refactor(simulstreaming): extract backend + online module into separate files from whisper streaming

whisperlivekit/audio_processor.py

@@ -6,8 +6,7 @@ import logging
 import traceback
 from datetime import timedelta
 from whisperlivekit.timed_objects import ASRToken
-from whisperlivekit.whisper_streaming_custom.whisper_online import online_factory
-from whisperlivekit.core import TranscriptionEngine
+from whisperlivekit.core import TranscriptionEngine, online_factory
 from whisperlivekit.ffmpeg_manager import FFmpegManager, FFmpegState
 
 # Set up logging once

whisperlivekit/core.py

@@ -1,9 +1,12 @@
 try:
     from whisperlivekit.whisper_streaming_custom.whisper_online import backend_factory, warmup_asr
+    from whisperlivekit.whisper_streaming_custom.online_asr import VACOnlineASRProcessor, OnlineASRProcessor
 except ImportError:
     from .whisper_streaming_custom.whisper_online import backend_factory, warmup_asr
-from argparse import Namespace
+    from .whisper_streaming_custom.online_asr import VACOnlineASRProcessor, OnlineASRProcessor
 
+from argparse import Namespace
+import sys
 
 class TranscriptionEngine:
     _instance = None
@@ -78,8 +81,32 @@ class TranscriptionEngine:
         self.diarization = None
         
         if self.args.transcription:
-            self.asr, self.tokenizer = backend_factory(self.args)
-            warmup_asr(self.asr, self.args.warmup_file)
+            if self.args.backend == "simulstreaming": 
+                from simul_whisper import SimulStreamingASR
+                self.tokenizer = None
+                simulstreaming_kwargs = {}
+                for attr in ['frame_threshold', 'beams', 'decoder_type', 'audio_max_len', 'audio_min_len', 
+                            'cif_ckpt_path', 'never_fire', 'init_prompt', 'static_init_prompt', 
+                            'max_context_tokens', 'model_path']:
+                    if hasattr(self.args, attr):
+                        simulstreaming_kwargs[attr] = getattr(self.args, attr)
+        
+                # Add segment_length from min_chunk_size
+                simulstreaming_kwargs['segment_length'] = getattr(self.args, 'min_chunk_size', 0.5)
+                simulstreaming_kwargs['task'] = self.args.task
+                
+                size = self.args.model
+                self.asr = SimulStreamingASR(
+                    modelsize=size,
+                    lan=self.args.lan,
+                    cache_dir=getattr(self.args, 'model_cache_dir', None),
+                    model_dir=getattr(self.args, 'model_dir', None),
+                    **simulstreaming_kwargs
+                )
+
+            else:
+                self.asr, self.tokenizer = backend_factory(self.args)
+                warmup_asr(self.asr, self.args.warmup_file)
 
         if self.args.diarization:
             from whisperlivekit.diarization.diarization_online import DiartDiarization
@@ -90,3 +117,35 @@ class TranscriptionEngine:
             )
             
         TranscriptionEngine._initialized = True
+
+
+
+def online_factory(args, asr, tokenizer, logfile=sys.stderr):
+    if args.backend == "simulstreaming":    
+        from simul_whisper import SimulStreamingOnlineProcessor
+        online = SimulStreamingOnlineProcessor(
+            asr,
+            tokenizer,
+            logfile=logfile,
+            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
+            confidence_validation=args.confidence_validation
+        )
+    elif args.vac:
+        online = VACOnlineASRProcessor(
+            args.min_chunk_size,
+            asr,
+            tokenizer,
+            logfile=logfile,
+            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
+            confidence_validation = args.confidence_validation
+        )
+    else:
+        online = OnlineASRProcessor(
+            asr,
+            tokenizer,
+            logfile=logfile,
+            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
+            confidence_validation = args.confidence_validation
+        )
+    return online
+  

whisperlivekit/simul_whisper/__init__.py

@@ -0,0 +1,6 @@
+from .backend import SimulStreamingASR, SimulStreamingOnlineProcessor
+
+__all__ = [
+    "SimulStreamingASR",
+    "SimulStreamingOnlineProcessor",
+]

whisperlivekit/simul_whisper/backend.py

@@ -0,0 +1,331 @@
+import sys
+import numpy as np
+import logging
+from typing import List, Tuple, Optional
+import logging
+from whisperlivekit.timed_objects import ASRToken, Sentence, Transcript
+from whisperlivekit.simul_whisper.license_simulstreaming import SIMULSTREAMING_LICENSE
+logger = logging.getLogger(__name__)
+
+try:
+    import torch
+    from whisperlivekit.simul_whisper.config import AlignAttConfig
+    from whisperlivekit.simul_whisper.simul_whisper import PaddedAlignAttWhisper, DEC_PAD
+    from whisperlivekit.simul_whisper.whisper import tokenizer
+    SIMULSTREAMING_AVAILABLE = True
+except ImportError as e:
+    raise ImportError(
+        """SimulStreaming dependencies are not available.
+        Please install WhisperLiveKit using pip install "whisperlivekit[simulstreaming]".""")
+
+class SimulStreamingOnlineProcessor:
+    SAMPLING_RATE = 16000
+
+    def __init__(
+        self,
+        asr,
+        tokenize_method: Optional[callable] = None,
+        buffer_trimming: Tuple[str, float] = ("segment", 15),
+        confidence_validation = False,
+        logfile=sys.stderr,
+    ):
+        if not SIMULSTREAMING_AVAILABLE:
+            raise ImportError("SimulStreaming dependencies are not available.")
+        
+        self.asr = asr
+        self.tokenize = tokenize_method
+        self.logfile = logfile
+        self.confidence_validation = confidence_validation
+        self.init()
+        
+        # buffer does not work yet
+        self.buffer_trimming_way, self.buffer_trimming_sec = buffer_trimming
+
+    def init(self, offset: Optional[float] = None):
+        """Initialize or reset the processing state."""
+        self.audio_chunks = []
+        self.offset = offset if offset is not None else 0.0
+        self.is_last = False
+        self.beg = self.offset
+        self.end = self.offset
+        self.cumulative_audio_duration = 0.0
+        self.last_audio_stream_end_time = self.offset
+        
+        self.committed: List[ASRToken] = []
+        self.last_result_tokens: List[ASRToken] = []        
+        self.buffer_content = ""
+        self.processed_audio_duration = 0.0
+
+    def get_audio_buffer_end_time(self) -> float:
+        """Returns the absolute end time of the current audio buffer."""
+        return self.end
+
+    def insert_audio_chunk(self, audio: np.ndarray, audio_stream_end_time: Optional[float] = None):
+        """Append an audio chunk to be processed by SimulStreaming."""
+        if torch is None:
+            raise ImportError("PyTorch is required for SimulStreaming but not available")
+            
+        # Convert numpy array to torch tensor
+        audio_tensor = torch.from_numpy(audio).float()
+        self.audio_chunks.append(audio_tensor)
+        
+        # Update timing
+        chunk_duration = len(audio) / self.SAMPLING_RATE
+        self.cumulative_audio_duration += chunk_duration
+        
+        if audio_stream_end_time is not None:
+            self.last_audio_stream_end_time = audio_stream_end_time
+            self.end = audio_stream_end_time
+        else:
+            self.end = self.offset + self.cumulative_audio_duration
+
+    def prompt(self) -> Tuple[str, str]:
+        """
+        Returns a tuple: (prompt, context).
+        SimulStreaming handles prompting internally, so we return empty strings.
+        """
+        return "", ""
+
+    def get_buffer(self):
+        """
+        Get the unvalidated buffer content.
+        """
+        buffer_end = self.end if hasattr(self, 'end') else None
+        return Transcript(
+            start=None, 
+            end=buffer_end, 
+            text=self.buffer_content, 
+            probability=None
+        )
+
+    def timestamped_text(self, tokens, generation):
+        # From the simulstreaming repo. self.model to self.asr.model
+        pr = generation["progress"]
+        if "result" not in generation:
+            split_words, split_tokens = self.asr.model.tokenizer.split_to_word_tokens(tokens)
+        else:
+            split_words, split_tokens = generation["result"]["split_words"], generation["result"]["split_tokens"]
+
+        frames = [p["most_attended_frames"][0] for p in pr]
+        tokens = tokens.copy()
+        ret = []
+        for sw,st in zip(split_words,split_tokens):
+            b = None
+            for stt in st:
+                t,f = tokens.pop(0), frames.pop(0)
+                if t != stt:
+                    raise ValueError(f"Token mismatch: {t} != {stt} at frame {f}.")
+                if b is None:
+                    b = f
+            e = f
+            out = (b*0.02, e*0.02, sw)
+            ret.append(out)
+            logger.debug(f"TS-WORD:\t{' '.join(map(str, out))}")
+        return ret
+
+    def process_iter(self) -> Tuple[List[ASRToken], float]:
+        """
+        Process accumulated audio chunks using SimulStreaming.
+        
+        Returns a tuple: (list of committed ASRToken objects, float representing the audio processed up to time).
+        """
+        if not self.audio_chunks:
+            return [], self.end
+
+        try:
+            # concatenate all audio chunks
+            if len(self.audio_chunks) == 1:
+                audio = self.audio_chunks[0]
+            else:
+                audio = torch.cat(self.audio_chunks, dim=0)
+            
+            audio_duration = audio.shape[0] / self.SAMPLING_RATE if audio.shape[0] > 0 else 0
+            self.processed_audio_duration += audio_duration
+            
+            self.audio_chunks = []
+            
+            logger.debug(f"SimulStreaming processing audio shape: {audio.shape}, duration: {audio_duration:.2f}s")
+            logger.debug(f"Current end time: {self.end:.2f}s, last stream time: {self.last_audio_stream_end_time:.2f}s")
+            
+            self.asr.model.insert_audio(audio)
+            tokens, generation_progress = self.asr.model.infer(is_last=self.is_last)
+            ts_words = self.timestamped_text(tokens, generation_progress)
+            text = self.asr.model.tokenizer.decode(tokens)
+            
+            new_tokens = []
+            for ts_word in ts_words:
+                
+                start, end, word = ts_word
+                token = ASRToken(
+                    start=start,
+                    end=end,
+                    text=word,
+                    probability=0.95  # fake prob. Maybe we can extract it from the model?
+                )
+                new_tokens.append(token)
+                self.committed.extend(new_tokens)
+            
+            return new_tokens, self.end
+
+            
+        except Exception as e:
+            logger.exception(f"SimulStreaming processing error: {e}")
+            return [], self.end
+
+    def finish(self) -> Tuple[List[ASRToken], float]:
+        logger.debug("SimulStreaming finish() called")
+        self.is_last = True        
+        final_tokens, final_time = self.process_iter()
+        self.is_last = False
+        return final_tokens, final_time
+
+    def concatenate_tokens(
+        self,
+        tokens: List[ASRToken],
+        sep: Optional[str] = None,
+        offset: float = 0
+    ) -> Transcript:
+        """Concatenate tokens into a Transcript object."""
+        sep = sep if sep is not None else self.asr.sep
+        text = sep.join(token.text for token in tokens)
+        probability = sum(token.probability for token in tokens if token.probability) / len(tokens) if tokens else None
+        if tokens:
+            start = offset + tokens[0].start
+            end = offset + tokens[-1].end
+        else:
+            start = None
+            end = None
+        return Transcript(start, end, text, probability=probability)
+
+    def chunk_at(self, time: float):
+        """
+        useless but kept for compatibility
+        """
+        logger.debug(f"SimulStreaming chunk_at({time:.2f}) - handled internally")
+        pass
+
+    def words_to_sentences(self, tokens: List[ASRToken]) -> List[Sentence]:
+        """
+        Create simple sentences.
+        """
+        if not tokens:
+            return []
+        
+        full_text = " ".join(token.text for token in tokens)
+        sentence = Sentence(
+            start=tokens[0].start,
+            end=tokens[-1].end,
+            text=full_text
+        )
+        return [sentence]
+
+
+
+class SimulStreamingASR():
+    """SimulStreaming backend with AlignAtt policy."""
+    sep = ""
+
+    def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None, logfile=sys.stderr, **kwargs):
+        logger.warning(SIMULSTREAMING_LICENSE)
+        self.logfile = logfile
+        self.transcribe_kargs = {}
+        self.original_language = None if lan == "auto" else lan
