whisper_live/trt_server.py

import websockets
import time
import threading
import json
import textwrap

import logging
logging.basicConfig(level = logging.INFO)

from websockets.sync.server import serve

import torch
import numpy as np
import queue

from whisper_live.vad import VoiceActivityDetection
from whisper_live.trt_transcriber import WhisperTRTLLM


from scipy.io.wavfile import write
import functools

save_counter = 0
def save_wav(normalized_float32):
    global save_counter
    scaled_int16 = (normalized_float32 * 32768).astype(np.int16)
    write(f"outputs/output{save_counter}.wav", 16000, scaled_int16)
    save_counter += 1



class TranscriptionServer:
    """
    Represents a transcription server that handles incoming audio from clients.

    Attributes:
        RATE (int): The audio sampling rate (constant) set to 16000.
        vad_model (torch.Module): The voice activity detection model.
        vad_threshold (float): The voice activity detection threshold.
        clients (dict): A dictionary to store connected clients.
        websockets (dict): A dictionary to store WebSocket connections.
        clients_start_time (dict): A dictionary to track client start times.
        max_clients (int): Maximum allowed connected clients.
        max_connection_time (int): Maximum allowed connection time in seconds.
    """

    RATE = 16000

    def __init__(self):
        # voice activity detection model
        
        self.clients = {}
        self.websockets = {}
        self.clients_start_time = {}
        self.max_clients = 4
        self.max_connection_time = 600
        self.transcriber = None

    def get_wait_time(self):
        """
        Calculate and return the estimated wait time for clients.

        Returns:
            float: The estimated wait time in minutes.
        """
        wait_time = None

        for k, v in self.clients_start_time.items():
            current_client_time_remaining = self.max_connection_time - (time.time() - v)

            if wait_time is None or current_client_time_remaining < wait_time:
                wait_time = current_client_time_remaining

        return wait_time / 60

    def recv_audio(self, websocket, transcription_queue=None, llm_queue=None, whisper_tensorrt_path=None):
        """
        Receive audio chunks from a client in an infinite loop.
        
        Continuously receives audio frames from a connected client
        over a WebSocket connection. It processes the audio frames using a
        voice activity detection (VAD) model to determine if they contain speech
        or not. If the audio frame contains speech, it is added to the client's
        audio data for ASR.
        If the maximum number of clients is reached, the method sends a
        "WAIT" status to the client, indicating that they should wait
        until a slot is available.
        If a client's connection exceeds the maximum allowed time, it will
        be disconnected, and the client's resources will be cleaned up.

        Args:
            websocket (WebSocket): The WebSocket connection for the client.
        
        Raises:
            Exception: If there is an error during the audio frame processing.
        """
        self.vad_model = VoiceActivityDetection()
        self.vad_threshold = 0.5

        logging.info("[Whisper INFO:] New client connected")
        options = websocket.recv()
        options = json.loads(options)

        if len(self.clients) >= self.max_clients:
            logging.warning("Client Queue Full. Asking client to wait ...")
            wait_time = self.get_wait_time()
            response = {
                "uid": options["uid"],
                "status": "WAIT",
                "message": wait_time,
            }
            websocket.send(json.dumps(response))
            websocket.close()
            del websocket
            return
        
        if self.transcriber is None:
            self.transcriber = WhisperTRTLLM(whisper_tensorrt_path, assets_dir="assets", device="cuda")

        client = ServeClient(
            websocket,
            multilingual=options["multilingual"],
            language=options["language"],
            task=options["task"],
            client_uid=options["uid"],
            transcription_queue=transcription_queue,
            llm_queue=llm_queue,
            transcriber=self.transcriber
        )

        self.clients[websocket] = client
        self.clients_start_time[websocket] = time.time()
        no_voice_activity_chunks = 0
        print()
        while True:
            try:
                frame_data = websocket.recv()
                frame_np = np.frombuffer(frame_data, dtype=np.float32)

