restore a functionnal buffer_diarization

whisperlivekit/audio_processor.py

@@ -453,12 +453,11 @@ class AudioProcessor:
                 
                 # Handle undiarized text
                 if undiarized_text:
-                    combined_undiarized = sep.join(undiarized_text)
+                    combined = sep.join(undiarized_text)
                     if buffer_transcription:
-                        buffer_transcription = combined_undiarized + sep + buffer_transcription
-                    else:
-                        buffer_transcription = combined_undiarized
-                buffer_diarization = ""
+                        combined += sep
+                    await self.update_diarization(end_attributed_speaker, combined)
+                    buffer_diarization = combined
                 
                 response_status = "active_transcription"
                 final_lines_for_response = lines.copy()

whisperlivekit/token_buffer.py

@@ -0,0 +1,73 @@
+import torch
+import sys
+class TokenBuffer:
+
+    def __init__(self, text="", tokenizer=None, device=None, prefix_token_ids=[]):
+        self.text = text
+        self.prefix_token_ids = prefix_token_ids
+        self.tokenizer = tokenizer
+        self.device = device
+
+    def as_token_ids(self, tokenizer=None):
+
+        if tokenizer is None:
+            tokenizer = self.tokenizer
+        if tokenizer is None:
+            raise ValueError("Tokenizer is not set.") 
+        return self.prefix_token_ids + tokenizer.encode(self.text)
+
+    def as_tensor(self, device=None):
+        if device is None:
+            device = self.device
+        if device is None:
+            raise ValueError("Device is not set.")
+        tok_ids = self.as_token_ids()
+        return torch.tensor(tok_ids, 
+                     dtype=torch.long, device=device).unsqueeze(0)
+
+    def as_tensor_beam(self, beam, device=None):
+        t = self.as_tensor(device=device)
+        return t.repeat_interleave(beam, dim=0)
+
+
+    def as_text(self):
+        return self.text
+
+    @staticmethod
+    def empty(*a, **kw):
+        return TokenBuffer(*a,**kw)
+
+    @staticmethod
+    def from_text(text, *a, **kw):
+        return TokenBuffer(*a, text=text, **kw)
+    
+    def is_empty(self):
+        return self.text is None or self.text == ""
+
+    def trim_words(self, num=1, after=0):
+        '''
+        num: how many words to trim from the beginning
+        after: how many characters to skip (length of the static prompt)
+        '''
+        tokenizer = self.tokenizer
+        assert tokenizer is not None, "Tokenizer is not set."
+
+        ids = tokenizer.encode(self.text[after:])
+        words, wids = self.tokenizer.split_to_word_tokens(ids)
+        print(words, file=sys.stderr)
+        print(wids, file=sys.stderr)
+        if not words:
+            return 0
+        self.text = self.text[:after] + "".join(words[num:])
+        return sum(len(wi) for wi in wids[:num])
+
+    def append_token_ids(self, token_ids):
+        tokenizer = self.tokenizer
+        assert tokenizer is not None, "Tokenizer is not set."
+        self.text += self.tokenizer.decode(token_ids)
+
+    def as_split_word_tokens(self):
+        tokenizer = self.tokenizer
+        assert tokenizer is not None, "Tokenizer is not set."
+        ids = tokenizer.encode(self.text)
+        return tokenizer.split_to_word_tokens(ids)

whisperlivekit/whisper_streaming_custom/backends.py

@@ -337,7 +337,7 @@ class SimulStreamingASR(ASRBase):
         
         if model_dir is not None:
             self.model_path = model_dir
-        elif modelsize is not None:
+        elif modelsize is not None: #For the moment the .en.pt models do not work!
             model_mapping = {
                 'tiny': './tiny.pt',
                 'base': './base.pt',

whisperlivekit/whisper_streaming_custom/online_asr.py

@@ -545,11 +545,8 @@ class SimulStreamingOnlineProcessor:
         self.end = self.offset
         self.cumulative_audio_duration = 0.0
         
-        # Keep track of committed tokens for compatibility with existing interface
         self.committed: List[ASRToken] = []
-        self.last_result_tokens: List[ASRToken] = []
-        
-        # Buffer for unvalidated content
+        self.last_result_tokens: List[ASRToken] = []        
         self.buffer_content = ""
 
     def get_audio_buffer_end_time(self) -> float:

whisperlivekit/whisper_streaming_custom/whisper_online.py

@@ -77,7 +77,6 @@ def backend_factory(args):
                 "See the documentation for installation instructions."
             )
         
-        # Extract SimulStreaming-specific arguments
         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',