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import gradio as gr |
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from pyannote_viewer import PyannoteViewer |
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from pyannote.audio import Pipeline |
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import os |
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from huggingface_hub import HfApi |
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def apply_pipeline(audio: str, pipeline_name: str) -> tuple: |
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pipeline = Pipeline.from_pretrained( |
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pipeline_name, use_auth_token=os.environ["HF_TOKEN"] |
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) |
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outputs = pipeline(audio) |
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if isinstance(outputs, tuple): |
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return outputs |
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else: |
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return (outputs, 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('# pyannote pretrained pipelines') |
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gr.Markdown( |
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"You like [pyannote.audio](https://github.com/pyannote/pyannote-audio)? Consider using [pyannoteAI](https://pyannote.ai/) for better and faster options.\n" |
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"\nGo [here](https://huggingface.co/pyannote) for more detail on each pipeline available in this space." |
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) |
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gr.Markdown() |
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gr.Markdown("#### Select a pretrained pipeline:") |
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available_pipelines = [p.modelId for p in HfApi().list_models(filter="pyannote-audio-pipeline")] |
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available_pipelines = list(filter(lambda p: p.startswith("pyannote/"), available_pipelines)) |
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dropdown = gr.Dropdown(choices=available_pipelines, value=available_pipelines[0], interactive=True, label="Pretrained pipeline") |
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gr.Markdown("#### Upload or record an audio:") |
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audio = gr.Audio(type="filepath") |
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btn = gr.Button("Apply pipeline") |
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source_viewer = PyannoteViewer(interactive=False) |
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btn.click(fn=apply_pipeline, inputs=[audio, dropdown], outputs=[source_viewer]) |
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if __name__ == "__main__": |
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demo.launch() |
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