test as an API

app.py

@@ -1,502 +1,19 @@
-# 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 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, MultiBandDiffusion
-
-
-MODEL = None  # Last used model
-IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
-print(IS_BATCHED)
-MAX_BATCH_SIZE = 12
-BATCHED_DURATION = 15
-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
-
-
-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
-    
-    
-
-# write a similar function to make_waveform, but for video generated using an image with ans aspect ration of 16:9
-
-def make_video(*args, **kwargs):
-    # Further remove some warnings.
-    be = time.time()
-    with warnings.catch_warnings():
-        warnings.simplefilter('ignore')
-        out = gr.make_video(*args, **kwargs)
-        print("Make a video took", time.time() - be)
-        return out 
-    
-# write make video functions for other aspect ratios and use ffmpeg to combine them into a single video
-
-
-
-
-# def load_model(version='facebook/musicgen-melody'):
-def load_model(version='facebook/musicgen-small'):
-    global MODEL
-    print("Loading model", version)
-    if MODEL is None or MODEL.name != version:
-        MODEL = MusicGen.get_pretrained(version)
-
-
-def load_diffusion():
-    global MBD
-    if MBD is None:
-        print("loading MBD")
-        MBD = MultiBandDiffusion.get_mbd_musicgen()
-
-
-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,
-            return_tokens=USE_DIFFUSION
-        )
-    else:
-        outputs = MODEL.generate(
-            texts, progress=progress, return_tokens=USE_DIFFUSION)
-    if USE_DIFFUSION:
-        outputs_diffusion = MBD.tokens_to_wav(outputs[1])
-        outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
-    outputs = outputs.detach().cpu().float()
-    # return outputs
-    pending_videos = []
-    out_wavs = []
-    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)
-            pending_videos.append(pool.submit(make_waveform, file.name))
-            # pending_videos.append(pool.submit(make_video, 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))
-    # here I could ipload this to youtube music
-    # return out_wavs
-    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/musicgen-small')
-    res = _do_predictions(texts, melodies, BATCHED_DURATION)
-    return res
-
-
-def predict_full(model, decoder, text, melody, duration, topk, topp, temperature, cfg_coef, progress=gr.Progress()):
-    global INTERRUPTING
-    global USE_DIFFUSION
-    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)
-    if decoder == "MultiBand_Diffusion":
-        USE_DIFFUSION = True
-        load_diffusion()
-    else:
-        USE_DIFFUSION = False
-    load_model(model)
-
-    def _progress(generated, to_generate):
-        progress((min(generated, to_generate), to_generate))
-        if INTERRUPTING:
-            raise gr.Error("Interrupted.")
-    MODEL.set_custom_progress_callback(_progress)
-
-    videos, wavs = _do_predictions(
-        [text], [melody], duration, progress=True,
-        top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
-    if USE_DIFFUSION:
-        return videos[0], wavs[0], videos[1], wavs[1]
-    return videos[0], wavs[0], None, None
-
-
-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 toggle_diffusion(choice):
-    if choice == "MultiBand_Diffusion":
-        return [gr.update(visible=True)] * 2
-    else:
-        return [gr.update(visible=False)] * 2
-
-
-def ui_full(launch_kwargs):
-    with gr.Blocks() as interface:
-        gr.Markdown(
-            """
-            # MusicGen
-            This is your private 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", value="Chill and relaxing downtempo for the shower", 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(["facebook/musicgen-melody", "facebook/musicgen-medium", "facebook/musicgen-small",
-                                      "facebook/musicgen-large"],
-                                     label="Model", value="facebook/musicgen-small", interactive=True)
-                with gr.Row():
-                    decoder = gr.Radio(["Default", "MultiBand_Diffusion"],
-                                       label="Decoder", value="Default", interactive=True)
-                with gr.Row():
-                    duration = gr.Slider(
-                        minimum=1, maximum=120, value=20, 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")
-                audio_output = gr.Audio(
-                    label="Generated Music (wav)", type='filepath')
-                diffusion_output = gr.Video(
-                    label="MultiBand Diffusion Decoder")
-                audio_diffusion = gr.Audio(
-                    label="MultiBand Diffusion Decoder (wav)", type='filepath')
-        submit.click(toggle_diffusion, decoder, [diffusion_output, audio_diffusion], queue=False,
-                     show_progress=False, api_name="generate").then(predict_full, inputs=[model, decoder, text, melody, duration, topk, topp,
-                                                                                          temperature, cfg_coef],
-                                                                    outputs=[output, audio_output, diffusion_output, audio_diffusion])
-        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",
-                    "./assets/bach.mp3",
-                    "facebook/musicgen-melody",
-                    "Default"
-                ],
-                [
-                    "90s rock song with electric guitar and heavy drums",
-                    None,
-                    "facebook/musicgen-medium",
-                    "Default"
-                ],
-                [
-                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
-                    "./assets/bach.mp3",
-                    "facebook/musicgen-melody",
-                    "Default"
-                ],
-                [
-                    "lofi slow bpm electro chill with organic samples",
-                    None,
-                    "facebook/musicgen-medium",
-                    "Default"
-                ],
-                [
-                    "Punk rock with loud drum and power guitar",
-                    None,
-                    "facebook/musicgen-medium",
-                    "MultiBand_Diffusion"
-                ],
-            ],
-            inputs=[text, melody, model, decoder],
-            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. facebook/musicgen-melody -- a music generation model capable of generating music condition
-                on text and melody inputs. **Note**, you can also use text only.
