rm old app

app_batched.py

@@ -1,222 +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.
-"""
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import warnings
-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
-
-_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
-pool = ProcessPoolExecutor(3)
-pool.__enter__()
-
-
-def make_waveform(*args, **kwargs):
-    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():
-    print("Loading model")
-    return MusicGen.get_pretrained("melody")
-
-
-def predict(texts, melodies):
-    global MODEL
-    if MODEL is None:
-        MODEL = load_model()
-
-    duration = 12
-    max_text_length = 512
-    texts = [text[:max_text_length] for text in texts]
-    MODEL.set_generation_params(duration=duration)
-
-    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)
-
-    outputs = MODEL.generate_with_chroma(
-        descriptions=texts,
-        melody_wavs=processed_melodies,
-        melody_sample_rate=target_sr,
-        progress=False
-    )
-
-    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))
-    res = [[out_file.result() for out_file in out_files]]
-    print("batch finished", len(texts), time.time() - be)
-    return res
-
-
-def ui(**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/musicgen/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)
-                    melody = gr.Audio(source="upload", type="numpy", label="Condition on a melody (optional)", interactive=True)
-                with gr.Row():
-                    submit = gr.Button("Generate")
-            with gr.Column():
-                output = gr.Video(label="Generated Music")
-        submit.click(predict, inputs=[text, melody], outputs=[output], batch=True, max_batch_size=8)
-        gr.Examples(
-            fn=predict,
-            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.
-        """)
-
-        # Show the interface
-        launch_kwargs = {}
-        username = kwargs.get('username')
-        password = kwargs.get('password')
-        server_port = kwargs.get('server_port', 0)
-        inbrowser = kwargs.get('inbrowser', False)
-        share = kwargs.get('share', False)
-        server_name = kwargs.get('listen')
-
-        launch_kwargs['server_name'] = server_name
-
-        if username and password:
-            launch_kwargs['auth'] = (username, password)
-        if server_port > 0:
-            launch_kwargs['server_port'] = server_port
-        if inbrowser:
-            launch_kwargs['inbrowser'] = inbrowser
-        if share:
-            launch_kwargs['share'] = share
-        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',
-        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()
-
-    ui(
-        username=args.username,
-        password=args.password,
-        inbrowser=args.inbrowser,
-        server_port=args.server_port,
-        share=args.share,
-        listen=args.listen
-    )