ASR evaluator

examples/asr/asr_chunked_inference/ctc/speech_to_text_buffered_infer_ctc.py

@@ -41,6 +41,7 @@
 from dataclasses import dataclass, is_dataclass
 from typing import Optional
 
+import pytorch_lightning as pl
 import torch
 from omegaconf import OmegaConf
 
@@ -71,6 +72,7 @@ class TranscriptionConfig:
     num_workers: int = 0
     append_pred: bool = False  # Sets mode of work, if True it will add new field transcriptions.
     pred_name_postfix: Optional[str] = None  # If you need to use another model name, rather than standard one.
+    random_seed: Optional[int] = None  # seed number going to be used in seed_everything()
 
     # Chunked configs
     chunk_len_in_secs: float = 1.6  # Chunk length in seconds
@@ -96,6 +98,9 @@ def main(cfg: TranscriptionConfig) -> TranscriptionConfig:
     if is_dataclass(cfg):
         cfg = OmegaConf.structured(cfg)
 
+    if cfg.random_seed:
+        pl.seed_everything(cfg.random_seed)
+
     if cfg.model_path is None and cfg.pretrained_name is None:
         raise ValueError("Both cfg.model_path and cfg.pretrained_name cannot be None!")
     if cfg.audio_dir is None and cfg.dataset_manifest is None:

examples/asr/asr_chunked_inference/rnnt/speech_to_text_buffered_infer_rnnt.py

@@ -62,6 +62,7 @@
 from dataclasses import dataclass, is_dataclass
 from typing import Optional
 
+import pytorch_lightning as pl
 import torch
 from omegaconf import OmegaConf, open_dict
 
@@ -95,6 +96,7 @@ class TranscriptionConfig:
     num_workers: int = 0
     append_pred: bool = False  # Sets mode of work, if True it will add new field transcriptions.
     pred_name_postfix: Optional[str] = None  # If you need to use another model name, rather than standard one.
+    random_seed: Optional[int] = None  # seed number going to be used in seed_everything()
 
     # Chunked configs
     chunk_len_in_secs: float = 1.6  # Chunk length in seconds
@@ -127,6 +129,9 @@ def main(cfg: TranscriptionConfig) -> TranscriptionConfig:
     if is_dataclass(cfg):
         cfg = OmegaConf.structured(cfg)
 
+    if cfg.random_seed:
+        pl.seed_everything(cfg.random_seed)
+
     if cfg.model_path is None and cfg.pretrained_name is None:
         raise ValueError("Both cfg.model_path and cfg.pretrained_name cannot be None!")
     if cfg.audio_dir is None and cfg.dataset_manifest is None:

examples/asr/transcribe_speech.py

@@ -108,13 +108,15 @@ class TranscriptionConfig:
     dataset_manifest: Optional[str] = None  # Path to dataset's JSON manifest
     channel_selector: Optional[int] = None  # Used to select a single channel from multi-channel files
     audio_key: str = 'audio_filepath'  # Used to override the default audio key in dataset_manifest
+    eval_config_yaml: Optional[str] = None  # Path to a yaml file of config of evaluation
 
     # General configs
     output_filename: Optional[str] = None
     batch_size: int = 32
     num_workers: int = 0
     append_pred: bool = False  # Sets mode of work, if True it will add new field transcriptions.
     pred_name_postfix: Optional[str] = None  # If you need to use another model name, rather than standard one.
+    random_seed: Optional[int] = None  # seed number going to be used in seed_everything()
 
     # Set to True to output greedy timestamp information (only supported models)
     compute_timestamps: bool = False
@@ -152,11 +154,21 @@ def main(cfg: TranscriptionConfig) -> TranscriptionConfig:
     if is_dataclass(cfg):
         cfg = OmegaConf.structured(cfg)
 
+    if cfg.random_seed:
+        pl.seed_everything(cfg.random_seed)
+
     if cfg.model_path is None and cfg.pretrained_name is None:
         raise ValueError("Both cfg.model_path and cfg.pretrained_name cannot be None!")
     if cfg.audio_dir is None and cfg.dataset_manifest is None:
         raise ValueError("Both cfg.audio_dir and cfg.dataset_manifest cannot be None!")
 
