TTS/utils/synthesis.py

import pkg_resources
installed = {pkg.key for pkg in pkg_resources.working_set}  #pylint: disable=not-an-iterable
if 'tensorflow' in installed or 'tensorflow-gpu' in installed:
    import tensorflow as tf
import torch
import numpy as np
from .text import text_to_sequence, phoneme_to_sequence


def text_to_seqvec(text, CONFIG):
    text_cleaner = [CONFIG.text_cleaner]
    # text ot phonemes to sequence vector
    if CONFIG.use_phonemes:
        seq = np.asarray(
            phoneme_to_sequence(text, text_cleaner, CONFIG.phoneme_language,
                                CONFIG.enable_eos_bos_chars,
                                tp=CONFIG.characters if 'characters' in CONFIG.keys() else None),
            dtype=np.int32)
    else:
        seq = np.asarray(text_to_sequence(text, text_cleaner, tp=CONFIG.characters if 'characters' in CONFIG.keys() else None), dtype=np.int32)
    return seq


def numpy_to_torch(np_array, dtype, cuda=False):
    if np_array is None:
        return None
    tensor = torch.as_tensor(np_array, dtype=dtype)
    if cuda:
        return tensor.cuda()
    return tensor


def numpy_to_tf(np_array, dtype):
    if np_array is None:
        return None
    tensor = tf.convert_to_tensor(np_array, dtype=dtype)
    return tensor


def compute_style_mel(style_wav, ap):
    style_mel = ap.melspectrogram(
        ap.load_wav(style_wav)).expand_dims(0)
    return style_mel


def run_model_torch(model, inputs, CONFIG, truncated, speaker_id=None, style_mel=None):
    if CONFIG.use_gst:
        decoder_output, postnet_output, alignments, stop_tokens = model.inference(
            inputs, style_mel=style_mel, speaker_ids=speaker_id)
    else:
        if truncated:
            decoder_output, postnet_output, alignments, stop_tokens = model.inference_truncated(
                inputs, speaker_ids=speaker_id)
        else:
            decoder_output, postnet_output, alignments, stop_tokens = model.inference(
                inputs, speaker_ids=speaker_id)
    return decoder_output, postnet_output, alignments, stop_tokens


def run_model_tf(model, inputs, CONFIG, truncated, speaker_id=None, style_mel=None):
    if CONFIG.use_gst and style_mel is not None:
        raise NotImplementedError(' [!] GST inference not implemented for TF')
    if truncated:
        raise NotImplementedError(' [!] Truncated inference not implemented for TF')
    if speaker_id is not None:
        raise NotImplementedError(' [!] Multi-Speaker not implemented for TF')
    # TODO: handle multispeaker case
    decoder_output, postnet_output, alignments, stop_tokens = model(
        inputs, training=False)
    return decoder_output, postnet_output, alignments, stop_tokens


def parse_outputs_torch(postnet_output, decoder_output, alignments, stop_tokens):
    postnet_output = postnet_output[0].data.cpu().numpy()
    decoder_output = decoder_output[0].data.cpu().numpy()
    alignment = alignments[0].cpu().data.numpy()
    stop_tokens = stop_tokens[0].cpu().numpy()
    return postnet_output, decoder_output, alignment, stop_tokens


def parse_outputs_tf(postnet_output, decoder_output, alignments, stop_tokens):
    postnet_output = postnet_output[0].numpy()
    decoder_output = decoder_output[0].numpy()
    alignment = alignments[0].numpy()
    stop_tokens = stop_tokens[0].numpy()
    return postnet_output, decoder_output, alignment, stop_tokens


def trim_silence(wav, ap):
    return wav[:ap.find_endpoint(wav)]


def inv_spectrogram(postnet_output, ap, CONFIG):
    if CONFIG.model in ["Tacotron", "TacotronGST"]:
        wav = ap.inv_spectrogram(postnet_output.T)
    else:
        wav = ap.inv_melspectrogram(postnet_output.T)
    return wav


def id_to_torch(speaker_id):
    if speaker_id is not None:
        speaker_id = np.asarray(speaker_id)
        speaker_id = torch.from_numpy(speaker_id).unsqueeze(0)
    return speaker_id


