to 0.2.11

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "whisperlivekit"
-version = "0.2.10"
+version = "0.2.11"
 description = "Real-time speech-to-text with speaker diarization using Whisper"
 readme = "README.md"
 authors = [

whisperlivekit/audio_processor.py

@@ -4,7 +4,7 @@ from time import time, sleep
 import math
 import logging
 import traceback
-from whisperlivekit.timed_objects import ASRToken, Silence, Line, FrontData, State, Transcript
+from whisperlivekit.timed_objects import ASRToken, Silence, Line, FrontData, State, Transcript, ChangeSpeaker
 from whisperlivekit.core import TranscriptionEngine, online_factory, online_diarization_factory, online_translation_factory
 from whisperlivekit.silero_vad_iterator import FixedVADIterator
 from whisperlivekit.results_formater import format_output
@@ -59,7 +59,6 @@ class AudioProcessor:
         self.tokens = []
         self.translated_segments = []
         self.buffer_transcription = Transcript()
-        self.buffer_diarization = ""
         self.end_buffer = 0
         self.end_attributed_speaker = 0
         self.lock = asyncio.Lock()
@@ -68,7 +67,9 @@ class AudioProcessor:
         self.last_response_content = FrontData()
         self.last_detected_speaker = None
         self.speaker_languages = {}
-        
+        self.cumulative_pcm_len = 0
+        self.diarization_before_transcription = False
+
         # Models and processing
         self.asr = models.asr
         self.tokenizer = models.tokenizer
@@ -101,13 +102,14 @@ class AudioProcessor:
         self.diarization_task = None
         self.watchdog_task = None
         self.all_tasks_for_cleanup = []
+        self.online_translation = None
         
         if self.args.transcription:
             self.online = online_factory(self.args, models.asr, models.tokenizer)        
             self.sep = self.online.asr.sep   
         if self.args.diarization:
             self.diarization = online_diarization_factory(self.args, models.diarization_model)
-        if self.args.target_language:
+        if models.translation_model:
             self.online_translation = online_translation_factory(self.args, models.translation_model)
 
     def convert_pcm_to_float(self, pcm_buffer):
@@ -142,7 +144,6 @@ class AudioProcessor:
                 tokens=self.tokens.copy(),
                 translated_segments=self.translated_segments.copy(),
                 buffer_transcription=self.buffer_transcription,
-                buffer_diarization=self.buffer_diarization,
                 end_buffer=self.end_buffer,
                 end_attributed_speaker=self.end_attributed_speaker,
                 remaining_time_transcription=remaining_transcription,
@@ -154,7 +155,7 @@ class AudioProcessor:
         async with self.lock:
             self.tokens = []
             self.translated_segments = []
-            self.buffer_transcription = self.buffer_diarization = Transcript()
+            self.buffer_transcription = Transcript()
             self.end_buffer = self.end_attributed_speaker = 0
             self.beg_loop = time()
 
@@ -201,11 +202,11 @@ class AudioProcessor:
                 await asyncio.sleep(0.2)
 
         logger.info("FFmpeg stdout processing finished. Signaling downstream processors if needed.")
-        if self.args.transcription and self.transcription_queue:
+        if not self.diarization_before_transcription and self.transcription_queue:
             await self.transcription_queue.put(SENTINEL)
         if self.args.diarization and self.diarization_queue:
             await self.diarization_queue.put(SENTINEL)
-        if self.args.target_language and self.translation_queue:
+        if self.online_translation:
             await self.translation_queue.put(SENTINEL)
 
     async def transcription_processor(self):
@@ -219,11 +220,6 @@ class AudioProcessor:
                     logger.debug("Transcription processor received sentinel. Finishing.")
                     self.transcription_queue.task_done()
                     break
-                
-                if not self.online:
-                    logger.warning("Transcription processor: self.online not initialized.")
-                    self.transcription_queue.task_done()
-                    continue
 
                 asr_internal_buffer_duration_s = len(getattr(self.online, 'audio_buffer', [])) / self.online.SAMPLING_RATE
                 transcription_lag_s = max(0.0, time() - self.beg_loop - self.end_buffer)
@@ -236,12 +232,12 @@ class AudioProcessor:
                     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)
+                elif isinstance(item, ChangeSpeaker):
-                
+                    self.online.new_speaker(item)
-                if isinstance(item, np.ndarray):
+                elif isinstance(item, np.ndarray):
                     pcm_array = item
-                else:
+                
-                    raise Exception('item should be pcm_array')
+                logger.info(asr_processing_logs)
                 
