89 lines
3.3 KiB
Python
Executable file
89 lines
3.3 KiB
Python
Executable file
#!/usr/bin/env python3
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# Copyright (c) 2024 Julian Müller (ChaoticByte)
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# Disable FutureWarnings
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import warnings
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warnings.simplefilter(action='ignore', category=FutureWarning)
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# Imports
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from argparse import ArgumentParser
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from pathlib import Path
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from typing import List
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from faster_whisper import WhisperModel
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from semantic_text_splitter import TextSplitter
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from tokenizers import Tokenizer
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from transformers import pipeline
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# Transcription
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def transcribe(model_name: str, audio_file: str) -> str:
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'''Transcribe the media using faster-whisper'''
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t_chunks = []
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print("* Loading model ", end="", flush=True)
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model = WhisperModel(model_name, device="auto", compute_type="int8")
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segments, _ = model.transcribe(audio_file, language="en", beam_size=5, condition_on_previous_text=False)
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print()
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print("* Transcribing audio ", end="", flush=True)
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for s in segments:
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print(".", end="", flush=True)
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t_chunks.append(s.text)
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print()
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t = "".join(t_chunks)
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return t
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# NLP
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NLP_MODEL = "facebook/bart-large-cnn"
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def split_text(t: str, max_tokens: int) -> List[str]:
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'''Split text into semantic segments'''
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print("* Splitting up transcript into semantic segments")
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tokenizer = Tokenizer.from_pretrained(NLP_MODEL)
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splitter = TextSplitter.from_huggingface_tokenizer(
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tokenizer, (int(max_tokens*0.8), max_tokens))
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chunks = splitter.chunks(t)
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return chunks
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def summarize(chunks: List[str], summary_min: int, summary_max: int) -> str:
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'''Summarize all segments (chunks) using a language model'''
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print("* Summarizing transcript segments ", end="", flush=True)
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chunks_summarized = []
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summ = pipeline("summarization", model=NLP_MODEL)
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for c in chunks:
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print(".", end="", flush=True)
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chunks_summarized.append(
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summ(c, max_length=summary_max, min_length=summary_min, do_sample=False)[0]['summary_text'].strip())
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print()
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return "\n".join(chunks_summarized)
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# Main
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if __name__ == "__main__":
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# parse commandline arguments
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argp = ArgumentParser()
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argp.add_argument("--summin", metavar="n", type=int, default=10, help="The minimum lenght of a segment summary [10] (min: 5)")
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argp.add_argument("--summax", metavar="n", type=int, default=90, help="The maximum lenght of a segment summary [90] (min: 5)")
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argp.add_argument("--segmax", metavar="n", type=int, default=375, help="The maximum number of tokens per segment [375] (5 - 500)")
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argp.add_argument("-m", metavar="name", type=str, default="small.en", help="The name of the whisper model to be used [small.en]")
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argp.add_argument("-i", required=True, metavar="filepath", type=Path, help="The path to the media file")
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argp.add_argument("-o", required=True, metavar="filepath", type=Path, help="Where to save the output text to")
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args = argp.parse_args()
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# Clamp values
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args.summin = max(5, args.summin)
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args.summax = max(5, args.summax)
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args.segmax = max(5, min(args.segmax, 500))
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# transcribe
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text = transcribe(args.m, args.i).strip()
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# split up into semantic segments & summarize
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chunks = split_text(text, args.segmax)
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summary = summarize(chunks, args.summin, args.summax)
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print(f"\n{summary}\n")
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print(f"* Saving summary to {args.o.__str__()}")
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with args.o.open("w+") as f: # overwrites
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f.write(summary)
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