Finish preparing training datasets.

egs/librispeech/ASR/prepare_giga_speech.sh

@@ -0,0 +1,109 @@
+#!/usr/bin/env bash
+
+set -eou pipefail
+
+nj=15
+stage=-1
+stop_stage=100
+
+# We assume dl_dir (download dir) contains the following
+# directories and files. If not, they will be downloaded
+# by this script automatically.
+#
+#  - $dl_dir/GigaSpeech
+#      You can find audio, dict, GigaSpeech.json inside it.
+#      You can apply for the download credentials by following
+#      https://github.com/SpeechColab/GigaSpeech#download
+
+# Number of hours for GigaSpeech subsets
+# XL 10k hours
+# L  2.5k hours
+# M  1k hours
+# S  250 hours
+# XS 10 hours
+# DEV 12 hours
+# Test 40 hours
+
+dl_dir=$PWD/download
+
+. shared/parse_options.sh || exit 1
+
+# All files generated by this script are saved in "data".
+# You can safely remove "data" and rerun this script to regenerate it.
+mkdir -p data
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+log "dl_dir: $dl_dir"
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Download data"
+
+  [ ! -e $dl_dir/GigaSpeech ] && mkdir -p $dl_dir/GigaSpeech
+
+  # If you have pre-downloaded it to /path/to/GigaSpeech,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/GigaSpeech $dl_dir/GigaSpeech
+  #
+  if [ ! -d $dl_dir/GigaSpeech/audio ] && [ ! -f $dl_dir/GigaSpeech.json ]; then
+    # Check credentials.
+    if [ ! -f $dl_dir/password ]; then
+      echo -n "$0: Please apply for the download credentials by following"
+      echo -n "https://github.com/SpeechColab/GigaSpeech#dataset-download"
+      echo " and save it to $dl_dir/password."
+      exit 1;
+    fi
+    PASSWORD=`cat $dl_dir/password 2>/dev/null`
+    if [ -z "$PASSWORD" ]; then
+      echo "$0: Error, $dl_dir/password is empty."
+      exit 1;
+    fi
+    PASSWORD_MD5=`echo $PASSWORD | md5sum | cut -d ' ' -f 1`
+    if [[ $PASSWORD_MD5 != "dfbf0cde1a3ce23749d8d81e492741b8" ]]; then
+      echo "$0: Error, invalid $dl_dir/password."
+      exit 1;
+    fi
+    # Download XL, DEV and TEST sets by default.
+    lhotse download gigaspeech \
+      --subset XL \
+      --subset L \
+      --subset M \
+      --subset S \
+      --subset XS \
+      --subset DEV \
+      --subset TEST \
+      --host tsinghua \
+      $dl_dir/password $dl_dir/GigaSpeech
+  fi
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare GigaSpeech manifest (may take 30 minutes)"
+  # We assume that you have downloaded the GigaSpeech corpus
+  # to $dl_dir/GigaSpeech
+  mkdir -p data/manifests
+  lhotse prepare gigaspeech \
+    --subset XL \
+    --subset L \
+    --subset M \
+    --subset S \
+    --subset XS \
+    --subset DEV \
+    --subset TEST \
+    -j $nj \
+    $dl_dir/GigaSpeech data/manifests
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Preprocess GigaSpeech manifest"
+  if [ ! -f data/fbank/.preprocess_complete ]; then
+   log "It may take 2 hours for this stage"
+   python3 ./local/preprocess_gigaspeech.py
+   touch data/fbank/.preprocess_complete
+  fi
+fi

egs/librispeech/ASR/transducer_stateless_multi_datasets/asr_datamodule.py

@@ -16,12 +16,28 @@
 # limitations under the License.
 
 import argparse
+import logging
+from pathlib import Path
+from typing import Optional
+
+from lhotse import CutSet, Fbank, FbankConfig
+from lhotse.dataset import (
+    BucketingSampler,
+    CutMix,
+    DynamicBucketingSampler,
+    K2SpeechRecognitionDataset,
+    SpecAugment,
+)
+from lhotse.dataset.input_strategies import (
+    OnTheFlyFeatures,
+    PrecomputedFeatures,
+)
+from torch.utils.data import DataLoader
 
-from lhotse import CutSet
 from icefall.utils import str2bool
 
 
-class AsrDataset:
+class AsrDataModule:
     def __init__(self, args: argparse.Namespace):
         self.args = args
 
