update

app.py

@@ -13,201 +13,197 @@
 # limitations under the License.
 
 import os
-import sys
-import torch
-import gradio as gr
-import torchaudio
-import datetime, hashlib
-from inspiremusic.cli.inference import InspireMusicUnified, set_env_variables
 
-# Prepare environment and model files (unchanged from original)
 os.system('nvidia-smi')
 os.system('apt update -y && apt-get install -y apt-utils && apt install -y unzip')
 os.environ['PYTHONPATH'] = 'third_party/Matcha-TTS'
-os.system(
-    'mkdir pretrained_models && cd pretrained_models && '
-    'git clone https://huggingface.co/FunAudioLLM/InspireMusic-Base.git && '
-    'git clone https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-Long.git && '
-    'git clone https://huggingface.co/FunAudioLLM/InspireMusic-1.5B.git && '
-    'git clone https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-24kHz.git && '
-    'git clone https://huggingface.co/FunAudioLLM/InspireMusic-Base-24kHz.git && '
-    # Fix paths in YAML files
-    'for i in InspireMusic-Base InspireMusic-Base-24kHz InspireMusic-1.5B InspireMusic-1.5B-24kHz InspireMusic-1.5B-Long; '
-    'do sed -i -e "s/..\/..\///g" ${i}/inspiremusic.yaml; done && cd ..'
-)
+os.system('mkdir pretrained_models && cd pretrained_models && git clone https://huggingface.co/FunAudioLLM/InspireMusic-Base.git &&git clone https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-Long.git &&git clone https://huggingface.co/FunAudioLLM/InspireMusic-1.5B.git &&git clone https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-24kHz.git &&git clone https://huggingface.co/FunAudioLLM/InspireMusic-Base-24kHz.git && for i in InspireMusic-Base InspireMusic-Base-24kHz InspireMusic-1.5B InspireMusic-1.5B-24kHz InspireMusic-1.5B-Long; do sed -i -e "s/\.\.\/\.\.\///g" ${i}/inspiremusic.yaml; done && cd ..')
+
+import sys
+import torch
 print(torch.backends.cudnn.version())
+
 ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(f"{ROOT_DIR}/third_party/Matcha-TTS")
+sys.path.append('{}/third_party/Matcha-TTS'.format(ROOT_DIR))
+
+import spaces
+import gradio as gr
+from inspiremusic.cli.inference import InspireMusicUnified, set_env_variables
+import torchaudio
+import datetime
+import hashlib
+import importlib
 
-# Define available model options
-MODELS = ["InspireMusic-1.5B-Long", "InspireMusic-1.5B", "InspireMusic-Base",
-          "InspireMusic-1.5B-24kHz", "InspireMusic-Base-24kHz"]
+MODELS = ["InspireMusic-1.5B-Long", "InspireMusic-1.5B", "InspireMusic-Base", "InspireMusic-1.5B-24kHz", "InspireMusic-Base-24kHz"]
 AUDIO_PROMPT_DIR = "demo/audio_prompts"
 OUTPUT_AUDIO_DIR = "demo/outputs"
 
