| import datetime |
| import glob |
| import os |
| import random |
| import re |
|
|
| import numpy as np |
| from scipy import signal |
|
|
| from TTS.encoder.models.lstm import LSTMSpeakerEncoder |
| from TTS.encoder.models.resnet import ResNetSpeakerEncoder |
| from TTS.utils.io import save_fsspec |
|
|
|
|
| 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 |
| |
| 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] |
| |
| 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 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_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".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, epoch): |
| 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, |
| "epoch": epoch, |
| "loss": model_loss, |
| "date": datetime.date.today().strftime("%B %d, %Y"), |
| } |
| best_loss = model_loss |
| bestmodel_path = "best_model.pth" |
| 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 |
|
|
