feat(python tcp server): TTS tmp inference

.gitignore

@@ -24,6 +24,7 @@ debug.log
 leon.json
 bridges/python/src/Pipfile.lock
 tcp_server/src/Pipfile.lock
+tcp_server/src/lib/tts/models/*.pth
 !tcp_server/**/.gitkeep
 !bridges/python/**/.gitkeep
 !bridges/nodejs/**/.gitkeep
@@ -32,7 +33,6 @@ tcp_server/src/Pipfile.lock
 skills/**/src/settings.json
 skills/**/memory/*.json
 core/data/models/*.nlp
-core/data/models/tts/*.pth
 core/data/models/llm/*
 package.json.backup
 .python-version

tcp_server/src/lib/constants.py

@@ -1,16 +1,13 @@
 import os
 
-MODELS_PATH = os.path.join(
-    os.getcwd(),
-    'core',
-    'data',
-    'models'
-)
+SRC_PATH = os.path.join(os.getcwd(), 'tcp_server', 'src')
 
+# TTS
 TTS_MODEL_VERSION = 'V1'
 TTS_MODEL_NAME = f'EN-Leon-{TTS_MODEL_VERSION}'
 TTS_MODEL_FILE_NAME = f'{TTS_MODEL_NAME}.pth'
-TTS_MODEL_FOLDER_PATH = os.path.join(MODELS_PATH, 'tts')
-TTS_MODEL_PATH = os.path.join(TTS_MODEL_FOLDER_PATH, TTS_MODEL_FILE_NAME)
+TTS_LIB_PATH = os.path.join(SRC_PATH, 'lib', 'tts')
+TTS_MODEL_FOLDER_PATH = os.path.join(TTS_LIB_PATH, 'models')
 TTS_MODEL_CONFIG_PATH = os.path.join(TTS_MODEL_FOLDER_PATH, 'config.json')
+TTS_MODEL_PATH = os.path.join(TTS_MODEL_FOLDER_PATH, TTS_MODEL_FILE_NAME)
 IS_TTS_ENABLED = os.environ.get('LEON_TTS', 'true') == 'true'

tcp_server/src/lib/tcp_server.py

@@ -1,9 +1,11 @@
 import socket
 import json
+import os
 from typing import Union
 
 import lib.nlp as nlp
-from .tts.tts import TTS
+from .tts.api import TTS
+from .constants import TTS_MODEL_CONFIG_PATH, TTS_MODEL_PATH, IS_TTS_ENABLED
 
 
 class TCPServer:
@@ -13,12 +15,40 @@ class TCPServer:
         self.tcp_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
         self.conn = None
         self.addr = None
-        self.tts = TTS()
+        self.tts = None
 
     @staticmethod
     def log(*args, **kwargs):
         print('[TCP Server]', *args, **kwargs)
 
+    def init_tts(self):
+        print('IS_TTS_ENABLED', IS_TTS_ENABLED)
+        # TODO: FIX IT
+        if not IS_TTS_ENABLED:
+            self.log('TTS is disabled')
+            return
+
+        if not os.path.exists(TTS_MODEL_CONFIG_PATH):
+            self.log(f'TTS model config not found at {TTS_MODEL_CONFIG_PATH}')
+            return
+
+        if not os.path.exists(TTS_MODEL_PATH):
+            self.log(f'TTS model not found at {TTS_MODEL_PATH}')
+            return
+
+        self.tts = TTS(language='EN',
+                       device='auto',
+                       config_path=TTS_MODEL_CONFIG_PATH,
+                       ckpt_path=TTS_MODEL_PATH
+        )
+
+        text = 'Hello, I am Leon. How can I help you?'
+        speaker_ids = self.tts.hps.data.spk2id
+        output_path = 'output.wav'
+        speed = 1.0
+
+        self.tts.tts_to_file(text, speaker_ids['EN-Leon-V1'], output_path, speed=speed)
+
     def init(self):
         # Make sure to establish TCP connection by reusing the address so it does not conflict with port already in use
         self.tcp_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)

