Update demos/musicgen_app.py

demos/musicgen_app.py

@@ -1,5 +1,4 @@
 import argparse
-from concurrent.futures import ProcessPoolExecutor
 import logging
 import os
 from pathlib import Path
@@ -7,7 +6,7 @@ import subprocess as sp
 import sys
 import time
 import typing as tp
-import warnings
+from tempfile import NamedTemporaryFile
 
 from einops import rearrange
 import torch
@@ -21,7 +20,7 @@ import multiprocessing as mp
 
 # --- Utility Functions and Classes ---
 
-class FileCleaner:  # Unchanged from previous example, included for completeness
+class FileCleaner:  # Unchanged
     def __init__(self, file_lifetime: float = 3600):
         self.file_lifetime = file_lifetime
         self.files = []
@@ -154,220 +153,4 @@ class Predictor:
             result_task_id, result = self.result_queue.get()
             if result_task_id == task_id:
                 if isinstance(result, Exception):
-                    raise result  # Re-raise the exception in the main process
-                return result # (wav, diffusion_wav) or (wav, None)
-
-    def shutdown(self):
-        """
-        Shuts down the worker process.
-        """
-        self.task_queue.put(None)  # Send sentinel value to stop the worker
-        self.process.join()  # Wait for the process to terminate
-
-
-# Global predictor instance
-_predictor = None
-
-def get_predictor(model_name:str = 'facebook/musicgen-melody'):
-    global _predictor
-    if _predictor is None:
-        _predictor = Predictor(model_name)
-    return _predictor
-
-def predict_full(model, model_path, use_mbd, text, melody, duration, topk, topp, temperature, cfg_coef):
-
-    predictor = get_predictor(model)
-    task_id = predictor.predict(
-        text=text,
-        melody=melody,
-        duration=duration,
-        use_diffusion=use_mbd,
-        top_k=topk,
-        top_p=topp,
-        temperature=temperature,
-        cfg_coef=cfg_coef,
-    )
-
-    wav, diffusion_wav = predictor.get_result(task_id)
-
-    # Save and return audio files
-    wav_paths = []
-    video_paths = []
-
-    # Save standard output
-    with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
-        audio_write(
-            file.name, wav[0], 32000, strategy="loudness", #hardcoded sample rate
-            loudness_headroom_db=16, loudness_compressor=True, add_suffix=False
-        )
-        wav_paths.append(file.name)
-        video_paths.append(make_waveform(file.name)) # Make and clean up video
-        file_cleaner.add(file.name)
-
-    # Save MBD output if used
-    if diffusion_wav is not None:
-        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
-            audio_write(
-                file.name, diffusion_wav[0], 32000, strategy="loudness", #hardcoded sample rate
-                loudness_headroom_db=16, loudness_compressor=True, add_suffix=False
-            )
-            wav_paths.append(file.name)
-            video_paths.append(make_waveform(file.name)) # Make and clean up video
-            file_cleaner.add(file.name)
-
-    if use_mbd:
-         return video_paths[0], wav_paths[0], video_paths[1], wav_paths[1]
-    return video_paths[0], wav_paths[0], None, None
-
-
-def toggle_audio_src(choice):
-    if choice == "mic":
-        return gr.update(sources="microphone", value=None, label="Microphone")
-    else:
-        return gr.update(sources="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
-# --- Gradio UI ---
-
-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", interactive=True)
-                    with gr.Column():
-                        radio = gr.Radio(["file", "mic"], value="file",
-                                         label="Condition on a melody (optional) File or Mic")
-                        melody = gr.Audio(sources="upload", type="numpy", label="File",
-                                          interactive=True, elem_id="melody-input")
-                with gr.Row():
-                    submit = gr.Button("Submit")
-                    # _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)  # Interrupt is now handled implicitly
-                with gr.Row():
-                    model = gr.Radio(["facebook/musicgen-melody", "facebook/musicgen-medium", "facebook/musicgen-small",
-                                      "facebook/musicgen-large", "facebook/musicgen-melody-large",
-                                      "facebook/musicgen-stereo-small", "facebook/musicgen-stereo-medium",
-                                      "facebook/musicgen-stereo-melody", "facebook/musicgen-stereo-large",
-                                      "facebook/musicgen-stereo-melody-large"],
-                                     label="Model", value="facebook/musicgen-melody", interactive=True)
-                    model_path = gr.Text(label="Model Path (custom models)", interactive=False, visible=False)  # Keep, but hide
-                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=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")
-                audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
-                diffusion_output = gr.Video(label="MultiBand Diffusion Decoder", visible=False)
-                audio_diffusion = gr.Audio(label="MultiBand Diffusion Decoder (wav)", type='filepath', visible=False)
-
-        submit.click(
-            toggle_diffusion, decoder, [diffusion_output, audio_diffusion], queue=False
-        ).then(
-            predict_full,
-            inputs=[model, model_path, 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"
-                ],
-                [
-                    "A cheerful country song with acoustic guitars",
-                    "./assets/bolero_ravel.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.
-
-            The model was trained with description 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).
-
-            Using one of the `melody` model (e.g. `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.
-            For best results, the melody should be 30 seconds long (I know, the samples we provide are not...)
-
-            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 10 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.
-            5. facebook/musicgen-melody-large -- a 3.3B transformer decoder conditioned on and melody.
-            6. facebook/musicgen-stereo-*: same as the previous models but fine tuned to output stereo audio.
-
-            We also present two way of decoding the audio tokens
-            1. Use the default GAN based compression model. It can suffer from artifacts especially
-                for crashes, snares etc.
-            2. Use [MultiBand Diffusion](https://arxiv.org/abs/2308.02560). Should improve the audio quality,
-                at an extra computational cost. When this is selected, we provide both the GAN based decoded
-                audio, and the one obtained with MBD.
+                    raise result