Fix py3.11 dataclasses issue (#7616)

* Fix py3.11 dataclasses issue  (#7582)

* Update ASR configs to support Python 3.11

Signed-off-by: smajumdar <titu1994@gmail.com>

* Update TTS configs to support Python 3.11

Signed-off-by: smajumdar <titu1994@gmail.com>

* Guard MeCab and Ipadic

Signed-off-by: smajumdar <titu1994@gmail.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Fix remaining ASR dataclasses

Signed-off-by: smajumdar <titu1994@gmail.com>

* Fix remaining ASR dataclasses

Signed-off-by: smajumdar <titu1994@gmail.com>

* Fix scripts

Signed-off-by: smajumdar <titu1994@gmail.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* [pre-commit.ci] auto fixes from pre-commit.com hooks

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---------

Signed-off-by: smajumdar <titu1994@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* Update name to ConfidenceMethodConfig

Signed-off-by: smajumdar <titu1994@gmail.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Broadcast loss only when using pipeline parallelism and within the pipeline parallel domain (#7576) (#7586)

* Broadcast loss only when using pipeline parallelism and within the pipeline parallel domain
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Signed-off-by: Sangkug Lym <slym@nvidia.com>
Co-authored-by: Sangkug Lym <slym@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* Safeguard nemo_text_processing installation on ARM (#7485)

* safeguard nemo_text_processing installing

Signed-off-by: Jason <jasoli@nvidia.com>

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* update check

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---------

Signed-off-by: Jason <jasoli@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* Fix changes to confidence measure

Signed-off-by: smajumdar <titu1994@gmail.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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---------

Signed-off-by: smajumdar <titu1994@gmail.com>
Signed-off-by: Sangkug Lym <slym@nvidia.com>
Signed-off-by: Jason <jasoli@nvidia.com>
Co-authored-by: Somshubra Majumdar <titu1994@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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Co-authored-by: Jason <jasoli@nvidia.com>

examples/asr/experimental/k2/align_speech_parallel.py

@@ -74,7 +74,7 @@
 
 
 import os
-from dataclasses import dataclass, is_dataclass
+from dataclasses import dataclass, field, is_dataclass
 from typing import Optional
 
 import pytorch_lightning as ptl
@@ -94,12 +94,14 @@
 @dataclass
 class ParallelAlignmentConfig:
     model: Optional[str] = None  # name
-    predict_ds: ASRDatasetConfig = ASRDatasetConfig(return_sample_id=True, num_workers=4)
-    aligner_args: K2AlignerWrapperModelConfig = K2AlignerWrapperModelConfig()
+    predict_ds: ASRDatasetConfig = field(
+        default_factory=lambda: ASRDatasetConfig(return_sample_id=True, num_workers=4)
+    )
+    aligner_args: K2AlignerWrapperModelConfig = field(default_factory=lambda: K2AlignerWrapperModelConfig())
     output_path: str = MISSING
     model_stride: int = 8
 
-    trainer: TrainerConfig = TrainerConfig(gpus=-1, accelerator="ddp")
+    trainer: TrainerConfig = field(default_factory=lambda: TrainerConfig(gpus=-1, accelerator="ddp"))
 
     # there arguments will be ignored
     return_predictions: bool = False

nemo/collections/asr/metrics/rnnt_wer.py

@@ -15,7 +15,7 @@
 import copy
 import re
 from abc import abstractmethod
-from dataclasses import dataclass, is_dataclass
+from dataclasses import dataclass, field, is_dataclass
 from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import editdistance
@@ -1299,7 +1299,7 @@ class RNNTDecodingConfig:
     preserve_alignments: Optional[bool] = None
 
     #  confidence config
-    confidence_cfg: ConfidenceConfig = ConfidenceConfig()
+    confidence_cfg: ConfidenceConfig = field(default_factory=lambda: ConfidenceConfig())
 
     # RNNT Joint fused batch size
     fused_batch_size: Optional[int] = None
@@ -1317,10 +1317,10 @@ class RNNTDecodingConfig:
     rnnt_timestamp_type: str = "all"  # can be char, word or all for both
 
     # greedy decoding config
-    greedy: greedy_decode.GreedyRNNTInferConfig = greedy_decode.GreedyRNNTInferConfig()
+    greedy: greedy_decode.GreedyRNNTInferConfig = field(default_factory=lambda: greedy_decode.GreedyRNNTInferConfig())
 
     # beam decoding config
-    beam: beam_decode.BeamRNNTInferConfig = beam_decode.BeamRNNTInferConfig(beam_size=4)
+    beam: beam_decode.BeamRNNTInferConfig = field(default_factory=lambda: beam_decode.BeamRNNTInferConfig(beam_size=4))
 
     # can be used to change temperature for decoding
     temperature: float = 1.0

nemo/collections/asr/metrics/wer.py

@@ -14,7 +14,7 @@
 
 import re
 from abc import abstractmethod
-from dataclasses import dataclass, is_dataclass
+from dataclasses import dataclass, field, is_dataclass
 from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import editdistance
@@ -1297,13 +1297,17 @@ class CTCDecodingConfig:
     batch_dim_index: int = 0
 
     # greedy decoding config
-    greedy: ctc_greedy_decoding.GreedyCTCInferConfig = ctc_greedy_decoding.GreedyCTCInferConfig()
+    greedy: ctc_greedy_decoding.GreedyCTCInferConfig = field(
+        default_factory=lambda: ctc_greedy_decoding.GreedyCTCInferConfig()
+    )
 
     # beam decoding config
-    beam: ctc_beam_decoding.BeamCTCInferConfig = ctc_beam_decoding.BeamCTCInferConfig(beam_size=4)
+    beam: ctc_beam_decoding.BeamCTCInferConfig = field(
+        default_factory=lambda: ctc_beam_decoding.BeamCTCInferConfig(beam_size=4)
+    )
 
     # confidence config
-    confidence_cfg: ConfidenceConfig = ConfidenceConfig()
+    confidence_cfg: ConfidenceConfig = field(default_factory=lambda: ConfidenceConfig())
 
     # can be used to change temperature for decoding
     temperature: float = 1.0

nemo/collections/asr/models/configs/aligner_config.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 
 from nemo.collections.asr.parts.k2.classes import GraphModuleConfig
 
@@ -35,10 +35,10 @@ class AlignerWrapperModelConfig:
     word_output: bool = True
     cpu_decoding: bool = False
     decode_batch_size: int = 0
-    ctc_cfg: AlignerCTCConfig = AlignerCTCConfig()
-    rnnt_cfg: AlignerRNNTConfig = AlignerRNNTConfig()
+    ctc_cfg: AlignerCTCConfig = field(default_factory=lambda: AlignerCTCConfig())
+    rnnt_cfg: AlignerRNNTConfig = field(default_factory=lambda: AlignerRNNTConfig())
 
 
 @dataclass
 class K2AlignerWrapperModelConfig(AlignerWrapperModelConfig):
-    decoder_module_cfg: GraphModuleConfig = GraphModuleConfig()
+    decoder_module_cfg: GraphModuleConfig = field(default_factory=lambda: GraphModuleConfig())

nemo/collections/asr/models/configs/asr_models_config.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional
 
 from omegaconf import MISSING
@@ -74,24 +74,32 @@ class EncDecCTCConfig(model_cfg.ModelConfig):
     labels: List[str] = MISSING
 
     # Dataset configs
-    train_ds: ASRDatasetConfig = ASRDatasetConfig(manifest_filepath=None, shuffle=True)
-    validation_ds: ASRDatasetConfig = ASRDatasetConfig(manifest_filepath=None, shuffle=False)
-    test_ds: ASRDatasetConfig = ASRDatasetConfig(manifest_filepath=None, shuffle=False)
+    train_ds: ASRDatasetConfig = field(default_factory=lambda: ASRDatasetConfig(manifest_filepath=None, shuffle=True))
+    validation_ds: ASRDatasetConfig = field(
+        default_factory=lambda: ASRDatasetConfig(manifest_filepath=None, shuffle=False)
+    )
+    test_ds: ASRDatasetConfig = field(default_factory=lambda: ASRDatasetConfig(manifest_filepath=None, shuffle=False))
 
     # Optimizer / Scheduler config
-    optim: Optional[model_cfg.OptimConfig] = model_cfg.OptimConfig(sched=model_cfg.SchedConfig())
+    optim: Optional[model_cfg.OptimConfig] = field(
+        default_factory=lambda: model_cfg.OptimConfig(sched=model_cfg.SchedConfig())
+    )
 
