Recherche avancée

Sur d’autres sites (10646)

• SIGSEGV from ffmpeg on Amazon Lambda

  1er août 2024, par Serge

  Trying out Amazon Lambda / nodejs 8. My goal is to launch ffmpeg, generate a short clip and upload it to S3 bucket.

  



  I created the function following the image resize tutorial. Edited the code to get output from simple linux commands like ls or cat /proc/cpuinfo - all works.

  



  Now, added the ffmpeg binary for i686 - ffmpeg static build by JohnVan Sickle (thanks !). Changed the code to launch simple ffmpeg command that is supposed to create sa 2-seconds small video clip.

  



  That fails, according to logs, with the signal SIGSEGV returned to the "close" event handler of child_process.spawn()

  



  As far as I understand, this could be caused by the ffmpeg binary incompatibility with the static build. Or by some mistake in my code.

  



  Several npm modules rely on the static builds from johnvansickle.com/ffmpeg and there are no such issues filed on their github. Maybe there's some other mistake I made ?

  



  Should I compile ffmpeg myself under Amazon Linux AMI amzn-ami-hvm-2017.03.1.20170812-x86_64-gp2 which is under the hood of AWS Lambda ?

  



  ---

  



  upd. Launched EC2 t2.micro instance from the same AMI, downloaded the same ffmpeg static build, and it works just fine from the command line. Now I doubt that it is a compilation issue.

  



  Also tried copying ffmpeg executable to /tmp/ffmpeg and chmod 755 just to make sure.
Running simple ffmpeg --help command via child_process.execSync() returns "Error : Command failed : /tmp/ffmpeg —help"

  



  ---

  



  const join = require('path').join;
const tmpdir = require('os').tmpdir;
const process = require('process');
const fs = require('fs');
const spawn = require('child_process').spawn;
const exec = require('child_process').exec;

const async = require('async');
const AWS = require('aws-sdk');
const util = require('util');

process.env['PATH'] = process.env['PATH'] + ':' + process.env['LAMBDA_TASK_ROOT'];


const tempDir = process.env['TEMP'] || tmpdir();
const filename = join(tempDir, 'test.mp4');
const s3 = new AWS.S3();


exports.handler = function(event, context, callback) {
  var dstBucket = srcBucket + "resized";
  var dstKey  = "render-test.mp4";

  async.waterfall([
    function transform(next) {
      var args = [
        '-filter_complex',
        '"testsrc=r=25:s=640x480:d=3"',
        '-an',
        '-y',
        '-hide_banner',
        '-c:v', 'libx264',
        filename,
      ];

      console.log("Will launch ffmpeg");
      const childProcess = spawn('ffmpeg', args);

      childProcess.on('close', function(e) {
        console.log('ffmpeg close event: ' + JSON.stringify(arguments));
        next();
      });

      console.log("After launched ffmpeg");
    },

    function upload(next) {
      ...
    }
  ], function (err) {
    ...
  });
};


  


• How to Stream Audio from Google Cloud Storage in Chunks and Convert Each Chunk to WAV for Whisper Transcription

  14 novembre 2024, par Douglas Landvik

  I'm working on a project where I need to transcribe audio stored in a Google Cloud Storage bucket using OpenAI's Whisper model. The audio is stored in WebM format with Opus encoding, and due to the file size, I'm streaming the audio in 30-second chunks.

  


  To convert each chunk to WAV (16 kHz, mono, 16-bit PCM) compatible with Whisper, I'm using FFmpeg. The first chunk converts successfully, but subsequent chunks fail to convert. I suspect this is because each chunk lacks the WebM container's header, which FFmpeg needs to interpret the Opus codec correctly.

  


  Here’s a simplified version of my approach :

  


  Download Chunk : I download each chunk from GCS as bytes.
Convert with FFmpeg : I pass the bytes to FFmpeg to convert each chunk from WebM/Opus to WAV.

  


  async def handle_transcription_and_notify(
    consultation_service: ConsultationService,
    consultation_processor: ConsultationProcessor,
    consultation: Consultation,
    language: str,
    notes: str,
    clinic_id: str,
    vet_email: str,
    trace_id: str,
    blob_path: str,
    max_retries: int = 3,
    retry_delay: int = 5,
    max_concurrent_tasks: int = 3
):
    """
    Handles the transcription process by streaming the file from GCS, converting to a compatible format, 
    and notifying the client via WebSocket.
    """
    chunk_duration_sec = 30  # 30 seconds per chunk
    logger.info(f"Starting transcription process for consultation {consultation.consultation_id}",
                extra={'trace_id': trace_id})

    # Initialize GCS client
    service_account_key = os.environ.get('SERVICE_ACCOUNT_KEY_BACKEND')
    if not service_account_key:
        logger.error("Service account key not found in environment variables", extra={'trace_id': trace_id})
        await send_discord_alert(
            f"Service account key not found for consultation {consultation.consultation_id}.\nTrace ID: {trace_id}"
        )
        return

    try:
        service_account_info = json.loads(service_account_key)
        credentials = service_account.Credentials.from_service_account_info(service_account_info)
    except Exception as e:
        logger.error(f"Error loading service account credentials: {str(e)}", extra={'trace_id': trace_id})
        await send_discord_alert(
            f"Error loading service account credentials for consultation {consultation.consultation_id}.\nError: {str(e)}\nTrace ID: {trace_id}"
        )
        return

    # Initialize GCS client
    service_account_key = os.environ.get('SERVICE_ACCOUNT_KEY_BACKEND')
    if not service_account_key:
        logger.error("Service account key not found in environment variables", extra={'trace_id': trace_id})
        await send_discord_alert(
            f"Service account key not found for consultation {consultation.consultation_id}.\nTrace ID: {trace_id}"
        )
        return

    try:
        service_account_info = json.loads(service_account_key)
        credentials = service_account.Credentials.from_service_account_info(service_account_info)
    except Exception as e:
        logger.error(f"Error loading service account credentials: {str(e)}", extra={'trace_id': trace_id})
        await send_discord_alert(
            f"Error loading service account credentials for consultation {consultation.consultation_id}.\nError: {str(e)}\nTrace ID: {trace_id}"
        )
        return

    storage_client = storage.Client(credentials=credentials)
    bucket_name = 'vetz_consultations'
    blob = storage_client.bucket(bucket_name).get_blob(blob_path)
    bytes_per_second = 16000 * 2  # 32,000 bytes per second
    chunk_size_bytes = 30 * bytes_per_second
    size = blob.size

    async def stream_blob_in_chunks(blob, chunk_size):
        loop = asyncio.get_running_loop()
        start = 0
        size = blob.size
        while start < size:
            end = min(start + chunk_size - 1, size - 1)
            try:
                logger.info(f"Requesting chunk from {start} to {end}", extra={'trace_id': trace_id})
                chunk = await loop.run_in_executor(
                    None, lambda: blob.download_as_bytes(start=start, end=end)
                )
                if not chunk:
                    break
                logger.info(f"Yielding chunk from {start} to {end}, size: {len(chunk)} bytes",
                            extra={'trace_id': trace_id})
                yield chunk
                start += chunk_size
            except Exception as e:
                logger.error(f"Error downloading chunk from {start} to {end}: {str(e)}", exc_info=True,
                             extra={'trace_id': trace_id})
                raise e

    async def convert_to_wav(chunk_bytes, chunk_idx):
        """
        Convert audio chunk to WAV format compatible with Whisper, ensuring it's 16 kHz, mono, and 16-bit PCM.
        """
        try:
            logger.debug(f"Processing chunk {chunk_idx}: size = {len(chunk_bytes)} bytes")

            detected_format = await detect_audio_format(chunk_bytes)
            logger.info(f"Detected audio format for chunk {chunk_idx}: {detected_format}")
            input_io = io.BytesIO(chunk_bytes)
            output_io = io.BytesIO()

