chore: import upstream snapshot with attribution

scripts/dataset_processing/tts/compute_features.py

@@ -0,0 +1,130 @@
+# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script computes features for TTS models prior to training, such as pitch and energy.
+The resulting features will be stored in the provided 'feature_dir'.
+
+$ python <nemo_root_path>/scripts/dataset_processing/tts/compute_features.py \
+    --feature_config_path=<nemo_root_path>/examples/tts/conf/features/feature_22050.yaml \
+    --manifest_path=<data_root_path>/manifest.json \
+    --audio_dir=<data_root_path>/audio \
+    --feature_dir=<data_root_path>/features \
+    --overwrite \
+    --num_workers=1
+"""
+
+import argparse
+from pathlib import Path
+
+from joblib import Parallel, delayed
+from omegaconf import OmegaConf
+from tqdm import tqdm
+
+from nemo.collections.asr.parts.utils.manifest_utils import read_manifest
+from nemo.core.classes.common import safe_instantiate
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+        description="Compute TTS features.",
+    )
+    parser.add_argument(
+        "--feature_config_path",
+        required=True,
+        type=Path,
+        help="Path to feature config file.",
+    )
+    parser.add_argument(
+        "--manifest_path",
+        required=True,
+        type=Path,
+        help="Path to training manifest.",
+    )
+    parser.add_argument(
+        "--audio_dir",
+        required=True,
+        type=Path,
+        help="Path to base directory with audio data.",
+    )
+    parser.add_argument(
+        "--feature_dir",
+        required=True,
+        type=Path,
+        help="Path to directory where feature data will be stored.",
+    )
+    parser.add_argument(
+        "--dedupe_files",
+        action=argparse.BooleanOptionalAction,
+        help="If given, will only process the first manifest entry found for each audio file.",
+    )
+    parser.add_argument(
+        "--overwrite",
+        action=argparse.BooleanOptionalAction,
+        help="Whether to overwrite existing feature files.",
+    )
+    parser.add_argument(
+        "--num_workers", default=1, type=int, help="Number of parallel threads to use. If -1 all CPUs are used."
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+    feature_config_path = args.feature_config_path
+    manifest_path = args.manifest_path
+    audio_dir = args.audio_dir
+    feature_dir = args.feature_dir
+    dedupe_files = args.dedupe_files
+    overwrite = args.overwrite
+    num_workers = args.num_workers
+
+    if not manifest_path.exists():
+        raise ValueError(f"Manifest {manifest_path} does not exist.")
+
+    if not audio_dir.exists():
+        raise ValueError(f"Audio directory {audio_dir} does not exist.")
+
+    feature_config = OmegaConf.load(feature_config_path)
+    feature_config = safe_instantiate(feature_config)
+    featurizers = feature_config.featurizers
+
+    entries = read_manifest(manifest_path)
+
+    if dedupe_files:
+        final_entries = []
+        audio_filepath_set = set()
+        for entry in entries:
+            audio_filepath = entry["audio_filepath"]
+            if audio_filepath in audio_filepath_set:
+                continue
+            final_entries.append(entry)
+            audio_filepath_set.add(audio_filepath)
+        entries = final_entries
+
+    for feature_name, featurizer in featurizers.items():
+        print(f"Computing: {feature_name}")
+        Parallel(n_jobs=num_workers)(
+            delayed(featurizer.save)(
+                manifest_entry=entry, audio_dir=audio_dir, feature_dir=feature_dir, overwrite=overwrite
+            )
+            for entry in tqdm(entries)
+        )
+
+
+if __name__ == "__main__":
+    main()