update prediction method

app.py

@@ -1,7 +1,8 @@
 import os
 
 import gradio as gr
-from gradio_components.prediction import load_model, predict
+
+from gradio_components.prediction import predict
 
 theme = gr.themes.Glass(
 	primary_hue="fuchsia",
@@ -60,7 +61,6 @@ theme = gr.themes.Glass(
 	button_cancel_background_fill_hover_dark='*button_cancel_background_fill'
 	)
 
-
 _AUDIOCRAFT_MODELS = ["facebook/musicgen-melody",
                       "facebook/musicgen-medium",
                       "facebook/musicgen-small",
@@ -79,6 +79,7 @@ def generate_prompt(difficulty, style):
 		)
 	return prompt
 
+
 def UI():
 	with gr.Blocks() as demo:
 		with gr.Tab("Generate Music by melody"):
@@ -104,9 +105,19 @@ def UI():
 						temperature = gr.Number(
 							label="Temperature", value=1.0, interactive=True
 							)
-						sample_rate = gr.Number(label="output music sample rate", value=32000, interactive=True)
-						difficulty = gr.Radio(["Easy", "Medium", "Hard"], label="Difficulty", value="Easy", interactive=True)
-						style = gr.Radio(["Jazz", "Classical Music", "Hip Hop", "Others"], value="Classical Music", label="music genre", interactive=True)
+						sample_rate = gr.Number(
+							label="output music sample rate", value=32000,
+							interactive=True
+							)
+						difficulty = gr.Radio(
+							["Easy", "Medium", "Hard"], label="Difficulty",
+							value="Easy", interactive=True
+							)
+						style = gr.Radio(
+							["Jazz", "Classical Music", "Hip Hop", "Others"],
+							value="Classical Music", label="music genre",
+							interactive=True
+							)
 						if style == "Others":
 							style = gr.Textbox(label="Type your music genre")
 						prompt = generate_prompt(difficulty.value, style.value)
@@ -119,29 +130,27 @@ def UI():
 					with gr.Row():
 						melody = gr.Audio(
 							sources=["microphone", "upload"],
-							streaming=True,
 							label="Record or upload your audio",
-							interactive=True,
-							type="numpy",
+							#interactive=True,
 							show_label=True,
-						)
+							)
 					with gr.Row():
 						submit = gr.Button("Generate Music")
 						output = gr.Audio("listen to the generated music")
-						submit.click(fn=predict, inputs=melody, outputs=output)
 
-			generate_music = gr.Button("Generate Music")
-			output = gr.Audio("listen to the music")
-			generate_music.click(fn=predict,
-			                     inputs=[model_path, prompt, melody, duration, topk, topp, temperature, sample_rate],
-			                     outputs=output)
+			submit.click(
+				fn=predict,
+				inputs=[model_path, prompt, melody, duration, topk, topp, temperature,
+				        sample_rate],
+				outputs=output
+				)
 
 		gr.Examples(
 			examples=[
 				[
 					os.path.join(
 						os.path.dirname(__file__),
-						"./data/audio/twinkle_twinkle_little_stars_mozart.mp3"
+						"./data/audio/twinkle_twinkle_little_stars_mozart_20sec.mp3"
 						),
 					"Easy",
 					32000,
@@ -150,7 +159,7 @@ def UI():
 				[
 					os.path.join(
 						os.path.dirname(__file__),
-						"./data/audio/golden_hour.mp3"
+						"./data/audio/golden_hour_20sec.mp3"
 						),
 					"Easy",
 					32000,
@@ -159,7 +168,7 @@ def UI():
 				[
 					os.path.join(
 						os.path.dirname(__file__),
-						"./data/audio/turkish_march_mozart.mp3"
+						"./data/audio/turkish_march_mozart_20sec.mp3"
 						),
 					"Easy",
 					32000,
@@ -168,7 +177,7 @@ def UI():
 				[
 					os.path.join(
 						os.path.dirname(__file__),
-						"./data/audio/golden_hour.mp3"
+						"./data/audio/golden_hour_20sec.mp3"
 						),
 					"Hard",
 					32000,
@@ -177,7 +186,7 @@ def UI():
 				[
 					os.path.join(
 						os.path.dirname(__file__),
-						"./data/audio/golden_hour.mp3"
+						"./data/audio/golden_hour_20sec.mp3"
 						),
 					"Hard",
 					32000,
@@ -186,7 +195,7 @@ def UI():
 				[
 					os.path.join(
 						os.path.dirname(__file__),
-						"./data/audio/golden_hour.mp3"
+						"./data/audio/golden_hour_20sec.mp3"
 						),
 					"Hard",
 					16000,
@@ -200,6 +209,6 @@ def UI():
 			)
 	demo.queue().launch()
 
+
 if __name__ == "__main__":
 	UI()
-

data/audio/golden_hour_20sec.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b4288206a5e10ba85e38f9f734ab21a01ae2e038028fc4d58b2e0404eaa6b38
+size 150626

data/audio/turkish_march_mozart_20sec.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b503d887b6f567819f74bdfb491540e7e49708858341d02c83a045a53d1f7dd
+size 146599

data/audio/twinkle_twinkle_little_stars_mozart.mp3

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ed46f25fb0031b270dafc14981e67121ecf094e15c6c6c138f7998672de8ce7a
+oid sha256:5f0082e0f5f042b80d91385c8f68c9f9109a0abc164972c0535705ebeb6708c2
 size 20276397

data/audio/twinkle_twinkle_little_stars_mozart_20sec.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d959a1125db5075112d4be8086f81722d216154fe721f4d8463cd8a2e06b3f3
+size 154344

gradio_components/prediction.py

@@ -8,6 +8,7 @@ from audiocraft.models import MusicGen
 
 from tempfile import NamedTemporaryFile
 from pathlib import Path
+from transformers import AutoModelForSeq2SeqLM
 
 
 def load_model(version='facebook/musicgen-melody'):
@@ -23,6 +24,7 @@ def _do_predictions(model, texts, melodies, duration, progress=False, gradio_pro
             processed_melodies.append(None)
         else:
             sr, melody = melody[0], torch.from_numpy(melody[1]).to(model.device).float().t()
+            print(f"Input audio sample rate is {sr}")
             if melody.dim() == 1:
                 melody = melody[None]
             melody = melody[..., :int(sr * duration)]
@@ -63,12 +65,12 @@ def predict(model_path, text, melody, duration, topk, topp, temperature, target_
     INTERRUPTING = False
     progress(0, desc="Loading model...")
     model_path = model_path.strip()
-    if model_path:
-        if not Path(model_path).exists():
-            raise gr.Error(f"Model path {model_path} doesn't exist.")
-        if not Path(model_path).is_dir():
-            raise gr.Error(f"Model path {model_path} must be a folder containing "
-                           "state_dict.bin and compression_state_dict_.bin.")
+    # if model_path:
+    #     if not Path(model_path).exists():
+    #         raise gr.Error(f"Model path {model_path} doesn't exist.")
+    #     if not Path(model_path).is_dir():
+    #         raise gr.Error(f"Model path {model_path} must be a folder containing "
+    #                        "state_dict.bin and compression_state_dict_.bin.")
     if temperature < 0:
         raise gr.Error("Temperature must be >= 0.")
     if topk < 0: