rework structure of gradio app
This commit is contained in:
Jaikinator
@@ -1,2 +1,7 @@
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from .qtfaststart import *
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from .gradio_app import *
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from .activity_tracker import *
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from .interface import *
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from .stg import *
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from .interactions import *
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from .global_var import *
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from .app import *
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@@ -0,0 +1,37 @@
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"""
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This file contains the functions which are related to monitoring the actual app usage.
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Therefore, the app is to be more efficient in the usage of the resources.
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By for example, unloading or reloading the model.
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"""
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import time
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import threading
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import torch
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import gc
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import gradio as gr
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timeout = 30 #seconds
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USER_ACTIVE = True
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user_active_lock = threading.Lock() # dummy for now
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# Create a thread to monitor user activity
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def monitor_activity(model, pipe, timeout=timeout):
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global USER_ACTIVE
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while True:
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time.sleep(timeout) # Check user activity every second
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with user_active_lock:
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if not USER_ACTIVE:
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del model
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del pipe
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gc.collect()
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torch.cuda.empty_cache()
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print("Model deleted empty memory")
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gr.Warning("Model unloaded due to inactivity. Please reload the model to continue.")
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break
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USER_ACTIVE = False
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@@ -0,0 +1,9 @@
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"""
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Stores global variables for the app.
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"""
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# Global variable to store the model
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MODEL = None
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# Global variable to track user activity
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USER_ACTIVE = False
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@@ -1,504 +0,0 @@
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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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Scraibe 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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"""
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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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Scraibe 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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from math import pi
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import os
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import gradio as gr
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import threading
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from tqdm import tqdm
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import time
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from scraibe import Scraibe, 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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"Bosnian", "Bulgarian", "Catalan", "Chinese", "Croatian",
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"Czech", "Danish", "Dutch", "English", "Estonian",
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"Finnish", "French", "Galician", "German", "Greek",
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"Hebrew", "Hindi", "Hungarian", "Icelandic", "Indonesian",
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"Italian", "Japanese", "Kannada", "Kazakh", "Korean",
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"Latvian", "Lithuanian", "Macedonian", "Malay", "Marathi",
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"Maori", "Nepali", "Norwegian", "Persian", "Polish",
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"Portuguese", "Romanian", "Russian", "Serbian", "Slovak",
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"Slovenian", "Spanish", "Swahili", "Swedish", "Tagalog",
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"Tamil", "Thai", "Turkish", "Ukrainian", "Urdu",
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"Vietnamese", "Welsh"
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]
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CURRENT_PATH = os.path.dirname(os.path.realpath(__file__))
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# Global variable to track user activity
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USER_ACTIVE = True
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# Lock to synchronize access to user_active variable
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user_active_lock = threading.Lock()
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# Function to reset the user activity flag
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def reset_user_activity():
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global USER_ACTIVE
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with user_active_lock:
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USER_ACTIVE = True
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class GradioTranscriptionInterface:
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"""
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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 __init__(self, model: Scraibe):
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"""
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Initializes the GradioTranscriptionInterface with a transcription model.
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Args:
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model (Scraibe): 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 Scraibe 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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"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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if isinstance(source, str):
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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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elif isinstance(source, list):
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source_names = [s.split("/")[-1] for s in source]
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result = []
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for s in tqdm(source, total=len(source),desc = "Transcribing audio files"):
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try:
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res = self.model.autotranscribe(s, **kwargs)
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except ValueError:
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_name = s.split("/")[-1]
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res = f"NO TRANSCRIPT FOUND FOR {_name}"
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gr.Warning(f"Couldn't detect any speech in {_name} will skip this file.")
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result.append(res)
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out = ''
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out_dict = {}
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for i, r in enumerate(result):
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out += f"TRANSCRIPT FOR {source_names[i]}:\n\n"
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out += str(r)
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out += "\n\n"
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if isinstance(r, str):
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out_dict[source_names[i]] = r
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else:
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out_dict[source_names[i]] = r.get_dict()
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return out, json.dumps(out_dict, indent=4)
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else:
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raise gr.Error("Please provide a valid audio file.")
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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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if isinstance(source, str):
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result = self.model.transcribe(source, **kwargs)
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return str(result)
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elif isinstance(source, list):
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source_names = [s.split("/")[-1] for s in source]
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result = []
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for s in tqdm(source, total=len(source),desc = "Transcribing audio files"):
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res = self.model.transcribe(s, **kwargs)
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result.append(res)
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out = ''
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for i, res in enumerate(result):
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out += f"TRANSCRIPT FOR {source_names[i]}:\n\n"
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out += str(res)
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out += "\n\n"
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return out
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else:
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raise gr.Error("Please provide a valid audio file.")
