Delete app.py

app.py

@@ -1,408 +0,0 @@
-# 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.
-
-# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
-# also released under the MIT license.
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import os
-from pathlib import Path
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import typing as tp
-import warnings
-#import modin.pandas as pd
-import torch
-import gradio as gr
-
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import MusicGen
-
-
-MODEL = None  # Last used model
-IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
-MAX_BATCH_SIZE = 6
-BATCHED_DURATION = 15
-INTERRUPTING = False
-# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
-_old_call = sp.call
-
-
-def _call_nostderr(*args, **kwargs):
-    # Avoid ffmpeg vomitting 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 = ProcessPoolExecutor(3)
-pool.__enter__()
-
-
-def interrupt():
-    global INTERRUPTING
-    INTERRUPTING = False
-
-
-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='melody'):
-    global MODEL
-    print("Loading model", version)
-    if MODEL is None or MODEL.name != version:
-        MODEL = MusicGen.get_pretrained(version)
-
-
-def _do_predictions(texts, melodies, duration, progress=False, **gen_kwargs):
-    MODEL.set_generation_params(duration=duration, **gen_kwargs)
-    print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies])
-    be = time.time()
-    processed_melodies = []
-    target_sr = 32000
-    target_ac = 1
-    for melody in melodies:
-        if melody is None:
-            processed_melodies.append(None)
-        else:
-            sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
-            if melody.dim() == 1:
-                melody = melody[None]
-            melody = melody[..., :int(sr * duration)]
-            melody = convert_audio(melody, sr, target_sr, target_ac)
-            processed_melodies.append(melody)
-
-    if any(m is not None for m in processed_melodies):
-        outputs = MODEL.generate_with_chroma(
-            descriptions=texts,
-            melody_wavs=processed_melodies,
-            melody_sample_rate=target_sr,
-            progress=progress,
-        )
-    else:
-        outputs = MODEL.generate(texts, progress=progress)
-
-    outputs = outputs.detach().cpu().float()
-    out_files = []
-    for output in 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)
-            out_files.append(pool.submit(make_waveform, file.name))
-            file_cleaner.add(file.name)
-    res = [out_file.result() for out_file in out_files]
-    for file in res:
-        file_cleaner.add(file)
-    print("batch finished", len(texts), time.time() - be)
-    print("Tempfiles currently stored: ", len(file_cleaner.files))
-    return res
-
-
-def predict_batched(texts, melodies):
-    max_text_length = 512
-    texts = [text[:max_text_length] for text in texts]
-    load_model('melody')
-    res = _do_predictions(texts, melodies, BATCHED_DURATION)
-    return [res]
-
-
-def predict_full(model, text, melody, duration, topk, topp, temperature, cfg_coef, progress=gr.Progress()):
-    global INTERRUPTING
-    INTERRUPTING = False
-    if temperature < 0:
-        raise gr.Error("Temperature must be >= 0.")
-    if topk < 0:
-        raise gr.Error("Topk must be non-negative.")
-    if topp < 0:
-        raise gr.Error("Topp must be non-negative.")
-
-    topk = int(topk)
-    load_model(model)
-
-    def _progress(generated, to_generate):
-        progress((generated, to_generate))
-        if INTERRUPTING:
-            raise gr.Error("Interrupted.")
-    MODEL.set_custom_progress_callback(_progress)
-
-    outs = _do_predictions(
-        [text], [melody], duration, progress=True,
-        top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
-    return outs[0]
-
-
-def toggle_audio_src(choice):
-    if choice == "mic":
-        return gr.update(source="microphone", value=None, label="Microphone")
-    else:
-        return gr.update(source="upload", value=None, label="File")
-
-
-def ui_full(launch_kwargs):
-    with gr.Blocks() as interface:
-        gr.Markdown(
-            """
-            # MusicGen
-            This is a demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
-            a simple and controllable model for music generation
-            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text = gr.Text(label="Input Text", interactive=True)
-                    with gr.Column():
-                        radio = gr.Radio(["file", "mic"], value="file",
-                                         label="Condition on a melody (optional) File or Mic")
-                        melody = gr.Audio(source="upload", type="numpy", label="File",
-                                          interactive=True, elem_id="melody-input")
-                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(["melody", "medium", "small", "large"],
-                                     label="Model", value="melody", interactive=True)
-                with gr.Row():
-                    duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
-                with gr.Row():
-                    topk = gr.Number(label="Top-k", value=250, interactive=True)
-                    topp = gr.Number(label="Top-p", value=0, interactive=True)
-                    temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
-                    cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
-            with gr.Column():
-                output = gr.Video(label="Generated Music")
-        submit.click(predict_full,
-                     inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef],
-                     outputs=[output])
-        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
-        gr.Examples(
-            fn=predict_full,
-            examples=[
-                [
-                    "An 80s driving pop song with heavy drums and synth pads in the background",
-                    "./bach.mp3",
-                    "melody"
-                ],
-                [
-                    "A cheerful country song with acoustic guitars",
-                    "./bolero_ravel.mp3",
-                    "melody"
-                ],
-                [
-                    "90s rock song with electric guitar and heavy drums",
-                    None,
-                    "medium"
-                ],
-                [
-                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
-                    "./bach.mp3",
-                    "melody"
-                ],
-                [
-                    "lofi slow bpm electro chill with organic samples",
-                    None,
-                    "medium",
-                ],
-            ],
-            inputs=[text, melody, model],
-            outputs=[output]
-        )
-        gr.Markdown(
-            """
-            ### More details
-
-            The model will generate a short music extract based on the description you provided.
