增加了pytorch框架下的音频处理模型FastSpeech和ECAPA-TDNN的测试代码

PyTorch/Speech/FastSpeech2/evaluate.py

+import argparse
+import os
+
+import torch
+import yaml
+import torch.nn as nn
+from torch.utils.data import DataLoader
+
+from utils.model import get_model, get_vocoder
+from utils.tools import to_device, log, synth_one_sample
+from model import FastSpeech2Loss
+from dataset import Dataset
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def evaluate(model, step, configs, logger=None, vocoder=None):
+    preprocess_config, model_config, train_config = configs
+
+    # Get dataset
+    dataset = Dataset(
+        "val.txt", preprocess_config, train_config, sort=False, drop_last=False
+    )
+    batch_size = train_config["optimizer"]["batch_size"]
+    loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=False,
+        collate_fn=dataset.collate_fn,
+    )
+
+    # Get loss function
+    Loss = FastSpeech2Loss(preprocess_config, model_config).to(device)
+
+    # Evaluation
+    loss_sums = [0 for _ in range(6)]
+    for batchs in loader:
+        for batch in batchs:
+            batch = to_device(batch, device)
+            with torch.no_grad():
+                # Forward
+                output = model(*(batch[2:]))
+
+                # Cal Loss
+                losses = Loss(batch, output)
+
+                for i in range(len(losses)):
+                    loss_sums[i] += losses[i].item() * len(batch[0])
+
+    loss_means = [loss_sum / len(dataset) for loss_sum in loss_sums]
+
+    message = "Validation Step {}, Total Loss: {:.4f}, Mel Loss: {:.4f}, Mel PostNet Loss: {:.4f}, Pitch Loss: {:.4f}, Energy Loss: {:.4f}, Duration Loss: {:.4f}".format(
+        *([step] + [l for l in loss_means])
+    )
+
+    if logger is not None:
+        fig, wav_reconstruction, wav_prediction, tag = synth_one_sample(
+            batch,
+            output,
+            vocoder,
+            model_config,
+            preprocess_config,
+        )
+
+        log(logger, step, losses=loss_means)
+        log(
+            logger,
+            fig=fig,
+            tag="Validation/step_{}_{}".format(step, tag),
+        )
+        sampling_rate = preprocess_config["preprocessing"]["audio"]["sampling_rate"]
+        log(
+            logger,
+            audio=wav_reconstruction,
+            sampling_rate=sampling_rate,
+            tag="Validation/step_{}_{}_reconstructed".format(step, tag),
+        )
+        log(
+            logger,
+            audio=wav_prediction,
+            sampling_rate=sampling_rate,
+            tag="Validation/step_{}_{}_synthesized".format(step, tag),
+        )
+
+    return message
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--restore_step", type=int, default=30000)
+    parser.add_argument(
+        "-p",
+        "--preprocess_config",
+        type=str,
+        required=True,
+        help="path to preprocess.yaml",
+    )
+    parser.add_argument(
+        "-m", "--model_config", type=str, required=True, help="path to model.yaml"
+    )
+    parser.add_argument(
+        "-t", "--train_config", type=str, required=True, help="path to train.yaml"
+    )
+    args = parser.parse_args()
+
+    # Read Config
+    preprocess_config = yaml.load(
+        open(args.preprocess_config, "r"), Loader=yaml.FullLoader
+    )
+    model_config = yaml.load(open(args.model_config, "r"), Loader=yaml.FullLoader)
+    train_config = yaml.load(open(args.train_config, "r"), Loader=yaml.FullLoader)
+    configs = (preprocess_config, model_config, train_config)
+
+    # Get model
+    model = get_model(args, configs, device, train=False).to(device)
+
+    message = evaluate(model, args.restore_step, configs)
+    print(message)
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PyTorch/Speech/FastSpeech2/hifigan/LICENSE

+MIT License
+
+Copyright (c) 2020 Jungil Kong
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
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