rewored webapp
This commit is contained in:
Jaikinator
File diff suppressed because one or more lines are too long
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@@ -15,8 +15,34 @@ Usage:
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"""
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from autotranscript import AutoTranscribe
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"""
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Gradio Audio Transcription App.
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--------------------------------
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This module provides an interface to transcribe audio files using the
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AutoTranscribe model. Users can either upload an audio file or record their speech
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live for transcription. The application supports multiple languages and provides
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options to specify the number of speakers and the language of the audio.
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Attributes:
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LANGUAGES (list): A list of supported languages for transcription.
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Usage:
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Run this script to start the Gradio web interface for audio transcription.
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"""
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import json
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import gradio as gr
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from autotranscript import AutoTranscribe, Transcript
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theme = gr.themes.Soft(
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primary_hue="green",
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secondary_hue='orange',
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neutral_hue="gray",
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)
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LANGUAGES = [
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"Afrikaans", "Arabic", "Armenian", "Azerbaijani", "Belarusian",
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@@ -33,70 +59,282 @@ LANGUAGES = [
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"Vietnamese", "Welsh"
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]
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def gradio_server(model : AutoTranscribe):
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class GradioTranscriptionInterface:
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"""
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Sets up and launches the Gradio interface for audio transcription.
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Args:
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model (AutoTranscribe): An instance of the AutoTranscribe model for transcription.
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Interface handling the interaction between Gradio UI and the Audio Transcription system.
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"""
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def transcribe(audio, microphone, number_of_speakers, language):
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def __init__(self, model: AutoTranscribe):
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"""
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Transcribes the provided audio input based on the given parameters.
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Initializes the GradioTranscriptionInterface with a transcription model.
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Args:
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audio (str): Filepath to the uploaded audio file.
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microphone (str): Filepath to the recorded audio.
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number_of_speakers (int): Number of speakers in the audio.
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language (str): Language of the audio content.
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model (AutoTranscribe): Model responsible for audio transcription tasks.
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"""
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self.model = model
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def auto_transcribe(self, source,
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num_speakers : int,
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translation : bool,
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language : str):
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"""
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Shortcut method for the AutoTranscribe task.
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Returns:
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tuple: Transcribed text (str), JSON output (dict)
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"""
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kwargs = {}
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if number_of_speakers != 0:
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kwargs["num_speakers"] = number_of_speakers
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if language != "None":
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kwargs["language"] = language
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print()
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if audio is not None:
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out = model.transcribe(audio, **kwargs)
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elif microphone is not None:
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out = model.transcribe(microphone , **kwargs)
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else:
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out = "Please upload an audio file or record one."
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kwargs = {
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"num_speakers": num_speakers if num_speakers != 0 else None,
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"language": language if language != "None" else None,
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"task": 'translate' if translation else None
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}
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return str(out), out.get_json(), out.get_md()
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try:
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result = self.model.autotranscribe(source, **kwargs)
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except ValueError:
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raise gr.Error("Couldn't detect any speech in the provided audio. \
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Please try again!")
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return str(result), result.get_json()
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gr.Interface(
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fn=transcribe,
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inputs=[
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gr.Audio(source= "upload", type="filepath", label="Upload Your Audio File",
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interactive=True),
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gr.Audio(source= "microphone", type="filepath", label="Record Your Audio",
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interactive=True, container= False),
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gr.Number(value=0, label= "Number of speakers (optional)",
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info = "Number of speakers in the audio file. If you don't know, leave it at 0."),
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gr.Dropdown(LANGUAGES,
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label="Language (optional)", value = "None",
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info="Language of the audio file. If you don't know, leave it at None.")
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],
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outputs=[
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gr.Textbox(label="Transcription"),
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gr.JSON(label="Raw Output", container= False),
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],
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title="Audio Transcription",
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description="Upload an audio file to transcribe its content. Powered by AutoTranscribe!",
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theme="soft", # Example of a more modern theme
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server_port=7860,
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server_name="autotranscribe",
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).queue().launch()
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def transcribe(self, source, translation, language):
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"""
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Shortcut method for the Transcribe task.
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Returns:
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str: Transcribed text.
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"""
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kwargs = {
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"language": language if language != "None" else None,
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"task": 'translate' if translation == "Yes" else None
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}
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result = self.model.transcribe(source, **kwargs)
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return str(result)
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def perform_diarisation(self, source, num_speakers):
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"""
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Shortcut method for the Diarisation task.
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Returns:
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str: JSON output of diarisation result.
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"""
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kwargs = {
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"num_speakers": num_speakers if num_speakers != 0 else None,
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}
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try:
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result = self.model.diarization(source, **kwargs)
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except ValueError:
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raise gr.Error("Couldn't detect any speech in the provided audio. \
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Please try again!")
