Patch transcribe and support offline transcribe for hybrid model

examples/asr/transcribe_speech.py

@@ -19,11 +19,11 @@
 
 import pytorch_lightning as pl
 import torch
-from omegaconf import OmegaConf
+from omegaconf import OmegaConf, open_dict
 
 from nemo.collections.asr.metrics.rnnt_wer import RNNTDecodingConfig
 from nemo.collections.asr.metrics.wer import CTCDecodingConfig
-from nemo.collections.asr.models.ctc_models import EncDecCTCModel
+from nemo.collections.asr.models import EncDecCTCModel, EncDecHybridRNNTCTCModel
 from nemo.collections.asr.modules.conformer_encoder import ConformerChangeConfig
 from nemo.collections.asr.parts.utils.transcribe_utils import (
     compute_output_filename,
@@ -154,6 +154,9 @@ class TranscriptionConfig:
 def main(cfg: TranscriptionConfig) -> TranscriptionConfig:
     logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
 
+    for key in cfg:
+        cfg[key] = None if cfg[key] == 'None' else cfg[key]
+
     if is_dataclass(cfg):
         cfg = OmegaConf.structured(cfg)
 
@@ -223,7 +226,6 @@ def main(cfg: TranscriptionConfig) -> TranscriptionConfig:
                 decoding_cfg.preserve_alignments = cfg.compute_timestamps
             if 'compute_langs' in decoding_cfg:
                 decoding_cfg.compute_langs = cfg.compute_langs
-
             asr_model.change_decoding_strategy(decoding_cfg, decoder_type=cfg.decoder_type)
 
         # Check if ctc or rnnt model
@@ -243,6 +245,15 @@ def main(cfg: TranscriptionConfig) -> TranscriptionConfig:
 
             asr_model.change_decoding_strategy(cfg.ctc_decoding)
 
+    # Setup decoding config based on model type and decoder_type
+    with open_dict(cfg):
+        if isinstance(asr_model, EncDecCTCModel) or (
+            isinstance(asr_model, EncDecHybridRNNTCTCModel) and cfg.decoder_type == "ctc"
+        ):
+            cfg.decoding = cfg.ctc_decoding
+        else:
+            cfg.decoding = cfg.rnnt_decoding
+
     # prepare audio filepaths and decide wether it's partical audio
     filepaths, partial_audio = prepare_audio_data(cfg)
 

nemo/collections/asr/parts/utils/transcribe_utils.py

@@ -289,14 +289,14 @@ def write_transcription(
 
     if isinstance(transcriptions[0], rnnt_utils.Hypothesis):  # List[rnnt_utils.Hypothesis]
         best_hyps = transcriptions
-        assert cfg.ctc_decoding.beam.return_best_hypothesis, "Works only with return_best_hypothesis=true"
+        assert cfg.decoding.beam.return_best_hypothesis, "Works only with return_best_hypothesis=true"
     elif isinstance(transcriptions[0], list) and isinstance(
         transcriptions[0][0], rnnt_utils.Hypothesis
     ):  # List[List[rnnt_utils.Hypothesis]] NBestHypothesis
         best_hyps, beams = [], []
         for hyps in transcriptions:
             best_hyps.append(hyps[0])
-            if not cfg.ctc_decoding.beam.return_best_hypothesis:
+            if not cfg.decoding.beam.return_best_hypothesis:
                 beam = []
                 for hyp in hyps:
                     beam.append((hyp.text, hyp.score))

tools/asr_evaluator/conf/eval.yaml

@@ -13,7 +13,7 @@ engine:
         chunk_len_in_secs: 1.6 #null # Need to specify if use buffered inference (default for offline_by_chunked is 20)
         total_buffer_in_secs: 4 #null # Need to specify if use buffered inference (default for offline_by_chunked is 22)
         model_stride: 4 # Model downsampling factor, 8 for Citrinet models and 4 for Conformer models
-
+        decoder_type: null # Used for hybrid CTC RNNT model only. Specify decoder_type *ctc* or *rnnt* for hybrid CTC RNNT model.
     test_ds:
         manifest_filepath: null
         sample_rate: 16000

tools/asr_evaluator/utils.py

@@ -154,7 +154,8 @@ def run_offline_inference(cfg: DictConfig) -> DictConfig:
             f"output_filename={cfg.output_filename} "
             f"batch_size={cfg.test_ds.batch_size} "
             f"random_seed={cfg.random_seed} "
-            f"eval_config_yaml={f.name} ",
+            f"eval_config_yaml={f.name} "
+            f"decoder_type={cfg.inference.decoder_type} ",
             shell=True,
             check=True,
         )