@@ -55,19 +71,11 @@ class AsrDataset:
             "--num-buckets",
             type=int,
             default=30,
-            help="The number of buckets for the BucketingSampler"
+            help="The number of buckets for the BucketingSampler "
+            "and DynamicBucketingSampler."
             "(you might want to increase it for larger datasets).",
         )
 
-        group.add_argument(
-            "--on-the-fly-feats",
-            type=str2bool,
-            default=False,
-            help="When enabled, use on-the-fly cut mixing and feature "
-            "extraction. Will drop existing precomputed feature manifests "
-            "if available.",
-        )
-
         group.add_argument(
             "--shuffle",
             type=str2bool,
@@ -126,8 +134,25 @@ class AsrDataset:
         )
 
     def train_dataloaders(
-        self, cuts_train: CutSet, cuts_musan: Optional[CutSet] = None
+        self,
+        cuts_train: CutSet,
+        dynamic_bucketing: bool,
+        on_the_fly_feats: bool,
+        cuts_musan: Optional[CutSet] = None,
     ) -> DataLoader:
+        """
+        Args:
+          cuts_train:
+            Cuts for training.
+          cuts_musan:
+            If not None, it is the cuts for mixing.
+          dynamic_bucketing:
+            True to use DynamicBucketingSampler;
+            False to use BucketingSampler.
+          on_the_fly_feats:
+            True to use OnTheFlyFeatures;
+            False to use PrecomputedFeatures.
+        """
         transforms = []
         if cuts_musan is not None:
             logging.info("Enable MUSAN")
@@ -177,21 +202,34 @@ class AsrDataset:
         # Drop feats to be on the safe side.
         train = K2SpeechRecognitionDataset(
             cut_transforms=transforms,
-            input_strategy=OnTheFlyFeatures(
-                Fbank(FbankConfig(num_mel_bins=80))
+            input_strategy=(
+                OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+                if on_the_fly_feats
+                else PrecomputedFeatures()
             ),
             input_transforms=input_transforms,
             return_cuts=self.args.return_cuts,
         )
 
-        logging.info("Using DynamicBucketingSampler.")
-        train_sampler = DynamicBucketingSampler(
-            cuts_train,
-            max_duration=self.args.max_duration,
-            shuffle=self.args.shuffle,
-            num_buckets=self.args.num_buckets,
-            drop_last=True,
-        )
+        if dynamic_bucketing:
+            logging.info("Using DynamicBucketingSampler.")
+            train_sampler = DynamicBucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                drop_last=True,
+            )
+        else:
+            logging.info("Using BucketingSampler.")
+            train_sampler = BucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                bucket_method="equal_duration",
+                drop_last=True,
+            )
 
         logging.info("About to create train dataloader")
         train_dl = DataLoader(

egs/librispeech/ASR/transducer_stateless_multi_datasets/gigaspeech.py

@@ -17,7 +17,7 @@
 
 
 import logging
-from typing import Path
+from pathlib import Path
 
 from lhotse import CutSet, load_manifest
 
@@ -29,29 +29,47 @@ class GigaSpeech:
           manifest_dir:
             It is expected to contain the following files::
 
-                - cuts_L.jsonl.gz
-                - cuts_XL.jsonl.gz
-                - cuts_TEST.jsonl.gz
-                - cuts_DEV.jsonl.gz
+                - cuts_XL_raw.jsonl.gz
+                - cuts_L_raw.jsonl.gz
+                - cuts_M_raw.jsonl.gz
+                - cuts_S_raw.jsonl.gz
+                - cuts_XS_raw.jsonl.gz
+                - cuts_DEV_raw.jsonl.gz
+                - cuts_TEST_raw.jsonl.gz
         """
         self.manifest_dir = Path(manifest_dir)
 
-    def train_L_cuts(self) -> CutSet:
-        f = self.manifest_dir / "cuts_L.json.gz"
-        logging.info(f"About to get train-L cuts from {f}")
-        return CutSet.from_jsonl_lazy(f)
-
     def train_XL_cuts(self) -> CutSet:
-        f = self.manifest_dir / "cuts_XL.json.gz"
+        f = self.manifest_dir / "cuts_XL_raw.jsonl.gz"
         logging.info(f"About to get train-XL cuts from {f}")
         return CutSet.from_jsonl_lazy(f)
 