                # VAD
                try:
                    speech_prob = self.vad_model(torch.from_numpy(frame_np.copy()), self.RATE).item()
                    if speech_prob < self.vad_threshold:
                        no_voice_activity_chunks += 1
                        if no_voice_activity_chunks > 3:
                            if not self.clients[websocket].eos:
                                self.clients[websocket].set_eos(True)
                            time.sleep(0.1)    # EOS stop receiving frames for a 100ms(to send output to LLM.)
                        continue
                    no_voice_activity_chunks = 0
                    self.clients[websocket].set_eos(False)

                except Exception as e:
                    logging.error(e)
                    return
                self.clients[websocket].add_frames(frame_np)

                elapsed_time = time.time() - self.clients_start_time[websocket]
                if elapsed_time >= self.max_connection_time:
                    self.clients[websocket].disconnect()
                    logging.warning(f"{self.clients[websocket]} Client disconnected due to overtime.")
                    self.clients[websocket].cleanup()
                    self.clients.pop(websocket)
                    self.clients_start_time.pop(websocket)
                    websocket.close()
                    del websocket
                    break

            except Exception as e:
                logging.error(e)
                self.clients[websocket].cleanup()
                self.clients.pop(websocket)
                self.clients_start_time.pop(websocket)
                logging.info("[Whisper INFO:] Connection Closed.")
                del websocket
                break

    def run(self, host, port=9090, transcription_queue=None, llm_queue=None, whisper_tensorrt_path=None):
        """
        Run the transcription server.

        Args:
            host (str): The host address to bind the server.
            port (int): The port number to bind the server.
        """
        with serve(
            functools.partial(
                self.recv_audio,
                transcription_queue=transcription_queue,
                llm_queue=llm_queue,
                whisper_tensorrt_path=whisper_tensorrt_path
            ),
            host,
            port
        ) as server:
            server.serve_forever()


class ServeClient:
    """
    Attributes:
        RATE (int): The audio sampling rate (constant) set to 16000.
        SERVER_READY (str): A constant message indicating that the server is ready.
        DISCONNECT (str): A constant message indicating that the client should disconnect.
        client_uid (str): A unique identifier for the client.
        data (bytes): Accumulated audio data.
        frames (bytes): Accumulated audio frames.
        language (str): The language for transcription.
        task (str): The task type, e.g., "transcribe."
        transcriber (WhisperModel): The Whisper model for speech-to-text.
        timestamp_offset (float): The offset in audio timestamps.
        frames_np (numpy.ndarray): NumPy array to store audio frames.
        frames_offset (float): The offset in audio frames.
        exit (bool): A flag to exit the transcription thread.
        transcript (list): List of transcribed segments.
        websocket: The WebSocket connection for the client.
    """
    RATE = 16000
    SERVER_READY = "SERVER_READY"
    DISCONNECT = "DISCONNECT"

    def __init__(
        self,
        websocket,
        task="transcribe",
        device=None,
        multilingual=False,
        language=None, 
        client_uid=None,
        transcription_queue=None,
        llm_queue=None,
        transcriber=None
        ):
        """
        Initialize a ServeClient instance.
        The Whisper model is initialized based on the client's language and device availability.
        The transcription thread is started upon initialization. A "SERVER_READY" message is sent
        to the client to indicate that the server is ready.

        Args:
            websocket (WebSocket): The WebSocket connection for the client.
            task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe".
            device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
            multilingual (bool, optional): Whether the client supports multilingual transcription. Defaults to False.
            language (str, optional): The language for transcription. Defaults to None.
            client_uid (str, optional): A unique identifier for the client. Defaults to None.

        """
        if transcriber is None:
            raise ValueError("Transcriber is None.")
        self.transcriber = transcriber
        self.client_uid = client_uid
        self.transcription_queue = transcription_queue
        self.llm_queue = llm_queue
        self.data = b""
        self.frames = b""
        self.task = task
        self.last_prompt = None

        self.timestamp_offset = 0.0
        self.frames_np = None
        self.frames_offset = 0.0
        self.exit = False
        self.transcript = []
        self.prompt = None
        self.segment_inference_time = []

        # threading
        self.websocket = websocket
        self.lock = threading.Lock()
        self.eos = False
        self.trans_thread = threading.Thread(target=self.speech_to_text)
        self.trans_thread.start()

        self.websocket.send(
            json.dumps(
                {
                    "uid": self.client_uid,
                    "message": self.SERVER_READY
                }
            )
        )
    
    def set_eos(self, eos):
        self.lock.acquire()
        self.eos = eos
        self.lock.release()
    
    def add_frames(self, frame_np):
        """
        Add audio frames to the ongoing audio stream buffer.

        This method is responsible for maintaining the audio stream buffer, allowing the continuous addition
        of audio frames as they are received. It also ensures that the buffer does not exceed a specified size
        to prevent excessive memory usage.

        If the buffer size exceeds a threshold (45 seconds of audio data), it discards the oldest 30 seconds
        of audio data to maintain a reasonable buffer size. If the buffer is empty, it initializes it with the provided
        audio frame. The audio stream buffer is used for real-time processing of audio data for transcription.

        Args:
            frame_np (numpy.ndarray): The audio frame data as a NumPy array.

