import os import jax import jax.numpy as jnp import librosa import numpy as np import pax # from text import english_cleaners from utils import ( create_tacotron_model, load_tacotron_ckpt, load_tacotron_config, load_wavegru_ckpt, load_wavegru_config, ) from wavegru import WaveGRU # os.environ["PHONEMIZER_ESPEAK_LIBRARY"] = "./espeak/usr/lib/libespeak-ng.so.1.1.51" # from phonemizer.backend import EspeakBackend # backend = EspeakBackend("en-us", preserve_punctuation=True, with_stress=True) def load_tacotron_model(alphabet_file, config_file, model_file): """load tacotron model to memory""" with open(alphabet_file, "r", encoding="utf-8") as f: alphabet = f.read().split("\n") config = load_tacotron_config(config_file) net = create_tacotron_model(config) _, net, _ = load_tacotron_ckpt(net, None, model_file) net = net.eval() net = jax.device_put(net) return alphabet, net, config tacotron_inference_fn = pax.pure(lambda net, text: net.inference(text, max_len=2400)) def text_to_mel(net, text, alphabet, config): """convert text to mel spectrogram""" # text = english_cleaners(text) # text = backend.phonemize([text], strip=True)[0] text = text + config["END_CHARACTER"] text = text + config["PAD"] * (100 - (len(text) % 100)) tokens = [] for c in text: if c in alphabet: tokens.append(alphabet.index(c)) tokens = jnp.array(tokens, dtype=jnp.int32) mel = tacotron_inference_fn(net, tokens[None]) return mel def load_wavegru_net(config_file, model_file): """load wavegru to memory""" config = load_wavegru_config(config_file) net = WaveGRU( mel_dim=config["mel_dim"], rnn_dim=config["rnn_dim"], upsample_factors=config["upsample_factors"], has_linear_output=True, ) _, net, _ = load_wavegru_ckpt(net, None, model_file) net = net.eval() net = jax.device_put(net) return config, net wavegru_inference = pax.pure(lambda net, mel: net.inference(mel, no_gru=True)) def mel_to_wav(net, netcpp, mel, config): """convert mel to wav""" if len(mel.shape) == 2: mel = mel[None] pad = config["num_pad_frames"] // 2 + 2 mel = np.pad(mel, [(0, 0), (pad, pad), (0, 0)], mode="edge") ft = wavegru_inference(net, mel) ft = jax.device_get(ft[0]) wav = netcpp.inference(ft, 1.0) wav = np.array(wav) wav = librosa.mu_expand(wav - 127, mu=255) wav = librosa.effects.deemphasis(wav, coef=0.86) wav = wav * 2.0 wav = wav / max(1.0, np.max(np.abs(wav))) wav = wav * 2**15 wav = np.clip(wav, a_min=-(2**15), a_max=(2**15) - 1) wav = wav.astype(np.int16) return wav