switch to piper-phonemize

egs/ljspeech/TTS/.gitignore

@@ -0,0 +1,4 @@
+build
+core.c
+*.so
+my-output*

egs/ljspeech/TTS/local/compute_fbank_ljspeech.py

@@ -0,0 +1,141 @@
+#!/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 LJSpeech 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 lhotse import (
+    CutSet,
+    Fbank,
+    FbankConfig,
+    LilcomChunkyWriter,
+    load_manifest,
+    load_manifest_lazy,
+)
+from lhotse.audio import RecordingSet
+from lhotse.supervision import SupervisionSet
+
+from icefall.utils import get_executor
+
+# 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_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_ljspeech(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}")
+
+    sampling_rate = 22050
+    frame_length = 1024 / sampling_rate  # (in second)
+    frame_shift = 256 / sampling_rate  # (in second)
+
+    prefix = "ljspeech"
+    suffix = "jsonl.gz"
+    partition = "all"
+
+    recordings = load_manifest(
+        src_dir / f"{prefix}_recordings_{partition}.{suffix}", RecordingSet
+    )
+    supervisions = load_manifest(
+        src_dir / f"{prefix}_supervisions_{partition}.{suffix}", SupervisionSet
+    )
+
+    # Differences with matcha-tts
+    # 1. we use pre-emphasis
+    # 2. we remove dc offset
+    # 3. we use a different window
+    # 4. we use a different mel filter bank matrix
+    # 5. we don't normalize features
+    config = FbankConfig(
+        sampling_rate=sampling_rate,
+        frame_length=frame_length,
+        frame_shift=frame_shift,
+        use_fft_mag=True,
+        low_freq=0,
+        high_freq=8000,
+        # should be identical to n_feats in ../matcha/train.py
+        num_filters=80,
+    )
+    extractor = Fbank(config)
+
+    with get_executor() as ex:  # Initialize the executor only once.
+        cuts_filename = f"{prefix}_cuts_{partition}.{suffix}"
+        if (output_dir / cuts_filename).is_file():
+            logging.info(f"{cuts_filename} already exists - skipping.")
+            return
+        logging.info(f"Processing {partition}")
+        cut_set = CutSet.from_manifests(
+            recordings=recordings, supervisions=supervisions
+        )
+
+        cut_set = cut_set.compute_and_store_features(
+            extractor=extractor,
+            storage_path=f"{output_dir}/{prefix}_feats_{partition}",
+            # when an executor is specified, make more partitions
+            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__":
+    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_ljspeech(args.num_jobs)

egs/ljspeech/TTS/local/prepare_tokens_ljspeech.py

@@ -28,17 +28,33 @@ try:
 except ModuleNotFoundError as ex:
     raise RuntimeError(f"{ex}\nPlease run\n  pip install espnet_tts_frontend\n")
 
+import argparse
+
 from lhotse import CutSet, load_manifest
 from piper_phonemize import phonemize_espeak
 
 
-def prepare_tokens_ljspeech():
+def get_parser():
-    output_dir = Path("data/spectrogram")
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--in-out-dir",
+        type=Path,
+        required=True,
+        help="Input and output directory",
+    )
+
+    return parser
+
+
+def prepare_tokens_ljspeech(in_out_dir):
     prefix = "ljspeech"
     suffix = "jsonl.gz"
     partition = "all"
 
-    cut_set = load_manifest(output_dir / f"{prefix}_cuts_{partition}.{suffix}")
+    cut_set = load_manifest(in_out_dir / f"{prefix}_cuts_{partition}.{suffix}")
 
     new_cuts = []
     for cut in cut_set:
@@ -56,11 +72,13 @@ def prepare_tokens_ljspeech():
         new_cuts.append(cut)
 
     new_cut_set = CutSet.from_cuts(new_cuts)
-    new_cut_set.to_file(output_dir / f"{prefix}_cuts_with_tokens_{partition}.{suffix}")
+    new_cut_set.to_file(in_out_dir / f"{prefix}_cuts_with_tokens_{partition}.{suffix}")
 
 
 if __name__ == "__main__":
     formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
     logging.basicConfig(format=formatter, level=logging.INFO)
 