        """
        self.lock.acquire()
        if self.frames_np is not None and self.frames_np.shape[0] > 45*self.RATE:
            self.frames_offset += 30.0
            self.frames_np = self.frames_np[int(30*self.RATE):]
        if self.frames_np is None:
            self.frames_np = frame_np.copy()
        else:
            self.frames_np = np.concatenate((self.frames_np, frame_np), axis=0)
        self.lock.release()

    def speech_to_text(self):
        """
        Process an audio stream in an infinite loop, continuously transcribing the speech.

        This method continuously receives audio frames, performs real-time transcription, and sends
        transcribed segments to the client via a WebSocket connection.

        If the client's language is not detected, it waits for 30 seconds of audio input to make a language prediction.
        It utilizes the Whisper ASR model to transcribe the audio, continuously processing and streaming results. Segments
        are sent to the client in real-time, and a history of segments is maintained to provide context.Pauses in speech 
        (no output from Whisper) are handled by showing the previous output for a set duration. A blank segment is added if 
        there is no speech for a specified duration to indicate a pause.

        Raises:
            Exception: If there is an issue with audio processing or WebSocket communication.

        """
        while True:
            # send the LLM outputs
            try:
                llm_response = None
                if self.llm_queue is not None:
                    while not self.llm_queue.empty():
                        llm_response = self.llm_queue.get()
                    
                    if llm_response:
                        eos = llm_response["eos"]
                        if eos:
                            self.websocket.send(json.dumps(llm_response))
            except queue.Empty:
                pass
            
            if self.exit:
                logging.info("[Whisper INFO:] Exiting speech to text thread")
                break
            
            if self.frames_np is None:
                time.sleep(0.02)    # wait for any audio to arrive
                continue

            # clip audio if the current chunk exceeds 30 seconds, this basically implies that
            # no valid segment for the last 30 seconds from whisper
            if self.frames_np[int((self.timestamp_offset - self.frames_offset)*self.RATE):].shape[0] > 25 * self.RATE:
                duration = self.frames_np.shape[0] / self.RATE
                self.timestamp_offset = self.frames_offset + duration - 5
    
            samples_take = max(0, (self.timestamp_offset - self.frames_offset)*self.RATE)
            input_bytes = self.frames_np[int(samples_take):].copy()
            duration = input_bytes.shape[0] / self.RATE
            if duration<0.4:
                time.sleep(0.01)    # 5ms sleep to wait for some voice active audio to arrive
                continue

            try:
                input_sample = input_bytes.copy()
                start = time.time()
                mel, duration = self.transcriber.log_mel_spectrogram(input_sample)
                last_segment = self.transcriber.transcribe(mel)
                infer_time = time.time() - start
                self.segment_inference_time.append(infer_time)

                segments = []
                if len(last_segment):
                    segments.append({"text": last_segment})
                    try:
                        self.prompt = ' '.join(segment['text'] for segment in segments)
                        if self.last_prompt != self.prompt:
                            self.websocket.send(
                                json.dumps({
                                    "uid": self.client_uid,
                                    "segments": segments,
                                    "eos": self.eos,
                                    "latency": infer_time
                                })
                            )
                            
                        self.transcription_queue.put({"uid": self.client_uid, "prompt": self.prompt, "eos": self.eos})
                        if self.eos:
                            self.timestamp_offset += duration
                            logging.info(f"[Whisper INFO]: {self.prompt}, eos: {self.eos}")
                            logging.info(
                                f"[Whisper INFO]: Average inference time {sum(self.segment_inference_time) / len(self.segment_inference_time)}\n\n")
                            self.segment_inference_time = []
                        
                            
                            
                    except Exception as e:
                        logging.error(f"[ERROR]: {e}")

            except Exception as e:
                logging.error(f"[ERROR]: {e}")
    
    def disconnect(self):
        """
        Notify the client of disconnection and send a disconnect message.

        This method sends a disconnect message to the client via the WebSocket connection to notify them
        that the transcription service is disconnecting gracefully.

        """
        self.websocket.send(
            json.dumps(
                {
                    "uid": self.client_uid,
                    "message": self.DISCONNECT
                }
            )
        )
    
    def cleanup(self):
        """
        Perform cleanup tasks before exiting the transcription service.

        This method performs necessary cleanup tasks, including stopping the transcription thread, marking
        the exit flag to indicate the transcription thread should exit gracefully, and destroying resources
        associated with the transcription process.

        """
        logging.info("Cleaning up.")
        self.exit = True
        # self.transcriber.destroy()