Add libriheavy recipe

egs/libriheavy/TTS/hifigan/models.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/hifigan/models.py

egs/libriheavy/TTS/hifigan/train.py

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+#!/usr/bin/env python3
+# Copyright         2023-2024  Xiaomi Corp.        (authors: Zengwei Yao,
+#                                                               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 argparse
+import logging
+from pathlib import Path
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple, Union
+import itertools
+import json
+import copy
+import math
+import os
+import random
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+import torch.multiprocessing as mp
+import torch.nn as nn
+from lhotse.cut import Cut
+from lhotse.utils import fix_random_seed
+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 LibriheavyTtsDataModule
+
+from torch.optim.lr_scheduler import ExponentialLR, LRScheduler
+from torch.optim import Optimizer
+
+from utils import load_checkpoint, save_checkpoint, plot_spectrogram
+
+from icefall import diagnostics
+from icefall.dist import cleanup_dist, setup_dist
+from icefall.env import get_env_info
+from icefall.hooks import register_inf_check_hooks
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    get_parameter_groups_with_lrs,
+)
+from models import (
+    HiFiGAN,
+    feature_loss,
+    generator_loss,
+    discriminator_loss,
+)
+from lhotse import Fbank, FbankConfig
+from lhotse.utils import fix_random_seed
+
+
+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=12354,
+        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=30,
+        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(
+        "--start-batch",
+        type=int,
+        default=0,
+        help="""If positive, --start-epoch is ignored and
+        it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="hifigan/exp",
+        help="""The experiment dir.
+        It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--learning-rate", type=float, default=0.0002, help="The learning rate."
+    )
+
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="The seed for random generators intended for reproducibility",
+    )
+
+    parser.add_argument(
+        "--print-diagnostics",
+        type=str2bool,
+        default=False,
+        help="Accumulate stats on activations, print them and exit.",
+    )
+
+    parser.add_argument(
+        "--inf-check",
+        type=str2bool,
+        default=False,
+        help="Add hooks to check for infinite module outputs and gradients.",
+    )
+
+    parser.add_argument(
+        "--save-every-n",
+        type=int,
+        default=4000,
+        help="""Save checkpoint after processing this number of batches"
+        periodically. We save checkpoint to exp-dir/ whenever
+        params.batch_idx_train % save_every_n == 0. The checkpoint filename
+        has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt'
+        Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the
+        end of each epoch where `xxx` is the epoch number counting from 1.
+        """,
+    )
+
+    parser.add_argument(
+        "--keep-last-k",
+        type=int,
+        default=30,
+        help="""Only keep this number of checkpoints on disk.
+        For instance, if it is 3, there are only 3 checkpoints
+        in the exp-dir with filenames `checkpoint-xxx.pt`.
+        It does not affect checkpoints with name `epoch-xxx.pt`.
+        """,
+    )
+
+    parser.add_argument(
+        "--average-period",
+        type=int,
+        default=200,
+        help="""Update the averaged model, namely `model_avg`, after processing
+        this number of batches. `model_avg` is a separate version of model,
+        in which each floating-point parameter is the average of all the
+        parameters from the start of training. Each time we take the average,
+        we do: `model_avg = model * (average_period / batch_idx_train) +
+            model_avg * ((batch_idx_train - average_period) / batch_idx_train)`.
+        """,
+    )
+
+    parser.add_argument(
+        "--use-fp16",
+        type=str2bool,
+        default=False,
+        help="Whether to use half precision training.",
+    )
+
+    parser.add_argument(
+        "--hifigan-version",
+        type=str,
+        default="v1",
+        choices=["v1", "v2", "v3"],
+        help="Version of hifigan.",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    """Return a dict containing training parameters.
+
+    All training related parameters that are not passed from the commandline
+    are saved in the variable `params`.
+
+    Commandline options are merged into `params` after they are parsed, so
+    you can also access them via `params`.
+
+    Explanation of options saved in `params`:
+
+        - best_train_loss: Best training loss so far. It is used to select
+                           the model that has the lowest training loss. It is
+                           updated during the training.
+
+        - best_valid_loss: Best validation loss so far. It is used to select
+                           the model that has the lowest validation loss. It is
+                           updated during the training.
+
+        - best_train_epoch: It is the epoch that has the best training loss.
+
+        - best_valid_epoch: It is the epoch that has the best validation loss.
+
+        - batch_idx_train: Used to writing statistics to tensorboard. It
+                           contains number of batches trained so far across
+                           epochs.
