svoice_demo / app.py
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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()