/espnet/espnet)
 import inspect
 
-from paddlespeech.s2t.transform.transform_interface import TransformInterface
-from paddlespeech.s2t.utils.check_kwargs import check_kwargs
+from paddlespeech.audio.transform.transform_interface import TransformInterface
+from paddlespeech.audio.utils.check_kwargs import check_kwargs
 
 
 class FuncTrans(TransformInterface):

paddlespeech/s2t/transform/perturb.py → paddlespeech/audio/transform/perturb.py

@@ -17,8 +17,97 @@ import numpy
 import scipy
 import soundfile
 
-from paddlespeech.s2t.io.reader import SoundHDF5File
+import io
+import os
+import h5py
+import numpy as np
 
+class SoundHDF5File():
+    """Collecting sound files to a HDF5 file
+
+    >>> f = SoundHDF5File('a.flac.h5', mode='a')
+    >>> array = np.random.randint(0, 100, 100, dtype=np.int16)
+    >>> f['id'] = (array, 16000)
+    >>> array, rate = f['id']
+
+
+    :param: str filepath:
+    :param: str mode:
+    :param: str format: The type used when saving wav. flac, nist, htk, etc.
+    :param: str dtype:
+
+    """
+
+    def __init__(self,
+                 filepath,
+                 mode="r+",
+                 format=None,
+                 dtype="int16",
+                 **kwargs):
+        self.filepath = filepath
+        self.mode = mode
+        self.dtype = dtype
+
+        self.file = h5py.File(filepath, mode, **kwargs)
+        if format is None:
+            # filepath = a.flac.h5 -> format = flac
+            second_ext = os.path.splitext(os.path.splitext(filepath)[0])[1]
+            format = second_ext[1:]
+            if format.upper() not in soundfile.available_formats():
+                # If not found, flac is selected
+                format = "flac"
+
+        # This format affects only saving
+        self.format = format
+
+    def __repr__(self):
+        return '<SoundHDF5 file "{}" (mode {}, format {}, type {})>'.format(
+            self.filepath, self.mode, self.format, self.dtype)
+
+    def create_dataset(self, name, shape=None, data=None, **kwds):
+        f = io.BytesIO()
+        array, rate = data
+        soundfile.write(f, array, rate, format=self.format)
+        self.file.create_dataset(
+            name, shape=shape, data=np.void(f.getvalue()), **kwds)
+
+    def __setitem__(self, name, data):
+        self.create_dataset(name, data=data)
+
+    def __getitem__(self, key):
+        data = self.file[key][()]
+        f = io.BytesIO(data.tobytes())
+        array, rate = soundfile.read(f, dtype=self.dtype)
+        return array, rate
+
+    def keys(self):
+        return self.file.keys()
+
+    def values(self):
+        for k in self.file:
+            yield self[k]
+
+    def items(self):
+        for k in self.file:
+            yield k, self[k]
+
+    def __iter__(self):
+        return iter(self.file)
+
+    def __contains__(self, item):
+        return item in self.file
+
+    def __len__(self, item):
+        return len(self.file)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.file.close()
+
+    def close(self):
+        self.file.close()
 
 class SpeedPerturbation():
     """SpeedPerturbation
@@ -469,3 +558,4 @@ class RIRConvolve():
                 [scipy.convolve(x, r, mode="same") for r in rir], axis=-1)
         else:
             return scipy.convolve(x, rir, mode="same")
+

paddlespeech/s2t/transform/spec_augment.py → paddlespeech/audio/transform/spec_augment.py

@@ -17,7 +17,7 @@ import random
 import numpy
 from PIL import Image
 
-from paddlespeech.s2t.transform.functional import FuncTrans
+from .functional import FuncTrans
 
 
 def time_warp(x, max_time_warp=80, inplace=False, mode="PIL"):

paddlespeech/s2t/transform/spectrogram.py → paddlespeech/audio/transform/spectrogram.py

@@ -17,7 +17,7 @@ import numpy as np
 import paddle
 from python_speech_features import logfbank
 
-import paddlespeech.audio.compliance.kaldi as kaldi
+from ..compliance import kaldi
 
 
 def stft(x,

paddlespeech/s2t/transform/transformation.py → paddlespeech/audio/transform/transformation.py

@@ -22,32 +22,32 @@ from inspect import signature
 
 import yaml
 
-from paddlespeech.s2t.utils.dynamic_import import dynamic_import
+from ..utils.dynamic_import import dynamic_import
 
 import_alias = dict(
-    identity="paddlespeech.s2t.transform.transform_interface:Identity",
-    time_warp="paddlespeech.s2t.transform.spec_augment:TimeWarp",
-    time_mask="paddlespeech.s2t.transform.spec_augment:TimeMask",
-    freq_mask="paddlespeech.s2t.transform.spec_augment:FreqMask",
-    spec_augment="paddlespeech.s2t.transform.spec_augment:SpecAugment",
-    speed_perturbation="paddlespeech.s2t.transform.perturb:SpeedPerturbation",
-    speed_perturbation_sox="paddlespeech.s2t.transform.perturb:SpeedPerturbationSox",
-    volume_perturbation="paddlespeech.s2t.transform.perturb:VolumePerturbation",
-    noise_injection="paddlespeech.s2t.transform.perturb:NoiseInjection",
-    bandpass_perturbation="paddlespeech.s2t.transform.perturb:BandpassPerturbation",
-    rir_convolve="paddlespeech.s2t.transform.perturb:RIRConvolve",
-    delta="paddlespeech.s2t.transform.add_deltas:AddDeltas",
-    cmvn="paddlespeech.s2t.transform.cmvn:CMVN",
-    utterance_cmvn="paddlespeech.s2t.transform.cmvn:UtteranceCMVN",
-    fbank="paddlespeech.s2t.transform.spectrogram:LogMelSpectrogram",
-    spectrogram="paddlespeech.s2t.transform.spectrogram:Spectrogram",
-    stft="paddlespeech.s2t.transform.spectrogram:Stft",
-    istft="paddlespeech.s2t.transform.spectrogram:IStft",
-    stft2fbank="paddlespeech.s2t.transform.spectrogram:Stft2LogMelSpectrogram",
-    wpe="paddlespeech.s2t.transform.wpe:WPE",
-    channel_selector="paddlespeech.s2t.transform.channel_selector:ChannelSelector",
-    fbank_kaldi="paddlespeech.s2t.transform.spectrogram:LogMelSpectrogramKaldi",
-    cmvn_json="paddlespeech.s2t.transform.cmvn:GlobalCMVN")
+    identity="paddlespeech.audio.transform.transform_interface:Identity",
+    time_warp="paddlespeech.audio.transform.spec_augment:TimeWarp",
+    time_mask="paddlespeech.audio.transform.spec_augment:TimeMask",
+    freq_mask="paddlespeech.audio.transform.spec_augment:FreqMask",
+    spec_augment="paddlespeech.audio.transform.spec_augment:SpecAugment",
+    speed_perturbation="paddlespeech.audio.transform.perturb:SpeedPerturbation",
+    speed_perturbation_sox="paddlespeech.audio.transform.perturb:SpeedPerturbationSox",
+    volume_perturbation="paddlespeech.audio.transform.perturb:VolumePerturbation",
+    noise_injection="paddlespeech.audio.transform.perturb:NoiseInjection",
+    bandpass_perturbation="paddlespeech.audio.transform.perturb:BandpassPerturbation",
+    rir_convolve="paddlespeech.audio.transform.perturb:RIRConvolve",
+    delta="paddlespeech.audio.transform.add_deltas:AddDeltas",
+    cmvn="paddlespeech.audio.transform.cmvn:CMVN",
+    utterance_cmvn="paddlespeech.audio.transform.cmvn:UtteranceCMVN",
+    fbank="paddlespeech.audio.transform.spectrogram:LogMelSpectrogram",
+    spectrogram="paddlespeech.audio.transform.spectrogram:Spectrogram",
+    stft="paddlespeech.audio.transform.spectrogram:Stft",
+    istft="paddlespeech.audio.transform.spectrogram:IStft",
+    stft2fbank="paddlespeech.audio.transform.spectrogram:Stft2LogMelSpectrogram",
+    wpe="paddlespeech.audio.transform.wpe:WPE",
+    channel_selector="paddlespeech.audio.transform.channel_selector:ChannelSelector",
+    fbank_kaldi="paddlespeech.audio.transform.spectrogram:LogMelSpectrogramKaldi",
+    cmvn_json="paddlespeech.audio.transform.cmvn:GlobalCMVN")
 
