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| 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 torch | |
| 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 = "[email protected]" | |
| 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) | |
| # @title GradIO helper | |
| def prepare_media(source_path_or_url: os.PathLike, | |
| source_type: Literal['audio_filepath', 'youtube_url'], | |
| delete_video: bool = True, | |
| simulate = False) -> 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), | |
| } | |
| 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 | |
| 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/OXXRoa1U6xU?si=nhJ6lzGenCmk4P7R", | |
| "https://youtu.be/EOJ0wH6h3rE?si=a99k6BnSajvNmXcn", | |
| "https://youtu.be/7mjQooXt28o?si=qqmMxCxwqBlLPDI2", | |
| "https://youtu.be/bnS-HK_lTHA?si=PQLVAab3QHMbv0S3https://youtu.be/zJB0nnOc7bM?si=EA1DN8nHWJcpQWp_", | |
| "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%;} | |
| } | |
| #mylog {font-size: 12pt; line-height: 1.2; min-height: 6.4em; max-height: 6.4em;} | |
| """ | |
| 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: | |
| - Currently running on CPU, and it takes longer than 3 minutes for a 30-second input. Please try [GPU-HuggingFace-demo](mimbres/YourMT3) for fast inference. | |
| - For acadmic reproduction purpose, we strongly recommend to use [Colab Demo](https://colab.research.google.com/drive/1AgOVEBfZknDkjmSRA7leoa81a2vrnhBG?usp=sharing) with multiple checkpoints. | |
| <div style="display: inline-block;"> | |
| <a href="https://arxiv.org/abs/2407.04822"> | |
| <img src="https://img.shields.io/badge/arXiv:2407.04822-B31B1B?logo=arxiv&logoColor=fff&style=plastic" alt="arXiv Badge"/> | |
| </a> | |
| </div> | |
| <div style="display: inline-block;"> | |
| <a href="https://github.com/mimbres/YourMT3"> | |
| <img src="https://img.shields.io/badge/GitHub-181717?logo=github&logoColor=fff&style=plastic" alt="GitHub Badge"/> | |
| </a> | |
| </div> | |
| <div style="display: inline-block;"> | |
| <a href="https://colab.research.google.com/drive/1AgOVEBfZknDkjmSRA7leoa81a2vrnhBG?usp=sharing"> | |
| <img src="https://img.shields.io/badge/Google%20Colab-F9AB00?logo=googlecolab&logoColor=fff&style=plastic"/> | |
| </a> | |
| </div> | |
| """) | |
| 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) | |
| # 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): | |
| with gr.Row(): | |
| # 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) | |