mirror of https://github.com/coqui-ai/TTS.git
229 lines
8.1 KiB
Python
229 lines
8.1 KiB
Python
import datetime
|
|
import glob
|
|
import os
|
|
import random
|
|
import re
|
|
from multiprocessing import Manager
|
|
|
|
import numpy as np
|
|
from scipy import signal
|
|
|
|
from TTS.speaker_encoder.models.lstm import LSTMSpeakerEncoder
|
|
from TTS.speaker_encoder.models.resnet import ResNetSpeakerEncoder
|
|
from TTS.utils.io import save_fsspec
|
|
|
|
|
|
class Storage(object):
|
|
def __init__(self, maxsize, storage_batchs, num_speakers_in_batch, num_threads=8):
|
|
# use multiprocessing for threading safe
|
|
self.storage = Manager().list()
|
|
self.maxsize = maxsize
|
|
self.num_speakers_in_batch = num_speakers_in_batch
|
|
self.num_threads = num_threads
|
|
self.ignore_last_batch = False
|
|
|
|
if storage_batchs >= 3:
|
|
self.ignore_last_batch = True
|
|
|
|
# used for fast random sample
|
|
self.safe_storage_size = self.maxsize - self.num_threads
|
|
if self.ignore_last_batch:
|
|
self.safe_storage_size -= self.num_speakers_in_batch
|
|
|
|
def __len__(self):
|
|
return len(self.storage)
|
|
|
|
def full(self):
|
|
return len(self.storage) >= self.maxsize
|
|
|
|
def append(self, item):
|
|
# if storage is full, remove an item
|
|
if self.full():
|
|
self.storage.pop(0)
|
|
|
|
self.storage.append(item)
|
|
|
|
def get_random_sample(self):
|
|
# safe storage size considering all threads remove one item from storage in same time
|
|
storage_size = len(self.storage) - self.num_threads
|
|
|
|
if self.ignore_last_batch:
|
|
storage_size -= self.num_speakers_in_batch
|
|
|
|
return self.storage[random.randint(0, storage_size)]
|
|
|
|
def get_random_sample_fast(self):
|
|
"""Call this method only when storage is full"""
|
|
return self.storage[random.randint(0, self.safe_storage_size)]
|
|
|
|
|
|
class AugmentWAV(object):
|
|
def __init__(self, ap, augmentation_config):
|
|
|
|
self.ap = ap
|
|
self.use_additive_noise = False
|
|
|
|
if "additive" in augmentation_config.keys():
|
|
self.additive_noise_config = augmentation_config["additive"]
|
|
additive_path = self.additive_noise_config["sounds_path"]
|
|
if additive_path:
|
|
self.use_additive_noise = True
|
|
# get noise types
|
|
self.additive_noise_types = []
|
|
for key in self.additive_noise_config.keys():
|
|
if isinstance(self.additive_noise_config[key], dict):
|
|
self.additive_noise_types.append(key)
|
|
|
|
additive_files = glob.glob(os.path.join(additive_path, "**/*.wav"), recursive=True)
|
|
|
|
self.noise_list = {}
|
|
|
|
for wav_file in additive_files:
|
|
noise_dir = wav_file.replace(additive_path, "").split(os.sep)[0]
|
|
# ignore not listed directories
|
|
if noise_dir not in self.additive_noise_types:
|
|
continue
|
|
if not noise_dir in self.noise_list:
|
|
self.noise_list[noise_dir] = []
|
|
self.noise_list[noise_dir].append(wav_file)
|
|
|
|
print(
|
|
f" | > Using Additive Noise Augmentation: with {len(additive_files)} audios instances from {self.additive_noise_types}"
|
|
)
|
|
|
|
self.use_rir = False
|
|
|
|
if "rir" in augmentation_config.keys():
|
|
self.rir_config = augmentation_config["rir"]
|
|
if self.rir_config["rir_path"]:
|
|
self.rir_files = glob.glob(os.path.join(self.rir_config["rir_path"], "**/*.wav"), recursive=True)
|
|
self.use_rir = True
|
|
|
|
print(f" | > Using RIR Noise Augmentation: with {len(self.rir_files)} audios instances")
|
|
|
|
self.create_augmentation_global_list()
|
|
|
|
def create_augmentation_global_list(self):
|
|
if self.use_additive_noise:
|
|
self.global_noise_list = self.additive_noise_types
|
|
else:
|
|
self.global_noise_list = []
|
|
if self.use_rir:
|
|
self.global_noise_list.append("RIR_AUG")
|
|
|
|
def additive_noise(self, noise_type, audio):
|
|
|
|
clean_db = 10 * np.log10(np.mean(audio ** 2) + 1e-4)
|
|
|
|
noise_list = random.sample(
|
|
self.noise_list[noise_type],
|
|
random.randint(
|
|
self.additive_noise_config[noise_type]["min_num_noises"],
|
|
self.additive_noise_config[noise_type]["max_num_noises"],
|
|
),
|
|
)
|
|
|
|
audio_len = audio.shape[0]
|
|
noises_wav = None
|
|
for noise in noise_list:
|
|
noiseaudio = self.ap.load_wav(noise, sr=self.ap.sample_rate)[:audio_len]
|
|
|
|
if noiseaudio.shape[0] < audio_len:
|
|
continue
|
|
|
|
noise_snr = random.uniform(
|
|
self.additive_noise_config[noise_type]["min_snr_in_db"],
|
|
self.additive_noise_config[noise_type]["max_num_noises"],
|
|
)
|
|
noise_db = 10 * np.log10(np.mean(noiseaudio ** 2) + 1e-4)
|
|
noise_wav = np.sqrt(10 ** ((clean_db - noise_db - noise_snr) / 10)) * noiseaudio
|
|
|
|
if noises_wav is None:
|
|
noises_wav = noise_wav
|
|
else:
|
|
noises_wav += noise_wav
|
|
|
|
# if all possible files is less than audio, choose other files
|
|
if noises_wav is None:
|
|
return self.additive_noise(noise_type, audio)
|
|
|
|
return audio + noises_wav
|
|
|
|
def reverberate(self, audio):
|
|
audio_len = audio.shape[0]
|
|
|
|
rir_file = random.choice(self.rir_files)
|
|
rir = self.ap.load_wav(rir_file, sr=self.ap.sample_rate)
|
|
rir = rir / np.sqrt(np.sum(rir ** 2))
|
|
return signal.convolve(audio, rir, mode=self.rir_config["conv_mode"])[:audio_len]
|
|
|
|
def apply_one(self, audio):
|
|
noise_type = random.choice(self.global_noise_list)
|
|
if noise_type == "RIR_AUG":
|
|
return self.reverberate(audio)
|
|
|
|
return self.additive_noise(noise_type, audio)
|
|
|
|
|
|
def to_camel(text):
|
|
text = text.capitalize()
|
|
return re.sub(r"(?!^)_([a-zA-Z])", lambda m: m.group(1).upper(), text)
|
|
|
|
|
|
def setup_speaker_encoder_model(config: "Coqpit"):
|
|
if config.model_params["model_name"].lower() == "lstm":
|
|
model = LSTMSpeakerEncoder(
|
|
config.model_params["input_dim"],
|
|
config.model_params["proj_dim"],
|
|
config.model_params["lstm_dim"],
|
|
config.model_params["num_lstm_layers"],
|
|
use_torch_spec=config.model_params.get("use_torch_spec", False),
|
|
audio_config=config.audio,
|
|
)
|
|
elif config.model_params["model_name"].lower() == "resnet":
|
|
model = ResNetSpeakerEncoder(
|
|
input_dim=config.model_params["input_dim"],
|
|
proj_dim=config.model_params["proj_dim"],
|
|
log_input=config.model_params.get("log_input", False),
|
|
use_torch_spec=config.model_params.get("use_torch_spec", False),
|
|
audio_config=config.audio,
|
|
)
|
|
return model
|
|
|
|
|
|
def save_checkpoint(model, optimizer, criterion, model_loss, out_path, current_step, epoch):
|
|
checkpoint_path = "checkpoint_{}.pth.tar".format(current_step)
|
|
checkpoint_path = os.path.join(out_path, checkpoint_path)
|
|
print(" | | > Checkpoint saving : {}".format(checkpoint_path))
|
|
|
|
new_state_dict = model.state_dict()
|
|
state = {
|
|
"model": new_state_dict,
|
|
"optimizer": optimizer.state_dict() if optimizer is not None else None,
|
|
"criterion": criterion.state_dict(),
|
|
"step": current_step,
|
|
"epoch": epoch,
|
|
"loss": model_loss,
|
|
"date": datetime.date.today().strftime("%B %d, %Y"),
|
|
}
|
|
save_fsspec(state, checkpoint_path)
|
|
|
|
|
|
def save_best_model(model, optimizer, criterion, model_loss, best_loss, out_path, current_step):
|
|
if model_loss < best_loss:
|
|
new_state_dict = model.state_dict()
|
|
state = {
|
|
"model": new_state_dict,
|
|
"optimizer": optimizer.state_dict(),
|
|
"criterion": criterion.state_dict(),
|
|
"step": current_step,
|
|
"loss": model_loss,
|
|
"date": datetime.date.today().strftime("%B %d, %Y"),
|
|
}
|
|
best_loss = model_loss
|
|
bestmodel_path = "best_model.pth.tar"
|
|
bestmodel_path = os.path.join(out_path, bestmodel_path)
|
|
print("\n > BEST MODEL ({0:.5f}) : {1:}".format(model_loss, bestmodel_path))
|
|
save_fsspec(state, bestmodel_path)
|
|
return best_loss
|