tcp_server/src/lib/tts/api.py

@@ -17,14 +17,21 @@ class TTS(nn.Module):
                 config_path=None,
                 ckpt_path=None):
         super().__init__()
+
+        self.log('Loading model...')
+
         if device == 'auto':
             device = 'cpu'
+
             if torch.cuda.is_available(): device = 'cuda'
+            else: self.log('GPU not available. CUDA is not installed?')
+
             if torch.backends.mps.is_available(): device = 'mps'
         if 'cuda' in device:
             assert torch.cuda.is_available()
 
-        # config_path =
+        self.log(f'Device: {device}')
+
         hps = utils.get_hparams_from_file(config_path)
 
         num_languages = hps.num_languages
@@ -54,6 +61,8 @@ class TTS(nn.Module):
         language = language.split('_')[0]
         self.language = 'ZH_MIX_EN' if language == 'ZH' else language # we support a ZH_MIX_EN model
 
+        self.log('Model loaded')
+
     @staticmethod
     def audio_numpy_concat(segment_data_list, sr, speed=1.):
         audio_segments = []
@@ -125,3 +134,8 @@ class TTS(nn.Module):
                 soundfile.write(output_path, audio, self.hps.data.sampling_rate, format=format)
             else:
                 soundfile.write(output_path, audio, self.hps.data.sampling_rate)
+
+
+    @staticmethod
+    def log(*args, **kwargs):
+        print('[TTS]', *args, **kwargs)

tcp_server/src/lib/tts/models.py

@@ -3,15 +3,15 @@ import torch
 from torch import nn
 from torch.nn import functional as F
 
-from melo import commons
-from melo import modules
-from melo import attentions
+from lib.tts import commons
+from lib.tts import modules
+from lib.tts import attentions
 
 from torch.nn import Conv1d, ConvTranspose1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
 
-from melo.commons import init_weights, get_padding
-import melo.monotonic_align as monotonic_align
+from lib.tts.commons import init_weights, get_padding
+import lib.tts.monotonic_align as monotonic_align
 