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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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if isinstance(source, str):
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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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elif isinstance(source, list):
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source_names = [s.split("/")[-1] for s in source]
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result = []
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for s in tqdm(source, total=len(source),desc = "Performing diarisation"):
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try:
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res = self.model.diarization(s, **kwargs)
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except ValueError:
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res = f"NO DIARISATION FOUND FOR {s}"
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gr.Warning(f"Couldn't detect any speech in {s} will skip this file.")
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result.append(res)
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out = {}
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for i, res in enumerate(result):
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out[source_names[i]] = res
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return json.dumps(out, indent=4)
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else:
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gr.Error("Please provide a valid audio file.")
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####
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# Gradio Interface
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####
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def gradio_Interface(model : Scraibe = None, timeout = 1):
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"""
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Gradio Web interface for audio transcription.
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:param model: Scraibe model, defaults to None
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:type model: Scraibe, optional
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:param timeout: Time until model is unloaded, defaults to 600 seconds
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:type timeout: int, optional
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:return: Gradio Interface
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:rtype: gradio.Interface
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"""
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if model is None:
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model = Scraibe()
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save_model_params = model.params
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pipe = GradioTranscriptionInterface(model)
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def select_task(choice):
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# tell the app that it is still in use
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reset_user_activity()
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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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# tell the app that it is still in use
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reset_user_activity()
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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 or Files":
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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,
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num_speakers,
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translate,
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language,
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audio1,
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audio2,
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video1,
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video2,
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file_in,
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progress = gr.Progress(track_tqdm= True)):
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# get *args which are not None
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if not "model" in locals():
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gr.Warning("Model unloaded due to inactivity. Reloading the model, please wait.")
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model = Scraibe(**save_model_params)
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pipe = GradioTranscriptionInterface(model)
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# # tell the app that it is still in use
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reset_user_activity()
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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 isinstance(source, list):
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source = [s.name for s in source]
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if len(source) == 1:
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source = source[0]
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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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if isinstance(source, str):
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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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else:
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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 = False),
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gr.update(visible = False))
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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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# Create a thread to monitor user activity
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def monitor_activity(model, pipe, timeout=timeout):
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global USER_ACTIVE
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while True:
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time.sleep(timeout) # Check user activity every second
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with user_active_lock:
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if not USER_ACTIVE:
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del model
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del pipe
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print("Model deleted empty memory")
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gr.Warning("Model unloaded due to inactivity. Please reload the model to continue.")
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break
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USER_ACTIVE = False
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# Start the monitoring thread
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activity_thread = threading.Thread(target=monitor_activity, args=(model, pipe))
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activity_thread.daemon = True
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activity_thread.start()
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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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hname = os.path.join(CURRENT_PATH, "header.html")
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header = open(hname, "r").read()
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# ugly hack to get the logo to work
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header = header.replace("/file=logo.svg", f"/file={CURRENT_PATH}/logo.svg" )
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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 or Files"], 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",include_audio= True,
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interactive= True, visible= False)
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file_in = gr.Files(label="Upload File or Files", 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)
|
||||
|
||||
annoation = gr.Textbox(label="Name your speaker's",
|
||||
info= "Please provide a list of the speakers arranged \
|
||||
in the order in which they appear in the input. Use comma ',' \
|
||||
as a seperator. Be aware that the first name is given \
|
||||
to SPEAKER_00 the second to SPEAKER_01 and so on.",
|
||||
visible= False, interactive= True)
|
||||
|
||||
annotate = gr.Button(value="Annotate", visible= False, interactive= True)
|
||||
|
||||
# Define usage of components
|
||||
input.change(fn=select_origin, inputs=[input],
|
||||
outputs=[audio1, audio2, video1, video2, file_in])
|
||||
|
||||
task.change(fn=select_task, inputs=[task],
|
||||
outputs=[num_speakers, translate, language])
|
||||
|
||||
translate.change(fn= lambda x : gr.update(value = x),
|
||||
inputs=[translate], outputs=[translate])
|
||||
num_speakers.change(fn= lambda x : gr.update(value = x),
|
||||
inputs=[num_speakers], outputs=[num_speakers])
|
||||
language.change(fn= lambda x : gr.update(value = x),
|
||||
inputs=[language], outputs=[language])
|
||||
|
||||
submit.click(fn = run_scribe,
|
||||
inputs=[task, num_speakers, translate, language, audio1,
|
||||
audio2, video1, video2, file_in],
|
||||
outputs=[out_txt, out_json, annoation, annotate])
|
||||
|
||||
annotate.click(fn = annotate_output, inputs=[annoation, out_json],
|
||||
outputs=[out_txt, out_json])
|
||||
|
||||
return demo
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
gradio_Interface().queue().launch()
|
||||
@@ -0,0 +1,145 @@
|
||||
"""
|
||||
This file contains ervery function that will be called when the user interacts with the
|
||||
UI like pressing a button or uploading a file.
