Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 
 from whisperplus.pipelines.whisper import SpeechToTextPipeline
-from whisperplus.pipelines.whisper_diarize import ASRDiarizationPipeline
 from whisperplus.utils.download_utils import download_and_convert_to_mp3
 from whisperplus.utils.text_utils import format_speech_to_dialogue
 
@@ -27,35 +26,6 @@ def youtube_url_to_text(url, model_id, language_choice):
     return transcript, video_path
 
 
-def speaker_diarization(url, model_id):
-    """
-    Main function that downloads and converts a video to MP3 format, performs speech-to-text conversion using
-    a specified model, and returns the transcript along with the video path.
-
-    Args:
-        url (str): The URL of the video to download and convert.
-        model_id (str): The ID of the speech-to-text model to use.
-        language_choice (str): The language choice for the speech-to-text conversion.
-
-    Returns:
-        transcript (str): The transcript of the speech-to-text conversion.
-        video_path (str): The path of the downloaded video.
-    """
-
-    pipeline = ASRDiarizationPipeline.from_pretrained(
-        asr_model=model_id,
-        diarizer_model="pyannote/speaker-diarization",
-        use_auth_token="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",
-        chunk_length_s=30,
-        device="cuda",
-    )
-
-    audio_path = download_and_convert_to_mp3(url)
-    output_text = pipeline(audio_path)
-    dialogue = format_speech_to_dialogue(output_text)
-    return dialogue, audio_path
-
-
 def youtube_url_to_text_app():
     with gr.Blocks():
         with gr.Row():
@@ -104,44 +74,6 @@ def youtube_url_to_text_app():
         )
 
 
-def speaker_diarization_app():
-    with gr.Blocks():
-        with gr.Row():
-            with gr.Column():
-                youtube_url_path = gr.Text(placeholder="Enter Youtube URL", label="Youtube URL")
-
-                whisper_model_id = gr.Dropdown(
-                    choices=[
-                        "openai/whisper-large-v3",
-                        "openai/whisper-large",
-                        "openai/whisper-medium",
-                        "openai/whisper-base",
-                        "openai/whisper-small",
-                        "openai/whisper-tiny",
-                    ],
-                    value="openai/whisper-large-v3",
-                    label="Whisper Model",
-                )
-
-                num_speakers = gr.Number(value=2, label="Number of Speakers")
-                min_speaker = gr.Number(value=1, label="Minimum Number of Speakers")
-                max_speaker = gr.Number(value=2, label="Maximum Number of Speakers")
-                whisperplus_in_predict = gr.Button(value="Generator")
-
-            with gr.Column():
-                output_text = gr.Textbox(label="Output Text")
-                output_audio = gr.Audio(label="Output Audio")
-
-        whisperplus_in_predict.click(
-            fn=speaker_diarization,
-            inputs=[
-                youtube_url_path,
-                whisper_model_id,
-            ],
-            outputs=[output_text, output_audio],
-        )
-
-
 gradio_app = gr.Blocks()
 with gradio_app:
     gr.HTML(
@@ -161,8 +93,6 @@ with gradio_app:
         with gr.Column():
             with gr.Tab(label="Youtube URL to Text"):
                 youtube_url_to_text_app()
-            with gr.Tab(label="Speaker Diarization"):
-                speaker_diarization_app()
 
 gradio_app.queue()
 gradio_app.launch(debug=True)