import spaces import sys import os sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), 'amt/src'))) import subprocess from typing import Tuple, Dict, Literal from ctypes import ArgumentError from html_helper import * from model_helper import * import torchaudio import glob import gradio as gr from gradio_log import Log from pathlib import Path # gradio_log log_file = 'amt/log.txt' Path(log_file).touch() # @title Load Checkpoint model_name = 'YPTF.MoE+Multi (noPS)' # @param ["YMT3+", "YPTF+Single (noPS)", "YPTF+Multi (PS)", "YPTF.MoE+Multi (noPS)", "YPTF.MoE+Multi (PS)"] precision = '16'# if torch.cuda.is_available() else '32'# @param ["32", "bf16-mixed", "16"] project = '2024' if model_name == "YMT3+": checkpoint = "notask_all_cross_v6_xk2_amp0811_gm_ext_plus_nops_b72@model.ckpt" args = [checkpoint, '-p', project, '-pr', precision] elif model_name == "YPTF+Single (noPS)": checkpoint = "ptf_all_cross_rebal5_mirst_xk2_edr005_attend_c_full_plus_b100@model.ckpt" args = [checkpoint, '-p', project, '-enc', 'perceiver-tf', '-ac', 'spec', '-hop', '300', '-atc', '1', '-pr', precision] elif model_name == "YPTF+Multi (PS)": checkpoint = "mc13_256_all_cross_v6_xk5_amp0811_edr005_attend_c_full_plus_2psn_nl26_sb_b26r_800k@model.ckpt" args = [checkpoint, '-p', project, '-tk', 'mc13_full_plus_256', '-dec', 'multi-t5', '-nl', '26', '-enc', 'perceiver-tf', '-ac', 'spec', '-hop', '300', '-atc', '1', '-pr', precision] elif model_name == "YPTF.MoE+Multi (noPS)": checkpoint = "mc13_256_g4_all_v7_mt3f_sqr_rms_moe_wf4_n8k2_silu_rope_rp_b36_nops@last.ckpt" args = [checkpoint, '-p', project, '-tk', 'mc13_full_plus_256', '-dec', 'multi-t5', '-nl', '26', '-enc', 'perceiver-tf', '-sqr', '1', '-ff', 'moe', '-wf', '4', '-nmoe', '8', '-kmoe', '2', '-act', 'silu', '-epe', 'rope', '-rp', '1', '-ac', 'spec', '-hop', '300', '-atc', '1', '-pr', precision] elif model_name == "YPTF.MoE+Multi (PS)": checkpoint = "mc13_256_g4_all_v7_mt3f_sqr_rms_moe_wf4_n8k2_silu_rope_rp_b80_ps2@model.ckpt" args = [checkpoint, '-p', project, '-tk', 'mc13_full_plus_256', '-dec', 'multi-t5', '-nl', '26', '-enc', 'perceiver-tf', '-sqr', '1', '-ff', 'moe', '-wf', '4', '-nmoe', '8', '-kmoe', '2', '-act', 'silu', '-epe', 'rope', '-rp', '1', '-ac', 'spec', '-hop', '300', '-atc', '1', '-pr', precision] else: raise ValueError(model_name) model = load_model_checkpoint(args=args, device="cpu") model.to("cuda") # @title GradIO helper def prepare_media(source_path_or_url: os.PathLike, source_type: Literal['audio_filepath', 'youtube_url'], delete_video: bool = True) -> Dict: """prepare media from source path or youtube, and return audio info""" # Get audio_file if source_type == 'audio_filepath': audio_file = source_path_or_url elif source_type == 'youtube_url': # Download from youtube with open(log_file, 'w') as lf: audio_file = './downloaded/yt_audio' command = ['yt-dlp', '-x', source_path_or_url, '-f', 'bestaudio', '-o', audio_file, '--audio-format', 'mp3', '--restrict-filenames', '--force-overwrites', '--username', 'oauth2', '--password', '', '-v'] if simulate: command = command + ['-s'] process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True) for line in iter(process.stdout.readline, ''): # Filter out unnecessary messages if "www.google.com/device" in line: hl_text = line.replace("https://www.google.com/device", "\033[93mhttps://www.google.com/device\x1b[0m").split() hl_text[-1] = "\x1b[31;1m" + hl_text[-1] + "\x1b[0m" lf.write(' '.join(hl_text[])); lf.flush() process.stdout.close() process.wait() audio_file += '.mp3' else: raise ValueError(source_type) # Create info info = torchaudio.info(audio_file) return { "filepath": audio_file, "track_name": os.path.basename(audio_file).split('.')