all text-related classes now share a common TimedText base class

src/whisper_streaming/asr_token.py

@@ -1,15 +0,0 @@
-class ASRToken:
-    """
-    A token (word) from the ASR system with start/end times and text.
-    """
-    def __init__(self, start: float, end: float, text: str):
-        self.start = start
-        self.end = end
-        self.text = text
-
-    def with_offset(self, offset: float) -> "ASRToken":
-        """Return a new token with the time offset added."""
-        return ASRToken(self.start + offset, self.end + offset, self.text)
-
-    def __repr__(self):
-        return f"ASRToken(start={self.start:.2f}, end={self.end:.2f}, text={self.text!r})"

src/whisper_streaming/backends.py

@@ -6,7 +6,7 @@ import math
 import torch
 from typing import List
 import numpy as np
-from src.whisper_streaming.asr_token import ASRToken
+from src.whisper_streaming.timed_objects import ASRToken
 
 logger = logging.getLogger(__name__)
 

src/whisper_streaming/online_asr.py

@@ -2,37 +2,10 @@ import sys
 import numpy as np
 import logging
 from typing import List, Tuple, Optional
-from src.whisper_streaming.asr_token import ASRToken
+from src.whisper_streaming.timed_objects import ASRToken, Sentence, Transcript
 
 logger = logging.getLogger(__name__)
 
-class Sentence:
-    """
-    A sentence assembled from tokens.
-    """
-    def __init__(self, start: float, end: float, text: str):
-        self.start = start
-        self.end = end
-        self.text = text
-
-    def __repr__(self):
-        return f"Sentence(start={self.start:.2f}, end={self.end:.2f}, text={self.text!r})"
-
-class Transcript:
-    """
-    A transcript that bundles a start time, an end time, and a concatenated text.
-    """
-    def __init__(self, start: Optional[float], end: Optional[float], text: str):
-        self.start = start
-        self.end = end
-        self.text = text
-
-    def __iter__(self):
-        return iter((self.start, self.end, self.text))
-
-    def __repr__(self):
-        return f"Transcript(start={self.start}, end={self.end}, text={self.text!r})"
-
 
 class HypothesisBuffer:
     """
@@ -111,10 +84,6 @@ class HypothesisBuffer:
         while self.committed_in_buffer and self.committed_in_buffer[0].end <= time:
             self.committed_in_buffer.pop(0)
 
-    def complete(self) -> List[ASRToken]:
-        """Return any remaining tokens (i.e. the current buffer)."""
-        return self.buffer
-
 
 class OnlineASRProcessor:
     """
@@ -211,7 +180,7 @@ class OnlineASRProcessor:
         self.committed.extend(committed_tokens)
         completed = self.concatenate_tokens(committed_tokens)
         logger.debug(f">>>> COMPLETE NOW: {completed.text}")
-        incomp = self.concatenate_tokens(self.transcript_buffer.complete())
+        incomp = self.concatenate_tokens(self.transcript_buffer.buffer)
         logger.debug(f"INCOMPLETE: {incomp.text}")
 
         if committed_tokens and self.buffer_trimming_way == "sentence":
@@ -318,7 +287,7 @@ class OnlineASRProcessor:
         """
         Flush the remaining transcript when processing ends.
         """
-        remaining_tokens = self.transcript_buffer.complete()
+        remaining_tokens = self.transcript_buffer.buffer
         final_transcript = self.concatenate_tokens(remaining_tokens)
         logger.debug(f"Final non-committed transcript: {final_transcript}")
         self.buffer_time_offset += len(self.audio_buffer) / self.SAMPLING_RATE

src/whisper_streaming/timed_objects.py

@@ -0,0 +1,22 @@
+from dataclasses import dataclass
+from typing import Optional
+
+@dataclass
+class TimedText:
+    start: Optional[float]
+    end: Optional[float]
+    text: str
+
+@dataclass
+class ASRToken(TimedText):
+    def with_offset(self, offset: float) -> "ASRToken":
+        """Return a new token with the time offset added."""
+        return ASRToken(self.start + offset, self.end + offset, self.text)
+
+@dataclass
+class Sentence(TimedText):
+    pass
+
+@dataclass
+class Transcript(TimedText):
+    pass

whisper_fastapi_online_server.py

@@ -201,17 +201,17 @@ async def websocket_endpoint(websocket: WebSocket):
                     )
                     pcm_buffer = bytearray()
                     online.insert_audio_chunk(pcm_array)
-                    beg_trans, end_trans, trans = online.process_iter()
+                    transcription = online.process_iter()
                     
-                    if trans:
+                    if transcription:
                         chunk_history.append({
-                        "beg": beg_trans,
-                        "end": end_trans,
-                        "text": trans,
+                        "beg": transcription.start,
+                        "end": transcription.end,
+                        "text": transcription.text,
                         "speaker": "0"
                         })
                     
-                    full_transcription += trans
+                    full_transcription += transcription.text
                     if args.vac:
                         transcript = online.online.concatenate_tokens(online.online.transcript_buffer.buffer)
                     else: