hls or RTSP dont work

[gasparuben]

Hi all,
Sorry if this is not the right way to contact you. Just wanted to report that the RTSP or HLS client doesnt look to work at least for me. So I was wondering if something special should be done, in order to make those work.
File and microphone use cases do work perfectly.

I run my server via docker on a sever with GPU's: docker run -it --gpus all --network=host -p 9090:9090 ghcr.io/collabora/whisperlive-gpu:latest
Then I run a client from my mac, as per your example:

from whisper_live.client import TranscriptionClient
client = TranscriptionClient(
  "server",
  9090,
  lang="en",
  translate=False,
  model="small",                                      # also support hf_model => `Systran/faster-whisper-small`
  use_vad=False,
  save_output_recording=False,                         # Only used for microphone input, False by Default
  output_recording_filename="./output_recording.wav", # Only used for microphone input
  max_clients=4,
  max_connection_time=600,
  mute_audio_playback=False,                          # Only used for file input, False by Default
)
client(hls_url="https://xxxx.cern.ch/whisper/smil:1273117_camera_delayed_all.smil/playlist.m3u8")
client(rtsp_url="rtsp://ip:1935/whisper/_definst_/1273117_camera_delayed_1200-360p")

But it remains on connecting and I dont get a flow of transcripts. Both do work from Safari for the HLS or VLC player for the RTSP.

I would like to also ask what means, translate parameter on the TranscriptionClient, could the software translate from English to French for example?

Thank you very much,
Ruben

[andreclerigo]

I can reproduce this issue! It was working before, so something in the code must have changed.
@makaveli10

RTSP Server is set up like so:

$ docker run --rm -it \
  -p 8554:8554/tcp \
  -p 8000:8000/udp -p 8001:8001/udp \
  bluenviron/mediamtx:latest

and using a random .mp3 lying around:

ffmpeg -re -i nlu_5s.mp3 \
  -c:a aac -b:a 64k -ar 48000 -ac 2 \
  -f rtsp -rtsp_transport tcp \
  rtsp://127.0.0.1:8554/mic

On the server side:

[INFO]: * recording
[INFO]: Waiting for server ready ...
[INFO]: Opened connection
[INFO]: Server Running with backend faster_whisper
[INFO]: Server Ready!
[INFO]: Connecting to RTSP stream...
[INFO]: Websocket connection closed: None: None
[INFO]: RTSP stream processing finished.

[makaveli10]

I can reproduce this issue! It was working before, so something in the code must have changed. @makaveli10

RTSP Server is set up like so:

$ docker run --rm -it
-p 8554:8554/tcp
-p 8000:8000/udp -p 8001:8001/udp
bluenviron/mediamtx:latest
and using a random .mp3 lying around:

ffmpeg -re -i nlu_5s.mp3
-c:a aac -b:a 64k -ar 48000 -ac 2
-f rtsp -rtsp_transport tcp
rtsp://127.0.0.1:8554/mic
On the server side:

[INFO]: * recording
[INFO]: Waiting for server ready ...
[INFO]: Opened connection
[INFO]: Server Running with backend faster_whisper
[INFO]: Server Ready!
[INFO]: Connecting to RTSP stream...
[INFO]: Websocket connection closed: None: None
[INFO]: RTSP stream processing finished.

@gasparuben @andreclerigo I just tested an audio file by creating a rtsp stream with the docker setup as yours and it works without any issues for me. Here is my client code

import os
import shutil
import wave

import logging
import numpy as np
import pyaudio
import threading
import json
import websocket
import uuid
import time
import av
import whisper_live.utils as utils


class Client:
    """
    Handles communication with a server using WebSocket.
    """
    INSTANCES = {}
    END_OF_AUDIO = "END_OF_AUDIO"

    def __init__(
        self,
        host=None,
        port=None,
        lang=None,
        translate=False,
        model="small",
        srt_file_path="output.srt",
        use_vad=True,
        use_wss=False,
        log_transcription=True,
        send_last_n_segments=10,
        no_speech_thresh=0.45,
        clip_audio=False,
        same_output_threshold=10,
        transcription_callback=None,
        enable_translation=False,
        target_language="fr",
        translation_callback=None,
        translation_srt_file_path="output_translated.srt",
    ):
        """
        Initializes a Client instance for audio recording and streaming to a server.

        If host and port are not provided, the WebSocket connection will not be established.
        When translate is True, the task will be set to "translate" instead of "transcribe".
        he audio recording starts immediately upon initialization.

        Args:
            host (str): The hostname or IP address of the server.
            port (int): The port number for the WebSocket server.
            lang (str, optional): The selected language for transcription. Default is None.
            translate (bool, optional): Specifies if the task is translation. Default is False.
            model (str, optional): The whisper model to use (e.g., "small", "medium", "large"). Default is "small".
            srt_file_path (str, optional): The file path to save the output SRT file. Default is "output.srt".
            use_vad (bool, optional): Whether to enable voice activity detection. Default is True.
            log_transcription (bool, optional): Whether to log transcription output to the console. Default is True.
            send_last_n_segments (int, optional): Number of most recent segments to send to the client. Defaults to 10.
            no_speech_thresh (float, optional): Segments with no speech probability above this threshold will be discarded. Defaults to 0.45.
            clip_audio (bool, optional): Whether to clip audio with no valid segments. Defaults to False.
            same_output_threshold (int, optional): Number of repeated outputs before considering it as a valid segment. Defaults to 10.
            transcription_callback (callable, optional): A callback function to handle transcription results. Default is None.
            enable_translation (float, optional): Whether to enable translation from any to any language. Defaults to False.
            target_language (str, optional): Target language for translation. Defaults to 'fr'.
            translation_callback (callable, optional): A callback function to handle translation results. Default is None.
            translation_srt_file_path (str, optional): The file path to save the translated output SRT file. Default is "output_translated.srt".
        """
        self.recording = False
        self.task = "transcribe"
        self.uid = str(uuid.uuid4())
        self.waiting = False
        self.last_response_received = None
        self.disconnect_if_no_response_for = 15
        self.language = lang
        self.model = model
        self.server_error = False
        self.srt_file_path = srt_file_path
        self.use_vad = use_vad
        self.use_wss = use_wss
        self.last_segment = None
        self.last_received_segment = None
        self.log_transcription = log_transcription
        self.send_last_n_segments = send_last_n_segments
        self.no_speech_thresh = no_speech_thresh
        self.clip_audio = clip_audio
        self.same_output_threshold = same_output_threshold
        self.transcription_callback = transcription_callback

        # Translation-specific attributes
        self.enable_translation = enable_translation
        self.target_language = target_language
        self.translation_callback = translation_callback
        self.translation_srt_file_path = translation_srt_file_path
        self.last_translated_segment = None
        if translate:
            self.task = "translate"

        self.audio_bytes = None

        if host is not None and port is not None:
            socket_protocol = 'wss' if self.use_wss else "ws"
            socket_url = f"{socket_protocol}://{host}:{port}"
            self.client_socket = websocket.WebSocketApp(
                socket_url,
                on_open=lambda ws: self.on_open(ws),
                on_message=lambda ws, message: self.on_message(ws, message),
                on_error=lambda ws, error: self.on_error(ws, error),
                on_close=lambda ws, close_status_code, close_msg: self.on_close(
                    ws, close_status_code, close_msg
                ),
            )
        else:
            print("[ERROR]: No host or port specified.")
            return

        Client.INSTANCES[self.uid] = self

        # start websocket client in a thread
        self.ws_thread = threading.Thread(target=self.client_socket.run_forever)
        self.ws_thread.daemon = True
        self.ws_thread.start()

        self.transcript = []
        self.translated_transcript = []
        print("[INFO]: * recording")

    def handle_status_messages(self, message_data):
        """Handles server status messages."""
        status = message_data["status"]
        if status == "WAIT":
            self.waiting = True
            print(f"[INFO]: Server is full. Estimated wait time {round(message_data['message'])} minutes.")
        elif status == "ERROR":
            print(f"Message from Server: {message_data['message']}")
            self.server_error = True
        elif status == "WARNING":
            print(f"Message from Server: {message_data['message']}")

    def process_segments(self, segments, translated=False):
        """Processes transcript segments."""
        text = []
        for i, seg in enumerate(segments):
            if not text or text[-1] != seg["text"]:
                text.append(seg["text"].strip())
                if i == len(segments) - 1 and not seg.get("completed", False):
                    self.last_segment = seg
                elif self.server_backend == "faster_whisper" and seg.get("completed", False):
                    if translated:
                        if (not self.translated_transcript or float(seg['start']) >= float(self.translated_transcript[-1]['end'])):
                            self.translated_transcript.append(seg)
                    else:
                        if (not self.transcript or float(seg['start']) >= float(self.transcript[-1]['end'])):
                            self.transcript.append(seg)
        # update last received segment and last valid response time
        if not translated:
            if self.last_received_segment is None or self.last_received_segment != segments[-1]["text"]:
                self.last_response_received = time.time()
                self.last_received_segment = segments[-1]["text"]

        # call the transcription callback if provided
        if translated:
            if self.translation_callback and callable(self.translation_callback):
                try:
                    self.translation_callback(" ".join(text), segments) # string, list
                except Exception as e:
                    print(f"[WARN] translation_callback raised: {e}")
                return
        else:
            if self.transcription_callback and callable(self.transcription_callback):
                try:
                    self.transcription_callback(" ".join(text), segments) # string, list
                except Exception as e:
                    print(f"[WARN] transcription_callback raised: {e}")
                return
        
        if self.log_transcription:
            original_text = [seg["text"] for seg in self.transcript[-4:]]
            if self.last_segment is not None and self.last_segment["text"] not in original_text:
                original_text.append(self.last_segment["text"])
            
            # utils.clear_screen()
            utils.print_transcript(original_text)
            if self.enable_translation:
                print(f"\n\nTRANSLATION to {self.target_language}:")
                utils.print_transcript([seg["text"] for seg in self.translated_transcript[-4:]], translated=True)

    def on_message(self, ws, message):
        """
        Callback function called when a message is received from the server.

