synthesize.py

import os
import re
import argparse
from string import punctuation

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

from utils.model import get_model, set_noise_schedule
from utils.tools import to_device, synth_samples, compute_rtf
from dataset import TextDataset
from text import text_to_sequence
from datetime import datetime

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, step, configs, batchs, STFT, control_values):
    preprocess_config, model_config, train_config = configs
    duration_control = control_values
    sampling_rate = preprocess_config["preprocessing"]["audio"]["sampling_rate"]

    rtfs = []
    for batch in batchs:
        batch = to_device(batch, device)
        with torch.no_grad():
            # Forward
            start = datetime.now()
            output = model.inference(
                *(batch[2:]),
                d_control=duration_control,
            )
            end = datetime.now()
            inference_time = (end - start).total_seconds()
            rtf = compute_rtf(output, inference_time, sample_rate=sampling_rate)
            rtfs.append(rtf)

            synth_samples(
                output,
                batch,
                STFT,
                preprocess_config,
                train_config["path"]["result_path"],
            )
    print(f'Synthesis Done. RTF estimate: {np.mean(rtfs)} ± {np.std(rtfs)}')


if __name__ == "__main__":

    parser = argparse.ArgumentParser()
    parser.add_argument("--restore_step", type=int, required=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=int,
        default=0,
        help="speaker ID for multi-speaker synthesis, for single-sentence mode only",
    )
    parser.add_argument(
        "-p",
        "--preprocess_config",
        type=str,
        required=True,
        help="path to preprocess.yaml",
    )
    parser.add_argument(
        "-m", "--model_config", type=str, required=True, help="path to model.yaml"
    )
    parser.add_argument(
        "-t", "--train_config", type=str, required=True, help="path to train.yaml"
    )
    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.source is not None and args.text is None
    if args.mode == "single":
        assert args.source is None and args.text is not None

    # Read Config
    preprocess_config = yaml.load(
        open(args.preprocess_config, "r"), Loader=yaml.FullLoader
    )
    model_config = yaml.load(open(args.model_config, "r"), Loader=yaml.FullLoader)
    train_config = yaml.load(open(args.train_config, "r"), Loader=yaml.FullLoader)
    configs = (preprocess_config, model_config, train_config)

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

    # Set noise schedule
    noise_schedule_path = os.path.join(
        train_config["path"]["noise_schedule_path"], "{}iters.pt".format(train_config["window"]["noise_iter"])
    )
    set_noise_schedule(model, noise_schedule_path)

    # Build STFT
    STFT = Audio.stft.TacotronSTFT(
        preprocess_config["preprocessing"]["stft"]["filter_length"],
        preprocess_config["preprocessing"]["stft"]["hop_length"],
        preprocess_config["preprocessing"]["stft"]["win_length"],
        preprocess_config["preprocessing"]["mel"]["n_mel_channels"],
        preprocess_config["preprocessing"]["audio"]["sampling_rate"],
        preprocess_config["preprocessing"]["mel"]["mel_fmin"],
        preprocess_config["preprocessing"]["mel"]["mel_fmax"],
    )

    # Preprocess texts
    if args.mode == "batch":
        # Get dataset
        dataset = TextDataset(args.source, preprocess_config)
        batchs = DataLoader(
            dataset,
            batch_size=8,
            collate_fn=dataset.collate_fn,
        )
    if args.mode == "single":
        ids = raw_texts = [args.text[:100]]
        speakers = np.array([args.speaker_id])
        if preprocess_config["preprocessing"]["text"]["language"] == "en":
            texts = np.array([preprocess_english(args.text, preprocess_config)])
        elif preprocess_config["preprocessing"]["text"]["language"] == "zh":
            texts = np.array([preprocess_mandarin(args.text, preprocess_config)])
        text_lens = np.array([len(texts[0])])
        batchs = [(ids, raw_texts, speakers, texts, text_lens, max(text_lens))]

    control_values = args.duration_control

    synthesize(model, args.restore_step, configs, batchs, STFT, control_values)