-            2. facebook/musicgen-small -- a 300M transformer decoder conditioned on text only.
-            3. facebook/musicgen-medium -- a 1.5B transformer decoder conditioned on text only.
-            4. facebook/musicgen-large -- a 3.3B transformer decoder conditioned on text only.
-
-            We also present two way of decoding the audio tokens
-            1. Use the default GAN based compression model
-            2. Use MultiBand Diffusion from (paper linknano )
-
-            When using `facebook/musicgen-melody`, you can optionally 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)
-        # interface.queue().launch(**launch_kwargs, share=True)
-
-
-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",
-                                   value="Chill and relaxing downtempo for the shower", 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")
-                audio_output = gr.Audio(
-                    label="Generated Music (wav)", type='filepath')
-        submit.click(predict_batched, inputs=[text, melody],
-                     outputs=[output, audio_output], batch=True, max_batch_size=MAX_BATCH_SIZE, api_name="create")
-        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 optionally 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().launch(**launch_kwargs)
-        # demo.queue(max_size=8 * 4).launch(**launch_kwargs, share=True)
-
-
-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
-        launch_kwargs['share'] = True
-
-    global USE_DIFFUSION
-    # Show the interface
-    if IS_BATCHED:
-        USE_DIFFUSION = False
-        ui_batched(launch_kwargs)
-        # ui_full(launch_kwargs)
-    else:
-        # Space > https://huggingface.co/spaces/MWire/zest-2023
-        USE_DIFFUSION = False
-        # ui_full(launch_kwargs)
-        ui_batched(launch_kwargs)
+import gradio
+
+def my_inference_function(name):
+  return "Hello " + name + "!"
+
+gradio_interface = gradio.Interface(
+  fn=my_inference_function,
+  inputs="text",
+  outputs="text",
+  examples=[
+    ["Jill"],
+    ["Sam"]
+  ],
+  title="REST API with Gradio and Huggingface Spaces",
+  description="This is a demo of how to build an AI powered REST API with Gradio and Huggingface Spaces – for free! Based on [this article](https://www.tomsoderlund.com/ai/building-ai-powered-rest-api). See the **Use via API** link at the bottom of this page.",
+  article="Test 2023"
+)
+
+gradio_interface.launch()

app_full.py

@@ -0,0 +1,502 @@
+# 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 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, MultiBandDiffusion
+
+
+MODEL = None  # Last used model
+IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
+print(IS_BATCHED)
+MAX_BATCH_SIZE = 12
+BATCHED_DURATION = 15
+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
+
+
+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
+    
+    
+
+# write a similar function to make_waveform, but for video generated using an image with ans aspect ration of 16:9
+
+def make_video(*args, **kwargs):
+    # Further remove some warnings.
+    be = time.time()
+    with warnings.catch_warnings():
+        warnings.simplefilter('ignore')
+        out = gr.make_video(*args, **kwargs)
+        print("Make a video took", time.time() - be)
+        return out 
+    
+# write make video functions for other aspect ratios and use ffmpeg to combine them into a single video
+
+
+
+
+# def load_model(version='facebook/musicgen-melody'):
+def load_model(version='facebook/musicgen-small'):
+    global MODEL
+    print("Loading model", version)
+    if MODEL is None or MODEL.name != version:
+        MODEL = MusicGen.get_pretrained(version)
+
+
+def load_diffusion():
+    global MBD
+    if MBD is None:
+        print("loading MBD")
+        MBD = MultiBandDiffusion.get_mbd_musicgen()
+
+
+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,
+            return_tokens=USE_DIFFUSION
+        )
+    else:
+        outputs = MODEL.generate(
+            texts, progress=progress, return_tokens=USE_DIFFUSION)
+    if USE_DIFFUSION:
+        outputs_diffusion = MBD.tokens_to_wav(outputs[1])
+        outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
+    outputs = outputs.detach().cpu().float()
+    # return outputs
+    pending_videos = []
+    out_wavs = []
+    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)
+            pending_videos.append(pool.submit(make_waveform, file.name))
+            # pending_videos.append(pool.submit(make_video, 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))
+    # here I could ipload this to youtube music
+    # return out_wavs
+    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/musicgen-small')
+    res = _do_predictions(texts, melodies, BATCHED_DURATION)
+    return res
+
+
+def predict_full(model, decoder, text, melody, duration, topk, topp, temperature, cfg_coef, progress=gr.Progress()):
+    global INTERRUPTING
+    global USE_DIFFUSION
+    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)
+    if decoder == "MultiBand_Diffusion":
+        USE_DIFFUSION = True
+        load_diffusion()
+    else:
+        USE_DIFFUSION = False
+    load_model(model)
+
+    def _progress(generated, to_generate):
+        progress((min(generated, to_generate), to_generate))
+        if INTERRUPTING:
+            raise gr.Error("Interrupted.")