 
 class Transformation():

paddlespeech/audio/utils/check_kwargs.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from espnet(https://github.com/espnet/espnet)
+import inspect
+
+
+def check_kwargs(func, kwargs, name=None):
+    """check kwargs are valid for func
+
+    If kwargs are invalid, raise TypeError as same as python default
+    :param function func: function to be validated
+    :param dict kwargs: keyword arguments for func
+    :param str name: name used in TypeError (default is func name)
+    """
+    try:
+        params = inspect.signature(func).parameters
+    except ValueError:
+        return
+    if name is None:
+        name = func.__name__
+    for k in kwargs.keys():
+        if k not in params:
+            raise TypeError(
+                f"{name}() got an unexpected keyword argument '{k}'")

paddlespeech/audio/utils/dynamic_import.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Modified from espnet(https://github.com/espnet/espnet)
+import importlib
+
+__all__ = ["dynamic_import"]
+
+
+def dynamic_import(import_path, alias=dict()):
+    """dynamic import module and class
+
+    :param str import_path: syntax 'module_name:class_name'
+        e.g., 'paddlespeech.s2t.models.u2:U2Model'
+    :param dict alias: shortcut for registered class
+    :return: imported class
+    """
+    if import_path not in alias and ":" not in import_path:
+        raise ValueError(
+            "import_path should be one of {} or "
+            'include ":", e.g. "paddlespeech.s2t.models.u2:U2Model" : '
+            "{}".format(set(alias), import_path))
+    if ":" not in import_path:
+        import_path = alias[import_path]
+
+    module_name, objname = import_path.split(":")
+    m = importlib.import_module(module_name)
+    return getattr(m, objname)

paddlespeech/audio/utils/log.py

@@ -65,6 +65,7 @@ class Logger(object):
 
     def __init__(self, name: str=None):
         name = 'PaddleAudio' if not name else name
+        self.name = name
         self.logger = logging.getLogger(name)
 
         for key, conf in log_config.items():
@@ -101,7 +102,7 @@ class Logger(object):
         if not self.is_enable:
             return
 
-        self.logger.log(log_level, msg)
+        self.logger.log(log_level, self.name + " | " + msg)
 
     @contextlib.contextmanager
     def use_terminator(self, terminator: str):

paddlespeech/audio/utils/tensor_utils.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Unility functions for Transformer."""
+from typing import List
+from typing import Tuple
+
+import paddle
+
+from .log import Logger
+
+__all__ = ["pad_sequence", "add_sos_eos", "th_accuracy", "has_tensor"]
+
+logger = Logger(__name__)
+
+
+def has_tensor(val):
+    if isinstance(val, (list, tuple)):
+        for item in val:
+            if has_tensor(item):
+                return True
+    elif isinstance(val, dict):
+        for k, v in val.items():
+            print(k)
+            if has_tensor(v):
+                return True
+    else:
+        return paddle.is_tensor(val)
+
+
+def pad_sequence(sequences: List[paddle.Tensor],
+                 batch_first: bool=False,
+                 padding_value: float=0.0) -> paddle.Tensor:
+    r"""Pad a list of variable length Tensors with ``padding_value``
+
+    ``pad_sequence`` stacks a list of Tensors along a new dimension,
+    and pads them to equal length. For example, if the input is list of
+    sequences with size ``L x *`` and if batch_first is False, and ``T x B x *``
+    otherwise.
+
+    `B` is batch size. It is equal to the number of elements in ``sequences``.
+    `T` is length of the longest sequence.
+    `L` is length of the sequence.
+    `*` is any number of trailing dimensions, including none.
+
+    Example:
+        >>> from paddle.nn.utils.rnn import pad_sequence
+        >>> a = paddle.ones(25, 300)
+        >>> b = paddle.ones(22, 300)
+        >>> c = paddle.ones(15, 300)
+        >>> pad_sequence([a, b, c]).shape
+        paddle.Tensor([25, 3, 300])
+
+    Note:
+        This function returns a Tensor of size ``T x B x *`` or ``B x T x *``
+        where `T` is the length of the longest sequence. This function assumes
+        trailing dimensions and type of all the Tensors in sequences are same.
+
+    Args:
+        sequences (list[Tensor]): list of variable length sequences.
+        batch_first (bool, optional): output will be in ``B x T x *`` if True, or in
+            ``T x B x *`` otherwise
+        padding_value (float, optional): value for padded elements. Default: 0.
+
+    Returns:
+        Tensor of size ``T x B x *`` if :attr:`batch_first` is ``False``.
+        Tensor of size ``B x T x *`` otherwise
+    """
+
+    # assuming trailing dimensions and type of all the Tensors
+    # in sequences are same and fetching those from sequences[0]
+    max_size = paddle.shape(sequences[0])
+    # (TODO Hui Zhang): slice not supprot `end==start`
+    # trailing_dims = max_size[1:]
+    trailing_dims = tuple(
+        max_size[1:].numpy().tolist()) if sequences[0].ndim >= 2 else ()
+    max_len = max([s.shape[0] for s in sequences])
+    if batch_first:
+        out_dims = (len(sequences), max_len) + trailing_dims
+    else:
+        out_dims = (max_len, len(sequences)) + trailing_dims
+    out_tensor = paddle.full(out_dims, padding_value, sequences[0].dtype)
+    for i, tensor in enumerate(sequences):
+        length = tensor.shape[0]
+        # use index notation to prevent duplicate references to the tensor
+        if batch_first:
+            # TODO (Hui Zhang): set_value op not supprot `end==start`
+            # TODO (Hui Zhang): set_value op not support int16
+            # TODO (Hui Zhang): set_varbase 2 rank not support [0,0,...]
+            # out_tensor[i, :length, ...] = tensor
+            if length != 0:
+                out_tensor[i, :length] = tensor
+            else:
+                out_tensor[i, length] = tensor
+        else:
+            # TODO (Hui Zhang): set_value op not supprot `end==start`
+            # out_tensor[:length, i, ...] = tensor
+            if length != 0:
+                out_tensor[:length, i] = tensor
+            else:
+                out_tensor[length, i] = tensor
+
+    return out_tensor
+
+
+def add_sos_eos(ys_pad: paddle.Tensor, sos: int, eos: int,
+                ignore_id: int) -> Tuple[paddle.Tensor, paddle.Tensor]:
+    """Add <sos> and <eos> labels.
+    Args:
+        ys_pad (paddle.Tensor): batch of padded target sequences (B, Lmax)
+        sos (int): index of <sos>
+        eos (int): index of <eeos>
+        ignore_id (int): index of padding
+    Returns:
+        ys_in (paddle.Tensor) : (B, Lmax + 1)
+        ys_out (paddle.Tensor) : (B, Lmax + 1)
+    Examples:
+        >>> sos_id = 10
+        >>> eos_id = 11
+        >>> ignore_id = -1
+        >>> ys_pad
+        tensor([[ 1,  2,  3,  4,  5],
+                [ 4,  5,  6, -1, -1],
+                [ 7,  8,  9, -1, -1]], dtype=paddle.int32)
+        >>> ys_in,ys_out=add_sos_eos(ys_pad, sos_id , eos_id, ignore_id)
+        >>> ys_in
+        tensor([[10,  1,  2,  3,  4,  5],
+                [10,  4,  5,  6, 11, 11],
+                [10,  7,  8,  9, 11, 11]])
+        >>> ys_out
+        tensor([[ 1,  2,  3,  4,  5, 11],
+                [ 4,  5,  6, 11, -1, -1],
+                [ 7,  8,  9, 11, -1, -1]])
+    """
+    # TODO(Hui Zhang): using comment code,
+    #_sos = paddle.to_tensor(
+    #    [sos], dtype=paddle.long, stop_gradient=True, place=ys_pad.place)
+    #_eos = paddle.to_tensor(
+    #    [eos], dtype=paddle.long, stop_gradient=True, place=ys_pad.place)
+    #ys = [y[y != ignore_id] for y in ys_pad]  # parse padded ys
+    #ys_in = [paddle.cat([_sos, y], dim=0) for y in ys]
+    #ys_out = [paddle.cat([y, _eos], dim=0) for y in ys]
+    #return pad_sequence(ys_in, padding_value=eos), pad_sequence(ys_out, padding_value=ignore_id)
+    B = ys_pad.shape[0]
+    _sos = paddle.ones([B, 1], dtype=ys_pad.dtype) * sos
+    _eos = paddle.ones([B, 1], dtype=ys_pad.dtype) * eos
+    ys_in = paddle.cat([_sos, ys_pad], dim=1)
+    mask_pad = (ys_in == ignore_id)
+    ys_in = ys_in.masked_fill(mask_pad, eos)
+
+    ys_out = paddle.cat([ys_pad, _eos], dim=1)
+    ys_out = ys_out.masked_fill(mask_pad, eos)
+    mask_eos = (ys_out == ignore_id)
+    ys_out = ys_out.masked_fill(mask_eos, eos)
+    ys_out = ys_out.masked_fill(mask_pad, ignore_id)
+    return ys_in, ys_out
+
+
+def th_accuracy(pad_outputs: paddle.Tensor,
+                pad_targets: paddle.Tensor,
+                ignore_label: int) -> float:
+    """Calculate accuracy.
+    Args:
+        pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
+        pad_targets (LongTensor): Target label tensors (B, Lmax, D).
+        ignore_label (int): Ignore label id.
+    Returns:
+        float: Accuracy value (0.0 - 1.0).
+    """
+    pad_pred = pad_outputs.view(pad_targets.shape[0], pad_targets.shape[1],
+                                pad_outputs.shape[1]).argmax(2)
+    mask = pad_targets != ignore_label
+    #TODO(Hui Zhang): sum not support bool type
+    # numerator = paddle.sum(
+    #     pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
+    numerator = (
+        pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
+    numerator = paddle.sum(numerator.type_as(pad_targets))
+    #TODO(Hui Zhang): sum not support bool type
+    # denominator = paddle.sum(mask)
+    denominator = paddle.sum(mask.type_as(pad_targets))
+    return float(numerator) / float(denominator)

paddlespeech/cli/asr/infer.py

@@ -33,8 +33,8 @@ from ..log import logger
 from ..utils import CLI_TIMER
 from ..utils import stats_wrapper
 from ..utils import timer_register
+from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
-from paddlespeech.s2t.transform.transformation import Transformation
 from paddlespeech.s2t.utils.utility import UpdateConfig
 
 __all__ = ['ASRExecutor']

paddlespeech/s2t/exps/deepspeech2/model.py

@@ -23,7 +23,7 @@ import paddle
 from paddle import distributed as dist
 from paddle import inference
 
-from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
+from paddlespeech.audio.text.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.io.dataloader import BatchDataLoader
 from paddlespeech.s2t.models.ds2 import DeepSpeech2InferModel
 from paddlespeech.s2t.models.ds2 import DeepSpeech2Model

paddlespeech/s2t/exps/u2/bin/test_wav.py

@@ -20,10 +20,10 @@ import paddle
 import soundfile
 from yacs.config import CfgNode
 
+from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.models.u2 import U2Model
 from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.transform.transformation import Transformation
 from paddlespeech.s2t.utils.log import Log
 from paddlespeech.s2t.utils.utility import UpdateConfig
 logger = Log(__name__).getlog()

paddlespeech/s2t/exps/u2/model.py

@@ -26,6 +26,8 @@ from paddle import distributed as dist
 
 from paddlespeech.s2t.frontend.featurizer import TextFeaturizer
 from paddlespeech.s2t.io.dataloader import BatchDataLoader
+from paddlespeech.s2t.io.dataloader import StreamDataLoader
+from paddlespeech.s2t.io.dataloader import DataLoaderFactory
 from paddlespeech.s2t.models.u2 import U2Model
 from paddlespeech.s2t.training.optimizer import OptimizerFactory
 from paddlespeech.s2t.training.reporter import ObsScope
@@ -106,7 +108,8 @@ class U2Trainer(Trainer):
     @paddle.no_grad()
     def valid(self):
         self.model.eval()
-        logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
+        if not self.use_streamdata:
+            logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
         valid_losses = defaultdict(list)
         num_seen_utts = 1
         total_loss = 0.0
@@ -132,7 +135,8 @@ class U2Trainer(Trainer):
                 msg = f"Valid: Rank: {dist.get_rank()}, "
                 msg += "epoch: {}, ".format(self.epoch)
                 msg += "step: {}, ".format(self.iteration)
-                msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader))
+                if not self.use_streamdata:
+                    msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader))
                 msg += ', '.join('{}: {:>.6f}'.format(k, v)
                                  for k, v in valid_dump.items())
                 logger.info(msg)
@@ -152,7 +156,8 @@ class U2Trainer(Trainer):
 
         self.before_train()
 
-        logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
+        if not self.use_streamdata:
+            logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
         while self.epoch < self.config.n_epoch:
             with Timer("Epoch-Train Time Cost: {}"):
                 self.model.train()
@@ -170,7 +175,8 @@ class U2Trainer(Trainer):
                             self.train_batch(batch_index, batch, msg)
                             self.after_train_batch()
                             report('iter', batch_index + 1)
-                            report('total', len(self.train_loader))
+                            if not self.use_streamdata:
+                                report('total', len(self.train_loader))
                             report('reader_cost', dataload_time)
                         observation['batch_cost'] = observation[
                             'reader_cost'] + observation['step_cost']
@@ -191,7 +197,6 @@ class U2Trainer(Trainer):
                 except Exception as e:
                     logger.error(e)
                     raise e
-
             with Timer("Eval Time Cost: {}"):
                 total_loss, num_seen_utts = self.valid()
                 if dist.get_world_size() > 1:
@@ -218,92 +223,16 @@ class U2Trainer(Trainer):
 
     def setup_dataloader(self):
         config = self.config.clone()
-
+        self.use_streamdata = config.get("use_stream_data", False)
         if self.train:
-            # train/valid dataset, return token ids
-            self.train_loader = BatchDataLoader(
-                json_file=config.train_manifest,
-                train_mode=True,
-                sortagrad=config.sortagrad,
-                batch_size=config.batch_size,
-                maxlen_in=config.maxlen_in,
-                maxlen_out=config.maxlen_out,
-                minibatches=config.minibatches,
-                mini_batch_size=self.args.ngpu,
-                batch_count=config.batch_count,
-                batch_bins=config.batch_bins,
-                batch_frames_in=config.batch_frames_in,
-                batch_frames_out=config.batch_frames_out,
-                batch_frames_inout=config.batch_frames_inout,
-                preprocess_conf=config.preprocess_config,
-                n_iter_processes=config.num_workers,
-                subsampling_factor=1,
-                num_encs=1,
-                dist_sampler=config.get('dist_sampler', False),
-                shortest_first=False)
-
-            self.valid_loader = BatchDataLoader(
-                json_file=config.dev_manifest,
-                train_mode=False,
-                sortagrad=False,
-                batch_size=config.batch_size,
-                maxlen_in=float('inf'),
-                maxlen_out=float('inf'),
-                minibatches=0,
-                mini_batch_size=self.args.ngpu,
-                batch_count='auto',
-                batch_bins=0,
-                batch_frames_in=0,
-                batch_frames_out=0,
-                batch_frames_inout=0,
-                preprocess_conf=config.preprocess_config,
-                n_iter_processes=config.num_workers,
-                subsampling_factor=1,
-                num_encs=1,
-                dist_sampler=config.get('dist_sampler', False),
-                shortest_first=False)
+            self.train_loader = DataLoaderFactory.get_dataloader('train', config, self.args)
+            self.valid_loader = DataLoaderFactory.get_dataloader('valid', config, self.args)
             logger.info("Setup train/valid Dataloader!")
         else:
             decode_batch_size = config.get('decode', dict()).get(
                 'decode_batch_size', 1)
-            # test dataset, return raw text
-            self.test_loader = BatchDataLoader(
-                json_file=config.test_manifest,
-                train_mode=False,
-                sortagrad=False,
-                batch_size=decode_batch_size,
-                maxlen_in=float('inf'),
-                maxlen_out=float('inf'),
-                minibatches=0,
-                mini_batch_size=1,
-                batch_count='auto',
-                batch_bins=0,
-                batch_frames_in=0,
-                batch_frames_out=0,
-                batch_frames_inout=0,
-                preprocess_conf=config.preprocess_config,
-                n_iter_processes=1,
-                subsampling_factor=1,
-                num_encs=1)
-
-            self.align_loader = BatchDataLoader(
-                json_file=config.test_manifest,
-                train_mode=False,
-                sortagrad=False,
-                batch_size=decode_batch_size,
-                maxlen_in=float('inf'),
-                maxlen_out=float('inf'),
-                minibatches=0,
-                mini_batch_size=1,
-                batch_count='auto',
-                batch_bins=0,
-                batch_frames_in=0,
-                batch_frames_out=0,
-                batch_frames_inout=0,
-                preprocess_conf=config.preprocess_config,
-                n_iter_processes=1,
-                subsampling_factor=1,
-                num_encs=1)
+            self.test_loader = DataLoaderFactory.get_dataloader('test', config, self.args)
+            self.align_loader = DataLoaderFactory.get_dataloader('align', config, self.args)
             logger.info("Setup test/align Dataloader!")
 
     def setup_model(self):
@@ -452,7 +381,8 @@ class U2Tester(U2Trainer):
     def test(self):
         assert self.args.result_file
         self.model.eval()
-        logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
+        if not self.use_streamdata:
+            logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
 
         stride_ms = self.config.stride_ms
         error_rate_type = None

paddlespeech/s2t/exps/u2_kaldi/model.py

@@ -25,7 +25,7 @@ from paddle import distributed as dist
 
 from paddlespeech.s2t.frontend.featurizer import TextFeaturizer
 from paddlespeech.s2t.frontend.utility import load_dict
-from paddlespeech.s2t.io.dataloader import BatchDataLoader
+from paddlespeech.s2t.io.dataloader import DataLoaderFactory
 from paddlespeech.s2t.models.u2 import U2Model
 from paddlespeech.s2t.training.optimizer import OptimizerFactory
 from paddlespeech.s2t.training.scheduler import LRSchedulerFactory
@@ -104,7 +104,8 @@ class U2Trainer(Trainer):
     @paddle.no_grad()
     def valid(self):
         self.model.eval()
-        logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
+        if not self.use_streamdata:
+            logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
         valid_losses = defaultdict(list)
         num_seen_utts = 1
         total_loss = 0.0
@@ -131,7 +132,8 @@ class U2Trainer(Trainer):
                 msg = f"Valid: Rank: {dist.get_rank()}, "
                 msg += "epoch: {}, ".format(self.epoch)
                 msg += "step: {}, ".format(self.iteration)
-                msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader))
+                if not self.use_streamdata:
+                    msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader))
                 msg += ', '.join('{}: {:>.6f}'.format(k, v)
                                  for k, v in valid_dump.items())
                 logger.info(msg)
@@ -150,8 +152,8 @@ class U2Trainer(Trainer):
         # paddle.jit.save(script_model, script_model_path)
 
         self.before_train()
-
-        logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
+        if not self.use_streamdata:
+            logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
         while self.epoch < self.config.n_epoch:
             with Timer("Epoch-Train Time Cost: {}"):
                 self.model.train()
@@ -162,7 +164,8 @@ class U2Trainer(Trainer):
                         msg = "Train: Rank: {}, ".format(dist.get_rank())
                         msg += "epoch: {}, ".format(self.epoch)
                         msg += "step: {}, ".format(self.iteration)
-                        msg += "batch : {}/{}, ".format(batch_index + 1,
+                        if not self.use_streamdata:
+                            msg += "batch : {}/{}, ".format(batch_index + 1,
                                                         len(self.train_loader))
                         msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
                         msg += "data time: {:>.3f}s, ".format(dataload_time)
@@ -198,87 +201,23 @@ class U2Trainer(Trainer):
             self.new_epoch()
 
     def setup_dataloader(self):
-        config = self.config.clone()
-        # train/valid dataset, return token ids
-        self.train_loader = BatchDataLoader(
-            json_file=config.train_manifest,
-            train_mode=True,
-            sortagrad=False,
-            batch_size=config.batch_size,
-            maxlen_in=float('inf'),
-            maxlen_out=float('inf'),
-            minibatches=0,
-            mini_batch_size=self.args.ngpu,
-            batch_count='auto',
-            batch_bins=0,
-            batch_frames_in=0,
-            batch_frames_out=0,
-            batch_frames_inout=0,
-            preprocess_conf=config.preprocess_config,
-            n_iter_processes=config.num_workers,
-            subsampling_factor=1,
-            num_encs=1)
-
-        self.valid_loader = BatchDataLoader(
-            json_file=config.dev_manifest,
-            train_mode=False,
-            sortagrad=False,
-            batch_size=config.batch_size,
-            maxlen_in=float('inf'),
-            maxlen_out=float('inf'),
-            minibatches=0,
-            mini_batch_size=self.args.ngpu,
-            batch_count='auto',
-            batch_bins=0,
-            batch_frames_in=0,
-            batch_frames_out=0,
-            batch_frames_inout=0,
-            preprocess_conf=None,
-            n_iter_processes=config.num_workers,
-            subsampling_factor=1,
-            num_encs=1)
-
-        decode_batch_size = config.get('decode', dict()).get(
-            'decode_batch_size', 1)
-        # test dataset, return raw text
-        self.test_loader = BatchDataLoader(
-            json_file=config.test_manifest,
-            train_mode=False,
-            sortagrad=False,
-            batch_size=decode_batch_size,
-            maxlen_in=float('inf'),
-            maxlen_out=float('inf'),
-            minibatches=0,
-            mini_batch_size=1,
-            batch_count='auto',
-            batch_bins=0,
-            batch_frames_in=0,
-            batch_frames_out=0,
-            batch_frames_inout=0,
-            preprocess_conf=None,
-            n_iter_processes=1,
-            subsampling_factor=1,
-            num_encs=1)
-
-        self.align_loader = BatchDataLoader(
-            json_file=config.test_manifest,
-            train_mode=False,
-            sortagrad=False,
-            batch_size=decode_batch_size,
-            maxlen_in=float('inf'),
-            maxlen_out=float('inf'),
-            minibatches=0,
-            mini_batch_size=1,
-            batch_count='auto',
-            batch_bins=0,
-            batch_frames_in=0,
-            batch_frames_out=0,
-            batch_frames_inout=0,
-            preprocess_conf=None,
-            n_iter_processes=1,
-            subsampling_factor=1,
-            num_encs=1)
-        logger.info("Setup train/valid/test/align Dataloader!")
+        self.use_streamdata = config.get("use_stream_data", False)
+        if self.train:
+            config = self.config.clone()
+            self.train_loader = DataLoaderFactory.get_dataloader('train', config, self.args)
+            config = self.config.clone()
+            config['preprocess_config'] = None
+            self.valid_loader = DataLoaderFactory.get_dataloader('valid', config, self.args)
+            logger.info("Setup train/valid Dataloader!")
+        else:
+            config = self.config.clone()
+            config['preprocess_config'] = None
+            self.test_loader = DataLoaderFactory.get_dataloader('test', config, self.args)
+            config = self.config.clone()
+            config['preprocess_config'] = None
+            self.align_loader = DataLoaderFactory.get_dataloader('align', config, self.args)
+            logger.info("Setup test/align Dataloader!")
+
 
     def setup_model(self):
         config = self.config
@@ -406,7 +345,8 @@ class U2Tester(U2Trainer):
     def test(self):
         assert self.args.result_file
         self.model.eval()
-        logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
+        if not self.use_streamdata:
+            logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
 
         stride_ms = self.config.stride_ms
         error_rate_type = None

paddlespeech/s2t/exps/u2_st/model.py

@@ -25,7 +25,7 @@ import paddle
 from paddle import distributed as dist
 
 from paddlespeech.s2t.frontend.featurizer import TextFeaturizer
-from paddlespeech.s2t.io.dataloader import BatchDataLoader
+from paddlespeech.s2t.io.dataloader import DataLoaderFactory
 from paddlespeech.s2t.models.u2_st import U2STModel
 from paddlespeech.s2t.training.optimizer import OptimizerFactory
 from paddlespeech.s2t.training.reporter import ObsScope
@@ -120,7 +120,8 @@ class U2STTrainer(Trainer):
     @paddle.no_grad()
     def valid(self):
         self.model.eval()
-        logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
+        if not self.use_streamdata:
+            logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
         valid_losses = defaultdict(list)
         num_seen_utts = 1
         total_loss = 0.0
@@ -153,7 +154,8 @@ class U2STTrainer(Trainer):
                 msg = f"Valid: Rank: {dist.get_rank()}, "
                 msg += "epoch: {}, ".format(self.epoch)
                 msg += "step: {}, ".format(self.iteration)
-                msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader))
+                if not self.use_streamdata:
+                    msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader))
                 msg += ', '.join('{}: {:>.6f}'.format(k, v)
                                  for k, v in valid_dump.items())
                 logger.info(msg)
@@ -172,8 +174,8 @@ class U2STTrainer(Trainer):
         # paddle.jit.save(script_model, script_model_path)
 
