ready to train

egs/baker_zh/TTS/local/audio.py

@@ -0,0 +1 @@
+../matcha/audio.py

egs/baker_zh/TTS/local/compute_fbank_baker_zh.py

@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+# Copyright    2021-2023  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                       Zengwei Yao)
+#
+# 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 baker-zh 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
+
+import torch
+from fbank import MatchaFbank, MatchaFbankConfig
+from lhotse import CutSet, LilcomChunkyWriter, load_manifest
+from lhotse.audio import RecordingSet
+from lhotse.supervision import SupervisionSet
+
+from icefall.utils import get_executor
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--num-jobs",
+        type=int,
+        default=4,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        """,
+    )
+    return parser
+
+
+def compute_fbank_baker_zh(num_jobs: int):
+    src_dir = Path("data/manifests")
+    output_dir = Path("data/fbank")
+
+    if num_jobs < 1:
+        num_jobs = os.cpu_count()
+
+    logging.info(f"num_jobs: {num_jobs}")
+    logging.info(f"src_dir: {src_dir}")
+    logging.info(f"output_dir: {output_dir}")
+    config = MatchaFbankConfig(
+        n_fft=1024,
+        n_mels=80,
+        sampling_rate=22050,
+        hop_length=256,
+        win_length=1024,
+        f_min=0,
+        f_max=8000,
+    )
+
+    prefix = "baker_zh"
+    suffix = "jsonl.gz"
+
+    extractor = MatchaFbank(config)
+
+    with get_executor() as ex:  # Initialize the executor only once.
+        cuts_filename = f"{prefix}_cuts.{suffix}"
+        logging.info(f"Processing {cuts_filename}")
+        cut_set = load_manifest(src_dir / cuts_filename).resample(22050)
+
+        cut_set = cut_set.compute_and_store_features(
+            extractor=extractor,
+            storage_path=f"{output_dir}/{prefix}_feats",
+            num_jobs=num_jobs if ex is None else 80,
+            executor=ex,
+            storage_type=LilcomChunkyWriter,
+        )
+
+        cut_set.to_file(output_dir / cuts_filename)
+
+
+if __name__ == "__main__":
+    # 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)
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    args = get_parser().parse_args()
+    compute_fbank_baker_zh(args.num_jobs)

egs/baker_zh/TTS/local/compute_fbank_statistics.py

@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+# Copyright    2024  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.
+"""
+This script compute the mean and std of the fbank features.
+"""
+
+import argparse
+import json
+import logging
+from pathlib import Path
+
+import torch
+from lhotse import CutSet, load_manifest_lazy
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "manifest",
+        type=Path,
+        help="Path to the manifest file",
+    )
+
+    parser.add_argument(
+        "cmvn",
+        type=Path,
+        help="Path to the cmvn.json",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+
+    manifest = args.manifest
+    logging.info(
+        f"Computing fbank mean and std for {manifest} and saving to {args.cmvn}"
+    )
+
+    assert manifest.is_file(), f"{manifest} does not exist"
+    cut_set = load_manifest_lazy(manifest)
+    assert isinstance(cut_set, CutSet), type(cut_set)
+
+    feat_dim = cut_set[0].features.num_features
+    num_frames = 0
+    s = 0
+    sq = 0
+    for c in cut_set:
+        f = torch.from_numpy(c.load_features())
+        num_frames += f.shape[0]
+        s += f.sum()
+        sq += f.square().sum()
+
+    fbank_mean = s / (num_frames * feat_dim)
+    fbank_var = sq / (num_frames * feat_dim) - fbank_mean * fbank_mean
+    print("fbank var", fbank_var)
+    fbank_std = fbank_var.sqrt()
+    with open(args.cmvn, "w") as f:
+        json.dump({"fbank_mean": fbank_mean.item(), "fbank_std": fbank_std.item()}, f)
+        f.write("\n")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    main()

egs/baker_zh/TTS/local/convert_text_to_tokens.py

@@ -0,0 +1,119 @@
+#!/usr/bin/env python3
+
+import argparse
+import re
+from typing import List
+
+import jieba
+from lhotse import load_manifest
+from pypinyin import lazy_pinyin, load_phrases_dict, Style
+
+load_phrases_dict(
+    {
+        "行长": [["hang2"], ["zhang3"]],
+        "银行行长": [["yin2"], ["hang2"], ["hang2"], ["zhang3"]],
+    }
+)
+
+whiter_space_re = re.compile(r"\s+")
+
+punctuations_re = [
+    (re.compile(x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("，", ","),
+        ("。", "."),
+        ("！", "!"),
+        ("？", "?"),
+        ("“", '"'),
+        ("”", '"'),
+        ("‘", "'"),
+        ("’", "'"),
+        ("：", ":"),
+        ("、", ","),
+    ]
+]
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        "--in-file",
+        type=str,
+        required=True,
+        help="Input cutset.",
+    )
+
+    parser.add_argument(
+        "--out-file",
+        type=str,
+        required=True,
+        help="Output cutset.",
+    )
+
+    return parser
+
+
+def normalize_white_spaces(text):
+    return whiter_space_re.sub(" ", text)
+
+
+def normalize_punctuations(text):
+    for regex, replacement in punctuations_re:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def split_text(text: str) -> List[str]:
+    """
+    Example input:  '你好呀，You are 一个好人。   去银行存钱？How about    you?'
+    Example output: ['你好', '呀', ',', 'you are', '一个', '好人', '.', '去', '银行', '存钱', '?', 'how about you', '?']
+    """
+    text = text.lower()
+    text = normalize_white_spaces(text)
+    text = normalize_punctuations(text)
+    ans = []
+
+    for seg in jieba.cut(text):
+        if seg in ",.!?:\"'":
+            ans.append(seg)
+        elif seg == " " and len(ans) > 0:
+            if ord("a") <= ord(ans[-1][-1]) <= ord("z"):
+                ans[-1] += seg
+        elif ord("a") <= ord(seg[0]) <= ord("z"):
+            if len(ans) == 0:
+                ans.append(seg)
+                continue
+
+            if ans[-1][-1] == " ":
+                ans[-1] += seg
+                continue
+
+            ans.append(seg)
+        else:
+            ans.append(seg)
+
+    ans = [s.strip() for s in ans]
+    return ans
+
+
+def main():
+    args = get_parser().parse_args()
+    cuts = load_manifest(args.in_file)
+    for c in cuts:
+        assert len(c.supervisions) == 1, (len(c.supervisions), c.supervisions)
+        text = c.supervisions[0].normalized_text
+
+        text_list = split_text(text)
+        tokens = lazy_pinyin(text_list, style=Style.TONE3, tone_sandhi=True)
+
+        c.supervisions[0].tokens = tokens
+
+    cuts.to_file(args.out_file)
+
+    print(f"saved to {args.out_file}")
+
+
+if __name__ == "__main__":
+    main()

