Add peoples_speech (#1101)

* update

* Small fix

* Update egs/peoples_speech/ASR/prepare.sh

Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>

* limit normalize log

* Update egs/peoples_speech/ASR/local/compute_fbank_peoples_speech_valid_test.py

Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>

* Update compute_fbank_peoples_speech_splits.py

* Update compute_fbank_peoples_speech_valid_test.py

---------

Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>

egs/peoples_speech/ASR/local/compute_fbank_musan.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/local/compute_fbank_musan.py

egs/peoples_speech/ASR/local/compute_fbank_peoples_speech_splits.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.             (Yifan Yang)
+#
+# 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.
+
+import argparse
+import logging
+from datetime import datetime
+from pathlib import Path
+
+import torch
+from lhotse import (
+    CutSet,
+    KaldifeatFbank,
+    KaldifeatFbankConfig,
+    LilcomChunkyWriter,
+    set_audio_duration_mismatch_tolerance,
+    set_caching_enabled,
+)
+
+# Torch's multithreaded behavior needs to be disabled or
+# it wastes a lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
+# even when we are not invoking the main (e.g. when spawning subprocesses).
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--num-workers",
+        type=int,
+        default=20,
+        help="Number of dataloading workers used for reading the audio.",
+    )
+
+    parser.add_argument(
+        "--batch-duration",
+        type=float,
+        default=600.0,
+        help="The maximum number of audio seconds in a batch."
+        "Determines batch size dynamically.",
+    )
+
+    parser.add_argument(
+        "--num-splits",
+        type=int,
+        required=True,
+        help="The number of splits of the train subset",
+    )
+
+    parser.add_argument(
+        "--start",
+        type=int,
+        default=0,
+        help="Process pieces starting from this number (inclusive).",
+    )
+
+    parser.add_argument(
+        "--stop",
+        type=int,
+        default=-1,
+        help="Stop processing pieces until this number (exclusive).",
+    )
+
+    return parser.parse_args()
+
+
+def compute_fbank_peoples_speech_splits(args):
+    subsets = ("dirty", "dirty_sa", "clean", "clean_sa")
+    num_splits = args.num_splits
+    output_dir = f"data/fbank/peoples_speech_train_split"
+    output_dir = Path(output_dir)
+    assert output_dir.exists(), f"{output_dir} does not exist!"
+
+    num_digits = 8
+
+    start = args.start
+    stop = args.stop
+    if stop < start:
+        stop = num_splits
+
+    stop = min(stop, num_splits)
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+    extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device))
+    logging.info(f"device: {device}")
+
+    set_audio_duration_mismatch_tolerance(0.01)  # 10ms tolerance
+    set_caching_enabled(False)
+
+    for partition in subsets:
+        for i in range(start, stop):
+            idx = f"{i + 1}".zfill(num_digits)
+            logging.info(f"Processing {partition}: {idx}")
+
+            cuts_path = output_dir / f"peoples_speech_cuts_{partition}.{idx}.jsonl.gz"
+            if cuts_path.is_file():
+                logging.info(f"{cuts_path} exists - skipping")
+                continue
+
+            raw_cuts_path = (
+                output_dir / f"peoples_speech_cuts_{partition}_raw.{idx}.jsonl.gz"
+            )
+
+            logging.info(f"Loading {raw_cuts_path}")
+            cut_set = CutSet.from_file(raw_cuts_path)
+
+            logging.info("Splitting cuts into smaller chunks.")
+            cut_set = cut_set.trim_to_supervisions(
+                keep_overlapping=False, min_duration=None
+            )
+
+            logging.info("Computing features")
+            cut_set = cut_set.compute_and_store_features_batch(
+                extractor=extractor,
+                storage_path=f"{output_dir}/peoples_speech_feats_{partition}_{idx}",
+                num_workers=args.num_workers,
+                batch_duration=args.batch_duration,
+                storage_type=LilcomChunkyWriter,
+                overwrite=True,
+            )
+
+            logging.info(f"Saving to {cuts_path}")
+            cut_set.to_file(cuts_path)
+
+
+def main():
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    args = get_args()
+    logging.info(vars(args))
+    compute_fbank_peoples_speech_splits(args)
+
+
+if __name__ == "__main__":
+    main()