        It updates various attributes of the client based on the received message, including
        recording status, language detection, and server messages. If a disconnect message
        is received, it sets the recording status to False.

        Args:
            ws (websocket.WebSocketApp): The WebSocket client instance.
            message (str): The received message from the server.

        """
        message = json.loads(message)

        if self.uid != message.get("uid"):
            print("[ERROR]: invalid client uid")
            return

        if "status" in message.keys():
            self.handle_status_messages(message)
            return

        if "message" in message.keys() and message["message"] == "DISCONNECT":
            print("[INFO]: Server disconnected due to overtime.")
            self.recording = False

        if "message" in message.keys() and message["message"] == "SERVER_READY":
            self.last_response_received = time.time()
            self.recording = True
            self.server_backend = message["backend"]
            print(f"[INFO]: Server Running with backend {self.server_backend}")
            return

        if "language" in message.keys():
            self.language = message.get("language")
            lang_prob = message.get("language_prob")
            print(
                f"[INFO]: Server detected language {self.language} with probability {lang_prob}"
            )
            return

        if "segments" in message.keys():
            self.process_segments(message["segments"])
        
        if "translated_segments" in message.keys():
            self.process_segments(message["translated_segments"], translated=True)

    def on_error(self, ws, error):
        print(f"[ERROR] WebSocket Error: {error}")
        self.server_error = True
        self.error_message = error

    def on_close(self, ws, close_status_code, close_msg):
        print(f"[INFO]: Websocket connection closed: {close_status_code}: {close_msg}")
        self.recording = False
        self.waiting = False

    def on_open(self, ws):
        """
        Callback function called when the WebSocket connection is successfully opened.

        Sends an initial configuration message to the server, including client UID,
        language selection, and task type.

        Args:
            ws (websocket.WebSocketApp): The WebSocket client instance.

        """
        print("[INFO]: Opened connection")
        ws.send(
            json.dumps(
                {
                    "uid": self.uid,
                    "language": self.language,
                    "task": self.task,
                    "model": self.model,
                    "use_vad": self.use_vad,
                    "send_last_n_segments": self.send_last_n_segments,
                    "no_speech_thresh": self.no_speech_thresh,
                    "clip_audio": self.clip_audio,
                    "same_output_threshold": self.same_output_threshold,
                    "enable_translation": self.enable_translation,
                    "target_language": self.target_language,
                }
            )
        )

    def send_packet_to_server(self, message):
        """
        Send an audio packet to the server using WebSocket.

        Args:
            message (bytes): The audio data packet in bytes to be sent to the server.

        """
        try:
            self.client_socket.send(message, websocket.ABNF.OPCODE_BINARY)
        except Exception as e:
            print(e)

    def close_websocket(self):
        """
        Close the WebSocket connection and join the WebSocket thread.

        First attempts to close the WebSocket connection using `self.client_socket.close()`. After
        closing the connection, it joins the WebSocket thread to ensure proper termination.

        """
        try:
            self.client_socket.close()
        except Exception as e:
            print("[ERROR]: Error closing WebSocket:", e)

        try:
            self.ws_thread.join()
        except Exception as e:
            print("[ERROR:] Error joining WebSocket thread:", e)

    def get_client_socket(self):
        """
        Get the WebSocket client socket instance.

        Returns:
            WebSocketApp: The WebSocket client socket instance currently in use by the client.
        """
        return self.client_socket

    def write_srt_file(self, output_path="output.srt"):
        """
        Writes out the transcript in .srt format.

        Args:
            message (output_path, optional): The path to the target file.  Default is "output.srt".

        """
        if self.server_backend == "faster_whisper":
            if not self.transcript and self.last_segment is not None:
                self.transcript.append(self.last_segment)
            elif self.last_segment and self.transcript[-1]["text"] != self.last_segment["text"]:
                self.transcript.append(self.last_segment)
            utils.create_srt_file(self.transcript, output_path)

        if self.enable_translation:
            utils.create_srt_file(self.translated_transcript, self.translation_srt_file_path)

    def wait_before_disconnect(self):
        """Waits a bit before disconnecting in order to process pending responses."""
        assert self.last_response_received
        while time.time() - self.last_response_received < self.disconnect_if_no_response_for:
            continue


class TranscriptionTeeClient:
    """
    Client for handling audio recording, streaming, and transcription tasks via one or more
    WebSocket connections.

