Create app.py

app.py

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+import torchaudio
+import matplotlib.pyplot as plt
+import gradio as gr
+from transformers import AutoProcessor, MusicgenForConditionalGeneration
+import torchaudio.transforms as T
+
+# Define available model options for dropdown
+model_options = {
+    "Small Model": "facebook/musicgen-small",
+    "Medium Model": "facebook/musicgen-medium",
+    "Large Model": "facebook/musicgen-large"
+}
+
+# Define style tags options
+style_tags_options = ["East Coast", "Trap", "Boom Bap", "Lo-Fi", "Experimental", 
+                      "Rock", "Electronic", "Pop", "Country", "Heavy Metal", 
+                      "Classical", "Jazz", "Reggae"]
+
+def generate_spectrogram(audio_tensor, sample_rate):
+    griffinlim_transform = T.GriffinLim(n_fft=400, win_length=400, hop_length=160)
+    waveform = griffinlim_transform(audio_tensor)
+
+    plt.figure(figsize=(10, 4))
+    plt.specgram(waveform.numpy()[0], Fs=sample_rate, cmap='viridis')
+    plt.colorbar(format='%+2.0f dB')
+    plt.title('Spectrogram')
+    plt.tight_layout()
+    plt.ylabel('Frequency (Hz)')
+    plt.xlabel('Time (s)')
+    spectrogram_path = "generated_spectrogram.png"
+    plt.savefig(spectrogram_path)
+    plt.close()
+    return spectrogram_path
+
+def generate_music(description, model_choice, style_tags, tempo, intensity, duration):
+    try:
+        processor = AutoProcessor.from_pretrained(model_options[model_choice])
+        model = MusicgenForConditionalGeneration.from_pretrained(model_options[model_choice])
+
+        # Convert the list of selected style tags into a single string
+        style_tags_str = " ".join(style_tags)
+
+        inputs = processor(text=[description + " " + style_tags_str], return_tensors="pt", padding=True)
+        audio_output = model.generate(**inputs, max_new_tokens=256)
+
+        sampling_rate = 16000
+        output_file = "generated_music.wav"
+        torchaudio.save(output_file, audio_output[0].cpu(), sampling_rate)
+        spectrogram_path = generate_spectrogram(audio_output[0].squeeze(), sampling_rate)
+
+        return output_file, spectrogram_path, None
+    except Exception as e:
+        error_message = f"An error occurred: {str(e)}"
+        return None, None, error_message
+
+iface = gr.Interface(
+    fn=generate_music,
+    inputs=[
+        gr.Textbox(label="Enter a description for the music"),
+        gr.Dropdown(label="Select Model", choices=list(model_options.keys())),
+        gr.CheckboxGroup(label="Style Tags", choices=style_tags_options),
+        gr.Slider(label="Tempo", minimum=60, maximum=240, step=1, value=120),
+        gr.Slider(label="Intensity", minimum=1, maximum=10, step=1, value=5),
+        gr.Slider(label="Duration (Seconds)", minimum=15, maximum=300, step=1, value=60)
+    ],
+    outputs=[
+        gr.Audio(label="Generated Music"),
+        gr.Image(label="Spectrogram"),
+        gr.Textbox(label="Error Message", visible=True)
+    ],
+    title="MusicGen Pro XL",
+    description="Generate original music from multiple genres with customizable parameters and style tags. Listen to the generated music, visualize the spectrogram, and receive error messages if any."
+)
+
+iface.launch()