app.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import argparse
from concurrent.futures import ThreadPoolExecutor
import logging
import os
import base64
from pathlib import Path
import subprocess as sp
import sys
from tempfile import NamedTemporaryFile
import time
import typing as tp
import warnings

import gradio as gr
from pydub import AudioSegment

from audiocraft.data.audio import audio_write
from audiocraft.models import MAGNeT


SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')

MODEL = None  # Last used model
SPACE_ID = os.environ.get('SPACE_ID', '')
MAX_BATCH_SIZE = 12
N_REPEATS = 1
INTERRUPTING = False
MBD = None
# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
_old_call = sp.call

PROD_STRIDE_1 = "prod-stride1 (new!)"


def _call_nostderr(*args, **kwargs):
    # Avoid ffmpeg vomiting on the logs.
    kwargs['stderr'] = sp.DEVNULL
    kwargs['stdout'] = sp.DEVNULL
    _old_call(*args, **kwargs)


sp.call = _call_nostderr
# Preallocating the pool of processes.
pool = ThreadPoolExecutor(4)
pool.__enter__()


def interrupt():
    global INTERRUPTING
    INTERRUPTING = True


class FileCleaner:
    def __init__(self, file_lifetime: float = 3600):
        self.file_lifetime = file_lifetime
        self.files = []

    def add(self, path: tp.Union[str, Path]):
        self._cleanup()
        self.files.append((time.time(), Path(path)))

    def _cleanup(self):
        now = time.time()
        for time_added, path in list(self.files):
            if now - time_added > self.file_lifetime:
                if path.exists():
                    path.unlink()
                self.files.pop(0)
            else:
                break


file_cleaner = FileCleaner()


def make_waveform(*args, **kwargs):
    # Further remove some warnings.
    be = time.time()
    with warnings.catch_warnings():
        warnings.simplefilter('ignore')
        out = gr.make_waveform(*args, **kwargs)
        print("Make a video took", time.time() - be)
        return out


def load_model(version='facebook/magnet-small-10secs'):
    global MODEL
    print("Loading model", version)
    if MODEL is None or MODEL.name != version:
        MODEL = None  # in case loading would crash
        MODEL = MAGNeT.get_pretrained(version)


def _do_predictions(texts, progress=False, gradio_progress=None, **gen_kwargs):
    MODEL.set_generation_params(**gen_kwargs)
    print("new batch", len(texts), texts)
    be = time.time()

    try:
        outputs = MODEL.generate(texts, progress=progress, return_tokens=False)
    except RuntimeError as e:
        raise gr.Error("Error while generating " + e.args[0])
    outputs = outputs.detach().cpu().float()
    pending_videos = []
    out_wavs = []
    for i, output in enumerate(outputs):
        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
            audio_write(
                file.name, output, MODEL.sample_rate, strategy="loudness",
                loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
            if i == 0:
                pending_videos.append(pool.submit(make_waveform, file.name))
            out_wavs.append(file.name)
            file_cleaner.add(file.name)
    out_videos = [pending_video.result() for pending_video in pending_videos]
    for video in out_videos:
        file_cleaner.add(video)
    print("batch finished", len(texts), time.time() - be)
    print("Tempfiles currently stored: ", len(file_cleaner.files))
    return out_videos, out_wavs


def predict_batched(texts, melodies):
    max_text_length = 512
    texts = [text[:max_text_length] for text in texts]
    load_model('facebook/magnet-small-10secs')
    res = _do_predictions(texts, melodies)
    return res

def predict_full(secre_token, model, model_path, text, temperature, topp,
                 max_cfg_coef, min_cfg_coef, 
                 decoding_steps1, decoding_steps2, decoding_steps3, decoding_steps4, 
                 span_score,
                 progress=gr.Progress()):
    if secret_token != SECRET_TOKEN:
        raise gr.Error(
            f'Invalid secret token. Please fork the original space if you want to use it for yourself.')

    global INTERRUPTING
    INTERRUPTING = False
    progress(0, desc="Loading model...")
    model_path = model_path.strip()
    if model_path:
        if not Path(model_path).exists():
            raise gr.Error(f"Model path {model_path} doesn't exist.")
        if not Path(model_path).is_dir():
            raise gr.Error(f"Model path {model_path} must be a folder containing "
                           "state_dict.bin and compression_state_dict_.bin.")
        model = model_path
    if temperature < 0:
        raise gr.Error("Temperature must be >= 0.")

    load_model(model)

    max_generated = 0

    def _progress(generated, to_generate):
        nonlocal max_generated
        max_generated = max(generated, max_generated)
        progress((min(max_generated, to_generate), to_generate))
        if INTERRUPTING:
            raise gr.Error("Interrupted.")
    MODEL.set_custom_progress_callback(_progress)
    
    videos, wavs = _do_predictions(
        [text], progress=True,
        temperature=temperature, top_p=topp,
        max_cfg_coef=max_cfg_coef, min_cfg_coef=min_cfg_coef, 
        decoding_steps=[decoding_steps1, decoding_steps2, decoding_steps3, decoding_steps4],
        span_arrangement='stride1' if (span_score == PROD_STRIDE_1) else 'nonoverlap',
        gradio_progress=progress)

    wav_path = wavs[0]
    wav_base64 = ""
    