                 duration_this_chunk = len(pcm_array) / self.sample_rate
                 cumulative_pcm_duration_stream_time += duration_this_chunk
@@ -297,8 +293,7 @@ class AudioProcessor:
 
     async def diarization_processor(self, diarization_obj):
         """Process audio chunks for speaker diarization."""
-        buffer_diarization = ""
+        self.current_speaker = 0
-        cumulative_pcm_duration_stream_time = 0.0
         while True:
             try:
                 item = await self.diarization_queue.get()
@@ -307,7 +302,6 @@ class AudioProcessor:
                     self.diarization_queue.task_done()
                     break
                 elif type(item) is Silence:
-                    cumulative_pcm_duration_stream_time += item.duration
                     diarization_obj.insert_silence(item.duration)
                     continue
                 elif isinstance(item, np.ndarray):
@@ -317,22 +311,26 @@ class AudioProcessor:
                 
                 # Process diarization
                 await diarization_obj.diarize(pcm_array)
+                segments = diarization_obj.get_segments()
                 
-                async with self.lock:
+                if self.diarization_before_transcription:
-                    self.tokens, last_segment = diarization_obj.assign_speakers_to_tokens(
+                    if segments and segments[-1].speaker != self.current_speaker:
-                        self.tokens,
+                        self.current_speaker = segments[-1].speaker
-                        use_punctuation_split=self.args.punctuation_split
+                        cut_at = int(segments[-1].start*16000 - (self.cumulative_pcm_len))
-                    )
+                        await self.transcription_queue.put(pcm_array[cut_at:])
-                    if len(self.tokens) > 0:
+                        await self.transcription_queue.put(ChangeSpeaker(speaker=self.current_speaker, start=cut_at))                        
-                        self.end_attributed_speaker = max(self.tokens[-1].end, self.end_attributed_speaker)
+                        await self.transcription_queue.put(pcm_array[:cut_at])
-                    if buffer_diarization:
+                    else:
-                        self.buffer_diarization = buffer_diarization
+                        await self.transcription_queue.put(pcm_array)
-
+                else:           
-                    # if last_segment is not None and last_segment.speaker != self.last_detected_speaker:
+                    async with self.lock:
-                    #     if not self.speaker_languages.get(last_segment.speaker, None):
+                        self.tokens = diarization_obj.assign_speakers_to_tokens(
-                    #         self.last_detected_speaker = last_segment.speaker
+                            self.tokens,
-                    #         self.online.on_new_speaker(last_segment)
+                            use_punctuation_split=self.args.punctuation_split
-       
+                        )
+                self.cumulative_pcm_len += len(pcm_array)            
+                if len(self.tokens) > 0:
+                    self.end_attributed_speaker = max(self.tokens[-1].end, self.end_attributed_speaker)
                 self.diarization_queue.task_done()
                 
             except Exception as e:
@@ -342,7 +340,7 @@ class AudioProcessor:
                     self.diarization_queue.task_done()
         logger.info("Diarization processor task finished.")
 
-    async def translation_processor(self, online_translation):
+    async def translation_processor(self):
         # the idea is to ignore diarization for the moment. We use only transcription tokens. 
         # And the speaker is attributed given the segments used for the translation
         # in the future we want to have different languages for each speaker etc, so it will be more complex.
@@ -354,7 +352,7 @@ class AudioProcessor:
                     self.translation_queue.task_done()
                     break
                 elif type(item) is Silence:
-                    online_translation.insert_silence(item.duration)
+                    self.online_translation.insert_silence(item.duration)
                     continue
                 