    Acts as a high-level client for audio transcription tasks using a WebSocket connection. It can be used
    to send audio data for transcription to one or more servers, and receive transcribed text segments.
    Args:
        clients (list): one or more previously initialized Client instances

    Attributes:
        clients (list): the underlying Client instances responsible for handling WebSocket connections.
    """
    def __init__(self, clients, save_output_recording=False, output_recording_filename="./output_recording.wav", mute_audio_playback=False):
        self.clients = clients
        if not self.clients:
            raise Exception("At least one client is required.")
        self.chunk = 4096
        self.format = pyaudio.paInt16
        self.channels = 1
        self.rate = 16000
        self.record_seconds = 60000
        self.save_output_recording = save_output_recording
        self.output_recording_filename = output_recording_filename
        self.mute_audio_playback = mute_audio_playback
        self.frames = b""
        self.p = pyaudio.PyAudio()
        try:
            self.stream = self.p.open(
                format=self.format,
                channels=self.channels,
                rate=self.rate,
                input=True,
                frames_per_buffer=self.chunk,
            )
        except OSError as error:
            print(f"[WARN]: Unable to access microphone. {error}")
            self.stream = None

    def __call__(self, audio=None, rtsp_url=None, hls_url=None, save_file=None):
        """
        Start the transcription process.

        Initiates the transcription process by connecting to the server via a WebSocket. It waits for the server
        to be ready to receive audio data and then sends audio for transcription. If an audio file is provided, it
        will be played and streamed to the server; otherwise, it will perform live recording.

        Args:
            audio (str, optional): Path to an audio file for transcription. Default is None, which triggers live recording.

        """
        assert sum(
            source is not None for source in [audio, rtsp_url, hls_url]
        ) <= 1, 'You must provide only one selected source'

        print("[INFO]: Waiting for server ready ...")
        for client in self.clients:
            while not client.recording:
                if client.waiting or client.server_error:
                    self.close_all_clients()
                    return

        print("[INFO]: Server Ready!")
        if hls_url is not None:
            self.process_hls_stream(hls_url, save_file)
        elif audio is not None:
            resampled_file = utils.resample(audio)
            self.play_file(resampled_file)
        elif rtsp_url is not None:
            self.process_rtsp_stream(rtsp_url)
        else:
            self.record()

    def close_all_clients(self):
        """Closes all client websockets."""
        for client in self.clients:
            client.close_websocket()

    def write_all_clients_srt(self):
        """Writes out .srt files for all clients."""
        for client in self.clients:
            client.write_srt_file(client.srt_file_path)

    def multicast_packet(self, packet, unconditional=False):
        """
        Sends an identical packet via all clients.

        Args:
            packet (bytes): The audio data packet in bytes to be sent.
            unconditional (bool, optional): If true, send regardless of whether clients are recording.  Default is False.
        """
        for client in self.clients:
            if (unconditional or client.recording):
                client.send_packet_to_server(packet)

    def play_file(self, filename):
        """
        Play an audio file and send it to the server for processing.

        Reads an audio file, plays it through the audio output, and simultaneously sends
        the audio data to the server for processing. It uses PyAudio to create an audio
        stream for playback. The audio data is read from the file in chunks, converted to
        floating-point format, and sent to the server using WebSocket communication.
        This method is typically used when you want to process pre-recorded audio and send it
        to the server in real-time.

        Args:
            filename (str): The path to the audio file to be played and sent to the server.
        """

        # read audio and create pyaudio stream
        with wave.open(filename, "rb") as wavfile:
            if self.mute_audio_playback:
                self.stream = None
            else:
                self.stream = self.p.open(
                    format=self.p.get_format_from_width(wavfile.getsampwidth()),
                    channels=wavfile.getnchannels(),
                    rate=wavfile.getframerate(),
                    input=True,
                    output=True,
                    frames_per_buffer=self.chunk,
                )

            chunk_duration = self.chunk / float(wavfile.getframerate())
            try:
                while any(client.recording for client in self.clients):
                    data = wavfile.readframes(self.chunk)
                    if data == b"":
                        break

                    audio_array = self.bytes_to_float_array(data)
                    self.multicast_packet(audio_array.tobytes())
                    if self.mute_audio_playback:
                        time.sleep(chunk_duration)
                    else:
                        self.stream.write(data)
    
                wavfile.close()

                for client in self.clients:
                    client.wait_before_disconnect()
                self.multicast_packet(Client.END_OF_AUDIO.encode('utf-8'), True)
                self.write_all_clients_srt()
                if self.stream:
                    self.stream.close()
                self.close_all_clients()

            except KeyboardInterrupt:
                wavfile.close()
                self.stream.stop_stream()
                self.stream.close()
                self.p.terminate()
                self.close_all_clients()
                self.write_all_clients_srt()
                print("[INFO]: Keyboard interrupt.")

    def process_rtsp_stream(self, rtsp_url):
        """
        Connect to an RTSP source, process the audio stream, and send it for transcription.

        Args:
            rtsp_url (str): The URL of the RTSP stream source.
        """
        print("[INFO]: Connecting to RTSP stream...")
        try:
            container = av.open(rtsp_url, format="rtsp", options={"rtsp_transport": "tcp"})
            self.process_av_stream(container, stream_type="RTSP")
        except Exception as e:
            print(f"[ERROR]: Failed to process RTSP stream: {e}")
        finally:
            for client in self.clients:
                client.wait_before_disconnect()
            self.multicast_packet(Client.END_OF_AUDIO.encode('utf-8'), True)
            self.close_all_clients()
            self.write_all_clients_srt()
        print("[INFO]: RTSP stream processing finished.")

    def process_hls_stream(self, hls_url, save_file=None):
        """
        Connect to an HLS source, process the audio stream, and send it for transcription.

        Args:
            hls_url (str): The URL of the HLS stream source.
            save_file (str, optional): Local path to save the network stream.
        """
        print("[INFO]: Connecting to HLS stream...")
        try:
            container = av.open(hls_url, format="hls")
            self.process_av_stream(container, stream_type="HLS", save_file=save_file)
        except Exception as e:
            print(f"[ERROR]: Failed to process HLS stream: {e}")
        finally:
            for client in self.clients:
                client.wait_before_disconnect()
            self.multicast_packet(Client.END_OF_AUDIO.encode('utf-8'), True)
            self.close_all_clients()
            self.write_all_clients_srt()
        print("[INFO]: HLS stream processing finished.")

    def process_av_stream(self, container, stream_type, save_file=None):
        """
        Process an AV container stream and send audio packets to the server.

        Args:
            container (av.container.InputContainer): The input container to process.
            stream_type (str): The type of stream being processed ("RTSP" or "HLS").
            save_file (str, optional): Local path to save the stream. Default is None.
        """
        audio_stream = next((s for s in container.streams if s.type == "audio"), None)
        if not audio_stream:
            print(f"[ERROR]: No audio stream found in {stream_type} source.")
            return

        output_container = None
        if save_file:
            output_container = av.open(save_file, mode="w")
            output_audio_stream = output_container.add_stream(codec_name="pcm_s16le", rate=self.rate)

        try:
            for packet in container.demux(audio_stream):
                for frame in packet.decode():
                    audio_data = frame.to_ndarray().tobytes()
                    self.multicast_packet(audio_data)

                    if save_file:
                        output_container.mux(frame)
        except Exception as e:
            print(f"[ERROR]: Error during {stream_type} stream processing: {e}")
        finally:
            # Wait for server to send any leftover transcription.
            time.sleep(5)
            self.multicast_packet(Client.END_OF_AUDIO.encode('utf-8'), True)
            if output_container:
                output_container.close()
            container.close()

    def save_chunk(self, n_audio_file):
        """
        Saves the current audio frames to a WAV file in a separate thread.

        Args:
        n_audio_file (int): The index of the audio file which determines the filename.
                            This helps in maintaining the order and uniqueness of each chunk.
        """
        t = threading.Thread(
            target=self.write_audio_frames_to_file,
            args=(self.frames[:], f"chunks/{n_audio_file}.wav",),
        )
        t.start()

    def finalize_recording(self, n_audio_file):
        """
        Finalizes the recording process by saving any remaining audio frames,
        closing the audio stream, and terminating the process.

        Args:
        n_audio_file (int): The file index to be used if there are remaining audio frames to be saved.
                            This index is incremented before use if the last chunk is saved.
        """
        if self.save_output_recording and len(self.frames):
            self.write_audio_frames_to_file(
                self.frames[:], f"chunks/{n_audio_file}.wav"
            )
            n_audio_file += 1
        self.stream.stop_stream()
        self.stream.close()
        self.p.terminate()
        self.close_all_clients()
        if self.save_output_recording:
            self.write_output_recording(n_audio_file)
        self.write_all_clients_srt()

    def record(self):
        """
        Record audio data from the input stream and save it to a WAV file.

        Continuously records audio data from the input stream, sends it to the server via a WebSocket
        connection, and simultaneously saves it to multiple WAV files in chunks. It stops recording when
        the `RECORD_SECONDS` duration is reached or when the `RECORDING` flag is set to `False`.

        Audio data is saved in chunks to the "chunks" directory. Each chunk is saved as a separate WAV file.
        The recording will continue until the specified duration is reached or until the `RECORDING` flag is set to `False`.
        The recording process can be interrupted by sending a KeyboardInterrupt (e.g., pressing Ctrl+C). After recording,
        the method combines all the saved audio chunks into the specified `out_file`.
        """
        n_audio_file = 0
        if self.save_output_recording:
            if os.path.exists("chunks"):
                shutil.rmtree("chunks")
            os.makedirs("chunks")
        try:
            for _ in range(0, int(self.rate / self.chunk * self.record_seconds)):
                if not any(client.recording for client in self.clients):
                    break
                data = self.stream.read(self.chunk, exception_on_overflow=False)
                self.frames += data

                audio_array = self.bytes_to_float_array(data)

                self.multicast_packet(audio_array.tobytes())

                # save frames if more than a minute
                if len(self.frames) > 60 * self.rate:
                    if self.save_output_recording:
                        self.save_chunk(n_audio_file)
                        n_audio_file += 1
                    self.frames = b""
            self.write_all_clients_srt()

        except KeyboardInterrupt:
            self.finalize_recording(n_audio_file)

    def write_audio_frames_to_file(self, frames, file_name):
        """
        Write audio frames to a WAV file.

        The WAV file is created or overwritten with the specified name. The audio frames should be
        in the correct format and match the specified channel, sample width, and sample rate.

