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# -*- coding: utf-8 -*-
# Copyright 2020 Minh Nguyen (@dathudeptrai)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Griffin-Lim phase reconstruction algorithm from mel spectrogram."""
import os
import librosa
import numpy as np
import soundfile as sf
import tensorflow as tf
from sklearn.preprocessing import StandardScaler
def griffin_lim_lb(
mel_spec, stats_path, dataset_config, n_iter=32, output_dir=None, wav_name="lb"
):
"""Generate wave from mel spectrogram with Griffin-Lim algorithm using Librosa.
Args:
mel_spec (ndarray): array representing the mel spectrogram.
stats_path (str): path to the `stats.npy` file containing norm statistics.
dataset_config (Dict): dataset configuration parameters.
n_iter (int): number of iterations for GL.
output_dir (str): output directory where audio file will be saved.
wav_name (str): name of the output file.
Returns:
gl_lb (ndarray): generated wave.
"""
scaler = StandardScaler()
scaler.mean_, scaler.scale_ = np.load(stats_path)
mel_spec = np.power(10.0, scaler.inverse_transform(mel_spec)).T
mel_basis = librosa.filters.mel(
dataset_config["sampling_rate"],
n_fft=dataset_config["fft_size"],
n_mels=dataset_config["num_mels"],
fmin=dataset_config["fmin"],
fmax=dataset_config["fmax"],
)
mel_to_linear = np.maximum(1e-10, np.dot(np.linalg.pinv(mel_basis), mel_spec))
gl_lb = librosa.griffinlim(
mel_to_linear,
n_iter=n_iter,
hop_length=dataset_config["hop_size"],
win_length=dataset_config["win_length"] or dataset_config["fft_size"],
)
if output_dir:
output_path = os.path.join(output_dir, f"{wav_name}.wav")
sf.write(output_path, gl_lb, dataset_config["sampling_rate"], "PCM_16")
return gl_lb
class TFGriffinLim(tf.keras.layers.Layer):
"""Griffin-Lim algorithm for phase reconstruction from mel spectrogram magnitude."""
def __init__(self, stats_path, dataset_config, normalized: bool = True):
"""Init GL params.
Args:
stats_path (str): path to the `stats.npy` file containing norm statistics.
dataset_config (Dict): dataset configuration parameters.
"""
super().__init__()
self.normalized = normalized
if normalized:
scaler = StandardScaler()
scaler.mean_, scaler.scale_ = np.load(stats_path)
self.scaler = scaler
self.ds_config = dataset_config
self.mel_basis = librosa.filters.mel(
self.ds_config["sampling_rate"],
n_fft=self.ds_config["fft_size"],
n_mels=self.ds_config["num_mels"],
fmin=self.ds_config["fmin"],
fmax=self.ds_config["fmax"],
) # [num_mels, fft_size // 2 + 1]
def save_wav(self, gl_tf, output_dir, wav_name):
"""Generate WAV file and save it.
Args:
gl_tf (tf.Tensor): reconstructed signal from GL algorithm.
output_dir (str): output directory where audio file will be saved.
wav_name (str): name of the output file.
"""
encode_fn = lambda x: tf.audio.encode_wav(x, self.ds_config["sampling_rate"])
gl_tf = tf.expand_dims(gl_tf, -1)
if not isinstance(wav_name, list):
wav_name = [wav_name]
if len(gl_tf.shape) > 2:
bs, *_ = gl_tf.shape
assert bs == len(wav_name), "Batch and 'wav_name' have different size."
tf_wav = tf.map_fn(encode_fn, gl_tf, dtype=tf.string)
for idx in tf.range(bs):
output_path = os.path.join(output_dir, f"{wav_name[idx]}.wav")
tf.io.write_file(output_path, tf_wav[idx])
else:
tf_wav = encode_fn(gl_tf)
tf.io.write_file(os.path.join(output_dir, f"{wav_name[0]}.wav"), tf_wav)
@tf.function(
input_signature=[
tf.TensorSpec(shape=[None, None, None], dtype=tf.float32),
tf.TensorSpec(shape=[], dtype=tf.int32),
]
)
def call(self, mel_spec, n_iter=32):
"""Apply GL algorithm to batched mel spectrograms.
Args:
mel_spec (tf.Tensor): normalized mel spectrogram.
n_iter (int): number of iterations to run GL algorithm.
Returns:
(tf.Tensor): reconstructed signal from GL algorithm.
"""
# de-normalize mel spectogram
if self.normalized:
mel_spec = tf.math.pow(
10.0, mel_spec * self.scaler.scale_ + self.scaler.mean_
)
else:
mel_spec = tf.math.pow(
10.0, mel_spec
) # TODO @dathudeptrai check if its ok without it wavs were too quiet
inverse_mel = tf.linalg.pinv(self.mel_basis)
# [:, num_mels] @ [fft_size // 2 + 1, num_mels].T
mel_to_linear = tf.linalg.matmul(mel_spec, inverse_mel, transpose_b=True)
mel_to_linear = tf.cast(tf.math.maximum(1e-10, mel_to_linear), tf.complex64)
init_phase = tf.cast(
tf.random.uniform(tf.shape(mel_to_linear), maxval=1), tf.complex64
)
phase = tf.math.exp(2j * np.pi * init_phase)
for _ in tf.range(n_iter):
inverse = tf.signal.inverse_stft(
mel_to_linear * phase,
frame_length=self.ds_config["win_length"] or self.ds_config["fft_size"],
frame_step=self.ds_config["hop_size"],
fft_length=self.ds_config["fft_size"],
window_fn=tf.signal.inverse_stft_window_fn(self.ds_config["hop_size"]),
)
phase = tf.signal.stft(
inverse,
self.ds_config["win_length"] or self.ds_config["fft_size"],
self.ds_config["hop_size"],
self.ds_config["fft_size"],
)
phase /= tf.cast(tf.maximum(1e-10, tf.abs(phase)), tf.complex64)
return tf.signal.inverse_stft(
mel_to_linear * phase,
frame_length=self.ds_config["win_length"] or self.ds_config["fft_size"],
frame_step=self.ds_config["hop_size"],
fft_length=self.ds_config["fft_size"],
window_fn=tf.signal.inverse_stft_window_fn(self.ds_config["hop_size"]),
)
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