    # Convert WAV to MP3
    mp3_path = wav_path.replace(".wav", ".mp3")
    sound = AudioSegment.from_wav(wav_path)
    sound.export(mp3_path, format="mp3")

    # Encode the MP3 file to base64
    mp3_base64 = ""
    with open(mp3_path, "rb") as mp3_file:
        mp3_base64 = base64.b64encode(mp3_file.read()).decode('utf-8')

    # Prepend the appropriate data URI header
    mp3_base64_data_uri = 'data:audio/mp3;base64,' + mp3_base64
    
    return mp3_base64_data_uri

def ui_full(launch_kwargs):
    with gr.Blocks() as interface:
        gr.HTML("""
            <div style="z-index: 100; position: fixed; top: 0px; right: 0px; left: 0px; bottom: 0px; width: 100%; height: 100%; background: white; display: flex; align-items: center; justify-content: center; color: black;">
            <div style="text-align: center; color: black;">
            <p style="color: black;">This space is a headless component of the cloud rendering engine used by AiTube.</p>
            <p style="color: black;">It is not available for public use, but you can use the <a href="https://huggingface.co/spaces/doevent/AnimateLCM-SVD" target="_blank">original space</a>.</p>
            </div>
            </div>""")
        with gr.Row():
            with gr.Column():
                secret_token = gr.Textbox(label="Secret Token")
                with gr.Row():
                    text = gr.Textbox(label="Input Text", value="Downtown New York, busy street, pedestrian, taxis", interactive=True)
                with gr.Row():
                    submit = gr.Button("Submit")
                    # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
                    _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
                with gr.Row():
                    model = gr.Radio(['facebook/magnet-small-10secs', 'facebook/magnet-medium-10secs',
                                      'facebook/magnet-small-30secs', 'facebook/magnet-medium-30secs',
                                      'facebook/audio-magnet-small', 'facebook/audio-magnet-medium'],
                                     label="Model", value='facebook/audio-magnet-medium', interactive=True)
                    model_path = gr.Textbox(label="Model Path (custom models)") 
                with gr.Row():
                    span_score = gr.Radio(["max-nonoverlap", PROD_STRIDE_1],
                                       label="Span Scoring", value=PROD_STRIDE_1, interactive=True)       
                with gr.Row():
                    decoding_steps1 = gr.Number(label="Decoding Steps (stage 1)", value=20, interactive=True)
                    decoding_steps2 = gr.Number(label="Decoding Steps (stage 2)", value=10, interactive=True)
                    decoding_steps3 = gr.Number(label="Decoding Steps (stage 3)", value=10, interactive=True)
                    decoding_steps4 = gr.Number(label="Decoding Steps (stage 4)", value=10, interactive=True)
                with gr.Row():
                    temperature = gr.Number(label="Temperature", value=3.0, step=0.25, minimum=0, interactive=True)
                    topp = gr.Number(label="Top-p", value=0.9, step=0.1, minimum=0, maximum=1, interactive=True)
                    max_cfg_coef = gr.Number(label="Max CFG coefficient", value=10.0, minimum=0, interactive=True)
                    min_cfg_coef = gr.Number(label="Min CFG coefficient", value=1.0, minimum=0, interactive=True)                
            with gr.Column():
                output = gr.Video(label="Generated Audio")
                base64_audio_output = gr.Textbox()
        submit.click(fn=predict_full, 
                        inputs=[secret_token, model, model_path, text, 
                                    temperature, topp,
                                    max_cfg_coef, min_cfg_coef,
                                    decoding_steps1, decoding_steps2, decoding_steps3, decoding_steps4,
                                    span_score],
                                    outputs=base64_audio_output)

        interface.queue(max_size=10).launch(**launch_kwargs)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--listen',
        type=str,
        default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
        help='IP to listen on for connections to Gradio',
    )
    parser.add_argument(
        '--username', type=str, default='', help='Username for authentication'
    )
    parser.add_argument(
        '--password', type=str, default='', help='Password for authentication'
    )
    parser.add_argument(
        '--server_port',
        type=int,
        default=0,
        help='Port to run the server listener on',
    )
    parser.add_argument(
        '--inbrowser', action='store_true', help='Open in browser'
    )
    parser.add_argument(
        '--share', action='store_true', help='Share the gradio UI'
    )

    args = parser.parse_args()

    launch_kwargs = {}
    launch_kwargs['server_name'] = args.listen

    if args.username and args.password:
        launch_kwargs['auth'] = (args.username, args.password)
    if args.server_port:
        launch_kwargs['server_port'] = args.server_port
    if args.inbrowser:
        launch_kwargs['inbrowser'] = args.inbrowser
    if args.share:
        launch_kwargs['share'] = args.share

    logging.basicConfig(level=logging.INFO, stream=sys.stderr)

    # Show the interface
    ui_full(launch_kwargs)