dependencies

app.py

@@ -0,0 +1,45 @@
+
+from transformers import AutoProcessor, MusicgenForConditionalGeneration
+import scipy
+import streamlit as st
+
+BASE_MODEL = "facebook/musicgen-small"
+
+
+processor = AutoProcessor.from_pretrained(BASE_MODEL)
+model = MusicgenForConditionalGeneration.from_pretrained(BASE_MODEL)
+
+def generate_audio(text):
+    inputs = processor(
+        text=[text],
+        padding=True,
+        return_tensors="pt",
+    )
+    audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=256)
+    cleaned_text = text.replace(" ", "_")
+    sampling_rate = model.config.audio_encoder.sampling_rate
+    scipy.io.wavfile.write(f'${cleaned_text}.wav', rate=sampling_rate, data=audio_values[0, 0].numpy())
+    return f'${cleaned_text}.wav'
+
+# Streamlit app title and description
+st.title("Text-to-Audio Generation")
+
+# User input for text prompt
+text = st.text_area("Enter Text Prompt:")
+
+# Generate audio when the user clicks the button
+if st.button("Generate Audio"):
+    if text:
+        # Generate audio using the ModelManager
+        audio_file_path = generate_audio(text)
+
+        # Display the audio player
+        audio_data = open(audio_file_path, "rb").read()
+        st.audio(audio_data, format="audio/wav")
+
+        # Provide a download link for the audio file
+        st.write("Audio Generated Successfully!")
+        st.success(f"Download the audio file [here](/{audio_file_path}).")
+    else:
+        st.warning("Please enter a text prompt.")
+