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Running
on
Zero
| import os | |
| import sys | |
| import gradio as gr | |
| from inspiremusic.cli.inference import InspireMusicUnified, set_env_variables | |
| import torchaudio | |
| import datetime | |
| import hashlib | |
| def generate_filename(): | |
| now = datetime.datetime.now() | |
| seconds_since_epoch = int(now.timestamp()) | |
| # Convert seconds to string | |
| seconds_str = str(seconds_since_epoch) | |
| # Hash the string using SHA-256 | |
| hash_object = hashlib.sha256(seconds_str.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 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, | |
| "model_dir" : os.path.join("pretrained_models", | |
| model_name), | |
| "result_dir" : "exp/inspiremusic", | |
| "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 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 update_text(): | |
| global text_input # Declare as global to modify the outer scope variable | |
| text_input = "New value set by button click" | |
| return text_input | |
| default_prompts = [ | |
| "Experience soothing and sensual instrumental jazz with a touch of Bossa Nova, perfect for a relaxing restaurant or spa ambiance.", | |
| "Compose an uplifting R&B song.", | |
| "Create an emotional, introspective folk song with acoustic guitar and soft vocals." | |
| ] | |
| def cut_audio(audio_file, cut_seconds=5): | |
| audio, sr = torchaudio.load(audio_file) | |
| num_samples = cut_seconds * sr | |
| cutted_audio = audio[:, :num_samples] | |
| output_path = os.path.join(os.getcwd(), "audio_prompt_" + generate_filename() + ".wav") | |
| torchaudio.save(output_path, cutted_audio, sr) | |
| return output_path | |
| def run_text2music(text, model_name, chorus, | |
| output_sample_rate, max_generate_audio_seconds): | |
| args = get_args( | |
| task='continuation', 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 run_continuation(text, audio, model_name, chorus, | |
| output_sample_rate, max_generate_audio_seconds): | |
| args = get_args( | |
| task='text-to-music', text=text, audio=cut_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) | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown(""" | |
| # InspireMusic | |
| - Support text-to-music, music continuation, audio super-resolution, audio reconstruction tasks with high audio quality, with available sampling rates of 24kHz, 48kHz. | |
| - Support long audio generation in multiple output audio formats, i.e., wav, flac, mp3, m4a. | |
| - Open-source [InspireMusic-Base](https://modelscope.cn/models/iic/InspireMusic/summary), [InspireMusic-Base-24kHz](https://modelscope.cn/models/iic/InspireMusic-Base-24kHz/summary), [InspireMusic-1.5B](https://modelscope.cn/models/iic/InspireMusic-1.5B/summary), [InspireMusic-1.5B-24kHz](https://modelscope.cn/models/iic/InspireMusic-1.5B-24kHz/summary), [InspireMusic-1.5B-Long](https://modelscope.cn/models/iic/InspireMusic-1.5B-Long/summary) models for music generation. | |
| - Currently only support English text prompts. | |
| """) | |
| with gr.Row(equal_height=True): | |
| model_name = gr.Dropdown(["InspireMusic-1.5B-Long", "InspireMusic-1.5B", "InspireMusic-1.5B-24kHz", "InspireMusic-Base", "InspireMusic-Base-24kHz"], label="Select Model Name", value="InspireMusic-Base") | |
| 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, 120, | |
| label="Generate Audio Length (s)", | |
| value=30) | |
| # with gr.Column(): | |
| # fast = gr.Checkbox(label="Fast Inference", value=False) | |
| # fade_out = gr.Checkbox(label="Apply Fade Out Effect", value=True) | |
| with gr.Row(equal_height=True): | |
| # Textbox for custom input | |
| 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") | |
| with gr.Row(): | |
| button = gr.Button("Text to Music") | |
| button.click(run_text2music, | |
| inputs=[text_input, model_name, | |
| chorus, | |
| output_sample_rate, | |
| max_generate_audio_seconds], | |
| outputs=music_output) | |
| generate_button = gr.Button("Music Continuation") | |
| generate_button.click(run_continuation, | |
| inputs=[text_input, audio_input, model_name, | |
| chorus, | |
| output_sample_rate, | |
| max_generate_audio_seconds], | |
| outputs=music_output) | |
| with gr.Column(): | |
| default_prompt_buttons = [] | |
| for prompt in default_prompts: | |
| button = gr.Button(value=prompt) | |
| button.click(run_text2music, | |
| inputs=[text_input, model_name, | |
| chorus, | |
| output_sample_rate, | |
| max_generate_audio_seconds], | |
| outputs=music_output) | |
| default_prompt_buttons.append(button) | |
| demo.launch() | |