        Args:
            frames (bytes): The audio frames to be written to the file.
            file_name (str): The name of the WAV file to which the frames will be written.

        """
        with wave.open(file_name, "wb") as wavfile:
            wavfile: wave.Wave_write
            wavfile.setnchannels(self.channels)
            wavfile.setsampwidth(2)
            wavfile.setframerate(self.rate)
            wavfile.writeframes(frames)

    def write_output_recording(self, n_audio_file):
        """
        Combine and save recorded audio chunks into a single WAV file.

        The individual audio chunk files are expected to be located in the "chunks" directory. Reads each chunk
        file, appends its audio data to the final recording, and then deletes the chunk file. After combining
        and saving, the final recording is stored in the specified `out_file`.


        Args:
            n_audio_file (int): The number of audio chunk files to combine.
            out_file (str): The name of the output WAV file to save the final recording.

        """
        input_files = [
            f"chunks/{i}.wav"
            for i in range(n_audio_file)
            if os.path.exists(f"chunks/{i}.wav")
        ]
        with wave.open(self.output_recording_filename, "wb") as wavfile:
            wavfile: wave.Wave_write
            wavfile.setnchannels(self.channels)
            wavfile.setsampwidth(2)
            wavfile.setframerate(self.rate)
            for in_file in input_files:
                with wave.open(in_file, "rb") as wav_in:
                    while True:
                        data = wav_in.readframes(self.chunk)
                        if data == b"":
                            break
                        wavfile.writeframes(data)
                # remove this file
                os.remove(in_file)
        wavfile.close()
        # clean up temporary directory to store chunks
        if os.path.exists("chunks"):
            shutil.rmtree("chunks")

    @staticmethod
    def bytes_to_float_array(audio_bytes):
        """
        Convert audio data from bytes to a NumPy float array.

        It assumes that the audio data is in 16-bit PCM format. The audio data is normalized to
        have values between -1 and 1.

        Args:
            audio_bytes (bytes): Audio data in bytes.

        Returns:
            np.ndarray: A NumPy array containing the audio data as float values normalized between -1 and 1.
        """
        raw_data = np.frombuffer(buffer=audio_bytes, dtype=np.int16)
        return raw_data.astype(np.float32) / 32768.0


class TranscriptionClient(TranscriptionTeeClient):
    """
    Client for handling audio transcription tasks via a single WebSocket connection.

    Acts as a high-level client for audio transcription tasksoutput_transcription_path using a WebSocket connection. It can be used
    to send audio data for transcription to a server and receive transcribed text segments.

    Args:
        host (str): The hostname or IP address of the server.
        port (int): The port number to connect to on the server.
        lang (str, optional): The primary language for transcription. Default is None, which defaults to English ('en').
        translate (bool, optional): If True, the task will be translation instead of transcription. Default is False.
        model (str, optional): The whisper model to use (e.g., "small", "base"). Default is "small".
        use_vad (bool, optional): Whether to enable voice activity detection. Default is True.
        save_output_recording (bool, optional): Whether to save the microphone recording. Default is False.
        output_recording_filename (str, optional): Path to save the output recording WAV file. Default is "./output_recording.wav".
        output_transcription_path (str, optional): File path to save the output transcription (SRT file). Default is "./output.srt".
        log_transcription (bool, optional): Whether to log transcription output to the console. Default is True.
        mute_audio_playback (bool, optional): If True, mutes audio playback during file playback. Default is False.
        send_last_n_segments (int, optional): Number of most recent segments to send to the client. Defaults to 10.
        no_speech_thresh (float, optional): Segments with no speech probability above this threshold will be discarded. Defaults to 0.45.
        clip_audio (bool, optional): Whether to clip audio with no valid segments. Defaults to False.
        same_output_threshold (int, optional): Number of repeated outputs before considering it as a valid segment. Defaults to 10.
        transcription_callback (callable, optional): A callback function to handle transcription results. Default is None.
        enable_translation (float, optional): Whether to enable translation from any to any language. Defaults to False.
        target_language (str, optional): Target language for translation. Defaults to 'fr'.
        translation_callback (callable, optional): A callback function to handle translation results. Default is None.
        translation_srt_file_path (str, optional): The file path to save the translated output SRT file. Default is "output_translated.srt".

    Attributes:
        client (Client): An instance of the underlying Client class responsible for handling the WebSocket connection.

    Example:
        To create a TranscriptionClient and start transcription on microphone audio:
        ```python
        transcription_client = TranscriptionClient(host="localhost", port=9090)
        transcription_client()
        ```
    """
    def __init__(
        self,
        host,
        port,
        lang=None,
        translate=False,
        model="small",
        use_vad=True,
        use_wss=False,
        save_output_recording=False,
        output_recording_filename="./output_recording.wav",
        output_transcription_path="./output.srt",
        log_transcription=True,
        mute_audio_playback=False,
        send_last_n_segments=10,
        no_speech_thresh=0.45,
        clip_audio=False,
        same_output_threshold=10,
        transcription_callback=None,
        enable_translation=False,
        target_language="fr",
        translation_callback=None,
        translation_srt_file_path="./output_translated.srt",
    ):
        self.client = Client(
            host,
            port,
            lang,
            translate,
            model,
            srt_file_path=output_transcription_path,
            use_vad=use_vad,
            use_wss=use_wss,
            log_transcription=log_transcription,
            send_last_n_segments=send_last_n_segments,
            no_speech_thresh=no_speech_thresh,
            clip_audio=clip_audio,
            same_output_threshold=same_output_threshold,
            transcription_callback=transcription_callback,
            enable_translation=enable_translation,
            target_language=target_language,
            translation_callback=translation_callback,
            translation_srt_file_path=translation_srt_file_path,
        )

