add demo loadin code

app.py

@@ -1,7 +1,76 @@
 import gradio as gr
+from transformers import Wav2Vec2FeatureExtractor
+from transformers import AutoModel
+import torch
+from torch import nn
+import torchaudio
+import torchaudio.transforms as T
 
-def greet(name):
-    return "Hello " + name + "!!"
+# input cr: https://huggingface.co/spaces/thealphhamerc/audio-to-text/blob/main/app.py
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+inputs = [gr.components.Audio(type="filepath", label="Add music audio file"), 
+          gr.inputs.Audio(source="microphone",optional=True, type="filepath"),
+          ]
+outputs = [gr.components.Textbox()]
+# outputs = [gr.components.Textbox(), transcription_df]
+title = "Output the tags of a (music) audio"
+description = "An example of using MERT-95M-public to conduct music tagging."
+article = ""
+audio_examples = [
+    ["input/example-1.wav"],
+    ["input/example-2.wav"],
+]
+
+# Load the model
+model = AutoModel.from_pretrained("m-a-p/MERT-v0-public", trust_remote_code=True)
+# loading the corresponding preprocessor config
+processor = Wav2Vec2FeatureExtractor.from_pretrained("m-a-p/MERT-v0-public",trust_remote_code=True)
+
+
+def convert_audio(inputs, microphone):
+    if (microphone is not None):
+        inputs = microphone
+    
+    waveform, sample_rate = torchaudio.load(inputs)
+    
+
+    resample_rate = processor.sampling_rate
+
+    # make sure the sample_rate aligned
+    if resample_rate != sample_rate:
+        print(f'setting rate from {sample_rate} to {resample_rate}')
+        resampler = T.Resample(sample_rate, resample_rate)
+        waveform = resampler(waveform)
+    
+    inputs = processor(waveform, sampling_rate=resample_rate, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs, output_hidden_states=True)
+
+    # take a look at the output shape, there are 13 layers of representation
+    # each layer performs differently in different downstream tasks, you should choose empirically
+    all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze()
+    # print(all_layer_hidden_states.shape) # [13 layer, Time steps, 768 feature_dim]
+    return str(all_layer_hidden_states.shape)
+
+
+# iface = gr.Interface(fn=convert_audio, inputs="audio", outputs="text")
+# iface.launch()
+
+audio_chunked = gr.Interface(
+    fn=convert_audio,
+    inputs=inputs,
+    outputs=outputs,
+    allow_flagging="never",
+    title=title,
+    description=description,
+    article=article,
+    examples=audio_examples,
+)
+
+
+demo = gr.Blocks()
+with demo:
+    gr.TabbedInterface([audio_chunked], [
+        "Audio File"])
+# demo.queue(concurrency_count=1, max_size=5)
+demo.launch(show_api=False)