Added script to extract ASR CTC and RNNT models from ASR hybrid models

examples/asr/asr_hybrid_transducer_ctc/helpers/convert_nemo_asr_hybrid_to_ctc.py

@@ -0,0 +1,184 @@
+# Copyright (c) 2023, 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.
+
+
+"""
+A script to convert a Nemo ASR Hybrid model file (.nemo) to a Nemo ASR CTC or RNNT model file (.nemo)
+
+This allows you to train a RNNT-CTC Hybrid model, but then convert it into a pure CTC or pure RNNT model for use
+in NeMo. The resulting .nemo file will be a pure CTC or RNNT model, and can be used like any other .nemo model
+including in nemo2riva.
+
+Usage: python convert_nemo_asr_hybrid_to_ctc.py -i /path/to/hybrid.nemo -o /path/to/saved_ctc_model.nemo -m ctc|rnnt
+
+"""
+
+
+import argparse
+import os
+from copy import deepcopy
+
+import torch
+from omegaconf import OmegaConf
+
+from nemo.collections.asr.models import (
+    ASRModel,
+    EncDecCTCModel,
+    EncDecCTCModelBPE,
+    EncDecRNNTBPEModel,
+    EncDecRNNTModel,
+)
+from nemo.utils import logging
+
+
+def extract_model_ctc(args, hybrid_model):
+    """
+    A function which converts a hybrid model to a pure ctc model.
+    Args:
+        args (argparse): the args collection from ArgumentParser created by running this script
+        hybrid_model (ASRModel): the loaded hybrid RNNT-CTC Nemo model
+    """
+    BPE = False
+    ctc_class = EncDecCTCModel
+    if 'tokenizer' in hybrid_model.cfg.keys():
+        BPE = True
+        ctc_class = EncDecCTCModelBPE
+
+    hybrid_model_cfg = OmegaConf.to_container(hybrid_model.cfg)
+
+    new_cfg = deepcopy(hybrid_model_cfg)
+    new_cfg['ctc_reduction'] = hybrid_model_cfg['aux_ctc']['ctc_reduction']
+    new_cfg['decoder'] = hybrid_model_cfg['aux_ctc']['decoder']
+    del new_cfg['compute_eval_loss']
+    del new_cfg['model_defaults']
+    del new_cfg['joint']
+    del new_cfg['decoding']
+    del new_cfg['aux_ctc']
+    del new_cfg['loss']
+    if BPE and 'labels' in new_cfg:
+        del new_cfg['labels']
+    elif (not BPE) and 'tokenizer' in new_cfg:
+        del new_cfg['tokenizer']
+    del new_cfg['target']
+    del new_cfg['nemo_version']
+
+    new_cfg_oc = OmegaConf.create(new_cfg)
+
+    # we call restore_from with strict=False because the .nemo file we're restoring from is a hybrid model, which will have named
+    # tensors in the state_dict that do not exist in the pure CTC model class, which would result in an exception with strict=True
+    ctc_model = ctc_class.restore_from(
+        args.input, map_location=torch.device('cpu'), override_config_path=new_cfg_oc, strict=False
+    )
+
+    assert all(
+        [
+            torch.allclose(hybrid_model.state_dict()[x], ctc_model.state_dict()[x])
+            for x in hybrid_model.state_dict().keys()
+            if x.split('.')[0] in ['preprocessor', 'encoder']
+        ]
+    ), "Encoder and preprocessor state dicts don't match!"
+
+    ctc_model.decoder.load_state_dict(hybrid_model.ctc_decoder.state_dict())
+
+    assert all(
+        [
+            torch.allclose(hybrid_model.ctc_decoder.state_dict()[x], ctc_model.decoder.state_dict()[x])
+            for x in hybrid_model.ctc_decoder.state_dict().keys()
+        ]
+    ), "Decoder state_dict load failed!"
+
+    assert isinstance(ctc_model, ctc_class), "Extracted CTC model is of the wrong expected class!"
+
+    return ctc_model
+
+
+def extract_model_rnnt(args, hybrid_model):
+    """
+    A function which converts a hybrid model to a pure rnnt model.
+    Args:
+        args (argparse): the args collection from ArgumentParser created by running this script
+        hybrid_model (ASRModel): the loaded hybrid RNNT-CTC Nemo model
+    """
+    BPE = False
+    rnnt_class = EncDecRNNTModel
+    if 'tokenizer' in hybrid_model.cfg.keys():
+        BPE = True
+        rnnt_class = EncDecRNNTBPEModel
+
+    hybrid_model_cfg = OmegaConf.to_container(hybrid_model.cfg)
+
+    new_cfg = deepcopy(hybrid_model_cfg)
+    del new_cfg['aux_ctc']
+    if BPE and 'labels' in new_cfg:
+        del new_cfg['labels']
+    elif (not BPE) and 'tokenizer' in new_cfg:
+        del new_cfg['tokenizer']
+    del new_cfg['target']
+    del new_cfg['nemo_version']
+
+    new_cfg_oc = OmegaConf.create(new_cfg)
+
+    # we call restore_from with strict=False because the .nemo file we're restoring from is a hybrid model, which will have named
+    # tensors in the state_dict that do not exist in the pure RNNT model class, which would result in an exception with strict=True
+    rnnt_model = rnnt_class.restore_from(
+        args.input, map_location=torch.device('cpu'), override_config_path=new_cfg_oc, strict=False
+    )
+
+    assert all(
+        [
+            torch.allclose(hybrid_model.state_dict()[x], rnnt_model.state_dict()[x])
+            for x in hybrid_model.state_dict().keys()
+            if x.split('.')[0] in ['preprocessor', 'encoder', 'decoder', 'joint']
+        ]
+    ), "State dict values mismatch, something went wrong!"
+
+    assert isinstance(rnnt_model, rnnt_class), "Extracted RNNT model is of the wrong expected class!"
+
+    return rnnt_model
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', '--input', required=True, type=str, help='path to Nemo Hybrid model .nemo file')
+    parser.add_argument('-o', '--output', required=True, type=str, help='path and name of output .nemo file')
+    parser.add_argument(
+        '-t',
+        '--model_type',
+        required=False,
+        type=str,
+        default='ctc',
+        choices=['ctc', 'rnnt'],
+        help='whether to output a ctc or rnnt model from the hybrid',
+    )
+
+    args = parser.parse_args()
+
+    if not os.path.exists(args.input):
+        logging.critical(f'Input file [ {args.input} ] does not exist or cannot be found. Aborting.')
+        exit(255)
+
+    hybrid_model = ASRModel.restore_from(args.input, map_location=torch.device('cpu'))
+
+    if args.model_type == 'ctc':
+        output_model = extract_model_ctc(args, hybrid_model)
+    elif args.model_type == 'rnnt':
+        output_model = extract_model_rnnt(args, hybrid_model)
+    else:
+        logging.critical(
+            f"the model_type arg must be one of 'ctc' or 'rnnt', received unknown value: '{args.model_type}'. Aborting."
+        )
+        exit(255)
+
+    output_model.save_to(args.output)
+    logging.info(f'Converted {args.model_type.upper()} model was successfully saved to {args.output}')