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import gradio as gr
from transformers import pipeline
import torch
from transformers import AutoProcessor, MusicgenForConditionalGeneration
import numpy as np # Import numpy
# Load emotion classifier
emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
# Load music generator (small for CPU)
music_model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
# Map emotion to style/genre prompts
EMOTION_TO_MUSIC = {
"joy": "happy upbeat piano melody",
"anger": "intense aggressive drums",
"sadness": "slow emotional violin",
"fear": "dark ambient synth",
"love": "soft romantic acoustic guitar",
"surprise": "quirky playful tune",
"neutral": "chill background lofi beat"
}
# Main generation function
def generate_music(user_input):
# Step 1: Detect emotion
emotion_scores = emotion_classifier(user_input)[0]
top_emotion = max(emotion_scores, key=lambda x: x["score"])["label"]
# Step 2: Generate prompt
music_prompt = EMOTION_TO_MUSIC.get(top_emotion.lower(), "ambient melody")
# Step 3: Generate music
inputs = processor(text=[music_prompt], return_tensors="pt")
audio_values = music_model.generate(**inputs, max_new_tokens=1024)
# Convert audio tensor to numpy array
audio_array = audio_values[0].cpu().numpy()
# --- FIX START ---
# Normalize the audio array to be within the range of a 16-bit PCM WAV file
# The default sampling rate for musicgen-small is 16000 Hz, and Gradio expects
# values to be scaled for 16-bit integers if not float.
# We'll normalize to -1 to 1 for float and let Gradio handle the 16-bit conversion.
# However, to be extra safe, ensure max amplitude is 1.0.
audio_array = audio_array / np.max(np.abs(audio_array))
# --- FIX END ---
# Return result
# The Musicgen model outputs audio at a sampling rate of 16kHz
sampling_rate = 16000
return f"Top Emotion: {top_emotion}", (sampling_rate, audio_array)
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("# Emotion-to-Music AI")
gr.Markdown("Describe how you feel and get a unique music track matching your mood!")
with gr.Row():
text_input = gr.Textbox(label="How are you feeling?")
generate_btn = gr.Button("Generate Music")
with gr.Row():
emotion_output = gr.Textbox(label="Detected Emotion")
audio_output = gr.Audio(label="Generated Music", type="numpy") # type="numpy" is correct here
generate_btn.click(fn=generate_music, inputs=text_input, outputs=[emotion_output, audio_output])
demo.launch()
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