linting and few fixes for chattts

STT/whisper_stt_handler.py

@@ -68,7 +68,9 @@ class WhisperSTTHandler(BaseHandler):
             # one should warmup with a number of generated tokens above max tokens targeted for subsequent generation
             # hence, having min_new_tokens < max_new_tokens in the future doesn't make sense
             warmup_gen_kwargs = {
-                "min_new_tokens": self.gen_kwargs["max_new_tokens"],  # Yes, assign max_new_tokens to min_new_tokens
+                "min_new_tokens": self.gen_kwargs[
+                    "max_new_tokens"
+                ],  # Yes, assign max_new_tokens to min_new_tokens
                 "max_new_tokens": self.gen_kwargs["max_new_tokens"],
                 **self.gen_kwargs,
             }

TTS/chatTTS_handler.py

@@ -18,15 +18,15 @@ class ChatTTSHandler(BaseHandler):
     def setup(
         self,
         should_listen,
-        device="mps",
+        device="cuda",
         gen_kwargs={},  # Unused
         stream=True,
         chunk_size=512,
     ):
         self.should_listen = should_listen
         self.device = device
-        self.model  = ChatTTS.Chat()
-        self.model.load(compile=True) # Set to True for better performance
+        self.model = ChatTTS.Chat()
+        self.model.load(compile=False)  # Doesn't work for me with True
         self.chunk_size = chunk_size
         self.stream = stream
         rnd_spk_emb = self.model.sample_random_speaker()
@@ -37,8 +37,7 @@ class ChatTTSHandler(BaseHandler):
 
     def warmup(self):
         logger.info(f"Warming up {self.__class__.__name__}")
-        _= self.model.infer("text")
-
+        _ = self.model.infer("text")
 
     def process(self, llm_sentence):
         console.print(f"[green]ASSISTANT: {llm_sentence}")
@@ -52,36 +51,32 @@ class ChatTTSHandler(BaseHandler):
                 time.time() - start
             )  # Removing this line makes it fail more often. I'm looking into it.
 
-        wavs_gen = self.model.infer(llm_sentence,params_infer_code=self.params_infer_code, stream=self.stream)
+        wavs_gen = self.model.infer(
+            llm_sentence, params_infer_code=self.params_infer_code, stream=self.stream
+        )
 
         if self.stream:
             wavs = [np.array([])]
             for gen in wavs_gen:
-                print('new chunk gen', len(gen[0]))
                 if len(gen[0]) == 0:
                     self.should_listen.set()
                     return
                 audio_chunk = librosa.resample(gen[0], orig_sr=24000, target_sr=16000)
-                audio_chunk = (audio_chunk * 32768).astype(np.int16)
-                print('audio_chunk:', audio_chunk.shape)
+                audio_chunk = (audio_chunk * 32768).astype(np.int16)[0]
                 while len(audio_chunk) > self.chunk_size:
-                    yield audio_chunk[:self.chunk_size]  # 返回前 chunk_size 字节的数据
-                    audio_chunk = audio_chunk[self.chunk_size:]  # 移除已返回的数据
-                yield np.pad(audio_chunk, (0,self.chunk_size-len(audio_chunk)))
+                    yield audio_chunk[: self.chunk_size]  # 返回前 chunk_size 字节的数据
+                    audio_chunk = audio_chunk[self.chunk_size :]  # 移除已返回的数据
+                yield np.pad(audio_chunk, (0, self.chunk_size - len(audio_chunk)))
         else:
-            print('check result', wavs_gen)
             wavs = wavs_gen
             if len(wavs[0]) == 0:
                 self.should_listen.set()
                 return
             audio_chunk = librosa.resample(wavs[0], orig_sr=24000, target_sr=16000)
             audio_chunk = (audio_chunk * 32768).astype(np.int16)
-            print('audio_chunk:', audio_chunk.shape)
             for i in range(0, len(audio_chunk), self.chunk_size):
                 yield np.pad(
                     audio_chunk[i : i + self.chunk_size],
                     (0, self.chunk_size - len(audio_chunk[i : i + self.chunk_size])),
                 )
         self.should_listen.set()
-
-

arguments_classes/chat_tts_arguments.py

@@ -5,14 +5,12 @@ from dataclasses import dataclass, field
 class ChatTTSHandlerArguments:
     chat_tts_stream: bool = field(
         default=True,
-        metadata={
-            "help": "The tts mode is stream Default is 'stream'."
-        },
+        metadata={"help": "The tts mode is stream Default is 'stream'."},
     )
     chat_tts_device: str = field(
-        default="mps",
+        default="cuda",
         metadata={
-            "help": "The device to be used for speech synthesis. Default is 'mps'."
+            "help": "The device to be used for speech synthesis. Default is 'cuda'."
         },
     )
     chat_tts_chunk_size: int = field(

arguments_classes/module_arguments.py

@@ -35,7 +35,7 @@ class ModuleArguments:
     tts: Optional[str] = field(
         default="parler",
         metadata={
-            "help": "The TTS to use. Either 'parler' or 'melo'. Default is 'parler'"
+            "help": "The TTS to use. Either 'parler', 'melo', or 'chatTTS'. Default is 'parler'"
         },
     )
     log_level: str = field(

requirements.txt

@@ -3,6 +3,6 @@ parler_tts @ git+https://github.com/huggingface/parler-tts.git
 melotts @ git+https://github.com/andimarafioti/MeloTTS.git#egg=MeloTTS  # made a copy of MeloTTS to have compatible versions of transformers
 torch==2.4.0
 sounddevice==0.5.0
-ChatTTS
-funasr
-modelscope
+ChatTTS>=0.1.1
+funasr>=1.1.6
+modelscope>=1.17.1
\ No newline at end of file

requirements_mac.txt

@@ -5,6 +5,6 @@ torch==2.4.0
 sounddevice==0.5.0
 lightning-whisper-mlx>=0.0.10
 mlx-lm>=0.14.0
-ChatTTS
+ChatTTS>=0.1.1
 funasr>=1.1.6
 modelscope>=1.17.1

s2s_pipeline.py

@@ -80,7 +80,7 @@ def main():
             MLXLanguageModelHandlerArguments,
             ParlerTTSHandlerArguments,
             MeloTTSHandlerArguments,
-            ChatTTSHandlerArguments
+            ChatTTSHandlerArguments,
         )
     )
 
@@ -190,7 +190,7 @@ def main():
     prepare_args(mlx_language_model_handler_kwargs, "mlx_lm")
     prepare_args(parler_tts_handler_kwargs, "tts")
     prepare_args(melo_tts_handler_kwargs, "melo")
-    prepare_args(chat_tts_handler_kwargs,"chat_tts")
+    prepare_args(chat_tts_handler_kwargs, "chat_tts")
 
     # 3. Build the pipeline
     stop_event = Event()
@@ -319,9 +319,7 @@ def main():
         try:
             from TTS.chatTTS_handler import ChatTTSHandler
         except RuntimeError as e:
-            logger.error(
-                "Error importing ChatTTSHandler"
-            )
+            logger.error("Error importing ChatTTSHandler")
             raise e
         tts = ChatTTSHandler(
             stop_event,
@@ -331,7 +329,7 @@ def main():
             setup_kwargs=vars(chat_tts_handler_kwargs),
         )
     else:
-        raise ValueError("The TTS should be either parler or melo")
+        raise ValueError("The TTS should be either parler, melo or chatTTS")
 
     # 4. Run the pipeline
     try: