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def merge_video_with_audio_ffmpeg(videoFilePath,audioFilePath,filePathOutput,start_time_audio="00:00:05"):
    subprocess.call(['ffmpeg', '-i', videoFilePath,
                     '-itsoffset', start_time_audio,
                     '-i', audioFilePath,
                     '-c:v', 'copy',
                     '-map', '0:v:0',
                     '-map', '1:a:0',
                     filePathOutput, '-y'])

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def text_to_speech(speak, languageCode, outputFilePath, speed=1.0):
    """Synthesizes speech from the input string of text or ssml.

    Note: ssml must be well-formed according to:
        https://www.w3.org/TR/speech-synthesis/
    """
    from google.cloud import texttospeech

    # Instantiates a client
    client = texttospeech.TextToSpeechClient()

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def translate(text,language):
    from google.cloud import translate_v2 as translate
    translate_client = translate.Client()

    if isinstance(text, bytes):
        text = text.decode('utf-8')

    # Text can also be a sequence of strings, in which case this method
    # will return a sequence of results for each text.
    result = translate_client.translate(

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def upload_blob(bucket_name, source_file_name, destination_blob_name):
    """Uploads a file to the bucket."""
    # bucket_name = "your-bucket-name"
    # source_file_name = "local/path/to/file"
    # destination_blob_name = "storage-object-name"

    storage_client = storage.Client()
    bucket = storage_client.bucket(bucket_name)
    blob = bucket.blob(destination_blob_name)

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def speech_to_text(bucket_name, audio_blob_name):
    client = speech_v1p1beta1.SpeechClient()
    # storage_uri = 'gs://cloud-samples-data/speech/brooklyn_bridge.mp3'
    storage_uri = 'gs://' + bucket_name + '/' + audio_blob_name
    # The language of the supplied audio
    language_code = "en-GB"
    # Sample rate in Hertz of the audio data sent
    sample_rate_hertz = 44100

    encoding = enums.RecognitionConfig.AudioEncoding.MP3