         self.before_train()
-
-        logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
+        if not self.use_streamdata:
+            logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
         while self.epoch < self.config.n_epoch:
             with Timer("Epoch-Train Time Cost: {}"):
                 self.model.train()
@@ -191,7 +193,8 @@ class U2STTrainer(Trainer):
                             self.train_batch(batch_index, batch, msg)
                             self.after_train_batch()
                             report('iter', batch_index + 1)
-                            report('total', len(self.train_loader))
+                            if not self.use_streamdata:
+                                report('total', len(self.train_loader))
                             report('reader_cost', dataload_time)
                         observation['batch_cost'] = observation[
                             'reader_cost'] + observation['step_cost']
@@ -241,79 +244,18 @@ class U2STTrainer(Trainer):
 
         load_transcript = True if config.model_conf.asr_weight > 0 else False
 
+        config = self.config.clone()
+        config['load_transcript'] = load_transcript
+        self.use_streamdata = config.get("use_stream_data", False)
         if self.train:
-            # train/valid dataset, return token ids
-            self.train_loader = BatchDataLoader(
-                json_file=config.train_manifest,
-                train_mode=True,
-                sortagrad=False,
-                batch_size=config.batch_size,
-                maxlen_in=config.maxlen_in,
-                maxlen_out=config.maxlen_out,
-                minibatches=0,
-                mini_batch_size=1,
-                batch_count='auto',
-                batch_bins=0,
-                batch_frames_in=0,
-                batch_frames_out=0,
-                batch_frames_inout=0,
-                preprocess_conf=config.
-                preprocess_config,  # aug will be off when train_mode=False
-                n_iter_processes=config.num_workers,
-                subsampling_factor=1,
-                load_aux_output=load_transcript,
-                num_encs=1,
-                dist_sampler=True)
-
-            self.valid_loader = BatchDataLoader(
-                json_file=config.dev_manifest,
-                train_mode=False,
-                sortagrad=False,
-                batch_size=config.batch_size,
-                maxlen_in=float('inf'),
-                maxlen_out=float('inf'),
-                minibatches=0,
-                mini_batch_size=1,
-                batch_count='auto',
-                batch_bins=0,
-                batch_frames_in=0,
-                batch_frames_out=0,
-                batch_frames_inout=0,
-                preprocess_conf=config.
-                preprocess_config,  # aug will be off when train_mode=False
-                n_iter_processes=config.num_workers,
-                subsampling_factor=1,
-                load_aux_output=load_transcript,
-                num_encs=1,
-                dist_sampler=False)
+            self.train_loader = DataLoaderFactory.get_dataloader('train', config, self.args)
+            self.valid_loader = DataLoaderFactory.get_dataloader('valid', config, self.args)
             logger.info("Setup train/valid Dataloader!")
         else:
-            # test dataset, return raw text
-            decode_batch_size = config.get('decode', dict()).get(
-                'decode_batch_size', 1)
-            self.test_loader = BatchDataLoader(
-                json_file=config.test_manifest,
-                train_mode=False,
-                sortagrad=False,
-                batch_size=decode_batch_size,
-                maxlen_in=float('inf'),
-                maxlen_out=float('inf'),
-                minibatches=0,
-                mini_batch_size=1,
-                batch_count='auto',
-                batch_bins=0,
-                batch_frames_in=0,
-                batch_frames_out=0,
-                batch_frames_inout=0,
-                preprocess_conf=config.
-                preprocess_config,  # aug will be off when train_mode=False
-                n_iter_processes=config.num_workers,
-                subsampling_factor=1,
-                num_encs=1,
-                dist_sampler=False)
-
+            self.test_loader = DataLoaderFactory.get_dataloader('test', config, self.args)
             logger.info("Setup test Dataloader!")
 
+
     def setup_model(self):
         config = self.config
         model_conf = config
@@ -468,7 +410,8 @@ class U2STTester(U2STTrainer):
     def test(self):
         assert self.args.result_file
         self.model.eval()
-        logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
+        if not self.use_streamdata:
+            logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
 
         decode_cfg = self.config.decode
         bleu_func = bleu_score.char_bleu if decode_cfg.error_rate_type == 'char-bleu' else bleu_score.bleu

paddlespeech/s2t/io/dataloader.py

@@ -18,6 +18,7 @@ from typing import Text
 
 import jsonlines
 import numpy as np
+import paddle
 from paddle.io import BatchSampler
 from paddle.io import DataLoader
 from paddle.io import DistributedBatchSampler
@@ -28,7 +29,11 @@ from paddlespeech.s2t.io.dataset import TransformDataset
 from paddlespeech.s2t.io.reader import LoadInputsAndTargets
 from paddlespeech.s2t.utils.log import Log
 
-__all__ = ["BatchDataLoader"]
+import paddlespeech.audio.streamdata as streamdata
+from paddlespeech.audio.text.text_featurizer import TextFeaturizer
+from yacs.config import CfgNode
+
+__all__ = ["BatchDataLoader", "StreamDataLoader"]
 
 logger = Log(__name__).getlog()
 
@@ -56,6 +61,136 @@ def batch_collate(x):
     """
     return x[0]
 