# TODO: perform GL with pytorch for batching
def apply_griffin_lim(inputs, input_lens, CONFIG, ap):
    '''Apply griffin-lim to each sample iterating throught the first dimension.
    Args:
        inputs (Tensor or np.Array): Features to be converted by GL. First dimension is the batch size.
        input_lens (Tensor or np.Array): 1D array of sample lengths.
        CONFIG (Dict): TTS config.
        ap (AudioProcessor): TTS audio processor.
    '''
    wavs = []
    for idx, spec in enumerate(inputs):
        wav_len = (input_lens[idx] * ap.hop_length) - ap.hop_length  # inverse librosa padding
        wav = inv_spectrogram(spec, ap, CONFIG)
        # assert len(wav) == wav_len, f" [!] wav lenght: {len(wav)} vs expected: {wav_len}"
        wavs.append(wav[:wav_len])
    return wavs


def synthesis(model,
              text,
              CONFIG,
              use_cuda,
              ap,
              speaker_id=None,
              style_wav=None,
              truncated=False,
              enable_eos_bos_chars=False, #pylint: disable=unused-argument
              use_griffin_lim=False,
              do_trim_silence=False,
              backend='torch'):
    """Synthesize voice for the given text.

        Args:
            model (TTS.models): model to synthesize.
            text (str): target text
            CONFIG (dict): config dictionary to be loaded from config.json.
            use_cuda (bool): enable cuda.
            ap (TTS.utils.audio.AudioProcessor): audio processor to process
                model outputs.
            speaker_id (int): id of speaker
            style_wav (str): Uses for style embedding of GST.
            truncated (bool): keep model states after inference. It can be used
                for continuous inference at long texts.
            enable_eos_bos_chars (bool): enable special chars for end of sentence and start of sentence.
            do_trim_silence (bool): trim silence after synthesis.
            backend (str): tf or torch
    """
    # GST processing
    style_mel = None
    if CONFIG.model == "TacotronGST" and style_wav is not None:
        style_mel = compute_style_mel(style_wav, ap)
    # preprocess the given text
    inputs = text_to_seqvec(text, CONFIG)
    # pass tensors to backend
    if backend == 'torch':
        speaker_id = id_to_torch(speaker_id)
        style_mel = numpy_to_torch(style_mel, torch.float, cuda=use_cuda)
        inputs = numpy_to_torch(inputs, torch.long, cuda=use_cuda)
        inputs = inputs.unsqueeze(0)
    else:
        # TODO: handle speaker id for tf model
        style_mel = numpy_to_tf(style_mel, tf.float32)
        inputs = numpy_to_tf(inputs, tf.int32)
        inputs = tf.expand_dims(inputs, 0)
    # synthesize voice
    if backend == 'torch':
        decoder_output, postnet_output, alignments, stop_tokens = run_model_torch(
            model, inputs, CONFIG, truncated, speaker_id, style_mel)
        postnet_output, decoder_output, alignment, stop_tokens = parse_outputs_torch(
            postnet_output, decoder_output, alignments, stop_tokens)
    else:
        decoder_output, postnet_output, alignments, stop_tokens = run_model_tf(
            model, inputs, CONFIG, truncated, speaker_id, style_mel)
        postnet_output, decoder_output, alignment, stop_tokens = parse_outputs_tf(
            postnet_output, decoder_output, alignments, stop_tokens)
    # convert outputs to numpy
    # plot results
    wav = None
    if use_griffin_lim:
        wav = inv_spectrogram(postnet_output, ap, CONFIG)
        # trim silence
        if do_trim_silence:
            wav = trim_silence(wav, ap)
    return wav, alignment, decoder_output, postnet_output, stop_tokens, inputs