+
+        - log_interval:  Print training loss if batch_idx % log_interval` is 0
+
+        - reset_interval: Reset statistics if batch_idx % reset_interval is 0
+
+        - valid_interval:  Run validation if batch_idx % valid_interval is 0
+
+        - feature_dim: The model input dim. It has to match the one used
+                       in computing features.
+    """
+    params = AttributeDict(
+        {
+            "best_train_loss": float("inf"),
+            "best_valid_loss": float("inf"),
+            "best_train_epoch": -1,
+            "best_valid_epoch": -1,
+            "batch_idx_train": 0,
+            "log_interval": 50,
+            "reset_interval": 200,
+            "valid_interval": 500,
+            "feature_dim": 80,
+            "segment_size": 8192,
+            "adam_b1": 0.8,
+            "adam_b2": 0.99,
+            "lr_decay": 0.999,
+            "v1": {
+                "upsample_initial_channel": 512,
+                "resblock_version": "1",
+                "upsample_rates": [8, 8, 2, 2],
+                "upsample_kernel_sizes": [16, 16, 4, 4],
+                "resblock_kernel_sizes": [3, 7, 11],
+                "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+            },
+            "v2": {
+                "upsample_initial_channel": 128,
+                "resblock_version": "1",
+                "upsample_rates": [8, 8, 2, 2],
+                "upsample_kernel_sizes": [16, 16, 4, 4],
+                "resblock_kernel_sizes": [3, 7, 11],
+                "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+            },
+            "v3": {
+                "upsample_initial_channel": 256,
+                "resblock_version": "2",
+                "upsample_rates": [8, 8, 4],
+                "upsample_kernel_sizes": [16, 16, 8],
+                "resblock_kernel_sizes": [3, 5, 7],
+                "resblock_dilation_sizes": [[1, 2], [2, 6], [3, 12]],
+            },
+            "env_info": get_env_info(),
+        }
+    )
+
+    return params
+
+
+def fbank(
+    audio: torch.Tensor,
+    lengths: Optional[torch.Tensor] = None,
+    sampling_rate: int = 16000,
+    frame_length: int = 1024,
+    frame_shift: int = 256,
+    use_fft_mag: bool = True,
+):
+    sampling_rate = sampling_rate
+    config = FbankConfig(
+        sampling_rate=sampling_rate,
+        frame_length=frame_length / sampling_rate,  # (in second),
+        frame_shift=frame_shift / sampling_rate,  # (in second)
+        use_fft_mag=use_fft_mag,
+    )
+    fb = Fbank(config)
+    feat = fb.extract_batch(audio, sampling_rate=sampling_rate, lengths=lengths)
+    if feat.dim() == 2:
+        feat = feat.unsqueeze(0)
+    return feat
+
+
+def get_model(params: AttributeDict) -> nn.Module:
+    device = params.device
+    model = HiFiGAN(
+        in_channels=params.feature_dim,
+        upsample_initial_channel=params[params.hifigan_version][
+            "upsample_initial_channel"
+        ],
+        upsample_rates=params[params.hifigan_version]["upsample_rates"],
+        upsample_kernel_sizes=params[params.hifigan_version]["upsample_kernel_sizes"],
+        resblock_version=params[params.hifigan_version]["resblock_version"],
+        resblock_kernel_sizes=params[params.hifigan_version]["resblock_kernel_sizes"],
+        resblock_dilation_sizes=params[params.hifigan_version][
+            "resblock_dilation_sizes"
+        ],
+    ).to(device)
+    num_param_g = sum([p.numel() for p in model.generator.parameters()])
+    logging.info(f"Number of Generator parameters : {num_param_g}")
+    num_param_mpd = sum([p.numel() for p in model.mpd.parameters()])
+    logging.info(f"Number of MultiPeriodDiscriminator parameters : {num_param_mpd}")
+    num_param_msd = sum([p.numel() for p in model.msd.parameters()])
+    logging.info(f"Number of MultiScaleDiscriminator parameters : {num_param_msd}")
+    logging.info(
+        f"Number of model parameters : {num_param_g + num_param_mpd + num_param_msd}"
+    )
+    return model
+
+
+def load_checkpoint_if_available(
+    params: AttributeDict,
+    model: nn.Module,
+    model_avg: nn.Module = None,
+    optimizer_g: Optional[Optimizer] = None,
+    optimizer_d: Optional[Optimizer] = None,
+    scheduler_g: Optional[LRScheduler] = None,
+    scheduler_d: Optional[LRScheduler] = None,
+) -> Optional[Dict[str, Any]]:
+    """Load checkpoint from file.
+
+    If params.start_batch is positive, it will load the checkpoint from
+    `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, 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.
+      model_avg:
+        The stored model averaged from the start of training.