-# **Initialize global model state at startup**
-loaded_model = None
-current_model_name = None
-
-# Set environment variables once (e.g., for torch performance, precision settings)
-set_env_variables()
-
-# Load the default model into GPU memory
-current_model_name = "InspireMusic-1.5B-Long"  # default selected model in the UI
-loaded_model = InspireMusicUnified(
-    model_name=current_model_name,
-    model_dir=os.path.join("pretrained_models", current_model_name),
-    min_generate_audio_seconds=10.0,
-    max_generate_audio_seconds=30.0,
-    sample_rate=24000,
-    output_sample_rate=48000,   # 48kHz output for default (non-24kHz model)
-    load_jit=True,
-    load_onnx=False,
-    fast=False,                 # False because 48000 Hz output (not fast mode)
-    result_dir=OUTPUT_AUDIO_DIR
-)
-# (The model is now loaded on the GPU and ready for reuse)
+DEMO_TEXT_PROMPTS = ["Jazz music with drum beats.",
+					 "A captivating classical piano performance, this piece exudes a dynamic and intense atmosphere, showcasing intricate and expressive instrumental artistry.",
+					 "A soothing instrumental piece blending elements of light music and pop, featuring a gentle guitar rendition. The overall feel is serene and reflective, likely instrumental with no vocals.",
+					 "The instrumental rock piece features dynamic oscillations and wave-like progressions, creating an immersive and energetic atmosphere. The music is purely instrumental, with no vocals, and it blends elements of rock and post-rock for a powerful and evocative experience.",
+					 "The classical instrumental piece exudes a haunting and evocative atmosphere, characterized by its intricate guitar work and profound emotional depth.",
+					 "Experience a dynamic blend of instrumental electronic music with futuristic house vibes, featuring energetic beats and a captivating rhythm. The tracks are likely instrumental, focusing on the immersive soundscapes rather than vocal performances."]
 