[0], "sample_rate": int(info.sample_rate), "bits_per_sample": int(info.bits_per_sample), "num_channels": int(info.num_channels), "num_frames": int(info.num_frames), "duration": int(info.num_frames / info.sample_rate), "encoding": str.lower(info.encoding), } @spaces.GPU def process_audio(audio_filepath): if audio_filepath is None: return None audio_info = prepare_media(audio_filepath, source_type='audio_filepath') midifile = transcribe(model, audio_info) midifile = to_data_url(midifile) return create_html_from_midi(midifile) # html midiplayer @spaces.GPU def process_video(youtube_url): if 'youtu' not in youtube_url: return None audio_info = prepare_media(youtube_url, source_type='youtube_url') midifile = transcribe(model, audio_info) midifile = to_data_url(midifile) return create_html_from_midi(midifile) # html midiplayer def play_video(youtube_url): if 'youtu' not in youtube_url: return None return create_html_youtube_player(youtube_url) def oauth_google(): return create_html_oauth() AUDIO_EXAMPLES = glob.glob('examples/*.*', recursive=True) YOUTUBE_EXAMPLES = ["https://youtu.be/5vJBhdjvVcE?si=s3NFG_SlVju0Iklg", "https://www.youtube.com/watch?v=vMboypSkj3c", "https://youtu.be/vRd5KEjX8vw?si=b-qw633ZjaX6Uxy5", "https://youtu.be/bnS-HK_lTHA?si=PQLVAab3QHMbv0S3https://youtu.be/zJB0nnOc7bM?si=EA1DN8nHWJcpQWp_", "https://youtu.be/7mjQooXt28o?si=qqmMxCxwqBlLPDI2", "https://youtu.be/mIWYTg55h10?si=WkbtKfL6NlNquvT8"] theme = gr.Theme.from_hub("gradio/dracula_revamped") theme.text_md = '10px' theme.text_lg = '12px' theme.body_background_fill_dark = '#060a1c' #'#372037'# '#a17ba5' #'#73d3ac' theme.border_color_primary_dark = '#45507328' theme.block_background_fill_dark = '#3845685c' theme.body_text_color_dark = 'white' theme.block_title_text_color_dark = 'black' theme.body_text_color_subdued_dark = '#e4e9e9' css = """ .gradio-container { background: linear-gradient(-45deg, #ee7752, #e73c7e, #23a6d5, #23d5ab); background-size: 400% 400%; animation: gradient 15s ease infinite; height: 100vh; } @keyframes gradient { 0% {background-position: 0% 50%;} 50% {background-position: 100% 50%;} 100% {background-position: 0% 50%;} } """ with gr.Blocks(theme=theme, css=css) as demo: with gr.Row(): with gr.Column(scale=10): gr.Markdown( f""" ## 🎶YourMT3+: Multi-instrument Music Transcription with Enhanced Transformer Architectures and Cross-dataset Stem Augmentation ## Model card: - Model name: `{model_name}` - Encoder backbone: Perceiver-TF + Mixture of Experts (2/8) - Decoder backbone: Multi-channel T5-small - Tokenizer: MT3 tokens with Singing extension - Dataset: YourMT3 dataset - Augmentation strategy: Intra-/Cross dataset stem augment, No Pitch-shifting - FP Precision: BF16-mixed for training, FP16 for inference ## Caution: - For acadmic reproduction purpose, we strongly recommend to use [Colab Demo](https://colab.research.google.com/drive/1AgOVEBfZknDkjmSRA7leoa81a2vrnhBG?usp=sharing) with multiple checkpoints.
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""") with gr.Group(): with gr.Tab("Upload audio"): # Input audio_input = gr.Audio(label="Record Audio", type="filepath", show_share_button=True, show_download_button=True) # Display examples gr.Examples(examples=AUDIO_EXAMPLES, inputs=audio_input) # Submit button transcribe_audio_button = gr.Button("Transcribe", variant="primary") # Transcribe output_tab1 = gr.HTML() transcribe_audio_button.click(process_audio, inputs=audio_input, outputs=output_tab1) with gr.Tab("From YouTube"): with gr.Column(scale=4): # Input URL youtube_url = gr.Textbox(label="YouTube Link URL", placeholder="https://youtu.be/...") # Display examples gr.Examples(examples=YOUTUBE_EXAMPLES, inputs=youtube_url, examples_per_page=4) # Play button play_video_button = gr.Button("Get Audio from YouTube", variant="primary") # Play youtube youtube_player = gr.HTML(render=True) with gr.Column(scale=4): # Submit button transcribe_video_button = gr.Button("Transcribe", variant="primary") oauth_button = gr.Button("google.com/device", variant="primary") with gr.Column(scale=1): # Transcribe output_tab2 = gr.HTML(render=True) # video_output = gr.Text(label="Video Info") transcribe_video_button.click(process_video, inputs=youtube_url, outputs=output_tab2) # Play play_video_button.click(play_video, inputs=youtube_url, outputs=youtube_player) # Authetification oauth_button.click(oauth_google, outputs=youtube_player) with gr.Column(scale=1): Log(log_file, dark=True, xterm_font_size=12, elem_id='mylog') demo.launch(debug=True)