mirror of https://github.com/coqui-ai/TTS.git
remove extra from utils and move funcs to io.py
This commit is contained in:
Eren Gölge
parent
812dbc2b06
commit
ce2bba543e
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@ -1,9 +1,5 @@
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import datetime
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import os
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import re
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import torch
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from TTS.speaker_encoder.model import SpeakerEncoder
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@ -13,111 +9,6 @@ def to_camel(text):
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def setup_model(c):
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model = SpeakerEncoder(c.model["input_dim"], c.model["proj_dim"], c.model["lstm_dim"], c.model["num_lstm_layers"])
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return model
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def save_checkpoint(model, optimizer, model_loss, out_path, current_step, epoch):
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checkpoint_path = "checkpoint_{}.pth.tar".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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"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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torch.save(state, checkpoint_path)
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def save_best_model(model, optimizer, model_loss, best_loss, out_path, current_step):
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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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"step": current_step,
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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.tar"
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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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torch.save(state, bestmodel_path)
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return best_loss
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def check_config_speaker_encoder(c):
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...
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# """Check the config.json file of the speaker encoder"""
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# check_argument("run_name", c, restricted=True, val_type=str)
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# check_argument("run_description", c, val_type=str)
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# # audio processing parameters
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# check_argument("audio", c, restricted=True, val_type=dict)
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# check_argument("num_mels", c["audio"], restricted=True, val_type=int, min_val=10, max_val=2056)
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# check_argument("fft_size", c["audio"], restricted=True, val_type=int, min_val=128, max_val=4058)
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# check_argument("sample_rate", c["audio"], restricted=True, val_type=int, min_val=512, max_val=100000)
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# check_argument(
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# "frame_length_ms",
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# c["audio"],
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# restricted=True,
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# val_type=float,
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# min_val=10,
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# max_val=1000,
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# alternative="win_length",
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# )
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# check_argument(
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# "frame_shift_ms", c["audio"], restricted=True, val_type=float, min_val=1, max_val=1000, alternative="hop_length"
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# )
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# check_argument("preemphasis", c["audio"], restricted=True, val_type=float, min_val=0, max_val=1)
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# check_argument("min_level_db", c["audio"], restricted=True, val_type=int, min_val=-1000, max_val=10)
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# check_argument("ref_level_db", c["audio"], restricted=True, val_type=int, min_val=0, max_val=1000)
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# check_argument("power", c["audio"], restricted=True, val_type=float, min_val=1, max_val=5)
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# check_argument("griffin_lim_iters", c["audio"], restricted=True, val_type=int, min_val=10, max_val=1000)
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# # training parameters
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# check_argument("loss", c, enum_list=["ge2e", "angleproto"], restricted=True, val_type=str)
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# check_argument("grad_clip", c, restricted=True, val_type=float)
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# check_argument("epochs", c, restricted=True, val_type=int, min_val=1)
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# check_argument("lr", c, restricted=True, val_type=float, min_val=0)
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# check_argument("lr_decay", c, restricted=True, val_type=bool)
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# check_argument("warmup_steps", c, restricted=True, val_type=int, min_val=0)
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# check_argument("tb_model_param_stats", c, restricted=True, val_type=bool)
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# check_argument("num_speakers_in_batch", c, restricted=True, val_type=int)
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# check_argument("num_loader_workers", c, restricted=True, val_type=int)
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# check_argument("wd", c, restricted=True, val_type=float, min_val=0.0, max_val=1.0)
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# # checkpoint and output parameters
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# check_argument("steps_plot_stats", c, restricted=True, val_type=int)
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# check_argument("checkpoint", c, restricted=True, val_type=bool)
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# check_argument("save_step", c, restricted=True, val_type=int)
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# check_argument("print_step", c, restricted=True, val_type=int)
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# check_argument("output_path", c, restricted=True, val_type=str)
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# # model parameters
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# check_argument("model", c, restricted=True, val_type=dict)
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# check_argument("input_dim", c["model"], restricted=True, val_type=int)
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# check_argument("proj_dim", c["model"], restricted=True, val_type=int)
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# check_argument("lstm_dim", c["model"], restricted=True, val_type=int)
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# check_argument("num_lstm_layers", c["model"], restricted=True, val_type=int)
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# check_argument("use_lstm_with_projection", c["model"], restricted=True, val_type=bool)
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# # in-memory storage parameters
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# check_argument("storage", c, restricted=True, val_type=dict)
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# check_argument("sample_from_storage_p", c["storage"], restricted=True, val_type=float, min_val=0.0, max_val=1.0)
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# check_argument("storage_size", c["storage"], restricted=True, val_type=int, min_val=1, max_val=100)
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# check_argument("additive_noise", c["storage"], restricted=True, val_type=float, min_val=0.0, max_val=1.0)
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# # datasets - checking only the first entry
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# check_argument("datasets", c, restricted=True, val_type=list)
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# for dataset_entry in c["datasets"]:
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# check_argument("name", dataset_entry, restricted=True, val_type=str)
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# check_argument("path", dataset_entry, restricted=True, val_type=str)
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# check_argument("meta_file_train", dataset_entry, restricted=True, val_type=[str, list])
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# check_argument("meta_file_val", dataset_entry, restricted=True, val_type=str)
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model = SpeakerEncoder(c.model["input_dim"], c.model["proj_dim"],
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c.model["lstm_dim"], c.model["num_lstm_layers"])
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return model
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@ -0,0 +1,39 @@
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import os
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import datetime
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import torch
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def save_checkpoint(model, optimizer, model_loss, out_path, current_step):
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checkpoint_path = "checkpoint_{}.pth.tar".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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"step": current_step,
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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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torch.save(state, checkpoint_path)
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def save_best_model(model, optimizer, model_loss, best_loss, out_path,
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current_step):
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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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"step": current_step,
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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.tar"
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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(
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model_loss, bestmodel_path))
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torch.save(state, bestmodel_path)
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return best_loss
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