text/init.py (#203)

* Create all_process.py

* Create asr_transcript.py

* Update config.py

* Create extract_list.py

* Create clean_list.py

* Create custom.css

* Create compress_model.py

* Update all_process.py

* Update resample.py

* Update resample.py

* configs/config.json copy utils

* mirror: openi + token
bert models optimize

* text/__init__.py platform compatibility
compress_model.py output

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compress_model.py

@@ -0,0 +1,88 @@
+from collections import OrderedDict
+from text.symbols import symbols
+import torch
+from tools.log import logger
+import utils
+from models import SynthesizerTrn
+import os
+
+
+def copyStateDict(state_dict):
+    if list(state_dict.keys())[0].startswith("module"):
+        start_idx = 1
+    else:
+        start_idx = 0
+    new_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+        name = ",".join(k.split(".")[start_idx:])
+        new_state_dict[name] = v
+    return new_state_dict
+
+
+def removeOptimizer(config: str, input_model: str, ishalf: bool, output_model: str):
+    hps = utils.get_hparams_from_file(config)
+
+    net_g = SynthesizerTrn(
+        len(symbols),
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        **hps.model,
+    )
+
+    optim_g = torch.optim.AdamW(
+        net_g.parameters(),
+        hps.train.learning_rate,
+        betas=hps.train.betas,
+        eps=hps.train.eps,
+    )
+
+    state_dict_g = torch.load(input_model, map_location="cpu")
+    new_dict_g = copyStateDict(state_dict_g)
+    keys = []
+    for k, v in new_dict_g["model"].items():
+        if "enc_q" in k:
+            continue  # noqa: E701
+        keys.append(k)
+
+    new_dict_g = (
+        {k: new_dict_g["model"][k].half() for k in keys}
+        if ishalf
+        else {k: new_dict_g["model"][k] for k in keys}
+    )
+
+    torch.save(
+        {
+            "model": new_dict_g,
+            "iteration": 0,
+            "optimizer": optim_g.state_dict(),
+            "learning_rate": 0.0001,
+        },
+        output_model,
+    )
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-c", "--config", type=str, default="configs/config.json")
+    parser.add_argument("-i", "--input", type=str)
+    parser.add_argument("-o", "--output", type=str, default=None)
+    parser.add_argument(
+        "-hf", "--half", action="store_true", default=False, help="Save as FP16"
+    )
+
+    args = parser.parse_args()
+
+    output = args.output
+
+    if output is None:
+        import os.path
+
+        filename, ext = os.path.splitext(args.input)
+        half = "_half" if args.half else ""
+        output = filename + "_release" + half + ext
+
+    removeOptimizer(args.config, args.input, args.half, output)
+    logger.info(f"压缩模型成功, 输出模型: {os.path.abspath(output)}")