        if save_output_recording and not output_recording_filename.endswith(".wav"):
            raise ValueError(f"Please provide a valid `output_recording_filename`: {output_recording_filename}")
        if not output_transcription_path.endswith(".srt"):
            raise ValueError(f"Please provide a valid `output_transcription_path`: {output_transcription_path}. The file extension should be `.srt`.")
        if not translation_srt_file_path.endswith(".srt"):
            raise ValueError(f"Please provide a valid `translation_srt_file_path`: {translation_srt_file_path}. The file extension should be `.srt`.")
        TranscriptionTeeClient.__init__(
            self,
            [self.client],
            save_output_recording=save_output_recording,
            output_recording_filename=output_recording_filename,
            mute_audio_playback=mute_audio_playback
        )

the logs on the client side:

python run_client.py --rtsp rtsp://127.0.0.1:8554/mic
[INFO]: * recording
ALSA lib pcm.c:2721:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.rear
ALSA lib pcm.c:2721:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.center_lfe
ALSA lib pcm.c:2721:(snd_pcm_open_noupdate) Unknown PCM cards.pcm.side
ALSA lib pcm_route.c:878:(find_matching_chmap) Found no matching channel map
ALSA lib pcm_route.c:878:(find_matching_chmap) Found no matching channel map
ALSA lib pcm_route.c:878:(find_matching_chmap) Found no matching channel map
ALSA lib pcm_route.c:878:(find_matching_chmap) Found no matching channel map
Cannot connect to server socket err = No such file or directory
Cannot connect to server request channel
jack server is not running or cannot be started
JackShmReadWritePtr::~JackShmReadWritePtr - Init not done for -1, skipping unlock
JackShmReadWritePtr::~JackShmReadWritePtr - Init not done for -1, skipping unlock
[INFO]: Waiting for server ready ...
[INFO]: Opened connection
[INFO]: Server Running with backend faster_whisper
[INFO]: Server Ready!
[INFO]: Connecting to RTSP stream...
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 and put them away in the cupboard. The little girl and
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 and put them away in the cupboard. The little girl and
Captain Bill
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Captain Bill
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Captain Bill start
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Captain Bill started
 and put them away in the cupboard. The little girl and
Captain Bill started out
 and put them away in the cupboard. The little girl and
Captain Bill started out
 and put them away in the cupboard. The little girl and
Captain Bill started out

[makaveli10]

@gasparuben the translate=True is the whisper's inherent translation that works for any language to English translation.
enable-translation, translation-language is an external translation model that works alongside whisper to translate from any language to any language we use alirezamsh/small100 so, language support can be checkout here.

[andreclerigo]

It seems like the issue for me was using an older version of the client. Did a fresh clone and can confirm that it is working as expected @gasparuben.

[andreclerigo]

@makaveli10, regarding the translation model, should we expect an impact on latency when this flag is activated?

[makaveli10]

@andreclerigo It shouldnt effect the transcription part but the translation itself is not optimized. So, would be a bit slower

[gasparuben]

Thank you @ALL for the follow-up. I am off until September 2nd. I may have an old client, but if I recall correctly, I did a clone of the project and just set up an HLS and RTSP stream using Wowza, but I agree that it should work in the same way as your example.
I will retry once I'm back at the office.
Thanks again,
Ruben

[gasparuben]

HI everyone,

I still have the same behavior. As it looks related to my setup, please let me be as verbose as possible to explain what I have done.

I am running on a VM with an A100 GPU:

%docker run -it --gpus all -p 9090:9090 -v /usr/local/src/faster-whisper-small:/models/faster-whisper-small -e MODEL=/models/faster-whisper-small --network host ghcr.io/collabora/whisperlive-gpu:latest
Previously, I downloaded Whisper Small locally:

%git clone https://huggingface.co/Systran/faster-whisper-small

This was done to avoid any issues accessing Hugging Face.