+def read_preprocess_cfg(preprocess_conf_file):
+    augment_conf = dict()
+    preprocess_cfg = CfgNode(new_allowed=True)
+    preprocess_cfg.merge_from_file(preprocess_conf_file)
+    for idx, process in enumerate(preprocess_cfg["process"]):
+        opts = dict(process)
+        process_type = opts.pop("type")
+        if process_type == 'time_warp':
+            augment_conf['max_w'] = process['max_time_warp']
+            augment_conf['w_inplace'] = process['inplace']
+            augment_conf['w_mode'] = process['mode']
+        if process_type == 'freq_mask':
+            augment_conf['max_f'] = process['F']
+            augment_conf['num_f_mask'] = process['n_mask']
+            augment_conf['f_inplace'] = process['inplace']
+            augment_conf['f_replace_with_zero'] = process['replace_with_zero']
+        if process_type == 'time_mask':
+            augment_conf['max_t'] = process['T']
+            augment_conf['num_t_mask'] = process['n_mask']
+            augment_conf['t_inplace'] = process['inplace']
+            augment_conf['t_replace_with_zero'] = process['replace_with_zero']
+    return augment_conf 
+
+class StreamDataLoader():
+    def __init__(self,
+                 manifest_file: str,
+                 train_mode: bool,
+                 unit_type: str='char',
+                 batch_size: int=0,
+                 preprocess_conf=None,
+                 num_mel_bins=80,
+                 frame_length=25,
+                 frame_shift=10,
+                 dither=0.0,
+                 minlen_in: float=0.0, 
+                 maxlen_in: float=float('inf'),
+                 minlen_out: float=0.0,
+                 maxlen_out: float=float('inf'),
+                 resample_rate: int=16000,
+                 shuffle_size: int=10000, 
+                 sort_size: int=1000,
+                 n_iter_processes: int=1,
+                 prefetch_factor: int=2,
+                 dist_sampler: bool=False,
+                 cmvn_file="data/mean_std.json",
+                 vocab_filepath='data/lang_char/vocab.txt'):
+        self.manifest_file = manifest_file
+        self.train_model = train_mode
+        self.batch_size = batch_size
+        self.prefetch_factor = prefetch_factor
+        self.dist_sampler = dist_sampler
+        self.n_iter_processes = n_iter_processes
+
+        text_featurizer = TextFeaturizer(unit_type, vocab_filepath)
+        symbol_table = text_featurizer.vocab_dict
+        self.feat_dim = num_mel_bins 
+        self.vocab_size = text_featurizer.vocab_size 
+        
+        augment_conf = read_preprocess_cfg(preprocess_conf)
+        
+        # The list of shard
+        shardlist = []
+        with open(manifest_file, "r") as f:
+            for line in f.readlines():
+                shardlist.append(line.strip())
+        world_size = 1
+        try:
+            world_size = paddle.distributed.get_world_size() 
+        except Exception as e:
+            logger.warninig(e)
+            logger.warninig("can not get world_size using paddle.distributed.get_world_size(), use world_size=1")
+        assert(len(shardlist) >= world_size, "the length of shard list should >= number of gpus/xpus/...")
+
+        update_n_iter_processes = int(max(min(len(shardlist)/world_size - 1, self.n_iter_processes), 0))
+        logger.info(f"update_n_iter_processes {update_n_iter_processes}")
+        if update_n_iter_processes != self.n_iter_processes:
+            self.n_iter_processes = update_n_iter_processes         
+            logger.info(f"change nun_workers to {self.n_iter_processes}")
+
+        if self.dist_sampler:
+            base_dataset = streamdata.DataPipeline(
+                streamdata.SimpleShardList(shardlist),
+                streamdata.split_by_node if train_mode else streamdata.placeholder(),
+                streamdata.split_by_worker,
+                streamdata.tarfile_to_samples(streamdata.reraise_exception)
+            )
+        else:
+            base_dataset = streamdata.DataPipeline(
+                streamdata.SimpleShardList(shardlist),
+                streamdata.split_by_worker,
+                streamdata.tarfile_to_samples(streamdata.reraise_exception)
+            )
+
+        self.dataset = base_dataset.append_list(
+            streamdata.audio_tokenize(symbol_table),
+            streamdata.audio_data_filter(frame_shift=frame_shift, max_length=maxlen_in, min_length=minlen_in, token_max_length=maxlen_out, token_min_length=minlen_out),
+            streamdata.audio_resample(resample_rate=resample_rate),
+            streamdata.audio_compute_fbank(num_mel_bins=num_mel_bins, frame_length=frame_length, frame_shift=frame_shift, dither=dither),
+            streamdata.audio_spec_aug(**augment_conf) if train_mode else streamdata.placeholder(),  # num_t_mask=2, num_f_mask=2, max_t=40, max_f=30, max_w=80)
+            streamdata.shuffle(shuffle_size),
+            streamdata.sort(sort_size=sort_size),
+            streamdata.batched(batch_size),
+            streamdata.audio_padding(),
+            streamdata.audio_cmvn(cmvn_file)
+        )
+
+        if paddle.__version__ >= '2.3.2':
+            self.loader = streamdata.WebLoader(
+                self.dataset, 
+                num_workers=self.n_iter_processes, 
+                prefetch_factor = self.prefetch_factor, 
+                batch_size=None
+            )
+        else:
+            self.loader = streamdata.WebLoader(
+                self.dataset, 
+                num_workers=self.n_iter_processes, 
+                batch_size=None
+            )
+
+    def __iter__(self):
+        return self.loader.__iter__()
+
+    def __call__(self):
+        return self.__iter__()
+
+    def __len__(self):
+        logger.info("Stream dataloader does not support calculate the length of the dataset")
+        return -1
+
 
 class BatchDataLoader():
     def __init__(self,
@@ -199,3 +334,119 @@ class BatchDataLoader():
         echo += f"shortest_first: {self.shortest_first}, "
         echo += f"file: {self.json_file}"
         return echo
+
+
+class DataLoaderFactory():
+    @staticmethod
+    def get_dataloader(mode: str, config, args):
+        config = config.clone()
+        use_streamdata = config.get("use_stream_data", False)
+        if use_streamdata:
+            if mode == 'train':
+                config['manifest'] = config.train_manifest
+                config['train_mode'] = True
+            elif mode == 'valid':
+                config['manifest'] = config.dev_manifest
+                config['train_mode'] = False  
+            elif model == 'test' or mode == 'align':
+                config['manifest'] = config.test_manifest
+                config['train_mode'] = False
+                config['dither'] = 0.0
+                config['minlen_in'] = 0.0
+                config['maxlen_in'] = float('inf')
+                config['minlen_out'] = 0
+                config['maxlen_out'] = float('inf')
+                config['dist_sampler'] = False
+            else:
+                raise KeyError("not valid mode type!!, please input one of 'train, valid, test, align'")
+            return StreamDataLoader(
+                    manifest_file=config.manifest,
+                    train_mode=config.train_mode,
+                    unit_type=config.unit_type,
+                    preprocess_conf=config.preprocess_config,
+                    batch_size=config.batch_size,
+                    num_mel_bins=config.feat_dim,
+                    frame_length=config.window_ms,
+                    frame_shift=config.stride_ms,
+                    dither=config.dither,
+                    minlen_in=config.minlen_in,
+                    maxlen_in=config.maxlen_in,
+                    minlen_out=config.minlen_out,
+                    maxlen_out=config.maxlen_out,
+                    resample_rate=config.resample_rate,
+                    shuffle_size=config.shuffle_size, 
+                    sort_size=config.sort_size,
+                    n_iter_processes=config.num_workers,
+                    prefetch_factor=config.prefetch_factor,
+                    dist_sampler=config.dist_sampler,
+                    cmvn_file=config.cmvn_file,
+                    vocab_filepath=config.vocab_filepath,
+                )
+        else:
+            if mode == 'train':
+                config['manifest'] = config.train_manifest
+                config['train_mode'] = True
+                config['mini_batch_size'] = args.ngpu
+                config['subsampling_factor'] = 1
+                config['num_encs'] = 1
+            elif mode == 'valid':
+                config['manifest'] = config.dev_manifest
+                config['train_mode'] = False
+                config['sortagrad'] = False
+                config['maxlen_in'] = float('inf')
+                config['maxlen_out'] = float('inf')
+                config['minibatches'] = 0
+                config['mini_batch_size'] = args.ngpu
+                config['batch_count'] = 'auto'
+                config['batch_bins'] = 0
+                config['batch_frames_in'] = 0
+                config['batch_frames_out'] = 0
+                config['batch_frames_inout'] = 0
+                config['subsampling_factor'] = 1
+                config['num_encs'] = 1
+                config['shortest_first'] = False
+            elif mode == 'test' or mode == 'align':
+                config['manifest'] = config.test_manifest
+                config['train_mode'] = False
+                config['sortagrad'] = False
+                config['batch_size'] = config.get('decode', dict()).get(
+                'decode_batch_size', 1)
+                config['maxlen_in'] = float('inf')
+                config['maxlen_out'] = float('inf')
+                config['minibatches'] = 0
+                config['mini_batch_size'] = 1
+                config['batch_count'] = 'auto'
+                config['batch_bins'] = 0
+                config['batch_frames_in'] = 0
+                config['batch_frames_out'] = 0
+                config['batch_frames_inout'] = 0
+                config['num_workers'] = 1
+                config['subsampling_factor'] = 1
+                config['num_encs'] = 1
+                config['dist_sampler'] = False
+                config['shortest_first'] = False
+            else:
+                raise KeyError("not valid mode type!!, please input one of 'train, valid, test, align'")
+            
+            return BatchDataLoader(
+                json_file=config.manifest,
+                train_mode=config.train_mode,
+                sortagrad=config.sortagrad,
+                batch_size=config.batch_size,
+                maxlen_in=config.maxlen_in,
+                maxlen_out=config.maxlen_out,
+                minibatches=config.minibatches,
+                mini_batch_size=config.mini_batch_size,
+                batch_count=config.batch_count,
+                batch_bins=config.batch_bins,
+                batch_frames_in=config.batch_frames_in,
+                batch_frames_out=config.batch_frames_out,
+                batch_frames_inout=config.batch_frames_inout,
+                preprocess_conf=config.preprocess_config,
+                n_iter_processes=config.num_workers,
+                subsampling_factor=config.subsampling_factor,
+                load_aux_output=config.get('load_transcript', None),
+                num_encs=config.num_encs,
+                dist_sampler=config.dist_sampler,
+                shortest_first=config.shortest_first)
+           

paddlespeech/s2t/io/reader.py

@@ -19,7 +19,7 @@ import numpy as np
 import soundfile
 
 from .utility import feat_type
-from paddlespeech.s2t.transform.transformation import Transformation
+from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.s2t.utils.log import Log
 # from paddlespeech.s2t.frontend.augmentor.augmentation import AugmentationPipeline as Transformation
 

paddlespeech/s2t/models/u2/u2.py

@@ -48,9 +48,9 @@ from paddlespeech.s2t.utils import checkpoint
 from paddlespeech.s2t.utils import layer_tools
 from paddlespeech.s2t.utils.ctc_utils import remove_duplicates_and_blank
 from paddlespeech.s2t.utils.log import Log
-from paddlespeech.s2t.utils.tensor_utils import add_sos_eos
-from paddlespeech.s2t.utils.tensor_utils import pad_sequence
-from paddlespeech.s2t.utils.tensor_utils import th_accuracy
+from paddlespeech.audio.utils.tensor_utils import add_sos_eos
+from paddlespeech.audio.utils.tensor_utils import pad_sequence
+from paddlespeech.audio.utils.tensor_utils import th_accuracy
 from paddlespeech.s2t.utils.utility import log_add
 from paddlespeech.s2t.utils.utility import UpdateConfig
 

paddlespeech/s2t/models/u2_st/u2_st.py

@@ -38,8 +38,8 @@ from paddlespeech.s2t.modules.mask import subsequent_mask
 from paddlespeech.s2t.utils import checkpoint
 from paddlespeech.s2t.utils import layer_tools
 from paddlespeech.s2t.utils.log import Log
-from paddlespeech.s2t.utils.tensor_utils import add_sos_eos
-from paddlespeech.s2t.utils.tensor_utils import th_accuracy
+from paddlespeech.audio.utils.tensor_utils import add_sos_eos
+from paddlespeech.audio.utils.tensor_utils import th_accuracy
 from paddlespeech.s2t.utils.utility import UpdateConfig
 
 __all__ = ["U2STModel", "U2STInferModel"]

paddlespeech/server/engine/asr/online/onnx/asr_engine.py

@@ -26,7 +26,7 @@ from paddlespeech.cli.log import logger
 from paddlespeech.resource import CommonTaskResource
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.modules.ctc import CTCDecoder
-from paddlespeech.s2t.transform.transformation import Transformation
+from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.s2t.utils.utility import UpdateConfig
 from paddlespeech.server.engine.base_engine import BaseEngine
 from paddlespeech.server.utils import onnx_infer

paddlespeech/server/engine/asr/online/paddleinference/asr_engine.py

@@ -24,9 +24,9 @@ from yacs.config import CfgNode
 from paddlespeech.cli.asr.infer import ASRExecutor
 from paddlespeech.cli.log import logger
 from paddlespeech.resource import CommonTaskResource
+from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.modules.ctc import CTCDecoder
-from paddlespeech.s2t.transform.transformation import Transformation
 from paddlespeech.s2t.utils.utility import UpdateConfig
 from paddlespeech.server.engine.base_engine import BaseEngine
 from paddlespeech.server.utils.paddle_predictor import init_predictor

paddlespeech/server/engine/asr/online/python/asr_engine.py

@@ -24,9 +24,9 @@ from yacs.config import CfgNode
 from paddlespeech.cli.asr.infer import ASRExecutor
 from paddlespeech.cli.log import logger
 from paddlespeech.resource import CommonTaskResource
+from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.modules.ctc import CTCDecoder
-from paddlespeech.s2t.transform.transformation import Transformation
 from paddlespeech.s2t.utils.tensor_utils import add_sos_eos
 from paddlespeech.s2t.utils.tensor_utils import pad_sequence
 from paddlespeech.s2t.utils.utility import UpdateConfig

setup.py

@@ -69,7 +69,9 @@ base = [
     "prettytable",
     "zhon",
     "colorlog",
-    "pathos == 0.2.8"
+    "pathos == 0.2.8",
+    "braceexpand",
+    "pyyaml"
 ]
 
 server = [