app.py

from pyharp import ModelCard, build_endpoint

import gradio as gr
import torchaudio
import torch
import os

timbre_trap = torch.load('model-8750.pt', map_location='cpu')

card = ModelCard(
    name='Timbre-Trap',
    description='De-timbre your audio!',
    author='Frank Cwitkowitz',
    tags=['example', 'music transcription', 'multi-pitch estimation', 'timbre filtering']
)


def process_fn(audio_path, de_timbre):
    # Load the audio with torchaudio
    audio, fs = torchaudio.load(audio_path)
    # Average channels to obtain mono-channel
    audio = torch.mean(audio, dim=0, keepdim=True)
    # Resample audio to the specified sampling rate
    audio = torchaudio.functional.resample(audio, fs, 22050)
    # Add a batch dimension
    audio = audio.unsqueeze(0)
    # Determine original number of samples
    n_samples = audio.size(-1)
    # Pad audio to next multiple of block length
    audio = timbre_trap.sliCQ.pad_to_block_length(audio)

    # Encode raw audio into latent vectors
    latents, embeddings, _ = timbre_trap.encode(audio)
    # Apply skip connections if they are turned on
    embeddings = timbre_trap.apply_skip_connections(embeddings)
    # Obtain transcription or reconstructed spectral coefficients
    coefficients = timbre_trap.decode(latents, embeddings, de_timbre)

    # Invert reconstructed spectral coefficients
    audio = timbre_trap.sliCQ.decode(coefficients)
    # Trim to original number of samples
    audio = audio[..., :n_samples]
    # Remove batch dimension
    audio = audio.squeeze(0)

    if de_timbre and audio.abs().max():
        # Normalize audio to [-1, 1]
        audio /= audio.abs().max()

    # Create a temporary directory for output
    os.makedirs('_outputs', exist_ok=True)
    # Create a path for saving the audio
    save_path = os.path.join('_outputs', 'output.wav')
    # Save the audio
    torchaudio.save(save_path, audio, 22050)

    return save_path


with gr.Blocks() as demo:
    inputs = [
        gr.Audio(
            label='Audio Input',
            type='filepath'
        ),
        #gr.Checkbox(
        #    value=False,
        #    label='De-Timbre'
        #)
        gr.Slider(
            minimum=0,
            maximum=1,
            step=1,
            value=0,
            label='De-Timbre'
        )
    ]

    output = gr.Audio(label='Audio Output', type='filepath')

    ctrls_data, ctrls_button, process_button = build_endpoint(inputs, output, process_fn, card)

demo.launch(share=True)