Seamless Streaming Inference (#169)

* Adding README

* Remove unnecessary try catch block

* Adding ASR task

* Adding  README

* Fix WER scorer

* Change default strip silence behavior

* Fix vocoder sample rate fetching and add disbale standardize audio

* Standardizing audio logic in strip silence

* Addressing comments

* Addressing comments

* miinor README change

* renaming and reorganizing pipeline files

src/seamless_communication/cli/eval_utils/compute_metrics.py

@@ -4,23 +4,23 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
+import json
 import logging
+from pathlib import Path
+from typing import Optional, Tuple, Union
+
 import pandas as pd
 import whisper
-
 from fairseq2.typing import Device
 from jiwer import cer, wer
-from pathlib import Path
 from sacrebleu.metrics.base import Score, Signature
 from sacrebleu.metrics.bleu import BLEU
 from sacrebleu.metrics.chrf import CHRF
 from seamless_communication.cli.eval_utils.lang_mapping import LANG3_LANG2
 from tqdm import tqdm
-from typing import Optional, Tuple, Union
 from whisper import Whisper
 from whisper.normalizers import BasicTextNormalizer, EnglishTextNormalizer
 
-
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s %(levelname)s -- %(name)s: %(message)s",
@@ -190,7 +190,7 @@ def compute_asr_error_rate(
     ref_text_series: pd.Series,
     lang: str,
     whisper_normalize_text: bool = True,
-) -> Tuple[Score, str]:
+) -> Tuple[float, str]:
     """Wraps normalization functions and computes ASR WER/CER score
     Args:
         hyp_text_series (pd.Series): each line contains s2t model prediction or first pass prediction
@@ -348,17 +348,24 @@ def compute_quality_metrics(
         logger.info(f"{task} ASR Normalized BLEU:\n{asr_bleu_normalized_json}")
 
     if task == "ASR":
-        _, asr_error_rate_signature = compute_asr_error_rate(
+        asr_error_rate, asr_error_rate_signature = compute_asr_error_rate(
             hyp_text_series=df[pred_text_col_name],
             ref_text_series=df[ref_text_col_name],
             lang=tgt_lang,
             whisper_normalize_text=whisper_normalize_text_output,
         )
+        d = {
+            "name": "WER",
+            "score": asr_error_rate,
+            "signature": asr_error_rate_signature,
+        }
+        asr_error_rate_json = json.dumps(d, indent=1, ensure_ascii=False)
+
         filename = "asr_error_rate.json"
 
         with open(output_path / filename, "w") as f:
-            f.write(asr_error_rate_signature)
+            f.write(asr_error_rate_json)
 
-        logger.info(f"ASR : {asr_error_rate_signature}")
+        logger.info(f"ASR : {asr_error_rate_json}")
 
