diff --git a/src/wordcab_transcribe/models.py b/src/wordcab_transcribe/models.py
index 86a3e93..8203a04 100644
--- a/src/wordcab_transcribe/models.py
+++ b/src/wordcab_transcribe/models.py
@@ -53,6 +53,16 @@ class Word(BaseModel):
     probability: float
 
 
+class MultiChannelSegment(NamedTuple):
+    """Multi-channel segment model for the API."""
+
+    start: float
+    end: float
+    text: str
+    words: List[Word]
+    speaker: int
+
+
 class Utterance(BaseModel):
     """Utterance model for the API."""
 
@@ -513,6 +523,12 @@ class DiarizationRequest(BaseModel):
     num_speakers: int
 
 
+class MultiChannelTranscriptionOutput(BaseModel):
+    """Multi-channel transcription output model for the API."""
+
+    segments: List[MultiChannelSegment]
+
+
 class TranscriptionOutput(BaseModel):
     """Transcription output model for the API."""
 
diff --git a/src/wordcab_transcribe/services/post_processing_service.py b/src/wordcab_transcribe/services/post_processing_service.py
index 23011be..420e8c2 100644
--- a/src/wordcab_transcribe/services/post_processing_service.py
+++ b/src/wordcab_transcribe/services/post_processing_service.py
@@ -24,6 +24,7 @@
 from wordcab_transcribe.models import (
     DiarizationOutput,
     DiarizationSegment,
+    MultiChannelTranscriptionOutput,
     Timestamps,
     TranscriptionOutput,
     Utterance,
@@ -75,13 +76,13 @@ def single_channel_speaker_mapping(
         return utterances
 
     def multi_channel_speaker_mapping(
-        self, multi_channel_segments: List[TranscriptionOutput]
+        self, multi_channel_segments: List[MultiChannelTranscriptionOutput]
     ) -> TranscriptionOutput:
         """
         Run the multi-channel post-processing functions on the inputs by merging the segments based on the timestamps.
 
         Args:
-            multi_channel_segments (List[TranscriptionOutput]):
+            multi_channel_segments (List[MultiChannelTranscriptionOutput]):
                 List of segments from multi speakers.
 
         Returns:
@@ -93,7 +94,7 @@ def multi_channel_speaker_mapping(
             for segment in output.segments
             for word in segment.words
         ]
-        words_with_speaker_mapping.sort(key=lambda _, word: word.start)
+        words_with_speaker_mapping.sort(key=lambda x: x[1].start)
 
         utterances: List[Utterance] = self.reconstruct_multi_channel_utterances(
             words_with_speaker_mapping
diff --git a/src/wordcab_transcribe/services/transcribe_service.py b/src/wordcab_transcribe/services/transcribe_service.py
index eba00f4..4a0fa9b 100644
--- a/src/wordcab_transcribe/services/transcribe_service.py
+++ b/src/wordcab_transcribe/services/transcribe_service.py
@@ -26,7 +26,12 @@
 from loguru import logger
 from tensorshare import Backend, TensorShare
 
-from wordcab_transcribe.models import TranscriptionOutput
+from wordcab_transcribe.models import (
+    MultiChannelSegment,
+    MultiChannelTranscriptionOutput,
+    TranscriptionOutput,
+    Word,
+)
 
 
 class FasterWhisperModel(NamedTuple):
@@ -312,7 +317,7 @@ def multi_channel(
         no_speech_threshold: float = 0.6,
         condition_on_previous_text: bool = False,
         prompt: Optional[str] = None,
-    ) -> TranscriptionOutput:
+    ) -> MultiChannelTranscriptionOutput:
         """
         Transcribe an audio file using the faster-whisper original pipeline.
 
@@ -342,7 +347,7 @@ def multi_channel(
             prompt (Optional[str]): Initial prompt to use for the generation.
 
         Returns:
-            TranscriptionOutput: Transcription output.
+            MultiChannelTranscriptionOutput: Multi-channel transcription segments in a list.
         """
         if isinstance(audio, torch.Tensor):
             _audio = audio.numpy()
@@ -374,27 +379,13 @@ def multi_channel(
         )
 
         for segment in segments:
-            segment_dict: dict = {
-                "start": None,
-                "end": None,
-                "text": segment.text,
-                "words": [],
-                "speaker": speaker_id,
-            }
-
-            for word in segment.words:
-                segment_dict["words"].append(
-                    {
-                        "start": word.start,
-                        "end": word.end,
-                        "word": word.word,
-                        "score": word.probability,
-                    }
-                )
-
-            segment_dict["start"] = segment_dict["words"][0]["start"]
-            segment_dict["end"] = segment_dict["words"][-1]["end"]
-
-            final_segments.append(segment_dict)
+            _segment = MultiChannelSegment(
+                start=segment.start,
+                end=segment.end,
+                text=segment.text,
+                words=[Word(**word._asdict()) for word in segment.words],
+                speaker=speaker_id,
+            )
+            final_segments.append(_segment)
 
-        return TranscriptionOutput(segments=final_segments)
+        return MultiChannelTranscriptionOutput(segments=final_segments)