app.py

"""Gradio demo for denoisers."""
import tempfile
from pathlib import Path

import gradio as gr
import numpy as np
import torch
import torchaudio
from denoisers import UNet1DModel, WaveUNetModel
from tqdm import tqdm

MODELS = [
    "wrice/unet1d-vctk-48khz",
    "wrice/waveunet-vctk-48khz",
    "wrice/waveunet-vctk-24khz",
]


def denoise(model_name: str, audio_path: str):
    """Denoise audio."""
    if "unet1d" in model_name:
        model = UNet1DModel.from_pretrained(model_name)
    else:
        model = WaveUNetModel.from_pretrained(model_name)

    if torch.cuda.is_available():
        model = model.cuda()

    stream_reader = torchaudio.io.StreamReader(audio_path)
    stream_reader.add_basic_audio_stream(
        frames_per_chunk=model.config.max_length,
        sample_rate=model.config.sample_rate,
        num_channels=1,
    )

    stream_writer = torchaudio.io.StreamWriter("denoised.wav")
    stream_writer.add_audio_stream(sample_rate=model.config.sample_rate, num_channels=1)

    chunk_size = model.config.max_length

    with stream_writer.open():
        for (audio_chunk,) in tqdm(stream_reader.stream()):
            if audio_chunk is None:
                break

            audio_chunk = audio_chunk.permute(1, 0)
            original_chunk_size = audio_chunk.size(-1)

            if audio_chunk.size(-1) < chunk_size:
                padding = chunk_size - audio_chunk.size(-1)
                audio_chunk = torch.nn.functional.pad(audio_chunk, (0, padding))

            if torch.cuda.is_available():
                audio_chunk = audio_chunk.cuda()

            with torch.no_grad():
                denoised_chunk = model(audio_chunk[None]).audio
                denoised_chunk = denoised_chunk[:, :, :original_chunk_size]

            stream_writer.write_audio_chunk(
                0, denoised_chunk.squeeze(0).permute(1, 0).cpu()
            )

    return "denoised.wav"


iface = gr.Interface(
    fn=denoise,
    inputs=[gr.Dropdown(choices=MODELS, value=MODELS[0]), gr.Audio(type="filepath")],
    outputs=gr.Audio(type="filepath"),
)
iface.launch()