 
 class DurationDiscriminator(nn.Module):  # vits2

tcp_server/src/lib/tts/utils.py

@@ -1,4 +1,328 @@
+import os
+import glob
+import argparse
+import logging
 import json
+import subprocess
+import torch
+from lib.tts.text import cleaned_text_to_sequence, get_bert
+from lib.tts.text.cleaner import clean_text
+from lib.tts import commons
+
+MATPLOTLIB_FLAG = False
+
+logger = logging.getLogger(__name__)
+
+
+
+def get_text_for_tts_infer(text, language_str, hps, device, symbol_to_id=None):
+    norm_text, phone, tone, word2ph = clean_text(text, language_str)
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str, symbol_to_id)
+
+    if hps.data.add_blank:
+        phone = commons.intersperse(phone, 0)
+        tone = commons.intersperse(tone, 0)
+        language = commons.intersperse(language, 0)
+        for i in range(len(word2ph)):
+            word2ph[i] = word2ph[i] * 2
+        word2ph[0] += 1
+
+    if getattr(hps.data, "disable_bert", False):
+        bert = torch.zeros(1024, len(phone))
+        ja_bert = torch.zeros(768, len(phone))
+    else:
+        bert = get_bert(norm_text, word2ph, language_str, device)
+        print('bert', bert)
+        del word2ph
+        assert bert.shape[-1] == len(phone), phone
+
+        if language_str == "ZH":
+            bert = bert
+            ja_bert = torch.zeros(768, len(phone))
+        elif language_str in ["JP", "EN", "ZH_MIX_EN", 'KR', 'SP', 'ES', 'FR', 'DE', 'RU']:
+            ja_bert = bert
+            bert = torch.zeros(1024, len(phone))
+        else:
+            raise NotImplementedError()
+
+    assert bert.shape[-1] == len(
+        phone
+    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
+
+    phone = torch.LongTensor(phone)
+    tone = torch.LongTensor(tone)
+    language = torch.LongTensor(language)
+    return bert, ja_bert, phone, tone, language
+
+def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
+    assert os.path.isfile(checkpoint_path)
+    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
+    iteration = checkpoint_dict.get("iteration", 0)
+    learning_rate = checkpoint_dict.get("learning_rate", 0.)
+    if (
+        optimizer is not None
+        and not skip_optimizer
+        and checkpoint_dict["optimizer"] is not None
+    ):
+        optimizer.load_state_dict(checkpoint_dict["optimizer"])
+    elif optimizer is None and not skip_optimizer:
+        # else:      Disable this line if Infer and resume checkpoint,then enable the line upper
+        new_opt_dict = optimizer.state_dict()
+        new_opt_dict_params = new_opt_dict["param_groups"][0]["params"]
+        new_opt_dict["param_groups"] = checkpoint_dict["optimizer"]["param_groups"]
+        new_opt_dict["param_groups"][0]["params"] = new_opt_dict_params
+        optimizer.load_state_dict(new_opt_dict)
+
+    saved_state_dict = checkpoint_dict["model"]
+    if hasattr(model, "module"):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+
+    new_state_dict = {}
+    for k, v in state_dict.items():
+        try:
+            # assert "emb_g" not in k
+            new_state_dict[k] = saved_state_dict[k]
+            assert saved_state_dict[k].shape == v.shape, (
+                saved_state_dict[k].shape,
+                v.shape,
+            )
+        except Exception as e:
+            print(e)
+            # For upgrading from the old version
+            if "ja_bert_proj" in k:
+                v = torch.zeros_like(v)
+                logger.warn(
+                    f"Seems you are using the old version of the model, the {k} is automatically set to zero for backward compatibility"
+                )
+            else:
+                logger.error(f"{k} is not in the checkpoint")
+
+            new_state_dict[k] = v
+
+    if hasattr(model, "module"):
+        model.module.load_state_dict(new_state_dict, strict=False)
+    else:
+        model.load_state_dict(new_state_dict, strict=False)
+
+    logger.info(
+        "Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
+    )
+
+    return model, optimizer, learning_rate, iteration
+
+
+def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
+    logger.info(
+        "Saving model and optimizer state at iteration {} to {}".format(
+            iteration, checkpoint_path
+        )
+    )
+    if hasattr(model, "module"):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    torch.save(
+        {
+            "model": state_dict,
+            "iteration": iteration,
+            "optimizer": optimizer.state_dict(),
+            "learning_rate": learning_rate,
+        },
+        checkpoint_path,
+    )
+
+
+def summarize(
+    writer,
+    global_step,
+    scalars={},
+    histograms={},
+    images={},
+    audios={},
+    audio_sampling_rate=22050,
+):
+    for k, v in scalars.items():
+        writer.add_scalar(k, v, global_step)
+    for k, v in histograms.items():
+        writer.add_histogram(k, v, global_step)
+    for k, v in images.items():
+        writer.add_image(k, v, global_step, dataformats="HWC")
+    for k, v in audios.items():
+        writer.add_audio(k, v, global_step, audio_sampling_rate)
+
+