            # ffmpeg command to convert webm/opus to WAV with 16 kHz, mono, and 16-bit PCM

            # ffmpeg command with debug information
            ffmpeg_command = [
                "ffmpeg",
                "-loglevel", "debug",
                "-f", "s16le",            # Treat input as raw PCM data
                "-ar", "48000",           # Set input sample rate
                "-ac", "1",               # Set input to mono
                "-i", "pipe:0",
                "-ar", "16000",           # Set output sample rate to 16 kHz
                "-ac", "1",               # Ensure mono output
                "-sample_fmt", "s16",     # Set output format to 16-bit PCM
                "-f", "wav",              # Output as WAV format
                "pipe:1"
            ]

            process = subprocess.Popen(
                ffmpeg_command,
                stdin=subprocess.PIPE,
                stdout=subprocess.PIPE,
                stderr=subprocess.PIPE
            )

            stdout, stderr = process.communicate(input=input_io.read())

            if process.returncode == 0:
                logger.info(f"FFmpeg conversion completed successfully for chunk {chunk_idx}")
                output_io.write(stdout)
                output_io.seek(0)

                # Save the WAV file locally for listening
                output_dir = "converted_chunks"
                os.makedirs(output_dir, exist_ok=True)
                file_path = os.path.join(output_dir, f"chunk_{chunk_idx}.wav")

                with open(file_path, "wb") as f:
                    f.write(stdout)
                logger.info(f"Chunk {chunk_idx} saved to {file_path}")

                return output_io
            else:
                logger.error(f"FFmpeg failed for chunk {chunk_idx} with return code {process.returncode}")
                logger.error(f"Chunk {chunk_idx} - FFmpeg stderr: {stderr.decode()}")
                return None

        except Exception as e:
            logger.error(f"Unexpected error in FFmpeg conversion for chunk {chunk_idx}: {str(e)}")
            return None

    async def transcribe_chunk(idx, chunk_bytes):
        for attempt in range(1, max_retries + 1):
            try:
                logger.info(f"Transcribing chunk {idx + 1} (attempt {attempt}).", extra={'trace_id': trace_id})

                # Convert to WAV format
                wav_io = await convert_to_wav(chunk_bytes, idx)
                if not wav_io:
                    logger.error(f"Failed to convert chunk {idx + 1} to WAV format.")
                    return ""

                wav_io.name = "chunk.wav"
                chunk_transcription = await consultation_processor.transcribe_audio_whisper(wav_io)
                logger.info(f"Chunk {idx + 1} transcribed successfully.", extra={'trace_id': trace_id})
                return chunk_transcription
            except Exception as e:
                logger.error(f"Error transcribing chunk {idx + 1} (attempt {attempt}): {str(e)}", exc_info=True,
                             extra={'trace_id': trace_id})
                if attempt < max_retries:
                    await asyncio.sleep(retry_delay)
                else:
                    await send_discord_alert(
                        f"Max retries reached for chunk {idx + 1} in consultation {consultation.consultation_id}.\nError: {str(e)}\nTrace ID: {trace_id}"
                    )
                    return ""  # Return empty string for failed chunk

    await notification_manager.send_personal_message(
        f"Consultation {consultation.consultation_id} is being transcribed.", vet_email
    )

    try:
        idx = 0
        full_transcription = []
        async for chunk in stream_blob_in_chunks(blob, chunk_size_bytes):
            transcription = await transcribe_chunk(idx, chunk)
            if transcription:
                full_transcription.append(transcription)
            idx += 1

        combined_transcription = " ".join(full_transcription)
        consultation.full_transcript = (consultation.full_transcript or "") + " " + combined_transcription
        consultation_service.save_consultation(clinic_id, vet_email, consultation)
        logger.info(f"Transcription saved for consultation {consultation.consultation_id}.",
                    extra={'trace_id': trace_id})

    except Exception as e:
        logger.error(f"Error during transcription process: {str(e)}", exc_info=True, extra={'trace_id': trace_id})
        await send_discord_alert(
            f"Error during transcription process for consultation {consultation.consultation_id}.\nError: {str(e)}\nTrace ID: {trace_id}"
        )
        return

    await notification_manager.send_personal_message(
        f"Consultation {consultation.consultation_id} has been transcribed.", vet_email
    )