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return json.dumps(result, indent=2)
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####
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# Gradio Interface
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####
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def gradio_Interface(model : AutoTranscribe = None):
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if model is None:
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model = AutoTranscribe()
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pipe = GradioTranscriptionInterface(model)
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def select_task(choice):
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if choice == 'Auto Transcribe':
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return (gr.update(visible = True),
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gr.update(visible = True),
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gr.update(visible = True))
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elif choice == 'Transcribe':
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return (gr.update(visible = False),
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gr.update(visible = True),
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gr.update(visible = True))
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elif choice == 'Diarisation':
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return (gr.update(visible = True),
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gr.update(visible = False),
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gr.update(visible = False))
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def select_origin(choice):
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if choice == "Upload Audio":
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return (gr.update(visible = True),
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gr.update(visible = False, value = None),
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gr.update(visible = False, value = None),
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gr.update(visible = False, value = None),
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gr.update(visible = False, value = None))
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elif choice == "Record Audio":
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return (gr.update(visible = False, value = None),
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gr.update(visible = True),
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gr.update(visible = False, value = None),
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gr.update(visible = False, value = None),
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gr.update(visible = False, value = None))
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elif choice == "Upload Video":
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return (gr.update(visible = False, value = None),
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gr.update(visible = False, value = None),
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gr.update(visible = True),
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gr.update(visible = False, value = None),
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gr.update(visible = False, value = None))
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elif choice == "Record Video":
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return (gr.update(visible = False, value = None),
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gr.update(visible = False, value = None),
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gr.update(visible = False, value = None),
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gr.update(visible = True),
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gr.update(visible = False, value = None))
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elif choice == "File":
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return (gr.update(visible = False, value = None),
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gr.update(visible = False, value = None),
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gr.update(visible = False, value = None),
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gr.update(visible = False, value = None),
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gr.update(visible = True))
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def run_scribe(task, num_speakers, translate, language, audio1, audio2, video1, video2, file_in, progress = gr.Progress(track_tqdm= True)):
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# get *args which are not None
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progress(0, desc='Starting task...')
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source = audio1 or audio2 or video1 or video2 or file_in
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if task == 'Auto Transcribe':
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out_str , out_json = pipe.auto_transcribe(source = source,
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num_speakers = num_speakers,
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translation = translate,
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language = language)
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return (gr.update(value = out_str, visible = True),
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gr.update(value = out_json, visible = True),
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gr.update(visible = True),
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gr.update(visible = True))
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elif task == 'Transcribe':
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out = pipe.transcribe(source = source,
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translation = translate,
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language = language)
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return (gr.update(value = out, visible = True),
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gr.update(value = None, visible = False),
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gr.update(visible = False),
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gr.update(visible = False))
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elif task == 'Diarisation':
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out = pipe.perform_diarisation(source = source,
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num_speakers = num_speakers)
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return (gr.update(value = None, visible = False),
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gr.update(value = out, visible = True),
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gr.update(visible = False),
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gr.update(visible = False))
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def annotate_output(annoation : str, out_json : dict):
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# get *args which are not None
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trans = Transcript.from_json(out_json)
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trans = trans.annotate(*annoation.split(","))
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return gr.update(value = str(trans)),gr.update(value = trans.get_json())
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with gr.Blocks(theme=theme,title='ScrAIbe: Automatic Audio Transcription') as demo:
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# Define components
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header = open("header.html", "r").read()
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gr.HTML(header, visible= True, show_label=False)
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with gr.Row():
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with gr.Column():
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task = gr.Radio(["Auto Transcribe", "Transcribe", "Diarisation"], label="Task",
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value= 'Auto Transcribe')
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num_speakers = gr.Number(value=0, label= "Number of speakers (optional)",
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info = "Number of speakers in the audio file. If you don't know,\
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leave it at 0.", visible= True)
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translate = gr.Checkbox(label="Translation", choices=[True, False], value = False,
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info="Select 'Yes' to have the output translated into English.",
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visible= True)
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language = gr.Dropdown(LANGUAGES,
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label="Language (optional)", value = "None",
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info="Language of the audio file. If you don't know,\
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leave it at None.", visible= True)
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input = gr.Radio(["Upload Audio", "Record Audio", "Upload Video","Record Video"
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,"File"], label="Input Type", value="Upload Audio")
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audio1 = gr.Audio(source="upload", type="filepath", label="Upload Audio",
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interactive= True, visible= True)
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audio2 = gr.Audio(source="microphone", label="Record Audio", type="filepath",
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interactive= True, visible= False)
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video1 = gr.Video(source="upload", type="filepath", label="Upload Video",
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interactive= True, visible= False)
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video2 = gr.Video(source="webcam", label="Record Video", type="filepath",
