feat: implement first version of space for HARP

.gitignore

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+*__pycache__
+_outputs
+src

app.py

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+import gradio as gr
+from audiocraft.data.audio_utils import normalize_audio
+from audiocraft.models import MusicGen
+from audiotools import AudioSignal
+
+from pyharp import ModelCard, build_endpoint, save_and_return_filepath
+
+card = ModelCard(
+    name='Micro Musicgen Jungle',
+    description="The jungle version of the micro-musicgen model series. Use a prompt duration of 0 to generate unconditional audio. (WHICH WORKS BETTER) Outpainting is not really tested by me, I just thought it would be cool to have it here because you work with input audio.\n\n HAVE FUNNNNNNNNN",
+    author='Aaron Abebe',
+    tags=['musicgen', 'jungle', 'micro-musicgen', 'unconditional', 'generation']
+)
+
+model = MusicGen.get_pretrained("pharoAIsanders420/micro-musicgen-jungle")
+
+
+def process_fn(input_audio_path, gen_duration, prompt_duration):
+    """
+    Process the input audio and generate new audio by sampling from the micro-musicgen-jungle model.
+    Supports both unconditional and conditional generation.
+
+    Args:
+        input_audio_path (str): the audio filepath to be processed.
+        gen_duration (int): the duration of the generated audio.
+        prompt_duration (int): the duration of the input conditioning audio.
+
+    Returns:
+        output_audio_path (str): the filepath of the processed audio.
+    """
+    sig = AudioSignal(input_audio_path)
+    y, sr = sig.audio_data[0], sig.sample_rate
+
+    model.set_generation_params(
+        duration=gen_duration,
+        temperature=1.05,
+        cfg_coef=3,
+    )
+
+    if prompt_duration is None or prompt_duration == 0:
+        output = model.generate_unconditional(1)
+    else:
+        num_samples = int(prompt_duration * sr)
+        if y.shape[1] < num_samples:
+            raise ValueError("The existing audio is too short for the specified prompt duration.")
+
+        start_sample = y.shape[1] - num_samples
+        prompt_waveform = y[..., start_sample:]
+
+
+        output = model.generate_continuation(prompt_waveform, prompt_sample_rate=sr)
+
+    output = normalize_audio(
+        output,
+        sample_rate=model.sample_rate, # musicgen outputs at 32kHz
+        strategy="loudness",
+        loudness_headroom_db=10,
+        loudness_compressor=True,
+    )
+    sig.audio_data = output.cpu()
+    return save_and_return_filepath(sig)
+
+
+with gr.Blocks() as demo:
+    inputs = [
+        gr.Audio(
+            label="Ignore Me: I only generate, I don't consume",
+            type='filepath'
+        ),
+        gr.Slider(
+            minimum=10,
+            maximum=30,
+            step=1,
+            value=10,
+            label="Generation Duration"
+        ),
+        gr.Slider(
+            minimum=0,
+            maximum=10,
+            step=1,
+            value=2,
+            label="Input Conditioning Duration"
+        ),
+    ]
+
+    output = gr.Audio(label='Audio Output', type='filepath')
+    widgets = build_endpoint(inputs, output, process_fn, card)
+
+demo.queue()
+demo.launch(share=True)

requirements.txt

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+-e git+https://github.com/audacitorch/pyharp.git#egg=pyharp
+torch==2.1.0
+descript-audiotools
+audiocraft @ https://github.com/aaronabebe/audiocraft.git