egs/peoples_speech/ASR/local/compute_fbank_peoples_speech_valid_test.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Yifan Yang)
+#
+# 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.
+
+
+"""
+This file computes fbank features of the People's Speech dataset.
+It looks for manifests in the directory data/manifests.
+
+The generated fbank features are saved in data/fbank.
+"""
+
+import argparse
+import logging
+import os
+from pathlib import Path
+from typing import Optional
+
+import torch
+from filter_cuts import filter_cuts
+from lhotse import CutSet, KaldifeatFbank, KaldifeatFbankConfig, LilcomChunkyWriter
+
+# Torch's multithreaded behavior needs to be disabled or
+# it wastes a lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
+# even when we are not invoking the main (e.g. when spawning subprocesses).
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+
+def compute_fbank_peoples_speech_valid_test():
+    src_dir = Path(f"data/manifests")
+    output_dir = Path(f"data/fbank")
+    num_workers = 42
+    batch_duration = 600
+
+    subsets = ("validation", "test")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+    extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device))
+
+    logging.info(f"device: {device}")
+
+    for partition in subsets:
+        cuts_path = output_dir / f"peoples_speech_cuts_{partition}.jsonl.gz"
+        if cuts_path.is_file():
+            logging.info(f"{partition} already exists - skipping.")
+            continue
+
+        raw_cuts_path = output_dir / f"peoples_speech_cuts_{partition}_raw.jsonl.gz"
+
+        logging.info(f"Loading {raw_cuts_path}")
+        cut_set = CutSet.from_file(raw_cuts_path)
+
+        logging.info("Splitting cuts into smaller chunks")
+        cut_set = cut_set.trim_to_supervisions(
+            keep_overlapping=False, min_duration=None
+        )
+
+        logging.info("Computing features")
+        cut_set = cut_set.compute_and_store_features_batch(
+            extractor=extractor,
+            storage_path=f"{output_dir}/peoples_speech_feats_{partition}",
+            num_workers=num_workers,
+            batch_duration=batch_duration,
+            storage_type=LilcomChunkyWriter,
+            overwrite=True,
+        )
+
+        logging.info(f"Saving to {cuts_path}")
+        cut_set.to_file(cuts_path)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    compute_fbank_peoples_speech_valid_test()

egs/peoples_speech/ASR/local/filter_cuts.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/local/filter_cuts.py

egs/peoples_speech/ASR/local/prepare_lang_bpe.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/local/prepare_lang_bpe.py

egs/peoples_speech/ASR/local/preprocess_peoples_speech.py

@@ -0,0 +1,123 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Yifan Yang)
+#
+# 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.
+
+import argparse
+import logging
+import re
+from pathlib import Path
+from typing import Optional
+
+from lhotse import CutSet, SupervisionSegment
+from lhotse.recipes.utils import read_manifests_if_cached
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        help="""Dataset parts to compute fbank. If None, we will use all""",
+    )
+
+    return parser.parse_args()
+
+
+def normalize_text(utt: str) -> str:
+    utt = re.sub(r"[{0}]+".format("-"), " ", utt)
+    return re.sub(r"[^a-zA-Z\s]", "", utt).upper()
+
+
+def preprocess_peoples_speech(dataset: Optional[str] = None):
+    src_dir = Path(f"data/manifests")
+    output_dir = Path(f"data/fbank")
+    output_dir.mkdir(exist_ok=True)
+
+    if dataset is None:
+        dataset_parts = (
+            "validation",
+            "test",
+            "dirty",
+            "dirty_sa",
+            "clean",
+            "clean_sa",
+        )
+    else:
+        dataset_parts = dataset.split(" ", -1)
+
+    logging.info("Loading manifest, it may takes 8 minutes")
+    prefix = f"peoples_speech"
+    suffix = "jsonl.gz"
+    manifests = read_manifests_if_cached(
+        dataset_parts=dataset_parts,
+        output_dir=src_dir,
+        suffix=suffix,
+        prefix=prefix,
+    )
+    assert manifests is not None
+
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
+    for partition, m in manifests.items():
+        logging.info(f"Processing {partition}")
+        raw_cuts_path = output_dir / f"{prefix}_cuts_{partition}_raw.{suffix}"
+        if raw_cuts_path.is_file():
+            logging.info(f"{partition} already exists - skipping")
+            continue
+
+        logging.info(f"Normalizing text in {partition}")
+        i = 0
+        for sup in m["supervisions"]:
+            text = str(sup.text)
+            orig_text = text
+            sup.text = normalize_text(sup.text)
+            text = str(sup.text)
+            if i < 10 and len(orig_text) != len(text):
+                logging.info(
+                    f"\nOriginal text vs normalized text:\n{orig_text}\n{text}"
+                )
+                i += 1
+
+        # Create long-recording cut manifests.
+        cut_set = CutSet.from_manifests(
+            recordings=m["recordings"],
+            supervisions=m["supervisions"],
+        ).resample(16000)
+
+        # Run data augmentation that needs to be done in the
+        # time domain.
+        logging.info(f"Saving to {raw_cuts_path}")
+        cut_set.to_file(raw_cuts_path)
+
+
+def main():
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    args = get_args()
+    logging.info(vars(args))
+    preprocess_peoples_speech(dataset=args.dataset)
+    logging.info("Done")
+
+
+if __name__ == "__main__":
+    main()

egs/peoples_speech/ASR/local/train_bpe_model.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/local/train_bpe_model.py

egs/peoples_speech/ASR/local/validate_bpe_lexicon.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/local/validate_bpe_lexicon.py

egs/peoples_speech/ASR/prepare.sh

@@ -0,0 +1,247 @@
+#!/usr/bin/env bash
+
+set -eou pipefail
+
+nj=32
+stage=-1
+stop_stage=100
+
+# Split data/set to a number of pieces
+# This is to avoid OOM during feature extraction.
+num_per_split=4000
+
+# We assume dl_dir (download dir) contains the following
+# directories and files. If not, they will be downloaded
+# by this script automatically.
+#
+#  - $dl_dir/peoples_speech
+#      This directory contains the following files downloaded from
+#       https://huggingface.co/datasets/MLCommons/peoples_speech
+# 
+#     - test
+#     - train
+#     - validation
+#
+#  - $dl_dir/musan
+#      This directory contains the following directories downloaded from
+#       http://www.openslr.org/17/
+#
+#     - music
+#     - noise
+#     - speech
+
+dl_dir=$PWD/download
+
+. shared/parse_options.sh || exit 1
+
+# vocab size for sentence piece models.
+# It will generate data/lang_bpe_xxx,
+# data/lang_bpe_yyy if the array contains xxx, yyy
+vocab_sizes=(
+  # 5000
+  # 2000
+  # 1000
+  500
+)
+
+# 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"
+
+  # If you have pre-downloaded it to /path/to/peoples_speech,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/peoples_speech $dl_dir/peoples_speech
+  #
+  if [ ! -d $dl_dir/peoples_speech/train ]; then
+    git lfs install
+    git clone https://huggingface.co/datasets/MLCommons/peoples_speech
+  fi
+
+  # If you have pre-downloaded it to /path/to/musan,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/musan $dl_dir/
+  #
+  if [ ! -d $dl_dir/musan ]; then
+    lhotse download musan $dl_dir
+  fi
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare People's Speech manifest"
+  # We assume that you have downloaded the People's Speech corpus
+  # to $dl_dir/peoples_speech
+  mkdir -p data/manifests
+  if [ ! -e data/manifests/.peoples_speech.done ]; then
+    lhotse prepare peoples-speech -j $nj $dl_dir/peoples_speech data/manifests
+    touch data/manifests/.peoples_speech.done
+  fi
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Prepare musan manifest"
+  # We assume that you have downloaded the musan corpus
+  # to data/musan
+  mkdir -p data/manifests
+  if [ ! -e data/manifests/.musan.done ]; then
+    lhotse prepare musan $dl_dir/musan data/manifests
+    touch data/manifests/.musan.done
+  fi
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Preprocess People's Speech manifest"
+  mkdir -p data/fbank
+  if [ ! -e data/fbank/.preprocess_complete ]; then
+    ./local/preprocess_peoples_speech.py
+    touch data/fbank/.preprocess_complete
+  fi
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Compute fbank for valid and test subsets of People's Speech"
+  if [ ! -e data/fbank/.peoples_speech_valid_test.done ]; then
+    ./local/compute_fbank_peoples_speech_valid_test.py
+    touch data/fbank/.peoples_speech_valid_test.done
+  fi
+fi
+
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  log "Stage 5: Split train subset into pieces"
+  split_dir=data/fbank/peoples_speech_train_split
+  if [ ! -e $split_dir/.peoples_speech_dirty_split.done ]; then
+    lhotse split-lazy ./data/fbank/peoples_speech_cuts_dirty_raw.jsonl.gz $split_dir $num_per_split
+    touch $split_dir/.peoples_speech_dirty_split.done
+  fi
+
+  if [ ! -e $split_dir/.peoples_speech_dirty_sa_split.done ]; then
+    lhotse split-lazy ./data/fbank/peoples_speech_cuts_dirty_sa_raw.jsonl.gz $split_dir $num_per_split
+    touch $split_dir/.peoples_speech_dirty_sa_split.done
+  fi
+
