transcribe/server.py

import json
import logging
import threading
import time
import config
import librosa
import numpy as np
import soundfile
from pywhispercpp.model import Model

logging.basicConfig(level=logging.INFO)

class ServeClientBase(object):
    RATE = 16000
    SERVER_READY = "SERVER_READY"
    DISCONNECT = "DISCONNECT"

    def __init__(self, client_uid, websocket):
        self.client_uid = client_uid
        self.websocket = websocket
        self.frames = b""
        self.timestamp_offset = 0.0
        self.frames_np = None
        self.frames_offset = 0.0
        self.text = []
        self.current_out = ''
        self.prev_out = ''
        self.t_start = None
        self.exit = False
        self.same_output_count = 0
        self.show_prev_out_thresh = 5  # if pause(no output from whisper) show previous output for 5 seconds
        self.add_pause_thresh = 3  # add a blank to segment list as a pause(no speech) for 3 seconds
        self.transcript = []
        self.send_last_n_segments = 10

        # text formatting
        self.pick_previous_segments = 2

        # threading
        self.lock = threading.Lock()

    def speech_to_text(self):
        raise NotImplementedError

    def transcribe_audio(self):
        raise NotImplementedError

    def handle_transcription_output(self):
        raise NotImplementedError

    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):]
            # check timestamp offset(should be >= self.frame_offset)
            # this basically means that there is no speech as timestamp offset hasnt updated
            # and is less than frame_offset
            if self.timestamp_offset < self.frames_offset:
                self.timestamp_offset = self.frames_offset
        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 clip_audio_if_no_valid_segment(self):
        """
        Update the timestamp offset based on audio buffer status.
        Clip audio if the current chunk exceeds 30 seconds, this basically implies that
        no valid segment for the last 30 seconds from whisper
        """
        with self.lock:
            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

    def get_audio_chunk_for_processing(self):
        """
        Retrieves the next chunk of audio data for processing based on the current offsets.

        Calculates which part of the audio data should be processed next, based on
        the difference between the current timestamp offset and the frame's offset, scaled by
        the audio sample rate (RATE). It then returns this chunk of audio data along with its
        duration in seconds.

        Returns:
            tuple: A tuple containing:
                - input_bytes (np.ndarray): The next chunk of audio data to be processed.
                - duration (float): The duration of the audio chunk in seconds.
        """
        with self.lock:
            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
        return input_bytes, duration

    def prepare_segments(self, last_segment=None):
        """
        Prepares the segments of transcribed text to be sent to the client.

        This method compiles the recent segments of transcribed text, ensuring that only the
        specified number of the most recent segments are included. It also appends the most
        recent segment of text if provided (which is considered incomplete because of the possibility
        of the last word being truncated in the audio chunk).

        Args:
            last_segment (str, optional): The most recent segment of transcribed text to be added
                                          to the list of segments. Defaults to None.

        Returns:
            list: A list of transcribed text segments to be sent to the client.
        """
        segments = []
        if len(self.transcript) >= self.send_last_n_segments:
            segments = self.transcript[-self.send_last_n_segments:].copy()
        else:
            segments = self.transcript.copy()
        if last_segment is not None:
            segments = segments + [last_segment]
        logging.info(f"{segments}")
        return segments

    def get_audio_chunk_duration(self, input_bytes):
        """
        Calculates the duration of the provided audio chunk.

        Args:
            input_bytes (numpy.ndarray): The audio chunk for which to calculate the duration.

        Returns:
            float: The duration of the audio chunk in seconds.
        """
        return input_bytes.shape[0] / self.RATE

    def send_transcription_to_client(self, segments):
        """
        Sends the specified transcription segments to the client over the websocket connection.

        This method formats the transcription segments into a JSON object and attempts to send
        this object to the client. If an error occurs during the send operation, it logs the error.

        Returns:
            segments (list): A list of transcription segments to be sent to the client.
        """
        try:
            self.websocket.send(
                json.dumps({
                    "uid": self.client_uid,
                    "segments": segments,
                })
            )
        except Exception as e:
            logging.error(f"[ERROR]: Sending data to client: {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


class ServeClientWhisperCPP(ServeClientBase):
    SINGLE_MODEL = None
    SINGLE_MODEL_LOCK = threading.Lock()

    def __init__(self, websocket, language=None, client_uid=None,
                 single_model=False):
        """
        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.
            language (str, optional): The language for transcription. Defaults to None.
            client_uid (str, optional): A unique identifier for the client. Defaults to None.
            single_model (bool, optional): Whether to instantiate a new model for each client connection. Defaults to False.