+      optimizer:
+        The optimizer that we are using.
+      scheduler:
+        The scheduler that we are using.
+    Returns:
+      Return a dict containing previously saved training info.
+    """
+    if params.start_batch > 0:
+        filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt"
+    elif 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,
+        model_avg=model_avg,
+        optimizer_g=optimizer_g,
+        optimizer_d=optimizer_d,
+        scheduler_g=scheduler_g,
+        scheduler_d=scheduler_d,
+    )
+
+    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]
+
+    if params.start_batch > 0:
+        if "cur_epoch" in saved_params:
+            params["start_epoch"] = saved_params["cur_epoch"]
+
+    return saved_params
+
+
+def compute_generator_loss(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    features: Tensor,
+    audios: Tensor,
+) -> Tuple[Tensor, MetricsTracker]:
+    device = params.device
+    model = model.module if isinstance(model, DDP) else model
+
+    audios = audios.unsqueeze(1)  # (B, 1, T)
+
+    gen_audios = model(features)  # (B, 1, T)
+
+    gen_features = fbank(gen_audios.squeeze(1)).permute(0, 2, 1).to(device)  # (B, F, T)
+
+    # L1 Mel-Spectrogram Loss
+    loss_mel = F.l1_loss(features, gen_features) * 45
+
+    y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = model.mpd(audios, gen_audios)
+    y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = model.msd(audios, gen_audios)
+
+    loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
+    loss_fm_s = feature_loss(fmap_s_r, fmap_s_g)
+
+    loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)
+    loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)
+
+    loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel
+
+    assert loss_gen_all.requires_grad == True
+
+    info = MetricsTracker()
+    info["frames"] = 1
+    info["loss_gen"] = loss_gen_all.detach().cpu().item()
+    info["loss_mel"] = loss_mel.detach().cpu().item()
+    info["loss_mel_error"] = loss_mel.detach().cpu().item() / 45
+    info["loss_feature_msd"] = loss_fm_s.detach().cpu().item()
+    info["loss_feature_mpd"] = loss_fm_f.detach().cpu().item()
+    info["loss_gen_msd"] = loss_gen_s.detach().cpu().item()
+    info["loss_gen_mpd"] = loss_gen_f.detach().cpu().item()
+
+    return loss_gen_all, info
+
+
+def compute_discriminator_loss(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    features: Tensor,
+    audios: Tensor,
+) -> Tuple[Tensor, MetricsTracker]:
+    device = params.device
+    model = model.module if isinstance(model, DDP) else model
+
+    audios = audios.unsqueeze(1)
+
+    gen_audios = model(features)  # (B, 1, T)
+
+    # MPD
+    y_df_hat_r, y_df_hat_g, _, _ = model.mpd(audios, gen_audios.detach())
+    loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(
+        y_df_hat_r, y_df_hat_g
+    )
+
+    # MSD
+    y_ds_hat_r, y_ds_hat_g, _, _ = model.msd(audios, gen_audios.detach())
+    loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(
+        y_ds_hat_r, y_ds_hat_g
+    )
+
+    loss_disc_all = loss_disc_s + loss_disc_f
+
+    info = MetricsTracker()
+    # MetricsTracker will norm the loss value with "frames", set it to 1 here to
+    # make tot_loss look normal.
+    info["frames"] = 1
+    info["loss_disc"] = loss_disc_all.detach().cpu().item()
+    info["loss_disc_msd"] = loss_disc_s.detach().cpu().item()
+    info["loss_disc_mpd"] = loss_disc_f.detach().cpu().item()
+
+    return loss_disc_all, info
+
+
+def train_one_epoch(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    optimizer_g: Optimizer,
+    optimizer_d: Optimizer,
+    scheduler_g: ExponentialLR,
+    scheduler_d: ExponentialLR,
+    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.
+      scheduler:
+        The learning rate scheduler, we call step() every epoch.
+      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.
+      world_size:
+        Number of nodes in DDP training. If it is 1, DDP is disabled.
+      rank:
+        The rank of the node in DDP training. If no DDP is used, it should
+        be set to 0.
