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| from svoice.separate import * | |
| import scipy.io.wavfile as wav | |
| import gradio as gr | |
| import os | |
| import torch | |
| import soundfile as sf | |
| from transformers import pipeline | |
| from glob import glob | |
| load_model() | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| BASE_PATH = os.path.dirname(os.path.abspath(__file__)) | |
| os.makedirs('input', exist_ok=True) | |
| os.makedirs('separated', exist_ok=True) | |
| print(f"Loading ASR model on {device}...") | |
| pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small", device=0 if device == "cuda" else -1) | |
| print("ASR model loaded!") | |
| def transcribe_audio(audiopath): | |
| audio_input, sr = sf.read(audiopath) | |
| return pipe(audio_input, max_new_tokens=500)['text'] | |
| def separator(audio, rec_audio, example): | |
| outputs= {} | |
| for f in glob('input/*'): | |
| os.remove(f) | |
| for f in glob('separated/*'): | |
| os.remove(f) | |
| if audio: | |
| wav.write('input/original.wav', audio[0], audio[1]) | |
| elif rec_audio: | |
| wav.write('input/original.wav', rec_audio[0], rec_audio[1]) | |
| else: | |
| os.system(f'cp {example} input/original.wav') | |
| separate_demo(mix_dir="./input") | |
| separated_files = glob(os.path.join('separated', "*.wav")) | |
| separated_files = sorted([f for f in separated_files if "original.wav" not in f]) | |
| outputs["transcripts"] = [] | |
| for i, f in enumerate(separated_files): | |
| print(f"Transcribing separated audio {i+1} ...") | |
| outputs["transcripts"].append(transcribe_audio(f)) | |
| print("Text:", outputs["transcripts"][-1]) | |
| return separated_files + outputs['transcripts'] | |
| def set_example_audio(example: list) -> dict: | |
| return gr.Audio.update(value=example[0]) | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown(''' | |
| <center> | |
| <h1>Multiple Voice Separation with Transcription DEMO</h1> | |
| <div style="display:flex;align-items:center;justify-content:center;"><iframe src="https://streamable.com/e/0x8osl?autoplay=1&nocontrols=1" frameborder="0" allow="autoplay"></iframe></div> | |
| <p> | |
| This is a demo for the multiple voice separation algorithm. The algorithm is trained on the LibriMix7 dataset and can be used to separate multiple voices from a single audio file. | |
| *This is an intermediate checkpoint just for experimentation purpose. It isn't performing well on 16k sample rate so you can go here <b><a href="https://github.com/muhammad-ahmed-ghani/svoice_demo">svoice_demo</a></b> to train it on 8k. | |
| </p> | |
| </center> | |
| ''') | |
| with gr.Row(): | |
| input_audio = gr.Audio(label="Input audio", type="numpy") | |
| rec_audio = gr.Audio(label="Record Using Microphone", type="numpy", source="microphone") | |
| with gr.Row(): | |
| output_audio1 = gr.Audio(label='Speaker 1', interactive=False) | |
| output_text1 = gr.Text(label='Speaker 1', interactive=False) | |
| output_audio2 = gr.Audio(label='Speaker 2', interactive=False) | |
| output_text2 = gr.Text(label='Speaker 2', interactive=False) | |
| with gr.Row(): | |
| output_audio3 = gr.Audio(label='Speaker 3', interactive=False) | |
| output_text3 = gr.Text(label='Speaker 3', interactive=False) | |
| output_audio4 = gr.Audio(label='Speaker 4', interactive=False) | |
| output_text4 = gr.Text(label='Speaker 4', interactive=False) | |
| with gr.Row(): | |
| output_audio5 = gr.Audio(label='Speaker 5', interactive=False) | |
| output_text5 = gr.Text(label='Speaker 5', interactive=False) | |
| output_audio6 = gr.Audio(label='Speaker 6', interactive=False) | |
| output_text6 = gr.Text(label='Speaker 6', interactive=False) | |
| with gr.Row(): | |
| output_audio7 = gr.Audio(label='Speaker 7', interactive=False) | |
| output_text7 = gr.Text(label='Speaker 7', interactive=False) | |
| outputs_audio = [output_audio1, output_audio2, output_audio3, output_audio4, output_audio5, output_audio6, output_audio7] | |
| outputs_text = [output_text1, output_text2, output_text3, output_text4, output_text5, output_text6, output_text7] | |
| button = gr.Button("Separate") | |
| examples = [ | |
| "samples/mixture1.wav", | |
| "samples/mixture2.wav", | |
| "samples/mixture3.wav" | |
| ] | |
| example_selector = gr.inputs.Radio(examples, label="Example Audio") | |
| button.click(separator, inputs=[input_audio, rec_audio, example_selector], outputs=outputs_audio + outputs_text) | |
| demo.launch() |