+    # Load augmentor from exteranl yaml file which contains eval info, could be extend to other feature such VAD, P&C
+    augmentor = None
+    if cfg.eval_config_yaml:
+        eval_config = OmegaConf.load(cfg.eval_config_yaml)
+        augmentor = eval_config.test_ds.get("augmentor")
+        logging.info(f"Will apply on-the-fly augmentation on samples during transcription: {augmentor} ")
+
     # setup GPU
     if cfg.cuda is None:
         if torch.cuda.is_available():
@@ -253,6 +265,7 @@ def autocast():
                         num_workers=cfg.num_workers,
                         return_hypotheses=return_hypotheses,
                         channel_selector=cfg.channel_selector,
+                        augmentor=augmentor,
                     )
                 else:
                     logging.warning(
@@ -264,6 +277,7 @@ def autocast():
                         num_workers=cfg.num_workers,
                         return_hypotheses=return_hypotheses,
                         channel_selector=cfg.channel_selector,
+                        augmentor=augmentor,
                     )
             else:
                 transcriptions = asr_model.transcribe(
@@ -272,6 +286,7 @@ def autocast():
                     num_workers=cfg.num_workers,
                     return_hypotheses=return_hypotheses,
                     channel_selector=cfg.channel_selector,
+                    augmentor=augmentor,
                 )
 
     logging.info(f"Finished transcribing {len(filepaths)} files !")

nemo/collections/asr/models/ctc_bpe_models.py

@@ -227,6 +227,9 @@ def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLo
             'use_start_end_token': self.cfg.validation_ds.get('use_start_end_token', False),
         }
 
+        if config.get("augmentor"):
+            dl_config['augmentor'] = config.get("augmentor")
+
         temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config))
         return temporary_datalayer
 

nemo/collections/asr/models/ctc_models.py

@@ -116,8 +116,12 @@ def transcribe(
         return_hypotheses: bool = False,
         num_workers: int = 0,
         channel_selector: Optional[ChannelSelectorType] = None,
+        augmentor: DictConfig = None,
     ) -> List[str]:
         """
+        If modify this function, please remember update transcribe_partial_audio() in 
+        nemo/collections/asr/parts/utils/trancribe_utils.py
+
         Uses greedy decoding to transcribe audio files. Use this method for debugging and prototyping.
 
         Args:
@@ -131,7 +135,7 @@ def transcribe(
                 With hypotheses can do some postprocessing like getting timestamp or rescoring
             num_workers: (int) number of workers for DataLoader
             channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`.
-
+            augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied.
         Returns:
             A list of transcriptions (or raw log probabilities if logprobs is True) in the same order as paths2audio_files
         """
@@ -182,6 +186,9 @@ def transcribe(
                     'channel_selector': channel_selector,
                 }
 
+                if augmentor:
+                    config['augmentor'] = augmentor
+
                 temporary_datalayer = self._setup_transcribe_dataloader(config)
                 for test_batch in tqdm(temporary_datalayer, desc="Transcribing"):
                     logits, logits_len, greedy_predictions = self.forward(
@@ -724,6 +731,8 @@ def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLo
             'pin_memory': True,
             'channel_selector': config.get('channel_selector', None),
         }
+        if config.get("augmentor"):
+            dl_config['augmentor'] = config.get("augmentor")
 
         temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config))
         return temporary_datalayer

nemo/collections/asr/models/rnnt_bpe_models.py

@@ -579,5 +579,8 @@ def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLo
             'use_start_end_token': self.cfg.validation_ds.get('use_start_end_token', False),
         }
 
