Spaces:
Building
on
A10G
Building
on
A10G
linting and adding file cleaner
Browse files
app.py
CHANGED
@@ -10,9 +10,11 @@
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import argparse
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from concurrent.futures import ProcessPoolExecutor
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import os
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import subprocess as sp
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from tempfile import NamedTemporaryFile
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import time
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import warnings
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import torch
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@@ -50,6 +52,29 @@ def interrupt():
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INTERRUPTING = True
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def make_waveform(*args, **kwargs):
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# Further remove some warnings.
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be = time.time()
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@@ -103,8 +128,12 @@ def _do_predictions(texts, melodies, duration, progress=False, **gen_kwargs):
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file.name, output, MODEL.sample_rate, strategy="loudness",
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loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
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out_files.append(pool.submit(make_waveform, file.name))
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res = [out_file.result() for out_file in out_files]
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print("batch finished", len(texts), time.time() - be)
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return res
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@@ -140,18 +169,21 @@ def predict_full(model, text, melody, duration, topk, topp, temperature, cfg_coe
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top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
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return outs[0]
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def toggle_audio_src(choice):
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if choice == "mic":
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return gr.update(source="microphone", value=None, label="Microphone")
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else:
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return gr.update(source="upload", value=None, label="File")
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-
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def ui_full(launch_kwargs):
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with gr.Blocks() as interface:
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gr.Markdown(
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"""
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# MusicGen
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-
This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
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presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
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"""
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)
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@@ -160,14 +192,17 @@ def ui_full(launch_kwargs):
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with gr.Row():
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text = gr.Text(label="Input Text", interactive=True)
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with gr.Column():
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radio = gr.Radio(["file", "mic"], value="file",
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with gr.Row():
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submit = gr.Button("Submit")
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# Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
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_ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
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with gr.Row():
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model = gr.Radio(["melody", "medium", "small", "large"],
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with gr.Row():
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duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
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with gr.Row():
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@@ -177,7 +212,9 @@ def ui_full(launch_kwargs):
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cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
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with gr.Column():
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output = gr.Video(label="Generated Music")
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submit.click(predict_full,
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radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
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gr.Examples(
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fn=predict_full,
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@@ -221,17 +258,20 @@ def ui_full(launch_kwargs):
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This can take a long time, and the model might lose consistency. The model might also
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decide at arbitrary positions that the song ends.
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**WARNING:** Choosing long durations will take a long time to generate (2min might take ~10min).
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is kept with the previously generated chunk, and 18 "new" seconds
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We present 4 model variations:
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1. Melody -- a music generation model capable of generating music condition
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2. Small -- a 300M transformer decoder conditioned on text only.
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3. Medium -- a 1.5B transformer decoder conditioned on text only.
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4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
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When using `melody`, ou can optionaly provide a reference audio from
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which a broad melody will be extracted. The model will then try to follow both
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You can also use your own GPU or a Google Colab by following the instructions on our repo.
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See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
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@@ -248,11 +288,14 @@ def ui_batched(launch_kwargs):
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"""
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# MusicGen
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-
This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
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presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
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<br/>
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<a href="https://huggingface.co/spaces/facebook/MusicGen?duplicate=true"
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for longer sequences, more control and no queue.</p>
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"""
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)
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@@ -261,13 +304,16 @@ def ui_batched(launch_kwargs):
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with gr.Row():
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text = gr.Text(label="Describe your music", lines=2, interactive=True)
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with gr.Column():
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radio = gr.Radio(["file", "mic"], value="file",
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-
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with gr.Row():
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submit = gr.Button("Generate")
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with gr.Column():
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output = gr.Video(label="Generated Music")
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submit.click(predict_batched, inputs=[text, melody],
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radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
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gr.Examples(
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fn=predict_batched,
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import argparse
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from concurrent.futures import ProcessPoolExecutor
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import os
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from pathlib import Path
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import subprocess as sp
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from tempfile import NamedTemporaryFile
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import time
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import typing as tp
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import warnings
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import torch
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INTERRUPTING = True
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class FileCleaner:
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def __init__(self, file_lifetime: float = 3600):
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self.file_lifetime = file_lifetime
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self.files = []
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def add(self, path: tp.Union[str, Path]):
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self._cleanup()
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self.files.append((time.time(), Path(path)))
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def _cleanup(self):
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now = time.time()
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for time_added, path in list(self.files):
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if now - time_added > self.file_lifetime:
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if path.exists():
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path.unlink()
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self.files.pop(0)
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else:
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break
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file_cleaner = FileCleaner()
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def make_waveform(*args, **kwargs):
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# Further remove some warnings.
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be = time.time()
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file.name, output, MODEL.sample_rate, strategy="loudness",
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loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
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out_files.append(pool.submit(make_waveform, file.name))
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file_cleaner.add(file.name)
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res = [out_file.result() for out_file in out_files]
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for file in res:
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file_cleaner.add(file)
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print("batch finished", len(texts), time.time() - be)
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print("Tempfiles currently stored: ", len(file_cleaner.files))
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return res
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top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef)
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return outs[0]
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def toggle_audio_src(choice):
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if choice == "mic":
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return gr.update(source="microphone", value=None, label="Microphone")
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else:
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return gr.update(source="upload", value=None, label="File")
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def ui_full(launch_kwargs):
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with gr.Blocks() as interface:
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gr.Markdown(
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"""
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# MusicGen
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+
This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
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a simple and controllable model for music generation
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presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
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"""
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)
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with gr.Row():
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text = gr.Text(label="Input Text", interactive=True)
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with gr.Column():
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radio = gr.Radio(["file", "mic"], value="file",
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label="Condition on a melody (optional) File or Mic")
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melody = gr.Audio(source="upload", type="numpy", label="File",
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interactive=True, elem_id="melody-input")
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with gr.Row():
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submit = gr.Button("Submit")
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# Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
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_ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
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with gr.Row():
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model = gr.Radio(["melody", "medium", "small", "large"],
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label="Model", value="melody", interactive=True)
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with gr.Row():
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duration = gr.Slider(minimum=1, maximum=120, value=10, label="Duration", interactive=True)
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with gr.Row():
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cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
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with gr.Column():
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output = gr.Video(label="Generated Music")
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submit.click(predict_full,
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inputs=[model, text, melody, duration, topk, topp, temperature, cfg_coef],
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outputs=[output])
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radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
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gr.Examples(
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fn=predict_full,
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This can take a long time, and the model might lose consistency. The model might also
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decide at arbitrary positions that the song ends.
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+
**WARNING:** Choosing long durations will take a long time to generate (2min might take ~10min).
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An overlap of 12 seconds is kept with the previously generated chunk, and 18 "new" seconds
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are generated each time.
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We present 4 model variations:
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+
1. Melody -- a music generation model capable of generating music condition
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+
on text and melody inputs. **Note**, you can also use text only.
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2. Small -- a 300M transformer decoder conditioned on text only.
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3. Medium -- a 1.5B transformer decoder conditioned on text only.
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4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
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When using `melody`, ou can optionaly provide a reference audio from
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+
which a broad melody will be extracted. The model will then try to follow both
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+
the description and melody provided.
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You can also use your own GPU or a Google Colab by following the instructions on our repo.
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See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
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"""
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# MusicGen
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+
This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
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+
a simple and controllable model for music generation
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presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
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<br/>
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<a href="https://huggingface.co/spaces/facebook/MusicGen?duplicate=true"
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style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
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<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;"
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src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
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for longer sequences, more control and no queue.</p>
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"""
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)
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with gr.Row():
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text = gr.Text(label="Describe your music", lines=2, interactive=True)
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with gr.Column():
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radio = gr.Radio(["file", "mic"], value="file",
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label="Condition on a melody (optional) File or Mic")
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melody = gr.Audio(source="upload", type="numpy", label="File",
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interactive=True, elem_id="melody-input")
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with gr.Row():
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submit = gr.Button("Generate")
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with gr.Column():
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output = gr.Video(label="Generated Music")
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submit.click(predict_batched, inputs=[text, melody],
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outputs=[output], batch=True, max_batch_size=MAX_BATCH_SIZE)
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radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
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gr.Examples(
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fn=predict_batched,
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