     # Model component configs
-    preprocessor: AudioToMelSpectrogramPreprocessorConfig = AudioToMelSpectrogramPreprocessorConfig()
-    spec_augment: Optional[SpectrogramAugmentationConfig] = SpectrogramAugmentationConfig()
-    encoder: ConvASREncoderConfig = ConvASREncoderConfig()
-    decoder: ConvASRDecoderConfig = ConvASRDecoderConfig()
-    decoding: CTCDecodingConfig = CTCDecodingConfig()
+    preprocessor: AudioToMelSpectrogramPreprocessorConfig = field(
+        default_factory=lambda: AudioToMelSpectrogramPreprocessorConfig()
+    )
+    spec_augment: Optional[SpectrogramAugmentationConfig] = field(
+        default_factory=lambda: SpectrogramAugmentationConfig()
+    )
+    encoder: ConvASREncoderConfig = field(default_factory=lambda: ConvASREncoderConfig())
+    decoder: ConvASRDecoderConfig = field(default_factory=lambda: ConvASRDecoderConfig())
+    decoding: CTCDecodingConfig = field(default_factory=lambda: CTCDecodingConfig())
 
 
 @dataclass
 class EncDecCTCModelConfig(model_cfg.NemoConfig):
-    model: EncDecCTCConfig = EncDecCTCConfig()
+    model: EncDecCTCConfig = field(default_factory=lambda: EncDecCTCConfig())
 
 
 @dataclass

nemo/collections/asr/models/configs/classification_models_config.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional
 
 from omegaconf import MISSING
@@ -72,30 +72,40 @@ class EncDecClassificationConfig(model_cfg.ModelConfig):
     timesteps: int = MISSING
 
     # Dataset configs
-    train_ds: EncDecClassificationDatasetConfig = EncDecClassificationDatasetConfig(
-        manifest_filepath=None, shuffle=True, trim_silence=False
+    train_ds: EncDecClassificationDatasetConfig = field(
+        default_factory=lambda: EncDecClassificationDatasetConfig(
+            manifest_filepath=None, shuffle=True, trim_silence=False
+        )
     )
-    validation_ds: EncDecClassificationDatasetConfig = EncDecClassificationDatasetConfig(
-        manifest_filepath=None, shuffle=False
+    validation_ds: EncDecClassificationDatasetConfig = field(
+        default_factory=lambda: EncDecClassificationDatasetConfig(manifest_filepath=None, shuffle=False)
     )
-    test_ds: EncDecClassificationDatasetConfig = EncDecClassificationDatasetConfig(
-        manifest_filepath=None, shuffle=False
+    test_ds: EncDecClassificationDatasetConfig = field(
+        default_factory=lambda: EncDecClassificationDatasetConfig(manifest_filepath=None, shuffle=False)
     )
 
     # Optimizer / Scheduler config
-    optim: Optional[model_cfg.OptimConfig] = model_cfg.OptimConfig(sched=model_cfg.SchedConfig())
+    optim: Optional[model_cfg.OptimConfig] = field(
+        default_factory=lambda: model_cfg.OptimConfig(sched=model_cfg.SchedConfig())
+    )
 
     # Model component configs
-    preprocessor: AudioToMFCCPreprocessorConfig = AudioToMFCCPreprocessorConfig()
-    spec_augment: Optional[SpectrogramAugmentationConfig] = SpectrogramAugmentationConfig()
-    crop_or_pad_augment: Optional[CropOrPadSpectrogramAugmentationConfig] = CropOrPadSpectrogramAugmentationConfig(
-        audio_length=timesteps
+    preprocessor: AudioToMFCCPreprocessorConfig = field(default_factory=lambda: AudioToMFCCPreprocessorConfig())
+    spec_augment: Optional[SpectrogramAugmentationConfig] = field(
+        default_factory=lambda: SpectrogramAugmentationConfig()
+    )
+    crop_or_pad_augment: Optional[CropOrPadSpectrogramAugmentationConfig] = field(
+        default_factory=lambda: CropOrPadSpectrogramAugmentationConfig(audio_length=-1)
     )
 
-    encoder: ConvASREncoderConfig = ConvASREncoderConfig()
-    decoder: ConvASRDecoderClassificationConfig = ConvASRDecoderClassificationConfig()
+    encoder: ConvASREncoderConfig = field(default_factory=lambda: ConvASREncoderConfig())
+    decoder: ConvASRDecoderClassificationConfig = field(default_factory=lambda: ConvASRDecoderClassificationConfig())
+
+    def __post_init__(self):
+        if self.crop_or_pad_augment is not None:
+            self.crop_or_pad_augment.audio_length = self.timesteps
 
 
 @dataclass
 class EncDecClassificationModelConfig(model_cfg.NemoConfig):
-    model: EncDecClassificationConfig = EncDecClassificationConfig()
+    model: EncDecClassificationConfig = field(default_factory=lambda: EncDecClassificationConfig())

nemo/collections/asr/models/configs/diarizer_config.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import asdict, dataclass
+from dataclasses import asdict, dataclass, field
 from typing import Any, Dict, Optional, Tuple, Union
 