+        if config.get("augmentor"):
+            dl_config['augmentor'] = config.get("augmentor")
+
         temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config))
         return temporary_datalayer

nemo/collections/asr/models/rnnt_models.py

@@ -217,6 +217,7 @@ def transcribe(
         partial_hypothesis: Optional[List['Hypothesis']] = None,
         num_workers: int = 0,
         channel_selector: Optional[ChannelSelectorType] = None,
+        augmentor: DictConfig = None,
     ) -> Tuple[List[str], Optional[List['Hypothesis']]]:
         """
         Uses greedy decoding to transcribe audio files. Use this method for debugging and prototyping.
@@ -232,7 +233,7 @@ def transcribe(
         With hypotheses can do some postprocessing like getting timestamp or rescoring
             num_workers: (int) number of workers for DataLoader
             channel_selector (int | Iterable[int] | str): select a single channel or a subset of channels from multi-channel audio. If set to `'average'`, it performs averaging across channels. Disabled if set to `None`. Defaults to `None`. Uses zero-based indexing.
-
+            augmentor: (DictConfig): Augment audio samples during transcription if augmentor is applied.
         Returns:
             A list of transcriptions in the same order as paths2audio_files. Will also return
         """
@@ -277,6 +278,9 @@ def transcribe(
                     'channel_selector': channel_selector,
                 }
 
+                if augmentor:
+                    config['augmentor'] = augmentor
+
                 temporary_datalayer = self._setup_transcribe_dataloader(config)
                 for test_batch in tqdm(temporary_datalayer, desc="Transcribing"):
                     encoded, encoded_len = self.forward(
@@ -938,6 +942,9 @@ def _setup_transcribe_dataloader(self, config: Dict) -> 'torch.utils.data.DataLo
             'pin_memory': True,
         }
 
+        if config.get("augmentor"):
+            dl_config['augmentor'] = config.get("augmentor")
+
         temporary_datalayer = self._setup_dataloader_from_config(config=DictConfig(dl_config))
         return temporary_datalayer
 

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

@@ -334,13 +334,16 @@ def write_transcription(
 
 def transcribe_partial_audio(
     asr_model,
-    path2manifest: str,
+    path2manifest: str = None,
     batch_size: int = 4,
     logprobs: bool = False,
     return_hypotheses: bool = False,
     num_workers: int = 0,
     channel_selector: Optional[int] = None,
+    augmentor: DictConfig = None,
 ) -> List[str]:
+    """
+    See description of this function in trancribe() in nemo/collections/asr/models/ctc_models.py    """
 
     assert isinstance(asr_model, EncDecCTCModel), "Currently support CTC model only."
 
@@ -377,6 +380,8 @@ def transcribe_partial_audio(
             'num_workers': num_workers,
             'channel_selector': channel_selector,
         }
+        if augmentor:
+            config['augmentor'] = augmentor
 
         temporary_datalayer = asr_model._setup_transcribe_dataloader(config)
         for test_batch in tqdm(temporary_datalayer, desc="Transcribing"):