egs/baker_zh/TTS/local/fbank.py

@@ -0,0 +1 @@
+../matcha/fbank.py

egs/baker_zh/TTS/local/generate_tokens.py

@@ -46,9 +46,13 @@ def generate_token_list() -> List[str]:
     ans = list(token_set)
     ans.sort()
 
+    punctuations = list(",.!?:\"'")
+    ans = punctuations + ans
+
     # use ID 0 for blank
-    # We use blank for padding
+    # Use ID 1 of _ for padding
     ans.insert(0, " ")
+    ans.insert(1, "_")  #
 
     return ans
 

egs/baker_zh/TTS/local/validate_manifest.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+# Copyright    2022-2023  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                       Zengwei Yao)
+#
+# 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 script checks the following assumptions of the generated manifest:
+
+- Single supervision per cut
+
+We will add more checks later if needed.
+
+Usage example:
+
+    python3 ./local/validate_manifest.py \
+            ./data/spectrogram/baker_zh_cuts_all.jsonl.gz
+
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+from lhotse import CutSet, load_manifest_lazy
+from lhotse.dataset.speech_synthesis import validate_for_tts
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "manifest",
+        type=Path,
+        help="Path to the manifest file",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+
+    manifest = args.manifest
+    logging.info(f"Validating {manifest}")
+
+    assert manifest.is_file(), f"{manifest} does not exist"
+    cut_set = load_manifest_lazy(manifest)
+    assert isinstance(cut_set, CutSet), type(cut_set)
+
+    validate_for_tts(cut_set)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    main()

egs/baker_zh/TTS/matcha/tokenizer.py

@@ -1 +0,0 @@
-../../../ljspeech/TTS/matcha/tokenizer.py

egs/baker_zh/TTS/matcha/tokenizer.py

@@ -0,0 +1,119 @@
+# Copyright      2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+import logging
+from typing import Dict, List
+
+import tacotron_cleaner.cleaners
+
+try:
+    from piper_phonemize import phonemize_espeak
+except Exception as ex:
+    raise RuntimeError(
+        f"{ex}\nPlease run\n"
+        "pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html"
+    )
+
+from utils import intersperse
+
+
+# This tokenizer supports both English and Chinese.
+# We assume you have used
+# ../local/convert_text_to_tokens.py
+# to process your text
+class Tokenizer(object):
+    def __init__(self, tokens: str):
+        """
+        Args:
+            tokens: the file that maps tokens to ids
+        """
+        # Parse token file
+        self.token2id: Dict[str, int] = {}
+        with open(tokens, "r", encoding="utf-8") as f:
+            for line in f.readlines():
+                info = line.rstrip().split()
+                if len(info) == 1:
+                    # case of space
+                    token = " "
+                    id = int(info[0])
+                else:
+                    token, id = info[0], int(info[1])
+                assert token not in self.token2id, token
+                self.token2id[token] = id
+
+        # Refer to https://github.com/rhasspy/piper/blob/master/TRAINING.md
+        self.pad_id = self.token2id["_"]  # padding
+        self.space_id = self.token2id[" "]  # word separator (whitespace)
+
+        self.vocab_size = len(self.token2id)
+
+    def texts_to_token_ids(
+        self,
+        sentence_list: List[List[str]],
+        intersperse_blank: bool = True,
+        lang: str = "en-us",
+    ) -> List[List[int]]:
+        """
+        Args:
+          sentence_list:
+            A list of sentences.
+          intersperse_blank:
+            Whether to intersperse blanks in the token sequence.
+          lang:
+            Language argument passed to phonemize_espeak().
+
+        Returns:
+          Return a list of token id list [utterance][token_id]
+        """
+        token_ids_list = []
+
+        for sentence in sentence_list:
+            tokens_list = []
+            for word in sentence:
+                if word in self.token2id:
+                    tokens_list.append(word)
+                    continue
+
+                tmp_tokens_list = phonemize_espeak(word, lang)
+                for t in tmp_tokens_list:
+                    tokens_list.extend(t)
+
+            token_ids = []
+            for t in tokens_list:
+                if t not in self.token2id:
+                    logging.warning(f"Skip OOV {t}")
+                    continue
+
+                if t == " " and len(token_ids) > 0 and token_ids[-1] == self.space_id:
+                    continue
+
+                token_ids.append(self.token2id[t])
+
+            if intersperse_blank:
+                token_ids = intersperse(token_ids, self.pad_id)
+
+            token_ids_list.append(token_ids)
+
+        return token_ids_list
+
+
+def test_tokenizer():
+    import jieba
+    from pypinyin import lazy_pinyin, Style
+
+    tokenizer = Tokenizer("data/tokens.txt")
+    text1 = "今天is Monday, tomorrow is 星期二"
+    text2 = "你好吗? 我很好, how about you?"
+
+    text1 = list(jieba.cut(text1))
+    text2 = list(jieba.cut(text2))
+    tokens1 = lazy_pinyin(text1, style=Style.TONE3, tone_sandhi=True)
+    tokens2 = lazy_pinyin(text2, style=Style.TONE3, tone_sandhi=True)
+    print(tokens1)
+    print(tokens2)
+
+    ids = tokenizer.texts_to_token_ids([tokens1, tokens2])
+    print(ids)
+
+
+if __name__ == "__main__":
+    test_tokenizer()