        """
        super().__init__(client_uid, websocket)
        self.language = language
        self.eos = False

        if single_model:
            if ServeClientWhisperCPP.SINGLE_MODEL is None:
                self.create_model()
                ServeClientWhisperCPP.SINGLE_MODEL = self.transcriber
            else:
                self.transcriber = ServeClientWhisperCPP.SINGLE_MODEL
        else:
            self.create_model()

        # threading
        logging.info('Create a thread to process audio.')
        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,
            "backend": "pywhispercpp"
        }))

    def create_model(self, warmup=True):
        """
        Instantiates a new model, sets it as the transcriber and does warmup if desired.
        """
    
        self.transcriber = Model(model=config.WHISPER_MODEL, models_dir=config.MODEL_DIR)
        if warmup:
            self.warmup()

    def warmup(self, warmup_steps=1):
        """
        Warmup TensorRT since first few inferences are slow.

        Args:
            warmup_steps (int): Number of steps to warm up the model for.
        """
        logging.info("[INFO:] Warming up whisper.cpp engine..")
        mel, _, = soundfile.read("assets/jfk.flac")
        for i in range(warmup_steps):
            self.transcriber.transcribe(mel, print_progress=False)

    def set_eos(self, eos):
        """
        Sets the End of Speech (EOS) flag.

        Args:
            eos (bool): The value to set for the EOS flag.
        """
        self.lock.acquire()
        self.eos = eos
        self.lock.release()

    def handle_transcription_output(self, last_segment, duration):
        """
        Handle the transcription output, updating the transcript and sending data to the client.

        Args:
            last_segment (str): The last segment from the whisper output which is considered to be incomplete because
                                of the possibility of word being truncated.
            duration (float): Duration of the transcribed audio chunk.
        """
        segments = self.prepare_segments({"text": last_segment})
        self.send_transcription_to_client(segments)
        if self.eos:
            self.update_timestamp_offset(last_segment, duration)

    def transcribe_audio(self, input_bytes):
        """
        Transcribe the audio chunk and send the results to the client.

        Args:
            input_bytes (np.array): The audio chunk to transcribe.
        """
        if ServeClientWhisperCPP.SINGLE_MODEL:
            ServeClientWhisperCPP.SINGLE_MODEL_LOCK.acquire()
        logging.info(f"[pywhispercpp:] Processing audio with duration: {input_bytes.shape[0] / self.RATE}")
        mel = input_bytes
        duration = librosa.get_duration(y=input_bytes, sr=self.RATE)

        if self.language == "zh":
            prompt = '以下是简体中文普通话的句子。'
        else:
            prompt = 'The following is an English sentence.'
 
        segments = self.transcriber.transcribe(
            mel,
            language=self.language,
            initial_prompt=prompt,
            token_timestamps=True,
            # max_len=max_len,
            print_progress=False
        )
        text = []
        for segment in segments:
            content = segment.text
            text.append(content)
        last_segment = ' '.join(text)

        logging.info(f"[pywhispercpp:] Last segment: {last_segment}")

        if ServeClientWhisperCPP.SINGLE_MODEL:
            ServeClientWhisperCPP.SINGLE_MODEL_LOCK.release()
        if last_segment:
            self.handle_transcription_output(last_segment, duration)

    def update_timestamp_offset(self, last_segment, duration):
        """
        Update timestamp offset and transcript.

        Args:
            last_segment (str): Last transcribed audio from the whisper model.
            duration (float): Duration of the last audio chunk.
        """
        if not len(self.transcript):
            self.transcript.append({"text": last_segment + " "})
        elif self.transcript[-1]["text"].strip() != last_segment:
            self.transcript.append({"text": last_segment + " "})

        logging.info(f'Transcript list context: {self.transcript}')

        with self.lock:
            self.timestamp_offset += duration

    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:
            if self.exit:
                logging.info("Exiting speech to text thread")
                break

            if self.frames_np is None:
                time.sleep(0.02)  # wait for any audio to arrive
                continue

            self.clip_audio_if_no_valid_segment()

            input_bytes, duration = self.get_audio_chunk_for_processing()
            if duration < 1:
                continue

            try:
                input_sample = input_bytes.copy()
                logging.info(f"[pywhispercpp:] Processing audio with duration: {duration}")
                self.transcribe_audio(input_sample)

            except Exception as e:
                logging.error(f"[ERROR]: {e}")