File size: 6,316 Bytes
a7c2243 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | #!/usr/bin/env python3
# Copyright (c) 2026, NVIDIA CORPORATION. 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.
"""
Convert .nemo checkpoints that were trained with ``preprocessor.use_torchaudio=True``
to the current format (non-torchaudio FilterbankFeatures).
After torchaudio was removed as a dependency (PR #15211), models trained with the
torchaudio-based preprocessor (FilterbankFeaturesTA) fail to load because the
state dict keys no longer match:
Old (torchaudio):
preprocessor.featurizer._mel_spec_extractor.spectrogram.window
preprocessor.featurizer._mel_spec_extractor.mel_scale.fb
New (current):
preprocessor.featurizer.window
preprocessor.featurizer.fb
This script renames those keys and also sets ``use_torchaudio: false`` in the model
config so that the correct featurizer class is instantiated on load.
Usage
-----
python convert_torchaudio_nemo.py --nemo_file model.nemo --output_file model_converted.nemo
"""
import argparse
import os
import tarfile
import tempfile
import torch
import yaml
MODEL_CONFIG_YAML = "model_config.yaml"
MODEL_WEIGHTS_CKPT = "model_weights.ckpt"
# Old torchaudio key suffix -> new key suffix
KEY_MIGRATION = {
"featurizer._mel_spec_extractor.spectrogram.window": "featurizer.window",
"featurizer._mel_spec_extractor.mel_scale.fb": "featurizer.fb",
}
def migrate_state_dict(state_dict: dict) -> tuple[dict, list[tuple[str, str]]]:
"""Rename torchaudio-era keys. Returns (new_state_dict, list of (old, new) renames)."""
renames = []
for key in list(state_dict.keys()):
for old_suffix, new_suffix in KEY_MIGRATION.items():
if key.endswith(old_suffix):
new_key = key[: -len(old_suffix)] + new_suffix
if "featurizer.fb" in new_suffix:
state_dict[new_key] = state_dict.pop(key).T.unsqueeze(0)
else:
state_dict[new_key] = state_dict.pop(key)
renames.append((key, new_key))
break
return state_dict, renames
def migrate_config(cfg: dict) -> bool:
"""Set ``use_torchaudio: false`` in the preprocessor config. Returns True if changed."""
preprocessor = cfg.get("preprocessor", {})
if preprocessor.get("use_torchaudio", False):
preprocessor["use_torchaudio"] = False
return True
return False
def convert_nemo_file(nemo_path: str, output_path: str) -> None:
"""Extract, migrate, and repack a .nemo archive."""
with tempfile.TemporaryDirectory() as tmpdir:
def _safe_extract_all(tar_obj: tarfile.TarFile, dest_dir: str) -> None:
"""Safely extract all members of a tar file into dest_dir.
Ensures that no member escapes dest_dir via absolute paths or '..' components.
"""
dest_dir_abs = os.path.abspath(dest_dir)
for member in tar_obj.getmembers():
member_path = os.path.join(dest_dir_abs, member.name)
member_path_abs = os.path.abspath(member_path)
if os.path.commonpath([dest_dir_abs, member_path_abs]) != dest_dir_abs:
raise ValueError(f"Illegal tar archive entry path: {member.name!r}")
tar_obj.extract(member, path=dest_dir_abs)
# --- Unpack --------------------------------------------------------
# Older checkpoints may be gzipped; newer ones are plain tar.
try:
tar = tarfile.open(nemo_path, "r:")
except tarfile.ReadError:
tar = tarfile.open(nemo_path, "r:gz")
_safe_extract_all(tar, tmpdir)
tar.close()
# --- Migrate state dict --------------------------------------------
weights_path = os.path.join(tmpdir, MODEL_WEIGHTS_CKPT)
if not os.path.isfile(weights_path):
raise FileNotFoundError(
f"Could not find {MODEL_WEIGHTS_CKPT} inside the .nemo archive. "
"Are you sure this is a valid .nemo file?"
)
state_dict = torch.load(weights_path, map_location="cpu", weights_only=True)
state_dict, renames = migrate_state_dict(state_dict)
if not renames:
print("No torchaudio keys found in state dict — nothing to migrate.")
return
for old, new in renames:
print(f" Renamed: {old} -> {new}")
torch.save(state_dict, weights_path)
# --- Migrate config ------------------------------------------------
config_path = os.path.join(tmpdir, MODEL_CONFIG_YAML)
if os.path.isfile(config_path):
with open(config_path) as f:
cfg = yaml.safe_load(f)
if migrate_config(cfg):
print(" Config: set use_torchaudio=false")
with open(config_path, "w") as f:
yaml.dump(cfg, f, default_flow_style=False)
# --- Repack --------------------------------------------------------
with tarfile.open(output_path, "w:") as tar:
tar.add(tmpdir, arcname=".")
print(f"\nConverted checkpoint saved to: {output_path}")
def main():
parser = argparse.ArgumentParser(
description="Convert .nemo checkpoints from torchaudio preprocessor format to the current format.",
)
parser.add_argument(
"--nemo_file",
required=True,
help="Path to the source .nemo file.",
)
parser.add_argument(
"--output_file",
required=True,
help="Path to write the converted .nemo file.",
)
args = parser.parse_args()
if not os.path.isfile(args.nemo_file):
raise FileNotFoundError(f"File not found: {args.nemo_file}")
convert_nemo_file(args.nemo_file, args.output_file)
if __name__ == "__main__":
main()
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