     return filename

src/seamless_communication/cli/streaming/README.md

@@ -0,0 +1,45 @@
+# Evaluating SeamlessStreaming and Seamless models
+SeamlessStreaming is the streaming only model and Seamless is the expressive streaming model.
+
+## Quick start:
+
+Evaluation can be run with the `streaming_evaluate` CLI.
+
+We use the `seamless_streaming_unity` for loading the speech encoder and T2U models, and `seamless_streaming_monotonic_decoder` for loading the text decoder for streaming evaluation. This is already set as defaults for the `streaming_evaluate` CLI, but can be overridden using the `--unity-model-name` and  `--monotonic-decoder-model-name` args if required.
+
+Note that the numbers in the paper use single precision floating point format (fp32) for evaluation by setting `--dtype fp32`.
+
+### S2TT:
+Set the task to `s2tt` for evaluating the speech-to-text translation part of the SeamlessStreaming model.
+
+```bash
+streaming_evaluate --task s2tt --data-file <path_to_data_tsv_file> --audio-root-dir <path_to_audio_root_directory> --output <path_to_evaluation_output_directory> --tgt-lang <3_letter_lang_code>
+```
+
+Note: The `--ref-field` can be used to specify the name of the reference column in the dataset.
+
+### ASR:
+Set the task to `asr` for evaluating the automatic speech recognition part of the SeamlessStreaming model. Make sure to pass the source language as the `--tgt-lang` arg.
+
+```bash
+streaming_evaluate --task s2tt --data-file <path_to_data_tsv_file> --audio-root-dir <path_to_audio_root_directory> --output <path_to_evaluation_output_directory> --tgt-lang <3_letter_source_lang_code> 
+```
+
+### S2ST:
+
+#### SeamlessStreaming:
+
+Set the task to `s2st` for evaluating the speech-to-speech translation part of the SeamlessStreaming model. 
+
+```bash
+streaming_evaluate --task s2st --data-file <path_to_data_tsv_file> --audio-root-dir <path_to_audio_root_directory> --output <path_to_evaluation_output_directory> --tgt-lang <3_letter_lang_code>
+```
+
+#### Seamless:
+The Seamless model is an unified model for streaming expressive speech-to-speech tranlsation. Use the `--expressive` arg for running evaluation of this unified model.
+
+```bash
+streaming_evaluate --task s2st --data-file <path_to_data_tsv_file> --audio-root-dir <path_to_audio_root_directory> --output <path_to_evaluation_output_directory> --tgt-lang <3_letter_lang_code> --expressive
+```
+
+Note: In the current version of our paper, we use vocoder_pretssel_16khz for the evaluation , so in order to reproduce those results please add this arg to the above command: `--vocoder-name vocoder_pretssel_16khz`

src/seamless_communication/cli/streaming/evaluate.py

@@ -5,19 +5,29 @@
 # LICENSE file in the root directory of this source tree.
 
 import argparse
+import logging
 
 from fairseq2.assets import asset_store, download_manager
-from seamless_communication.streaming.agents.mma_m4t_s2st import (
-    MonotonicM4TS2STAgent,
-    SeamlessS2STAgent,
-)
 from seamless_communication.cli.streaming.scorers.seamless_quality_scorer import (
     SeamlessQualityScorer,
 )
-from seamless_communication.streaming.agents.mma_m4t_s2t import MonotonicM4TS2TAgent
+from seamless_communication.streaming.agents.seamless_s2st import SeamlessS2STAgent
+from seamless_communication.streaming.agents.seamless_streaming_s2st import (
+    SeamlessStreamingS2STAgent,
+)
+from seamless_communication.streaming.agents.seamless_streaming_s2t import (
+    SeamlessStreamingS2TAgent,
+)
 from simuleval.evaluator import build_evaluator
 from simuleval.utils.agent import EVALUATION_SYSTEM_LIST, build_system_args
 
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s -- %(name)s: %(message)s",
+)
+
+logger = logging.getLogger(__name__)
+
 
 def main() -> None:
     parser = argparse.ArgumentParser(
@@ -28,7 +38,7 @@ def main() -> None:
 
     parser.add_argument(
         "--task",
-        choices=["s2st", "s2tt"],
+        choices=["s2st", "s2tt", "asr"],
         required=True,
         type=str,
         help="Target language to translate/transcribe into.",
@@ -39,46 +49,33 @@ def main() -> None:
         default=False,
         help="Expressive streaming S2ST inference",
     )
-    parser.add_argument(
-        "--dtype",
-        default="fp16",
-        type=str,
-    )
 
     args, _ = parser.parse_known_args()
 
     model_configs = dict(
         source_segment_size=320,
         device="cuda:0",
-        dtype=args.dtype,
+        dtype="fp16",
         min_starting_wait_w2vbert=192,
         decision_threshold=0.5,
         no_early_stop=True,
-        max_len_a=1,
-        max_len_b=200,
+        max_len_a=0,
+        max_len_b=100,
     )
 