 def generate_filename():
-    # ... (unchanged: generates a unique filename for outputs)
-    timestamp = str(int(datetime.datetime.now().timestamp())).encode()
-    hash_object = hashlib.sha256(timestamp)
-    return hash_object.hexdigest()[:10]
-
-def get_args(task, text="", audio=None, model_name="InspireMusic-Base",
-             chorus="intro", output_sample_rate=48000, max_generate_audio_seconds=30.0,
-             time_start=0.0, time_end=30.0, trim=False):
-    """Prepare the arguments dictionary for a generation task."""
-    # If a 24kHz model is selected, force output sample rate to 24000
-    if "24kHz" in model_name:
-        output_sample_rate = 24000
-    # Determine fast mode (True if using 24k output, which skips upsampling)
-    fast = True if output_sample_rate == 24000 else False
-    args = {
-        "task": task,
-        "text": text,
-        "audio_prompt": audio,
-        "model_name": model_name,
-        "chorus": chorus,
-        "fast": fast,
-        "fade_out": True,
-        "trim": trim,
-        "output_sample_rate": output_sample_rate,
-        "min_generate_audio_seconds": 10.0,
-        "max_generate_audio_seconds": max_generate_audio_seconds,
-        "max_audio_prompt_length": 5.0,
-        "model_dir": os.path.join("pretrained_models", model_name),
-        "result_dir": OUTPUT_AUDIO_DIR,
-        "output_fn": generate_filename(),
-        "format": "wav",
-        "time_start": time_start or 0.0,
-        "time_end": time_end or (time_start + max_generate_audio_seconds),
-        "fade_out_duration": 1.0,
-    }
-    return args
-
-# **Refactored inference function using the preloaded model**
-@spaces.GPU()
-def music_generation(args):
-    """Generate music using the InspireMusic model, reusing a preloaded model if available."""
-    global loaded_model, current_model_name
-    requested_model = args["model_name"]
-    # If the requested model is not the one currently loaded, load the new model
-    if loaded_model is None or requested_model != current_model_name:
-        # Free GPU memory from the old model
-        if loaded_model is not None:
-            del loaded_model
-            torch.cuda.empty_cache()  # free cached memory​:contentReference[oaicite:10]{index=10}
-        # Load the requested model into GPU memory
-        loaded_model = InspireMusicUnified(
-            model_name=requested_model,
-            model_dir=args["model_dir"],
-            min_generate_audio_seconds=args["min_generate_audio_seconds"],
-            max_generate_audio_seconds=args["max_generate_audio_seconds"],
-            sample_rate=24000,
-            output_sample_rate=args["output_sample_rate"],
-            load_jit=True,
-            load_onnx=False,
-            fast=args["fast"],
-            result_dir=args["result_dir"]
-        )
-        current_model_name = requested_model
-    # Perform inference with the loaded model (no gradient computation needed)
-    with torch.no_grad():  # disable grad to save memory​:contentReference[oaicite:11]{index=11}​:contentReference[oaicite:12]{index=12}
-        output_path = loaded_model.inference(
-            task=args["task"],
-            text=args["text"],
-            audio_prompt=args["audio_prompt"],
-            chorus=args["chorus"],
-            time_start=args["time_start"],
-            time_end=args["time_end"],
-            output_fn=args["output_fn"],
-            max_audio_prompt_length=args["max_audio_prompt_length"],
-            fade_out_duration=args["fade_out_duration"],
-            output_format=args["format"],
-            fade_out_mode=args["fade_out"],
-            trim=args["trim"]
-        )
-    return output_path
-
-# Demo helper functions (using music_generation internally)
-def demo_inspiremusic_t2m(text, model_name, chorus, output_sample_rate, max_generate_audio_seconds):
-    args = get_args(task="text-to-music", text=text, audio=None,
-                    model_name=model_name, chorus=chorus,
-                    output_sample_rate=output_sample_rate,
-                    max_generate_audio_seconds=max_generate_audio_seconds)
-    return music_generation(args)
-
-def demo_inspiremusic_con(text, audio, model_name, chorus, output_sample_rate, max_generate_audio_seconds):
-    # Trim the audio prompt to 5 seconds and use it for continuation
-    trimmed_audio = trim_audio(audio, cut_seconds=5)
-    args = get_args(task="continuation", text=text, audio=trimmed_audio,
-                    model_name=model_name, chorus=chorus,
-                    output_sample_rate=output_sample_rate,
-                    max_generate_audio_seconds=max_generate_audio_seconds)
-    return music_generation(args)
+	hash_object = hashlib.sha256(str(int(datetime.datetime.now().timestamp())).encode())
+	hash_string = hash_object.hexdigest()
+	return hash_string
+
+def get_args(
+		task, text="", audio=None, model_name="InspireMusic-Base",
+		chorus="intro",
+		output_sample_rate=48000, max_generate_audio_seconds=30.0, time_start = 0.0, time_end=30.0, trim=False):
+
+	if "24kHz" in model_name:
+		output_sample_rate = 24000
+
+	if output_sample_rate == 24000:
+		fast = True
+	else:
+		fast = False
+	# This function constructs the arguments required for InspireMusic
+	args = {
+		"task"                      : task,
+		"text"                      : text,
+		"audio_prompt"              : audio,
+		"model_name"                : model_name,
+		"chorus"                    : chorus,
+		"fast"                      : fast,
+		"fade_out"                  : True,
+		"trim"                      : trim,
+		"output_sample_rate"        : output_sample_rate,
+		"min_generate_audio_seconds": 10.0,
+		"max_generate_audio_seconds": max_generate_audio_seconds,
+		"max_audio_prompt_length": 5.0,
+		"model_dir"                 : os.path.join("pretrained_models",
+												   model_name),
+		"result_dir"                : OUTPUT_AUDIO_DIR,
+		"output_fn"                 : generate_filename(),
+		"format"                    : "wav",
+		"time_start" : time_start,
+		"time_end": time_end,
+		"fade_out_duration": 1.0,
+	}
+
+	if args["time_start"] is None:
+		args["time_start"] = 0.0
+	args["time_end"] = args["time_start"] + args["max_generate_audio_seconds"]
+
+	print(args)
+	return args
+
 
 def trim_audio(audio_file, cut_seconds=5):
-    # ... (unchanged: load audio and trim to first 5 seconds)
-    audio_tensor, sr = torchaudio.load(audio_file)
-    num_samples = int(cut_seconds * sr)
-    trimmed_audio = audio_tensor[:, :num_samples]
-    output_path = os.path.join(AUDIO_PROMPT_DIR, "audio_prompt_" + generate_filename() + ".wav")
-    torchaudio.save(output_path, trimmed_audio, sr)
-    return output_path
+	audio, sr = torchaudio.load(audio_file)
+	num_samples = cut_seconds * sr
+	cutted_audio = audio[:, :num_samples]
+	output_path = os.path.join(AUDIO_PROMPT_DIR, "audio_prompt_" + generate_filename() + ".wav")
+	torchaudio.save(output_path, cutted_audio, sr)
+	return output_path
+
+@spaces.GPU(duration=120)
+def music_generation(args):
+	set_env_variables()
+	model = InspireMusicUnified(
+			model_name=args["model_name"],
+			model_dir=args["model_dir"],
+			min_generate_audio_seconds=args["min_generate_audio_seconds"],
+			max_generate_audio_seconds=args["max_generate_audio_seconds"],
+			sample_rate=24000,
+			output_sample_rate=args["output_sample_rate"],
+			load_jit=True,
+			load_onnx=False,
+			fast=args["fast"],
+			result_dir=args["result_dir"])
+
+	output_path = model.inference(
+			task=args["task"],
+			text=args["text"],
+			audio_prompt=args["audio_prompt"],
+			chorus=args["chorus"],
+			time_start=args["time_start"],
+			time_end=args["time_end"],
+			output_fn=args["output_fn"],
+			max_audio_prompt_length=args["max_audio_prompt_length"],
+			fade_out_duration=args["fade_out_duration"],
+			output_format=args["format"],
+			fade_out_mode=args["fade_out"],
+			trim=args["trim"])
+	return output_path
+
+
+def demo_inspiremusic_t2m(text, model_name, chorus,
+					 output_sample_rate, max_generate_audio_seconds):
+	args = get_args(
+			task='text-to-music', text=text, audio=None,
+			model_name=model_name, chorus=chorus,
+			output_sample_rate=output_sample_rate,
+			max_generate_audio_seconds=max_generate_audio_seconds)
+	return music_generation(args)
+
+def demo_inspiremusic_con(text, audio, model_name, chorus,
+					 output_sample_rate, max_generate_audio_seconds):
+	args = get_args(
+			task='continuation', text=text, audio=trim_audio(audio, cut_seconds=5),
+			model_name=model_name, chorus=chorus,
+			output_sample_rate=output_sample_rate,
+			max_generate_audio_seconds=max_generate_audio_seconds)
+	return music_generation(args)
 