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interactive= True, visible= False)
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file_in = gr.File(label="Upload File", interactive= True, visible= False)
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submit = gr.Button()
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with gr.Column():
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out_txt = gr.Textbox(label="Output",
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visible= True, show_copy_button=True)
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out_json = gr.JSON(label="JSON Output",
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visible= False, show_copy_button=True)
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annoation = gr.Textbox(label="Name your speaker's",
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info= "Please provide a list of the speakers arranged \
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in the order in which they appear in the input. Use comma ',' \
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as a seperator. Be aware that the first name is given \
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to SPEAKER_00 the second to SPEAKER_01 and so on.",
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visible= False, interactive= True)
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annotate = gr.Button(value="Annotate", visible= False, interactive= True)
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# Define usage of components
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input.change(fn=select_origin, inputs=[input],
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outputs=[audio1, audio2, video1, video2, file_in])
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task.change(fn=select_task, inputs=[task],
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outputs=[num_speakers, translate, language])
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translate.change(fn= lambda x : gr.update(value = x),
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inputs=[translate], outputs=[translate])
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num_speakers.change(fn= lambda x : gr.update(value = x),
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inputs=[num_speakers], outputs=[num_speakers])
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language.change(fn= lambda x : gr.update(value = x),
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inputs=[language], outputs=[language])
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submit.click(fn = run_scribe,
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inputs=[task, num_speakers, translate, language, audio1,
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audio2, video1, video2, file_in],
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outputs=[out_txt, out_json, annoation, annotate])
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annotate.click(fn = annotate_output, inputs=[annoation, out_json],
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outputs=[out_txt, out_json])
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return demo
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if __name__ == "__main__":
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model = AutoTranscribe()
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gradio_server(model)
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gradio_Interface().queue().launch()
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@@ -0,0 +1,66 @@
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<!-- Importing Cormorant Garamond font from Google Fonts -->
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<link href="https://fonts.googleapis.com/css2?family=Cormorant+Garamond:wght@400;700&display=swap" rel="stylesheet">
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<style>
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.header-container {
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display: flex;
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align-items: center;
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justify-content: center;
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position: relative;
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padding-top: 30px;
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}
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.logo-container {
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position: absolute;
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top: 50%;
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right: 20px;
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transform: translateY(-50%);
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width: 300px;
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}
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.logo {
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width: 100%;
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height: auto;
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}
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h1 {
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font-family: 'Cormorant Garamond', serif;
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font-size: 50px !important; /* Increased font size */
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font-weight: bold;
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color: #50AF31;
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margin: 0;
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position: relative;
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padding: 0.5em 0;
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}
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h1::before, h1::after {
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content: "";
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position: absolute;
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height: 2px;
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width: 80%;
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background-color: #50AF31;
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left: 10%;
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}
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h1::before {
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top: 0.5em;
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}
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h1::after {
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bottom: 0.5em;
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}
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p, h2 {
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font-size: 16px;
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margin: 10px 0;
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line-height: 1.4;
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}
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</style>
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<div class="header-container">
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<h1>ScrAIbe</h1>
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<div class="logo-container">
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<a href="https://www.kida-bmel.de/"> <!-- Replace with your actual URL -->
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<img src="file/Logo_KIDA_bmel_green.svg" alt="KIDA Logo" class="logo">
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</a>
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</div>
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</div>
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<div style="text-align: center; padding: 20px 10%;">
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<p>
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Upload, record, or provide a video with audio for transcription. Our toolkit is designed to transcribe content from multiple languages accurately. The integrated speaker diarisation feature identifies different speakers, ensuring a smooth transcription experience. For optimal results, indicate the number of speakers and the original language of the content.
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</p>
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<h2 style="font-weight: bold; color: #50AF31;">What would you like to do next?</h2>
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</div>
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@@ -11,6 +11,7 @@ import json
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from sympy import use
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from .autotranscript import AutoTranscribe
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from .app.gradio_app import gradio_Interface
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from whisper.tokenizer import LANGUAGES , TO_LANGUAGE_CODE
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from torch.cuda import is_available
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@@ -160,7 +161,7 @@ def cli():
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if start_server: # unfinished code
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from .app.gradio_app import gradio_Interface
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gradio_Interface(model).queue().launch(server_port=args.port, server_name=args.server_name)
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@@ -1,5 +1,6 @@
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import json
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import time
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from traceback import print_stack
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from typing import Union
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@@ -50,6 +51,7 @@ class Transcript:
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if args:
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for arg, speaker in zip(args, sorted(self.speakers)):
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annotations[speaker] = arg
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invalid_speakers = set(kwargs.keys()) - set(self.speakers)
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