+    """
+    model.train()
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+
+    # 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_g=optimizer_g,
+            optimizer_d=optimizer_d,
+            scheduler_g=scheduler_g,
+            scheduler_d=scheduler_d,
+            sampler=train_dl.sampler,
+            scaler=scaler,
+            rank=0,
+        )
+
+    for batch_idx, batch in enumerate(train_dl):
+        params.batch_idx_train += 1
+        batch_size = batch["features_lens"].size(0)
+
+        features = batch["features"].to(device)  # (B, T, F)
+        features_lens = batch["features_lens"].to(device)
+        audios = batch["audio"].to(device)
+
+        # 8192 samples is 29 frames
+        segment_frames = (
+            params.segment_size - params.frame_length
+        ) // params.frame_shift + 1
+        start_p = random.randint(0, features_lens.min() - (segment_frames + 1))
+
+        features = features[:, start_p : start_p + segment_frames, :].permute(
+            0, 2, 1
+        )  # (B, F, T)
+
+        audios = audios[
+            :,
+            start_p * params.frame_shift : start_p * params.frame_shift
+            + params.segment_size,
+        ]  # (B, T)
+
+        try:
+
+            optimizer_d.zero_grad()
+
+            loss_disc, loss_disc_info = compute_discriminator_loss(
+                params=params,
+                model=model,
+                features=features,
+                audios=audios,
+            )
+
+            loss_disc.backward()
+            optimizer_d.step()
+
+            optimizer_g.zero_grad()
+            loss_gen, loss_gen_info = compute_generator_loss(
+                params=params,
+                model=model,
+                features=features,
+                audios=audios,
+            )
+
+            loss_gen.backward()
+            optimizer_g.step()
+
+            loss_info = loss_gen_info + loss_disc_info
+            tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_gen_info
+
+        except Exception as e:
+            logging.info(f"Caught exception : {e}.")
+            save_bad_model()
+            raise
+
+        if params.print_diagnostics and batch_idx == 5:
+            return
+
+        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_lr_g = max(scheduler_g.get_last_lr())
+            cur_lr_d = max(scheduler_d.get_last_lr())
+            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}, batch size: {batch_size}, "
+                f"loss[{loss_info}], tot_loss[{tot_loss}], "
+                f"cur_lr_g: {cur_lr_g:.2e}, "
+                f"cur_lr_d: {cur_lr_d:.2e}, "
+                + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
+            )
+
+            if tb_writer is not None:
+                tb_writer.add_scalar(
+                    "train/learning_rate_gen", cur_lr_g, params.batch_idx_train
+                )
+                tb_writer.add_scalar(
+                    "train/learning_rate_disc", cur_lr_d, params.batch_idx_train
+                )
+                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 == 0
+            and not params.print_diagnostics
+        ):
+            logging.info("Computing validation loss")
+            valid_info = compute_validation_loss(
+                params=params,
+                model=model,
+                valid_dl=valid_dl,
+                world_size=world_size,
+                rank=rank,
+                tb_writer=tb_writer,
+            )
+            model.train()
+            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
+            logging.info(
+                f"Maximum memory allocated so far is {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
+                )
+
+    scheduler_g.step()
+    scheduler_d.step()
+    loss_value = tot_loss["loss_gen"]
+    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 compute_validation_loss(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    valid_dl: torch.utils.data.DataLoader,
+    world_size: int = 1,
+    rank: int = 0,
+    tb_writer: Optional[SummaryWriter] = None,
+) -> MetricsTracker:
+    """Run the validation process."""
+
+    model.eval()
+    torch.cuda.empty_cache()
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+
+    # used to summary the stats over iterations
+    tot_loss = MetricsTracker()
+
+    with torch.no_grad():
+        for batch_idx, batch in enumerate(valid_dl):
+            features = batch["features"]  # (B, T, F)
+            audios = batch["audio"]
+
+            x = features.permute(0, 2, 1)  # (B, F, T)
+            y = batch["audio"]  # (B, T)
+            y_mel = x.clone().to(device)  # (B, F, T)
+
+            y_g_hat = model(x.to(device))  # (B, 1, T)
+
+            y_g_hat_mel = (
+                fbank(y_g_hat.squeeze(1)).permute(0, 2, 1).to(device)
+            )  # (B, F, T)
+
+            loss_mel_error = F.l1_loss(y_mel, y_g_hat_mel)
+
+            loss_info = MetricsTracker()
+            # MetricsTracker will norm the loss value with "frames", set it to 1 here to
+            # make tot_loss look normal.
+            loss_info["frames"] = 1
+            loss_info["loss_mel_error"] = loss_mel_error.item()
+
+            tot_loss = tot_loss + loss_info
+
+            if batch_idx <= 5 and rank == 0 and tb_writer is not None:
+                if params.batch_idx_train == params.valid_interval:
+                    tb_writer.add_audio(
+                        "gt/y_{}".format(batch_idx),
+                        y[0],
+                        params.batch_idx_train,
+                        params.sampling_rate,
+                    )
+                    tb_writer.add_figure(
+                        "gt/y_spec_{}".format(batch_idx),
+                        plot_spectrogram(x[0].cpu().numpy()),
+                        params.batch_idx_train,
+                    )
+                tb_writer.add_audio(
+                    "generated/y_hat_{}".format(batch_idx),
+                    y_g_hat[0],
+                    params.batch_idx_train,
+                    params.sampling_rate,
+                )
+
+                tb_writer.add_figure(
+                    "generated/y_hat_spec_{}".format(batch_idx),
+                    plot_spectrogram(y_g_hat_mel[0].detach().cpu().numpy()),
+                    params.batch_idx_train,
+                )
+
+        if world_size > 1:
+            tot_loss.reduce(device)
+
+        loss_value = tot_loss["loss_mel_error"]
+        if loss_value < params.best_valid_loss:
+            params.best_valid_epoch = params.cur_epoch
+            params.best_valid_loss = loss_value
+
+        return tot_loss
+
+
+def run(rank, world_size, args):
+    """
+    Args:
+      rank:
+        It is a value between 0 and `world_size-1`, which is
+        passed automatically by `mp.spawn()` in :func:`main`.
+        The node with rank 0 is responsible for saving checkpoint.
+      world_size:
+        Number of GPUs for DDP training.
+      args:
+        The return value of get_parser().parse_args()
+    """
+    params = get_params()
+    params.update(vars(args))
+    torch.autograd.set_detect_anomaly(True)
+
+    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}")
+    params.device = device
+    logging.info(params)
+    logging.info("About to create model")
+
+    model = get_model(params)
+
+    assert params.start_epoch > 0, params.start_epoch
+    checkpoints = load_checkpoint_if_available(params=params, model=model)
+
+    model = model.to(device)
+    generator = model.generator
+    msd = model.msd
+    mpd = model.mpd
+    if world_size > 1:
+        logging.info("Using DDP")
+        model = DDP(model, device_ids=[rank], find_unused_parameters=True)
+
+    optimizer_g = torch.optim.AdamW(
+        generator.parameters(),
+        params.learning_rate,
+        betas=[params.adam_b1, params.adam_b2],
+    )
+    optimizer_d = torch.optim.AdamW(
+        itertools.chain(msd.parameters(), mpd.parameters()),
+        params.learning_rate,
+        betas=[params.adam_b1, params.adam_b2],
+    )
+
+    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
+        optimizer_g, gamma=params.lr_decay
+    )
+    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
+        optimizer_d, gamma=params.lr_decay
+    )
+
+    if checkpoints is not None:
+        # load state_dict for optimizers
+        if "optimizer_g" in checkpoints:
+            logging.info("Loading generator optimizer state dict")
+            optimizer_g.load_state_dict(checkpoints["optimizer_g"])
+        if "optimizer_d" in checkpoints:
+            logging.info("Loading discriminator optimizer state dict")
+            optimizer_d.load_state_dict(checkpoints["optimizer_d"])
+
+        # load state_dict for schedulers
+        if "scheduler_g" in checkpoints:
+            logging.info("Loading generator scheduler state dict")
+            scheduler_g.load_state_dict(checkpoints["scheduler_g"])
+        if "scheduler_d" in checkpoints:
+            logging.info("Loading discriminator scheduler state dict")
+            scheduler_d.load_state_dict(checkpoints["scheduler_d"])
+
+    if params.print_diagnostics:
+        opts = diagnostics.TensorDiagnosticOptions(
+            512
+        )  # allow 4 megabytes per sub-module
+        diagnostic = diagnostics.attach_diagnostics(model, opts)
+
+    if params.inf_check:
+        register_inf_check_hooks(model)
+
+    libriheavy = LibriheavyTtsDataModule(args)
+
+    train_cuts = libriheavy.train_cuts_small()
+
+    def remove_short_and_long_utt(c: Cut):
+        # Keep only utterances with duration between 1 second and 20 seconds
+        # You should use ../local/display_manifest_statistics.py to get
+        # an utterance duration distribution for your dataset to select
+        # the threshold
+        if c.duration < 1.0 or c.duration > 20.0:
+            return False
+        return True
+
+    train_cuts = train_cuts.filter(remove_short_and_long_utt)
+    train_dl = libriheavy.train_dataloaders(train_cuts)
+
+    valid_cuts = libriheavy.valid_cuts()
+    valid_dl = libriheavy.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)
+        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,
+            optimizer_g=optimizer_g,
+            optimizer_d=optimizer_d,
+            scheduler_g=scheduler_g,
+            scheduler_d=scheduler_d,
+            train_dl=train_dl,
+            valid_dl=valid_dl,
+            scaler=scaler,
+            tb_writer=tb_writer,
+            world_size=world_size,
+            rank=rank,
+        )
+
+        if params.print_diagnostics:
+            diagnostic.print_diagnostics()
+            break
+
+        filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
+        save_checkpoint(
+            filename=filename,
+            params=params,
+            model=model,
+            optimizer_g=optimizer_g,
+            optimizer_d=optimizer_d,
+            scheduler_g=scheduler_g,
+            scheduler_d=scheduler_d,
+            sampler=train_dl.sampler,
+            scaler=scaler,
+            rank=rank,
+        )
+
+        if params.batch_idx_train % params.save_every_n == 0:
+            filename = params.exp_dir / f"checkpoint-{params.batch_idx_train}.pt"
+            save_checkpoint(
+                filename=filename,
+                params=params,
