Update app.py

app.py

@@ -1,7 +1,194 @@
-from whisperplus.app import youtube_url_to_text_app, speaker_diarization_app
-
 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
+
+
+def youtube_url_to_text(url, model_id, language_choice):
+    """
+    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.
+    """
+    video_path = download_and_convert_to_mp3(url)
+    pipeline = SpeechToTextPipeline(model_id)
+    transcript = pipeline(audio_path=video_path, model_id=model_id, language=language_choice)
+
+    return transcript, video_path
+
+
+def speaker_diarization(url, model_id, device, num_speakers, min_speaker, max_speaker):
+    """
+    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=False,
+        chunk_length_s=30,
+        device=device,
+    )
+
+    audio_path = download_and_convert_to_mp3(url)
+    output_text = pipeline(
+        audio_path, num_speakers=num_speakers, min_speaker=min_speaker, max_speaker=max_speaker)
+    dialogue = format_speech_to_dialogue(output_text)
+    return dialogue, audio_path
+
+
+def youtube_url_to_text_app():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                youtube_url_path = gr.Text(placeholder="Enter Youtube URL", label="Youtube URL")
+
+                language_choice = gr.Dropdown(
+                    choices=[
+                        "English",
+                        "Turkish",
+                        "Spanish",
+                        "French",
+                        "Chinese",
+                        "Japanese",
+                        "Korean",
+                    ],
+                    value="Turkish",
+                    label="Language",
+                )
+                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",
+                )
+                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=youtube_url_to_text,
+            inputs=[
+                youtube_url_path,
+                whisper_model_id,
+                language_choice,
+            ],
+            outputs=[output_text, output_audio],
+        )
+        gr.Examples(
+            examples=[
+                [
+                    "https://www.youtube.com/watch?v=di3rHkEZuUw",
+                    "openai/whisper-large-v3",
+                    "English",
+                ],
+            ],
+            fn=youtube_url_to_text,
+            inputs=[
+                youtube_url_path,
+                whisper_model_id,
+                language_choice,
+            ],
+            outputs=[output_text, output_audio],
+            cache_examples=True,
+        )
+
+
+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",
+                )
+                device = gr.Dropdown(
+                    choices=["cpu", "cuda", "mps"],
+                    value="cuda",
+                    label="Device",
+                )
+                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,
+                device,
+                num_speakers,
+                min_speaker,
+                max_speaker,
+            ],
+            outputs=[output_text, output_audio],
+        )
+        gr.Examples(
+            examples=[
+                [
+                    "https://www.youtube.com/shorts/o8PgLUgte2k",
+                    "openai/whisper-large-v3",
+                    "cuda",
+                    2,
+                    1,
+                    2,
+                ],
+            ],
+            fn=speaker_diarization,
+            inputs=[
+                youtube_url_path,
+                whisper_model_id,
+                device,
+                num_speakers,
+                min_speaker,
+                max_speaker,
+            ],
+            outputs=[output_text, output_audio],
+            cache_examples=True,
+        )
 
 
 gradio_app = gr.Blocks()
@@ -27,4 +214,4 @@ with gradio_app:
                 speaker_diarization_app()
 
 gradio_app.queue()
-gradio_app.launch(debug=True)
+gradio_app.launch(debug=True)