import os import tempfile import torch import gradio as gr from huggingface_hub import hf_hub_download, snapshot_download import spaces # Download models from HuggingFace Hub on startup def download_models(): """Download all required model files from HuggingFace Hub.""" cache_dir = os.environ.get("HF_HOME", os.path.expanduser("/tmp")) model_dir = os.path.join(cache_dir, "heartmula_models") if not os.path.exists(model_dir): os.makedirs(model_dir, exist_ok=True) # Download HeartMuLaGen (tokenizer and gen_config) print("Downloading HeartMuLaGen files...") for filename in ["tokenizer.json", "gen_config.json"]: hf_hub_download( repo_id="HeartMuLa/HeartMuLaGen", filename=filename, local_dir=model_dir, ) # Download HeartMuLa-oss-3B print("Downloading HeartMuLa-oss-3B...") snapshot_download( repo_id="HeartMuLa/HeartMuLa-oss-3B", local_dir=os.path.join(model_dir, "HeartMuLa-oss-3B"), ) # Download HeartCodec-oss print("Downloading HeartCodec-oss...") snapshot_download( repo_id="HeartMuLa/HeartCodec-oss", local_dir=os.path.join(model_dir, "HeartCodec-oss"), ) print("All models downloaded successfully!") return model_dir from heartlib import HeartMuLaGenPipeline model_dir = download_models() # Determine device and dtype if torch.cuda.is_available(): device = torch.device("cuda") dtype = torch.bfloat16 else: device = torch.device("cpu") dtype = torch.float32 print(f"Loading pipeline on {device} with {dtype}...") pipe = HeartMuLaGenPipeline.from_pretrained( model_dir, device=device, dtype=dtype, version="3B", ) print("Pipeline loaded successfully!") @spaces.GPU(duration=130) def generate_music( lyrics: str, tags: str, max_duration_seconds: int, temperature: float, topk: int, cfg_scale: float, progress=gr.Progress(track_tqdm=True), ): """Generate music from lyrics and tags.""" if not lyrics.strip(): raise gr.Error("Please enter some lyrics!") if not tags.strip(): raise gr.Error("Please enter at least one tag!") # Create a temporary file for output with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as f: output_path = f.name max_audio_length_ms = max_duration_seconds * 1000 with torch.no_grad(): pipe( { "lyrics": lyrics, "tags": tags, }, max_audio_length_ms=max_audio_length_ms, save_path=output_path, topk=topk, temperature=temperature, cfg_scale=cfg_scale, ) return output_path # Example lyrics EXAMPLE_LYRICS = """[Intro] [Verse] The sun creeps in across the floor I hear the traffic outside the door The coffee pot begins to hiss It is another morning just like this [Prechorus] The world keeps spinning round and round Feet are planted on the ground I find my rhythm in the sound [Chorus] Every day the light returns Every day the fire burns We keep on walking down this street Moving to the same steady beat It is the ordinary magic that we meet [Verse] The hours tick deeply into noon Chasing shadows, chasing the moon Work is done and the lights go low Watching the city start to glow [Bridge] It is not always easy, not always bright Sometimes we wrestle with the night But we make it to the morning light [Chorus] Every day the light returns Every day the fire burns We keep on walking down this street Moving to the same steady beat [Outro] Just another day Every single day""" EXAMPLE_TAGS = "piano,happy,uplifting,pop" # Build the Gradio interface with gr.Blocks( title="HeartMuLa Music Generator", ) as demo: gr.Markdown( """ # HeartMuLa Music Generator Generate music from lyrics and tags using [HeartMuLa](https://github.com/HeartMuLa/heartlib), an open-source music foundation model. **Instructions:** 1. Enter your lyrics with structure tags like `[Verse]`, `[Chorus]`, `[Bridge]`, etc. 2. Add comma-separated tags describing the music style (e.g., `piano,happy,romantic`) 3. Adjust generation parameters as needed 4. Click "Generate Music" and wait for your song! *Note: Generation can take several minutes depending on the duration.* """ ) with gr.Row(): with gr.Column(scale=1): lyrics_input = gr.Textbox( label="Lyrics", placeholder="Enter lyrics with structure tags like [Verse], [Chorus], etc.", lines=20, value=EXAMPLE_LYRICS, ) tags_input = gr.Textbox( label="Tags", placeholder="piano,happy,romantic,synthesizer", value=EXAMPLE_TAGS, info="Comma-separated tags describing the music style", ) with gr.Accordion("Advanced Settings", open=False): max_duration = gr.Slider( minimum=30, maximum=240, value=120, step=10, label="Max Duration (seconds)", info="Maximum length of generated audio", ) temperature = gr.Slider( minimum=0.1, maximum=2.0, value=1.0, step=0.1, label="Temperature", info="Higher = more creative, Lower = more consistent", ) topk = gr.Slider( minimum=1, maximum=100, value=50, step=1, label="Top-K", info="Number of top tokens to sample from", ) cfg_scale = gr.Slider( minimum=1.0, maximum=3.0, value=1.5, step=0.1, label="CFG Scale", info="Classifier-free guidance scale", ) generate_btn = gr.Button("Generate Music", variant="primary", size="lg") with gr.Column(scale=1): audio_output = gr.Audio( label="Generated Music", type="filepath", ) gr.Markdown( """ ### Tips for Better Results - Use structured lyrics with section tags - Be specific with your style tags - Try different temperature values for variety - Shorter durations generate faster ### Example Tags - **Instruments:** piano, guitar, drums, synthesizer, violin, bass - **Mood:** happy, sad, romantic, energetic, calm, melancholic - **Genre:** pop, rock, jazz, classical, electronic, folk - **Tempo:** fast, slow, upbeat, relaxed """ ) generate_btn.click( fn=generate_music, inputs=[ lyrics_input, tags_input, max_duration, temperature, topk, cfg_scale, ], outputs=audio_output, ) gr.Markdown( """ --- **Model:** [HeartMuLa-oss-3B](https://huggingface.co/HeartMuLa/HeartMuLa-oss-3B) | **Paper:** [arXiv](https://arxiv.org/abs/2601.10547) | **Code:** [GitHub](https://github.com/HeartMuLa/heartlib) *Licensed under Apache 2.0* """ ) demo.launch()