+def latest_checkpoint_path(dir_path, regex="G_*.pth"):
+    f_list = glob.glob(os.path.join(dir_path, regex))
+    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
+    x = f_list[-1]
+    return x
+
+
+def plot_spectrogram_to_numpy(spectrogram):
+    global MATPLOTLIB_FLAG
+    if not MATPLOTLIB_FLAG:
+        import matplotlib
+
+        matplotlib.use("Agg")
+        MATPLOTLIB_FLAG = True
+        mpl_logger = logging.getLogger("matplotlib")
+        mpl_logger.setLevel(logging.WARNING)
+    import matplotlib.pylab as plt
+    import numpy as np
+
+    fig, ax = plt.subplots(figsize=(10, 2))
+    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
+    plt.colorbar(im, ax=ax)
+    plt.xlabel("Frames")
+    plt.ylabel("Channels")
+    plt.tight_layout()
+
+    fig.canvas.draw()
+    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    plt.close()
+    return data
+
+
+def plot_alignment_to_numpy(alignment, info=None):
+    global MATPLOTLIB_FLAG
+    if not MATPLOTLIB_FLAG:
+        import matplotlib
+
+        matplotlib.use("Agg")
+        MATPLOTLIB_FLAG = True
+        mpl_logger = logging.getLogger("matplotlib")
+        mpl_logger.setLevel(logging.WARNING)
+    import matplotlib.pylab as plt
+    import numpy as np
+
+    fig, ax = plt.subplots(figsize=(6, 4))
+    im = ax.imshow(
+        alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
+    )
+    fig.colorbar(im, ax=ax)
+    xlabel = "Decoder timestep"
+    if info is not None:
+        xlabel += "\n\n" + info
+    plt.xlabel(xlabel)
+    plt.ylabel("Encoder timestep")
+    plt.tight_layout()
+
+    fig.canvas.draw()
+    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    plt.close()
+    return data
+
+def load_filepaths_and_text(filename, split="|"):
+    with open(filename, encoding="utf-8") as f:
+        filepaths_and_text = [line.strip().split(split) for line in f]
+    return filepaths_and_text
+
+
+def get_hparams(init=True):
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-c",
+        "--config",
+        type=str,
+        default="./configs/base.json",
+        help="JSON file for configuration",
+    )
+    parser.add_argument('--local_rank', type=int, default=0)
+    parser.add_argument('--world-size', type=int, default=1)
+    parser.add_argument('--port', type=int, default=10000)
+    parser.add_argument("-m", "--model", type=str, required=True, help="Model name")
+    parser.add_argument('--pretrain_G', type=str, default=None,
+                            help='pretrain model')
+    parser.add_argument('--pretrain_D', type=str, default=None,
+                            help='pretrain model D')
+    parser.add_argument('--pretrain_dur', type=str, default=None,
+                            help='pretrain model duration')
+
+    args = parser.parse_args()
+    model_dir = os.path.join("./logs", args.model)
+
+    os.makedirs(model_dir, exist_ok=True)
+
+    config_path = args.config
+    config_save_path = os.path.join(model_dir, "config.json")
+    if init:
+        with open(config_path, "r") as f:
+            data = f.read()
+        with open(config_save_path, "w") as f:
+            f.write(data)
+    else:
+        with open(config_save_path, "r") as f:
+            data = f.read()
+    config = json.loads(data)
+
+    hparams = HParams(**config)
+    hparams.model_dir = model_dir
+    hparams.pretrain_G = args.pretrain_G
+    hparams.pretrain_D = args.pretrain_D
+    hparams.pretrain_dur = args.pretrain_dur
+    hparams.port = args.port
+    return hparams
+
+
+def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True):
+    """Freeing up space by deleting saved ckpts
+
+    Arguments:
+    path_to_models    --  Path to the model directory
+    n_ckpts_to_keep   --  Number of ckpts to keep, excluding G_0.pth and D_0.pth
+    sort_by_time      --  True -> chronologically delete ckpts
+                          False -> lexicographically delete ckpts
+    """
+    import re
+
+    ckpts_files = [
+        f
+        for f in os.listdir(path_to_models)
+        if os.path.isfile(os.path.join(path_to_models, f))
+    ]
+
+    def name_key(_f):
+        return int(re.compile("._(\\d+)\\.pth").match(_f).group(1))
+
+    def time_key(_f):
+        return os.path.getmtime(os.path.join(path_to_models, _f))
+
+    sort_key = time_key if sort_by_time else name_key
+
+    def x_sorted(_x):
+        return sorted(
+            [f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
+            key=sort_key,
+        )
+
+    to_del = [
+        os.path.join(path_to_models, fn)
+        for fn in (x_sorted("G")[:-n_ckpts_to_keep] + x_sorted("D")[:-n_ckpts_to_keep])
+    ]
+
+    def del_info(fn):
+        return logger.info(f".. Free up space by deleting ckpt {fn}")
+
+    def del_routine(x):
+        return [os.remove(x), del_info(x)]
+
+    [del_routine(fn) for fn in to_del]
+
+
+def get_hparams_from_dir(model_dir):
+    config_save_path = os.path.join(model_dir, "config.json")
+    with open(config_save_path, "r", encoding="utf-8") as f:
+        data = f.read()
+    config = json.loads(data)
+
+    hparams = HParams(**config)
+    hparams.model_dir = model_dir
+    return hparams
+
 
