Update app.py

app.py

@@ -1,21 +1,55 @@
 import gradio as gr
-from transformers import pipeline
-import numpy as np
+from pydub import AudioSegment
+from time import sleep
 
-transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
+with gr.Blocks() as demo:
+    input_audio = gr.Audio(label="Input Audio", type="filepath", format="mp3")
+    with gr.Row():
+        with gr.Column():
+            stream_as_file_btn = gr.Button("Stream as File")
+            format = gr.Radio(["wav", "mp3"], value="wav", label="Format")
+            stream_as_file_output = gr.Audio(streaming=True)
 
-def transcribe(audio):
-    sr, y = audio
-    y = y.astype(np.float32)
-    y /= np.max(np.abs(y))
+            def stream_file(audio_file, format):
+                audio = AudioSegment.from_file(audio_file)
+                i = 0
+                chunk_size = 1000
+                while chunk_size * i < len(audio):
+                    chunk = audio[chunk_size * i : chunk_size * (i + 1)]
+                    i += 1
+                    if chunk:
+                        file = f"/tmp/{i}.{format}"
+                        chunk.export(file, format=format)
+                        yield file
+                        sleep(0.5)
 
-    return transcriber({"sampling_rate": sr, "raw": y})["text"]
+            stream_as_file_btn.click(
+                stream_file, [input_audio, format], stream_as_file_output
+            )
 
+            gr.Examples(
+                [["audio/cantina.wav", "wav"], ["audio/cantina.wav", "mp3"]],
+                [input_audio, format],
+                fn=stream_file,
+                outputs=stream_as_file_output,
+                cache_examples=True,
+            )
 
-demo = gr.Interface(
-    fn=transcribe,
-    inputs=gr.Audio(sources="upload"),
-    outputs=gr.outputs.Textbox(),
-)
+        with gr.Column():
+            stream_as_bytes_btn = gr.Button("Stream as Bytes")
+            stream_as_bytes_output = gr.Audio(format="bytes", streaming=True)
 
-demo.launch(debug=True)
+            def stream_bytes(audio_file):
+                chunk_size = 20_000
+                with open(audio_file, "rb") as f:
+                    while True:
+                        chunk = f.read(chunk_size)
+                        if chunk:
+                            yield chunk
+                            sleep(1)
+                        else:
+                            break
+            stream_as_bytes_btn.click(stream_bytes, input_audio, stream_as_bytes_output)
+
+if __name__ == "__main__":
+    demo.queue().launch()