|
||||
"""
|
||||
|
||||
from math import pi
|
||||
import gradio as gr
|
||||
import scraibe.app.global_var as gv
|
||||
from scraibe import Transcript
|
||||
|
||||
def select_task(choice):
|
||||
# tell the app that it is still in use
|
||||
if choice == 'Auto Transcribe':
|
||||
|
||||
return (gr.update(visible = True),
|
||||
gr.update(visible = True),
|
||||
gr.update(visible = True))
|
||||
|
||||
|
||||
elif choice == 'Transcribe':
|
||||
|
||||
return (gr.update(visible = False),
|
||||
gr.update(visible = True),
|
||||
gr.update(visible = True))
|
||||
|
||||
|
||||
elif choice == 'Diarisation':
|
||||
|
||||
return (gr.update(visible = True),
|
||||
gr.update(visible = False),
|
||||
gr.update(visible = False))
|
||||
|
||||
def select_origin(choice):
|
||||
|
||||
# tell the app that it is still in use
|
||||
if choice == "Upload Audio":
|
||||
|
||||
return (gr.update(visible = True),
|
||||
gr.update(visible = False, value = None),
|
||||
gr.update(visible = False, value = None),
|
||||
gr.update(visible = False, value = None),
|
||||
gr.update(visible = False, value = None))
|
||||
|
||||
elif choice == "Record Audio":
|
||||
|
||||
return (gr.update(visible = False, value = None),
|
||||
gr.update(visible = True),
|
||||
gr.update(visible = False, value = None),
|
||||
gr.update(visible = False, value = None),
|
||||
gr.update(visible = False, value = None))
|
||||
|
||||
elif choice == "Upload Video":
|
||||
|
||||
return (gr.update(visible = False, value = None),
|
||||
gr.update(visible = False, value = None),
|
||||
gr.update(visible = True),
|
||||
gr.update(visible = False, value = None),
|
||||
gr.update(visible = False, value = None))
|
||||
|
||||
elif choice == "Record Video":
|
||||
|
||||
return (gr.update(visible = False, value = None),
|
||||
gr.update(visible = False, value = None),
|
||||
gr.update(visible = False, value = None),
|
||||
gr.update(visible = True),
|
||||
gr.update(visible = False, value = None))
|
||||
|
||||
elif choice == "File or Files":
|
||||
|
||||
return (gr.update(visible = False, value = None),
|
||||
gr.update(visible = False, value = None),
|
||||
gr.update(visible = False, value = None),
|
||||
gr.update(visible = False, value = None),
|
||||
gr.update(visible = True))
|
||||
|
||||
def run_scraibe(task,
|
||||
num_speakers,
|
||||
translate,
|
||||
language,
|
||||
audio1,
|
||||
audio2,
|
||||
video1,
|
||||
video2,
|
||||
file_in,
|
||||
progress = gr.Progress(track_tqdm= True)):
|
||||
|
||||
# get *args which are not None
|
||||
|
||||
pipe = gv.MODEL
|
||||
|
||||
progress(0, desc='Starting task...')
