Delete app.py

app.py

@@ -1,23 +0,0 @@
-import torchaudio
-from audiocraft.models import MusicGen
-from audiocraft.data.audio import audio_write
-import gradio as gr
-import modin.pandas as pd 
-from tempfile import NamedTemporaryFile
-
-model = MusicGen.get_pretrained('large')
-
-def genie(Prompt, Duration):
-    model.set_generation_params(duration=Duration)
-    wav = model.generate(Prompt)
-    for idx, one_wav in enumerate(wav):
-        with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
-            audio_write(
-                file.name, one_wav.cpu(), model.sample_rate, strategy="loudness",
-                loudness_headroom_db=16, add_suffix=False)                
-    return file.name
-    
-title = 'MusicGen'
-description = ("Audiocraft provides the code and models for MusicGen, a simple and controllable model for music generation. MusicGen is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods like MusicLM, MusicGen doesn't not require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio.")
-article = ('MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation. <br><br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>')
-gr.Interface(fn=genie, inputs=[gr.Textbox(label='Text Prompt. Warning: Longer Prompts may cause reset.'), gr.Slider(minimum=1, maximum=8, value=6, label='Duration')], outputs=gr.Audio(), title=title, description=description, article=article).queue(max_size=2).launch(debug=True)