Add config file for pretrained

synthesizer/hparams.py

@@ -1,5 +1,6 @@
 import ast
 import pprint
+import json
 
 class HParams(object):
     def __init__(self, **kwargs): self.__dict__.update(kwargs)
@@ -18,6 +19,18 @@ def parse(self, string):
                 self.__dict__[k] = ast.literal_eval(values[keys.index(k)])
         return self
 
+    def loadJson(self, dict):
+        print("\Loading the json with %s\n", dict)
+        for k in dict.keys():
+            self.__dict__[k] = dict[k]
+        return self
+
+    def dumpJson(self, fp):
+        print("\Saving the json with %s\n", fp)
+        with fp.open("w", encoding="utf-8") as f:
+            json.dump(self.__dict__, f)
+        return self
+
 hparams = HParams(
         ### Signal Processing (used in both synthesizer and vocoder)
         sample_rate = 16000,

synthesizer/inference.py

@@ -10,6 +10,7 @@
 import numpy as np
 import librosa
 from utils import logmmse
+import json
 from pypinyin import lazy_pinyin, Style
 
 class Synthesizer:
@@ -44,6 +45,11 @@ def is_loaded(self):
         return self._model is not None
 
     def load(self):
+        # Try to scan config file
+        model_config_fpaths = list(self.model_fpath.parent.rglob("*.json"))
+        if len(model_config_fpaths)>0 and model_config_fpaths[0].exists():
+            with model_config_fpaths[0].open("r", encoding="utf-8") as f:
+                hparams.loadJson(json.load(f))
         """
         Instantiates and loads the model given the weights file that was passed in the constructor.
         """

synthesizer/train.py

@@ -12,6 +12,7 @@
 from synthesizer.utils.text import sequence_to_text
 from vocoder.display import *
 from datetime import datetime
+import json
 import numpy as np
 from pathlib import Path
 import sys
@@ -75,6 +76,13 @@ def train(run_id: str, syn_dir: str, models_dir: str, save_every: int,
         if num_chars != loaded_shape[0]:
             print("WARNING: you are using compatible mode due to wrong sympols length, please modify varible _characters in `utils\symbols.py`")
             num_chars != loaded_shape[0]
+                # Try to scan config file
+        model_config_fpaths = list(weights_fpath.parent.rglob("*.json"))
+        if len(model_config_fpaths)>0 and model_config_fpaths[0].exists():
+            with model_config_fpaths[0].open("r", encoding="utf-8") as f:
+                hparams.loadJson(json.load(f))
+        else:  # save a config
+            hparams.dumpJson(weights_fpath.parent.joinpath(run_id).with_suffix(".json"))
 
 
     model = Tacotron(embed_dims=hparams.tts_embed_dims,