    try:
        template_service = TemplateService()
        medical_record_template = template_service.get_template_by_name(
            consultation.medical_record_template_id).sections

        sections = await consultation_processor.extract_structured_sections(
            transcription=consultation.full_transcript,
            notes=notes,
            language=language,
            template=medical_record_template,
        )
        consultation.sections = sections
        consultation_service.save_consultation(clinic_id, vet_email, consultation)
        logger.info(f"Sections processed for consultation {consultation.consultation_id}.",
                    extra={'trace_id': trace_id})
    except Exception as e:
        logger.error(f"Error processing sections for consultation {consultation.consultation_id}: {str(e)}",
                     exc_info=True, extra={'trace_id': trace_id})
        await send_discord_alert(
            f"Error processing sections for consultation {consultation.consultation_id}.\nError: {str(e)}\nTrace ID: {trace_id}"
        )
        raise e

    await notification_manager.send_personal_message(
        f"Consultation {consultation.consultation_id} is fully processed.", vet_email
    )
    logger.info(f"Successfully processed consultation {consultation.consultation_id}.",
                extra={'trace_id': trace_id})



  


• How exactly do I extract and merge a audio file using @ffmpeg/ffmpeg

  5 octobre 2022, par jeff.gogooma

  I have a lot of issues about audio transfer to server and frontend.

  


  I am building a solution that is extracting and merging a file received from frontend.

  


  I am using local ffmpeg file with node.js child_process package.

  


  My Env :

  


  framework : nest.js

  


  runtime : node.js

  


  lang : typescript

  


  here is console.log(file : Express.Multer.File).
I am storing to S3 bucket these files. So I am not using multer's memory, disk storage.

  


  // console.log(file);
 {
  fieldname: 'file',
  originalname: 'input1.mp3',
  encoding: '7bit',
  mimetype: 'audio/mpeg'
}


  


  import cp from "child_process";

type Section = { from: number, to: number };

const files: Array = [];

export const setupAudioFileSection = async (file: Express.Multer.File, section: Section) => {
  try {
    const file1 = await cp.exec(`ffmpeg -i ${file} -y -ss 0 -t ${section.from}`);
    const file2 = await cp.exec(`ffmpeg -i ${file} -y -ss ${section.from} -t ${section.to}`);
    const file3 = await cp.exec(`ffmpeg -i ${file} -y -ss ${section.to}`);
    files.push(file1);
    files.push(file3);

    return file2;
  } catch (error) {
    console.log(error);
    throw new Error('setupAudioFileSection error');
  }


  


  But, My Local ffmpeg is printing the error. because of [object Object]. I think local ffmpeg is not recognized the audio file received from client. So, I am resolving this issue by writing a file to disk or memory directly. but I am confusing this solution whether is right or not.

  


  error Error: Command failed: ffmpeg -i [object Object] -y -ss 10 -t 20 -f mp3 ./TestOutput
ffmpeg version 5.1.1 Copyright (c) 2000-2022 the FFmpeg developers
  built with Apple clang version 13.1.6 (clang-1316.0.21.2.5)
  configuration: --prefix=/opt/homebrew/Cellar/ffmpeg/5.1.1 --enable-shared --enable-pthreads --enable-version3 --cc=clang --host-cflags= --host-ldflags= --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libbluray --enable-libdav1d --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librist --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvmaf --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-videotoolbox --enable-neon
  libavutil      57. 28.100 / 57. 28.100
  libavcodec     59. 37.100 / 59. 37.100
  libavformat    59. 27.100 / 59. 27.100
  libavdevice    59.  7.100 / 59.  7.100
  libavfilter     8. 44.100 /  8. 44.100
  libswscale      6.  7.100 /  6.  7.100
  libswresample   4.  7.100 /  4.  7.100
  libpostproc    56.  6.100 / 56.  6.100

[object: No such file or directory



  


  How can I write file on my server and read ? if that can be, I can resolve [object Object] of ffmpeg issue maybe. Please some answer or advise my issue. Thank you !