Introduce code from Rust monorepo. This is a mess

tts/vocodes_model_check_talknet.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python
+
+"""
+Vocodes Tacotron Model Check
+"""
+
+import argparse
+
+parser = argparse.ArgumentParser(description='Check that the model is valid')
+
+parser.add_argument('--synthesizer_checkpoint_path', type=str, help='path the TTS synthesizer model', required=True)
+parser.add_argument('--output_metadata_filename', type=str, help='where to save extra metadata', required=True)
+
+args = parser.parse_args()
+
+import os
+import json
+
+# TODO: Enable model check/unzip/etc.
+"""
+import sys
+sys.path.append('waveglow/')
+import json
+
+import torch
+
+from tacotron2.hparams import create_hparams
+from tacotron2.model import Tacotron2
+
+print('========================================')
+print('Python interpreter', sys.executable)
+print('PyTorch version', torch.__version__)
+print('CUDA Available?', torch.cuda.is_available())
+print('CUDA Device count', torch.cuda.device_count())
+print('========================================', flush=True)
+
+# NB(bt, 2021-05-31): Trying to get everything on the same device
+#torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+hparams = create_hparams()
+hparams.sampling_rate = 22050 # Don't change this
+hparams.max_decoder_steps = 1000 # How long the audio will be before it cuts off (1000 is about 11 seconds)
+hparams.gate_threshold = 0.1 # Model must be 90% sure the clip is over before ending generation (the higher this number is, the more likely that the AI will keep generating until it reaches the Max Decoder Steps)
+
+# Load synthesizer
+checkpoint_path = args.synthesizer_checkpoint_path
+
+#model = load_model(hparams)
+model = Tacotron2(hparams)
+
+model.load_state_dict(torch.load(checkpoint_path)['state_dict'], strict=True)
+
+_ = model.cuda().eval().half()
+
+print('Model is valid')
+
+"""
+
+file_size_bytes = os.path.getsize(args.synthesizer_checkpoint_path)
+
+metadata = {
+    'file_size_bytes': file_size_bytes,
+}
+
+with open(args.output_metadata_filename, 'w') as json_file:
+    json.dump(metadata, json_file)