+        
+        self.model_path = kwargs.get('model_path', './large-v3.pt')
+        self.frame_threshold = kwargs.get('frame_threshold', 25)
+        self.audio_max_len = kwargs.get('audio_max_len', 30.0)
+        self.audio_min_len = kwargs.get('audio_min_len', 0.0)
+        self.segment_length = kwargs.get('segment_length', 0.5)
+        self.beams = kwargs.get('beams', 1)
+        self.decoder_type = kwargs.get('decoder_type', 'greedy' if self.beams == 1 else 'beam')
+        self.task = kwargs.get('task', 'transcribe')
+        self.cif_ckpt_path = kwargs.get('cif_ckpt_path', None)
+        self.never_fire = kwargs.get('never_fire', False)
+        self.init_prompt = kwargs.get('init_prompt', None)
+        self.static_init_prompt = kwargs.get('static_init_prompt', None)
+        self.max_context_tokens = kwargs.get('max_context_tokens', None)
+        
+        if model_dir is not None:
+            self.model_path = model_dir
+        elif modelsize is not None: #For the moment the .en.pt models do not work!
+            model_mapping = {
+                'tiny': './tiny.pt',
+                'base': './base.pt',
+                'small': './small.pt',
+                'medium': './medium.pt',
+                'medium.en': './medium.en.pt',
+                'large-v1': './large-v1.pt',
+                'base.en': './base.en.pt',
+                'small.en': './small.en.pt',
+                'tiny.en': './tiny.en.pt',
+                'large-v2': './large-v2.pt',
+                'large-v3': './large-v3.pt',
+                'large': './large-v3.pt'
+            }
+            self.model_path = model_mapping.get(modelsize, f'./{modelsize}.pt')
+        
+        self.model = self.load_model(modelsize, cache_dir, model_dir)
+        
+        # Set up tokenizer for translation if needed
+        if self.task == "translate":
+            self.set_translate_task()
+
+    def load_model(self, modelsize, cache_dir, model_dir):
+        try:
+            cfg = AlignAttConfig(
+                model_path=self.model_path,
+                segment_length=self.segment_length,
+                frame_threshold=self.frame_threshold,
+                language=self.original_language,
+                audio_max_len=self.audio_max_len,
+                audio_min_len=self.audio_min_len,
+                cif_ckpt_path=self.cif_ckpt_path,
+                decoder_type="beam",
+                beam_size=self.beams,
+                task=self.task,
+                never_fire=self.never_fire,
+                init_prompt=self.init_prompt,
+                max_context_tokens=self.max_context_tokens,
+                static_init_prompt=self.static_init_prompt,
+            )
+            
+            logger.info(f"Loading SimulStreaming model with language: {self.original_language}")
+            model = PaddedAlignAttWhisper(cfg)
+            return model
+            
+        except Exception as e:
+            logger.error(f"Failed to load SimulStreaming model: {e}")
+            raise
+
+    def segments_end_ts(self, result) -> List[float]:
+        """Get segment end timestamps."""
+        if torch.is_tensor(result):
+            num_tokens = len(result)
+            return [num_tokens * 0.1]  # rough estimate
+        return [1.0]
+
+    def set_translate_task(self):
+        """Set up translation task."""
+        try:
+            self.model.tokenizer = tokenizer.get_tokenizer(
+                multilingual=True,
+                language=self.model.cfg.language,
+                num_languages=self.model.model.num_languages,
+                task="translate"
+            )
+            logger.info("SimulStreaming configured for translation task")
+        except Exception as e:
+            logger.error(f"Failed to configure SimulStreaming for translation: {e}")
+            raise
+
+    def warmup(self, audio, init_prompt=""):
+        """Warmup the SimulStreaming model."""
+        try:
+            if isinstance(audio, np.ndarray):
+                audio = torch.from_numpy(audio).float()
+            self.model.insert_audio(audio)
+            self.model.infer(True)
+            self.model.refresh_segment(complete=True)
+            logger.info("SimulStreaming model warmed up successfully")
+        except Exception as e:
+            logger.exception(f"SimulStreaming warmup failed: {e}")