-    prepare_tokens_ljspeech()
+    args = get_parser().parse_args()
+
+    prepare_tokens_ljspeech(args.in_out_dir)

egs/ljspeech/TTS/matcha/models/matcha_tts.py

@@ -71,9 +71,12 @@ class MatchaTTS(torch.nn.Module):  # 🍵
             spk_emb_dim=spk_emb_dim,
         )
 
-        #  self.update_data_statistics(data_statistics)
+        if data_statistics is not None:
-        self.register_buffer("mel_mean", torch.tensor(data_statistics["mel_mean"]))
+            self.register_buffer("mel_mean", torch.tensor(data_statistics["mel_mean"]))
-        self.register_buffer("mel_std", torch.tensor(data_statistics["mel_std"]))
+            self.register_buffer("mel_std", torch.tensor(data_statistics["mel_std"]))
+        else:
+            self.register_buffer("mel_mean", torch.tensor(0.0))
+            self.register_buffer("mel_std", torch.tensor(1.0))
 
     @torch.inference_mode()
     def synthesise(

egs/ljspeech/TTS/matcha/tokenizer.py

@@ -0,0 +1 @@
+../vits/tokenizer.py

egs/ljspeech/TTS/matcha/train.py

@@ -8,20 +8,24 @@ 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 matcha.data.text_mel_datamodule import TextMelDataModule
-from icefall.env import get_env_info
 from matcha.models.matcha_tts import MatchaTTS
+from matcha.tokenizer import Tokenizer
+from matcha.utils.model import fix_len_compatibility
 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 utils2 import MetricsTracker, plot_feature
+from tts_datamodule import LJSpeechTtsDataModule
+from utils2 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
 
 
@@ -30,6 +34,20 @@ def get_parser():
         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,
@@ -64,6 +82,13 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        default="data/tokens.txt",
+        help="""Path to vocabulary.""",
+    )
+
     parser.add_argument(
         "--seed",
         type=int,
@@ -91,20 +116,14 @@ def get_parser():
         help="Whether to use half precision training.",
     )
 
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        default=32,
-    )
-
     return parser
 
 
 def get_data_statistics():
     return AttributeDict(
         {
-            "mel_mean": -5.517028331756592,
+            "mel_mean": 0.0,
-            "mel_std": 2.0643954277038574,
+            "mel_std": 1.0,
         }
     )
 
@@ -141,7 +160,6 @@ def _get_model_params() -> AttributeDict:
     encoder_params_p_dropout = 0.1
     params = AttributeDict(
         {
-            "n_vocab": 178,
             "n_spks": 1,  # for ljspeech.
             "spk_emb_dim": 64,
             "n_feats": n_feats,
@@ -216,8 +234,8 @@ def get_params():
             "best_train_epoch": -1,
             "best_valid_epoch": -1,
             "batch_idx_train": -1,  # 0
-            "log_interval": 50,
+            "log_interval": 10,
-            "valid_interval": 2000,
+            "valid_interval": 1500,
             "env_info": get_env_info(),
         }
     )
@@ -271,9 +289,39 @@ def load_checkpoint_if_available(
     return saved_params
 
 
+def prepare_input(batch: dict, tokenizer: Tokenizer, device: torch.device):
+    """Parse batch data"""
+    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, add_sos=True, add_eos=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, tokens, tokens_lens
+
+
 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,
@@ -281,19 +329,35 @@ def compute_validation_loss(
     """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):
-            for key, value in batch.items():
-                if isinstance(value, torch.Tensor):
-                    batch[key] = value.to(device)
-            losses = model.get_losses(batch)
-            loss = sum(losses.values())
 
