test on test app

README.md

@@ -7,7 +7,7 @@ sdk: gradio
 sdk_version: 4.21.0
 python_version: 3.9
 suggested_hardware: "a10g-large"
-app_file: app.py
+app_file: test_app.py
 pinned: false
 tags: ["MusicAI", "MultiModal", "Audio", "Text", "Image"]
 license: mit

test_app.py

@@ -0,0 +1,42 @@
+from math import log2, pow
+import os
+
+import numpy as np
+from scipy.fftpack import fft
+import basic_pitch
+import basic_pitch.inference
+from basic_pitch import ICASSP_2022_MODEL_PATH
+from tempfile import NamedTemporaryFile
+
+import gradio as gr
+
+def transcribe(audio_path):
+    # model_output, midi_data, note_events = predict("generated_0.wav")
+    model_output, midi_data, note_events = basic_pitch.inference.predict(
+        audio_path=audio_path,
+        model_or_model_path=ICASSP_2022_MODEL_PATH,
+        )
+
+    with NamedTemporaryFile("wb", suffix=".mid", delete=False) as file:
+        try:
+            midi_data.write(file)
+            print(f"midi file saved to {file.name}")
+        except Exception as e:
+            print(f"Error while writing midi file: {e}")
+            raise e
+
+    return gr.DownloadButton(
+        value=file.name,
+        label=f"Download MIDI file {file.name}",
+        visible=True)
+
+
+with gr.Blocks() as demo:
+    transcribe_button = gr.Button("Transcribe")
+    audio = gr.Audio("audio", type="filepath")
+
+    d = gr.DownloadButton("Download the file", visible=False)
+    transcribe_button.click(transcribe, inputs=[audio], outputs=d)
+if __name__ == "__main__":
+    demo.launch()
+