Merge branch 'main' into ScriptProcessorNode-to-AudioWorklet

DEV_NOTES.md

@@ -18,8 +18,29 @@ Decoder weights: 59110771 bytes
 Encoder weights: 15268874 bytes
 
 
+# 2. Translation: Faster model for each system
 
-# 2. SortFormer Diarization: 4-to-2 Speaker Constraint Algorithm
+## Benchmark Results
+
+Testing on MacBook M3 with NLLB-200-distilled-600M model:
+
+### Standard Transformers vs CTranslate2
+
+| Test Text | Standard Inference Time | CTranslate2 Inference Time | Speedup |
+|-----------|-------------------------|---------------------------|---------|
+| UN Chief says there is no military solution in Syria | 0.9395s | 2.0472s | 0.5x |
+| The rapid advancement of AI technology is transforming various industries | 0.7171s | 1.7516s | 0.4x |
+| Climate change poses a significant threat to global ecosystems | 0.8533s | 1.8323s | 0.5x |
+| International cooperation is essential for addressing global challenges | 0.7209s | 1.3575s | 0.5x |
+| The development of renewable energy sources is crucial for a sustainable future | 0.8760s | 1.5589s | 0.6x |
+
+**Results:**
+- Total Standard time: 4.1068s
+- Total CTranslate2 time: 8.5476s
+- CTranslate2 is slower on this system --> Use Transformers, and ideally we would have an mlx implementation.
+
+
+# 3. SortFormer Diarization: 4-to-2 Speaker Constraint Algorithm
 
 Transform a diarization model that predicts up to 4 speakers into one that predicts up to 2 speakers by mapping the output predictions.
 
@@ -67,4 +88,4 @@ ELSE:
     AS_2 ← B
 
 to finish
-```
+```

README.md

@@ -198,6 +198,11 @@ An important list of parameters can be changed. But what *should* you change?
 | `--embedding-model` | Hugging Face model ID for Diart embedding model. [Available models](https://github.com/juanmc2005/diart/tree/main?tab=readme-ov-file#pre-trained-models) | `speechbrain/spkrec-ecapa-voxceleb` |
 
 
+| Translation options | Description | Default |
+|-----------|-------------|---------|
+| `--nllb-backend` | `transformers` or `ctranslate2` | `ctranslate2` |
+| `--nllb-size` | `600M` or `1.3B` | `600M` |
+
 > For diarization using Diart, you need access to pyannote.audio models:
 > 1. [Accept user conditions](https://huggingface.co/pyannote/segmentation) for the `pyannote/segmentation` model
 > 2. [Accept user conditions](https://huggingface.co/pyannote/segmentation-3.0) for the `pyannote/segmentation-3.0` model

whisperlivekit/audio_processor.py

@@ -179,12 +179,11 @@ class AudioProcessor:
                     asr_processing_logs += f" + Silence of = {item.duration:.2f}s"
                     if self.tokens:
                         asr_processing_logs += f" | last_end = {self.tokens[-1].end} |"
-                logger.info(asr_processing_logs)
+                    logger.info(asr_processing_logs)
-                
-                if type(item) is Silence:
                     cumulative_pcm_duration_stream_time += item.duration
                     self.online.insert_silence(item.duration, self.tokens[-1].end if self.tokens else 0)
                     continue
+                logger.info(asr_processing_logs)
                 
                 if isinstance(item, np.ndarray):
                     pcm_array = item
@@ -223,7 +222,7 @@ class AudioProcessor:
                     new_tokens, buffer_text, new_end_buffer
                 )
                 
-                if new_tokens and self.args.target_language and self.translation_queue:
+                if self.translation_queue:
                     for token in new_tokens:
                         await self.translation_queue.put(token)
                         
@@ -256,13 +255,11 @@ class AudioProcessor:
                     logger.debug("Diarization processor received sentinel. Finishing.")
                     self.diarization_queue.task_done()
                     break
-                
+                elif type(item) is Silence:
-                if type(item) is Silence:
                     cumulative_pcm_duration_stream_time += item.duration
                     diarization_obj.insert_silence(item.duration)
                     continue
-    
+                elif isinstance(item, np.ndarray):
-                if isinstance(item, np.ndarray):
                     pcm_array = item
                 else:
                     raise Exception('item should be pcm_array') 
@@ -295,14 +292,17 @@ class AudioProcessor:
         # in the future we want to have different languages for each speaker etc, so it will be more complex.
         while True:
             try:
-                token = await self.translation_queue.get() #block until at least 1 token
+                item = await self.translation_queue.get() #block until at least 1 token
-                if token is SENTINEL:
+                if item is SENTINEL:
                     logger.debug("Translation processor received sentinel. Finishing.")
                     self.translation_queue.task_done()
                     break
+                elif type(item) is Silence:
+                    online_translation.insert_silence(item.duration)
+                    continue
                 
