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remove more unused code
This commit is contained in:
Fangjun Kuang
parent
c558328dc5
commit
ed569a938a
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#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang)
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import torch
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from icefall.utils import AttributeDict
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from matcha.models.matcha_tts import MatchaTTS
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from matcha.data.text_mel_datamodule import TextMelDataModule
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def _get_data_params() -> AttributeDict:
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params = AttributeDict(
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{
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"name": "ljspeech",
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"train_filelist_path": "./filelists/ljs_audio_text_train_filelist.txt",
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"valid_filelist_path": "./filelists/ljs_audio_text_val_filelist.txt",
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"batch_size": 32,
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"num_workers": 3,
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"pin_memory": False,
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"cleaners": ["english_cleaners2"],
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"add_blank": True,
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"n_spks": 1,
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"n_fft": 1024,
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"n_feats": 80,
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"sample_rate": 22050,
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"hop_length": 256,
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"win_length": 1024,
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"f_min": 0,
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"f_max": 8000,
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"seed": 1234,
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"load_durations": False,
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"data_statistics": AttributeDict(
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{
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"mel_mean": -5.517028331756592,
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"mel_std": 2.0643954277038574,
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}
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),
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}
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)
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return params
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def _get_model_params() -> AttributeDict:
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n_feats = 80
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filter_channels_dp = 256
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encoder_params_p_dropout = 0.1
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params = AttributeDict(
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{
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"n_vocab": 178,
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"n_spks": 1, # for ljspeech.
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"spk_emb_dim": 64,
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"n_feats": n_feats,
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"out_size": None, # or use 172
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"prior_loss": True,
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"use_precomputed_durations": False,
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"encoder": AttributeDict(
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{
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"encoder_type": "RoPE Encoder", # not used
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"encoder_params": AttributeDict(
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{
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"n_feats": n_feats,
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"n_channels": 192,
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"filter_channels": 768,
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"filter_channels_dp": filter_channels_dp,
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"n_heads": 2,
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"n_layers": 6,
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"kernel_size": 3,
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"p_dropout": encoder_params_p_dropout,
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"spk_emb_dim": 64,
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"n_spks": 1,
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"prenet": True,
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}
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),
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"duration_predictor_params": AttributeDict(
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{
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"filter_channels_dp": filter_channels_dp,
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"kernel_size": 3,
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"p_dropout": encoder_params_p_dropout,
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}
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),
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}
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),
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"decoder": AttributeDict(
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{
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"channels": [256, 256],
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"dropout": 0.05,
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"attention_head_dim": 64,
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"n_blocks": 1,
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"num_mid_blocks": 2,
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"num_heads": 2,
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"act_fn": "snakebeta",
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}
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),
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"cfm": AttributeDict(
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{
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"name": "CFM",
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"solver": "euler",
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"sigma_min": 1e-4,
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}
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),
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"optimizer": AttributeDict(
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{
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"lr": 1e-4,
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"weight_decay": 0.0,
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}
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),
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}
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)
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return params
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def get_params():
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params = AttributeDict(
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{
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"model": _get_model_params(),
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"data": _get_data_params(),
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}
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)
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return params
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def get_model(params):
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m = MatchaTTS(**params.model)
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return m
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def main():
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params = get_params()
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data_module = TextMelDataModule(hparams=params.data)
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if False:
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for b in data_module.train_dataloader():
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assert isinstance(b, dict)
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# b.keys()
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# ['x', 'x_lengths', 'y', 'y_lengths', 'spks', 'filepaths', 'x_texts', 'durations']
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# x: [batch_size, 289], torch.int64
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# x_lengths: [batch_size], torch.int64
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# y: [batch_size, n_feats, num_frames], torch.float32
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# y_lengths: [batch_size], torch.int64
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# spks: None
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# filepaths: list, (batch_size,)
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# x_texts: list, (batch_size,)
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# durations: None
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m = get_model(params)
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print(m)
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num_param = sum([p.numel() for p in m.parameters()])
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print(f"Number of parameters: {num_param}")
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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if __name__ == "__main__":
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main()
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@ -1,122 +0,0 @@
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from typing import Any, Dict, List, Optional, Tuple
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import hydra
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import lightning as L
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import rootutils
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from lightning import Callback, LightningDataModule, LightningModule, Trainer
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from lightning.pytorch.loggers import Logger
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from omegaconf import DictConfig
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from matcha import utils
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rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
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# ------------------------------------------------------------------------------------ #
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# the setup_root above is equivalent to:
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# - adding project root dir to PYTHONPATH
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# (so you don't need to force user to install project as a package)
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# (necessary before importing any local modules e.g. `from src import utils`)
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# - setting up PROJECT_ROOT environment variable
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# (which is used as a base for paths in "configs/paths/default.yaml")
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# (this way all filepaths are the same no matter where you run the code)
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# - loading environment variables from ".env" in root dir
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#
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# you can remove it if you:
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# 1. either install project as a package or move entry files to project root dir
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# 2. set `root_dir` to "." in "configs/paths/default.yaml"
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#
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# more info: https://github.com/ashleve/rootutils
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# ------------------------------------------------------------------------------------ #
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log = utils.get_pylogger(__name__)
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@utils.task_wrapper
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def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
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"""Trains the model. Can additionally evaluate on a testset, using best weights obtained during
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training.