 
@@ -78,9 +78,9 @@ class ASRDiarizerParams(DiarizerComponentConfig):
 @dataclass
 class ASRDiarizerConfig(DiarizerComponentConfig):
     model_path: Optional[str] = "stt_en_conformer_ctc_large"
-    parameters: ASRDiarizerParams = ASRDiarizerParams()
-    ctc_decoder_parameters: ASRDiarizerCTCDecoderParams = ASRDiarizerCTCDecoderParams()
-    realigning_lm_parameters: ASRRealigningLMParams = ASRRealigningLMParams()
+    parameters: ASRDiarizerParams = field(default_factory=lambda: ASRDiarizerParams())
+    ctc_decoder_parameters: ASRDiarizerCTCDecoderParams = field(default_factory=lambda: ASRDiarizerCTCDecoderParams())
+    realigning_lm_parameters: ASRRealigningLMParams = field(default_factory=lambda: ASRRealigningLMParams())
 
 
 @dataclass
@@ -102,7 +102,7 @@ class VADParams(DiarizerComponentConfig):
 class VADConfig(DiarizerComponentConfig):
     model_path: str = "vad_multilingual_marblenet"  # .nemo local model path or pretrained VAD model name
     external_vad_manifest: Optional[str] = None
-    parameters: VADParams = VADParams()
+    parameters: VADParams = field(default_factory=lambda: VADParams())
 
 
 @dataclass
@@ -121,7 +121,7 @@ class SpeakerEmbeddingsParams(DiarizerComponentConfig):
 class SpeakerEmbeddingsConfig(DiarizerComponentConfig):
     # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet)
     model_path: Optional[str] = None
-    parameters: SpeakerEmbeddingsParams = SpeakerEmbeddingsParams()
+    parameters: SpeakerEmbeddingsParams = field(default_factory=lambda: SpeakerEmbeddingsParams())
 
 
 @dataclass
@@ -142,7 +142,7 @@ class ClusteringParams(DiarizerComponentConfig):
 
 @dataclass
 class ClusteringConfig(DiarizerComponentConfig):
-    parameters: ClusteringParams = ClusteringParams()
+    parameters: ClusteringParams = field(default_factory=lambda: ClusteringParams())
 
 
 @dataclass
@@ -166,7 +166,7 @@ class MSDDParams(DiarizerComponentConfig):
 @dataclass
 class MSDDConfig(DiarizerComponentConfig):
     model_path: Optional[str] = "diar_msdd_telephonic"
-    parameters: MSDDParams = MSDDParams()
+    parameters: MSDDParams = field(default_factory=lambda: MSDDParams())
 
 
 @dataclass
@@ -176,16 +176,16 @@ class DiarizerConfig(DiarizerComponentConfig):
     oracle_vad: bool = False  # If True, uses RTTM files provided in the manifest file to get VAD timestamps
     collar: float = 0.25  # Collar value for scoring
     ignore_overlap: bool = True  # Consider or ignore overlap segments while scoring
-    vad: VADConfig = VADConfig()
-    speaker_embeddings: SpeakerEmbeddingsConfig = SpeakerEmbeddingsConfig()
-    clustering: ClusteringConfig = ClusteringConfig()
-    msdd_model: MSDDConfig = MSDDConfig()
-    asr: ASRDiarizerConfig = ASRDiarizerConfig()
+    vad: VADConfig = field(default_factory=lambda: VADConfig())
+    speaker_embeddings: SpeakerEmbeddingsConfig = field(default_factory=lambda: SpeakerEmbeddingsConfig())
+    clustering: ClusteringConfig = field(default_factory=lambda: ClusteringConfig())
+    msdd_model: MSDDConfig = field(default_factory=lambda: MSDDConfig())
+    asr: ASRDiarizerConfig = field(default_factory=lambda: ASRDiarizerConfig())
 
 
 @dataclass
 class NeuralDiarizerInferenceConfig(DiarizerComponentConfig):
-    diarizer: DiarizerConfig = DiarizerConfig()
+    diarizer: DiarizerConfig = field(default_factory=lambda: DiarizerConfig())
     device: str = "cpu"
     verbose: bool = False
     batch_size: int = 64

nemo/collections/asr/models/configs/k2_sequence_models_config.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 
 from nemo.collections.asr.models.configs.asr_models_config import EncDecCTCConfig
 from nemo.collections.asr.parts.k2.classes import GraphModuleConfig as BackendConfig
@@ -26,14 +26,14 @@ class GraphModuleConfig:
     split_batch_size: int = 0
     dec_type: str = "topo"
     transcribe_training: bool = True
-    backend_cfg: BackendConfig = BackendConfig()
+    backend_cfg: BackendConfig = field(default_factory=lambda: BackendConfig())
 