-    if args.dtype == "fp16":
-        model_configs.update(dict(fp16=True))
-
     EVALUATION_SYSTEM_LIST.clear()
     eval_configs = dict(quality_metrics="SEAMLESS_QUALITY_SCORER")
     if args.task == "s2st":
-        model_configs.update(
-            dict(
-                min_unit_chunk_size=50,
-            )
-        )
+        model_configs["min_unit_chunk_size"] = 50
         eval_configs["latency_metrics"] = "StartOffset EndOffset"
 
         if args.expressive:
             EVALUATION_SYSTEM_LIST.append(SeamlessS2STAgent)
-            model_configs.update(dict(vocoder_name="vocoder_pretssel"))
         else:
-            EVALUATION_SYSTEM_LIST.append(MonotonicM4TS2STAgent)
-    elif args.task == "s2tt":
+            EVALUATION_SYSTEM_LIST.append(SeamlessStreamingS2STAgent)
+    elif args.task in ["s2tt", "asr"]:
         assert args.expressive is False, "S2TT inference cannot be expressive."
-        EVALUATION_SYSTEM_LIST.append(MonotonicM4TS2TAgent)
+        EVALUATION_SYSTEM_LIST.append(SeamlessStreamingS2TAgent)
         parser.add_argument(
             "--unity-model-name",
             type=str,
@@ -104,6 +101,9 @@ def main() -> None:
         {**base_config, **model_configs, **eval_configs}, parser
     )
 
+    if args.fp16:
+        logger.warn("--fp16 arg will be ignorned, use --dtype instead")
+
     evaluator = build_evaluator(args)
     evaluator(system)
 

src/seamless_communication/cli/streaming/scorers/seamless_quality_scorer.py

@@ -5,23 +5,19 @@
 # LICENSE file in the root directory of this source tree.
 
 from __future__ import annotations
-import pandas
-from fairseq2.typing import Device
-from pathlib import Path
-from typing import Optional
+
 import json
 from argparse import ArgumentParser, Namespace
-from typing import Dict
+from pathlib import Path
+from typing import Dict, Optional
 
+import pandas
+from fairseq2.typing import Device
+from seamless_communication.cli.eval_utils import compute_quality_metrics
+from simuleval.evaluator.instance import LogInstance
 from simuleval.evaluator.scorers.quality_scorer import (
-    register_quality_scorer,
     QualityScorer,
-)
-
-from simuleval.evaluator.instance import LogInstance
-
-from seamless_communication.cli.eval_utils import (
-    compute_quality_metrics,
+    register_quality_scorer,
 )
 
 
@@ -90,19 +86,13 @@ class SeamlessQualityScorer(QualityScorer):  # type: ignore
 
     @staticmethod
     def add_args(parser: ArgumentParser) -> None:
-        try:
-            parser.add_argument(
-                "--task", type=str, help="Task to evaluate", required=True
-            )
-            parser.add_argument(
-                "--tgt-lang",
-                type=str,
-                help="Target language to translate/transcribe into.",
-                required=True,
-            )
-        except:
-            pass
-
+        parser.add_argument("--task", type=str, help="Task to evaluate", required=True)
+        parser.add_argument(
+            "--tgt-lang",
+            type=str,
+            help="Target language to translate/transcribe into.",
+            required=True,
+        )
         parser.add_argument(
             "--whisper-model-name", type=str, help="Whisper model name", default="large"
         )

src/seamless_communication/streaming/agents/online_vocoder.py

@@ -5,21 +5,37 @@
 # LICENSE file in the root directory of this source tree.
 from __future__ import annotations
 
+import logging
 from argparse import ArgumentParser, Namespace
 from typing import Any, Dict
-import torch
 
-from seamless_communication.models.vocoder.vocoder import Vocoder
+import torch
+from seamless_communication.models.vocoder.loader import load_vocoder_model
 from simuleval.agents import AgentStates, TextToSpeechAgent
 from simuleval.agents.actions import ReadAction, WriteAction
 from simuleval.data.segments import SpeechSegment
 