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This method is wrapped in optional @task_wrapper decorator, that controls the behavior during
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failure. Useful for multiruns, saving info about the crash, etc.
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:param cfg: A DictConfig configuration composed by Hydra.
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:return: A tuple with metrics and dict with all instantiated objects.
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"""
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# set seed for random number generators in pytorch, numpy and python.random
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if cfg.get("seed"):
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L.seed_everything(cfg.seed, workers=True)
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log.info(f"Instantiating datamodule <{cfg.data._target_}>") # pylint: disable=protected-access
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datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)
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log.info(f"Instantiating model <{cfg.model._target_}>") # pylint: disable=protected-access
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model: LightningModule = hydra.utils.instantiate(cfg.model)
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log.info("Instantiating callbacks...")
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callbacks: List[Callback] = utils.instantiate_callbacks(cfg.get("callbacks"))
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log.info("Instantiating loggers...")
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logger: List[Logger] = utils.instantiate_loggers(cfg.get("logger"))
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log.info(f"Instantiating trainer <{cfg.trainer._target_}>") # pylint: disable=protected-access
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trainer: Trainer = hydra.utils.instantiate(cfg.trainer, callbacks=callbacks, logger=logger)
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object_dict = {
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"cfg": cfg,
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"datamodule": datamodule,
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"model": model,
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"callbacks": callbacks,
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"logger": logger,
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"trainer": trainer,
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}
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if logger:
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log.info("Logging hyperparameters!")
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utils.log_hyperparameters(object_dict)
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if cfg.get("train"):
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log.info("Starting training!")
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trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path"))
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train_metrics = trainer.callback_metrics
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if cfg.get("test"):
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log.info("Starting testing!")
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ckpt_path = trainer.checkpoint_callback.best_model_path
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if ckpt_path == "":
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log.warning("Best ckpt not found! Using current weights for testing...")
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ckpt_path = None
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trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
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log.info(f"Best ckpt path: {ckpt_path}")
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test_metrics = trainer.callback_metrics
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# merge train and test metrics
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metric_dict = {**train_metrics, **test_metrics}
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return metric_dict, object_dict
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@hydra.main(version_base="1.3", config_path="../configs", config_name="train.yaml")
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def main(cfg: DictConfig) -> Optional[float]:
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"""Main entry point for training.
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:param cfg: DictConfig configuration composed by Hydra.
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:return: Optional[float] with optimized metric value.
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"""
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# apply extra utilities
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# (e.g. ask for tags if none are provided in cfg, print cfg tree, etc.)
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utils.extras(cfg)
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# train the model
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metric_dict, _ = train(cfg)
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# safely retrieve metric value for hydra-based hyperparameter optimization
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metric_value = utils.get_metric_value(metric_dict=metric_dict, metric_name=cfg.get("optimized_metric"))
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# return optimized metric
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return metric_value
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if __name__ == "__main__":
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main() # pylint: disable=no-value-for-parameter
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