add prepare.sh

egs/libriheavy/TTS/local/extract_speech_tokens.py

@@ -0,0 +1,289 @@
+#!/usr/bin/env python3
+# Copyright    2024  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 math
+import os
+from pathlib import Path
+from typing import Optional
+
+import fairseq
+import joblib
+import numpy as np
+import torch
+from lhotse import CutSet, SupervisionSegment
+from lhotse.utils import fastcopy
+from tqdm import tqdm
+
+# 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)
+
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:512"
+
+
+class ApplyKmeans(object):
+    def __init__(self, km_path):
+        self.km_model = joblib.load(km_path)
+        self.C_np = self.km_model.cluster_centers_.transpose()
+        self.Cnorm_np = (self.C_np**2).sum(0, keepdims=True)
+
+        self.C = torch.from_numpy(self.C_np)
+        self.Cnorm = torch.from_numpy(self.Cnorm_np)
+        if torch.cuda.is_available():
+            self.C = self.C.cuda()
+            self.Cnorm = self.Cnorm.cuda()
+
+    def __call__(self, x):
+        if isinstance(x, torch.Tensor):
+            dist = (
+                x.pow(2).sum(1, keepdim=True) - 2 * torch.matmul(x, self.C) + self.Cnorm
+            )
+            return dist.argmin(dim=1).cpu().numpy()
+        else:
+            dist = (
+                (x**2).sum(1, keepdims=True)
+                - 2 * np.matmul(x, self.C_np)
+                + self.Cnorm_np
+            )
+            return np.argmin(dist, axis=1)
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--subset",
+        type=str,
+        default="small",
+    )
+
+    parser.add_argument(
+        "--model-path",
+        type=str,
+        default="download/hubert_base_ls960.pt",
+    )
+
+    parser.add_argument(
+        "--kmeans-model-path",
+        type=str,
+        default="download/hubert_base_ls960_L9_km500.bin",
+    )
+
+    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).",
+    )
+
+    parser.add_argument(
+        "--window-duration",
+        type=float,
+        default=300.0,
+    )
+
+    parser.add_argument(
+        "--shift-duration",
+        type=float,
+        default=250.0,
+    )
+
+    return parser.parse_args()
+
+
+@torch.no_grad()
+def extract_and_save_one_cuts(
+    raw_cuts_path,
+    cuts_path,
+    model,
+    apply_kmeans,
+    do_normalize,
+    window_duration,
+    shift_duration,
+):
+    logging.info(f"Loading {raw_cuts_path}")
+    cut_set = CutSet.from_file(raw_cuts_path)
+
+    logging.info("Extracting kmeans")
+    cuts = []
+
+    assert window_duration >= shift_duration
+    window_size = int(window_duration * 16000)
+    shift_size = int(shift_duration * 16000)
+    overlap_size = window_size - shift_size
+    out_overlap_size = get_out_length(overlap_size)
+
+    for cut in tqdm(cut_set):
+        assert cut.sampling_rate == 16000, f"Sampling rate: {cut.sampling_rate}"
+
+        audio = cut.load_audio()
+
+        T = audio.shape[1]
+        start = 0
+        kmeans = []
+        while start < T:
+            real_window_size = min(window_size, T - start)
+            audio_window = audio[:, start : start + real_window_size]
+
+            x = (
+                torch.from_numpy(audio_window)
+                .float()
+                .to(next(model.parameters()).device)
+            )
+            if do_normalize:
+                x = torch.nn.functional.layer_norm(x, x.shape)
+
+            feature, _ = model.extract_features(
+                source=x,
+                padding_mask=None,
+                mask=False,
+                output_layer=9,
+            )
+            feature = feature.squeeze(0)
+
+            current_kmeans = apply_kmeans(feature).tolist()
+
+            if start == 0:
+                kmeans.extend(current_kmeans)
+            else:
+                kmeans.extend(current_kmeans[out_overlap_size:])
+
+            if T - start <= window_size:
+                break
+
+            start += shift_size
+
+        kmeans = " ".join(map(str, kmeans))
+
+        cut_with_kmeans = fastcopy(
+            cut,
+            custom={"kmeans": kmeans},
+        )
+        cuts.append(cut_with_kmeans)
+
+    cuts = CutSet(cuts)
+
+    logging.info(f"Saving to {cuts_path}")
+    cuts.to_file(cuts_path)
+
+
+def extract_kmeans(args):
+    assert args.subset in ("small", "medium", "large"), f"{args.subset}"
+
+    output_dir = (
+        f"data/kmeans/{args.subset}_split" if args.subset != "small" else "data/kmeans"
+    )
+    output_dir = Path(output_dir)
+    assert output_dir.exists(), f"{output_dir} does not exist!"
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+    logging.info(f"device: {device}")
+
+    prefix = "librilight"
+
+    apply_kmeans = ApplyKmeans(args.kmeans_model_path)
+    model, _, task = fairseq.checkpoint_utils.load_model_ensemble_and_task(
+        [args.model_path]
+    )
+    model = model[0].eval().to(device)
+    do_normalize = task.cfg.normalize
+
+    window_duration = args.window_duration
+    shift_duration = args.shift_duration
+
+    if args.subset == "small":
+        cuts_path = output_dir / f"{prefix}_cuts_{args.subset}.jsonl.gz"
+        if cuts_path.is_file():
+            logging.info(f"{cuts_path} exists - skipping")
+            return
+
+        raw_cuts_path = output_dir / f"{prefix}_cuts_{args.subset}_raw.jsonl.gz"
+        if not raw_cuts_path.is_file():
+            logging.info(f"{raw_cuts_path} does not exist - skipping it")
+            return
+
+        extract_and_save_one_cuts(
+            raw_cuts_path,
+            cuts_path,
+            model,
+            apply_kmeans,
+            do_normalize,
+            window_duration,
+            shift_duration,
+        )
+    else:
+        num_digits = 8  # num_digits is fixed by lhotse split-lazy
+        start = args.start
+        stop = args.stop
+        assert stop > start, "stop must be larger than start!"
+
+        for i in range(start, stop):
+            idx = f"{i}".zfill(num_digits)
+            logging.info(f"Processing {idx}/{stop - 1}")
+
+            cuts_path = output_dir / f"{prefix}_cuts_{args.subset}.{idx}.jsonl.gz"
+            if cuts_path.is_file():
+                logging.info(f"{cuts_path} exists - skipping")
+                continue
+
+            raw_cuts_path = (
+                output_dir / f"{prefix}_cuts_{args.subset}_raw.{idx}.jsonl.gz"
+            )
+            if not raw_cuts_path.is_file():
+                logging.info(f"{raw_cuts_path} does not exist - skipping it")
+                continue
+
+            extract_and_save_one_cuts(
+                raw_cuts_path,
+                cuts_path,
+                model,
+                apply_kmeans,
+                do_normalize,
+                window_duration,
+                shift_duration,
+            )
+
+
+def get_out_length(T):
+    conv_layers = [(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512, 2, 2)] * 2
+    for i, (out_channels, kernel_size, stride) in enumerate(conv_layers):
+        T = math.floor((T - kernel_size) / stride) + 1
+
+    return max(0, T)
+
+
+if __name__ == "__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))
+    extract_kmeans(args)

