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import torch |
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from transformers import pipeline |
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import numpy as np |
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import gradio as gr |
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set_seed(0) |
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def _grab_best_device(use_gpu=True): |
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if torch.cuda.device_count() > 0 and use_gpu: |
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device = "cuda" |
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else: |
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device = "cpu" |
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return device |
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device = _grab_best_device() |
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HUB_PATH = "ylacombe/vits_vctk_welsh_male" |
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pipe = pipeline(HUB_PATH, device=0) |
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title = "# 🐶 VITS" |
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description = """ |
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""" |
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num_speakers = pipe.model.config.num_speakers |
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def generate_audio(text, spkr_id): |
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forward_params = {"spkr_id": spkr_id} |
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output = pipeline(text, forward_params=forward_params) |
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return (output["sampling_rate"], output["audio"].squeeze()) |
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with gr.Blocks() as demo_blocks: |
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gr.Markdown(title) |
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gr.Markdown(description) |
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with gr.Row(): |
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with gr.Column(): |
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inp_text = gr.Textbox(label="Input Text", info="What would you like bark to synthesise?") |
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spkr = gr.Dropdown( |
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[i for i in range(num_speakers)], |
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value=None, |
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label="Speaker ID", |
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info="Default: Unconditional Generation" |
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) |
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btn = gr.Button("Generate Audio!") |
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with gr.Column(): |
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out_audio_vocos = gr.Audio(type="numpy", autoplay=False, label="Generated Audio", show_label=True) |
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btn.click(generate_audio, [inp_text, spk], [out_audio_vocos]) |
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demo_blocks.queue().launch(debug=True) |