egs/baker_zh/TTS/matcha/train.py

@@ -0,0 +1,717 @@
+#!/usr/bin/env python3
+# Copyright         2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+
+import argparse
+import json
+import logging
+from pathlib import Path
+from shutil import copyfile
+from typing import Any, Dict, Optional, Union
+
+import k2
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from lhotse.utils import fix_random_seed
+from model import fix_len_compatibility
+from models.matcha_tts import MatchaTTS
+from tokenizer import Tokenizer
+from torch.cuda.amp import GradScaler, autocast
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import Optimizer
+from torch.utils.tensorboard import SummaryWriter
+from tts_datamodule import BakerZhTtsDataModule
+from utils import MetricsTracker
+
+from icefall.checkpoint import load_checkpoint, save_checkpoint
+from icefall.dist import cleanup_dist, setup_dist
+from icefall.env import get_env_info
+from icefall.utils import AttributeDict, setup_logger, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--world-size",
+        type=int,
+        default=1,
+        help="Number of GPUs for DDP training.",
+    )
+
+    parser.add_argument(
+        "--master-port",
+        type=int,
+        default=12335,
+        help="Master port to use for DDP training.",
+    )
+
+    parser.add_argument(
+        "--tensorboard",
+        type=str2bool,
+        default=True,
+        help="Should various information be logged in tensorboard.",
+    )
+
+    parser.add_argument(
+        "--num-epochs",
+        type=int,
+        default=1000,
+        help="Number of epochs to train.",
+    )
+
+    parser.add_argument(
+        "--start-epoch",
+        type=int,
+        default=1,
+        help="""Resume training from this epoch. It should be positive.
+        If larger than 1, it will load checkpoint from
+        exp-dir/epoch-{start_epoch-1}.pt
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=Path,
+        default="matcha/exp",
+        help="""The experiment dir.
+        It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        default="data/tokens.txt",
+        help="""Path to vocabulary.""",
+    )
+
+    parser.add_argument(
+        "--cmvn",
+        type=str,
+        default="data/fbank/cmvn.json",
+        help="""Path to vocabulary.""",
+    )
+
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="The seed for random generators intended for reproducibility",
+    )
+
+    parser.add_argument(
+        "--save-every-n",
+        type=int,
+        default=10,
+        help="""Save checkpoint after processing this number of epochs"
+        periodically. We save checkpoint to exp-dir/ whenever
+        params.cur_epoch % save_every_n == 0. The checkpoint filename
+        has the form: f'exp-dir/epoch-{params.cur_epoch}.pt'.
+        Since it will take around 1000 epochs, we suggest using a large
+        save_every_n to save disk space.
+        """,
+    )
+
+    parser.add_argument(
+        "--use-fp16",
+        type=str2bool,
+        default=False,
+        help="Whether to use half precision training.",
+    )
+
+    return parser
+
+
+def get_data_statistics():
+    return AttributeDict(
+        {
+            "mel_mean": 0,
+            "mel_std": 1,
+        }
+    )
+
+
+def _get_data_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "name": "baker-zh",
+            "train_filelist_path": "./filelists/ljs_audio_text_train_filelist.txt",
+            "valid_filelist_path": "./filelists/ljs_audio_text_val_filelist.txt",
+            #  "batch_size": 64,
+            #  "num_workers": 1,
+            #  "pin_memory": False,
+            "cleaners": ["english_cleaners2"],
+            "add_blank": True,
+            "n_spks": 1,
+            "n_fft": 1024,
+            "n_feats": 80,
+            "sampling_rate": 22050,
+            "hop_length": 256,
+            "win_length": 1024,
+            "f_min": 0,
+            "f_max": 8000,
+            "seed": 1234,
+            "load_durations": False,
+            "data_statistics": get_data_statistics(),
+        }
+    )
+    return params
+
+
+def _get_model_params() -> AttributeDict:
+    n_feats = 80
+    filter_channels_dp = 256
+    encoder_params_p_dropout = 0.1
+    params = AttributeDict(
+        {
+            "n_spks": 1,  # for baker-zh.
+            "spk_emb_dim": 64,
+            "n_feats": n_feats,
+            "out_size": None,  # or use 172
+            "prior_loss": True,
+            "use_precomputed_durations": False,
+            "data_statistics": get_data_statistics(),
+            "encoder": AttributeDict(
+                {
+                    "encoder_type": "RoPE Encoder",  # not used
+                    "encoder_params": AttributeDict(
+                        {
+                            "n_feats": n_feats,
+                            "n_channels": 192,
+                            "filter_channels": 768,
+                            "filter_channels_dp": filter_channels_dp,
+                            "n_heads": 2,
+                            "n_layers": 6,
+                            "kernel_size": 3,
+                            "p_dropout": encoder_params_p_dropout,
+                            "spk_emb_dim": 64,
+                            "n_spks": 1,
+                            "prenet": True,
+                        }
+                    ),
+                    "duration_predictor_params": AttributeDict(
+                        {
+                            "filter_channels_dp": filter_channels_dp,
+                            "kernel_size": 3,