+    MODEL.set_custom_progress_callback(_progress)
+
+    videos, wavs = _do_predictions(
+        [text], [melody], duration, progress=True,
+        top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
+    if USE_DIFFUSION:
+        return videos[0], wavs[0], videos[1], wavs[1]
+    return videos[0], wavs[0], None, None
+
+
+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 toggle_diffusion(choice):
+    if choice == "MultiBand_Diffusion":
+        return [gr.update(visible=True)] * 2
+    else:
+        return [gr.update(visible=False)] * 2
+
+
+def ui_full(launch_kwargs):
+    with gr.Blocks() as interface:
+        gr.Markdown(
+            """
+            # MusicGen
+            This is your private 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", value="Chill and relaxing downtempo for the shower", 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(["facebook/musicgen-melody", "facebook/musicgen-medium", "facebook/musicgen-small",
+                                      "facebook/musicgen-large"],
+                                     label="Model", value="facebook/musicgen-small", interactive=True)
+                with gr.Row():
+                    decoder = gr.Radio(["Default", "MultiBand_Diffusion"],
+                                       label="Decoder", value="Default", interactive=True)
+                with gr.Row():
+                    duration = gr.Slider(
+                        minimum=1, maximum=120, value=20, 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")
+                audio_output = gr.Audio(
+                    label="Generated Music (wav)", type='filepath')
+                diffusion_output = gr.Video(
+                    label="MultiBand Diffusion Decoder")
+                audio_diffusion = gr.Audio(
+                    label="MultiBand Diffusion Decoder (wav)", type='filepath')
+        submit.click(toggle_diffusion, decoder, [diffusion_output, audio_diffusion], queue=False,
+                     show_progress=False, api_name="generate").then(predict_full, inputs=[model, decoder, text, melody, duration, topk, topp,
+                                                                                          temperature, cfg_coef],
+                                                                    outputs=[output, audio_output, diffusion_output, audio_diffusion])
+        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",
+                    "./assets/bach.mp3",
+                    "facebook/musicgen-melody",
+                    "Default"
+                ],
+                [
+                    "90s rock song with electric guitar and heavy drums",
+                    None,
+                    "facebook/musicgen-medium",
+                    "Default"
+                ],
+                [
+                    "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
+                    "./assets/bach.mp3",
+                    "facebook/musicgen-melody",
+                    "Default"
+                ],
+                [
+                    "lofi slow bpm electro chill with organic samples",
+                    None,
+                    "facebook/musicgen-medium",
+                    "Default"
+                ],
+                [
+                    "Punk rock with loud drum and power guitar",
+                    None,
+                    "facebook/musicgen-medium",
+                    "MultiBand_Diffusion"
+                ],
+            ],
+            inputs=[text, melody, model, decoder],
+            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. facebook/musicgen-melody -- a music generation model capable of generating music condition
+                on text and melody inputs. **Note**, you can also use text only.
+            2. facebook/musicgen-small -- a 300M transformer decoder conditioned on text only.
+            3. facebook/musicgen-medium -- a 1.5B transformer decoder conditioned on text only.
+            4. facebook/musicgen-large -- a 3.3B transformer decoder conditioned on text only.
+
+            We also present two way of decoding the audio tokens
+            1. Use the default GAN based compression model
+            2. Use MultiBand Diffusion from (paper linknano )
+
+            When using `facebook/musicgen-melody`, you can optionally 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)
+        # interface.queue().launch(**launch_kwargs, share=True)
+
+
+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",
+                                   value="Chill and relaxing downtempo for the shower", 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")
+                audio_output = gr.Audio(
+                    label="Generated Music (wav)", type='filepath')
+        submit.click(predict_batched, inputs=[text, melody],
+                     outputs=[output, audio_output], batch=True, max_batch_size=MAX_BATCH_SIZE, api_name="create")
+        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 optionally 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().launch(**launch_kwargs)
+        # demo.queue(max_size=8 * 4).launch(**launch_kwargs, share=True)
+
+
+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
+        launch_kwargs['share'] = True
+
+    global USE_DIFFUSION
+    # Show the interface
+    if IS_BATCHED:
+        USE_DIFFUSION = False
+        ui_batched(launch_kwargs)
+        # ui_full(launch_kwargs)
+    else:
+        # Space > https://huggingface.co/spaces/MWire/zest-2023
+        USE_DIFFUSION = False
+        # ui_full(launch_kwargs)
+        ui_batched(launch_kwargs)