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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 | |
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=1200)) | |
def text_to_mel(net, text, alphabet, config): | |
"""convert text to mel spectrogram""" | |
text = english_cleaners(text) | |
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"], | |
) | |
_, 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 + 4 | |
mel = np.pad( | |
mel, | |
[(0, 0), (pad, pad), (0, 0)], | |
constant_values=np.log(config["mel_min"]), | |
) | |
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 | |