Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+from pytube import YouTube
+import whisper
+
+# define function for transcription
+def whisper_transcript(model_size, url, audio_file):
+    if url:
+        link = YouTube(url)
+        source = link.streams.filter(only_audio=True)[0].download(filename="audio.mp4")
+
+    else:
+        source = audio_file
+
+    if model_size.endswith(".en"):
+        language = "english"
+
+    else:
+        language = None
+
+    options = whisper.DecodingOptions(without_timestamps=True)
+
+    loaded_model = whisper.load_model(model_size)
+    transcript = loaded_model.transcribe(source, language=language)
+
+    return transcript["text"]
+
+# define Gradio app interface
+gradio_ui = gr.Interface(
+    fn=whisper_transcript,
+    theme="Nymbo/Nymbo_Theme",
+    title="Transcribe multi-lingual audio clips with Whisper",
+    description="**How to use**: Select a model, paste in a Youtube link or upload an audio clip, then click submit. If your clip is **100% in English, select models ending in ‘.en’**. If the clip is in other languages, or a mix of languages, select models without ‘.en’",
+    article="**Note**: The larger the model size selected or the longer the audio clip, the more time it would take to process the transcript.",
+    inputs=[
+        gr.Dropdown(
+            label="Select Model",
+            choices=[
+                "tiny.en",
+                "base.en",
+                "small.en",
+                "medium.en",
+                "tiny",
+                "base",
+                "small",
+                "medium",
+                "large",
+            ],
+            value="base",
+        ),
+        gr.Textbox(label="Paste YouTube link here"),
+        gr.Audio(label="Upload Audio File", sources=["upload", "microphone"], type="filepath"),
+    ],
+    outputs=gr.Textbox(label="Whisper Transcript"),
+)
+
+gradio_ui.queue().launch()