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import gradio as gr |
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import numpy as np |
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import soundfile as sf |
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import spaces |
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import torch |
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import torchaudio |
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from sv import process_audio |
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@spaces.GPU |
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def model_inference(input_wav, language): |
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language = language if language else "auto" |
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if isinstance(input_wav, tuple): |
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fs, input_wav = input_wav |
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input_wav = input_wav.astype(np.float32) / np.iinfo(np.int16).max |
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input_wav = input_wav.mean(-1) if len(input_wav.shape) > 1 else input_wav |
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if fs != 16000: |
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resampler = torchaudio.transforms.Resample(fs, 16000) |
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input_wav = resampler(torch.from_numpy(input_wav).float()[None, :])[ |
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0 |
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].numpy() |
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with sf.SoundFile("temp.wav", "w", samplerate=16000, channels=1) as f: |
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f.write(input_wav) |
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result = process_audio("temp.wav", language=language) |
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return result |
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def launch(): |
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with gr.Blocks(theme=gr.themes.Soft()) as demo: |
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with gr.Row(): |
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with gr.Column(): |
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audio_inputs = gr.Audio(label="Upload audio or use the microphone") |
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language_inputs = gr.Dropdown( |
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choices=["auto", "zh", "en", "yue", "ja", "ko", "nospeech"], |
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value="auto", |
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label="Language", |
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) |
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fn_button = gr.Button("Start", variant="primary") |
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text_outputs = gr.Textbox(label="Results") |
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gr.Examples( |
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examples=[["example/mtr.mp3", "yue"]], |
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inputs=[audio_inputs, language_inputs], |
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examples_per_page=20, |
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) |
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fn_button.click( |
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model_inference, |
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inputs=[audio_inputs, language_inputs], |
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outputs=text_outputs, |
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) |
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demo.launch() |
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if __name__ == "__main__": |
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launch() |
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