whisperlivekit/whisper_streaming_custom/backends.py

@@ -3,32 +3,10 @@ import logging
 import io
 import soundfile as sf
 import math
-try: 
-    import torch
-except ImportError: 
-    torch = None
 from typing import List
 import numpy as np
 from whisperlivekit.timed_objects import ASRToken
-from whisperlivekit.simul_whisper.license_simulstreaming import SIMULSTREAMING_LICENSE
 logger = logging.getLogger(__name__)
-SIMULSTREAMING_ERROR_AND_INSTALLATION_INSTRUCTIONS = ImportError(
-"""SimulStreaming dependencies are not available.
-Please install WhisperLiveKit using pip install "whisperlivekit[simulstreaming]"
-""")
-
-try:
-    from whisperlivekit.simul_whisper.config import AlignAttConfig
-    from whisperlivekit.simul_whisper.simul_whisper import PaddedAlignAttWhisper, DEC_PAD
-    from whisperlivekit.simul_whisper.whisper import tokenizer
-    SIMULSTREAMING_AVAILABLE = True
-except ImportError:
-    SIMULSTREAMING_AVAILABLE = False
-    AlignAttConfig = None
-    PaddedAlignAttWhisper = None
-    DEC_PAD = None
-    tokenizer = None
-
 class ASRBase:
     sep = " "  # join transcribe words with this character (" " for whisper_timestamped,
               # "" for faster-whisper because it emits the spaces when needed)
@@ -309,181 +287,4 @@ class OpenaiApiASR(ASRBase):
         self.use_vad_opt = True
 