                 # get all the available tokens for translation. The more words, the more precise
@@ -368,9 +366,8 @@ class AudioProcessor:
                         break
                     tokens_to_process.append(additional_token)                
                 if tokens_to_process:
-                    online_translation.insert_tokens(tokens_to_process)
+                    self.online_translation.insert_tokens(tokens_to_process)
-                    self.translated_segments = await asyncio.to_thread(online_translation.process)
+                    self.translated_segments = await asyncio.to_thread(self.online_translation.process)
-                
                 self.translation_queue.task_done()
                 for _ in additional_tokens:
                     self.translation_queue.task_done()
@@ -423,23 +420,15 @@ class AudioProcessor:
                 )
                 if end_w_silence:
                     buffer_transcription = Transcript()
-                    buffer_diarization = Transcript()
                 else:
                     buffer_transcription = state.buffer_transcription
-                    buffer_diarization = state.buffer_diarization
 
-                # Handle undiarized text
+                buffer_diarization = ''
                 if undiarized_text:
-                    combined = self.sep.join(undiarized_text)
+                    buffer_diarization = self.sep.join(undiarized_text)
-                    if buffer_transcription:
-                        combined += self.sep
 
                     async with self.lock:
                         self.end_attributed_speaker = state.end_attributed_speaker
-                        if buffer_diarization:
-                            self.buffer_diarization = buffer_diarization
-
-                    buffer_diarization.text = combined
                 
                 response_status = "active_transcription"
                 if not state.tokens and not buffer_transcription and not buffer_diarization:
@@ -455,8 +444,8 @@ class AudioProcessor:
                 response = FrontData(
                     status=response_status,
                     lines=lines,
-                    buffer_transcription=buffer_transcription.text,
+                    buffer_transcription=buffer_transcription.text.strip(),
-                    buffer_diarization=buffer_transcription.text,
+                    buffer_diarization=buffer_diarization.strip(),
                     remaining_time_transcription=state.remaining_time_transcription,
                     remaining_time_diarization=state.remaining_time_diarization if self.args.diarization else 0
                 )
@@ -515,8 +504,8 @@ class AudioProcessor:
             self.all_tasks_for_cleanup.append(self.diarization_task)
             processing_tasks_for_watchdog.append(self.diarization_task)
         
-        if self.args.target_language and self.args.lan != 'auto':
+        if self.online_translation:
-            self.translation_task = asyncio.create_task(self.translation_processor(self.online_translation))
+            self.translation_task = asyncio.create_task(self.translation_processor())
             self.all_tasks_for_cleanup.append(self.translation_task)
             processing_tasks_for_watchdog.append(self.translation_task)
         
@@ -638,7 +627,7 @@ class AudioProcessor:
                 silence_buffer = Silence(duration=time() - self.start_silence)
 
         if silence_buffer:
-            if self.args.transcription and self.transcription_queue:
+            if not self.diarization_before_transcription and self.transcription_queue:
                 await self.transcription_queue.put(silence_buffer)
             if self.args.diarization and self.diarization_queue:
                 await self.diarization_queue.put(silence_buffer)
@@ -646,7 +635,7 @@ class AudioProcessor:
                 await self.translation_queue.put(silence_buffer)
 
         if not self.silence:
-            if self.args.transcription and self.transcription_queue:
+            if not self.diarization_before_transcription and self.transcription_queue:
                 await self.transcription_queue.put(pcm_array.copy())
 
             if self.args.diarization and self.diarization_queue:

whisperlivekit/basic_server.py

@@ -5,9 +5,6 @@ from fastapi.middleware.cors import CORSMiddleware
 from whisperlivekit import TranscriptionEngine, AudioProcessor, get_inline_ui_html, parse_args
 import asyncio
 import logging
-from starlette.staticfiles import StaticFiles
-import pathlib
-import whisperlivekit.web as webpkg
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 logging.getLogger().setLevel(logging.WARNING)
@@ -33,8 +30,6 @@ app.add_middleware(
     allow_methods=["*"],
     allow_headers=["*"],
 )
-web_dir = pathlib.Path(webpkg.__file__).parent
-app.mount("/web", StaticFiles(directory=str(web_dir)), name="web")
 
