Update app.py

app.py

@@ -1,28 +1,351 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under thmage license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+from concurrent.futures import ProcessPoolExecutor
+import logging
+import os
+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
-import torchaudio
+
+from audiocraft.data.audio import audio_write
 from audiocraft.models import MAGNeT
-from audiocraft. data. audio import audio_write
 
-model = MAGNeT.get_pretrained('facebook/magnet-small-10secs')
+
+MODEL = None  # Last used model
+SPACE_ID = os.environ.get('SPACE_ID', '')
+MAX_BATCH_SIZE = 12
+N_REPEATS = 2
+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 = ProcessPoolExecutor(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 infer(description):
-    descriptions = ['disco beat', 'energetic EDM']
+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()
 
-    wav = model.generate(descriptions) 
+    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(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()):
+    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)
     
-    for idx, one_wav in enumerate(wav):
-        print(idx)
-        audio_write(f'{idx}', 
-                    one_wav.cpu(), 
-                    model.sample_rate, 
-                    strategy="loudness", 
-                    loudness_compressor=True)
+    videos, wavs = _do_predictions(
+        [text] * N_REPEATS, 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)
+
+    outputs_ = [videos[0]] + [wav for wav in wavs]
+    return tuple(outputs_)
+
+def ui_full(launch_kwargs):
+    with gr.Blocks() as interface:
+        gr.Markdown(
+            """
+            # MAGNeT
+            This is your private demo for [MAGNeT](https://github.com/facebookresearch/audiocraft),
+            A fast text-to-music model, consists of a single, non-autoregressive transformer.
+            presented at: ["Masked Audio Generation using a Single Non-Autoregressive Transformer"] (https://huggingface.co/papers/2401.04577)
+            """
+        )
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    text = gr.Text(label="Input Text", value="80s electronic track with melodic synthesizers, catchy beat and groovy bass", 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/magnet-small-10secs', interactive=True)
+                    model_path = gr.Text(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 - variation 1")
+                audio_outputs = [gr.Audio(label=f"Generated Audio - variation {i+1}", type='filepath') for i in range(N_REPEATS)]
+        submit.click(fn=predict_full, 
+                        inputs=[model, model_path, text, 
+                                    temperature, topp,
+                                    max_cfg_coef, min_cfg_coef,
+                                    decoding_steps1, decoding_steps2, decoding_steps3, decoding_steps4,
+                                    span_score],
+                                    outputs=[output] + [o for o in audio_outputs])
+        gr.Examples(
+            fn=predict_full,
+            examples=[
+                [
+                    "80s electronic track with melodic synthesizers, catchy beat and groovy bass",
+                    'facebook/magnet-small-10secs',
+                    20, 3.0, 0.9, 10.0,
+                ],
+                [
+                    "80s electronic track with melodic synthesizers, catchy beat and groovy bass. 170 bpm",
+                    'facebook/magnet-small-10secs',
+                    20, 3.0, 0.9, 10.0,
+                ],
+                [
+                    "Earthy tones, environmentally conscious, ukulele-infused, harmonic, breezy, easygoing, organic instrumentation, gentle grooves",
+                    'facebook/magnet-medium-10secs',
+                    20, 3.0, 0.9, 10.0,
+                ],
+                [   "Funky groove with electric piano playing blue chords rhythmically",
+                    'facebook/magnet-medium-10secs',
+                    20, 3.0, 0.9, 10.0,
+                ],
+                [
+                    "Rock with saturated guitars, a heavy bass line and crazy drum break and fills.",
+                    'facebook/magnet-small-30secs',
+                    60, 3.0, 0.9, 10.0,
+                ],
+                [   "A grand orchestral arrangement with thunderous percussion, epic brass fanfares, and soaring strings, creating a cinematic atmosphere fit for a heroic battle",
+                    'facebook/magnet-medium-30secs',
+                    60, 3.0, 0.9, 10.0,
+                ],
+                [   "Seagulls squawking as ocean waves crash while wind blows heavily into a microphone.",
+                    'facebook/audio-magnet-small', 
+                    20, 3.5, 0.8, 20.0,
+                ],
+                [   "A toilet flushing as music is playing and a man is singing in the distance.",
+                    'facebook/audio-magnet-medium', 
+                    20, 3.5, 0.8, 20.0,
+                ],
+            ],
+
+            inputs=[text, model, decoding_steps1, temperature, topp, max_cfg_coef],
+            outputs=[output]
+        )
+
+        gr.Markdown(
+            """
+            ### More details
+            
+            #### Music Generation
+            "magnet" models will generate a short music extract based on the textual description you provided.
+            These models can generate either 10 seconds or 30 seconds of music.
+            These models were trained with descriptions from a stock music catalog. Descriptions that will work best
+            should include some level of details on the instruments present, along with some intended use case
+            (e.g. adding "perfect for a commercial" can somehow help).
+
+            We present 4 model variants:
+            1. facebook/magnet-small-10secs - a 300M non-autoregressive transformer capable of generating 10-second music conditioned
+                on text.
+            2. facebook/magnet-medium-10secs - 1.5B parameters, 10 seconds audio.
+            3. facebook/magnet-small-30secs - 300M parameters, 30 seconds audio.
+            4. facebook/magnet-medium-30secs - 1.5B parameters, 30 seconds audio.
         
-    return "done"
+            #### Sound-Effect Generation
+            "audio-magnet" models will generate a 10-second sound effect based on the description you provide. 
+
+            These models were trained on the following data sources: a subset of AudioSet (Gemmeke et al., 2017), 
+            [BBC sound effects](https://sound-effects.bbcrewind.co.uk/), AudioCaps (Kim et al., 2019), 
+            Clotho v2 (Drossos et al., 2020), VGG-Sound (Chen et al., 2020), FSD50K (Fonseca et al., 2021), 
+            [Free To Use Sounds](https://www.freetousesounds.com/all-in-one-bundle/), [Sonniss Game Effects](https://sonniss.com/gameaudiogdc), 
+            [WeSoundEffects](https://wesoundeffects.com/we-sound-effects-bundle-2020/), 
+            [Paramount Motion - Odeon Cinematic Sound Effects](https://www.paramountmotion.com/odeon-sound-effects).
+
+            We present 2 model variants:
+            1. facebook/audio-magnet-small - 10 second sound effect generation, 300M parameters.
+            2. facebook/audio-magnet-medium - 10 second sound effect generation, 1.5B parameters.
+
+            See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft/blob/main/docs/MAGNET.md)
+            for more details.
+            """
+        )
+
+        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)
 
-gr.Interface(
-    fn = infer,
-    inputs = gr.Textbox(value="gogo"),
-    outputs = gr.Textbox()
-).launch()
+    # Show the interface
+    ui_full(launch_kwargs)