-            batch_size = batch["x"].shape[0]
+            (
+                audio,
+                audio_lens,
+                features,
+                features_lens,
+                tokens,
+                tokens_lens,
+            ) = prepare_input(batch, tokenizer, device)
+
+            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
@@ -324,6 +388,7 @@ def compute_validation_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,
@@ -356,6 +421,7 @@ def train_one_epoch(
     """
     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()
@@ -374,20 +440,35 @@ def train_one_epoch(
             params=params,
             optimizer=optimizer,
             scaler=scaler,
-            rank=rank,
+            rank=0,
         )
 
     for batch_idx, batch in enumerate(train_dl):
         params.batch_idx_train += 1
-        for key, value in batch.items():
+        # audio: (N, T), float32
-            if isinstance(value, torch.Tensor):
+        # features: (N, T, C), float32
-                batch[key] = value.to(device)
+        # audio_lens, (N,), int32
+        # features_lens, (N,), int32
+        # tokens: List[List[str]], len(tokens) == N
 
-        batch_size = batch["x"].shape[0]
+        batch_size = len(batch["tokens"])
+
+        audio, audio_lens, features, features_lens, tokens, tokens_lens = prepare_input(
+            batch, tokenizer, device
+        )
 
         try:
             with autocast(enabled=params.use_fp16):
-                losses = model.get_losses(batch)
+                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())
 
@@ -458,6 +539,7 @@ def train_one_epoch(
             valid_info = compute_validation_loss(
                 params=params,
                 model=model,
+                tokenizer=tokenizer,
                 valid_dl=valid_dl,
                 world_size=world_size,
                 rank=rank,
@@ -479,28 +561,31 @@ def train_one_epoch(
         params.best_train_loss = params.train_loss
 
 
-def main():
+def run(rank, world_size, args):
-    parser = get_parser()
-    args = parser.parse_args()
     params = get_params()
-
     params.update(vars(args))
 
-    params.data_args.batch_size = params.batch_size
-    del params.batch_size
-
     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")
-    tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
+
+    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", 0)
+        device = torch.device("cuda", rank)
     logging.info(f"Device: {device}")
-    print(f"Device: {device}")
+
-    print(f"Device: {device}")
+    tokenizer = Tokenizer(params.tokens)
+    params.blank_id = tokenizer.pad_id
+    params.vocab_size = tokenizer.vocab_size
+    params.model_args.n_vocab = params.vocab_size
 
     logging.info(params)
     print(params)
@@ -512,28 +597,35 @@ def main():
     logging.info(f"Number of parameters: {num_param}")
     print(f"Number of parameters: {num_param}")
 
-    logging.info("About to create datamodule")
-    data_module = TextMelDataModule(hparams=params.data_args)
-
     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")
+    ljspeech = LJSpeechTtsDataModule(args)
+
+    train_cuts = ljspeech.train_cuts()
+    train_dl = ljspeech.train_dataloaders(train_cuts)
+
+    valid_cuts = ljspeech.valid_cuts()
+    valid_dl = ljspeech.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"])
 
-    train_dl = data_module.train_dataloader()
-    valid_dl = data_module.val_dataloader()
-
-    rank = 0
-
     for epoch in range(params.start_epoch, params.num_epochs + 1):
         logging.info(f"Start epoch {epoch}")
         fix_random_seed(params.seed + epoch - 1)
+        train_dl.sampler.set_epoch(epoch - 1)
 
         params.cur_epoch = epoch
 
@@ -543,11 +635,14 @@ def main():
         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:
@@ -571,6 +666,23 @@ def main():
 
     logging.info("Done!")
 