+                            "p_dropout": encoder_params_p_dropout,
+                        }
+                    ),
+                }
+            ),
+            "decoder": AttributeDict(
+                {
+                    "channels": [256, 256],
+                    "dropout": 0.05,
+                    "attention_head_dim": 64,
+                    "n_blocks": 1,
+                    "num_mid_blocks": 2,
+                    "num_heads": 2,
+                    "act_fn": "snakebeta",
+                }
+            ),
+            "cfm": AttributeDict(
+                {
+                    "name": "CFM",
+                    "solver": "euler",
+                    "sigma_min": 1e-4,
+                }
+            ),
+            "optimizer": AttributeDict(
+                {
+                    "lr": 1e-4,
+                    "weight_decay": 0.0,
+                }
+            ),
+        }
+    )
+
+    return params
+
+
+def get_params():
+    params = AttributeDict(
+        {
+            "model_args": _get_model_params(),
+            "data_args": _get_data_params(),
+            "best_train_loss": float("inf"),
+            "best_valid_loss": float("inf"),
+            "best_train_epoch": -1,
+            "best_valid_epoch": -1,
+            "batch_idx_train": -1,  # 0
+            "log_interval": 10,
+            "valid_interval": 1500,
+            "env_info": get_env_info(),
+        }
+    )
+    return params
+
+
+def get_model(params):
+    m = MatchaTTS(**params.model_args)
+    return m
+
+
+def load_checkpoint_if_available(
+    params: AttributeDict, model: nn.Module
+) -> Optional[Dict[str, Any]]:
+    """Load checkpoint from file.
+
+    If params.start_epoch is larger than 1, it will load the checkpoint from
+    `params.start_epoch - 1`.
+
+    Apart from loading state dict for `model` and `optimizer` it also updates
+    `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
+    and `best_valid_loss` in `params`.
+
+    Args:
+      params:
+        The return value of :func:`get_params`.
+      model:
+        The training model.
+    Returns:
+      Return a dict containing previously saved training info.
+    """
+    if params.start_epoch > 1:
+        filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
+    else:
+        return None
+
+    assert filename.is_file(), f"{filename} does not exist!"
+
+    saved_params = load_checkpoint(filename, model=model)
+
+    keys = [
+        "best_train_epoch",
+        "best_valid_epoch",
+        "batch_idx_train",
+        "best_train_loss",
+        "best_valid_loss",
+    ]
+    for k in keys:
+        params[k] = saved_params[k]
+
+    return saved_params
+
+
+def prepare_input(batch: dict, tokenizer: Tokenizer, device: torch.device, params):
+    """Parse batch data"""
+    mel_mean = params.data_args.data_statistics.mel_mean
+    mel_std_inv = 1 / params.data_args.data_statistics.mel_std
+    for i in range(batch["features"].shape[0]):
+        n = batch["features_lens"][i]
+        batch["features"][i : i + 1, :n, :] = (
+            batch["features"][i : i + 1, :n, :] - mel_mean
+        ) * mel_std_inv
+        batch["features"][i : i + 1, n:, :] = 0
+
+    audio = batch["audio"].to(device)
+    features = batch["features"].to(device)
+    audio_lens = batch["audio_lens"].to(device)
+    features_lens = batch["features_lens"].to(device)
+    tokens = batch["tokens"]
+
+    tokens = tokenizer.tokens_to_token_ids(tokens, intersperse_blank=True)
+    tokens = k2.RaggedTensor(tokens)
+    row_splits = tokens.shape.row_splits(1)
+    tokens_lens = row_splits[1:] - row_splits[:-1]
+    tokens = tokens.to(device)
+    tokens_lens = tokens_lens.to(device)
+    # a tensor of shape (B, T)
+    tokens = tokens.pad(mode="constant", padding_value=tokenizer.pad_id)
+
+    max_feature_length = fix_len_compatibility(features.shape[1])
+    if max_feature_length > features.shape[1]:
+        pad = max_feature_length - features.shape[1]
+        features = torch.nn.functional.pad(features, (0, 0, 0, pad))
+
+        #  features_lens[features_lens.argmax()] += pad
+
+    return audio, audio_lens, features, features_lens.long(), tokens, tokens_lens.long()
+
+
+def compute_validation_loss(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    tokenizer: Tokenizer,
+    valid_dl: torch.utils.data.DataLoader,
+    world_size: int = 1,
+    rank: int = 0,
+) -> MetricsTracker:
+    """Run the validation process."""
+    model.eval()
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+    get_losses = model.module.get_losses if isinstance(model, DDP) else model.get_losses
+
+    # used to summary the stats over iterations
+    tot_loss = MetricsTracker()
+
+    with torch.no_grad():
+        for batch_idx, batch in enumerate(valid_dl):
+            (
+                audio,
+                audio_lens,
+                features,
+                features_lens,
+                tokens,
+                tokens_lens,
+            ) = prepare_input(batch, tokenizer, device, params)
+
+            losses = get_losses(
+                {
+                    "x": tokens,
+                    "x_lengths": tokens_lens,
+                    "y": features.permute(0, 2, 1),
+                    "y_lengths": features_lens,
+                    "spks": None,  # should change it for multi-speakers
+                    "durations": None,
+                }
+            )
+
+            batch_size = len(batch["tokens"])
+
+            loss_info = MetricsTracker()
+            loss_info["samples"] = batch_size
+
+            s = 0
+
