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import gradio as gr |
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from gradio_sourceviewer import SourceViewer |
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from pyannote.audio import Pipeline |
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import os |
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def apply_pipeline(audio: str) -> tuple: |
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pipeline = Pipeline.from_pretrained( |
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"pyannote/speech-separation-ami-1.0", use_auth_token=os.environ["HF_TOKEN"] |
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) |
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return pipeline(audio) |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(scale=1): |
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gr.Markdown( |
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'<a href="https://github.com/pyannote/pyannote-audio"><img src="https://avatars.githubusercontent.com/u/7559051?s=200&v=4" alt="pyannote logo" width="170"/></a>' |
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) |
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with gr.Column(scale=10): |
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gr.Markdown('# Speaker diarization and speech separation pipeline') |
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gr.Markdown( |
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"This space is dedicated to showing the use of the speaker diarization and speech separation [pipeline](https://huggingface.co/pyannote/speech-separation-ami-1.0) integrated to `pyannote.audio`. To use this space:" |
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"\n - Load or record an audio" |
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"\n - Click on the apply pipeline button" |
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"\n - After pipeline processed the audio, you can then listen for each speaker separetely. Annotations on waveforms correspond to the speaker diarization produced by the pipeline, with one color per speaker." |
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) |
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audio = gr.Audio(type="filepath") |
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btn = gr.Button("Apply separation pipeline") |
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source_viewer = SourceViewer(interactive=False) |
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btn.click(fn=apply_pipeline, inputs=[audio], outputs=[source_viewer]) |
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if __name__ == "__main__": |
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demo.launch() |
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