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s -- %(name)s: %(message)s",
+)
+
+logger = logging.getLogger(__name__)
+
 
 class VocoderAgent(TextToSpeechAgent):  # type: ignore
-    def __init__(self, vocoder: Vocoder, args: Namespace) -> None:
+    def __init__(self, args: Namespace) -> None:
         super().__init__(args)
+
+        logger.info(
+            f"Loading the Vocoder model: {args.vocoder_name} on device={args.device}, dtype={args.dtype}"
+        )
+        self.vocoder = load_vocoder_model(
+            args.vocoder_name, device=args.device, dtype=args.dtype
+        )
+        self.vocoder.eval()
+
         self.sample_rate = args.sample_rate
-        self.vocoder = vocoder
         self.tgt_lang = args.tgt_lang
         self.speaker_id = args.vocoder_speaker_id
 
@@ -54,6 +70,12 @@ class VocoderAgent(TextToSpeechAgent):  # type: ignore
 
     @classmethod
     def add_args(cls, parser: ArgumentParser) -> None:
+        parser.add_argument(
+            "--vocoder-name",
+            type=str,
+            help="Vocoder name.",
+            default="vocoder_v2",
+        )
         parser.add_argument(
             "--vocoder-speaker-id",
             type=int,
@@ -64,6 +86,4 @@ class VocoderAgent(TextToSpeechAgent):  # type: ignore
 
     @classmethod
     def from_args(cls, args: Namespace, **kwargs: Dict[str, Any]) -> VocoderAgent:
-        vocoder = kwargs.get("vocoder", None)
-        assert isinstance(vocoder, Vocoder)
-        return cls(vocoder, args)
+        return cls(args)

src/seamless_communication/streaming/agents/pretssel_vocoder.py

@@ -5,27 +5,46 @@
 # LICENSE file in the root directory of this source tree.
 from __future__ import annotations
 
+import logging
 from argparse import ArgumentParser, Namespace
 from typing import Any, Dict, List
 
 import torch
+from fairseq2.assets import asset_store
 from fairseq2.data.audio import WaveformToFbankConverter, WaveformToFbankInput
-from seamless_communication.models.generator.vocoder import PretsselVocoder
+from seamless_communication.models.generator.loader import load_pretssel_vocoder_model
 from seamless_communication.models.unity import load_gcmvn_stats
-from seamless_communication.models.vocoder.vocoder import Vocoder
 from seamless_communication.streaming.agents.common import NoUpdateTargetMixin
 from simuleval.agents import AgentStates, TextToSpeechAgent
 from simuleval.agents.actions import ReadAction, WriteAction
 from simuleval.data.segments import SpeechSegment
 
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s -- %(name)s: %(message)s",
+)
+
+logger = logging.getLogger(__name__)
+
 
 class PretsselVocoderAgent(NoUpdateTargetMixin, TextToSpeechAgent):  # type: ignore
-    def __init__(self, vocoder: Vocoder, args: Namespace) -> None:
+    def __init__(self, args: Namespace) -> None:
         super().__init__(args)
-        self.vocoder = vocoder
+
+        logger.info(
+            f"Loading the Vocoder model: {args.vocoder_name} on device={args.device}, dtype={args.dtype}"
+        )
+        assert "pretssel" in args.vocoder_name
+        self.vocoder = load_pretssel_vocoder_model(
+            args.vocoder_name, device=args.device, dtype=args.dtype
+        )
+        self.vocoder.eval()
+
+        vocoder_model_card = asset_store.retrieve_card(args.vocoder_name)
+        self.vocoder_sample_rate = vocoder_model_card.field("sample_rate").as_(int)
+
         self.upstream_idx = args.upstream_idx
         self.sample_rate = args.sample_rate  # input sample rate
-        self.vocoder_sample_rate = args.vocoder_sample_rate  # output sample rate
         self.tgt_lang = args.tgt_lang
         self.convert_to_fbank = WaveformToFbankConverter(
             num_mel_bins=80,
@@ -110,23 +129,21 @@ class PretsselVocoderAgent(NoUpdateTargetMixin, TextToSpeechAgent):  # type: ign
 
     @classmethod
     def add_args(cls, parser: ArgumentParser) -> None:
+        parser.add_argument(
+            "--vocoder-name",
+            type=str,
+            help="Vocoder name.",
+            default="vocoder_pretssel",
+        )
         parser.add_argument(
             "--upstream-idx",
             type=int,
             default=0,
             help="index of encoder states where states.source contains input audio",
         )
-        parser.add_argument(
-            "--vocoder-sample-rate",
-            type=int,
-            default=16000,
-            help="sample rate out of the vocoder",
-        )
 
