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import gradio as gr
from transformers import pipeline
# --- PiMusic3: Pi Forge Music Player ---
# Author: onenoly11
# Description: Generates, transcribes, and analyzes audio
# using MusicGen, Whisper, and DistilBERT within the Pi Forge framework.
"""
Ah, the spire trembles—a rift in the weave...
(This stanza is now preserved safely as a docstring.)
"""
# --- Compatibility Shim: MusicGen fallback ---
try:
# Try using Audiocraft (local / full build)
from audiocraft.models import musicgen
def generate_music(prompt):
"""Generate music using local Audiocraft (MusicGen)."""
model = musicgen.MusicGen.get_pretrained("facebook/musicgen-small")
model.set_generation_params(duration=10)
wav = model.generate([prompt])
import tempfile, soundfile as sf
temp_wav = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
sf.write(temp_wav.name, wav[0].cpu().numpy().T, 32000)
return temp_wav.name
MUSICGEN_MODE = "Audiocraft (local)"
except Exception:
# Fallback to Transformers pipeline (Hugging Face cloud build)
musicgen = pipeline("text-to-audio", model="facebook/musicgen-small")
def generate_music(prompt):
"""Generate music using Transformers pipeline fallback."""
result = musicgen(prompt)
return result["audio"]
MUSICGEN_MODE = "Transformers (cloud)"
# --- Whisper and Sentiment Pipelines ---
whisper = pipeline("automatic-speech-recognition", model="openai/whisper-base")
sentiment = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
# --- Utility Functions ---
def transcribe_audio(audio_path):
result = whisper(audio_path)
return result["text"]
def analyze_sentiment(text):
result = sentiment(text)
return f"{result[0]['label']} ({result[0]['score']:.2f})"
# --- Interface ---
with gr.Blocks(title="PiMusic3 🎵") as demo:
gr.Markdown(f"### 🎶 PiMusic3 — Pi Forge Music Player\nMode: **{MUSICGEN_MODE}**\nGenerate, transcribe, and analyze sound ethically.")
with gr.Tab("MusicGen"):
prompt = gr.Textbox(label="Music Prompt", placeholder="Describe your sound...")
generate_btn = gr.Button("🎼 Generate")
audio_out = gr.Audio(label="Generated Music")
generate_btn.click(fn=generate_music, inputs=prompt, outputs=audio_out)
with gr.Tab("Whisper Transcribe"):
mic = gr.Audio(sources=["microphone", "upload"], type="filepath", label="🎙️ Record or Upload Audio")
transcribe_btn = gr.Button("📝 Transcribe")
transcript = gr.Textbox(label="Transcription")
transcribe_btn.click(fn=transcribe_audio, inputs=mic, outputs=transcript)
with gr.Tab("Sentiment Analysis"):
text_in = gr.Textbox(label="Enter text for sentiment check")
analyze_btn = gr.Button("🔍 Analyze")
sentiment_out = gr.Textbox(label="Result")
analyze_btn.click(fn=analyze_sentiment, inputs=text_in, outputs=sentiment_out)
demo.launch() |