inference.py

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import argparse
import numpy as np
import os
import sys
import time
import torch
from apex import amp
from common.audio_processing import griffin_lim
from common.layers import TacotronSTFT
from common.utils import save_wav
from scipy.io.wavfile import write
from tacotron2.loader import parse_tacotron2_args
from tacotron2.loader import get_tacotron2_model
from tacotron2.text import text_to_sequence
from train import parse_training_args
from dllogger.logger import LOGGER
import dllogger.logger as dllg
from dllogger.autologging import log_hardware, log_args


def parse_args(parser):
    """
    Parse commandline arguments.
    """
    parser.add_argument('-i', '--input-file', type=str, default="text.txt", help='full path to the input text (phareses separated by new line)')
    parser.add_argument('--checkpoint', type=str, default="logs/checkpoint_latest.pt", help='full path to the Tacotron2 model checkpoint file')
    parser.add_argument('-id', '--speaker-id', default=0, type=int, help='Speaker identity')
    parser.add_argument('-sn', '--speaker-num', default=1, type=int, help='Speaker number')
    parser.add_argument('--include-warmup', action='store_true', help='Include warmup')

    return parser


def load_checkpoint(checkpoint_path, model_name):
    assert os.path.isfile(checkpoint_path)
    model.load_state_dict(torch.load(checkpoint_path))
    print(f"Loaded checkpoint: {checkpoint_path}")
    return model


def load_and_setup_model(parser, args):
    checkpoint_path = args.checkpoint
    parser = parse_tacotron2_args(parser, add_help=False)
    args, _ = parser.parse_known_args()
    model = get_tacotron2_model(args, args.speaker_num, is_training=False)
    model.restore_checkpoint(checkpoint_path)
    model.eval()

    if args.amp_run:
        model, _ = amp.initialize(model, [], opt_level='O1')

    return model, args


# taken from tacotron2/data_function.py:TextMelCollate.__call__
def pad_sequences(sequences):
    # Right zero-pad all one-hot text sequences to max input length
    text_lengths, ids_sorted_decreasing = torch.sort(
        torch.IntTensor([len(x) for x in sequences]),
        dim=0, descending=True)
    max_text_len = text_lengths[0]

    texts = []
    for i in range(len(ids_sorted_decreasing)):
        text = sequences[ids_sorted_decreasing[i]]
        texts.append(np.pad(text, [0, max_text_len - len(text)], mode='constant'))

    texts = torch.from_numpy(np.stack(texts))
    return texts, text_lengths, ids_sorted_decreasing


def prepare_input_sequence(texts, speaker_id):
    sequences = [text_to_sequence(text, speaker_id, ['basic_cleaners'])[:] for text in texts]
    texts, text_lengths, ids_sorted_decreasing = pad_sequences(sequences)

    if torch.cuda.is_available():
        texts = texts.cuda().long()
        text_lengths = text_lengths.cuda().int()
    else:
        texts = texts.long()
        text_lengths = text_lengths.int()

    return texts, text_lengths, ids_sorted_decreasing


class MeasureTime():
    def __init__(self, measurements, key):
        self.measurements = measurements
        self.key = key

    def __enter__(self):
        torch.cuda.synchronize()
        self.t0 = time.perf_counter()

    def __exit__(self, exc_type, exc_value, exc_traceback):
        torch.cuda.synchronize()
        self.measurements[self.key] = time.perf_counter() - self.t0


def main():
    """
    Launches text to speech (inference).
    Inference is executed on a single GPU.
    """
    parser = argparse.ArgumentParser(description='PyTorch Tacotron 2 Inference')
    parser = parse_training_args(parser)
    parser = parse_args(parser)
    args, _ = parser.parse_known_args()

    LOGGER.set_model_name("Tacotron2_PyT")
    LOGGER.set_backends([
        dllg.StdOutBackend(log_file=None, logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=1),
        dllg.JsonBackend(log_file=args.log_file, logging_scope=dllg.TRAIN_ITER_SCOPE, iteration_interval=1)
    ])
    LOGGER.register_metric("tacotron2_frames_per_sec", metric_scope=dllg.TRAIN_ITER_SCOPE)
    LOGGER.register_metric("tacotron2_latency", metric_scope=dllg.TRAIN_ITER_SCOPE)
    LOGGER.register_metric("latency", metric_scope=dllg.TRAIN_ITER_SCOPE)

    model, args = load_and_setup_model(parser, args)

    log_hardware()
    log_args(args)

    try:
        f = open(args.input_file)
        sentences = list(map(lambda s : s.strip(), f.readlines()))
    except UnicodeDecodeError:
        f = open(args.input_file, encoding='gbk')
        sentences = list(map(lambda s : s.strip(), f.readlines()))

    os.makedirs(args.output_dir, exist_ok=True)

    LOGGER.iteration_start()

    measurements = {}

    sequences, text_lengths, ids_sorted_decreasing = prepare_input_sequence(sentences, args.speaker_id)

    with torch.no_grad(), MeasureTime(measurements, "tacotron2_time"):
        outputs = model.infer(sequences, text_lengths)
        _, mels, _, _, mel_lengths = [output.cpu() for output in outputs]

    tacotron2_infer_perf = mels.size(0)*mels.size(2)/measurements['tacotron2_time']

    LOGGER.log(key="tacotron2_frames_per_sec", value=tacotron2_infer_perf)
    LOGGER.log(key="tacotron2_latency", value=measurements['tacotron2_time'])
    LOGGER.log(key="latency", value=(measurements['tacotron2_time']))
    LOGGER.iteration_stop()
    LOGGER.finish()

    # recover to the original order and concatenate
    stft = TacotronSTFT(args.filter_length, args.hop_length, args.win_length,
                        args.n_mel_channels, args.sampling_rate, args.mel_fmin, args.mel_fmax)
    ids_sorted_decreasing = ids_sorted_decreasing.numpy().tolist()
    mels = [mel[:, :length] for mel, length in zip(mels, mel_lengths)]
    mels = [mels[ids_sorted_decreasing.index(i)] for i in range(len(ids_sorted_decreasing))]
    magnitudes = stft.inv_mel_spectrogram(torch.cat(mels, axis=-1))
    wav = griffin_lim(magnitudes, stft.stft_fn)
    save_wav(wav, os.path.join(args.output_dir, 'eval.wav'))
    np.save(os.path.join(args.output_dir, 'eval.npy'), np.concatenate(mels, axis=-1), allow_pickle=False)


if __name__ == '__main__':
    main()