Refined export_config

docs/source/asr/models.rst

@@ -218,7 +218,7 @@ You may find FastConformer variants of cache-aware streaming models under ``<NeM
 Note cache-aware streaming models are being exported without caching support by default.
 To include caching support, `model.set_export_config({'cache_support' : 'True'})` should be called before export.
 Or, if ``<NeMo_git_root>/scripts/export.py`` is being used:
-`python export.py cache_aware_conformer.nemo cache_aware_conformer.onnx --config cache_support=True`
+`python export.py cache_aware_conformer.nemo cache_aware_conformer.onnx --export-config cache_support=True`
 
 .. _LSTM-Transducer_model:
 
@@ -299,7 +299,7 @@ Similar example configs for FastConformer variants of Hybrid models can be found
 Note Hybrid models are being exported as RNNT (encoder and decoder+joint parts) by default.
 To export as CTC (single encoder+decoder graph), `model.set_export_config({'decoder_type' : 'ctc'})` should be called before export.
 Or, if ``<NeMo_git_root>/scripts/export.py`` is being used:
-`python export.py hybrid_transducer.nemo hybrid_transducer.onnx --config decoder_type=ctc`
+`python export.py hybrid_transducer.nemo hybrid_transducer.onnx --export-config decoder_type=ctc`
 
 .. _Conformer-HAT_model:
 

docs/source/core/export.rst

@@ -207,7 +207,7 @@ An example can be found in ``<NeMo_git_root>/nemo/collections/asr/models/rnnt_mo
 Here is example on now `set_export_config()` call is being tied to command line arguments in ``<NeMo_git_root>/scripts/export.py`` :
 
 .. code-block:: Python
-    python scripts/export.py  hybrid_conformer.nemo hybrid_conformer.onnx --config decoder_type=ctc
+    python scripts/export.py  hybrid_conformer.nemo hybrid_conformer.onnx --export-config decoder_type=ctc
 
 Exportable Model Code
 ~~~~~~~~~~~~~~~~~~~~~

nemo/collections/tts/models/base.py

@@ -68,6 +68,18 @@ def list_available_models(cls) -> 'List[PretrainedModelInfo]':
                 list_of_models.extend(subclass_models)
         return list_of_models
 
+    def set_export_config(self, args):
+        for k in ['enable_volume', 'enable_ragged_batches']:
+            if k in args:
+                self.export_config[k] = bool(args[k])
+                args.pop(k)
+        if 'num_speakers' in args:
+            self.export_config['num_speakers'] = int(args['num_speakers'])
+            args.pop('num_speakers')
+        if 'emb_range' in args:
+            raise Exception('embedding range is not user-settable')
+        super().set_export_config(args)
+
 
 class Vocoder(ModelPT, ABC):
     """

scripts/export.py

@@ -63,7 +63,7 @@ def get_args(argv):
     parser.add_argument("--device", default="cuda", help="Device to export for")
     parser.add_argument("--check-tolerance", type=float, default=0.01, help="tolerance for verification")
     parser.add_argument(
-        "--config",
+        "--export-config",
         metavar="KEY=VALUE",
         nargs='+',
         help="Set a number of key-value pairs to model.export_config dictionary "
@@ -142,8 +142,14 @@ def nemo_export(argv):
     if args.cache_support:
         model.set_export_config({"cache_support": "True"})
 
-    if args.config:
-        kv = dict(map(lambda s: s.split('='), args.config))
+    if args.export_config:
+        kv = {}
+        for key_value in args.export_config:
+            lst = key_value.split("=")
+            if len(lst) != 2:
+                raise Exception("Use correct format for --export_config: k=v")
+            k, v = lst
+            kv[k] = v
         model.set_export_config(kv)
 
     autocast = nullcontext

tests/collections/tts/test_tts_exportables.py

@@ -54,8 +54,7 @@ def radtts_model():
     model = RadTTSModel(cfg=cfg.model)
     app_state.is_model_being_restored = False
     model.eval()
-    model.export_config['enable_ragged_batches'] = True
-    model.export_config['enable_volume'] = True
+    model.set_export_config({'enable_ragged_batches': 'True', 'enable_volume': 'True'})
     return model