synthesize.py

import os
import re
import json
import argparse
from string import punctuation
from tqdm import tqdm

import torch
import yaml
import numpy as np
from torch.utils.data import DataLoader
from g2p_en import G2p
# from pypinyin import pinyin, Style

from utils.model import get_model, get_vocoder
from utils.tools import get_configs_of, to_device, synth_samples
from dataset import Dataset, TextDataset
from text import text_to_sequence

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


def read_lexicon(lex_path):
    lexicon = {}
    with open(lex_path) as f:
        for line in f:
            temp = re.split(r"\s+", line.strip("\n"))
            word = temp[0]
            phones = temp[1:]
            if word.lower() not in lexicon:
                lexicon[word.lower()] = phones
    return lexicon


def preprocess_english(text, preprocess_config):
    text = text.rstrip(punctuation)
    lexicon = read_lexicon(preprocess_config["path"]["lexicon_path"])

    g2p = G2p()
    phones = []
    words = re.split(r"([,;.\-\?\!\s+])", text)
    for w in words:
        if w.lower() in lexicon:
            phones += lexicon[w.lower()]
        else:
            phones += list(filter(lambda p: p != " ", g2p(w)))
    phones = "{" + "}{".join(phones) + "}"
    phones = re.sub(r"\{[^\w\s]?\}", "{sp}", phones)
    phones = phones.replace("}{", " ")

    print("Raw Text Sequence: {}".format(text))
    print("Phoneme Sequence: {}".format(phones))
    sequence = np.array(
        text_to_sequence(
            phones, preprocess_config["preprocessing"]["text"]["text_cleaners"]
        )
    )

    return np.array(sequence)


# def preprocess_mandarin(text, preprocess_config):
#     lexicon = read_lexicon(preprocess_config["path"]["lexicon_path"])

#     phones = []
#     pinyins = [
#         p[0]
#         for p in pinyin(
#             text, style=Style.TONE3, strict=False, neutral_tone_with_five=True
#         )
#     ]
#     for p in pinyins:
#         if p in lexicon:
#             phones += lexicon[p]
#         else:
#             phones.append("sp")

#     phones = "{" + " ".join(phones) + "}"
#     print("Raw Text Sequence: {}".format(text))
#     print("Phoneme Sequence: {}".format(phones))
#     sequence = np.array(
#         text_to_sequence(
#             phones, preprocess_config["preprocessing"]["text"]["text_cleaners"]
#         )
#     )

#     return np.array(sequence)


def synthesize(model, args, configs, vocoder, batchs, control_values):
    preprocess_config, model_config, train_config = configs
    pitch_control, energy_control, duration_control = control_values

    def synthesize_(batch):
        batch = to_device(batch, device)
        with torch.no_grad():
            # Forward
            output = model(
                *(batch[2:-1]),
                spker_embeds=batch[-1],
                p_control=pitch_control,
                e_control=energy_control,
                d_control=duration_control
            )[0]
            synth_samples(
                args,
                batch,
                output,
                vocoder,
                model_config,
                preprocess_config,
                train_config["path"]["result_path"],
                model.diffusion,
            )

    if args.teacher_forced:
        for batchs_ in batchs:
            for batch in tqdm(batchs_):
                batch = list(batch)
                batch[6] = None # set mel None for diffusion sampling
                synthesize_(batch)
    else:
        for batch in tqdm(batchs):
            synthesize_(batch)


if __name__ == "__main__":

    parser = argparse.ArgumentParser()
    parser.add_argument("--restore_step", type=int, required=True)
    parser.add_argument("--path_tag", type=str, default="")
    parser.add_argument(
        "--model",
        type=str,
        choices=["naive", "aux", "shallow"],
        required=True,
        help="training model type",
    )
    parser.add_argument("--teacher_forced", action="store_true")
    parser.add_argument(
        "--mode",
        type=str,
        choices=["batch", "single"],
        required=True,
        help="Synthesize a whole dataset or a single sentence",
    )
    parser.add_argument(
        "--source",
        type=str,
        default=None,
        help="path to a source file with format like train.txt and val.txt, for batch mode only",
    )
    parser.add_argument(
        "--text",
        type=str,
        default=None,
        help="raw text to synthesize, for single-sentence mode only",
    )
    parser.add_argument(
        "--speaker_id",
        type=str,
        default="p225",
        help="speaker ID for multi-speaker synthesis, for single-sentence mode only",
    )
    parser.add_argument(
        "--dataset",
        type=str,
        required=True,
        help="name of dataset",
    )
    parser.add_argument(
        "--pitch_control",
        type=float,
        default=1.0,
        help="control the pitch of the whole utterance, larger value for higher pitch",
    )
    parser.add_argument(
        "--energy_control",
        type=float,
        default=1.0,
        help="control the energy of the whole utterance, larger value for larger volume",
    )
    parser.add_argument(
        "--duration_control",
        type=float,
        default=1.0,
        help="control the speed of the whole utterance, larger value for slower speaking rate",
    )
    args = parser.parse_args()

