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# Install the latest release of Haystack in your own environment
#! pip install farm-haystack

# Install the latest main of Haystack
!pip install --upgrade pip
!pip install git+[colab]

# Imports needed to run this notebook

from pprint import pprint
from tqdm import tqdm
from haystack.nodes import QuestionGenerator, BM25Retriever, FARMReader
from haystack.document_stores import ElasticsearchDocumentStore
from haystack.pipelines import (
from haystack.utils import launch_es, print_questions
 # Option 2: In Colab / No Docker environments: Start Elasticsearch from source
! wget -q
! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
! chown -R daemon:daemon elasticsearch-7.9.2

import os
from subprocess import Popen, PIPE, STDOUT

es_server = Popen(
    ["elasticsearch-7.9.2/bin/elasticsearch"], stdout=PIPE, stderr=STDOUT, preexec_fn=lambda: os.setuid(1)  # as daemon
# wait until ES has started
! sleep 30

!tar -xvf xpdf-tools-linux-4.04.tar.gz && sudo cp xpdf-tools-linux-4.04/bin64/pdftotext /usr/local/bin

from haystack.nodes import TextConverter, PDFToTextConverter, DocxToTextConverter, PreProcessor

converter = TextConverter(remove_numeric_tables=True, valid_languages=["nl"])
doc_txt = converter.convert(file_path="/content/data/Chatbot_BVO DDK_22092022.txt", meta=None)[0]

from haystack.nodes import PreProcessor

# This is a default usage of the PreProcessor.
# Here, it performs cleaning of consecutive whitespaces
# and splits a single large document into smaller documents.
# Each document is up to 1000 words long and document breaks cannot fall in the middle of sentences
# Note how the single document passed into the document gets split into 5 smaller documents

preprocessor = PreProcessor(
docs = preprocessor.process([doc_txt])
print(f"n_docs_input: 1\nn_docs_output: {len(docs)}")

# Initialize Question Generator
question_generator = QuestionGenerator()

# Fill the document store with a German document.

# Load machine translation models
from haystack.nodes import TransformersTranslator
in_translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-nl-en")
out_translator = TransformersTranslator(model_name_or_path="Helsinki-NLP/opus-mt-en-nl")

reader = FARMReader("deepset/roberta-base-squad2")
qag_pipeline = QuestionAnswerGenerationPipeline(question_generator, reader)

# Wrap the previously defined QuestionAnswerGenerationPipeline
from haystack.pipelines import TranslationWrapperPipeline

pipeline_with_translation = TranslationWrapperPipeline(
    input_translator=in_translator, output_translator=out_translator, pipeline=qag_pipeline

for idx, document in enumerate(tqdm(document_store)):
    print(f"\n * Generating questions and answers for document {idx}: {document.content[:100]}...\n")
    result =[document])
ffmpeg -i video \
       -vf "select='between(t,4,6.5)+between(t,17,26)+between(t,74,91)',
            setpts=N/FRAME_RATE/TB" \
       -af "aselect='between(t,4,6.5)+between(t,17,26)+between(t,74,91)',
            asetpts=N/SR/TB" out.mp4
# Add the tables to the DocumentStore

import json
from haystack import Document
import pandas as pd

def read_tables(filename):
    processed_tables = []
    with open(filename) as tables:
        tables = json.load(tables)
        for key, table in tables.items():
            current_columns = table["header"]
            current_rows = table["data"]
            current_df = pd.DataFrame(columns=current_columns, data=current_rows)
            document = Document(content=current_df, content_type="table", id=key)

    return processed_tables

tables = read_tables(f"{doc_dir}/tables.json")
document_store.write_documents(tables, index=document_index)

# Showing content field and meta field of one of the Documents of content_type 'table'
from IPython.display import YouTubeVideo

YOUTUBE_ID = 'xxxxxxxxxxxx'

!rm -rf *.wav
!youtube-dl --extract-audio --audio-format wav --output "downloaded.%(ext)s"\?v\={YOUTUBE_ID}
!ffmpeg -loglevel panic -y -i downloaded.wav -acodec pcm_s16le -ac 1 -ar 16000 {project_name}/test.wav
find . -name '*.wav' -exec ffmpeg -i '{}' -i cars-passing-close-by-wet-road-23734.mp3 -filter_complex "[0:a][1:a]amerge=inputs=2[a]" -map "[a]" '{}.bkgnoise.wav' \;
find . -name '*.wav' -exec ffmpeg -i '{}' -filter:a "highpass=f=1200" '{}.phone.wav' \;
find . -name '*.wav' -exec ffmpeg -i '{}' -ar 16000 -ac 1 '{}.mono.wav' \;
ffmpeg -i VIDEO_IN -vcodec libx264 -crf 24 -filter:v scale=1024:-1 -acodec aac -ab 128k VIDEO_OUT.mp4
ffmpeg -nostdin -i VIDEO_IN -vf VIDEO_OUT < /dev/null

Wed Oct 26 2022 14:10:07 GMT+0000 (UTC)

#sh #ffmpeg

Thu Oct 21 2021 02:25:59 GMT+0000 (UTC)

#ffmpeg #captions #srt

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