 @app.get("/")
 async def get():

whisperlivekit/core.py

@@ -145,8 +145,8 @@ class TranscriptionEngine:
         
         self.translation_model = None
         if self.args.target_language:
-            if self.args.lan == 'auto':
+            if self.args.lan == 'auto' and self.args.backend != "simulstreaming":
-                raise Exception('Translation cannot be set with language auto')
+                raise Exception('Translation cannot be set with language auto when transcription backend is not simulstreaming')
             else:
                 from whisperlivekit.translation.translation import load_model
                 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

whisperlivekit/diarization/diart_backend.py

@@ -242,7 +242,7 @@ class DiartDiarization:
                         token.speaker = extract_number(segment.speaker) + 1
         else:
             tokens = add_speaker_to_tokens(segments, tokens)
-        return tokens, segments[-1]
+        return tokens
         
 def concatenate_speakers(segments):
     segments_concatenated = [{"speaker": 1, "begin": 0.0, "end": 0.0}]

whisperlivekit/diarization/sortformer_backend.py

@@ -296,7 +296,7 @@ class SortformerDiarizationOnline:
         
         if not segments or not tokens:
             logger.debug("No segments or tokens available for speaker assignment")
-            return tokens, None
+            return tokens
         
         logger.debug(f"Assigning speakers to {len(tokens)} tokens using {len(segments)} segments")
         use_punctuation_split = False
@@ -313,7 +313,7 @@ class SortformerDiarizationOnline:
             # Use punctuation-aware assignment (similar to diart_backend)
             tokens = self._add_speaker_to_tokens_with_punctuation(segments, tokens)
         
-        return tokens, segments[-1]
+        return tokens
 
     def _add_speaker_to_tokens_with_punctuation(self, segments: List[SpeakerSegment], tokens: list) -> list:
         """

whisperlivekit/results_formater.py

@@ -38,12 +38,16 @@ def new_line(
         text = token.text + debug_info,
         start = token.start,
         end = token.end,
+        detected_language=token.detected_language
     )
 
 def append_token_to_last_line(lines, sep, token, debug_info):
     if token.text:
         lines[-1].text += sep + token.text + debug_info
         lines[-1].end = token.end
+    if not lines[-1].detected_language and token.detected_language:
+        lines[-1].detected_language = token.detected_language
+         
 
 def format_output(state, silence, current_time, args, debug, sep):
     diarization = args.diarization

whisperlivekit/simul_whisper/backend.py

@@ -4,7 +4,7 @@ import logging
 from typing import List, Tuple, Optional
 import logging
 import platform
-from whisperlivekit.timed_objects import ASRToken, Transcript, SpeakerSegment
+from whisperlivekit.timed_objects import ASRToken, Transcript, ChangeSpeaker
 from whisperlivekit.warmup import load_file
 from .whisper import load_model, tokenizer
 from .whisper.audio import TOKENS_PER_SECOND
@@ -93,14 +93,16 @@ class SimulStreamingOnlineProcessor:
         self.end = audio_stream_end_time #Only to be aligned with what happens in whisperstreaming backend.
         self.model.insert_audio(audio_tensor)
 
-    def on_new_speaker(self, last_segment: SpeakerSegment):
+    def new_speaker(self, change_speaker: ChangeSpeaker):
-            self.model.on_new_speaker(last_segment)
+            self.process_iter(is_last=True)
             self.model.refresh_segment(complete=True)
-
+            self.model.speaker = change_speaker.speaker
+            self.global_time_offset = change_speaker.start
+            
     def get_buffer(self):
         concat_buffer = Transcript.from_tokens(tokens= self.buffer, sep='')
         return concat_buffer
-            
+
     def process_iter(self, is_last=False) -> Tuple[List[ASRToken], float]:
         """
         Process accumulated audio chunks using SimulStreaming.
@@ -108,9 +110,13 @@ class SimulStreamingOnlineProcessor:
         Returns a tuple: (list of committed ASRToken objects, float representing the audio processed up to time).
         """
         try:
-            timestamped_words, timestamped_buffer_language = self.model.infer(is_last=is_last)
+            timestamped_words = self.model.infer(is_last=is_last)
-            self.buffer = timestamped_buffer_language
+            if timestamped_words and timestamped_words[0].detected_language == None:
+                self.buffer.extend(timestamped_words)
+                return [], self.end
+            
             self.committed.extend(timestamped_words)
+            self.buffer = []
             return timestamped_words, self.end
 