tools/asr_evaluator/README.md

@@ -0,0 +1,44 @@
+ASR evaluator
+--------------------
+
+A tool for thoroughly evaluating the performance of ASR models and other features such as Voice Activity Detection. 
+
+Features:
+   - Simple step to evaluate a model in all three modes currently supported by NeMo: offline, chunked, and offline_by_chunked.
+   - On-the-fly data augmentation (such as silence, noise, etc.,) for ASR robustness evaluation. 
+   - Investigate the model's performance by detailed insertion, deletion, and substitution error rates for each and all samples.
+   - Evaluate models' reliability on different target groups such as gender, and audio length if metadata is presented.
+
+
+ASR evaluator contains two main parts: 
+- **ENGINE**. To conduct ASR inference.
+- **ANALYST**. To evaluate model performance based on predictions. 
+
+In Analyst, we can evaluate on metadata (such as duration, emotion, etc.) if it presents in manifest. For example, with the following config, we can calculate WERs for audios in different interval groups, where each group (in seconds) is defined by [[0,2],[2,5],[5,10],[10,20],[20,100000]]. Also, we can calculate the WERs for three groups of emotions, where each group is defined by [['happy','laugh'],['neutral'],['sad']]. Moreover, if we set save_wer_per_class=True, it will calculate WERs for audios in all classes presented in the data (i.e. above 5 classes + 'cry' which presented in data but not in the slot). 
+
+```
+analyst:   
+   metadata:
+        duration: 
+            enable: True
+            slot: [[0,2],[2,5],[5,10],[10,20],[20,100000]] 
+            save_wer_per_class: False # whether to save wer for each presented class.
+
+        emotion: 
+            enable: True
+            slot: [['happy','laugh'],['neutral'],['sad']] # we could have 'cry' in data but not in slot we focus on.
+            save_wer_per_class: False
+ ```           
+
+
+Check `./conf/eval.yaml` for the supported configuration. 
+
+If you plan to evaluate/add new tasks such as Punctuation and Capitalization, add it to the engine.
+
+Run
+```
+python asr_evaluator.py \
+engine.pretrained_name="stt_en_conformer_transducer_large" \
+engine.inference_mode.mode="offline" \
+engine.test_ds.augmentor.noise.manifest_path=<manifest file for noise data>
+```

tools/asr_evaluator/asr_evaluator.py

@@ -0,0 +1,92 @@
+# Copyright (c) 2022, 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.
+import json
+
+import git
+from omegaconf import OmegaConf
+from utils import cal_target_metadata_wer, cal_write_wer, run_asr_inference
+
+from nemo.core.config import hydra_runner
+from nemo.utils import logging
+
+
+"""
+This script serves as evaluator of ASR models
+Usage:
+  python python asr_evaluator.py \
+engine.pretrained_name="stt_en_conformer_transducer_large" \
+engine.inference.mode="offline" \
+engine.test_ds.augmentor.noise.manifest_path=<manifest file for noise data> \
+.....
+
+Check out parameters in ./conf/eval.yaml
+"""
+
+
+@hydra_runner(config_path="conf", config_name="eval.yaml")
+def main(cfg):
+    report = {}
+    logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
+
+    # Store git hash for reproducibility
+    if cfg.env.save_git_hash:
+        repo = git.Repo(search_parent_directories=True)
+        report['git_hash'] = repo.head.object.hexsha
+
+    ## Engine
+    # Could skip next line to use generated manifest
+
+    # If need to change more parameters for ASR inference, change it in
+    # 1) shell script in eval_utils.py in nemo/collections/asr/parts/utils or
+    # 2) TranscriptionConfig on top of the executed scripts such as transcribe_speech.py in examples/asr
+    cfg.engine = run_asr_inference(cfg=cfg.engine)
+
+    ## Analyst
+    cfg, total_res, eval_metric = cal_write_wer(cfg)
+    report.update({"res": total_res})
+
+    for target in cfg.analyst.metadata:
+        if cfg.analyst.metadata[target].enable:
+            occ_avg_wer = cal_target_metadata_wer(
+                manifest=cfg.analyst.metric_calculator.output_filename,
+                target=target,
+                meta_cfg=cfg.analyst.metadata[target],
+                eval_metric=eval_metric,
+            )
+            report[target] = occ_avg_wer
+
+    config_engine = OmegaConf.to_object(cfg.engine)
+    report.update(config_engine)
+
+    config_metric_calculator = OmegaConf.to_object(cfg.analyst.metric_calculator)
+    report.update(config_metric_calculator)
+
+    pretty = json.dumps(report, indent=4)
+    res = "%.3f" % (report["res"][eval_metric] * 100)
+    logging.info(pretty)
+    logging.info(f"Overall {eval_metric} is {res} %")
+
+    ## Writer
+    report_file = "report.json"
+    if "report_filename" in cfg.writer and cfg.writer.report_filename:
+        report_file = cfg.writer.report_filename
+
+    with open(report_file, "a") as fout:
+        json.dump(report, fout)
+        fout.write('\n')
+        fout.flush()
+
+
+if __name__ == "__main__":
+    main()