 def main():
-    with gr.Blocks(theme=gr.themes.Soft()) as demo:
-        gr.Markdown("# InspireMusic\n"
-                    "- A demo for music generation with high audio quality (up to 48kHz) and long-form capabilities.\n"
-                    "- GitHub: https://github.com/FunAudioLLM/InspireMusic\n"
-                    "- Available models: InspireMusic-1.5B-Long, InspireMusic-1.5B, InspireMusic-Base, InspireMusic-1.5B-24kHz, InspireMusic-Base-24kHz (on Hugging Face and ModelScope).\n"
-                    "*(Note: Only English text prompts are supported.)*")
-        # Input components
-        model_name = gr.Dropdown(MODELS, label="Select Model Name", value="InspireMusic-1.5B-Long")
-        chorus = gr.Dropdown(["intro", "verse", "chorus", "outro"], label="Chorus Mode", value="intro")
-        output_sample_rate = gr.Dropdown([48000, 24000], label="Output Audio Sample Rate (Hz)", value=48000)
-        max_generate_audio_seconds = gr.Slider(10, 300, label="Generate Audio Length (s)", value=30)
-        with gr.Row():
-            text_input = gr.Textbox(label="Input Text (For Text-to-Music Task)", value="Experience soothing ... ambiance.")
-            audio_input = gr.Audio(label="Input Audio Prompt (For Music Continuation Task)", type="filepath")
-        music_output = gr.Audio(label="Generated Music", type="filepath", autoplay=True, show_download_button=True)
-        # Buttons to trigger generation
-        with gr.Row():
-            t2m_button = gr.Button("Start Text-to-Music Task")
-            con_button = gr.Button("Start Music Continuation Task")
-        # Bind button clicks to the respective functions
-        t2m_button.click(fn=demo_inspiremusic_t2m,
-                         inputs=[text_input, model_name, chorus, output_sample_rate, max_generate_audio_seconds],
-                         outputs=music_output)
-        con_button.click(fn=demo_inspiremusic_con,
-                         inputs=[text_input, audio_input, model_name, chorus, output_sample_rate, max_generate_audio_seconds],
-                         outputs=music_output)
-        gr.Examples(examples=[...], inputs=[text_input])  # (example prompts list truncated for brevity)
-    demo.launch()
-
-if __name__ == "__main__":
-    # Ensure output directories exist
-    os.makedirs(AUDIO_PROMPT_DIR, exist_ok=True)
-    os.makedirs(OUTPUT_AUDIO_DIR, exist_ok=True)
-    main()
+	with gr.Blocks(theme=gr.themes.Soft()) as demo:
+		gr.Markdown("""
+		# InspireMusic
+		- Support music generation tasks with long-form and high audio quality, sampling rates up to 48kHz. 
+		- Github: https://github.com/FunAudioLLM/InspireMusic/  |  ModelScope Studio: https://modelscope.cn/studios/iic/InspireMusic
+		- Available music generation models: [InspireMusic-1.5B-Long](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-Long), [InspireMusic-1.5B](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B), [InspireMusic-Base](https://huggingface.co/FunAudioLLM/InspireMusic-Base), [InspireMusic-1.5B-24kHz](https://huggingface.co/FunAudioLLM/InspireMusic-1.5B-24kHz), [InspireMusic-Base-24kHz](https://huggingface.co/FunAudioLLM/InspireMusic-Base-24kHz). Both on Huggingface and ModelScope.
+		- Currently only support English text prompts.
+		- This page is for demo purpose, if you want to generate long-form audio, e.g., 5mins, please try to deploy locally. Thank you for your support.
+		""")
+
+		with gr.Row(equal_height=True):
+			model_name = gr.Dropdown(
+					MODELS, label="Select Model Name",
+					value="InspireMusic-1.5B-Long")
+			chorus = gr.Dropdown(["intro", "verse", "chorus", "outro"],
+								 label="Chorus Mode", value="intro")
+			output_sample_rate = gr.Dropdown([48000, 24000],
+											 label="Output Audio Sample Rate (Hz)",
+											 value=48000)
+			max_generate_audio_seconds = gr.Slider(10, 300,
+												   label="Generate Audio Length (s)",
+												   value=30)
+
+		with gr.Row(equal_height=True):
+			text_input = gr.Textbox(label="Input Text (For Text-to-Music Task)",
+									value="Experience soothing and sensual instrumental jazz with a touch of Bossa Nova, perfect for a relaxing restaurant or spa ambiance.")
+
+			audio_input = gr.Audio(
+				label="Input Audio Prompt (For Music Continuation Task)",
+				type="filepath")
+		music_output = gr.Audio(label="Generated Music", type="filepath", autoplay=True, show_download_button = True)
+
+		with gr.Row():
+			button = gr.Button("Start Text-to-Music Task")
+			button.click(demo_inspiremusic_t2m,
+						 inputs=[text_input, model_name,
+								 chorus,
+								 output_sample_rate,
+								 max_generate_audio_seconds],
+						 outputs=music_output)
+
+			generate_button = gr.Button("Start Music Continuation Task")
+			generate_button.click(demo_inspiremusic_con,
+								  inputs=[text_input, audio_input, model_name,
+										  chorus,
+										  output_sample_rate,
+										  max_generate_audio_seconds],
+								  outputs=music_output)
+		t2m_examples = gr.Examples(examples=DEMO_TEXT_PROMPTS, inputs=[text_input])
+	demo.launch()
+
+if __name__ == '__main__':
+	os.makedirs(AUDIO_PROMPT_DIR, exist_ok=True)
+	os.makedirs(OUTPUT_AUDIO_DIR, exist_ok=True)
+	main()