+            for key, value in losses.items():
+                v = value.detach().item()
+                loss_info[key] = v * batch_size
+                s += v * batch_size
+
+            loss_info["tot_loss"] = s
+
+            # summary stats
+            tot_loss = tot_loss + loss_info
+
+    if world_size > 1:
+        tot_loss.reduce(device)
+
+    loss_value = tot_loss["tot_loss"] / tot_loss["samples"]
+    if loss_value < params.best_valid_loss:
+        params.best_valid_epoch = params.cur_epoch
+        params.best_valid_loss = loss_value
+
+    return tot_loss
+
+
+def train_one_epoch(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    tokenizer: Tokenizer,
+    optimizer: Optimizer,
+    train_dl: torch.utils.data.DataLoader,
+    valid_dl: torch.utils.data.DataLoader,
+    scaler: GradScaler,
+    tb_writer: Optional[SummaryWriter] = None,
+    world_size: int = 1,
+    rank: int = 0,
+) -> None:
+    """Train the model for one epoch.
+
+    The training loss from the mean of all frames is saved in
+    `params.train_loss`. It runs the validation process every
+    `params.valid_interval` batches.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The model for training.
+      optimizer:
+        The optimizer.
+      train_dl:
+        Dataloader for the training dataset.
+      valid_dl:
+        Dataloader for the validation dataset.
+      scaler:
+        The scaler used for mix precision training.
+      tb_writer:
+        Writer to write log messages to tensorboard.
+    """
+    model.train()
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+    get_losses = model.module.get_losses if isinstance(model, DDP) else model.get_losses
+
+    # used to track the stats over iterations in one epoch
+    tot_loss = MetricsTracker()
+
+    saved_bad_model = False
+
+    def save_bad_model(suffix: str = ""):
+        save_checkpoint(
+            filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt",
+            model=model,
+            params=params,
+            optimizer=optimizer,
+            scaler=scaler,
+            rank=0,
+        )
+
+    for batch_idx, batch in enumerate(train_dl):
+        params.batch_idx_train += 1
+        # audio: (N, T), float32
+        # features: (N, T, C), float32
+        # audio_lens, (N,), int32
+        # features_lens, (N,), int32
+        # tokens: List[List[str]], len(tokens) == N
+
+        batch_size = len(batch["tokens"])
+
+        (
+            audio,
+            audio_lens,
+            features,
+            features_lens,
+            tokens,
+            tokens_lens,
+        ) = prepare_input(batch, tokenizer, device, params)
+        try:
+            with autocast(enabled=params.use_fp16):
+                losses = get_losses(
+                    {
+                        "x": tokens,
+                        "x_lengths": tokens_lens,
+                        "y": features.permute(0, 2, 1),
+                        "y_lengths": features_lens,
+                        "spks": None,  # should change it for multi-speakers
+                        "durations": None,
+                    }
+                )
+
+                loss = sum(losses.values())
+
+                scaler.scale(loss).backward()
+                scaler.step(optimizer)
+                scaler.update()
+                optimizer.zero_grad()
+
+                loss_info = MetricsTracker()
+                loss_info["samples"] = batch_size
+
+                s = 0
+
+                for key, value in losses.items():
+                    v = value.detach().item()
+                    loss_info[key] = v * batch_size
+                    s += v * batch_size
+
+                loss_info["tot_loss"] = s
+
+                tot_loss = tot_loss + loss_info
+        except:  # noqa
+            save_bad_model()
+            raise
+
+        if params.batch_idx_train % 100 == 0 and params.use_fp16:
+            # If the grad scale was less than 1, try increasing it.
+            # The _growth_interval of the grad scaler is configurable,
+            # but we can't configure it to have different
+            # behavior depending on the current grad scale.
+            cur_grad_scale = scaler._scale.item()
+
+            if cur_grad_scale < 8.0 or (
+                cur_grad_scale < 32.0 and params.batch_idx_train % 400 == 0
+            ):
+                scaler.update(cur_grad_scale * 2.0)
+            if cur_grad_scale < 0.01:
+                if not saved_bad_model:
+                    save_bad_model(suffix="-first-warning")
+                    saved_bad_model = True
+                logging.warning(f"Grad scale is small: {cur_grad_scale}")
+            if cur_grad_scale < 1.0e-05:
+                save_bad_model()
+                raise RuntimeError(
+                    f"grad_scale is too small, exiting: {cur_grad_scale}"
+                )
+
+        if params.batch_idx_train % params.log_interval == 0:
+            cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
+
+            logging.info(
+                f"Epoch {params.cur_epoch}, batch {batch_idx}, "
+                f"global_batch_idx: {params.batch_idx_train}, "
+                f"batch size: {batch_size}, "
+                f"loss[{loss_info}], tot_loss[{tot_loss}], "
+                + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
+            )
+
+            if tb_writer is not None:
+                loss_info.write_summary(
+                    tb_writer, "train/current_", params.batch_idx_train
+                )
+                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                if params.use_fp16:
+                    tb_writer.add_scalar(
+                        "train/grad_scale", cur_grad_scale, params.batch_idx_train
+                    )
+
+        if params.batch_idx_train % params.valid_interval == 1:
+            logging.info("Computing validation loss")
+            valid_info = compute_validation_loss(
+                params=params,
+                model=model,
+                tokenizer=tokenizer,
+                valid_dl=valid_dl,
+                world_size=world_size,
+                rank=rank,
+            )
+            model.train()
+            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
+            logging.info(
+                "Maximum memory allocated so far is "
+                f"{torch.cuda.max_memory_allocated()//1000000}MB"
+            )
+            if tb_writer is not None:
+                valid_info.write_summary(
+                    tb_writer, "train/valid_", params.batch_idx_train
+                )
+
+    loss_value = tot_loss["tot_loss"] / tot_loss["samples"]
+    params.train_loss = loss_value
+    if params.train_loss < params.best_train_loss:
+        params.best_train_epoch = params.cur_epoch
+        params.best_train_loss = params.train_loss
+
+
+def run(rank, world_size, args):
+    params = get_params()
+    params.update(vars(args))
+
+    fix_random_seed(params.seed)
+    if world_size > 1:
+        setup_dist(rank, world_size, params.master_port)
+
+    setup_logger(f"{params.exp_dir}/log/log-train")
+    logging.info("Training started")
+
+    if args.tensorboard and rank == 0:
+        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
+    else:
+        tb_writer = None
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", rank)
+    logging.info(f"Device: {device}")
+
+    tokenizer = Tokenizer(params.tokens)
+    params.pad_id = tokenizer.pad_id
+    params.vocab_size = tokenizer.vocab_size
+    params.model_args.n_vocab = params.vocab_size
+
+    with open(params.cmvn) as f:
+        stats = json.load(f)
+        params.data_args.data_statistics.mel_mean = stats["fbank_mean"]
+        params.data_args.data_statistics.mel_std = stats["fbank_std"]
+
+        params.model_args.data_statistics.mel_mean = stats["fbank_mean"]
+        params.model_args.data_statistics.mel_std = stats["fbank_std"]
+
+    logging.info(params)
+    print(params)
+
+    logging.info("About to create model")
+    model = get_model(params)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of parameters: {num_param}")
+
+    assert params.start_epoch > 0, params.start_epoch
+    checkpoints = load_checkpoint_if_available(params=params, model=model)
+
+    model.to(device)
+
+    if world_size > 1:
+        logging.info("Using DDP")
+        model = DDP(model, device_ids=[rank], find_unused_parameters=True)
+
+    optimizer = torch.optim.Adam(model.parameters(), **params.model_args.optimizer)
+
+    logging.info("About to create datamodule")
+
+    baker_zh = BakerZhTtsDataModule(args)
+
+    train_cuts = baker_zh.train_cuts()
+    train_dl = baker_zh.train_dataloaders(train_cuts)
+
+    valid_cuts = baker_zh.valid_cuts()
+    valid_dl = baker_zh.valid_dataloaders(valid_cuts)
+
+    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    if checkpoints and "grad_scaler" in checkpoints:
+        logging.info("Loading grad scaler state dict")
+        scaler.load_state_dict(checkpoints["grad_scaler"])
+
+    for epoch in range(params.start_epoch, params.num_epochs + 1):
+        logging.info(f"Start epoch {epoch}")
+        fix_random_seed(params.seed + epoch - 1)
+        if "sampler" in train_dl:
+            train_dl.sampler.set_epoch(epoch - 1)
+
+        params.cur_epoch = epoch
+
+        if tb_writer is not None:
+            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
+
+        train_one_epoch(
+            params=params,
+            model=model,
+            tokenizer=tokenizer,
+            optimizer=optimizer,
+            train_dl=train_dl,
+            valid_dl=valid_dl,
+            scaler=scaler,
+            tb_writer=tb_writer,
+            world_size=world_size,
+            rank=rank,
+        )
+
+        if epoch % params.save_every_n == 0 or epoch == params.num_epochs:
+            filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
+            save_checkpoint(
+                filename=filename,
+                params=params,
+                model=model,
+                optimizer=optimizer,
+                scaler=scaler,
+                rank=rank,
+            )
+            if rank == 0:
+                if params.best_train_epoch == params.cur_epoch:
+                    best_train_filename = params.exp_dir / "best-train-loss.pt"
+                    copyfile(src=filename, dst=best_train_filename)
+
+                if params.best_valid_epoch == params.cur_epoch:
+                    best_valid_filename = params.exp_dir / "best-valid-loss.pt"
+                    copyfile(src=filename, dst=best_valid_filename)
+
+    logging.info("Done!")
+
+    if world_size > 1:
+        torch.distributed.barrier()
+        cleanup_dist()
+
+
+def main():
+    parser = get_parser()
+    BakerZhTtsDataModule.add_arguments(parser)
+    args = parser.parse_args()
+
+    world_size = args.world_size
+    assert world_size >= 1
+    if world_size > 1:
+        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
+    else:
+        run(rank=0, world_size=1, args=args)
+
+
+if __name__ == "__main__":
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+    main()

egs/baker_zh/TTS/matcha/tts_datamodule.py

@@ -0,0 +1,340 @@
+# Copyright      2021  Piotr Żelasko
+# Copyright      2022-2023  Xiaomi Corporation     (Authors: Mingshuang Luo,
+#                                                            Zengwei Yao)
+#
+# 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 functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import torch
+from fbank import MatchaFbank, MatchaFbankConfig
+from lhotse import CutSet, load_manifest_lazy
+from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    PrecomputedFeatures,
+    SimpleCutSampler,
+    SpeechSynthesisDataset,
+)
+from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
+    AudioSamples,
+    OnTheFlyFeatures,
+)
+from lhotse.utils import fix_random_seed
+from torch.utils.data import DataLoader
+
+from icefall.utils import str2bool
+
+
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+
+class BakerZhTtsDataModule:
+    """
+    DataModule for tts experiments.
+    It assumes there is always one train and valid dataloader,
+    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
+    and test-other).
+
+    It contains all the common data pipeline modules used in ASR
+    experiments, e.g.:
+    - dynamic batch size,
+    - bucketing samplers,
+    - cut concatenation,
+    - on-the-fly feature extraction
+
+    This class should be derived for specific corpora used in ASR tasks.
+    """
+
+    def __init__(self, args: argparse.Namespace):
+        self.args = args
+
+    @classmethod
+    def add_arguments(cls, parser: argparse.ArgumentParser):
+        group = parser.add_argument_group(
+            title="TTS data related options",
+            description="These options are used for the preparation of "
+            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
+            "effective batch sizes, sampling strategies, applied data "
+            "augmentations, etc.",
+        )
+
+        group.add_argument(
+            "--manifest-dir",
+            type=Path,
+            default=Path("data/fbank"),
+            help="Path to directory with train/valid/test cuts.",
+        )
+        group.add_argument(
+            "--max-duration",
+            type=int,
+            default=200.0,
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
+        )
+        group.add_argument(
+            "--bucketing-sampler",
+            type=str2bool,
+            default=True,
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
+        )
+        group.add_argument(
+            "--num-buckets",
+            type=int,
+            default=30,
+            help="The number of buckets for the 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,
+            default=True,
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
+        )
+        group.add_argument(
+            "--drop-last",
+            type=str2bool,
+            default=True,
+            help="Whether to drop last batch. Used by sampler.",
+        )
+        group.add_argument(
+            "--return-cuts",
+            type=str2bool,
+            default=False,
+            help="When enabled, each batch will have the "
+            "field: batch['cut'] with the cuts that "
+            "were used to construct it.",
+        )
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=2,
+            help="The number of training dataloader workers that "
+            "collect the batches.",
+        )
+
+        group.add_argument(
+            "--input-strategy",
+            type=str,
+            default="PrecomputedFeatures",
+            help="AudioSamples or PrecomputedFeatures",
+        )
+
+    def train_dataloaders(
+        self,
+        cuts_train: CutSet,
+        sampler_state_dict: Optional[Dict[str, Any]] = None,
+    ) -> DataLoader:
+        """
+        Args:
+          cuts_train:
+            CutSet for training.
+          sampler_state_dict:
+            The state dict for the training sampler.
+        """
+        logging.info("About to create train dataset")
+        train = SpeechSynthesisDataset(
+            return_text=False,
+            return_tokens=True,
+            feature_input_strategy=eval(self.args.input_strategy)(),
+            return_cuts=self.args.return_cuts,
+        )
+
+        if self.args.on_the_fly_feats:
+            sampling_rate = 22050
+            config = MatchaFbankConfig(
+                n_fft=1024,
+                n_mels=80,
+                sampling_rate=sampling_rate,
+                hop_length=256,
+                win_length=1024,
+                f_min=0,
+                f_max=8000,
+            )
+            train = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=OnTheFlyFeatures(MatchaFbank(config)),
+                return_cuts=self.args.return_cuts,
+            )
+
+        if self.args.bucketing_sampler:
+            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,
+                buffer_size=self.args.num_buckets * 2000,
+                shuffle_buffer_size=self.args.num_buckets * 5000,
+                drop_last=self.args.drop_last,
+            )
+        else:
+            logging.info("Using SimpleCutSampler.")
+            train_sampler = SimpleCutSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+            )
+        logging.info("About to create train dataloader")
+
+        if sampler_state_dict is not None:
+            logging.info("Loading sampler state dict")
+            train_sampler.load_state_dict(sampler_state_dict)
+
+        # 'seed' is derived from the current random state, which will have
+        # previously been set in the main process.
+        seed = torch.randint(0, 100000, ()).item()
+        worker_init_fn = _SeedWorkers(seed)
+
+        train_dl = DataLoader(
+            train,
+            sampler=train_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=True,
+            pin_memory=True,
+            worker_init_fn=worker_init_fn,
+        )
+
+        return train_dl
+
+    def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
+        logging.info("About to create dev dataset")
+        if self.args.on_the_fly_feats:
+            sampling_rate = 22050
+            config = MatchaFbankConfig(
+                n_fft=1024,
+                n_mels=80,
+                sampling_rate=sampling_rate,
+                hop_length=256,
+                win_length=1024,
+                f_min=0,
+                f_max=8000,
+            )
+            validate = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=OnTheFlyFeatures(MatchaFbank(config)),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            validate = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=eval(self.args.input_strategy)(),
+                return_cuts=self.args.return_cuts,
+            )
+        valid_sampler = DynamicBucketingSampler(
+            cuts_valid,
+            max_duration=self.args.max_duration,
+            num_buckets=self.args.num_buckets,
+            shuffle=False,
+        )
+        logging.info("About to create valid dataloader")
+        valid_dl = DataLoader(
+            validate,
+            sampler=valid_sampler,
+            batch_size=None,
+            num_workers=2,
+            persistent_workers=True,
+            pin_memory=True,
+        )
+
+        return valid_dl
+
+    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
+        logging.info("About to create test dataset")
+        if self.args.on_the_fly_feats:
+            sampling_rate = 22050
+            config = MatchaFbankConfig(
+                n_fft=1024,
+                n_mels=80,
+                sampling_rate=sampling_rate,
+                hop_length=256,
+                win_length=1024,
+                f_min=0,
+                f_max=8000,
+            )
+            test = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=OnTheFlyFeatures(MatchaFbank(config)),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            test = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=eval(self.args.input_strategy)(),
+                return_cuts=self.args.return_cuts,
+            )
+        test_sampler = DynamicBucketingSampler(
+            cuts,
+            max_duration=self.args.max_duration,
+            num_buckets=self.args.num_buckets,
+            shuffle=False,
+        )
+        logging.info("About to create test dataloader")
+        test_dl = DataLoader(
+            test,
+            batch_size=None,
+            sampler=test_sampler,
+            num_workers=self.args.num_workers,
+        )
+        return test_dl
+
+    @lru_cache()
+    def train_cuts(self) -> CutSet:
+        logging.info("About to get train cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "baker_zh_cuts_train.jsonl.gz"
+        )
+
+    @lru_cache()
+    def valid_cuts(self) -> CutSet:
+        logging.info("About to get validation cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "baker_zh_cuts_valid.jsonl.gz"
+        )
+
+    @lru_cache()
+    def test_cuts(self) -> CutSet:
+        logging.info("About to get test cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "baker_zh_cuts_test.jsonl.gz"
+        )

