File size: 4,789 Bytes
7c6404e e988d7c 7c6404e e5ddb64 7c6404e e5ddb64 7c6404e e5ddb64 7c6404e e5ddb64 7c6404e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import os
import warnings
import torch
import librosa
import huggingface_hub
# Apply monkey-patch to gradio_client to prevent the bool TypeError in JSON schema parsing
try:
import gradio_client.utils
_orig_get_type = gradio_client.utils.get_type
def _patched_get_type(schema):
if isinstance(schema, bool):
schema = {}
return _orig_get_type(schema)
gradio_client.utils.get_type = _patched_get_type
_orig_json_schema_to_python_type = gradio_client.utils._json_schema_to_python_type
def _patched_json_schema_to_python_type(schema, defs):
if isinstance(schema, bool):
schema = {}
return _orig_json_schema_to_python_type(schema, defs)
gradio_client.utils._json_schema_to_python_type = _patched_json_schema_to_python_type
print("Successfully monkey-patched gradio_client.utils to handle boolean schemas.")
except Exception as e:
print(f"Failed to apply gradio_client monkey-patch: {e}")
import gradio as gr
from piano_transcription_inference import PianoTranscription, sample_rate
# Suppress warnings to keep logs clean
warnings.filterwarnings("ignore")
# Download the model weights eagerly
print("Downloading/fetching model weights...")
WEIGHTS_PATH = huggingface_hub.snapshot_download(
"Genius-Society/piano_trans",
) + "/CRNN_note_F1=0.9677_pedal_F1=0.9186.pth"
# Instantiate transcriptor on CPU
print(f"Initializing PianoTranscription on CPU using weights from: {WEIGHTS_PATH}")
transcriptor = PianoTranscription(
device="cpu",
checkpoint_path=WEIGHTS_PATH,
)
print("PianoTranscription initialized successfully!")
def transcribe_audio_to_midi(audio_path: str):
"""
Transcribes piano audio file to MIDI.
"""
if not audio_path:
return "Please upload an audio file first.", None
try:
print(f"Loading audio from {audio_path}")
# Load audio at the required sampling rate mono
audio, _ = librosa.load(audio_path, sr=sample_rate, mono=True)
print(f"Audio loaded successfully. Duration: {len(audio) / sample_rate:.2f} seconds")
# Generate output path
output_dir = "output"
os.makedirs(output_dir, exist_ok=True)
# Get base name without extension
base_name = os.path.splitext(os.path.basename(audio_path))[0]
midi_path = os.path.join(output_dir, f"{base_name}.mid")
print(f"Transcribing audio to MIDI...")
transcriptor.transcribe(audio, midi_path)
print(f"Transcription complete! Saved to {midi_path}")
return "Transcription complete! You can download your MIDI file below.", midi_path
except Exception as e:
import traceback
error_msg = f"Error during transcription: {str(e)}\n\n{traceback.format_exc()}"
print(error_msg)
return f"Error: {str(e)}", None
# Build clean Gradio interface
with gr.Blocks(theme=gr.themes.Soft(), css="#col-container { max-width: 800px; margin: 0 auto; }") as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("""
# 🎹 High-Resolution Piano Transcription
Convert audio recordings of piano performances into MIDI files with high accuracy.
Powered by ByteDance's High-Resolution Piano Transcription System of Qiuqiang Kong.
""")
with gr.Row():
audio_input = gr.Audio(
label="Upload Piano Audio (MP3, WAV, etc.)",
type="filepath"
)
with gr.Row():
submit_btn = gr.Button("Transcribe to MIDI", variant="primary")
with gr.Row():
status_output = gr.Textbox(label="Status", interactive=False)
with gr.Row():
midi_output = gr.File(label="Download MIDI File")
gr.HTML("""
<div style="margin-top: 20px; padding: 15px; background-color: var(--block-background-fill); border-radius: 8px;">
<h3>🎵 Recommended Playback</h3>
<p>For the best MIDI playback experience with a virtual 3D piano, sheet music representation, and high-quality sound:</p>
<ol>
<li>Download your transcribed MIDI file using the download button above.</li>
<li>Go to <a href="https://app.midiano.com/" target="_blank" style="color: var(--primary-500); text-decoration: underline;">MidiAno (app.midiano.com)</a>.</li>
<li>Drag and drop your MIDI file into the MidiAno browser tab to watch/listen to your performance!</li>
</ol>
</div>
""")
submit_btn.click(
fn=transcribe_audio_to_midi,
inputs=audio_input,
outputs=[status_output, midi_output]
)
if __name__ == "__main__":
demo.launch()
|