+                model=model,
+                optimizer_g=optimizer_g,
+                optimizer_d=optimizer_d,
+                scheduler_g=scheduler_g,
+                scheduler_d=scheduler_d,
+                sampler=train_dl.sampler,
+                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()
+    LibriheavyTtsDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    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/libriheavy/TTS/hifigan/tts_datamodule.py

@@ -0,0 +1,378 @@
+# Copyright      2021  Piotr Żelasko
+# Copyright      2022-2024  Xiaomi Corporation     (Authors: Mingshuang Luo,
+#                                                            Zengwei Yao,
+#                                                            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 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 LibriheavyTtsDataModule:
+    """
+    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=True,
+            help="When enabled, each batch will have the "
+            "field: batch['cut'] with the cuts that "
+            "were used to construct it.",
+        )
+        group.add_argument(
+            "--return-text",
+            type=str2bool,
+            default=True,
+            help="Whether to return the transcript of the audio.",
+        )
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=2,
+            help="The number of training dataloader workers that "
+            "collect the batches.",
+        )
+
+        group.add_argument(
+            "--sampling-rate",
+            type=int,
+            default=16000,
+            help="The sampleing rate of ljspeech dataset",
+        )
+
+        group.add_argument(
+            "--frame-shift",
+            type=int,
+            default=256,
+            help="Frame shift.",
+        )
+
+        group.add_argument(
+            "--frame-length",
+            type=int,
+            default=1024,
+            help="Frame shift.",
+        )
+
+        group.add_argument(
+            "--input-strategy",
+            type=str,
+            default="PrecomputedFeatures",
+            help="AudioSamples or PrecomputedFeatures",
+        )
+
+        group.add_argument(
+            "--use-fft-mag",
+            type=str2bool,
+            default=True,
+            help="Whether to use magnitude of fbank, false to use power energy.",
+        )
+
+    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=self.args.return_text,
+            return_tokens=False,
+            feature_input_strategy=eval(self.args.input_strategy)(),
+            return_cuts=self.args.return_cuts,
+        )
+
+        if self.args.on_the_fly_feats:
+            sampling_rate = self.args.sampling_rate
+            config = FbankConfig(
+                sampling_rate=sampling_rate,
+                frame_length=self.args.frame_length / sampling_rate,  # (in second),
+                frame_shift=self.args.frame_shift / sampling_rate,  # (in second)
+                use_fft_mag=self.args.use_fft_mag,
+            )
+            train = SpeechSynthesisDataset(
+                return_text=self.args.return_text,
+                return_tokens=False,
+                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 = self.args.sampling_rate
+            config = FbankConfig(
+                sampling_rate=sampling_rate,
+                frame_length=self.args.frame_length / sampling_rate,  # (in second),
+                frame_shift=self.args.frame_shift / sampling_rate,  # (in second)
+                use_fft_mag=self.args.use_fft_mag,
+            )
+            validate = SpeechSynthesisDataset(
+                return_text=self.args.return_text,
+                return_tokens=False,
+                feature_input_strategy=OnTheFlyFeatures(Fbank(config)),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            validate = SpeechSynthesisDataset(
+                return_text=self.args.return_text,
+                return_tokens=False,
+                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 = self.args.sampling_rate
+            config = FbankConfig(
+                sampling_rate=sampling_rate,
+                frame_length=self.args.frame_length / sampling_rate,  # (in second),
+                frame_shift=self.args.frame_shift / sampling_rate,  # (in second)
+                use_fft_mag=self.args.use_fft_mag,
+            )
+            test = SpeechSynthesisDataset(
+                return_text=self.args.return_text,
+                return_tokens=False,
+                feature_input_strategy=OnTheFlyFeatures(Fbank(config)),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            test = SpeechSynthesisDataset(
+                return_text=self.args.return_text,
+                return_tokens=False,
+                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_small(self) -> CutSet:
+        logging.info("About to get train cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "libriheavy_cuts_small.jsonl.gz"
+        )
+
+    @lru_cache()
+    def valid_cuts(self) -> CutSet:
+        logging.info("About to get validation cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "libriheavy_cuts_dev.jsonl.gz"
+        )
+
+    @lru_cache()
+    def train_cuts_finetune(self) -> CutSet:
+        logging.info("About to get train cuts finetune")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "libriheavy_cuts_small_finetune.jsonl.gz"
+        )
+
+    @lru_cache()
+    def valid_cuts_finetune(self) -> CutSet:
+        logging.info("About to get validation cuts finetune")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "libriheavy_cuts_dev_finetune.jsonl.gz"
+        )
+
+    @lru_cache()
+    def test_cuts(self) -> CutSet:
+        logging.info("About to get test cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "libriheavy_cuts_test_clean.jsonl.gz"
+        )

egs/libriheavy/TTS/hifigan/utils.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/hifigan/utils.py

egs/libriheavy/TTS/local/compute_fbank_libriheavy.py

@@ -0,0 +1,286 @@
+#!/usr/bin/env python3
+# Copyright    2023  Xiaomi Corp.        (authors: Xiaoyu Yang,
+#                                                  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.
