import functools import time import logging logging.basicConfig(level = logging.INFO) from websockets.sync.server import serve from whisperspeech.pipeline import Pipeline class WhisperSpeechTTS: def __init__(self): pass def initialize_model(self): self.pipe = Pipeline(s2a_ref='collabora/whisperspeech:s2a-q4-tiny-en+pl.model') self.last_llm_response = None def run(self, host, port, audio_queue=None): # initialize and warmup model self.initialize_model() for i in range(3): self.pipe.generate("Hello, I am warming up.") with serve( functools.partial(self.start_whisperspeech_tts, audio_queue=audio_queue), host, port ) as server: server.serve_forever() def start_whisperspeech_tts(self, websocket, audio_queue=None): self.eos = False self.output_audio = None while True: llm_response = audio_queue.get() if audio_queue.qsize() != 0: continue # check if this websocket exists try: websocket.ping() except Exception as e: del websocket break llm_output = llm_response["llm_output"][0] self.eos = llm_response["eos"] def should_abort(): if not audio_queue.empty(): raise TimeoutError() # only process if the output updated if self.last_llm_response != llm_output.strip(): logging.info("[WhisperSpeech INFO:] Tunning TTS inference ...") try: audio = self.pipe.generate(llm_output.strip(), step_callback=should_abort) self.output_audio = audio.cpu().numpy() self.last_llm_response = llm_output.strip() except TimeoutError: pass if self.eos and self.output_audio is not None: try: websocket.send(self.output_audio.tobytes()) except Exception as e: logging.error(f"[WhisperSpeech ERROR:] Audio error: {e}")