     def set_translate_task(self):
-        self.task = "translate"
-
-
-class SimulStreamingASR(ASRBase):
-    """SimulStreaming backend with AlignAtt policy."""
-    sep = ""
-
-    def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None, logfile=sys.stderr, **kwargs):
-        if not SIMULSTREAMING_AVAILABLE:
-            raise SIMULSTREAMING_ERROR_AND_INSTALLATION_INSTRUCTIONS
-        logger.warning(SIMULSTREAMING_LICENSE)
-        self.logfile = logfile
-        self.transcribe_kargs = {}
-        self.original_language = None if lan == "auto" else lan
-        
-        self.model_path = kwargs.get('model_path', './large-v3.pt')
-        self.frame_threshold = kwargs.get('frame_threshold', 25)
-        self.audio_max_len = kwargs.get('audio_max_len', 30.0)
-        self.audio_min_len = kwargs.get('audio_min_len', 0.0)
-        self.segment_length = kwargs.get('segment_length', 0.5)
-        self.beams = kwargs.get('beams', 1)
-        self.decoder_type = kwargs.get('decoder_type', 'greedy' if self.beams == 1 else 'beam')
-        self.task = kwargs.get('task', 'transcribe')
-        self.cif_ckpt_path = kwargs.get('cif_ckpt_path', None)
-        self.never_fire = kwargs.get('never_fire', False)
-        self.init_prompt = kwargs.get('init_prompt', None)
-        self.static_init_prompt = kwargs.get('static_init_prompt', None)
-        self.max_context_tokens = kwargs.get('max_context_tokens', None)
-        
-        if model_dir is not None:
-            self.model_path = model_dir
-        elif modelsize is not None: #For the moment the .en.pt models do not work!
-            model_mapping = {
-                'tiny': './tiny.pt',
-                'base': './base.pt',
-                'small': './small.pt',
-                'medium': './medium.pt',
-                'medium.en': './medium.en.pt',
-                'large-v1': './large-v1.pt',
-                'base.en': './base.en.pt',
-                'small.en': './small.en.pt',
-                'tiny.en': './tiny.en.pt',
-                'large-v2': './large-v2.pt',
-                'large-v3': './large-v3.pt',
-                'large': './large-v3.pt'
-            }
-            self.model_path = model_mapping.get(modelsize, f'./{modelsize}.pt')
-        
-        self.model = self.load_model(modelsize, cache_dir, model_dir)
-        
-        # Set up tokenizer for translation if needed
-        if self.task == "translate":
-            self.set_translate_task()
-
-    def load_model(self, modelsize, cache_dir, model_dir):
-        try:
-            cfg = AlignAttConfig(
-                model_path=self.model_path,
-                segment_length=self.segment_length,
-                frame_threshold=self.frame_threshold,
-                language=self.original_language,
-                audio_max_len=self.audio_max_len,
-                audio_min_len=self.audio_min_len,
-                cif_ckpt_path=self.cif_ckpt_path,
-                decoder_type="beam",
-                beam_size=self.beams,
-                task=self.task,
-                never_fire=self.never_fire,
-                init_prompt=self.init_prompt,
-                max_context_tokens=self.max_context_tokens,
-                static_init_prompt=self.static_init_prompt,
-            )
-            
-            logger.info(f"Loading SimulStreaming model with language: {self.original_language}")
-            model = PaddedAlignAttWhisper(cfg)
-            return model
-            
-        except Exception as e:
-            logger.error(f"Failed to load SimulStreaming model: {e}")
-            raise
-
-    def transcribe(self, audio, init_prompt=""):
-        """Transcribe audio using SimulStreaming."""
-        try:
-            if isinstance(audio, np.ndarray):
-                audio_tensor = torch.from_numpy(audio).float()
-            else:
-                audio_tensor = audio
-                
-            prompt = init_prompt if init_prompt else (self.init_prompt or "")
-            
-            result = self.model.infer(audio_tensor, init_prompt=prompt)
-            
-            if torch.is_tensor(result):
-                result = result[result < DEC_PAD]
-                
-            logger.debug(f"SimulStreaming transcription result: {result}")
-            return result
-            
-        except Exception as e:
-            logger.error(f"SimulStreaming transcription failed: {e}")
-            raise
-
-    def ts_words(self, result) -> List[ASRToken]:
-        """Convert SimulStreaming result to ASRToken list."""
-        tokens = []
-        
-        try:
-            if torch.is_tensor(result):
-                text = self.model.tokenizer.decode(result.cpu().numpy())
-            else:
-                text = str(result)
-                
-            if not text or len(text.strip()) == 0:
-                return tokens
-                
-            # We dont have word-level timestamps here. 1rst approach, should be improved later.
-            words = text.strip().split()
-            if not words:
-                return tokens
-            
-            duration_per_word = 0.1  # this will be modified based on actual audio duration 
-            #with the SimulStreamingOnlineProcessor
-            
-            for i, word in enumerate(words):
-                start_time = i * duration_per_word
-                end_time = (i + 1) * duration_per_word
-                
-                token = ASRToken(
-                    start=start_time,
-                    end=end_time,
-                    text=word,
-                    probability=1.0
-                )
-                tokens.append(token)
-                
-        except Exception as e:
-            logger.error(f"Error converting SimulStreaming result to tokens: {e}")
-            
-        return tokens
-
-    def segments_end_ts(self, result) -> List[float]:
-        """Get segment end timestamps."""
-        if torch.is_tensor(result):
-            num_tokens = len(result)
-            return [num_tokens * 0.1]  # rough estimate
-        return [1.0]
-
-    def use_vad(self):
-        """Enable VAD - SimulStreaming has different VAD handling."""
-        logger.info("VAD requested for SimulStreaming - handled internally by the model")
-        pass
-
-    def set_translate_task(self):
-        """Set up translation task."""
-        try:
-            self.model.tokenizer = tokenizer.get_tokenizer(
-                multilingual=True,
-                language=self.model.cfg.language,
-                num_languages=self.model.model.num_languages,
-                task="translate"
-            )
-            logger.info("SimulStreaming configured for translation task")
-        except Exception as e:
-            logger.error(f"Failed to configure SimulStreaming for translation: {e}")
-            raise
-
-    def warmup(self, audio, init_prompt=""):
-        """Warmup the SimulStreaming model."""
-        try:
-            if isinstance(audio, np.ndarray):
-                audio = torch.from_numpy(audio).float()
-            self.model.insert_audio(audio)
-            self.model.infer(True)
-            self.model.refresh_segment(complete=True)
-            logger.info("SimulStreaming model warmed up successfully")
-        except Exception as e:
-            logger.exception(f"SimulStreaming warmup failed: {e}")
+        self.task = "translate"

whisperlivekit/whisper_streaming_custom/online_asr.py

@@ -528,204 +528,3 @@ class VACOnlineASRProcessor:
         """
         return self.online.concatenate_tokens(self.online.transcript_buffer.buffer)
 
-
-class SimulStreamingOnlineProcessor:
-    SAMPLING_RATE = 16000
-
-    def __init__(
-        self,
-        asr,
-        tokenize_method: Optional[callable] = None,
-        buffer_trimming: Tuple[str, float] = ("segment", 15),
-        confidence_validation = False,
-        logfile=sys.stderr,
-    ):
-        if not SIMULSTREAMING_AVAILABLE:
-            raise ImportError("SimulStreaming dependencies are not available.")
-        
-        self.asr = asr
-        self.tokenize = tokenize_method
-        self.logfile = logfile
-        self.confidence_validation = confidence_validation
-        self.init()
-        
-        # buffer does not work yet
-        self.buffer_trimming_way, self.buffer_trimming_sec = buffer_trimming
-
-    def init(self, offset: Optional[float] = None):
-        """Initialize or reset the processing state."""
-        self.audio_chunks = []
-        self.offset = offset if offset is not None else 0.0
-        self.is_last = False
-        self.beg = self.offset
-        self.end = self.offset
-        self.cumulative_audio_duration = 0.0
-        self.last_audio_stream_end_time = self.offset
-        
-        self.committed: List[ASRToken] = []
-        self.last_result_tokens: List[ASRToken] = []        
-        self.buffer_content = ""
-        self.processed_audio_duration = 0.0
-
-    def get_audio_buffer_end_time(self) -> float:
-        """Returns the absolute end time of the current audio buffer."""
-        return self.end
-
-    def insert_audio_chunk(self, audio: np.ndarray, audio_stream_end_time: Optional[float] = None):
-        """Append an audio chunk to be processed by SimulStreaming."""
-        if torch is None:
-            raise ImportError("PyTorch is required for SimulStreaming but not available")
-            
-        # Convert numpy array to torch tensor
-        audio_tensor = torch.from_numpy(audio).float()
-        self.audio_chunks.append(audio_tensor)
-        
-        # Update timing
-        chunk_duration = len(audio) / self.SAMPLING_RATE
-        self.cumulative_audio_duration += chunk_duration
-        
-        if audio_stream_end_time is not None:
-            self.last_audio_stream_end_time = audio_stream_end_time
-            self.end = audio_stream_end_time
-        else:
-            self.end = self.offset + self.cumulative_audio_duration
-
-    def prompt(self) -> Tuple[str, str]:
-        """
-        Returns a tuple: (prompt, context).
-        SimulStreaming handles prompting internally, so we return empty strings.
-        """
-        return "", ""
-
-    def get_buffer(self):
-        """
-        Get the unvalidated buffer content.
-        """
-        buffer_end = self.end if hasattr(self, 'end') else None
-        return Transcript(
-            start=None, 
-            end=buffer_end, 
-            text=self.buffer_content, 
-            probability=None
-        )
-
-    def timestamped_text(self, tokens, generation):
-        # From the simulstreaming repo. self.model to self.asr.model
-        pr = generation["progress"]
-        if "result" not in generation:
-            split_words, split_tokens = self.asr.model.tokenizer.split_to_word_tokens(tokens)
-        else:
-            split_words, split_tokens = generation["result"]["split_words"], generation["result"]["split_tokens"]
-
-        frames = [p["most_attended_frames"][0] for p in pr]
-        tokens = tokens.copy()
-        ret = []
-        for sw,st in zip(split_words,split_tokens):
-            b = None
-            for stt in st:
-                t,f = tokens.pop(0), frames.pop(0)
-                if t != stt:
-                    raise ValueError(f"Token mismatch: {t} != {stt} at frame {f}.")
-                if b is None:
-                    b = f
-            e = f
-            out = (b*0.02, e*0.02, sw)
-            ret.append(out)
-            logger.debug(f"TS-WORD:\t{' '.join(map(str, out))}")
-        return ret
-
-    def process_iter(self) -> Tuple[List[ASRToken], float]:
-        """
-        Process accumulated audio chunks using SimulStreaming.
-        
-        Returns a tuple: (list of committed ASRToken objects, float representing the audio processed up to time).
-        """
-        if not self.audio_chunks:
-            return [], self.end
-
-        try:
-            # concatenate all audio chunks
-            if len(self.audio_chunks) == 1:
-                audio = self.audio_chunks[0]
-            else:
-                audio = torch.cat(self.audio_chunks, dim=0)
-            
-            audio_duration = audio.shape[0] / self.SAMPLING_RATE if audio.shape[0] > 0 else 0
-            self.processed_audio_duration += audio_duration
-            
-            self.audio_chunks = []
-            
-            logger.debug(f"SimulStreaming processing audio shape: {audio.shape}, duration: {audio_duration:.2f}s")
-            logger.debug(f"Current end time: {self.end:.2f}s, last stream time: {self.last_audio_stream_end_time:.2f}s")
-            
-            self.asr.model.insert_audio(audio)
-            tokens, generation_progress = self.asr.model.infer(is_last=self.is_last)
-            ts_words = self.timestamped_text(tokens, generation_progress)
-            text = self.asr.model.tokenizer.decode(tokens)
-            
-            new_tokens = []
-            for ts_word in ts_words:
-                
-                start, end, word = ts_word
-                token = ASRToken(
-                    start=start,
-                    end=end,
-                    text=word,
-                    probability=0.95  # fake prob. Maybe we can extract it from the model?
-                )
-                new_tokens.append(token)
-                self.committed.extend(new_tokens)
-            
-            return new_tokens, self.end
-
-            
-        except Exception as e:
-            logger.exception(f"SimulStreaming processing error: {e}")
-            return [], self.end
-
-    def finish(self) -> Tuple[List[ASRToken], float]:
-        logger.debug("SimulStreaming finish() called")
-        self.is_last = True        
-        final_tokens, final_time = self.process_iter()
-        self.is_last = False
-        return final_tokens, final_time
-
-    def concatenate_tokens(
-        self,
-        tokens: List[ASRToken],
-        sep: Optional[str] = None,
-        offset: float = 0
-    ) -> Transcript:
-        """Concatenate tokens into a Transcript object."""
-        sep = sep if sep is not None else self.asr.sep
-        text = sep.join(token.text for token in tokens)
-        probability = sum(token.probability for token in tokens if token.probability) / len(tokens) if tokens else None
-        if tokens:
-            start = offset + tokens[0].start
-            end = offset + tokens[-1].end
-        else:
-            start = None
-            end = None
-        return Transcript(start, end, text, probability=probability)
-
-    def chunk_at(self, time: float):
-        """
-        useless but kept for compatibility
-        """
-        logger.debug(f"SimulStreaming chunk_at({time:.2f}) - handled internally")
-        pass
-
-    def words_to_sentences(self, tokens: List[ASRToken]) -> List[Sentence]:
-        """
-        Create simple sentences.
-        """
-        if not tokens:
-            return []
-        
-        full_text = " ".join(token.text for token in tokens)
-        sentence = Sentence(
-            start=tokens[0].start,
-            end=tokens[-1].end,
-            text=full_text
-        )
-        return [sentence]