+
+
+"""
+This file computes fbank features of the Libriheavy dataset.
+It looks for manifests in the directory data/manifests.
+
+The generated fbank features are saved in data/fbank.
+"""
+
+import argparse
+import logging
+import os
+from pathlib import Path
+from typing import Optional
+
+import torch
+from lhotse import (
+    CutSet,
+    Fbank,
+    FbankConfig,
+    KaldifeatFbank,
+    KaldifeatFbankConfig,
+    LilcomChunkyWriter,
+)
+
+from icefall.utils import get_executor, str2bool
+
+# Torch's multithreaded behavior needs to be disabled or
+# it wastes a lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
+# even when we are not invoking the main (e.g. when spawning subprocesses).
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--manifest-dir",
+        type=str,
+        help="""The source directory that contains raw manifests.
+        """,
+        default="data/manifests",
+    )
+
+    parser.add_argument(
+        "--fbank-dir",
+        type=str,
+        help="""Fbank output dir
+        """,
+        default="data/fbank",
+    )
+
+    parser.add_argument(
+        "--sampling-rate",
+        type=int,
+        default=16000,
+    )
+
+    parser.add_argument(
+        "--num-mel-bins",
+        type=int,
+        default=80,
+    )
+
+    parser.add_argument(
+        "--frame-length",
+        type=int,
+        default=1024,
+    )
+
+    parser.add_argument(
+        "--frame-shift",
+        type=int,
+        default=256,
+    )
+
+    parser.add_argument(
+        "--use-fft-mag",
+        type=str2bool,
+        default=True,
+    )
+
+    parser.add_argument(
+        "--subset",
+        type=str,
+        help="""Dataset parts to compute fbank. If None, we will use all""",
+    )
+
+    parser.add_argument(
+        "--num-workers",
+        type=int,
+        default=20,
+        help="Number of dataloading workers used for reading the audio.",
+    )
+
+    parser.add_argument(
+        "--batch-duration",
+        type=float,
+        default=600.0,
+        help="The maximum number of audio seconds in a batch."
+        "Determines batch size dynamically.",
+    )
+
+    parser.add_argument(
+        "--use-splits",
+        type=str2bool,
+        default=False,
+        help="Whether to compute fbank on splits.",
+    )
+
+    parser.add_argument(
+        "--num-splits",
+        type=int,
+        help="""The number of splits of the medium and large subset.
+        Only needed when --use-splits is true.""",
+    )
+
+    parser.add_argument(
+        "--start",
+        type=int,
+        default=0,
+        help="""Process pieces starting from this number (inclusive).
+        Only needed when --use-splits is true.""",
+    )
+
+    parser.add_argument(
+        "--stop",
+        type=int,
+        default=-1,
+        help="""Stop processing pieces until this number (exclusive).
+        Only needed when --use-splits is true.""",
+    )
+
+    return parser.parse_args()
+
+
+def compute_fbank_libriheavy(args):
+    src_dir = Path(args.manifest_dir)
+    output_dir = Path(args.fbank_dir)
+    num_jobs = min(15, os.cpu_count())
+    num_mel_bins = args.num_mel_bins
+    subset = args.subset
+
+    sampling_rate = args.sampling_rate
+    frame_length = args.frame_length / sampling_rate  # (in second)
+    frame_shift = args.frame_shift / sampling_rate  # (in second)
+    use_fft_mag = args.use_fft_mag
+
+    config = FbankConfig(
+        sampling_rate=sampling_rate,
+        frame_length=frame_length,
+        frame_shift=frame_shift,
+        use_fft_mag=use_fft_mag,
+        num_mel_bins=num_mel_bins,
+    )
+    extractor = Fbank(config)
+
+    with get_executor() as ex:  # Initialize the executor only once.
+        output_cuts_path = output_dir / f"libriheavy_cuts_{subset}.jsonl.gz"
+        if output_cuts_path.exists():
+            logging.info(f"{output_cuts_path} exists - skipping")
+            return
+
+        input_cuts_path = src_dir / f"libriheavy_cuts_{subset}.jsonl.gz"
+        assert input_cuts_path.exists(), f"{input_cuts_path} does not exist!"
+        logging.info(f"Loading {input_cuts_path}")
+        cut_set = CutSet.from_file(input_cuts_path)
+
+        logging.info("Computing features")
+
+        cut_set = cut_set.compute_and_store_features(
+            extractor=extractor,
+            storage_path=f"{output_dir}/libriheavy_feats_{subset}",
+            # when an executor is specified, make more partitions
+            num_jobs=num_jobs if ex is None else 80,
+            executor=ex,
+            storage_type=LilcomChunkyWriter,
+        )
+
+        logging.info(f"Saving to {output_cuts_path}")
+        cut_set.to_file(output_cuts_path)
+
+
+def compute_fbank_libriheavy_splits(args):
+    num_splits = args.num_splits
+    subset = args.subset
+    src_dir = f"{args.manifest_dir}/libriheavy_{subset}_split"
+    src_dir = Path(src_dir)
+    output_dir = f"{args.fbank_dir}/libriheavy_{subset}_split"
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    start = args.start
+    stop = args.stop
+    if stop < start:
+        stop = num_splits
+
+    stop = min(stop, num_splits)
+
+    num_mel_bins = args.num_mel_bins
+    sampling_rate = args.sampling_rate
+    frame_length = args.frame_length / sampling_rate  # (in second)
+    frame_shift = args.frame_shift / sampling_rate  # (in second)
+    use_fft_mag = args.use_fft_mag
+
+    config = FbankConfig(
+        sampling_rate=sampling_rate,
+        frame_length=frame_length,
+        frame_shift=frame_shift,
+        use_fft_mag=use_fft_mag,
+        num_mel_bins=num_mel_bins,
+    )
+    extractor = Fbank(config)
+
+    num_digits = 8  # num_digits is fixed by lhotse split-lazy
+    for i in range(start, stop):
+        idx = f"{i + 1}".zfill(num_digits)
+        logging.info(f"Processing {idx}/{num_splits}")
+
+        cuts_path = output_dir / f"libriheavy_cuts_{subset}.{idx}.jsonl.gz"
+        if cuts_path.is_file():
+            logging.info(f"{cuts_path} exists - skipping")
+            continue
+
+        raw_cuts_path = src_dir / f"libriheavy_cuts_{subset}.{idx}.jsonl.gz"
+        if not raw_cuts_path.is_file():
+            logging.info(f"{raw_cuts_path} does not exist - skipping it")
+            continue
+
+        logging.info(f"Loading {raw_cuts_path}")
+        cut_set = CutSet.from_file(raw_cuts_path)
+
+        logging.info("Computing features")
+        if (output_dir / f"libriheavy_feats_{subset}_{idx}.lca").exists():
+            logging.info(f"Removing {output_dir}/libriheavy_feats_{subset}_{idx}.lca")
+            os.remove(output_dir / f"libriheavy_feats_{subset}_{idx}.lca")
+
+        cut_set = cut_set.compute_and_store_features_batch(
+            extractor=extractor,
+            storage_path=f"{output_dir}/libriheavy_feats_{subset}_{idx}",
+            num_workers=args.num_workers,
+            batch_duration=args.batch_duration,
+            overwrite=True,
+        )
+
+        logging.info("About to split cuts into smaller chunks.")
+        cut_set = cut_set.trim_to_supervisions(
+            keep_overlapping=False, min_duration=None
+        )
+
+        logging.info(f"Saving to {cuts_path}")
+        cut_set.to_file(cuts_path)
+        logging.info(f"Saved to {cuts_path}")
+
+
+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))
+
+    if args.use_splits:
+        assert args.num_splits is not None, "Please provide num_splits"
+        compute_fbank_libriheavy_splits(args)
+    else:
+        compute_fbank_libriheavy(args)