                 # get all the available tokens for translation. The more words, the more precise
-                tokens_to_process = [token]
+                tokens_to_process = [item]
                 additional_tokens = await get_all_from_queue(self.translation_queue)
                 
                 sentinel_found = False
@@ -326,7 +326,7 @@ class AudioProcessor:
             except Exception as e:
                 logger.warning(f"Exception in translation_processor: {e}")
                 logger.warning(f"Traceback: {traceback.format_exc()}")
-                if 'token' in locals() and token is not SENTINEL:
+                if 'token' in locals() and item is not SENTINEL:
                     self.translation_queue.task_done()
                 if 'additional_tokens' in locals():
                     for _ in additional_tokens:
@@ -367,7 +367,7 @@ class AudioProcessor:
                 if not state.tokens and not buffer_transcription and not buffer_diarization:
                     response_status = "no_audio_detected"
                     lines = []
-                elif response_status == "active_transcription" and not lines:
+                elif not lines:
                     lines = [Line(
                         speaker=1,
                         start=state.get("end_buffer", 0),
@@ -528,6 +528,8 @@ class AudioProcessor:
                 await self.transcription_queue.put(silence_buffer)
             if self.args.diarization and self.diarization_queue:
                 await self.diarization_queue.put(silence_buffer)
+            if self.translation_queue:
+                await self.translation_queue.put(silence_buffer)
 
         if not self.silence:
             if self.args.transcription and self.transcription_queue:

whisperlivekit/core.py

@@ -43,10 +43,12 @@ class TranscriptionEngine:
             "transcription": True,
             "vad": True,
             "pcm_input": False,
+            
             # whisperstreaming params:
             "buffer_trimming": "segment",
             "confidence_validation": False,
             "buffer_trimming_sec": 15,
+            
             # simulstreaming params:
             "disable_fast_encoder": False,
             "frame_threshold": 25,
@@ -61,10 +63,15 @@ class TranscriptionEngine:
             "max_context_tokens": None,
             "model_path": './base.pt',
             "diarization_backend": "sortformer",
+            
             # diarization params:
             "disable_punctuation_split" : False,
             "segmentation_model": "pyannote/segmentation-3.0",
-            "embedding_model": "pyannote/embedding",         
+            "embedding_model": "pyannote/embedding",  
+            
+            # translation params:
+            "nllb_backend": "ctranslate2",
+            "nllb_size": "600M"
         }
 
         config_dict = {**defaults, **kwargs}
@@ -142,8 +149,7 @@ class TranscriptionEngine:
                 raise Exception('Translation cannot be set with language auto')
             else:
                 from whisperlivekit.translation.translation import load_model
-                self.translation_model = load_model([self.args.lan]) #in the future we want to handle different languages for different speakers
+                self.translation_model = load_model([self.args.lan], backend=self.args.nllb_backend, model_size=self.args.nllb_size) #in the future we want to handle different languages for different speakers
-            
         TranscriptionEngine._initialized = True
 
 

whisperlivekit/parse_args.py

@@ -287,6 +287,20 @@ def parse_args():
         help="Optional. Number of models to preload in memory to speed up loading (set up to the expected number of concurrent instances).",
     )
 
+    simulstreaming_group.add_argument(
+        "--nllb-backend",
+        type=str,
+        default="ctranslate2",
+        help="transformers or ctranslate2",
+    )
+    
+    simulstreaming_group.add_argument(
+        "--nllb-size",
+        type=str,
+        default="600M",
+        help="600M or 1.3B",
+    )
+
     args = parser.parse_args()
     
     args.transcription = not args.no_transcription

whisperlivekit/remove_silences.py

@@ -39,7 +39,7 @@ def blank_to_silence(tokens):
                         )
                 else:
                     if silence_token: #there was silence but no more
-                        if silence_token.end - silence_token.start >= MIN_SILENCE_DURATION:
+                        if silence_token.duration() >= MIN_SILENCE_DURATION:
                             cleaned_tokens.append(
                                 silence_token
                             )

whisperlivekit/results_formater.py

@@ -123,14 +123,33 @@ def format_output(state, silence, current_time, args, debug, sep):
             