 def get_hparams_from_file(config_path):
     with open(config_path, "r", encoding="utf-8") as f:
@@ -8,6 +332,47 @@ def get_hparams_from_file(config_path):
     hparams = HParams(**config)
     return hparams
 
+
+def check_git_hash(model_dir):
+    source_dir = os.path.dirname(os.path.realpath(__file__))
+    if not os.path.exists(os.path.join(source_dir, ".git")):
+        logger.warn(
+            "{} is not a git repository, therefore hash value comparison will be ignored.".format(
+                source_dir
+            )
+        )
+        return
+
+    cur_hash = subprocess.getoutput("git rev-parse HEAD")
+
+    path = os.path.join(model_dir, "githash")
+    if os.path.exists(path):
+        saved_hash = open(path).read()
+        if saved_hash != cur_hash:
+            logger.warn(
+                "git hash values are different. {}(saved) != {}(current)".format(
+                    saved_hash[:8], cur_hash[:8]
+                )
+            )
+    else:
+        open(path, "w").write(cur_hash)
+
+
+def get_logger(model_dir, filename="train.log"):
+    global logger
+    logger = logging.getLogger(os.path.basename(model_dir))
+    logger.setLevel(logging.DEBUG)
+
+    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir, exist_ok=True)
+    h = logging.FileHandler(os.path.join(model_dir, filename))
+    h.setLevel(logging.DEBUG)
+    h.setFormatter(formatter)
+    logger.addHandler(h)
+    return logger
+
+
 class HParams:
     def __init__(self, **kwargs):
         for k, v in kwargs.items():

