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Duplicate from baaastien/Spleeter_and_ASR
Browse filesCo-authored-by: Bastien <baaastien@users.noreply.huggingface.co>
- .gitattributes +31 -0
- README.md +14 -0
- app.py +50 -0
- examples/MyBubbaMi-NothingMuch.mp3 +0 -0
- examples/audio_example.mp3 +0 -0
- license.txt +4 -0
- packages.txt +2 -0
- requirements.txt +6 -0
.gitattributes
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README.md
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---
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title: Spleeter And ASR
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emoji: 🚀
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colorFrom: gray
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colorTo: pink
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sdk: gradio
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sdk_version: 3.2
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app_file: app.py
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pinned: false
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license: mit
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duplicated_from: baaastien/Spleeter_and_ASR
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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import os
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import random2
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from spleeter.separator import Separator
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from transformers import pipeline, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM
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# Initiate a file separator with 2 stems (instruments and vocals) and 16khz bitrate, required for ASR
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separator = Separator('spleeter:2stems-16kHz')
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# Initiate Speech to text model with Wave2Vec english
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# https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english
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pipe = pipeline("automatic-speech-recognition", "jonatasgrosman/wav2vec2-large-xlsr-53-english")
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# Gradio function to split audio stems, transcribe vocals and return their filepaths
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def extract_stems(audio):
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# initiate a unique folder name for splitted files
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foldername = str(random2.randrange(100000000))
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# Separate audio input. Synchronous is true to wait for the end of split before going further
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separator.separate_to_file(audio, "output/", filename_format= foldername + "/{instrument}.wav", synchronous=True)
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# build filepaths for vocals and accompaniment files
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vocals = f"./output/"+ foldername +"/vocals.wav"
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accompaniment = f"./output/"+ foldername +"/accompaniment.wav"
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# Get a transcript of the vocals, by using the huggingface pipeline
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transcript = pipe(vocals, chunk_length_s=10, decoder=None)
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return vocals, accompaniment, transcript
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# Launch a Gradio interface
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# Input is an audio file,
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# Output is two audio files and a transcript
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title = "Demo: Deezer Spleeter + english Automatic Speech Recognition"
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description = "<p>This demo is a basic interface for <a href='https://research.deezer.com/projects/spleeter.html' target='_blank'>Deezer Spleeter</a>.</p><p>It uses the Spleeter library for separate audio file in two stems : accompaniments and vocals.</p><p>Once splitted, it performs ASR (Automatic Speech Recognition) based on a Wav2vec2 english model.</p>"
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examples = [["examples/" + mp3] for mp3 in os.listdir("examples/")]
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demo = gr.Interface(
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fn=extract_stems,
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inputs=gr.Audio(source="upload", type="filepath"),
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outputs=[gr.Audio(label="Vocals stem", source="upload", type="filepath"), gr.Audio(label="Accompaniment stem", source="upload", type="filepath"), gr.Textbox(label="Wave2vec2 Automatic Speech Recognition (English)")],
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title=title,
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description=description,
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examples=examples,
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allow_flagging="never"
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)
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demo.launch()
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examples/MyBubbaMi-NothingMuch.mp3
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Binary file (416 kB). View file
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examples/audio_example.mp3
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Binary file (263 kB). View file
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license.txt
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Music Nothing Much by My Bubba & Mi
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from https://freemusicarchive.org
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Under Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
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packages.txt
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libsndfile1
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ffmpeg
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requirements.txt
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transformers
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torch
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pyctcdecode
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pypi-kenlm
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spleeter
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random2
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