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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', torch_compile=True)
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
audio_queue.put(llm_response)
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():
try:
start = time.time()
audio = self.pipe.generate(llm_output.strip(), step_callback=should_abort)
inference_time = time.time() - start
logging.info(f"[WhisperSpeech INFO:] TTS inference done in {inference_time} ms.\n\n")
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}")
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