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import os
os.system("python -m unidic download")
from typing import Dict, Any, List, Generator
import torch
import os
import logging
from s2s_pipeline import main, prepare_all_args, get_default_arguments, setup_logger, initialize_queues_and_events, build_pipeline
import numpy as np
from queue import Queue, Empty
import threading
class EndpointHandler:
def __init__(self, path=""):
(
self.module_kwargs,
self.socket_receiver_kwargs,
self.socket_sender_kwargs,
self.vad_handler_kwargs,
self.whisper_stt_handler_kwargs,
self.paraformer_stt_handler_kwargs,
self.language_model_handler_kwargs,
self.mlx_language_model_handler_kwargs,
self.parler_tts_handler_kwargs,
self.melo_tts_handler_kwargs,
self.chat_tts_handler_kwargs,
) = get_default_arguments(mode='none', tts='melo', log_level='DEBUG')
setup_logger(self.module_kwargs.log_level)
prepare_all_args(
self.module_kwargs,
self.whisper_stt_handler_kwargs,
self.paraformer_stt_handler_kwargs,
self.language_model_handler_kwargs,
self.mlx_language_model_handler_kwargs,
self.parler_tts_handler_kwargs,
self.melo_tts_handler_kwargs,
self.chat_tts_handler_kwargs,
)
self.queues_and_events = initialize_queues_and_events()
self.pipeline_manager = build_pipeline(
self.module_kwargs,
self.socket_receiver_kwargs,
self.socket_sender_kwargs,
self.vad_handler_kwargs,
self.whisper_stt_handler_kwargs,
self.paraformer_stt_handler_kwargs,
self.language_model_handler_kwargs,
self.mlx_language_model_handler_kwargs,
self.parler_tts_handler_kwargs,
self.melo_tts_handler_kwargs,
self.chat_tts_handler_kwargs,
self.queues_and_events,
)
self.pipeline_manager.start()
# Add a new queue for collecting the final output
self.final_output_queue = Queue()
def _collect_output(self):
while True:
try:
output = self.queues_and_events['send_audio_chunks_queue'].get(timeout=5) # 2-second timeout
if isinstance(output, (str, bytes)) and output in (b"END", "END"):
self.final_output_queue.put("END")
break
elif isinstance(output, np.ndarray):
self.final_output_queue.put(output.tobytes())
else:
self.final_output_queue.put(output)
except Empty:
# If no output for 2 seconds, assume processing is complete
self.final_output_queue.put("END")
break
def __call__(self, data: Dict[str, Any]) -> Generator[Dict[str, Any], None, None]:
"""
Args:
data (Dict[str, Any]): The input data containing the necessary arguments.
Returns:
Generator[Dict[str, Any], None, None]: A generator yielding output chunks from the model or pipeline.
"""
# Start a thread to collect the final output
self.output_collector_thread = threading.Thread(target=self._collect_output)
self.output_collector_thread.start()
input_type = data.get("input_type", "text")
input_data = data.get("input", "")
if input_type == "speech":
# Convert input audio data to numpy array
audio_array = np.frombuffer(input_data, dtype=np.int16)
# Put audio data into the recv_audio_chunks_queue
self.queues_and_events['recv_audio_chunks_queue'].put(audio_array.tobytes())
elif input_type == "text":
# Put text data directly into the text_prompt_queue
self.queues_and_events['text_prompt_queue'].put(input_data)
else:
raise ValueError(f"Unsupported input type: {input_type}")
# Collect all output chunks
output_chunks = []
while True:
chunk = self.final_output_queue.get()
if chunk == "END":
break
output_chunks.append(chunk)
# Combine all audio chunks into a single byte string
combined_audio = b''.join(output_chunks)
return {"output": combined_audio}
def cleanup(self):
# Stop the pipeline
self.pipeline_manager.stop()
# Stop the output collector thread
self.queues_and_events['send_audio_chunks_queue'].put(b"END")
self.output_collector_thread.join() |