mirror of https://github.com/coqui-ai/TTS.git
177 lines
6.6 KiB
Python
177 lines
6.6 KiB
Python
import datetime
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import glob
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import os
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import random
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import numpy as np
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from scipy import signal
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from trainer.io import save_fsspec
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from TTS.encoder.models.lstm import LSTMSpeakerEncoder
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from TTS.encoder.models.resnet import ResNetSpeakerEncoder
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class AugmentWAV(object):
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def __init__(self, ap, augmentation_config):
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self.ap = ap
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self.use_additive_noise = False
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if "additive" in augmentation_config.keys():
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self.additive_noise_config = augmentation_config["additive"]
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additive_path = self.additive_noise_config["sounds_path"]
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if additive_path:
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self.use_additive_noise = True
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# get noise types
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self.additive_noise_types = []
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for key in self.additive_noise_config.keys():
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if isinstance(self.additive_noise_config[key], dict):
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self.additive_noise_types.append(key)
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additive_files = glob.glob(os.path.join(additive_path, "**/*.wav"), recursive=True)
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self.noise_list = {}
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for wav_file in additive_files:
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noise_dir = wav_file.replace(additive_path, "").split(os.sep)[0]
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# ignore not listed directories
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if noise_dir not in self.additive_noise_types:
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continue
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if not noise_dir in self.noise_list:
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self.noise_list[noise_dir] = []
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self.noise_list[noise_dir].append(wav_file)
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print(
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f" | > Using Additive Noise Augmentation: with {len(additive_files)} audios instances from {self.additive_noise_types}"
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)
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self.use_rir = False
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if "rir" in augmentation_config.keys():
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self.rir_config = augmentation_config["rir"]
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if self.rir_config["rir_path"]:
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self.rir_files = glob.glob(os.path.join(self.rir_config["rir_path"], "**/*.wav"), recursive=True)
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self.use_rir = True
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print(f" | > Using RIR Noise Augmentation: with {len(self.rir_files)} audios instances")
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self.create_augmentation_global_list()
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def create_augmentation_global_list(self):
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if self.use_additive_noise:
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self.global_noise_list = self.additive_noise_types
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else:
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self.global_noise_list = []
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if self.use_rir:
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self.global_noise_list.append("RIR_AUG")
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def additive_noise(self, noise_type, audio):
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clean_db = 10 * np.log10(np.mean(audio**2) + 1e-4)
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noise_list = random.sample(
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self.noise_list[noise_type],
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random.randint(
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self.additive_noise_config[noise_type]["min_num_noises"],
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self.additive_noise_config[noise_type]["max_num_noises"],
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),
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)
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audio_len = audio.shape[0]
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noises_wav = None
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for noise in noise_list:
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noiseaudio = self.ap.load_wav(noise, sr=self.ap.sample_rate)[:audio_len]
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if noiseaudio.shape[0] < audio_len:
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continue
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noise_snr = random.uniform(
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self.additive_noise_config[noise_type]["min_snr_in_db"],
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self.additive_noise_config[noise_type]["max_num_noises"],
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)
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noise_db = 10 * np.log10(np.mean(noiseaudio**2) + 1e-4)
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noise_wav = np.sqrt(10 ** ((clean_db - noise_db - noise_snr) / 10)) * noiseaudio
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if noises_wav is None:
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noises_wav = noise_wav
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else:
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noises_wav += noise_wav
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# if all possible files is less than audio, choose other files
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if noises_wav is None:
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return self.additive_noise(noise_type, audio)
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return audio + noises_wav
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def reverberate(self, audio):
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audio_len = audio.shape[0]
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rir_file = random.choice(self.rir_files)
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rir = self.ap.load_wav(rir_file, sr=self.ap.sample_rate)
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rir = rir / np.sqrt(np.sum(rir**2))
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return signal.convolve(audio, rir, mode=self.rir_config["conv_mode"])[:audio_len]
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def apply_one(self, audio):
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noise_type = random.choice(self.global_noise_list)
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if noise_type == "RIR_AUG":
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return self.reverberate(audio)
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return self.additive_noise(noise_type, audio)
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def setup_encoder_model(config: "Coqpit"):
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if config.model_params["model_name"].lower() == "lstm":
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model = LSTMSpeakerEncoder(
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config.model_params["input_dim"],
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config.model_params["proj_dim"],
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config.model_params["lstm_dim"],
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config.model_params["num_lstm_layers"],
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use_torch_spec=config.model_params.get("use_torch_spec", False),
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audio_config=config.audio,
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)
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elif config.model_params["model_name"].lower() == "resnet":
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model = ResNetSpeakerEncoder(
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input_dim=config.model_params["input_dim"],
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proj_dim=config.model_params["proj_dim"],
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log_input=config.model_params.get("log_input", False),
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use_torch_spec=config.model_params.get("use_torch_spec", False),
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audio_config=config.audio,
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)
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return model
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def save_checkpoint(model, optimizer, criterion, model_loss, out_path, current_step, epoch):
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checkpoint_path = "checkpoint_{}.pth".format(current_step)
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checkpoint_path = os.path.join(out_path, checkpoint_path)
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print(" | | > Checkpoint saving : {}".format(checkpoint_path))
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new_state_dict = model.state_dict()
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state = {
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"model": new_state_dict,
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"optimizer": optimizer.state_dict() if optimizer is not None else None,
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"criterion": criterion.state_dict(),
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"step": current_step,
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"epoch": epoch,
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"loss": model_loss,
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"date": datetime.date.today().strftime("%B %d, %Y"),
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}
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save_fsspec(state, checkpoint_path)
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def save_best_model(model, optimizer, criterion, model_loss, best_loss, out_path, current_step, epoch):
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if model_loss < best_loss:
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new_state_dict = model.state_dict()
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state = {
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"model": new_state_dict,
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"optimizer": optimizer.state_dict(),
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"criterion": criterion.state_dict(),
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"step": current_step,
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"epoch": epoch,
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"loss": model_loss,
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"date": datetime.date.today().strftime("%B %d, %Y"),
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}
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best_loss = model_loss
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bestmodel_path = "best_model.pth"
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bestmodel_path = os.path.join(out_path, bestmodel_path)
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print("\n > BEST MODEL ({0:.5f}) : {1:}".format(model_loss, bestmodel_path))
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save_fsspec(state, bestmodel_path)
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return best_loss
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