egs/baker_zh/TTS/prepare.sh

@@ -82,3 +82,70 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
     python3 ./local/generate_tokens.py --tokens data/tokens.txt
   fi
 fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Generate raw cutset"
+  if [ ! -e data/manifests/baker_zh_cuts_raw.jsonl.gz ]; then
+    lhotse cut simple \
+      -r ./data/manifests/baker_zh_recordings_all.jsonl.gz \
+      -s ./data/manifests/baker_zh_supervisions_all.jsonl.gz \
+      ./data/manifests/baker_zh_cuts_raw.jsonl.gz
+  fi
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Convert text to tokens"
+  if [ ! -e data/manifests/baker_zh_cuts.jsonl.gz ]; then
+    python3 ./local/convert_text_to_tokens.py \
+      --in-file ./data/manifests/baker_zh_cuts_raw.jsonl.gz \
+      --out-file ./data/manifests/baker_zh_cuts.jsonl.gz
+  fi
+fi
+
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  log "Stage 5: Generate fbank (used by ./matcha)"
+  mkdir -p data/fbank
+  if [ ! -e data/fbank/.baker-zh.done ]; then
+    ./local/compute_fbank_baker_zh.py
+    touch data/fbank/.baker-zh.done
+  fi
+
+  if [ ! -e data/fbank/.baker-zh-validated.done ]; then
+    log "Validating data/fbank for baker-zh (used by ./matcha)"
+    python3 ./local/validate_manifest.py \
+      data/fbank/baker_zh_cuts.jsonl.gz
+    touch data/fbank/.baker-zh-validated.done
+  fi
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Split the baker-zh cuts into train, valid and test sets (used by ./matcha)"
+  if [ ! -e data/fbank/.baker_zh_split.done ]; then
+    lhotse subset --last 600 \
+      data/fbank/baker_zh_cuts.jsonl.gz \
+      data/fbank/baker_zh_cuts_validtest.jsonl.gz
+    lhotse subset --first 100 \
+      data/fbank/baker_zh_cuts_validtest.jsonl.gz \
+      data/fbank/baker_zh_cuts_valid.jsonl.gz
+    lhotse subset --last 500 \
+      data/fbank/baker_zh_cuts_validtest.jsonl.gz \
+      data/fbank/baker_zh_cuts_test.jsonl.gz
+
+    rm data/fbank/baker_zh_cuts_validtest.jsonl.gz
+
+    n=$(( $(gunzip -c data/fbank/baker_zh_cuts.jsonl.gz | wc -l) - 600 ))
+
+    lhotse subset --first $n  \
+      data/fbank/baker_zh_cuts.jsonl.gz \
+      data/fbank/baker_zh_cuts_train.jsonl.gz
+
+    touch data/fbank/.baker_zh_split.done
+  fi
+fi
+
+if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
+  log "Stage 6: Compute fbank mean and std (used by ./matcha)"
+  if [ ! -f ./data/fbank/cmvn.json ]; then
+    ./local/compute_fbank_statistics.py ./data/fbank/baker_zh_cuts_train.jsonl.gz ./data/fbank/cmvn.json
+  fi
+fi