Fixes to bugs from original PR

LLM/mlx_language_model.py

@@ -99,13 +99,9 @@ class MLXLanguageModelHandler(BaseHandler):
             output += t
             curr_output += t
             if curr_output.endswith((".", "?", "!", "<|end|>")):
-                yield curr_output.replace("<|end|>", "")
+                yield (curr_output.replace("<|end|>", ""), language_code)
                 curr_output = ""
         generated_text = output.replace("<|end|>", "")
-        printable_text = generated_text
         torch.mps.empty_cache()
 
-        self.chat.append({"role": "assistant", "content": generated_text})
-
-        # don't forget last sentence
-        yield (printable_text, language_code)
\ No newline at end of file
+        self.chat.append({"role": "assistant", "content": generated_text})
\ No newline at end of file

STT/lightning_whisper_mlx_handler.py

@@ -39,11 +39,8 @@ class LightningWhisperSTTHandler(BaseHandler):
             model_name = model_name.split("/")[-1]
         self.device = device
         self.model = LightningWhisperMLX(model=model_name, batch_size=6, quant=None)
-        if language == 'auto':
-            language = None
+        self.start_language = language
         self.last_language = language
-        if self.last_language is not None:
-            self.gen_kwargs["language"] = self.last_language
 
         self.warmup()
 
@@ -63,25 +60,24 @@ class LightningWhisperSTTHandler(BaseHandler):
         global pipeline_start
         pipeline_start = perf_counter()
 
-        # language_code = self.processor.tokenizer.decode(pred_ids[0, 1])[2:-2]  # remove "<|" and "|>"
-
-        language_code = self.model.transcribe(spoken_prompt)["language"]
-
-        if language_code not in SUPPORTED_LANGUAGES:  # reprocess with the last language
-            logger.warning("Whisper detected unsupported language:", language_code)
-            gen_kwargs = copy(self.gen_kwargs) #####
-            gen_kwargs['language'] = self.last_language
-            language_code = self.last_language
-            # pred_ids = self.model.generate(input_features, **gen_kwargs)
+        if self.start_language != 'auto':
+            transcription_dict = self.model.transcribe(spoken_prompt, language=self.start_language)
         else:
-            self.last_language = language_code
-
-        pred_text = self.model.transcribe(spoken_prompt)["text"].strip()
+            transcription_dict = self.model.transcribe(spoken_prompt)
+            language_code = transcription_dict["language"]
+            if language_code not in SUPPORTED_LANGUAGES:
+                logger.warning(f"Whisper detected unsupported language: {language_code}")
+                if self.last_language in SUPPORTED_LANGUAGES:  # reprocess with the last language
+                    transcription_dict = self.model.transcribe(spoken_prompt, language=self.last_language)
+                else:
+                    transcription_dict = {"text": "", "language": "en"}
+            else:
+                self.last_language = language_code
+
+        pred_text = transcription_dict["text"].strip()
+        language_code = transcription_dict["language"]
         torch.mps.empty_cache()
 
-        # language_code = self.processor.tokenizer.decode(pred_ids[0, 1])[2:-2] # remove "<|" and "|>"
-        language_code = self.model.transcribe(spoken_prompt)["language"]
-
         logger.debug("finished whisper inference")
         console.print(f"[yellow]USER: {pred_text}")
         logger.debug(f"Language Code Whisper: {language_code}")

TTS/melo_handler.py

@@ -11,7 +11,7 @@ logger = logging.getLogger(__name__)
 console = Console()
 
 WHISPER_LANGUAGE_TO_MELO_LANGUAGE = {
-    "en": "EN",
+    "en": "EN_NEWEST",
     "fr": "FR",
     "es": "ES",
     "zh": "ZH",
@@ -20,7 +20,7 @@ WHISPER_LANGUAGE_TO_MELO_LANGUAGE = {
 }
 
 WHISPER_LANGUAGE_TO_MELO_SPEAKER = {
-    "en": "EN-BR",
+    "en": "EN-Newest",
     "fr": "FR",
     "es": "ES",
     "zh": "ZH",