egs/libriheavy/TTS/local/prepare_manifest.py

@@ -0,0 +1,76 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Wei Kang)
+#
+# 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 gzip
+import json
+import re
+import sys
+from pathlib import Path
+
+from tn.english.normalizer import Normalizer as EnNormalizer
+
+from icefall.utils import str2bool
+
+
+class TextNormlizer:
+    def __init__(self):
+        self.en_tn_model = EnNormalizer()
+
+    def __call__(self, text):
+        # brackets
+        # Always text inside brackets with numbers in them. Usually corresponds to "(Sam 23:17)"
+        text = re.sub(r"\([^\)]*\d[^\)]*\)", " ", text)
+        if remove_brackets:
+            text = re.sub(r"\([^\)]*\)", " ", text)
+
+        # Apply mappings
+        table = str.maketrans("’‘，。；？！（）：-《》、“”【】", "'',.;?!(): <>/\"\"[]")
+        text = text.translate(table)
+
+        # Remove extra spaces
+        text = re.sub(r"\s+", " ", text).strip()
+        normalized_text = re.sub(r"\s+", " ", normalized_text).strip()
+
+        text = self.en_tn_model.normalize(text)
+        return text.strip()
+
+
+# Assign text of the supervisions and remove unnecessary entries.
+def main():
+    assert (
+        len(sys.argv) == 4
+    ), "Usage: ./local/prepare_manifest.py INPUT OUTPUT_DIR KEEP_CUSTOM_FIELDS"
+    fname = Path(sys.argv[1]).name
+    oname = Path(sys.argv[2]) / fname
+    keep_custom_fields = str2bool(sys.argv[3])
+
+    tn = TextNormlizer()
+
+    with gzip.open(sys.argv[1], "r") as fin, gzip.open(oname, "w") as fout:
+        for line in fin:
+            cut = json.loads(line)
+            cut["supervisions"][0]["text"] = tn(
+                cut["supervisions"][0]["custom"]["texts"][0]
+            )
+            if not keep_custom_fields:
+                del cut["supervisions"][0]["custom"]
+                del cut["custom"]
+            fout.write((json.dumps(cut) + "\n").encode())
+
+
+if __name__ == "__main__":
+    main()

