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Browse files- app.py +61 -0
- requirements.txt +7 -0
app.py
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from transformers import pipeline
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from pytube import YouTube
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import gradio as gr
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import librosa
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import hopsworks
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project = hopsworks.login()
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fs = project.get_feature_store()
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dataset_api = project.get_dataset_api()
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dataset_api.download("Resources/best_model/model_scores.png", overwrite=True) # change link
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pipe = pipeline(model="ayberkuckun/whisper-small-se-hyperparameter-searched", task="automatic-speech-recognition",
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chunk_length_s=30)
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def transcribe(mic=None, url=None):
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if (mic is None) and (len(url) == 0):
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return None, "ERROR: You have to either use the microphone or paste a Youtube URL", None
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elif (mic is not None) and (len(url) > 0):
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return mic, pipe(mic)["text"], "model_scores.png"
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elif (len(url) > 0):
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selected_video = YouTube(url)
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try:
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audio = selected_video.streams.filter(only_audio=True)[0]
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except:
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raise Exception("Can't find an mp4 audio.")
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audio.download(filename="audio.mp3")
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speech_array, _ = librosa.load("audio.mp3", sr=16000)
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output = pipe(speech_array)
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return "audio.mp3", output["text"], "model_scores.png"
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else:
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return mic, pipe(mic)["text"], "model_scores.png"
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iface = gr.Interface(
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fn=transcribe,
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inputs=[
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gr.Audio(source="microphone", type="filepath", optional=True),
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gr.Textbox("https://www.youtube.com/shorts/1j2lRgtsDZk", label="Paste a YouTube video URL", optional=True),
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],
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outputs=[gr.Audio(label="Transcripted Audio"),
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gr.Textbox(label="Transcription"),
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gr.Image(label="Model Scores")
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],
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title="Whisper Small Swedish",
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description="Realtime demo for Swedish speech recognition using a fine-tuned Whisper small model.",
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allow_flagging="never"
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)
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iface.launch()
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requirements.txt
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hopsworks
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librosa
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gradio
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git+https://github.com/huggingface/transformers
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git+https://github.com/pytube/pytube
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torch
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sentencepiece
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