|
||||
source = audio1 or audio2 or video1 or video2 or file_in
|
||||
|
||||
if isinstance(source, list):
|
||||
source = [s.name for s in source]
|
||||
if len(source) == 1:
|
||||
source = source[0]
|
||||
|
||||
if task == 'Auto Transcribe':
|
||||
|
||||
out_str , out_json = pipe.auto_transcribe(source = source,
|
||||
num_speakers = num_speakers,
|
||||
translation = translate,
|
||||
language = language)
|
||||
|
||||
if isinstance(source, str):
|
||||
return (gr.update(value = out_str, visible = True),
|
||||
gr.update(value = out_json, visible = True),
|
||||
gr.update(visible = True),
|
||||
gr.update(visible = True))
|
||||
else:
|
||||
return (gr.update(value = out_str, visible = True),
|
||||
gr.update(value = out_json, visible = True),
|
||||
gr.update(visible = False),
|
||||
gr.update(visible = False))
|
||||
|
||||
elif task == 'Transcribe':
|
||||
|
||||
out = pipe.transcribe(source = source,
|
||||
translation = translate,
|
||||
language = language)
|
||||
|
||||
return (gr.update(value = out, visible = True),
|
||||
gr.update(value = None, visible = False),
|
||||
gr.update(visible = False),
|
||||
gr.update(visible = False))
|
||||
|
||||
elif task == 'Diarisation':
|
||||
|
||||
out = pipe.perform_diarisation(source = source,
|
||||
num_speakers = num_speakers)
|
||||
|
||||
return (gr.update(value = None, visible = False),
|
||||
gr.update(value = out, visible = True),
|
||||
gr.update(visible = False),
|
||||
gr.update(visible = False))
|
||||
|
||||
def annotate_output(annoation : str, out_json : dict):
|
||||
# get *args which are not None
|
||||
|
||||
trans = Transcript.from_json(out_json)
|
||||
trans = trans.annotate(*annoation.split(","))
|
||||
|
||||
return gr.update(value = str(trans)),gr.update(value = trans.get_json())
|
||||
|
||||
@@ -0,0 +1,129 @@
|
||||
"""
|
||||
This file contains the actual gradio Interface which is used to interact with the user.
|
||||
"""
|
||||
|
||||
import gradio as gr
|
||||
import os
|
||||
|
||||
import scraibe.app.global_var as gv
|
||||
from .interactions import *
|
||||
from .stg import *
|
||||
|
||||
from scraibe import Scraibe
|
||||
|
||||
theme = gr.themes.Soft(
|
||||
primary_hue="green",
|
||||
secondary_hue='orange',
|
||||
neutral_hue="gray",
|
||||
)
|
||||
|
||||
|
||||
LANGUAGES = [
|
||||
"Afrikaans", "Arabic", "Armenian", "Azerbaijani", "Belarusian",
|
||||
"Bosnian", "Bulgarian", "Catalan", "Chinese", "Croatian",
|
||||
"Czech", "Danish", "Dutch", "English", "Estonian",
|
||||
"Finnish", "French", "Galician", "German", "Greek",
|
||||
"Hebrew", "Hindi", "Hungarian", "Icelandic", "Indonesian",
|
||||
"Italian", "Japanese", "Kannada", "Kazakh", "Korean",
|
||||
"Latvian", "Lithuanian", "Macedonian", "Malay", "Marathi",
|
||||
"Maori", "Nepali", "Norwegian", "Persian", "Polish",
|
||||
"Portuguese", "Romanian", "Russian", "Serbian", "Slovak",
|
||||
"Slovenian", "Spanish", "Swahili", "Swedish", "Tagalog",
|
||||
"Tamil", "Thai", "Turkish", "Ukrainian", "Urdu",
|
||||
"Vietnamese", "Welsh"
|
||||
]
|
||||
|
||||
CURRENT_PATH = os.path.dirname(os.path.realpath(__file__))
|
||||
|
||||
|
||||
def gradio_Interface(pipe : Scraibe = None):
|
||||
|
||||
if pipe is not None:
|
||||
gv.MODEL = GradioTranscriptionInterface(pipe)
|
||||
|
||||
with gr.Blocks(theme=theme,title='ScrAIbe: Automatic Audio Transcription') as demo:
|
||||
|
||||
# Define components
|
||||
hname = os.path.join(CURRENT_PATH, "header.html")
|
||||
header = open(hname, "r").read()
|
||||
|
||||
# ugly hack to get the logo to work
|
||||
header = header.replace("/file=logo.svg", f"/file={CURRENT_PATH}/logo.svg" )
|
||||
|
||||
gr.HTML(header, visible= True, show_label=False)
|
||||
|
||||
with gr.Row():
|
||||
|
||||
with gr.Column():
|
||||
|
||||
task = gr.Radio(["Auto Transcribe", "Transcribe", "Diarisation"], label="Task",
|
||||
value= 'Auto Transcribe')
|
||||
|
||||
num_speakers = gr.Number(value=0, label= "Number of speakers (optional)",