             

whisperlivekit/simul_whisper/simul_whisper.py

@@ -66,7 +66,7 @@ class PaddedAlignAttWhisper:
             self.fw_feature_extractor = FeatureExtractor(feature_size=self.model.dims.n_mels)
             
         logger.info(f"Model dimensions: {self.model.dims}")
-
+        self.speaker = -1
         self.decode_options = DecodingOptions(
             language = cfg.language, 
             without_timestamps = True,
@@ -78,7 +78,6 @@ class PaddedAlignAttWhisper:
         self.detected_language = cfg.language if cfg.language != "auto" else None
         self.global_time_offset = 0.0
         self.reset_tokenizer_to_auto_next_call = False
-        self.sentence_start_time = 0.0
         
         self.max_text_len = self.model.dims.n_text_ctx
         self.num_decoder_layers = len(self.model.decoder.blocks)
@@ -153,7 +152,7 @@ class PaddedAlignAttWhisper:
         
         self.last_attend_frame = -self.cfg.rewind_threshold
         self.cumulative_time_offset = 0.0
-        self.sentence_start_time = self.cumulative_time_offset + self.segments_len()
+        self.first_timestamp = None
 
         if self.cfg.max_context_tokens is None:
             self.max_context_tokens = self.max_text_len
@@ -261,7 +260,6 @@ class PaddedAlignAttWhisper:
         self.init_context()
         logger.debug(f"Context: {self.context}")
         if not complete and len(self.segments) > 2:
-            logger.debug("keeping last two segments because they are and it is not complete.")
             self.segments = self.segments[-2:]
         else:
             logger.debug("removing all segments.")
@@ -434,18 +432,19 @@ class PaddedAlignAttWhisper:
         end_encode = time()
         # print('Encoder duration:', end_encode-beg_encode)
                 
-        if self.cfg.language == "auto" and self.detected_language is None:
+        if self.cfg.language == "auto" and self.detected_language is None and self.first_timestamp:
-            seconds_since_start = (self.cumulative_time_offset + self.segments_len()) - self.sentence_start_time
+            seconds_since_start = self.segments_len() - self.first_timestamp
-            if seconds_since_start >= 3.0:
+            if seconds_since_start >= 2.0:
                 language_tokens, language_probs = self.lang_id(encoder_feature) 
                 top_lan, p = max(language_probs[0].items(), key=lambda x: x[1])
                 print(f"Detected language: {top_lan} with p={p:.4f}")
                 self.create_tokenizer(top_lan)
-                self.refresh_segment(complete=True)
+                self.last_attend_frame = -self.cfg.rewind_threshold
+                self.cumulative_time_offset = 0.0
+                self.init_tokens()
+                self.init_context()
                 self.detected_language = top_lan
                 logger.info(f"Tokenizer language: {self.tokenizer.language}, {self.tokenizer.sot_sequence_including_notimestamps}")
-            else:
-                logger.debug(f"Skipping language detection: {seconds_since_start:.2f}s < 3.0s")
 
         self.trim_context()
         current_tokens = self._current_tokens()
@@ -590,6 +589,10 @@ class PaddedAlignAttWhisper:
         
         self._clean_cache()
 
+        if len(l_absolute_timestamps) >=2 and self.first_timestamp is None:
+            self.first_timestamp = l_absolute_timestamps[0]
+            
+
         timestamped_words = []
         timestamp_idx = 0
         for word, word_tokens in zip(split_words, split_tokens):
@@ -604,15 +607,11 @@ class PaddedAlignAttWhisper:
                     end=current_timestamp + 0.1,
                     text= word,
                     probability=0.95,
-                    language=self.detected_language
+                    speaker=self.speaker,
+                    detected_language=self.detected_language
                 ).with_offset(
                     self.global_time_offset
             )
             timestamped_words.append(timestamp_entry)
         
-        if self.detected_language is None and self.cfg.language == "auto":
+        return timestamped_words
-            timestamped_buffer_language, timestamped_words = timestamped_words, []
-        else:
-            timestamped_buffer_language = []
-        
-        return timestamped_words, timestamped_buffer_language

whisperlivekit/timed_objects.py

@@ -17,7 +17,7 @@ class TimedText:
     speaker: Optional[int] = -1
     probability: Optional[float] = None
     is_dummy: Optional[bool] = False
-    language: str = None
+    detected_language: Optional[str] = None
     
     def is_punctuation(self):
         return self.text.strip() in PUNCTUATION_MARKS
@@ -41,11 +41,11 @@ class TimedText:
         return bool(self.text)
 
