Upload app.py

app.py

@@ -0,0 +1,44 @@
+from transformers import pipeline
+import gradio as gr
+import os
+import subprocess
+
+pipe = pipeline(model="tilos/whisper-small-zh-HK")  # change to "your-username/the-name-you-picked"
+
+def video2mp3(video_file, output_ext="mp3"):
+    filename, ext = os.path.splitext(video_file)
+    subprocess.call(["ffmpeg", "-y", "-i", video_file, f"{filename}.{output_ext}"],
+                    stdout=subprocess.DEVNULL,
+                    stderr=subprocess.STDOUT)
+    return f"{filename}.{output_ext}"
+
+def transcribe(audio):
+    text = pipe(audio)["text"]
+    return text
+
+def video_identity(video):
+    audio_file = video2mp3(video)
+    text = pipe(audio_file)#["text"]
+    print(text)
+    return text
+
+video = gr.Interface(video_identity,
+                    gr.Video(),
+                    "playable_video",
+                    #examples=[
+                    #    os.path.join(os.path.dirname(__file__),
+                    #                 "video/video_sample.mp4")],
+                    cache_examples=True)
+
+voice = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs="text",
+    title="Whisper Small Cantonese",
+    description="Realtime demo for Cantonese speech recognition using a fine-tuned Whisper small model.",
+)
+
+
+
+demo = gr.TabbedInterface([video, voice])
+demo.launch()