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app.py

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+import streamlit as st
+import torch
+import torchaudio
+from audiocraft.models import MusicGen
+import os
+import numpy as np
+import base64
+
+@st.cache_resource()
+def load_model():
+    model = MusicGen.get_pretrained('facebook/musicgen-small')
+    return model
+
+
+@st.cache_resource()
+def generate_music_tensors(description, duration: int):
+    model = load_model()
+
+    model.set_generation_params(
+        use_sampling=True,
+        top_k=250,
+        duration=duration
+    )
+
+    output = model.generate(
+        descriptions=[description],
+        progress=True,
+        return_tokens=True
+    )
+    return output[0]
+
+
+def save_audio(samples: torch.Tensor):
+    """Renders an audio player for the given audio samples and saves them to a local directory.
+
+    Args:
+        samples (torch.Tensor): a Tensor of decoded audio samples
+            with shapes [B, C, T] or [C, T]
+        sample_rate (int): sample rate audio should be displayed with.
+        save_path (str): path to the directory where audio should be saved.
+    """
+
+    print("Samples (inside function): ", samples)
+    sample_rate = 30000
+    save_path = "audio_output/"
+    assert samples.dim() == 2 or samples.dim() == 3
+
+    samples = samples.detach().cpu()
+    if samples.dim() == 2:
+        samples = samples[None, ...]
+
+    for idx, audio in enumerate(samples):
+        audio_path = os.path.join(save_path, f"audio_{idx}.wav")
+        torchaudio.save(audio_path, audio, sample_rate)
+
+def get_binary_file_downloader_html(bin_file, file_label='File'):
+    with open(bin_file, 'rb') as f:
+        data = f.read()
+    bin_str = base64.b64encode(data).decode()
+    href = f'<a href="data:application/octet-stream;base64,{bin_str}" download="{os.path.basename(bin_file)}">Download {file_label}</a>'
+    return href
+
+st.set_page_config(
+    page_icon= "musical_note",
+    page_title= "Music Gen"
+)
+
+def main():
+    with st.sidebar:
+        st.header("""⚙️ Parameters ⚙️""",divider="rainbow")
+        st.text("")
+        st.subheader("1. Enter your music description.......")
+        text_area = st.text_area('Ex : 80s rock song with guitar and drums')
+        st.text('')
+        st.subheader("2. Select time duration (In Seconds)")
+
+        time_slider = st.slider("Select time duration (In Seconds)", 0, 20, 10)
+
+    st.title("""🎵 Text to Music Generator 🎵""")
+    st.text('')
+    left_co,right_co = st.columns(2)
+    left_co.write("""Music Generation using Meta AI, through a prompt""")
+    left_co.write(("""PS : First generation may take some time as it loads the full model and requirements"""))
+    #container1 = st.container()
+    #container1.write("""Music coupled with Image Generation using a prompt""")
+    #container1.write("""PS : First generation may take some time as it loads the full model and requirements""")
+
+
+    if st.sidebar.button('Generate !'):
+        gif_url = "https://media.giphy.com/media/26Fffy7jqQW8gVg8o/giphy.gif"
+        with right_co:
+            with st.spinner("Generating"):
+                st.image(gif_url,width=250)
+        with left_co:
+            st.text('')
+            st.text('')
+            st.text('')
+            st.text('')
+            st.text('')
+            st.text('')
+            st.subheader("Generated Music")
+
+            music_tensors = generate_music_tensors(text_area, time_slider)
+            save_music_file = save_audio(music_tensors)
+            audio_filepath = 'audio_output/audio_0.wav'
+            audio_file = open(audio_filepath, 'rb')
+            audio_bytes = audio_file.read()
+            st.audio(audio_bytes)
+            st.markdown(get_binary_file_downloader_html(audio_filepath, 'Audio'), unsafe_allow_html=True)
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+audiocraft
+numpy==1.23.5
+torch==2.0.1
+torchaudio==2.0.2
+huggingface_hub
+transformers==4.33.3
+torchmetrics
+encodec==0.1.1
+xformers==0.0.22
+streamlit
+librosa
+protobuf==3.20.0