+  if [ ! -e $split_dir/.peoples_speech_clean_split.done ]; then
+    lhotse split-lazy ./data/fbank/peoples_speech_cuts_clean_raw.jsonl.gz $split_dir $num_per_split
+    touch $split_dir/.peoples_speech_clean_split.done
+  fi
+
+  if [ ! -e $split_dir/.peoples_speech_clean_sa_split.done ]; then
+    lhotse split-lazy ./data/fbank/peoples_speech_cuts_clean_sa_raw.jsonl.gz $split_dir $num_per_split
+    touch $split_dir/.peoples_speech_clean_sa_split.done
+  fi
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Compute features for train subset of People's Speech"
+  if [ ! -e data/fbank/.peoples_speech_train.done ]; then
+    ./local/compute_fbank_peoples_speech_splits.py \
+      --num-workers $nj \
+      --batch-duration 600 \
+      --start 0 \
+      --num-splits 2000
+    touch data/fbank/.peoples_speech_train.done
+  fi
+fi
+
+if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
+  log "Stage 7: Compute fbank for musan"
+  mkdir -p data/fbank
+  if [ ! -e data/fbank/.musan.done ]; then
+    ./local/compute_fbank_musan.py
+    touch data/fbank/.musan.done
+  fi
+fi
+
+if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
+  log "Stage 8: Prepare BPE based lang"
+
+  for vocab_size in ${vocab_sizes[@]}; do
+    lang_dir=data/lang_bpe_${vocab_size}
+    mkdir -p $lang_dir
+
+    if [ ! -f $lang_dir/transcript_words.txt ]; then
+      log "Generate data for BPE training"
+      file=$(
+        find "data/fbank/peoples_speech_cuts_dirty_raw.jsonl.gz"
+	find "data/fbank/peoples_speech_cuts_dirty_sa_raw.jsonl.gz"
+	find "data/fbank/peoples_speech_cuts_clean_raw.jsonl.gz"
+	find "data/fbank/peoples_speech_cuts_clean_sa_raw.jsonl.gz"
+      )
+      gunzip -c ${file} | awk -F '"' '{print $30}' > $lang_dir/transcript_words.txt
+
+      # Ensure space only appears once
+      sed -i 's/\t/ /g' $lang_dir/transcript_words.txt
+      sed -i 's/ +/ /g' $lang_dir/transcript_words.txt
+    fi
+ 
+    if [ ! -f $lang_dir/words.txt ]; then
+      cat $lang_dir/transcript_words.txt | sed 's/ /\n/g' \
+        | sort -u | sed '/^$/d' > $lang_dir/words.txt
+      (echo '!SIL'; echo '<SPOKEN_NOISE>'; echo '<UNK>'; ) |
+        cat - $lang_dir/words.txt | sort | uniq | awk '
+        BEGIN {
+          print "<eps> 0";
+        }
+        {
+          if ($1 == "<s>") {
+            print "<s> is in the vocabulary!" | "cat 1>&2"
+            exit 1;
+          }
+          if ($1 == "</s>") {
+            print "</s> is in the vocabulary!" | "cat 1>&2"
+            exit 1;
+          }
+          printf("%s %d\n", $1, NR);
+        }
+        END {
+          printf("#0 %d\n", NR+1);
+          printf("<s> %d\n", NR+2);
+          printf("</s> %d\n", NR+3);
+        }' > $lang_dir/words || exit 1;
+      mv $lang_dir/words $lang_dir/words.txt
+    fi
+ 
+    if [ ! -f $lang_dir/bpe.model ]; then
+      ./local/train_bpe_model.py \
+        --lang-dir $lang_dir \
+        --vocab-size $vocab_size \
+        --transcript $lang_dir/transcript_words.txt
+    fi
+  
+    if [ ! -f $lang_dir/L_disambig.pt ]; then
+      ./local/prepare_lang_bpe.py --lang-dir $lang_dir
+
+      log "Validating $lang_dir/lexicon.txt"
+      ./local/validate_bpe_lexicon.py \
+        --lexicon $lang_dir/lexicon.txt \
+        --bpe-model $lang_dir/bpe.model
+    fi
+
+    if [ ! -f $lang_dir/L.fst ]; then
+      log "Converting L.pt to L.fst"
+      ./shared/convert-k2-to-openfst.py \
+        --olabels aux_labels \
+        $lang_dir/L.pt \
+        $lang_dir/L.fst
+    fi
+
+    if [ ! -f $lang_dir/L_disambig.fst ]; then
+      log "Converting L_disambig.pt to L_disambig.fst"
+      ./shared/convert-k2-to-openfst.py \
+        --olabels aux_labels \
+        $lang_dir/L_disambig.pt \
+        $lang_dir/L_disambig.fst
+    fi
+  done
+fi

egs/peoples_speech/ASR/shared

@@ -0,0 +1 @@
+../../../icefall/shared/