         append_token_to_last_line(lines, sep, token, debug_info)
     if lines and translated_segments:
-        cts_idx = 0 # current_translated_segment_idx
+        unassigned_translated_segments = []
-        for line in lines:
+        for ts in translated_segments:
-            while cts_idx < len(translated_segments):
+            assigned = False
-                ts = translated_segments[cts_idx]
+            for line in lines:
-                if ts and ts.start and ts.start >= line.start and ts.end <= line.end:
+                if ts and ts.overlaps_with(line):
-                    line.translation += ts.text + ' '
+                    if ts.is_within(line):
-                    cts_idx += 1
+                        line.translation += ts.text + ' '
-                else:
+                        assigned = True
-                    break
+                        break
-    return lines, undiarized_text, buffer_transcription, '' 
+                    else:
-
+                        ts0, ts1 = ts.approximate_cut_at(line.end)
+                        if ts0 and line.overlaps_with(ts0):
+                            line.translation += ts0.text + ' '
+                        if ts1:
+                            unassigned_translated_segments.append(ts1)
+                        assigned = True
+                        break
+            if not assigned:
+                unassigned_translated_segments.append(ts)
+        
+        if unassigned_translated_segments:
+            for line in lines:
+                remaining_segments = []
+                for ts in unassigned_translated_segments:
+                    if ts and ts.overlaps_with(line):
+                        line.translation += ts.text + ' '
+                    else:
+                        remaining_segments.append(ts)
+                unassigned_translated_segments = remaining_segments #maybe do smth in the future about that
+    return lines, undiarized_text, buffer_transcription, ''

whisperlivekit/timed_objects.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass, field
-from typing import Optional
+from typing import Optional, Any
 from datetime import timedelta
 
 def format_time(seconds: float) -> str:
@@ -15,6 +15,21 @@ class TimedText:
     speaker: Optional[int] = -1
     probability: Optional[float] = None
     is_dummy: Optional[bool] = False
+    
+    def overlaps_with(self, other: 'TimedText') -> bool:
+        return not (self.end <= other.start or other.end <= self.start)
+    
+    def is_within(self, other: 'TimedText') -> bool:
+        return other.contains_timespan(self)
+
+    def duration(self) -> float:
+        return self.end - self.start
+
+    def contains_time(self, time: float) -> bool:
+        return self.start <= time <= self.end
+
+    def contains_timespan(self, other: 'TimedText') -> bool:
+        return self.start <= other.start and self.end >= other.end
 
 @dataclass
 class ASRToken(TimedText):
@@ -41,6 +56,34 @@ class SpeakerSegment(TimedText):
 class Translation(TimedText):
     pass
 
+    def approximate_cut_at(self, cut_time):
+        """
+        Each word in text is considered to be of duration (end-start)/len(words in text)
+        """
+        if not self.text or not self.contains_time(cut_time):
+            return self, None
+
+        words = self.text.split()
+        num_words = len(words)
+        if num_words == 0:
+            return self, None
+
+        duration_per_word = self.duration() / num_words
+        
+        cut_word_index = int((cut_time - self.start) / duration_per_word)
+        
+        if cut_word_index >= num_words:
+            cut_word_index = num_words -1
+        
+        text0 = " ".join(words[:cut_word_index])
+        text1 = " ".join(words[cut_word_index:])
+
+        segment0 = Translation(start=self.start, end=cut_time, text=text0)
+        segment1 = Translation(start=cut_time, end=self.end, text=text1)
+
+        return segment0, segment1
+        
+
 @dataclass
 class Silence():
     duration: float
@@ -91,4 +134,4 @@ class State():
     end_buffer: float
     end_attributed_speaker: float
     remaining_time_transcription: float
-    remaining_time_diarization: float
+    remaining_time_diarization: float

whisperlivekit/translation/translation.py

@@ -1,3 +1,5 @@
+import logging
+import time
 import ctranslate2
 import torch
 import transformers
@@ -6,38 +8,42 @@ import huggingface_hub
 from whisperlivekit.translation.mapping_languages import get_nllb_code
 from whisperlivekit.timed_objects import Translation
 
+logger = logging.getLogger(__name__)
 
 #In diarization case, we may want to translate just one speaker, or at least start the sentences there
 
 PUNCTUATION_MARKS = {'.', '!', '?', '。', '！', '？'}
 
+MIN_SILENCE_DURATION_DEL_BUFFER = 3 #After a silence of x seconds, we consider the model should not use the buffer, even if the previous
+# sentence is not finished.
 