+    def train_L_cuts(self) -> CutSet:
+        f = self.manifest_dir / "cuts_L_raw.jsonl.gz"
+        logging.info(f"About to get train-L cuts from {f}")
+        return CutSet.from_jsonl_lazy(f)
+
+    def train_M_cuts(self) -> CutSet:
+        f = self.manifest_dir / "cuts_M_raw.jsonl.gz"
+        logging.info(f"About to get train-M cuts from {f}")
+        return CutSet.from_jsonl_lazy(f)
+
+    def train_S_cuts(self) -> CutSet:
+        f = self.manifest_dir / "cuts_S_raw.jsonl.gz"
+        logging.info(f"About to get train-S cuts from {f}")
+        return CutSet.from_jsonl_lazy(f)
+
+    def train_XS_cuts(self) -> CutSet:
+        f = self.manifest_dir / "cuts_XS_raw.jsonl.gz"
+        logging.info(f"About to get train-XS cuts from {f}")
+        return CutSet.from_jsonl_lazy(f)
+
     def test_cuts(self) -> CutSet:
-        f = self.manifest_dir / "cuts_TEST.json.gz"
+        f = self.manifest_dir / "cuts_TEST.jsonl.gz"
         logging.info(f"About to get TEST cuts from {f}")
         return load_manifest(f)
 
     def dev_cuts(self) -> CutSet:
-        f = self.manifest_dir / "cuts_DEV.json.gz"
+        f = self.manifest_dir / "cuts_DEV.jsonl.gz"
         logging.info(f"About to get DEV cuts from {f}")
         return load_manifest(f)

egs/librispeech/ASR/transducer_stateless_multi_datasets/librispeech.py

@@ -16,7 +16,7 @@
 # limitations under the License.
 
 import logging
-from typing import Path
+from pathlib import Path
 
 from lhotse import CutSet, load_manifest
 

egs/librispeech/ASR/transducer_stateless_multi_datasets/test_asr_datamodule.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python3
+# Copyright    2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# 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.
+
+"""
+To run this file, do:
+
+    cd icefall/egs/librispeech/ASR
+    python ./transducer_stateless_multi_datasets/test_asr_datamodule.py
+"""
+
+import argparse
+import random
+from pathlib import Path
+
+from asr_datamodule import AsrDataModule
+from gigaspeech import GigaSpeech
+from lhotse import load_manifest
+from librispeech import LibriSpeech
+
+
+def test_dataset():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    AsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    print(args)
+
+    if args.enable_musan:
+        cuts_musan = load_manifest(
+            Path(args.manifest_dir) / "cuts_musan.json.gz"
+        )
+    else:
+        cuts_musan = None
+
+    librispeech = LibriSpeech(manifest_dir=args.manifest_dir)
+    gigaspeech = GigaSpeech(manifest_dir=args.manifest_dir)
+
+    train_clean_100 = librispeech.train_clean_100_cuts()
+    train_S = gigaspeech.train_S_cuts()
+
+    asr_datamodule = AsrDataModule(args)
+
+    libri_train_dl = asr_datamodule.train_dataloaders(
+        train_clean_100,
+        dynamic_bucketing=False,
+        on_the_fly_feats=False,
+        cuts_musan=cuts_musan,
+    )
+
+    giga_train_dl = asr_datamodule.train_dataloaders(
+        train_S,
+        dynamic_bucketing=True,
+        on_the_fly_feats=True,
+        cuts_musan=cuts_musan,
+    )
+
+    seed = 20220216
+    rng = random.Random(seed)
+
+    for epoch in range(2):
+        print("epoch", epoch)
+        batch_idx = 0
+        libri_train_dl.sampler.set_epoch(epoch)
+        giga_train_dl.sampler.set_epoch(epoch)
+
+        iter_libri = iter(libri_train_dl)
+        iter_giga = iter(giga_train_dl)
+        while True:
+            idx = rng.choices((0, 1), weights=[0.8, 0.2], k=1)[0]
+            dl = iter_libri if idx == 0 else iter_giga
+            batch_idx += 1
+
+            print("dl idx", idx, "batch_idx", batch_idx)
+            batch = next(dl)
+            cuts = batch["supervisions"]["cut"]
+            for c in cuts:
+                print(c.id)
+
+            if batch_idx > 10:
+                break
+
+
+def main():
+    test_dataset()
+
+
+if __name__ == "__main__":
+    main()