|
||||
info = "Number of speakers in the audio file. If you don't know,\
|
||||
leave it at 0.", visible= True)
|
||||
|
||||
translate = gr.Checkbox(label="Translation", choices=[True, False], value = False,
|
||||
info="Select 'Yes' to have the output translated into English.",
|
||||
visible= True)
|
||||
|
||||
language = gr.Dropdown(LANGUAGES,
|
||||
label="Language (optional)", value = "None",
|
||||
info="Language of the audio file. If you don't know,\
|
||||
leave it at None.", visible= True)
|
||||
|
||||
input = gr.Radio(["Upload Audio", "Record Audio", "Upload Video","Record Video"
|
||||
,"File or Files"], label="Input Type", value="Upload Audio")
|
||||
|
||||
audio1 = gr.Audio(source="upload", type="filepath", label="Upload Audio",
|
||||
interactive= True, visible= True)
|
||||
audio2 = gr.Audio(source="microphone", label="Record Audio", type="filepath",
|
||||
interactive= True, visible= False)
|
||||
video1 = gr.Video(source="upload", type="filepath", label="Upload Video",
|
||||
interactive= True, visible= False)
|
||||
video2 = gr.Video(source="webcam", label="Record Video", type="filepath",include_audio= True,
|
||||
interactive= True, visible= False)
|
||||
file_in = gr.Files(label="Upload File or Files", interactive= True, visible= False)
|
||||
|
||||
submit = gr.Button()
|
||||
|
||||
with gr.Column():
|
||||
|
||||
out_txt = gr.Textbox(label="Output",
|
||||
visible= True, show_copy_button=True)
|
||||
|
||||
out_json = gr.JSON(label="JSON Output",
|
||||
visible= False, show_copy_button=True)
|
||||
|
||||
annoation = gr.Textbox(label="Name your speaker's",
|
||||
info= "Please provide a list of the speakers arranged \
|
||||
in the order in which they appear in the input. Use comma ',' \
|
||||
as a seperator. Be aware that the first name is given \
|
||||
to SPEAKER_00 the second to SPEAKER_01 and so on.",
|
||||
visible= False, interactive= True)
|
||||
|
||||
annotate = gr.Button(value="Annotate", visible= False, interactive= True)
|
||||
|
||||
# Define usage of components
|
||||
input.change(fn=select_origin, inputs=[input],
|
||||
outputs=[audio1, audio2, video1, video2, file_in])
|
||||
|
||||
task.change(fn=select_task, inputs=[task],
|
||||
outputs=[num_speakers, translate, language])
|
||||
|
||||
translate.change(fn= lambda x : gr.update(value = x),
|
||||
inputs=[translate], outputs=[translate])
|
||||
num_speakers.change(fn= lambda x : gr.update(value = x),
|
||||
inputs=[num_speakers], outputs=[num_speakers])
|
||||
language.change(fn= lambda x : gr.update(value = x),
|
||||
inputs=[language], outputs=[language])
|
||||
|
||||
submit.click(fn = run_scraibe,
|
||||
inputs=[task, num_speakers, translate, language, audio1,
|
||||
audio2, video1, video2, file_in],
|
||||
outputs=[out_txt, out_json, annoation, annotate])
|
||||
|
||||
annotate.click(fn = annotate_output, inputs=[annoation, out_json],
|
||||
outputs=[out_txt, out_json])
|
||||
|
||||
return demo
|
||||
@@ -0,0 +1,157 @@
|
||||
"""
|
||||
stg - scraibe to gradio interface
|
||||
|
||||
This file contains the code for the scraibe to gradio interface.
|
||||
It makes adds gradio interactions to the scraibe class in the back.
|
||||
|
||||
"""
|
||||
|
||||
import json
|
||||
import gradio as gr
|
||||
from tqdm import tqdm
|
||||
from scraibe import Scraibe
|
||||
|
||||
|
||||
class GradioTranscriptionInterface:
|
||||
"""
|
||||
Interface handling the interaction between Gradio UI and the Audio Transcription system.
|
||||
"""
|
||||
|
||||
def __init__(self, model: Scraibe):
|
||||
"""
|
||||
Initializes the GradioTranscriptionInterface with a transcription model.
|
||||
|
||||
Args:
|
||||
model (Scraibe): Model responsible for audio transcription tasks.
|
||||
"""
|
||||
self.model = model
|
||||
|
||||
def auto_transcribe(self, source,
|
||||
num_speakers : int,
|
||||
translation : bool,
|
||||
language : str):
|
||||
"""
|
||||
Shortcut method for the Scraibe task.
