mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-26 10:16:14 +00:00
switch to piper-phonemize
This commit is contained in:
Fangjun Kuang
parent
56d3b92f3f
commit
7077b4f99a
4
egs/ljspeech/TTS/.gitignore
vendored
Normal file
4
egs/ljspeech/TTS/.gitignore
vendored
Normal file
@ -0,0 +1,4 @@
|
||||
build
|
||||
core.c
|
||||
*.so
|
||||
my-output*
|
||||
141
egs/ljspeech/TTS/local/compute_fbank_ljspeech.py
Executable file
141
egs/ljspeech/TTS/local/compute_fbank_ljspeech.py
Executable file
@ -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)
|
||||
@ -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():
|
||||
output_dir = Path("data/spectrogram")
|
||||
def get_parser():
|
||||
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)
|
||||
|
||||
@ -71,9 +71,12 @@ class MatchaTTS(torch.nn.Module): # 🍵
|
||||
spk_emb_dim=spk_emb_dim,
|
||||
)
|
||||
|
||||
# self.update_data_statistics(data_statistics)
|
||||
self.register_buffer("mel_mean", torch.tensor(data_statistics["mel_mean"]))
|
||||
self.register_buffer("mel_std", torch.tensor(data_statistics["mel_std"]))
|
||||
if data_statistics is not None:
|
||||
self.register_buffer("mel_mean", torch.tensor(data_statistics["mel_mean"]))
|
||||
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(
|
||||
|
||||
1
egs/ljspeech/TTS/matcha/tokenizer.py
Symbolic link
1
egs/ljspeech/TTS/matcha/tokenizer.py
Symbolic link
@ -0,0 +1 @@
|
||||
../vits/tokenizer.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_std": 2.0643954277038574,
|
||||
"mel_mean": 0.0,
|
||||
"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,
|
||||
"valid_interval": 2000,
|
||||
"log_interval": 10,
|
||||
"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():
|
||||
if isinstance(value, torch.Tensor):
|
||||
batch[key] = value.to(device)
|
||||
# audio: (N, T), float32
|
||||
# features: (N, T, C), float32
|
||||
# audio_lens, (N,), int32
|
||||
# features_lens, (N,), int32
|
||||
# tokens: List[List[str]], len(tokens) == N
|
||||
|
||||
batch_size = 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():
|
||||
parser = get_parser()
|
||||
args = parser.parse_args()
|
||||
def run(rank, world_size, 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)
|
||||
|
||||
341
egs/ljspeech/TTS/matcha/tts_datamodule.py
Normal file
341
egs/ljspeech/TTS/matcha/tts_datamodule.py
Normal file
@ -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"
|
||||
)
|
||||
@ -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
|
||||
|
||||
@ -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
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user