egs/libriheavy/TTS/local/train_bpe_model.py

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

egs/libriheavy/TTS/prepare.sh

@@ -0,0 +1,131 @@
+#!/usr/bin/env bash
+
+# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+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/librilight
+#      You can find small, medium, large, etc. inside it.
+#
+#  - $dl_dir/libriheavy
+#      You can find libriheavy_cuts_small.jsonl.gz, libriheavy_cuts_medium.jsonl.gz, etc. inside it.
+dl_dir=$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=(
+  4000
+)
+
+# 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
+tokens_dir=data/tokens
+manifests_dir=data/manifests
+
+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 -1 ] && [ $stop_stage -ge -1 ]; then
+  log "Stage -1: Download audio data."
+  # If you have pre-downloaded it to /path/to/librilight,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/librilight $dl_dir/librilight
+  #
+  mkdir -p $dl_dir/librilight
+  for subset in small medium large; do
+    log "Downloading ${subset} subset."
+    if [ ! -d $dl_dir/librilight/${subset} ]; then
+      wget -P $dl_dir/librilight -c https://dl.fbaipublicfiles.com/librilight/data/${subset}.tar 
+      tar xf $dl_dir/librilight/${subset}.tar -C $dl_dir/librilight
+    else
+      log "Skipping download, ${subset} subset exists."
+    fi
+  done
+fi
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Download manifests from huggingface."
+
+  # If you have pre-downloaded it to /path/to/libriheavy,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/libriheavy $dl_dir/libriheavy
+  #
+  mkdir -p $dl_dir/libriheavy
+  for subset in small medium large dev test_clean test_other; do
+    if [ ! -e $dl_dir/libriheavy/libriheavy_cuts_${subset}.jsonl.gz ]; then
+      log "Downloading ${subset} subset."
+      wget -P $dl_dir/libriheavy -c https://huggingface.co/datasets/pkufool/libriheavy/resolve/main/libriheavy_cuts_${subset}.jsonl.gz
+    else
+      log "Skipping download, ${subset} subset exists."
+    fi
+  done
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Prepare Libriheavy manifests"
+  mkdir -p $manifests_dir
+  for subset in small medium large dev test_clean test_other; do
+    if [ ! -e $manifests_dir/libriheavy_cuts_${subset}.jsonl.gz ]; then
+      log "Prepare manifest for subset : ${subset}"
+      ./local/prepare_manifest.py $dl_dir/libriheavy/libriheavy_cuts_${subset}.jsonl.gz $manifests_dir False
+    fi
+  done
+fi
+
+num_per_split=200000
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Split medium and large subsets."
+  for subset in medium large; do
+    log "Spliting subset : $subset"
+    split_dir=$manifests_dir/libriheavy_${subset}_split
+    mkdir -p $split_dir
+    if [ ! -e $split_dir/.split_completed ]; then
+      lhotse split-lazy $manifests_dir/libriheavy_cuts_${subset}.jsonl.gz $split_dir $num_per_split
+      touch $split_dir/.split_completed
+    fi
+  done
+fi
+
+if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then
+  log "Stage 10: Train BPE model for unnormalized text"
+  if [ ! -f data/punc_texts ]; then
+    gunzip -c $manifests_dir/libriheavy_cuts_medium.jsonl.gz \
+      | jq '.supervisions[].text' | sed 's/"//;s/\\//g;s/"$//' > data/punc_texts
+  fi
+  for vocab_size in ${vocab_sizes[@]}; do
+    lang_dir=data/lang_punc_bpe_${vocab_size}
+    mkdir -p $lang_dir
+
+    cp data/punc_texts $lang_dir/text
+
+    if [ ! -f $lang_dir/bpe.model ]; then
+      ./local/train_bpe_model.py \
+        --lang-dir $lang_dir \
+        --byte-fallback \
+        --vocab-size ${vocab_size} \
+        --byte-fallback \
+        --character-coverage 0.99 \
+        --transcript $lang_dir/text
+    fi
+  done
+fi