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import pickle |
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from argparse import ArgumentParser |
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from pathlib import Path |
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import haiku as hk |
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import jax |
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import jax.numpy as jnp |
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import matplotlib.pyplot as plt |
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import numpy as np |
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from .config import FLAGS, DurationInput |
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from .data_loader import load_phonemes_set |
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from .model import AcousticModel, DurationModel |
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def load_lexicon(fn): |
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lines = open(fn, "r").readlines() |
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lines = [l.lower().strip().split("\t") for l in lines] |
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return dict(lines) |
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def predict_duration(tokens): |
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def fwd_(x): |
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return DurationModel(is_training=False)(x) |
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forward_fn = jax.jit(hk.transform_with_state(fwd_).apply) |
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with open(FLAGS.ckpt_dir / "duration_latest_ckpt.pickle", "rb") as f: |
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dic = pickle.load(f) |
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x = DurationInput( |
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np.array(tokens, dtype=np.int32)[None, :], |
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np.array([len(tokens)], dtype=np.int32), |
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None, |
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) |
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return forward_fn(dic["params"], dic["aux"], dic["rng"], x)[0] |
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def text2tokens(text, lexicon_fn): |
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phonemes = load_phonemes_set() |
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lexicon = load_lexicon(lexicon_fn) |
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words = text.strip().lower().split() |
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tokens = [FLAGS.sil_index] |
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for word in words: |
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if word in FLAGS.special_phonemes: |
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tokens.append(phonemes.index(word)) |
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elif word in lexicon: |
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p = lexicon[word] |
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p = p.split() |
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p = [phonemes.index(pp) for pp in p] |
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tokens.extend(p) |
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tokens.append(FLAGS.word_end_index) |
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else: |
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for p in word: |
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if p in phonemes: |
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tokens.append(phonemes.index(p)) |
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tokens.append(FLAGS.word_end_index) |
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tokens.append(FLAGS.sil_index) |
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return tokens |
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def predict_mel(tokens, durations): |
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ckpt_fn = FLAGS.ckpt_dir / "acoustic_latest_ckpt.pickle" |
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with open(ckpt_fn, "rb") as f: |
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dic = pickle.load(f) |
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last_step, params, aux, rng, optim_state = ( |
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dic["step"], |
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dic["params"], |
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dic["aux"], |
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dic["rng"], |
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dic["optim_state"], |
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) |
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@hk.transform_with_state |
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def forward(tokens, durations, n_frames): |
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net = AcousticModel(is_training=False) |
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return net.inference(tokens, durations, n_frames) |
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durations = durations * FLAGS.sample_rate / (FLAGS.n_fft // 4) |
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n_frames = int(jnp.sum(durations).item()) |
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predict_fn = jax.jit(forward.apply, static_argnums=[5]) |
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tokens = np.array(tokens, dtype=np.int32)[None, :] |
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return predict_fn(params, aux, rng, tokens, durations, n_frames)[0] |
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def text2mel( |
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text: str, lexicon_fn=FLAGS.data_dir / "lexicon.txt", silence_duration: float = -1.0 |
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): |
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tokens = text2tokens(text, lexicon_fn) |
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durations = predict_duration(tokens) |
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durations = jnp.where( |
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np.array(tokens)[None, :] == FLAGS.sil_index, |
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jnp.clip(durations, a_min=silence_duration, a_max=None), |
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durations, |
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) |
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durations = jnp.where( |
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np.array(tokens)[None, :] == FLAGS.word_end_index, 0.0, durations |
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) |
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mels = predict_mel(tokens, durations) |
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if tokens[-1] == FLAGS.sil_index: |
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end_silence = durations[0, -1].item() |
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silence_frame = int(end_silence * FLAGS.sample_rate / (FLAGS.n_fft // 4)) |
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mels = mels[:, : (mels.shape[1] - silence_frame)] |
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return mels |
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if __name__ == "__main__": |
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parser = ArgumentParser() |
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parser.add_argument("--text", type=str, required=True) |
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parser.add_argument("--output", type=Path, required=True) |
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args = parser.parse_args() |
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mel = text2mel(args.text) |
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plt.figure(figsize=(10, 5)) |
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plt.imshow(mel[0].T, origin="lower", aspect="auto") |
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plt.savefig(str(args.output)) |
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plt.close() |
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mel = jax.device_get(mel) |
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mel.tofile("clip.mel") |
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