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import torch
import torchaudio
import spaces
from typing import List
import soundfile as sf
import gradio as gr
import tempfile
import subprocess
def convert_to_16kHz_mono(input_file, output_file):
"""
Converts an audio file to 16KHz sample rate and single channel (mono) using ffmpeg.
Parameters:
input_file (str): Path to the input audio file.
output_file (str): Path to the output WAV file.
"""
try:
# Run the ffmpeg command
subprocess.run(['ffmpeg', '-y', '-i', input_file, '-ar', '16000', '-ac', '1', output_file], check=True)
print(f"Conversion complete: {output_file}")
return output_file
except subprocess.CalledProcessError as e:
print(f"An error occurred during conversion: {e}")
def create_temp_wav_file():
# Create a temporary file using NamedTemporaryFile
temp_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
# Get the path of the temporary file
temp_file_path = temp_file.name
return temp_file_path
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
knn_vc = torch.hub.load('bshall/knn-vc', 'knn_vc', prematched=True, trust_repo=True, pretrained=True, device=device)
def convert_voice(src_wav_path:str, ref_wav_paths, top_k:int):
tmp_src_wav_path = create_temp_wav_file()
tmp_ref_wav_path = create_temp_wav_file()
src_wav_path = convert_to_16kHz_mono(src_wav_path, tmp_src_wav_path)
ref_wav_paths = convert_to_16kHz_mono(ref_wav_paths, tmp_ref_wav_path)
query_seq = knn_vc.get_features(src_wav_path)
matching_set = knn_vc.get_matching_set([ref_wav_paths])
out_wav = knn_vc.match(query_seq, matching_set, topk=int(top_k))
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as converted_file:
sf.write(converted_file.name, out_wav, 16000, "PCM_24")
return converted_file.name
title = """
<div style="text-align: center; max-width: 700px; margin: 0 auto;">
<div
style="display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;"
> <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
KNN Voice Conversion
</h1> </div>
</div>
"""
description = """
Voice Conversion With Just k-Nearest Neighbors. The source and reference utterance(s) are encoded into self-supervised features using WavLM.
Each source feature is assigned to the mean of the k closest features from the reference.
The resulting feature sequence is then vocoded with HiFi-GAN to arrive at the converted waveform output.
"""
article = """
If the model contributes to your research please cite the following work:
Baas, M., van Niekerk, B., & Kamper, H. (2023). Voice conversion with just nearest neighbors. arXiv preprint arXiv:2305.18975.
demo contributed by [@wetdog](https://github.com/wetdog)
"""
demo = gr.Blocks()
with demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Interface(
fn=convert_voice,
inputs=[
gr.Audio(type='filepath'),
gr.Audio(type='filepath'),
#gr.File(file_count="multiple", type="filepath", label="Reference Audio Files"),
gr.Slider(
3,
10,
value=4,
step=1,
label="Top-k",
info=f"These default settings provide pretty good results, but feel free to modify the kNN topk",
)],
outputs=[gr.Audio(type='filepath')],
allow_flagging=False,)
gr.Markdown(article)
demo.queue(max_size=10)
demo.launch(show_api=False, server_name="0.0.0.0", server_port=7860)
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