tcp_server/src/lib/tts_to_be_implemented/utils.py

@@ -1,405 +0,0 @@
-import os
-import glob
-import argparse
-import logging
-import json
-import subprocess
-import torch
-from melo.text import cleaned_text_to_sequence, get_bert
-from melo.text.cleaner import clean_text
-from melo import commons
-
-MATPLOTLIB_FLAG = False
-
-logger = logging.getLogger(__name__)
-
-
-
-def get_text_for_tts_infer(text, language_str, hps, device, symbol_to_id=None):
-    norm_text, phone, tone, word2ph = clean_text(text, language_str)
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str, symbol_to_id)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-
-    if getattr(hps.data, "disable_bert", False):
-        bert = torch.zeros(1024, len(phone))
-        ja_bert = torch.zeros(768, len(phone))
-    else:
-        bert = get_bert(norm_text, word2ph, language_str, device)
-        print('bert', bert)
-        del word2ph
-        assert bert.shape[-1] == len(phone), phone
-
-        if language_str == "ZH":
-            bert = bert
-            ja_bert = torch.zeros(768, len(phone))
-        elif language_str in ["JP", "EN", "ZH_MIX_EN", 'KR', 'SP', 'ES', 'FR', 'DE', 'RU']:
-            ja_bert = bert
-            bert = torch.zeros(1024, len(phone))
-        else:
-            raise NotImplementedError()
-
-    assert bert.shape[-1] == len(
-        phone
-    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
-
-    phone = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-    return bert, ja_bert, phone, tone, language
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-    iteration = checkpoint_dict.get("iteration", 0)
-    learning_rate = checkpoint_dict.get("learning_rate", 0.)
-    if (
-        optimizer is not None
-        and not skip_optimizer
-        and checkpoint_dict["optimizer"] is not None
-    ):
-        optimizer.load_state_dict(checkpoint_dict["optimizer"])
-    elif optimizer is None and not skip_optimizer:
-        # else:      Disable this line if Infer and resume checkpoint,then enable the line upper
-        new_opt_dict = optimizer.state_dict()
-        new_opt_dict_params = new_opt_dict["param_groups"][0]["params"]
-        new_opt_dict["param_groups"] = checkpoint_dict["optimizer"]["param_groups"]
-        new_opt_dict["param_groups"][0]["params"] = new_opt_dict_params
-        optimizer.load_state_dict(new_opt_dict)
-
-    saved_state_dict = checkpoint_dict["model"]
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            # assert "emb_g" not in k
-            new_state_dict[k] = saved_state_dict[k]
-            assert saved_state_dict[k].shape == v.shape, (
-                saved_state_dict[k].shape,
-                v.shape,
-            )
-        except Exception as e:
-            print(e)
-            # For upgrading from the old version
-            if "ja_bert_proj" in k:
-                v = torch.zeros_like(v)
-                logger.warn(
-                    f"Seems you are using the old version of the model, the {k} is automatically set to zero for backward compatibility"
-                )
-            else:
-                logger.error(f"{k} is not in the checkpoint")
-
-            new_state_dict[k] = v
-
-    if hasattr(model, "module"):
-        model.module.load_state_dict(new_state_dict, strict=False)
-    else:
-        model.load_state_dict(new_state_dict, strict=False)
-
-    logger.info(
-        "Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
-    )
-
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info(
-        "Saving model and optimizer state at iteration {} to {}".format(
-            iteration, checkpoint_path
-        )
-    )
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    torch.save(
-        {
-            "model": state_dict,
-            "iteration": iteration,
-            "optimizer": optimizer.state_dict(),
-            "learning_rate": learning_rate,
-        },
-        checkpoint_path,
-    )
-
-
-def summarize(
-    writer,
-    global_step,
-    scalars={},
-    histograms={},
-    images={},
-    audios={},
-    audio_sampling_rate=22050,
-):
-    for k, v in scalars.items():
-        writer.add_scalar(k, v, global_step)
-    for k, v in histograms.items():
-        writer.add_histogram(k, v, global_step)
-    for k, v in images.items():
-        writer.add_image(k, v, global_step, dataformats="HWC")
-    for k, v in audios.items():
-        writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-    f_list = glob.glob(os.path.join(dir_path, regex))
-    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-    x = f_list[-1]
-    return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger("matplotlib")
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger("matplotlib")
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(
-        alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
-    )
-    fig.colorbar(im, ax=ax)
-    xlabel = "Decoder timestep"
-    if info is not None:
-        xlabel += "\n\n" + info
-    plt.xlabel(xlabel)
-    plt.ylabel("Encoder timestep")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding="utf-8") as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-c",
-        "--config",
-        type=str,
-        default="./configs/base.json",
-        help="JSON file for configuration",
-    )
-    parser.add_argument('--local_rank', type=int, default=0)
-    parser.add_argument('--world-size', type=int, default=1)
-    parser.add_argument('--port', type=int, default=10000)
-    parser.add_argument("-m", "--model", type=str, required=True, help="Model name")
-    parser.add_argument('--pretrain_G', type=str, default=None,
-                            help='pretrain model')
-    parser.add_argument('--pretrain_D', type=str, default=None,
-                            help='pretrain model D')
-    parser.add_argument('--pretrain_dur', type=str, default=None,
-                            help='pretrain model duration')
-
-    args = parser.parse_args()
-    model_dir = os.path.join("./logs", args.model)
-
-    os.makedirs(model_dir, exist_ok=True)
-
-    config_path = args.config
-    config_save_path = os.path.join(model_dir, "config.json")
-    if init:
-        with open(config_path, "r") as f:
-            data = f.read()
-        with open(config_save_path, "w") as f:
-            f.write(data)
-    else:
-        with open(config_save_path, "r") as f:
-            data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    hparams.pretrain_G = args.pretrain_G
-    hparams.pretrain_D = args.pretrain_D
-    hparams.pretrain_dur = args.pretrain_dur
-    hparams.port = args.port
-    return hparams
-
-
-def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True):
-    """Freeing up space by deleting saved ckpts
-
-    Arguments:
-    path_to_models    --  Path to the model directory
-    n_ckpts_to_keep   --  Number of ckpts to keep, excluding G_0.pth and D_0.pth
-    sort_by_time      --  True -> chronologically delete ckpts
-                          False -> lexicographically delete ckpts
-    """
-    import re
-
-    ckpts_files = [
-        f
-        for f in os.listdir(path_to_models)
-        if os.path.isfile(os.path.join(path_to_models, f))
-    ]
-
-    def name_key(_f):
-        return int(re.compile("._(\\d+)\\.pth").match(_f).group(1))
-
-    def time_key(_f):
-        return os.path.getmtime(os.path.join(path_to_models, _f))
-
-    sort_key = time_key if sort_by_time else name_key
-
-    def x_sorted(_x):
-        return sorted(
-            [f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
-            key=sort_key,
-        )
-
-    to_del = [
-        os.path.join(path_to_models, fn)
-        for fn in (x_sorted("G")[:-n_ckpts_to_keep] + x_sorted("D")[:-n_ckpts_to_keep])
-    ]
-
-    def del_info(fn):
-        return logger.info(f".. Free up space by deleting ckpt {fn}")
-
-    def del_routine(x):
-        return [os.remove(x), del_info(x)]
-
-    [del_routine(fn) for fn in to_del]
-
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r", encoding="utf-8") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r", encoding="utf-8") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn(
-            "{} is not a git repository, therefore hash value comparison will be ignored.".format(
-                source_dir
-            )
-        )
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn(
-                "git hash values are different. {}(saved) != {}(current)".format(
-                    saved_hash[:8], cur_hash[:8]
-                )
-            )
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir, exist_ok=True)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams:
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()

tcp_server/src/lib/tts_to_delete/utils.py

@@ -0,0 +1,40 @@
+import json
+
+def get_hparams_from_file(config_path):
+    with open(config_path, "r", encoding="utf-8") as f:
+        data = f.read()
+    config = json.loads(data)
+
+    hparams = HParams(**config)
+    return hparams
+
+class HParams:
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            if type(v) == dict:
+                v = HParams(**v)
+            self[k] = v
+
+    def keys(self):
+        return self.__dict__.keys()
+
+    def items(self):
+        return self.__dict__.items()
+
+    def values(self):
+        return self.__dict__.values()
+
+    def __len__(self):
+        return len(self.__dict__)
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+    def __setitem__(self, key, value):
+        return setattr(self, key, value)
+
+    def __contains__(self, key):
+        return key in self.__dict__
+
+    def __repr__(self):
+        return self.__dict__.__repr__()

tcp_server/src/main.py

@@ -14,4 +14,5 @@ tcp_server_host = os.environ.get('LEON_PY_TCP_SERVER_HOST', '0.0.0.0')
 tcp_server_port = os.environ.get('LEON_PY_TCP_SERVER_PORT', 1342)
 
 tcp_server = TCPServer(tcp_server_host, tcp_server_port)
+tcp_server.init_tts()
 tcp_server.init()