add audio option - needs follow up in transcriber.py

app.py

@@ -2,10 +2,28 @@ import gradio as gr
 from src.transcriber import transcriber
 
 def main():
+
+    audio_chunked = gr.Interface(
+            fn=transcribe_chunked_audio,
+            inputs=[
+                gr.Audio(sources=["upload"], label="Audio file", type="filepath"),
+                gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
+                gr.Checkbox(value=False, label="Return timestamps"),
+            ],
+            outputs=[
+                gr.Textbox(label="Transcription", show_copy_button=True),
+                gr.Textbox(label="Transcription Time (s)"),
+            ],
+            allow_flagging="never",
+            title=title,
+            description=description,
+            article=article,
+        )
+    
     with gr.Blocks(title='multilang-asr-transcriber', delete_cache=(86400, 86400), theme=gr.themes.Base()) as demo:
         gr.Markdown('## Multilang ASR Transcriber')
         gr.Markdown('An automatic speech recognition tool using [faster-whisper](https://github.com/SYSTRAN/faster-whisper). Supports multilingual video transcription and translation to english. Users may set the max words per line.')
-        video_file = gr.File(file_types=["video"],type="filepath", label="Upload a video")
+        file = gr.File(file_types=["video", "audio"],type="filepath", label="Upload video or audio")
         max_words_per_line = gr.Number(value=6, label="Max words per line")
         task = gr.Radio(choices=["transcribe", "translate"], value="transcribe", label="Select Task")
         model_version = gr.Radio(choices=["deepdml/faster-whisper-large-v3-turbo-ct2", "large-v3"], value="deepdml/faster-whisper-large-v3-turbo-ct2", label="Select Model")
@@ -13,7 +31,7 @@ def main():
         srt_file = gr.File(file_count="single", type="filepath", file_types=[".srt"], label="SRT file")
         text_clean_output = gr.Textbox(label="Text transcription", show_copy_button=True)
         gr.Interface(transcriber,
-                    inputs=[video_file, max_words_per_line, task, model_version],
+                    inputs=[file, max_words_per_line, task, model_version],
                     outputs=[text_output, srt_file, text_clean_output],
                     allow_flagging="never")
     demo.launch()