From my Mac, I installed the latest version:

%pip freeze | grep live
whisper-live==0.7.1
Python version:
python --version
Python 3.10.5

from whisper_live.client import TranscriptionClient
client = TranscriptionClient(
"ttaasexec03.cern.ch",
9090,
lang="en",
translate=False,
model="small",                                      # also supports hf_model => Systran/faster-whisper-small
use_vad=False,
save_output_recording=False,                        # Only used for microphone input, False by default
output_recording_filename="./output_recording.wav", # Only used for microphone input
mute_audio_playback=False,                          # Only used for file input, False by default
)

Then I try to transcribe HLS or RTSP from my Wowza infrastructure:

client(hls_url="https://wowzaqaedge.cern.ch/liveoutside/smil:1273117_camera_all.smil/playlist.m3u8")
client(rtsp_url="rtsp://wowzaqaorigin.cern.ch:1935/liveoutside/definst/1273117_camera")

Despite seeing my GPU server-side doing work, I don't see the same output on the screen as when I use a local microphone or MP3 file.
Since I have a somewhat cumbersome setup from home to access my institute's resources, I have also tried with a local RTSP following your example:

%docker run --rm -it -p 8554:8554/tcp -p 8000:8000/udp -p 8001:8001/udp bluenviron/mediamtx:latest

%ffmpeg -re -i 856696c138.mp4 
-c:a aac -b:a 64k -ar 48000 -ac 2 
-f rtsp -rtsp_transport tcp rtsp://127.0.0.1:8554/mic

However, I have the same result with:

%client(rtsp_url="rtsp://127.0.0.1:8554/mic")
I can see the server GPU working, but I don't see the output clearly displayed on my Mac console.
Please let me know if you have any suggestions.

Thank you for your support!

[gasparuben]

hi again,

I have now used the client code that @makaveli10 posted but nevertheless I have the same behaviour.
I am using a A100 GPU card on a different server.

So on my mac I do:

from run_client import TranscriptionClient
client = TranscriptionClient(
  "XXXXXX",
  9090,
  lang="en",
  translate=False,
  model="small",                                      # also support hf_model => `Systran/faster-whisper-small`
  use_vad=False,
  save_output_recording=False,                         # Only used for microphone input, False by Default
  output_recording_filename="./output_recording.wav", # Only used for microphone input
  mute_audio_playback=False,                          # Only used for file input, False by Default
)
INFO]: * recording
>>> [INFO]: Opened connection
[INFO]: Server Running with backend faster_whisper
client(rtsp_url="rtsp://127.0.0.1:8554/mic")
[INFO]: Waiting for server ready ...
[INFO]: Server Ready!
[INFO]: Connecting to RTSP stream...
[INFO]: Server disconnected due to overtime.
[INFO]: Websocket connection closed: 1000: 
Connection is already closed.
Connection is already closed.
[INFO]: RTSP stream processing finished.

On the server I can see:

..
INFO:faster_whisper:Processing audio with duration 00:15.312
INFO:faster_whisper:Processing audio with duration 00:15.952
INFO:faster_whisper:Processing audio with duration 00:16.336
INFO:faster_whisper:Processing audio with duration 00:16.848
INFO:faster_whisper:Processing audio with duration 00:16.848
INFO:faster_whisper:Processing audio with duration 00:17.360
INFO:faster_whisper:Processing audio with duration 00:17.872
INFO:faster_whisper:Processing audio with duration 00:18.384
INFO:faster_whisper:Processing audio with duration 00:19.408
INFO:faster_whisper:Processing audio with duration 00:20.048
WARNING:root:Client with uid '7146041e-8c2e-454a-be26-ddfb2941fb4d' disconnected due to overtime.
INFO:root:Cleaning up.
INFO:root:Exiting speech to text thread

So it does inference for a while and then stops due to some overtime parameter. The problem is that I dont see any text back on the client. I dont have a firewall on my mac (for those texts).

I am using Python 3.10.5, could that be an issue?

Thank you

[makaveli10]

@gasparuben Hello, Its hard to say whats going wrong, if i can get the rtsp stream, i can try to debug it on my end probably some time this week, if that works for you. One strange behavior is disconnecting due to overtime after 20 seconds, because the default overtime limit is like 600 seconds. Make sure you use the main branch but happy to take a look if you can pass the rtsp stream over. Thanks.

[gasparuben]

hi @makaveli10, thank you for having a look to it.
The issue I am facing it's reproducible on both using my wowza RTSP or the local RTSP server as you exposed on your test. I also tried to used the client from the python module:

from whisper_live.client import TranscriptionClient
client = TranscriptionClient(..

or the local one you did provide on your test:

from run_client import TranscriptionClient
client = TranscriptionClient(..

I think I am using all the latest versions:

% git branch -v
* main 5e6be74 Merge pull request #391 from makaveli10/integrate_live_translation
% git pull origin main 
From https://github.com/collabora/WhisperLive
 * branch            main       -> FETCH_HEAD
Already up to date.
% pip freeze | grep -i whisper
faster-whisper==1.1.0
whisper-live==0.7.1

I am happy to enable an RTSP from our Wowza qa but this is something I see on my local RTSP server too. I use ssh tuneling to communicate to my GPU's server as it's in the institute intranet but I have the impression the communication is well stablished. On Friday, 19.09, I will try to run the test from inside the institute. Nevertheless, I dont expect a change in behaviour.

Thank you for your support.
Ruben

[faxm0dem]

Probably unrelated, but @gasparuben I had a similar issue to "The problem is that I dont see any text back on the client" I activated save_output_recording and it helped me see that my microphone source was bad