task to direct-english-translation

README.md

@@ -141,8 +141,7 @@ async def websocket_endpoint(websocket: WebSocket):
 | `--model` | Whisper model size. List and recommandations [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/docs/available_models.md) | `small` |
 | `--model-path` | .pt file/directory containing whisper model. Overrides `--model`. Recommandations [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/docs/models_compatible_formats.md) | `None` |
 | `--language` | List [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/simul_whisper/whisper/tokenizer.py). If you use `auto`, the model attempts to detect the language automatically, but it tends to bias towards English. | `auto` |
-| `--target-language` | If sets, translate to using NLLB. Ex: `fr`. [200 languages available](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/docs/supported_languages.md). If you want to translate to english, you should rather use `--task translate`, since Whisper can do it directly. | `None` |
-| `--task` | Set to `translate` to translate *only* to english, using Whisper translation. | `transcribe` |
+| `--target-language` | If sets, translates using [NLLW](https://github.com/QuentinFuxa/NoLanguageLeftWaiting). [200 languages available](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/docs/supported_languages.md). If you want to translate to english, you can also use `--direct-english-translation`. The STT model will try to directly output the translation. | `None` |
 | `--diarization` | Enable speaker identification | `False` |
 | `--backend` | Processing backend. You can switch to `faster-whisper` if  `simulstreaming` does not work correctly | `simulstreaming` |
 | `--no-vac` | Disable Voice Activity Controller | `False` |

whisperlivekit/core.py

@@ -52,7 +52,7 @@ class TranscriptionEngine:
             "model_cache_dir": None,
             "model_dir": None,
             "lan": "auto",
-            "task": "transcribe",
+            "direct_english_translation": False,
         }
         transcription_common_params = update_with_kwargs(transcription_common_params, kwargs)                                            
 

whisperlivekit/local_agreement/backends.py

@@ -80,10 +80,6 @@ class WhisperTimestampedASR(ASRBase):
     def use_vad(self):
         self.transcribe_kargs["vad"] = True
 
-    def set_translate_task(self):
-        self.transcribe_kargs["task"] = "translate"
-
-
 class FasterWhisperASR(ASRBase):
     """Uses faster-whisper as the backend."""
     sep = ""
@@ -139,10 +135,6 @@ class FasterWhisperASR(ASRBase):
     def use_vad(self):
         self.transcribe_kargs["vad_filter"] = True
 
-    def set_translate_task(self):
-        self.transcribe_kargs["task"] = "translate"
-
-
 class MLXWhisper(ASRBase):
     """
     Uses MLX Whisper optimized for Apple Silicon.
@@ -218,10 +210,6 @@ class MLXWhisper(ASRBase):
     def use_vad(self):
         self.transcribe_kargs["vad_filter"] = True
 
-    def set_translate_task(self):
-        self.transcribe_kargs["task"] = "translate"
-
-
 class OpenaiApiASR(ASRBase):
     """Uses OpenAI's Whisper API for transcription."""
     def __init__(self, lan=None, temperature=0, logfile=sys.stderr):
@@ -232,7 +220,7 @@ class OpenaiApiASR(ASRBase):
         self.temperature = temperature
         self.load_model()
         self.use_vad_opt = False
-        self.task = "transcribe"
+        self.direct_english_translation = False
 
     def load_model(self, *args, **kwargs):
         from openai import OpenAI
@@ -274,7 +262,7 @@ class OpenaiApiASR(ASRBase):
             "temperature": self.temperature,
             "timestamp_granularities": ["word", "segment"],
         }
-        if self.task != "translate" and self.original_language:
+        if not self.direct_english_translation and self.original_language:
             params["language"] = self.original_language
         if prompt:
             params["prompt"] = prompt
@@ -284,7 +272,4 @@ class OpenaiApiASR(ASRBase):
         return transcript
 
     def use_vad(self):
-        self.use_vad_opt = True
-
-    def set_translate_task(self):
-        self.task = "translate"
+        self.use_vad_opt = True

whisperlivekit/local_agreement/whisper_online.py

@@ -70,7 +70,7 @@ def backend_factory(
             model_size,
             model_cache_dir,
             model_dir,
-            task,
+            direct_english_translation,
             buffer_trimming,
             buffer_trimming_sec,
             confidence_validation,
@@ -102,7 +102,7 @@ def backend_factory(
         e = time.time()
         logger.info(f"done. It took {round(e-t,2)} seconds.")
 
-    if task == "translate":
+    if direct_english_translation:
         tgt_language = "en"  # Whisper translates into English
     else:
         tgt_language = lan  # Whisper transcribes in this language

whisperlivekit/parse_args.py

@@ -114,11 +114,10 @@ def parse_args():
         help="Source language code, e.g. en,de,cs, or 'auto' for language detection.",
     )
     parser.add_argument(
-        "--task",
-        type=str,
-        default="transcribe",
-        choices=["transcribe", "translate"],
-        help="Transcribe or translate.",
+        "--direct-english-translation",
+        action="store_true",
+        default=False,
+        help="Use Whisper to directly translate to english.",
     )
     
     parser.add_argument(

whisperlivekit/simul_whisper/backend.py

@@ -211,7 +211,7 @@ class SimulStreamingASR():
                 cif_ckpt_path=self.cif_ckpt_path,
                 decoder_type="beam",
                 beam_size=self.beams,
-                task=self.task,
+                task=self.direct_english_translation,
                 never_fire=self.never_fire,
                 init_prompt=self.init_prompt,
                 max_context_tokens=self.max_context_tokens,
@@ -219,7 +219,7 @@ class SimulStreamingASR():
         )  
         
         # Set up tokenizer for translation if needed
-        if self.task == "translate":
+        if self.direct_english_translation:
             self.tokenizer = self.set_translate_task()
         else:
             self.tokenizer = None