     @classmethod
     def from_args(
         cls, args: Namespace, **kwargs: Dict[str, Any]
     ) -> PretsselVocoderAgent:
-        vocoder = kwargs.get("vocoder", None)
-        assert isinstance(vocoder, PretsselVocoder)
-        return cls(vocoder, args)
+        return cls(args)

src/seamless_communication/streaming/agents/mma_m4t_s2st.py → src/seamless_communication/streaming/agents/seamless_streaming_s2st.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
+from seamless_communication.streaming.agents.detokenizer import UnitYDetokenizerAgent
 from seamless_communication.streaming.agents.offline_w2v_bert_encoder import (
     OfflineWav2VecBertEncoderAgent,
 )
@@ -16,19 +17,15 @@ from seamless_communication.streaming.agents.online_text_decoder import (
 from seamless_communication.streaming.agents.online_unit_decoder import (
     NARUnitYUnitDecoderAgent,
 )
-from seamless_communication.streaming.agents.silero_vad import SileroVADAgent
 from seamless_communication.streaming.agents.online_vocoder import VocoderAgent
-from seamless_communication.streaming.agents.pretssel_vocoder import PretsselVocoderAgent
-
-from seamless_communication.streaming.agents.detokenizer import UnitYDetokenizerAgent
+from seamless_communication.streaming.agents.silero_vad import SileroVADAgent
 from seamless_communication.streaming.agents.unity_pipeline import (
     UnitYAgentPipeline,
     UnitYAgentTreePipeline,
 )
-from simuleval.utils import entrypoint
 
 
-class MonotonicM4TS2STAgent(UnitYAgentPipeline):
+class SeamlessStreamingS2STAgent(UnitYAgentPipeline):
     pipeline = [
         OnlineFeatureExtractorAgent,
         OfflineWav2VecBertEncoderAgent,
@@ -38,17 +35,7 @@ class MonotonicM4TS2STAgent(UnitYAgentPipeline):
     ]
 
 
-class SeamlessS2STAgent(UnitYAgentPipeline):
-    pipeline = [
-        OnlineFeatureExtractorAgent,
-        OfflineWav2VecBertEncoderAgent,
-        UnitYMMATextDecoderAgent,
-        NARUnitYUnitDecoderAgent,
-        PretsselVocoderAgent,
-    ]
-
-
-class MonotonicM4TS2STVADAgent(UnitYAgentPipeline):
+class SeamlessStreamingS2STVADAgent(UnitYAgentPipeline):
     pipeline = [
         SileroVADAgent,
         OnlineFeatureExtractorAgent,
@@ -59,7 +46,7 @@ class MonotonicM4TS2STVADAgent(UnitYAgentPipeline):
     ]
 
 
-class MonotonicM4TS2STJointVADAgent(UnitYAgentTreePipeline):
+class SeamlessStreamingS2STJointVADAgent(UnitYAgentTreePipeline):
     pipeline = {
         SileroVADAgent: [OnlineFeatureExtractorAgent],
         OnlineFeatureExtractorAgent: [OfflineWav2VecBertEncoderAgent],
@@ -69,15 +56,3 @@ class MonotonicM4TS2STJointVADAgent(UnitYAgentTreePipeline):
         NARUnitYUnitDecoderAgent: [VocoderAgent],
         VocoderAgent: [],
     }
-
-
-class SeamlessS2STJointVADAgent(UnitYAgentTreePipeline):
-    pipeline = {
-        SileroVADAgent: [OnlineFeatureExtractorAgent],
-        OnlineFeatureExtractorAgent: [OfflineWav2VecBertEncoderAgent],
-        OfflineWav2VecBertEncoderAgent: [UnitYMMATextDecoderAgent],
-        UnitYMMATextDecoderAgent: [UnitYDetokenizerAgent, NARUnitYUnitDecoderAgent],
-        UnitYDetokenizerAgent: [],
-        NARUnitYUnitDecoderAgent: [PretsselVocoderAgent],
-        PretsselVocoderAgent: [],
-    }

src/seamless_communication/streaming/agents/mma_m4t_s2t.py → src/seamless_communication/streaming/agents/seamless_streaming_s2t.py