 
-@dataclass
+@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, self.speaker, self.probability)
+        return ASRToken(self.start + offset, self.end + offset, self.text, self.speaker, self.probability, detected_language=self.detected_language)
 
 @dataclass
 class Sentence(TimedText):
@@ -123,7 +123,6 @@ class Silence():
 @dataclass
 class Line(TimedText):
     translation: str = ''
-    detected_language: str = None
     
     def to_dict(self):
         _dict = {
@@ -161,13 +160,17 @@ class FrontData():
         if self.error:
             _dict['error'] = self.error
         return _dict
-    
+
+@dataclass  
+class ChangeSpeaker:
+    speaker: int
+    start: int
+
 @dataclass  
 class State():
     tokens: list
     translated_segments: list
     buffer_transcription: str
-    buffer_diarization: str
     end_buffer: float
     end_attributed_speaker: float
     remaining_time_transcription: float

whisperlivekit/translation/translation.py

@@ -3,7 +3,7 @@ import time
 import ctranslate2
 import torch
 import transformers
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 import huggingface_hub
 from whisperlivekit.translation.mapping_languages import get_nllb_code
 from whisperlivekit.timed_objects import Translation
@@ -18,9 +18,20 @@ MIN_SILENCE_DURATION_DEL_BUFFER = 3 #After a silence of x seconds, we consider t
 @dataclass
 class TranslationModel():
     translator: ctranslate2.Translator
-    tokenizer: dict
     device: str
+    tokenizer: dict = field(default_factory=dict)
     backend_type: str = 'ctranslate2'
+    model_size: str = '600M'
+    
+    def get_tokenizer(self, input_lang):
+        if not self.tokenizer.get(input_lang, False):
+            self.tokenizer[input_lang] = transformers.AutoTokenizer.from_pretrained(
+                f"facebook/nllb-200-distilled-{self.model_size}",
+                src_lang=input_lang,
+                clean_up_tokenization_spaces=True
+            )
+        return self.tokenizer[input_lang]
+            
 
 def load_model(src_langs, backend='ctranslate2', model_size='600M'):
     device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -33,14 +44,20 @@ def load_model(src_langs, backend='ctranslate2', model_size='600M'):
         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)
+        if src_lang != 'auto':
+            tokenizer[src_lang] = transformers.AutoTokenizer.from_pretrained(MODEL, src_lang=src_lang, clean_up_tokenization_spaces=True)
 
-    return TranslationModel(
+    translation_model = TranslationModel(
         translator=translator,
         tokenizer=tokenizer,
         backend_type=backend,
-        device = device
+        device = device,
+        model_size = model_size
     )
+    for src_lang in src_langs:
+        if src_lang != 'auto':
+            translation_model.get_tokenizer(src_lang)
+    return translation_model
 
 class OnlineTranslation:
     def __init__(self, translation_model: TranslationModel, input_languages: list, output_languages: list):
@@ -63,16 +80,12 @@ class OnlineTranslation:
                     self.commited.extend(self.buffer[:i])
                     self.buffer = results[i:]
 
-    def translate(self, input, input_lang=None, output_lang=None):
+    def translate(self, input, input_lang, output_lang):
         if not input:
             return ""
-        if input_lang is None:
-            input_lang = self.input_languages[0]
-        if output_lang is None:
-            output_lang = self.output_languages[0]
         nllb_output_lang = get_nllb_code(output_lang)
             
-        tokenizer = self.translation_model.tokenizer[input_lang]
+        tokenizer = self.translation_model.get_tokenizer(input_lang)
         tokenizer_output = tokenizer(input, return_tensors="pt").to(self.translation_model.device)
         
         if self.translation_model.backend_type == 'ctranslate2':
@@ -90,7 +103,15 @@ class OnlineTranslation:
             text = ' '.join([token.text for token in tokens])
             start = tokens[0].start
             end = tokens[-1].end
-            translated_text = self.translate(text)
+            if self.input_languages[0] == 'auto':
+                input_lang = tokens[0].detected_language
+            else:
+                input_lang = self.input_languages[0]
+                
+            translated_text = self.translate(text,
+                                            input_lang,
+                                            self.output_languages[0]
+                                            )
             translation = Translation(
                 text=translated_text,
                 start=start,

whisperlivekit/web/live_transcription.css

@@ -534,22 +534,6 @@ label {
   color: var(--muted);
 }
 
-.label_language img {
-  width: 12px;
-  height: 12px;
-}
-
-.silence-icon {
-  width: 14px;
-  height: 14px;
-  vertical-align: text-bottom;
-}
-
-.speaker-icon {
-  width: 16px;
-  height: 16px;
-  vertical-align: text-bottom;
-}
 
 .speaker-badge {
   display: inline-flex;

whisperlivekit/web/live_transcription.js

@@ -40,6 +40,11 @@ const timerElement = document.querySelector(".timer");
 const themeRadios = document.querySelectorAll('input[name="theme"]');
 const microphoneSelect = document.getElementById("microphoneSelect");
 
+const translationIcon = `<svg xmlns="http://www.w3.org/2000/svg" height="12px" viewBox="0 -960 960 960" width="12px" fill="#5f6368"><path d="m603-202-34 97q-4 11-14 18t-22 7q-20 0-32.5-16.5T496-133l152-402q5-11 15-18t22-7h30q12 0 22 7t15 18l152 403q8 19-4 35.5T868-80q-13 0-22.5-7T831-106l-34-96H603ZM362-401 188-228q-11 11-27.5 11.5T132-228q-11-11-11-28t11-28l174-174q-35-35-63.5-80T190-640h84q20 39 40 68t48 58q33-33 68.5-92.5T484-720H80q-17 0-28.5-11.5T40-760q0-17 11.5-28.5T80-800h240v-40q0-17 11.5-28.5T360-880q17 0 28.5 11.5T400-840v40h240q17 0 28.5 11.5T680-760q0 17-11.5 28.5T640-720h-76q-21 72-63 148t-83 116l96 98-30 82-122-125Zm266 129h144l-72-204-72 204Z"/></svg>`
+const silenceIcon = `<svg xmlns="http://www.w3.org/2000/svg" style="vertical-align: text-bottom;" height="14px" viewBox="0 -960 960 960" width="14px" fill="#5f6368"><path d="M514-556 320-752q9-3 19-5.5t21-2.5q66 0 113 47t47 113q0 11-1.5 22t-4.5 22ZM40-200v-32q0-33 17-62t47-44q51-26 115-44t141-18q26 0 49.5 2.5T456-392l-56-54q-9 3-19 4.5t-21 1.5q-66 0-113-47t-47-113q0-11 1.5-21t4.5-19L84-764q-11-11-11-28t11-28q12-12 28.5-12t27.5 12l675 685q11 11 11.5 27.5T816-80q-11 13-28 12.5T759-80L641-200h39q0 33-23.5 56.5T600-120H120q-33 0-56.5-23.5T40-200Zm80 0h480v-32q0-14-4.5-19.5T580-266q-36-18-92.5-36T360-320q-71 0-127.5 18T140-266q-9 5-14.5 14t-5.5 20v32Zm240 0Zm560-400q0 69-24.5 131.5T829-355q-12 14-30 15t-32-13q-13-13-12-31t12-33q30-38 46.5-85t16.5-98q0-51-16.5-97T767-781q-12-15-12.5-33t12.5-32q13-14 31.5-13.5T829-845q42 51 66.5 113.5T920-600Zm-182 0q0 32-10 61.5T700-484q-11 15-29.5 15.5T638-482q-13-13-13.5-31.5T633-549q6-11 9.5-24t3.5-27q0-14-3.5-27t-9.5-25q-9-17-8.5-35t13.5-31q14-14 32.5-13.5T700-716q18 25 28 54.5t10 61.5Z"/></svg>`;
+const languageIcon = `<svg xmlns="http://www.w3.org/2000/svg" height="12" viewBox="0 -960 960 960" width="12" fill="#5f6368"><path d="M480-80q-82 0-155-31.5t-127.5-86Q143-252 111.5-325T80-480q0-83 31.5-155.5t86-127Q252-817 325-848.5T480-880q83 0 155.5 31.5t127 86q54.5 54.5 86 127T880-480q0 82-31.5 155t-86 127.5q-54.5 54.5-127 86T480-80Zm0-82q26-36 45-75t31-83H404q12 44 31 83t45 75Zm-104-16q-18-33-31.5-68.5T322-320H204q29 50 72.5 87t99.5 55Zm208 0q56-18 99.5-55t72.5-87H638q-9 38-22.5 73.5T584-178ZM170-400h136q-3-20-4.5-39.5T300-480q0-21 1.5-40.5T306-560H170q-5 20-7.5 39.5T160-480q0 21 2.5 40.5T170-400Zm216 0h188q3-20 4.5-39.5T580-480q0-21-1.5-40.5T574-560H386q-3 20-4.5 39.5T380-480q0 21 1.5 40.5T386-400Zm268 0h136q5-20 7.5-39.5T800-480q0-21-2.5-40.5T790-560H654q3 20 4.5 39.5T660-480q0 21-1.5 40.5T654-400Zm-16-240h118q-29-50-72.5-87T584-782q18 33 31.5 68.5T638-640Zm-234 0h152q-12-44-31-83t-45-75q-26 36-45 75t-31 83Zm-200 0h118q9-38 22.5-73.5T376-782q-56 18-99.5 55T204-640Z"/></svg>`
+const speakerIcon = `<svg xmlns="http://www.w3.org/2000/svg" height="16px" style="vertical-align: text-bottom;" viewBox="0 -960 960 960" width="16px" fill="#5f6368"><path d="M480-480q-66 0-113-47t-47-113q0-66 47-113t113-47q66 0 113 47t47 113q0 66-47 113t-113 47ZM160-240v-32q0-34 17.5-62.5T224-378q62-31 126-46.5T480-440q66 0 130 15.5T736-378q29 15 46.5 43.5T800-272v32q0 33-23.5 56.5T720-160H240q-33 0-56.5-23.5T160-240Zm80 0h480v-32q0-11-5.5-20T700-306q-54-27-109-40.5T480-360q-56 0-111 13.5T260-306q-9 5-14.5 14t-5.5 20v32Zm240-320q33 0 56.5-23.5T560-640q0-33-23.5-56.5T480-720q-33 0-56.5 23.5T400-640q0 33 23.5 56.5T480-560Zm0-80Zm0 400Z"/></svg>`;
+
 function getWaveStroke() {
   const styles = getComputedStyle(document.documentElement);
   const v = styles.getPropertyValue("--wave-stroke").trim();
@@ -335,19 +340,17 @@ function renderLinesWithBuffer(
 