+    if world_size > 1:
+        torch.distributed.barrier()
+        cleanup_dist()
+
+
+def main():
+    parser = get_parser()
+    LJSpeechTtsDataModule.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)
+
 
 torch.set_num_threads(1)
 torch.set_num_interop_threads(1)

egs/ljspeech/TTS/matcha/tts_datamodule.py

@@ -0,0 +1,341 @@
+# 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 lhotse import CutSet, Fbank, FbankConfig, load_manifest_lazy
+from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    PrecomputedFeatures,
+    SimpleCutSampler,
+    SpecAugment,
+    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 LJSpeechTtsDataModule:
+    """
+    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 = FbankConfig(
+                sampling_rate=sampling_rate,
+                frame_length=1024 / sampling_rate,  # (in second),
+                frame_shift=256 / sampling_rate,  # (in second)
+                use_fft_mag=True,
+                low_freq=0,
+                high_freq=8000,
+                # should be identical to n_feats in ./train.py
+                num_filters=80,
+            )
+            train = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=OnTheFlyFeatures(Fbank(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=False,
+            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 = FbankConfig(
+                sampling_rate=sampling_rate,
+                frame_length=1024 / sampling_rate,  # (in second),
+                frame_shift=256 / sampling_rate,  # (in second)
+                use_fft_mag=True,
+                low_freq=0,
+                high_freq=8000,
+                # should be identical to n_feats in ./train.py
+                num_filters=80,
+            )
+            validate = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=OnTheFlyFeatures(Fbank(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=False,
+        )
+
+        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 = FbankConfig(
+                sampling_rate=sampling_rate,
+                frame_length=1024 / sampling_rate,  # (in second),
+                frame_shift=256 / sampling_rate,  # (in second)
+                use_fft_mag=True,
+                low_freq=0,
+                high_freq=8000,
+                # should be identical to n_feats in ./train.py
+                num_filters=80,
+            )
+            test = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                feature_input_strategy=OnTheFlyFeatures(Fbank(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 / "ljspeech_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 / "ljspeech_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 / "ljspeech_cuts_test.jsonl.gz"
+        )

egs/ljspeech/TTS/matcha/utils/__init__.py

@@ -3,3 +3,4 @@
 #  from matcha.utils.pylogger import get_pylogger
 #  from matcha.utils.rich_utils import enforce_tags, print_config_tree
 #  from matcha.utils.utils import extras, get_metric_value, task_wrapper
+from matcha.utils.utils import intersperse

egs/ljspeech/TTS/prepare.sh

@@ -5,7 +5,7 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
 
 set -eou pipefail
 
-stage=0
+stage=-1
 stop_stage=100
 
 dl_dir=$PWD/download
@@ -31,7 +31,19 @@ if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
     python3 setup.py build_ext --inplace
     cd ../../
   else
-    log "monotonic_align lib already built"
+    log "monotonic_align lib for vits already built"
+  fi
+
+  if [ ! -f ./matcha/utils/monotonic_align/core.cpython-38-x86_64-linux-gnu.so  ]; then
+    pushd matcha/utils/monotonic_align
+    python3 setup.py build_ext --inplace
+    mv -v matcha/utils/monotonic_align/core.cpython-38-x86_64-linux-gnu.so ./
+    rm -rf matcha
+    rm -rf build
+    rm core.c
+    popd
+  else
+    log "monotonic_align lib for matcha-tts already built"
   fi
 fi
 
@@ -63,7 +75,7 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
 fi
 
 if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
-  log "Stage 2: Compute spectrogram for LJSpeech"
+  log "Stage 2: Compute spectrogram for LJSpeech (used by ./vits)"
   mkdir -p data/spectrogram
   if [ ! -e data/spectrogram/.ljspeech.done ]; then
     ./local/compute_spectrogram_ljspeech.py
@@ -71,7 +83,7 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
   fi
 
   if [ ! -e data/spectrogram/.ljspeech-validated.done ]; then
-    log "Validating data/spectrogram for LJSpeech"
+    log "Validating data/spectrogram for LJSpeech (used by ./vits)"
     python3 ./local/validate_manifest.py \
       data/spectrogram/ljspeech_cuts_all.jsonl.gz
     touch data/spectrogram/.ljspeech-validated.done
@@ -79,13 +91,13 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
 fi
 
 if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
-  log "Stage 3: Prepare phoneme tokens for LJSpeech"
+  log "Stage 3: Prepare phoneme tokens for LJSpeech (used by ./vits)"
   # We assume you have installed piper_phonemize and espnet_tts_frontend.
   # If not, please install them with:
   #   - piper_phonemize: pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html,
   #   - espnet_tts_frontend, `pip install espnet_tts_frontend`, refer to https://github.com/espnet/espnet_tts_frontend/
   if [ ! -e data/spectrogram/.ljspeech_with_token.done ]; then
-    ./local/prepare_tokens_ljspeech.py
+    ./local/prepare_tokens_ljspeech.py --in-out-dir ./data/spectrogram
     mv data/spectrogram/ljspeech_cuts_with_tokens_all.jsonl.gz \
       data/spectrogram/ljspeech_cuts_all.jsonl.gz
     touch data/spectrogram/.ljspeech_with_token.done
@@ -93,7 +105,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
 fi
 
 if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
-  log "Stage 4: Split the LJSpeech cuts into train, valid and test sets"
+  log "Stage 4: Split the LJSpeech cuts into train, valid and test sets (used by vits)"
   if [ ! -e data/spectrogram/.ljspeech_split.done ]; then
     lhotse subset --last 600 \
       data/spectrogram/ljspeech_cuts_all.jsonl.gz \
@@ -126,3 +138,56 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
     ./local/prepare_token_file.py --tokens data/tokens.txt
   fi
 fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Generate fbank (used by ./matcha)"
+  mkdir -p data/fbank
+  if [ ! -e data/fbank/.ljspeech.done ]; then
+    ./local/compute_fbank_ljspeech.py
+    touch data/fbank/.ljspeech.done
+  fi
+
+  if [ ! -e data/fbank/.ljspeech-validated.done ]; then
+    log "Validating data/fbank for LJSpeech (used by ./matcha)"
+    python3 ./local/validate_manifest.py \
+      data/fbank/ljspeech_cuts_all.jsonl.gz
+    touch data/fbank/.ljspeech-validated.done
+  fi
+fi
+
+if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
+  log "Stage 7: Prepare phoneme tokens for LJSpeech (used by ./matcha)"
+  # We assume you have installed piper_phonemize and espnet_tts_frontend.
+  # If not, please install them with:
+  #   - piper_phonemize: pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html,
+  #   - espnet_tts_frontend, `pip install espnet_tts_frontend`, refer to https://github.com/espnet/espnet_tts_frontend/
+  if [ ! -e data/fbank/.ljspeech_with_token.done ]; then
+    ./local/prepare_tokens_ljspeech.py --in-out-dir ./data/fbank
+    mv data/fbank/ljspeech_cuts_with_tokens_all.jsonl.gz \
+      data/fbank/ljspeech_cuts_all.jsonl.gz
+    touch data/fbank/.ljspeech_with_token.done
+  fi
+fi
+
+if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
+  log "Stage 8: Split the LJSpeech cuts into train, valid and test sets (used by ./matcha)"
+  if [ ! -e data/fbank/.ljspeech_split.done ]; then
+    lhotse subset --last 600 \
+      data/fbank/ljspeech_cuts_all.jsonl.gz \
+      data/fbank/ljspeech_cuts_validtest.jsonl.gz
+    lhotse subset --first 100 \
+      data/fbank/ljspeech_cuts_validtest.jsonl.gz \
+      data/fbank/ljspeech_cuts_valid.jsonl.gz
+    lhotse subset --last 500 \
+      data/fbank/ljspeech_cuts_validtest.jsonl.gz \
+      data/fbank/ljspeech_cuts_test.jsonl.gz
+
+    rm data/fbank/ljspeech_cuts_validtest.jsonl.gz
+
+    n=$(( $(gunzip -c data/fbank/ljspeech_cuts_all.jsonl.gz | wc -l) - 600 ))
+    lhotse subset --first $n  \
+      data/fbank/ljspeech_cuts_all.jsonl.gz \
+      data/fbank/ljspeech_cuts_train.jsonl.gz
+      touch data/fbank/.ljspeech_split.done
+  fi
+fi