Project Alpha 1

.gitignore

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+env/

README.md

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 # Meatgrinder
 
 Goals: to transcribe and auto-summarize meeting recordings
+
+## Installation
+
+1. install [Miniconda](https://docs.conda.io/en/latest/miniconda.html)

audio.py

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+import ffmpeg
+import uuid
+import pathlib
+
+def process(input_media):
+    temp_dir = "./env/tmp"
+    pathlib.Path(temp_dir).mkdir(parents=True, exist_ok=True)
+    temp_name = temp_dir + str(uuid.uuid4()) + ".wav"
+    stream = ffmpeg.input(input_media)
+    stream = ffmpeg.output(stream, temp_name)
+    ffmpeg.run(stream)
+    return temp_name

meatgrinder.py

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+import argparse
+import os
+import audio
+import transcribe
+import summarize
+
+parser = argparse.ArgumentParser(description='Transcribe and summarize meeting recordings')
+parser.add_argument('input', type=str, help='Path to the input media')
+parser.add_argument('--output', type=str, help='Path to the output file, will not print result to stdout if provided')
+parser.add_argument('--force', action='store_true', help='Overwrite existing output file without asking')
+args = parser.parse_args()
+
+if not args.input:
+    print("Please provide an input file")
+    exit(1)
+
+if args.output and os.path.isFile(args.output) and not args.force:
+    print("Output file already exists and will be overwritten")
+    if input("Continue? [y/N] ").lower() != "y":
+        exit(1)
+
+
+audio_file = audio.process(args.input)
+
+transcription = transcribe.process(audio_file)
+
+summary = summarize.process(transcription)
+
+if args.output:
+    with open(args.output, 'w') as f:
+        f.write(summary)
+else:
+    print(summary)

requirements.txt

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+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: osx-arm64
+accelerate=1.0.0=pypi_0
+aiohappyeyeballs=2.4.3=pypi_0
+aiohttp=3.10.9=pypi_0
+aiosignal=1.3.1=pypi_0
+argparse=1.4.0=pypi_0
+async-timeout=4.0.3=pypi_0
+attrs=24.2.0=pypi_0
+audioread=3.0.1=pypi_0
+bzip2=1.0.8=h80987f9_6
+ca-certificates=2024.9.24=hca03da5_0
+certifi=2024.8.30=pypi_0
+cffi=1.17.1=pypi_0
+charset-normalizer=3.3.2=pypi_0
+datasets=3.0.1=pypi_0
+decorator=5.1.1=pypi_0
+dill=0.3.8=pypi_0
+filelock=3.16.1=pypi_0
+frozenlist=1.4.1=pypi_0
+fsspec=2024.6.1=pypi_0
+huggingface-hub=0.25.1=pypi_0
+idna=3.10=pypi_0
+jinja2=3.1.4=pypi_0
+joblib=1.4.2=pypi_0
+lazy-loader=0.4=pypi_0
+libffi=3.4.4=hca03da5_1
+librosa=0.10.2.post1=pypi_0
+llvmlite=0.43.0=pypi_0
+markupsafe=3.0.0=pypi_0
+mpmath=1.3.0=pypi_0
+msgpack=1.1.0=pypi_0
+multidict=6.1.0=pypi_0
+multiprocess=0.70.16=pypi_0
+ncurses=6.4=h313beb8_0
+networkx=3.3=pypi_0
+numba=0.60.0=pypi_0
+numpy=2.0.2=pypi_0
+openssl=3.0.15=h80987f9_0
+packaging=24.1=pypi_0
+pandas=2.2.3=pypi_0
+pip=24.2=py310hca03da5_0
+platformdirs=4.3.6=pypi_0
+pooch=1.8.2=pypi_0
+propcache=0.2.0=pypi_0
+psutil=6.0.0=pypi_0
+pyarrow=17.0.0=pypi_0
+pycparser=2.22=pypi_0
+python=3.10.12=hb885b13_0
+python-dateutil=2.9.0.post0=pypi_0
+pytz=2024.2=pypi_0
+pyyaml=6.0.2=pypi_0
+readline=8.2=h1a28f6b_0
+regex=2024.9.11=pypi_0
+requests=2.32.3=pypi_0
+safetensors=0.4.5=pypi_0
+scikit-learn=1.5.2=pypi_0
+scipy=1.14.1=pypi_0
+sentencepiece=0.2.0=pypi_0
+setuptools=75.1.0=py310hca03da5_0
+six=1.16.0=pypi_0
+soundfile=0.12.1=pypi_0
+soxr=0.5.0.post1=pypi_0
+sqlite=3.45.3=h80987f9_0
+sympy=1.13.3=pypi_0
+threadpoolctl=3.5.0=pypi_0
+tk=8.6.14=h6ba3021_0
+tokenizers=0.20.0=pypi_0
+torch=2.4.1=pypi_0
+tqdm=4.66.5=pypi_0
+transformers=4.45.2=pypi_0
+typing-extensions=4.12.2=pypi_0
+tzdata=2024.2=pypi_0
+urllib3=2.2.3=pypi_0
+wheel=0.44.0=py310hca03da5_0
+xxhash=3.5.0=pypi_0
+xz=5.4.6=h80987f9_1
+yarl=1.14.0=pypi_0
+zlib=1.2.13=h18a0788_1

summarize.py

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+from transformers import pipeline
+
+def process(input_text):
+    summarizer = pipeline("summarization", model="marianna13/flan-t5-base-summarization", max_length=200, min_length=10, device="mps")
+    output = summarizer(input_text)[0]['summary_text']
+    return output

transcribe.py

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+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+def process(audio_path):
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        "openai/whisper-large-v3",
+        torch_dtype=torch.float32,
+        low_cpu_mem_usage=True,
+        use_safetensors=True
+    )
+    model.to("mps")
+    processor = AutoProcessor.from_pretrained("openai/whisper-large-v3")
+    pipe = pipeline(
+        "automatic-speech-recognition",
+        model=model,
+        tokenizer=processor.tokenizer,
+        feature_extractor=processor.feature_extractor,
+        torch_dtype=torch.float32,
+        device="mps"
+    )
+    output = pipe(audio_path)["text"]
+    return output