whisperlivekit/whisper_streaming_custom/whisper_online.py

@@ -5,8 +5,7 @@ import librosa
 from functools import lru_cache
 import time
 import logging
-from .backends import FasterWhisperASR, MLXWhisper, WhisperTimestampedASR, OpenaiApiASR, SimulStreamingASR, SIMULSTREAMING_AVAILABLE, SIMULSTREAMING_ERROR_AND_INSTALLATION_INSTRUCTIONS
-from .online_asr import OnlineASRProcessor, VACOnlineASRProcessor, SimulStreamingOnlineProcessor, SIMULSTREAMING_AVAILABLE as SIMULSTREAMING_ONLINE_AVAILABLE
+from .backends import FasterWhisperASR, MLXWhisper, WhisperTimestampedASR, OpenaiApiASR
 
 logger = logging.getLogger(__name__)
 
@@ -68,35 +67,7 @@ def backend_factory(args):
     backend = args.backend
     if backend == "openai-api":
         logger.debug("Using OpenAI API.")
-        asr = OpenaiApiASR(lan=args.lan)
-    elif backend == "simulstreaming":
-        logger.debug("Using SimulStreaming backend.")
-        if not SIMULSTREAMING_AVAILABLE:
-            raise SIMULSTREAMING_ERROR_AND_INSTALLATION_INSTRUCTIONS
-        
-        simulstreaming_kwargs = {}
-        for attr in ['frame_threshold', 'beams', 'decoder_type', 'audio_max_len', 'audio_min_len', 
-                     'cif_ckpt_path', 'never_fire', 'init_prompt', 'static_init_prompt', 
-                     'max_context_tokens', 'model_path']:
-            if hasattr(args, attr):
-                simulstreaming_kwargs[attr] = getattr(args, attr)
-        
-        # Add segment_length from min_chunk_size
-        simulstreaming_kwargs['segment_length'] = getattr(args, 'min_chunk_size', 0.5)
-        simulstreaming_kwargs['task'] = args.task
-        
-        size = args.model
-        t = time.time()
-        logger.info(f"Loading SimulStreaming {size} model for language {args.lan}...")
-        asr = SimulStreamingASR(
-            modelsize=size,
-            lan=args.lan,
-            cache_dir=getattr(args, 'model_cache_dir', None),
-            model_dir=getattr(args, 'model_dir', None),
-            **simulstreaming_kwargs
-        )
-        e = time.time()
-        logger.info(f"done. It took {round(e-t,2)} seconds.")
+        asr = OpenaiApiASR(lan=args.lan)        
     else:
         if backend == "faster-whisper":
             asr_cls = FasterWhisperASR
@@ -138,46 +109,6 @@ def backend_factory(args):
         tokenizer = None
     return asr, tokenizer
 
-def online_factory(args, asr, tokenizer, logfile=sys.stderr):
-    if args.backend == "simulstreaming":
-        if not SIMULSTREAMING_ONLINE_AVAILABLE:
-            raise SIMULSTREAMING_ERROR_AND_INSTALLATION_INSTRUCTIONS
-        
-        logger.debug("Creating SimulStreaming online processor")
-        online = SimulStreamingOnlineProcessor(
-            asr,
-            tokenizer,
-            logfile=logfile,
-            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
-            confidence_validation=args.confidence_validation
-        )
-    elif args.vac:
-        online = VACOnlineASRProcessor(
-            args.min_chunk_size,
-            asr,
-            tokenizer,
-            logfile=logfile,
-            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
-            confidence_validation = args.confidence_validation
-        )
-    else:
-        online = OnlineASRProcessor(
-            asr,
-            tokenizer,
-            logfile=logfile,
-            buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec),
-            confidence_validation = args.confidence_validation
-        )
-    return online
-  
-def asr_factory(args, logfile=sys.stderr):
-    """
-    Creates and configures an ASR and ASR Online instance based on the specified backend and arguments.
-    """
-    asr, tokenizer = backend_factory(args)
-    online = online_factory(args, asr, tokenizer, logfile=logfile)
-    return asr, online
-
 def warmup_asr(asr, warmup_file=None, timeout=5):
     """
     Warmup the ASR model by transcribing a short audio file.