Upload run.py

run.py

@@ -0,0 +1,105 @@
+import time
+
+import gradio as gr
+import librosa
+import numpy as np
+import soundfile as sf
+from transformers import pipeline
+
+TARGET_SAMPLE_RATE = 16_000
+AUDIO_SECONDS_THRESHOLD = 5
+pipe = pipeline("audio-classification", model="MIT/ast-finetuned-audioset-10-10-0.4593")
+prediction = [{"score": 1, "label": "recording..."}]
+
+
+def normalize_waveform(waveform, datatype=np.float32):  # source datatype: np.int16
+    waveform = waveform.astype(dtype=datatype)
+    waveform /= 32768.0
+    return waveform
+
+
+def streaming_recording_fn(stream, new_chunk):
+    global prediction
+    sr, y = new_chunk
+    y = normalize_waveform(y)
+    y = librosa.resample(y, orig_sr=sr, target_sr=TARGET_SAMPLE_RATE)
+    if stream is not None:
+        if (stream.shape[-1] / TARGET_SAMPLE_RATE) >= AUDIO_SECONDS_THRESHOLD:
+            prediction = pipe(stream)
+            file_name = f'./audio/{time.strftime("%Y%m%d_%H%M%S", time.localtime())}.wav'
+            sf.write(file_name, stream, TARGET_SAMPLE_RATE)
+            print(f"SAVE AUDIO: {file_name}")
+            print(f">>>>>>1\t{y.shape=}, {stream.shape=}\n\t{prediction[0]=}")
+            stream = None
+        else:
+            stream = np.concatenate([stream, y], axis=-1)
+            print(f">>>>>>2\t{y.shape=}, {stream.shape=}")
+    else:
+        stream = y
+        print(f">>>>>>3\t{y.shape=}, {stream.shape=}")
+
+    return stream, {i['label']: i['score'] for i in prediction}
+
+
+def microphone_fn(waveform):
+    print('-' * 120)
+    print(f"{waveform=}")
+    sr, y = waveform
+    y = normalize_waveform(y)
+    y = librosa.resample(y, orig_sr=sr, target_sr=TARGET_SAMPLE_RATE)
+    result = pipe(y)
+    file_name = f'./audio/{time.strftime("%Y%m%d_%H%M%S", time.localtime())}.wav'
+    sf.write(file_name, y, TARGET_SAMPLE_RATE)
+    return {i['label']: i['score'] for i in result}
+
+
+def file_fn(waveform):
+    print('-' * 120)
+    print(f"{waveform=}")
+    sr, y = waveform
+    y = normalize_waveform(y)
+    y = librosa.resample(y, orig_sr=sr, target_sr=TARGET_SAMPLE_RATE)
+    result = pipe(y)
+    file_name = f'./audio/{time.strftime("%Y%m%d_%H%M%S", time.localtime())}.wav'
+    sf.write(file_name, y, TARGET_SAMPLE_RATE)
+    return {i['label']: i['score'] for i in result}
+
+
+streaming_demo = gr.Interface(
+    fn=streaming_recording_fn,
+    inputs=["state", gr.Audio(sources=["microphone"], streaming=True)],
+    outputs=["state", "label"],
+    live=True,
+)
+
+microphone_demo = gr.Interface(
+    fn=microphone_fn,
+    inputs=[gr.Audio(sources=["microphone"], type="numpy")],
+    outputs=["label"]
+)
+
+file_demo = gr.Interface(
+    fn=file_fn,
+    inputs=[gr.Audio(sources=["upload"], type="numpy")],
+    outputs=["label"]
+)
+
+with gr.Blocks() as example:
+    inputs = [gr.Audio(sources=["upload"], type="numpy")]
+    output = gr.Label()
+
+    examples = [
+        ["audio/cantina.wav"],
+        ["audio/cat.mp3"]
+    ]
+    ex = gr.Examples(examples,
+                     fn=file_fn, inputs=inputs, outputs=output,
+                     run_on_click=True)
+
+with gr.Blocks() as demo:
+    gr.TabbedInterface([file_demo, streaming_demo, microphone_demo, example],
+                       ["Audio file", "Streaming", "Microphone", "example"])
+
+if __name__ == "__main__":
+
+    demo.launch(share=True)