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| # coding=utf-8 | |
| import gradio as gr | |
| import numpy as np | |
| import soundfile as sf | |
| import spaces | |
| import torch | |
| import torchaudio | |
| from sv import process_audio | |
| def model_inference(input_wav, language): | |
| # Simplify language selection | |
| language = language if language else "auto" | |
| # Handle input_wav format | |
| if isinstance(input_wav, tuple): | |
| fs, input_wav = input_wav | |
| input_wav = input_wav.astype(np.float32) / np.iinfo(np.int16).max | |
| input_wav = input_wav.mean(-1) if len(input_wav.shape) > 1 else input_wav | |
| if fs != 16000: | |
| resampler = torchaudio.transforms.Resample(fs, 16000) | |
| input_wav = resampler(torch.from_numpy(input_wav).float()[None, :])[ | |
| 0 | |
| ].numpy() | |
| # Process audio | |
| with sf.SoundFile("temp.wav", "w", samplerate=16000, channels=1) as f: | |
| f.write(input_wav) | |
| result = process_audio("temp.wav", language=language) | |
| return result | |
| def launch(): | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("# Cantonese Call Transcriber") | |
| gr.Markdown("## Try an example:") | |
| # Define components first | |
| audio_inputs = gr.Audio(label="Input") | |
| text_outputs = gr.Textbox(lines=10, label="Output") | |
| # Place the Examples component above the input | |
| with gr.Row(): | |
| gr.Examples( | |
| examples=[["example/scb.mp3"]], | |
| inputs=[audio_inputs], | |
| outputs=text_outputs, | |
| fn=lambda x: model_inference(x, "yue"), | |
| examples_per_page=1, | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| # Audio input is already defined, just reference it here | |
| audio_inputs | |
| fn_button = gr.Button("Process Audio", variant="primary") | |
| with gr.Column(scale=3): | |
| # Text output is already defined, just reference it here | |
| text_outputs | |
| demo.launch() | |
| if __name__ == "__main__": | |
| launch() | |