app_colab.py

"""
YourMT3+ with Instrument Conditioning - Google Colab Version

Instructions for use in Google Colab:

1. First, run this cell to install dependencies:
   !pip install torch torchaudio transformers gradio pytorch-lightning

2. Clone the YourMT3 repository:
   !git clone https://github.com/mimbres/YourMT3.git
   %cd YourMT3

3. Copy this code to a cell and run it to launch the interface

4. The Gradio interface will provide a public URL you can access
"""

import sys
import os

# Add the amt/src directory to Python path
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

# Create log file
log_file = 'amt/log.txt'
Path(log_file).touch()

# Model Configuration
model_name = 'YPTF.MoE+Multi (noPS)'  # You can change this
precision = '16'
project = '2024'

print(f"Loading model: {model_name}")

# Get model arguments based on selection
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(f"Unknown model: {model_name}")

# Load model
print("Loading model checkpoint...")
try:
    model = load_model_checkpoint(args=args, device="cpu")
    model.to("cuda")
    print("✓ Model loaded successfully!")
except Exception as e:
    print(f"✗ Error loading model: {e}")
    print("Make sure the model checkpoints are available in amt/logs/")

# Helper functions
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"""
    if source_type == 'audio_filepath':
        audio_file = source_path_or_url
    elif source_type == 'youtube_url':
        if os.path.exists('/content/yt_audio.mp3'):  # Colab path
            os.remove('/content/yt_audio.mp3')
        # Download from youtube
        with open(log_file, 'w') as lf:
            audio_file = '/content/yt_audio'  # Colab path
            command = ['yt-dlp', '-x', source_path_or_url, '-f', 'bestaudio',
                '-o', audio_file, '--audio-format', 'mp3', '--restrict-filenames',
                '--extractor-retries', '10', '--force-overwrites']
            if simulate:
                command = command + ['-s']
            process = subprocess.Popen(command,
                stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
        
            for line in iter(process.stdout.readline, ''):
                print(line)
                lf.write(line); 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, instrument_hint=None):
    """Process uploaded audio with optional instrument conditioning"""
    if audio_filepath is None:
        return None
    try:
        audio_info = prepare_media(audio_filepath, source_type='audio_filepath')
        midifile = transcribe(model, audio_info, instrument_hint)
        midifile = to_data_url(midifile)
        return create_html_from_midi(midifile)
    except Exception as e:
        return f"<p style='color: red;'>Error processing audio: {str(e)}</p>"

def process_video(youtube_url, instrument_hint=None):
    """Process YouTube video with optional instrument conditioning"""
    if 'youtu' not in youtube_url:
        return None
    try:
        audio_info = prepare_media(youtube_url, source_type='youtube_url')
        midifile = transcribe(model, audio_info, instrument_hint)
        midifile = to_data_url(midifile)
        return create_html_from_midi(midifile)
    except Exception as e:
        return f"<p style='color: red;'>Error processing YouTube video: {str(e)}</p>"

def play_video(youtube_url):
    if 'youtu' not in youtube_url:
        return None
    return create_html_youtube_player(youtube_url)

# Get example files
AUDIO_EXAMPLES = glob.glob('examples/*.*', recursive=True)
YOUTUBE_EXAMPLES = ["https://youtu.be/5vJBhdjvVcE?si=s3NFG_SlVju0Iklg",
                    "https://youtu.be/mw5VIEIvuMI?si=Dp9UFVw00Tl8CXe2",
                    "https://youtu.be/OXXRoa1U6xU?si=dpYMun4LjZHNydSb"]

# Gradio theme
theme = gr.Theme.from_hub("gradio/dracula_revamped")
css = """
.gradio-container {
    background: linear-gradient(-45deg, #ee7752, #e73c7e, #23a6d5, #23d5ab);
    background-size: 400% 400%;
    animation: gradient 15s ease infinite;
}
@keyframes gradient {
    0% {background-position: 0% 50%;}
    50% {background-position: 100% 50%;}
    100% {background-position: 0% 50%;}
}
"""

# Create Gradio interface
with gr.Blocks(theme=theme, css=css) as demo:
    
    gr.Markdown(f"""
    # 🎶 YourMT3+ with Instrument Conditioning
    
    **Enhanced music transcription with instrument-specific control!**
    
    **New Feature**: Select which instrument you want to transcribe from the dropdown menu.
    This solves the problem of the model switching between instruments mid-track.
    
    **Model**: `{model_name}` | **Running in**: Google Colab
    
    ---
    """)

    with gr.Tabs():
        
        with gr.Tab("🎵 Upload Audio"):
            with gr.Row():
                with gr.Column():
                    audio_input = gr.Audio(
                        label="Upload Audio File", 
                        type="filepath",
                        format="wav"
                    )
                    
                    instrument_selector = gr.Dropdown(
                        choices=[
                            "Auto (detect all instruments)", 
                            "Vocals/Singing", 
                            "Guitar", 
                            "Piano", 
                            "Violin", 
                            "Drums", 
                            "Bass", 
                            "Saxophone", 
                            "Flute"
                        ],
                        value="Auto (detect all instruments)",
                        label="🎯 Target Instrument",
                        info="NEW! Choose the specific instrument you want to transcribe"
                    )
                    
                    transcribe_button = gr.Button("🎼 Transcribe", variant="primary", size="lg")
                    
                    if AUDIO_EXAMPLES:
                        gr.Examples(examples=AUDIO_EXAMPLES[:5], inputs=audio_input)
            
            with gr.Row():
                output_audio = gr.HTML(label="Transcription Result")
        
        with gr.Tab("📺 YouTube"):
            with gr.Row():
                with gr.Column():
                    youtube_input = gr.Textbox(
                        label="YouTube URL", 
                        placeholder="https://youtu.be/..."
                    )
                    
                    youtube_instrument_selector = gr.Dropdown(
                        choices=[
                            "Auto (detect all instruments)", 
                            "Vocals/Singing", 
                            "Guitar", 
                            "Piano", 
                            "Violin", 
                            "Drums", 
                            "Bass", 
                            "Saxophone", 
                            "Flute"
                        ],
                        value="Auto (detect all instruments)",
                        label="🎯 Target Instrument",
                        info="Choose the specific instrument you want to transcribe"
                    )
                    
                    with gr.Row():
                        play_button = gr.Button("▶️ Preview Video", variant="secondary")
                        transcribe_yt_button = gr.Button("🎼 Transcribe", variant="primary")
                    
                    gr.Examples(examples=YOUTUBE_EXAMPLES, inputs=youtube_input)
            
            with gr.Row():
                with gr.Column():
                    youtube_player = gr.HTML(label="Video Preview")
                with gr.Column():
                    output_youtube = gr.HTML(label="Transcription Result")
    
    # Event handlers
    def process_with_instrument_audio(audio_file, instrument_choice):
        instrument_map = {
            "Auto (detect all instruments)": None,
            "Vocals/Singing": "vocals",
            "Guitar": "guitar", 
            "Piano": "piano",
            "Violin": "violin",
            "Drums": "drums",
            "Bass": "bass",
            "Saxophone": "saxophone",
            "Flute": "flute"
        }
        instrument_hint = instrument_map.get(instrument_choice, None)
        return process_audio(audio_file, instrument_hint)
    
    def process_with_instrument_youtube(url, instrument_choice):
        instrument_map = {
            "Auto (detect all instruments)": None,
            "Vocals/Singing": "vocals",
            "Guitar": "guitar", 
            "Piano": "piano",
            "Violin": "violin",
            "Drums": "drums",
            "Bass": "bass",
            "Saxophone": "saxophone",
            "Flute": "flute"
        }
        instrument_hint = instrument_map.get(instrument_choice, None)
        return process_video(url, instrument_hint)
    
    # Connect events
    transcribe_button.click(
        process_with_instrument_audio, 
        inputs=[audio_input, instrument_selector], 
        outputs=output_audio
    )
    
    transcribe_yt_button.click(
        process_with_instrument_youtube,
        inputs=[youtube_input, youtube_instrument_selector], 
        outputs=output_youtube
    )
    
    play_button.click(play_video, inputs=youtube_input, outputs=youtube_player)

print("🚀 Launching YourMT3+ with Instrument Conditioning...")
print("📝 Tips:")
print("   • Try 'Vocals/Singing' for vocal tracks to avoid instrument switching")
print("   • Use 'Guitar' for guitar solos to get complete transcriptions")
print("   • 'Auto' works like the original YourMT3+")

# Launch with share=True for Colab public URL
demo.launch(share=True, debug=True)