 
 @dataclass
 class EncDecK2SeqConfig(EncDecCTCConfig):
-    graph_module_cfg: GraphModuleConfig = GraphModuleConfig()
+    graph_module_cfg: GraphModuleConfig = field(default_factory=lambda: GraphModuleConfig())
 
 
 @dataclass
 class EncDecK2SeqModelConfig(NemoConfig):
-    model: EncDecK2SeqConfig = EncDecK2SeqConfig()
+    model: EncDecK2SeqConfig = field(default_factory=lambda: EncDecK2SeqConfig())

nemo/collections/asr/models/configs/matchboxnet_config.py

@@ -107,30 +107,38 @@ class MatchboxNetModelConfig(clf_cfg.EncDecClassificationConfig):
     labels: List[str] = MISSING
 
     # Dataset configs
-    train_ds: clf_cfg.EncDecClassificationDatasetConfig = clf_cfg.EncDecClassificationDatasetConfig(
-        manifest_filepath=None, shuffle=True, trim_silence=False
+    train_ds: clf_cfg.EncDecClassificationDatasetConfig = field(
+        default_factory=lambda: clf_cfg.EncDecClassificationDatasetConfig(
+            manifest_filepath=None, shuffle=True, trim_silence=False
+        )
     )
-    validation_ds: clf_cfg.EncDecClassificationDatasetConfig = clf_cfg.EncDecClassificationDatasetConfig(
-        manifest_filepath=None, shuffle=False
+    validation_ds: clf_cfg.EncDecClassificationDatasetConfig = field(
+        default_factory=lambda: clf_cfg.EncDecClassificationDatasetConfig(manifest_filepath=None, shuffle=False)
     )
-    test_ds: clf_cfg.EncDecClassificationDatasetConfig = clf_cfg.EncDecClassificationDatasetConfig(
-        manifest_filepath=None, shuffle=False
+    test_ds: clf_cfg.EncDecClassificationDatasetConfig = field(
+        default_factory=lambda: clf_cfg.EncDecClassificationDatasetConfig(manifest_filepath=None, shuffle=False)
     )
 
     # Optimizer / Scheduler config
-    optim: Optional[model_cfg.OptimConfig] = model_cfg.OptimConfig(sched=model_cfg.SchedConfig())
+    optim: Optional[model_cfg.OptimConfig] = field(
+        default_factory=lambda: model_cfg.OptimConfig(sched=model_cfg.SchedConfig())
+    )
 
     # Model general component configs
-    preprocessor: AudioToMFCCPreprocessorConfig = AudioToMFCCPreprocessorConfig(window_size=0.025)
-    spec_augment: Optional[SpectrogramAugmentationConfig] = SpectrogramAugmentationConfig(
-        freq_masks=2, time_masks=2, freq_width=15, time_width=25, rect_masks=5, rect_time=25, rect_freq=15
+    preprocessor: AudioToMFCCPreprocessorConfig = field(
+        default_factory=lambda: AudioToMFCCPreprocessorConfig(window_size=0.025)
+    )
+    spec_augment: Optional[SpectrogramAugmentationConfig] = field(
+        default_factory=lambda: SpectrogramAugmentationConfig(
+            freq_masks=2, time_masks=2, freq_width=15, time_width=25, rect_masks=5, rect_time=25, rect_freq=15
+        )
     )
-    crop_or_pad_augment: Optional[CropOrPadSpectrogramAugmentationConfig] = CropOrPadSpectrogramAugmentationConfig(
-        audio_length=128
+    crop_or_pad_augment: Optional[CropOrPadSpectrogramAugmentationConfig] = field(
+        default_factory=lambda: CropOrPadSpectrogramAugmentationConfig(audio_length=128)
     )
 
-    encoder: ConvASREncoderConfig = ConvASREncoderConfig(activation="relu")
-    decoder: ConvASRDecoderClassificationConfig = ConvASRDecoderClassificationConfig()
+    encoder: ConvASREncoderConfig = field(default_factory=lambda: ConvASREncoderConfig(activation="relu"))
+    decoder: ConvASRDecoderClassificationConfig = field(default_factory=lambda: ConvASRDecoderClassificationConfig())
 
 
 @dataclass