-            The model can generate up to 30 seconds of audio in one pass. It is now possible
-            to extend the generation by feeding back the end of the previous chunk of audio.
-            This can take a long time, and the model might lose consistency. The model might also
-            decide at arbitrary positions that the song ends.
-
-            **WARNING:** Choosing long durations will take a long time to generate (2min might take ~10min).
-            An overlap of 12 seconds is kept with the previously generated chunk, and 18 "new" seconds
-            are generated each time.
-
-            We present 4 model variations:
-            1. Melody -- a music generation model capable of generating music condition
-                on text and melody inputs. **Note**, you can also use text only.
-            2. Small -- a 300M transformer decoder conditioned on text only.
-            3. Medium -- a 1.5B transformer decoder conditioned on text only.
-            4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
-
-            When using `melody`, ou can optionaly provide a reference audio from
-            which a broad melody will be extracted. The model will then try to follow both
-            the description and melody provided.
-
-            You can also use your own GPU or a Google Colab by following the instructions on our repo.
-            See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
-            for more details.
-            """
-        )
-
-        interface.queue().launch(**launch_kwargs)
-
-
-def ui_batched(launch_kwargs):
-    with gr.Blocks() as demo:
-        gr.Markdown(
-            """
-            # MusicGen
-
-            This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
-            a simple and controllable model for music generation
-            presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-            <br/>
-            <a href="https://huggingface.co/spaces/facebook/MusicGen?duplicate=true"
-                style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
-            <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;"
-                src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            for longer sequences, more control and no queue.</p>
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text = gr.Text(label="Describe your music", lines=2, interactive=True)
-                    with gr.Column():
-                        radio = gr.Radio(["file", "mic"], value="file",
-                                         label="Condition on a melody (optional) File or Mic")
-                        melody = gr.Audio(source="upload", type="numpy", label="File",
-                                          interactive=True, elem_id="melody-input")
-                with gr.Row():
-                    submit = gr.Button("Generate")
-            with gr.Column():
-                output = gr.Video(label="Generated Music")
-        submit.click(predict_batched, inputs=[text, melody],
-                     outputs=[output], batch=True, max_batch_size=MAX_BATCH_SIZE)
-        radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
-        gr.Examples(
-            fn=predict_batched,
-            examples=[
-                [
-                    "An 80s driving pop song with heavy drums and synth pads in the background",
-                    "./assets/bach.mp3",
-                ],
-                [
-                    "A cheerful country song with acoustic guitars",
-                    "./assets/bolero_ravel.mp3",
-                ],
-                [
-                    "90s rock song with electric guitar and heavy drums",
-                    None,
-                ],
-                [
-                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
-                    "./assets/bach.mp3",
-                ],
-                [
-                    "lofi slow bpm electro chill with organic samples",
-                    None,
-                ],
-            ],
-            inputs=[text, melody],
-            outputs=[output]
-        )
-        gr.Markdown("""
-        ### More details
-
-        The model will generate 12 seconds of audio based on the description you provided.
-        You can optionaly provide a reference audio from which a broad melody will be extracted.
-        The model will then try to follow both the description and melody provided.
-        All samples are generated with the `melody` model.
-
-        You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
-        See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
-        for more details.
-        """)
-
-        demo.queue(max_size=8 * 4).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
-
-    # Show the interface
-    if IS_BATCHED:
-        ui_batched(launch_kwargs)
-    else:
-        ui_full(launch_kwargs)
-