       let speakerLabel = "";
       if (item.speaker === -2) {
-        const silenceIcon = `<img class="silence-icon" src="/web/src/silence.svg" alt="Silence" />`;
         speakerLabel = `<span class="silence">${silenceIcon}<span id='timeInfo'>${timeInfo}</span></span>`;
       } else if (item.speaker == 0 && !isFinalizing) {
         speakerLabel = `<span class='loading'><span class="spinner"></span><span id='timeInfo'><span class="loading-diarization-value">${fmt1(
           remaining_time_diarization
         )}</span> second(s) of audio are undergoing diarization</span></span>`;
       } else if (item.speaker !== 0) {
-        const speakerIcon = `<img class="speaker-icon" src="/web/src/speaker.svg" alt="Speaker ${item.speaker}" />`;
         const speakerNum = `<span class="speaker-badge">${item.speaker}</span>`;
         speakerLabel = `<span id="speaker">${speakerIcon}${speakerNum}<span id='timeInfo'>${timeInfo}</span></span>`;
 
         if (item.detected_language) {
-          speakerLabel += `<span class="label_language"><img src="/web/src/language.svg" alt="Detected language" width="12" height="12" /><span>${item.detected_language}</span></span>`;
+          speakerLabel += `<span class="label_language">${languageIcon}<span>${item.detected_language}</span></span>`;
         }
       }
 
@@ -388,7 +391,7 @@ function renderLinesWithBuffer(
       
       if (item.translation) {
         currentLineText += `<div class="label_translation">
-          <img src="/web/src/translate.svg" alt="Translation" width="12" height="12" />
+          ${translationIcon}
           <span>${item.translation}</span>
         </div>`;
       }