 @dataclass
 class TranslationModel():
     translator: ctranslate2.Translator
     tokenizer: dict
+    device: str
+    backend_type: str = 'ctranslate2'
 
-def load_model(src_langs):
+def load_model(src_langs, backend='ctranslate2', model_size='600M'):
-    MODEL = 'nllb-200-distilled-600M-ctranslate2'
-    MODEL_GUY = 'entai2965'
-    huggingface_hub.snapshot_download(MODEL_GUY + '/' + MODEL,local_dir=MODEL)
     device = "cuda" if torch.cuda.is_available() else "cpu"
-    translator = ctranslate2.Translator(MODEL,device=device)
+    MODEL = f'nllb-200-distilled-{model_size}-ctranslate2'
+    if backend=='ctranslate2':
+        MODEL_GUY = 'entai2965'
+        huggingface_hub.snapshot_download(MODEL_GUY + '/' + MODEL,local_dir=MODEL)
+        translator = ctranslate2.Translator(MODEL,device=device)
+    elif backend=='transformers':
+        translator = transformers.AutoModelForSeq2SeqLM.from_pretrained(f"facebook/nllb-200-distilled-{model_size}")
     tokenizer = dict()
     for src_lang in src_langs:
         tokenizer[src_lang] = transformers.AutoTokenizer.from_pretrained(MODEL, src_lang=src_lang, clean_up_tokenization_spaces=True)
+
     return TranslationModel(
         translator=translator,
-        tokenizer=tokenizer
+        tokenizer=tokenizer,
+        backend_type=backend,
+        device = device
     )
 
-def translate(input, translation_model, tgt_lang):
-    source = translation_model.tokenizer.convert_ids_to_tokens(translation_model.tokenizer.encode(input))
-    target_prefix = [tgt_lang]
-    results = translation_model.translator.translate_batch([source], target_prefix=[target_prefix])
-    target = results[0].hypotheses[0][1:]
-    return translation_model.tokenizer.decode(translation_model.tokenizer.convert_tokens_to_ids(target))
-
 class OnlineTranslation:
     def __init__(self, translation_model: TranslationModel, input_languages: list, output_languages: list):
         self.buffer = []
@@ -68,12 +74,19 @@ class OnlineTranslation:
             output_lang = self.output_languages[0]
         nllb_output_lang = get_nllb_code(output_lang)
             
-        source = self.translation_model.tokenizer[input_lang].convert_ids_to_tokens(self.translation_model.tokenizer[input_lang].encode(input))   
+        tokenizer = self.translation_model.tokenizer[input_lang]
-        results = self.translation_model.translator.translate_batch([source], target_prefix=[[nllb_output_lang]]) #we can use return_attention=True to try to optimize the stuff.
+        tokenizer_output = tokenizer(input, return_tensors="pt").to(self.translation_model.device)
-        target = results[0].hypotheses[0][1:]
+        
-        results = self.translation_model.tokenizer[input_lang].decode(self.translation_model.tokenizer[input_lang].convert_tokens_to_ids(target))
+        if self.translation_model.backend_type == 'ctranslate2':
-        return results
+            source = tokenizer.convert_ids_to_tokens(tokenizer_output['input_ids'][0])    
-
+            results = self.translation_model.translator.translate_batch([source], target_prefix=[[nllb_output_lang]])
+            target = results[0].hypotheses[0][1:]
+            result = tokenizer.decode(tokenizer.convert_tokens_to_ids(target))
+        else:
+            translated_tokens = self.translation_model.translator.generate(**tokenizer_output, forced_bos_token_id=tokenizer.convert_tokens_to_ids(nllb_output_lang))
+            result = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
+        return result
+    
     def translate_tokens(self, tokens):
         if tokens:
             text = ' '.join([token.text for token in tokens])
@@ -88,7 +101,6 @@ class OnlineTranslation:
             return translation
         return None
             
-        
 
     def insert_tokens(self, tokens):
         self.buffer.extend(tokens)
@@ -109,7 +121,11 @@ class OnlineTranslation:
         self.translation_remaining = self.translate_tokens(self.buffer)
         self.len_processed_buffer = len(self.buffer)
         return self.validated + [self.translation_remaining]
-                
+
+    def insert_silence(self, silence_duration: float):
+        if silence_duration >= MIN_SILENCE_DURATION_DEL_BUFFER:
+            self.buffer = []
+            self.validated += [self.translation_remaining]
 
 if __name__ == '__main__':
     output_lang = 'fr'
@@ -122,16 +138,13 @@ if __name__ == '__main__':
     test = test_string.split(' ')
     step = len(test) // 3
     
-    shared_model = load_model([input_lang])
+    shared_model = load_model([input_lang], backend='ctranslate2')
     online_translation = OnlineTranslation(shared_model, input_languages=[input_lang], output_languages=[output_lang])
-        
+    
+    beg_inference = time.time()    
     for id in range(5):
         val = test[id*step : (id+1)*step]
         val_str = ' '.join(val)
         result = online_translation.translate(val_str)
         print(result)
-    
+    print('inference time:', time.time() - beg_inference)
-    
-    
-    
-    # print(result)

whisperlivekit/web/live_transcription.css

@@ -438,7 +438,6 @@ label {
   font-size: 13px;
   border-radius: 30px;
   padding: 2px 10px;
-  display: none;
 }
 
 .loading {