    # Check source texts
    if args.mode == "batch":
        assert args.text is None
        if args.teacher_forced:
            assert args.source is None
        else:
            assert args.source is not None
    if args.mode == "single":
        assert args.source is None and args.text is not None and not args.teacher_forced

    # Read Config
    preprocess_config, model_config, train_config = get_configs_of(args.dataset)
    configs = (preprocess_config, model_config, train_config)
    if args.model == "shallow":
        assert args.restore_step >= train_config["step"]["total_step_aux"]
    if args.model in ["aux", "shallow"]:
        train_tag = "shallow"
    elif args.model == "naive":
        train_tag = "naive"
    else:
        raise NotImplementedError
    path_tag = "_{}".format(args.path_tag) if args.path_tag != "" else args.path_tag
    train_config["path"]["ckpt_path"] = train_config["path"]["ckpt_path"]+"_{}{}".format(train_tag, path_tag)
    train_config["path"]["log_path"] = train_config["path"]["log_path"]+"_{}{}".format(train_tag, path_tag)
    train_config["path"]["result_path"] = train_config["path"]["result_path"]+"_{}{}".format(args.model, path_tag)
    if preprocess_config["preprocessing"]["pitch"]["pitch_type"] == "cwt":
        from utils.pitch_tools import get_lf0_cwt
        preprocess_config["preprocessing"]["pitch"]["cwt_scales"] = get_lf0_cwt(np.ones(10))[1]
    os.makedirs(
        os.path.join(train_config["path"]["result_path"], str(args.restore_step)), exist_ok=True)

    # Log Configuration
    print("\n==================================== Inference Configuration ====================================")
    print(" ---> Type of Modeling:", args.model)
    print(" ---> Total Batch Size:", int(train_config["optimizer"]["batch_size"]))
    print(" ---> Path of ckpt:", train_config["path"]["ckpt_path"])
    print(" ---> Path of log:", train_config["path"]["log_path"])
    print(" ---> Path of result:", train_config["path"]["result_path"])
    print("================================================================================================")

    # Get model
    model = get_model(args, configs, device, train=False)

    # Load vocoder
    vocoder = get_vocoder(model_config, device)

    # Preprocess texts
    if args.mode == "batch":
        # Get dataset
        if args.teacher_forced:
            dataset = Dataset(
                "val.txt", args, preprocess_config, model_config, train_config, sort=False, drop_last=False
            )
        else:
            dataset = TextDataset(args.source, preprocess_config, model_config)
        batchs = DataLoader(
            dataset,
            batch_size=8,
            collate_fn=dataset.collate_fn,
        )
    if args.mode == "single":
        ids = raw_texts = [args.text[:100]]
        
        # Speaker Info
        load_spker_embed = model_config["multi_speaker"] \
            and preprocess_config["preprocessing"]["speaker_embedder"] != 'none'
        with open(os.path.join(preprocess_config["path"]["preprocessed_path"], "speakers.json")) as f:
            speaker_map = json.load(f)
        speakers = np.array([speaker_map[args.speaker_id]]) if model_config["multi_speaker"] else np.array([0]) # single speaker is allocated 0
        spker_embed = np.load(os.path.join(
            preprocess_config["path"]["preprocessed_path"],
            "spker_embed",
            "{}-spker_embed.npy".format(args.speaker_id),
        )) if load_spker_embed else None

        if preprocess_config["preprocessing"]["text"]["language"] == "en":
            texts = np.array([preprocess_english(args.text, preprocess_config)])
        elif preprocess_config["preprocessing"]["text"]["language"] == "zh":
            raise NotImplementedError
        text_lens = np.array([len(texts[0])])
        batchs = [(ids, raw_texts, speakers, texts, text_lens, max(text_lens), spker_embed)]

    control_values = args.pitch_control, args.energy_control, args.duration_control

    synthesize(model, args, configs, vocoder, batchs, control_values)