@@ -16,11 +16,9 @@ from seamless_communication.streaming.agents.online_text_decoder import (
 )
 from seamless_communication.streaming.agents.silero_vad import SileroVADAgent
 from seamless_communication.streaming.agents.unity_pipeline import UnitYAgentPipeline
-from simuleval.utils import entrypoint
 
 
-@entrypoint
-class MonotonicM4TS2TDetokAgent(UnitYAgentPipeline):
+class SeamlessStreamingS2TDetokAgent(UnitYAgentPipeline):
     pipeline = [
         OnlineFeatureExtractorAgent,
         OfflineWav2VecBertEncoderAgent,
@@ -29,8 +27,7 @@ class MonotonicM4TS2TDetokAgent(UnitYAgentPipeline):
     ]
 
 
-@entrypoint
-class MonotonicM4TS2TAgent(UnitYAgentPipeline):
+class SeamlessStreamingS2TAgent(UnitYAgentPipeline):
     pipeline = [
         OnlineFeatureExtractorAgent,
         OfflineWav2VecBertEncoderAgent,
@@ -38,7 +35,7 @@ class MonotonicM4TS2TAgent(UnitYAgentPipeline):
     ]
 
 
-class MonotonicM4TS2TVADAgent(UnitYAgentPipeline):
+class SeamlessStreamingS2TVADAgent(UnitYAgentPipeline):
     pipeline = [
         SileroVADAgent,
         OnlineFeatureExtractorAgent,

src/seamless_communication/streaming/agents/unity_pipeline.py

@@ -74,12 +74,7 @@ class UnitYPipelineMixin:
             help="Monotonic decoder model name.",
             default="seamless_streaming_monotonic_decoder",
         )
-        parser.add_argument(
-            "--vocoder-name",
-            type=str,
-            help="Vocoder name.",
-            default="vocoder_v2",
-        )
+
         parser.add_argument(
             "--sample-rate",
             default=16000,
@@ -147,22 +142,6 @@ class UnitYPipelineMixin:
         )
         monotonic_decoder_model.eval()
 
-        vocoder: Optional[Union[PretsselVocoder, Vocoder]] = None
-        if args.vocoder_name is not None and output_modality == Modality.SPEECH:
-            logger.info(
-                f"Loading the Vocoder model: {args.vocoder_name} on device={args.device}, dtype={args.dtype}"
-            )
-            if "pretssel" in args.vocoder_name:
-                vocoder = load_pretssel_vocoder_model(
-                    args.vocoder_name, device=args.device, dtype=args.dtype
-                )
-            else:
-                vocoder = load_vocoder_model(
-                    args.vocoder_name, device=args.device, dtype=args.dtype
-                )
-            assert vocoder is not None
-            vocoder.eval()
-
         return {
             "unity_model": unity_model,
             "unity_config": unity_config,
@@ -170,7 +149,6 @@ class UnitYPipelineMixin:
             "monotonic_decoder_config": monotonic_decoder_config,
             "text_tokenizer": text_tokenizer,
             "unit_tokenizer": unit_tokenizer,
-            "vocoder": vocoder,
         }
 
 

src/seamless_communication/streaming/dataloaders/s2tt.py

@@ -55,7 +55,13 @@ class SileroVADSilenceRemover:
             onnx=False,
         )
 
-    def __call__(self, sample_list: List[float]) -> List[float]:
+    def __call__(self, sample: torch.Tensor, is_standardized: bool) -> List[float]:
+        if not is_standardized:
+            # Standardizing here just for getting silence boundaries
+            standarized_sample_list = F.layer_norm(sample, sample.shape).tolist()
+        else:
+            standarized_sample_list = sample.tolist()
+
         (
             get_speech_timestamps,
             save_audio,
@@ -64,8 +70,10 @@ class SileroVADSilenceRemover:
             collect_chunks,
         ) = self.utils
         speech_timestamps = get_speech_timestamps(
-            sample_list, self.model, sampling_rate=self.sample_rate
+            standarized_sample_list, self.model, sampling_rate=self.sample_rate
         )
+
+        sample_list: List[float] = sample.tolist()
         if len(speech_timestamps) == 0:
             return sample_list
         speech_start_time = speech_timestamps[0]["start"]
@@ -75,7 +83,9 @@ class SileroVADSilenceRemover:
 
 @register_dataloader("fairseq2_s2tt")
 class SimulEvalSpeechToTextDataloader(SpeechToTextDataloader, IterableDataloader):  # type: ignore
-    def __init__(self, data_pipeline: DataPipeline, args: Namespace) -> None:
+    def __init__(
+        self, data_pipeline: DataPipeline, is_standardized: bool, args: Namespace
+    ) -> None:
         self.args = args
         self.data_file: Path = Path(getattr(self.args, "data_file", ""))
         if not self.data_file.exists():
@@ -83,10 +93,12 @@ class SimulEvalSpeechToTextDataloader(SpeechToTextDataloader, IterableDataloader
         self.start_index: int = getattr(self.args, "start_index", 0)
         self.end_index: int = getattr(self.args, "end_index", -1)
         self.data_pipeline = data_pipeline
+        self.is_standardized = is_standardized
         self.data_itr = iter(self.data_pipeline)
         self.cur_index = self.start_index - 1
+        self.no_strip_silence = self.args.no_strip_silence
         self.silence_remover = None
-        if self.args.strip_silence:
+        if not self.no_strip_silence:
             logger.warn(
                 "Stripping silence in the beginning and the end of audio with SileroVAD."
             )
@@ -113,12 +125,12 @@ class SimulEvalSpeechToTextDataloader(SpeechToTextDataloader, IterableDataloader
         return self.end_index - self.start_index
 
     def get_source(self, index: Optional[int] = None) -> List[float]:
-        source: List[float] = (
-            self.item["audio"]["data"]["waveform"]["seqs"].squeeze().tolist()
-        )
+        squeezed_item = self.item["audio"]["data"]["waveform"]["seqs"].squeeze()
 
-        if self.silence_remover is not None:
-            source = self.silence_remover(source)
+        if not self.no_strip_silence and self.silence_remover is not None:
+            source = self.silence_remover(squeezed_item, self.is_standardized)
+        else:
+            source = squeezed_item.tolist()
 
         return source
 
@@ -168,10 +180,13 @@ class SimulEvalSpeechToTextDataloader(SpeechToTextDataloader, IterableDataloader
             selector="audio.data",
         )
 
-        pipeline_builder.map(
-            lambda x: F.layer_norm(x, x.shape),
-            selector="audio.data.waveform",
-        )
+        is_standardized = False
+        if args.standardize_audio:
+            pipeline_builder.map(
+                lambda x: F.layer_norm(x, x.shape),
+                selector="audio.data.waveform",
+            )
+            is_standardized = True
 
         collate = Collater(pad_value=0, pad_to_multiple=1)
 
@@ -181,7 +196,7 @@ class SimulEvalSpeechToTextDataloader(SpeechToTextDataloader, IterableDataloader
 
         data_pipeline = pipeline_builder.and_return()
 
-        return cls(data_pipeline, args)
+        return cls(data_pipeline, is_standardized, args)
 
     @staticmethod
     def add_args(parser: ArgumentParser) -> None:
@@ -222,8 +237,13 @@ class SimulEvalSpeechToTextDataloader(SpeechToTextDataloader, IterableDataloader
             help="Output directory. Required if using iterable dataloader.",
         )
         parser.add_argument(
-            "--strip-silence",
+            "--no-strip-silence",
             action="store_true",
             default=False,
             help="Strip silence in the beginning and the end of audio.",
         )
+        parser.add_argument(
+            "--standardize-audio",
+            action="store_true",
+            help="Standardize audio.",
+        )