|
||||
|
||||
Returns:
|
||||
tuple: Transcribed text (str), JSON output (dict)
|
||||
"""
|
||||
|
||||
kwargs = {
|
||||
"num_speakers": num_speakers if num_speakers != 0 else None,
|
||||
"language": language if language != "None" else None,
|
||||
"task": 'translate' if translation else None
|
||||
}
|
||||
if isinstance(source, str):
|
||||
try:
|
||||
result = self.model.autotranscribe(source, **kwargs)
|
||||
except ValueError:
|
||||
raise gr.Error("Couldn't detect any speech in the provided audio. \
|
||||
Please try again!")
|
||||
|
||||
return str(result), result.get_json()
|
||||
|
||||
elif isinstance(source, list):
|
||||
source_names = [s.split("/")[-1] for s in source]
|
||||
result = []
|
||||
for s in tqdm(source, total=len(source),desc = "Transcribing audio files"):
|
||||
try:
|
||||
res = self.model.autotranscribe(s, **kwargs)
|
||||
except ValueError:
|
||||
_name = s.split("/")[-1]
|
||||
res = f"NO TRANSCRIPT FOUND FOR {_name}"
|
||||
gr.Warning(f"Couldn't detect any speech in {_name} will skip this file.")
|
||||
result.append(res)
|
||||
|
||||
out = ''
|
||||
out_dict = {}
|
||||
for i, r in enumerate(result):
|
||||
out += f"TRANSCRIPT FOR {source_names[i]}:\n\n"
|
||||
out += str(r)
|
||||
out += "\n\n"
|
||||
|
||||
if isinstance(r, str):
|
||||
out_dict[source_names[i]] = r
|
||||
else:
|
||||
out_dict[source_names[i]] = r.get_dict()
|
||||
|
||||
return out, json.dumps(out_dict, indent=4)
|
||||
|
||||
else:
|
||||
raise gr.Error("Please provide a valid audio file.")
|
||||
|
||||
|
||||
def transcribe(self, source, translation, language):
|
||||
"""
|
||||
Shortcut method for the Transcribe task.
|
||||
|
||||
Returns:
|
||||
str: Transcribed text.
|
||||
"""
|
||||
kwargs = {
|
||||
"language": language if language != "None" else None,
|
||||
"task": 'translate' if translation == "Yes" else None
|
||||
}
|
||||
|
||||
if isinstance(source, str):
|
||||
result = self.model.transcribe(source, **kwargs)
|
||||
|
||||
return str(result)
|
||||
|
||||
elif isinstance(source, list):
|
||||
source_names = [s.split("/")[-1] for s in source]
|
||||
result = []
|
||||
for s in tqdm(source, total=len(source),desc = "Transcribing audio files"):
|
||||
res = self.model.transcribe(s, **kwargs)
|
||||
result.append(res)
|
||||
|
||||
out = ''
|
||||
for i, res in enumerate(result):
|
||||
out += f"TRANSCRIPT FOR {source_names[i]}:\n\n"
|
||||
out += str(res)
|
||||
out += "\n\n"
|
||||
|
||||
return out
|
||||
|
||||
else:
|
||||
raise gr.Error("Please provide a valid audio file.")
|
||||
|
||||
def perform_diarisation(self, source, num_speakers):
|
||||
"""
|
||||
Shortcut method for the Diarisation task.
|
||||
|
||||
Returns:
|
||||
str: JSON output of diarisation result.
|
||||
"""
|
||||
kwargs = {
|
||||
"num_speakers": num_speakers if num_speakers != 0 else None,
|
||||
}
|
||||
|
||||
if isinstance(source, str):
|
||||
try:
|
||||
result = self.model.diarization(source, **kwargs)
|
||||
except ValueError:
|
||||
raise gr.Error("Couldn't detect any speech in the provided audio. \
|
||||
Please try again!")
|
||||
|
||||
return json.dumps(result, indent=2)
|
||||
elif isinstance(source, list):
|
||||
source_names = [s.split("/")[-1] for s in source]
|
||||
result = []
|
||||
for s in tqdm(source, total=len(source),desc = "Performing diarisation"):
|
||||
try:
|
||||
res = self.model.diarization(s, **kwargs)
|
||||
except ValueError:
|
||||
res = f"NO DIARISATION FOUND FOR {s}"
|
||||
gr.Warning(f"Couldn't detect any speech in {s} will skip this file.")
|
||||
result.append(res)
|
||||
|
||||
out = {}
|
||||
|
||||
for i, res in enumerate(result):
|
||||
out[source_names[i]] = res
|
||||
|
||||
return json.dumps(out, indent=4)
|
||||
|
||||
else:
|
||||
gr.Error("Please provide a valid audio file.")
|
||||
Reference in New Issue
Block a user